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  12.    <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
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  15.      <title>Data Format Standardization and DICOM Integration for Hyperpolarized &lt;sup&gt;13&lt;/sup&gt;C MRI</title>
  16.      <link>https://pubmed.ncbi.nlm.nih.gov/38710970/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  17.      <description>Hyperpolarized (HP) ^(13)C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard....</description>
  18.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">J Imaging Inform Med. 2024 May 6. doi: 10.1007/s10278-024-01100-2. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Hyperpolarized (HP) <sup>13</sup>C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP <sup>13</sup>C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP <sup>13</sup>C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP <sup>13</sup>C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP <sup>13</sup>C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP <sup>13</sup>C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38710970/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38710970</a> | DOI:<a href=https://doi.org/10.1007/s10278-024-01100-2>10.1007/s10278-024-01100-2</a></p></div>]]></content:encoded>
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  20.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  21.      <dc:creator>Ernesto Diaz</dc:creator>
  22.      <dc:creator>Renuka Sriram</dc:creator>
  23.      <dc:creator>Jeremy W Gordon</dc:creator>
  24.      <dc:creator>Avantika Sinha</dc:creator>
  25.      <dc:creator>Xiaoxi Liu</dc:creator>
  26.      <dc:creator>Sule I Sahin</dc:creator>
  27.      <dc:creator>Jason C Crane</dc:creator>
  28.      <dc:creator>Marram P Olson</dc:creator>
  29.      <dc:creator>Hsin-Yu Chen</dc:creator>
  30.      <dc:creator>Jenna M L Bernard</dc:creator>
  31.      <dc:creator>Daniel B Vigneron</dc:creator>
  32.      <dc:creator>Zhen Jane Wang</dc:creator>
  33.      <dc:creator>Duan Xu</dc:creator>
  34.      <dc:creator>Peder E Z Larson</dc:creator>
  35.      <dc:date>2024-05-06</dc:date>
  36.      <dc:source>Journal of imaging informatics in medicine</dc:source>
  37.      <dc:title>Data Format Standardization and DICOM Integration for Hyperpolarized &lt;sup&gt;13&lt;/sup&gt;C MRI</dc:title>
  38.      <dc:identifier>pmid:38710970</dc:identifier>
  39.      <dc:identifier>doi:10.1007/s10278-024-01100-2</dc:identifier>
  40.    </item>
  41.    <item>
  42.      <title>Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors</title>
  43.      <link>https://pubmed.ncbi.nlm.nih.gov/38710715/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  44.      <description>Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a)...</description>
  45.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Sci Rep. 2024 May 6;14(1):10378. doi: 10.1038/s41598-024-57634-6.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38710715/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38710715</a> | DOI:<a href=https://doi.org/10.1038/s41598-024-57634-6>10.1038/s41598-024-57634-6</a></p></div>]]></content:encoded>
  46.      <guid isPermaLink="false">pubmed:38710715</guid>
  47.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  48.      <dc:creator>Abhishek M Dedhe</dc:creator>
  49.      <dc:creator>Aakash A Chowkase</dc:creator>
  50.      <dc:creator>Niramay V Gogate</dc:creator>
  51.      <dc:creator>Manas M Kshirsagar</dc:creator>
  52.      <dc:creator>Rohan Naphade</dc:creator>
  53.      <dc:creator>Atharv Naphade</dc:creator>
  54.      <dc:creator>Pranav Kulkarni</dc:creator>
  55.      <dc:creator>Mrunmayi Naik</dc:creator>
  56.      <dc:creator>Aarya Dharm</dc:creator>
  57.      <dc:creator>Soham Raste</dc:creator>
  58.      <dc:creator>Shravan Patankar</dc:creator>
  59.      <dc:creator>Chinmay M Jogdeo</dc:creator>
  60.      <dc:creator>Aalok Sathe</dc:creator>
  61.      <dc:creator>Soham Kulkarni</dc:creator>
  62.      <dc:creator>Vibha Bapat</dc:creator>
  63.      <dc:creator>Rohinee Joshi</dc:creator>
  64.      <dc:creator>Kshitij Deshmukh</dc:creator>
  65.      <dc:creator>Subhash Lele</dc:creator>
  66.      <dc:creator>Kody J Manke-Miller</dc:creator>
  67.      <dc:creator>Jessica F Cantlon</dc:creator>
  68.      <dc:creator>Pranav S Pandit</dc:creator>
  69.      <dc:date>2024-05-06</dc:date>
  70.      <dc:source>Scientific reports</dc:source>
  71.      <dc:title>Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors</dc:title>
  72.      <dc:identifier>pmid:38710715</dc:identifier>
  73.      <dc:identifier>doi:10.1038/s41598-024-57634-6</dc:identifier>
  74.    </item>
  75.    <item>
  76.      <title>A road surface reconstruction dataset for autonomous driving</title>
  77.      <link>https://pubmed.ncbi.nlm.nih.gov/38710687/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  78.      <description>Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by...</description>
  79.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Sci Data. 2024 May 6;11(1):459. doi: 10.1038/s41597-024-03261-9.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38710687/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38710687</a> | DOI:<a href=https://doi.org/10.1038/s41597-024-03261-9>10.1038/s41597-024-03261-9</a></p></div>]]></content:encoded>
  80.      <guid isPermaLink="false">pubmed:38710687</guid>
  81.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  82.      <dc:creator>Tong Zhao</dc:creator>
  83.      <dc:creator>Yichen Xie</dc:creator>
  84.      <dc:creator>Mingyu Ding</dc:creator>
  85.      <dc:creator>Lei Yang</dc:creator>
  86.      <dc:creator>Masayoshi Tomizuka</dc:creator>
  87.      <dc:creator>Yintao Wei</dc:creator>
  88.      <dc:date>2024-05-06</dc:date>
  89.      <dc:source>Scientific data</dc:source>
  90.      <dc:title>A road surface reconstruction dataset for autonomous driving</dc:title>
  91.      <dc:identifier>pmid:38710687</dc:identifier>
  92.      <dc:identifier>doi:10.1038/s41597-024-03261-9</dc:identifier>
  93.    </item>
  94.    <item>
  95.      <title>Long-Chain Lipids Facilitate Insertion of Large Nanoparticles into Membranes of Small Unilamellar Vesicles</title>
  96.      <link>https://pubmed.ncbi.nlm.nih.gov/38710504/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  97.      <description>Insertion of hydrophobic nanoparticles into phospholipid bilayers is limited to small particles that can incorporate into a hydrophobic membrane core between two lipid leaflets. Incorporation of nanoparticles above this size limit requires the development of challenging surface engineering methodologies. In principle, increasing the long-chain lipid component in the lipid mixture should facilitate incorporation of larger nanoparticles. Here, we explore the effect of incorporating very long...</description>
  98.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Langmuir. 2024 May 6. doi: 10.1021/acs.langmuir.3c03471. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Insertion of hydrophobic nanoparticles into phospholipid bilayers is limited to small particles that can incorporate into a hydrophobic membrane core between two lipid leaflets. Incorporation of nanoparticles above this size limit requires the development of challenging surface engineering methodologies. In principle, increasing the long-chain lipid component in the lipid mixture should facilitate incorporation of larger nanoparticles. Here, we explore the effect of incorporating very long phospholipids (C24:1) into small unilamellar vesicles on the membrane insertion efficiency of hydrophobic nanoparticles that are 5-11 nm in diameter. To this end, we improve an existing vesicle preparation protocol and utilized cryogenic electron microscopy imaging to examine the mode of interaction and evaluate the insertion efficiency of membrane-inserted nanoparticles. We also perform classical coarse-grained molecular dynamics simulations to identify changes in lipid membrane structural properties that may increase insertion efficiency. Our results indicate that long-chain lipids increase the insertion efficiency by preferentially accumulating near membrane-inserted nanoparticles to reduce the thermodynamically unfavorable disruption of the membrane.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38710504/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38710504</a> | DOI:<a href=https://doi.org/10.1021/acs.langmuir.3c03471>10.1021/acs.langmuir.3c03471</a></p></div>]]></content:encoded>
  99.      <guid isPermaLink="false">pubmed:38710504</guid>
  100.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  101.      <dc:creator>Adan Marzouq</dc:creator>
  102.      <dc:creator>Lion Morgenstein</dc:creator>
  103.      <dc:creator>Carlos A Huang-Zhu</dc:creator>
  104.      <dc:creator>Shimon Yudovich</dc:creator>
  105.      <dc:creator>Ayelet Atkins</dc:creator>
  106.      <dc:creator>Asaf Grupi</dc:creator>
  107.      <dc:creator>Reid C Van Lehn</dc:creator>
  108.      <dc:creator>Shimon Weiss</dc:creator>
  109.      <dc:date>2024-05-06</dc:date>
  110.      <dc:source>Langmuir : the ACS journal of surfaces and colloids</dc:source>
  111.      <dc:title>Long-Chain Lipids Facilitate Insertion of Large Nanoparticles into Membranes of Small Unilamellar Vesicles</dc:title>
  112.      <dc:identifier>pmid:38710504</dc:identifier>
  113.      <dc:identifier>doi:10.1021/acs.langmuir.3c03471</dc:identifier>
  114.    </item>
  115.    <item>
  116.      <title>Bottlebrush Block Copolymers at the Interface of Immiscible Liquids: Adsorption and Lateral Packing</title>
  117.      <link>https://pubmed.ncbi.nlm.nih.gov/38710503/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  118.      <description>Amphiphilic bottlebrush block copolymers (BBCPs), having a hydrophilic bottlebrush polymer (BP) linked covalently to a hydrophobic BP, were found to segregate to liquid-liquid interfaces to minimize the free energy of the system. The key parameter influencing the outcome of the experiments is the ratio between the degree of polymerization of the backbone (N(BB)) and that of the side-chain brushes (N(SC)). Specifically, a spherical, star-like configuration results when N(BB) &lt; N(SC), while a...</description>
  119.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">J Am Chem Soc. 2024 May 6. doi: 10.1021/jacs.3c13817. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Amphiphilic bottlebrush block copolymers (BBCPs), having a hydrophilic bottlebrush polymer (BP) linked covalently to a hydrophobic BP, were found to segregate to liquid-liquid interfaces to minimize the free energy of the system. The key parameter influencing the outcome of the experiments is the ratio between the degree of polymerization of the backbone (<i>N</i><sub><i>BB</i></sub>) and that of the side-chain brushes (<i>N</i><sub><i>SC</i></sub>). Specifically, a spherical, star-like configuration results when <i>N</i><sub><i>BB</i></sub> &lt; <i>N</i><sub><i>SC</i></sub>, while a cylindrical, bottlebrush-like shape is preferred when <i>N</i><sub><i>BB</i></sub> &gt; <i>N</i><sub><i>SC</i></sub>. Dynamic interfacial tension (γ) and fluorescence recovery after photobleaching (FRAP) measurements show that the BBCP configuration influences the areal density and <i>in-plane</i> diffusion at the fluid interface. The characteristic relaxation times associated with BBCP adsorption (<i>τ</i><sub><i>A</i></sub>) and reorganization (<i>τ</i><sub><i>R</i></sub>) were determined by fitting time-dependent interfacial tension measurements to a sum of two exponential relaxation functions. Both <i>τ</i><sub><i>A</i></sub> and <i>τ</i><sub><i>R</i></sub> initially increased with <i>N</i><sub><i>BB</i></sub> up to 92 repeat units, due to the larger hydrodynamic radius in solution and slower <i>in-plane</i> diffusivity, attributed to a shorter cross-sectional diameter of the side-chains near the block junction. This trend reversed at <i>N</i><sub><i>BB</i></sub> = 190, with shorter <i>τ</i><sub><i>A</i></sub> and <i>τ</i><sub><i>R</i></sub> attributed to increased segregation strength and exposure of the bare water/toluene interface due to tilting and/or wiggling of the backbone chains, respectively. The adsorption energy barrier decreased with higher <i>N</i><sub><i>BB</i></sub>, due to a reduced BBCP packing density at the fluid interface. This study provides fundamental insights into macromolecular assembly at fluid interfaces, as it pertains to unique bottlebrush block architectures.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38710503/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38710503</a> | DOI:<a href=https://doi.org/10.1021/jacs.3c13817>10.1021/jacs.3c13817</a></p></div>]]></content:encoded>
  120.      <guid isPermaLink="false">pubmed:38710503</guid>
  121.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  122.      <dc:creator>Hong-Gyu Seong</dc:creator>
  123.      <dc:creator>Zichen Jin</dc:creator>
  124.      <dc:creator>Zhan Chen</dc:creator>
  125.      <dc:creator>Mingqiu Hu</dc:creator>
  126.      <dc:creator>Todd Emrick</dc:creator>
  127.      <dc:creator>Thomas P Russell</dc:creator>
  128.      <dc:date>2024-05-06</dc:date>
  129.      <dc:source>Journal of the American Chemical Society</dc:source>
  130.      <dc:title>Bottlebrush Block Copolymers at the Interface of Immiscible Liquids: Adsorption and Lateral Packing</dc:title>
  131.      <dc:identifier>pmid:38710503</dc:identifier>
  132.      <dc:identifier>doi:10.1021/jacs.3c13817</dc:identifier>
  133.    </item>
  134.    <item>
  135.      <title>California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities</title>
  136.      <link>https://pubmed.ncbi.nlm.nih.gov/38709962/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  137.      <description>In March 2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4 percent after policy implementation. Furthermore, an...</description>
  138.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Health Aff (Millwood). 2024 May;43(5):632-640. doi: 10.1377/hlthaff.2023.01163.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">In March 2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4 percent after policy implementation. Furthermore, an estimated 160,892 COVID-19 cases, 10,248 hospitalizations, and 679 deaths in the least-advantaged communities were averted by the policy. Despite these improvements, the share of COVID-19 cases, hospitalizations, and deaths in prioritized communities remained elevated. These estimates were robust in sensitivity analyses that tested exchangeability between prioritized communities and those not prioritized by the policy; model specifications; and potential temporal confounders, including prior infections. Correcting for disparities by strategically allocating limited resources to the least-advantaged or most-affected communities can reduce the impacts of COVID-19 and other diseases but might not eliminate health disparities.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709962/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709962</a> | DOI:<a href=https://doi.org/10.1377/hlthaff.2023.01163>10.1377/hlthaff.2023.01163</a></p></div>]]></content:encoded>
  139.      <guid isPermaLink="false">pubmed:38709962</guid>
  140.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  141.      <dc:creator>Christopher M Hoover</dc:creator>
  142.      <dc:creator>Emily Estus</dc:creator>
  143.      <dc:creator>Ada Kwan</dc:creator>
  144.      <dc:creator>Kristal Raymond</dc:creator>
  145.      <dc:creator>Tanu Sreedharan</dc:creator>
  146.      <dc:creator>Tomás León</dc:creator>
  147.      <dc:creator>Seema Jain</dc:creator>
  148.      <dc:creator>Priya B Shete</dc:creator>
  149.      <dc:date>2024-05-06</dc:date>
  150.      <dc:source>Health affairs (Project Hope)</dc:source>
  151.      <dc:title>California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities</dc:title>
  152.      <dc:identifier>pmid:38709962</dc:identifier>
  153.      <dc:identifier>doi:10.1377/hlthaff.2023.01163</dc:identifier>
  154.    </item>
  155.    <item>
  156.      <title>Eviction-driven infanticide and sexually selected adoption and infanticide in a neotropical parrot</title>
  157.      <link>https://pubmed.ncbi.nlm.nih.gov/38709919/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  158.      <description>Infanticide and adoption have been attributed to sexual selection, where an individual later reproduces with the parent whose offspring it killed or adopted. While sexually selected infanticide is well known, evidence for sexually selected adoption is anecdotal. We report on both behaviors at 346 nests over 27 y in green-rumped parrotlets (Forpus passerinus) in Venezuela. Parrotlets are monogamous with long-term pair bonds, exhibit a strongly male-biased adult sex ratio, and nest in cavities...</description>
  159.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Proc Natl Acad Sci U S A. 2024 May 14;121(20):e2317305121. doi: 10.1073/pnas.2317305121. Epub 2024 May 6.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Infanticide and adoption have been attributed to sexual selection, where an individual later reproduces with the parent whose offspring it killed or adopted. While sexually selected infanticide is well known, evidence for sexually selected adoption is anecdotal. We report on both behaviors at 346 nests over 27 y in green-rumped parrotlets (<i>Forpus passerinus</i>) in Venezuela. Parrotlets are monogamous with long-term pair bonds, exhibit a strongly male-biased adult sex ratio, and nest in cavities that are in short supply, creating intense competition for nest sites and mates. Infanticide attacks occurred at 256 nests in two distinct contexts: 1) Attacks were primarily committed by nonbreeding pairs (69%) attempting to evict parents from the cavity. Infanticide attacks per nest were positively correlated with population size and evicting pairs never adopted abandoned offspring. Competition for limited nest sites was a primary cause of eviction-driven infanticide, and 2) attacks occurred less frequently at nests where one mate died (31%), was perpetrated primarily by stepparents of both sexes, and was independent of population size. Thus, within a single species and mating system, infanticide occurred in multiple contexts due to multiple drivers. Nevertheless, 48% of stepparents of both sexes adopted offspring, and another 23% of stepfathers exhibited both infanticide and long-term care. Stepfathers were often young males who subsequently nested with widows, reaching earlier ages of first breeding than competitors and demonstrating sexually selected adoption. Adoption and infanticide conferred similar fitness benefits to stepfathers and appeared to be equivalent strategies driven by limited breeding opportunities, male-biased sex ratios, and long-term monogamy.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709919/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709919</a> | DOI:<a href=https://doi.org/10.1073/pnas.2317305121>10.1073/pnas.2317305121</a></p></div>]]></content:encoded>
  160.      <guid isPermaLink="false">pubmed:38709919</guid>
  161.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  162.      <dc:creator>Steven R Beissinger</dc:creator>
  163.      <dc:creator>Karl S Berg</dc:creator>
  164.      <dc:date>2024-05-06</dc:date>
  165.      <dc:source>Proceedings of the National Academy of Sciences of the United States of America</dc:source>
  166.      <dc:title>Eviction-driven infanticide and sexually selected adoption and infanticide in a neotropical parrot</dc:title>
  167.      <dc:identifier>pmid:38709919</dc:identifier>
  168.      <dc:identifier>doi:10.1073/pnas.2317305121</dc:identifier>
  169.    </item>
  170.    <item>
  171.      <title>Social-ecological predictors of spotted hyena navigation through a shared landscape</title>
  172.      <link>https://pubmed.ncbi.nlm.nih.gov/38709888/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  173.      <description>Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes. As a widely reviled and behaviorally plastic apex predator, the spotted hyena (Crocuta crocuta) is a...</description>
  174.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Ecol Evol. 2024 Apr 25;14(4):e11293. doi: 10.1002/ece3.11293. eCollection 2024 Apr.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Human-wildlife interactions are increasing in severity due to climate change and proliferating urbanization. Regions where human infrastructure and activity are rapidly densifying or newly appearing constitute novel environments in which wildlife must learn to coexist with people, thereby serving as ideal case studies with which to infer future human-wildlife interactions in shared landscapes. As a widely reviled and behaviorally plastic apex predator, the spotted hyena (<i>Crocuta crocuta</i>) is a model species for understanding how large carnivores navigate these human-caused 'landscapes of fear' in a changing world. Using high-resolution GPS collar data, we applied resource selection functions and step selection functions to assess spotted hyena landscape navigation and fine-scale movement decisions in relation to social-ecological features in a rapidly developing region comprising two protected areas: Lake Nakuru National Park and Soysambu Conservancy, Kenya. We then used camera trap imagery and Barrier Behavior Analysis (BaBA) to further examine hyena interactions with barriers. Our results show that environmental factors, linear infrastructure, human-carnivore conflict hotspots, and human tolerance were all important predictors for landscape-scale resource selection by hyenas, while human experience elements were less important for fine-scale hyena movement decisions. Hyena selection for these characteristics also changed seasonally and across land management types. Camera traps documented an exceptionally high number of individual spotted hyenas (234) approaching the national park fence at 16 sites during the study period, and BaBA results suggested that hyenas perceive protected area boundaries' semi-permeable electric fences as risky but may cross them out of necessity. Our findings highlight that the ability of carnivores to flexibly respond within human-caused landscapes of fear may be expressed differently depending on context, scale, and climatic factors. These results also point to the need to incorporate societal factors into multiscale analyses of wildlife movement to effectively plan for human-wildlife coexistence.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709888/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709888</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11045923/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11045923</a> | DOI:<a href=https://doi.org/10.1002/ece3.11293>10.1002/ece3.11293</a></p></div>]]></content:encoded>
  175.      <guid isPermaLink="false">pubmed:38709888</guid>
  176.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  177.      <dc:creator>Christine E Wilkinson</dc:creator>
  178.      <dc:creator>Wenjing Xu</dc:creator>
  179.      <dc:creator>Amalie Luneng Solli</dc:creator>
  180.      <dc:creator>Justin S Brashares</dc:creator>
  181.      <dc:creator>Christine Chepkisich</dc:creator>
  182.      <dc:creator>Gerald Osuka</dc:creator>
  183.      <dc:creator>Maggi Kelly</dc:creator>
  184.      <dc:date>2024-05-06</dc:date>
  185.      <dc:source>Ecology and evolution</dc:source>
  186.      <dc:title>Social-ecological predictors of spotted hyena navigation through a shared landscape</dc:title>
  187.      <dc:identifier>pmid:38709888</dc:identifier>
  188.      <dc:identifier>pmc:PMC11045923</dc:identifier>
  189.      <dc:identifier>doi:10.1002/ece3.11293</dc:identifier>
  190.    </item>
  191.    <item>
  192.      <title>Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization</title>
  193.      <link>https://pubmed.ncbi.nlm.nih.gov/38709828/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  194.      <description>Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical...</description>
  195.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">PLoS Comput Biol. 2024 May 6;20(5):e1012053. doi: 10.1371/journal.pcbi.1012053. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709828/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709828</a> | DOI:<a href=https://doi.org/10.1371/journal.pcbi.1012053>10.1371/journal.pcbi.1012053</a></p></div>]]></content:encoded>
  196.      <guid isPermaLink="false">pubmed:38709828</guid>
  197.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  198.      <dc:creator>Benjamin Antin</dc:creator>
  199.      <dc:creator>Masato Sadahiro</dc:creator>
  200.      <dc:creator>Marta Gajowa</dc:creator>
  201.      <dc:creator>Marcus A Triplett</dc:creator>
  202.      <dc:creator>Hillel Adesnik</dc:creator>
  203.      <dc:creator>Liam Paninski</dc:creator>
  204.      <dc:date>2024-05-06</dc:date>
  205.      <dc:source>PLoS computational biology</dc:source>
  206.      <dc:title>Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization</dc:title>
  207.      <dc:identifier>pmid:38709828</dc:identifier>
  208.      <dc:identifier>doi:10.1371/journal.pcbi.1012053</dc:identifier>
  209.    </item>
  210.    <item>
  211.      <title>MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations</title>
  212.      <link>https://pubmed.ncbi.nlm.nih.gov/38709820/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  213.      <description>Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or...</description>
  214.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">PLoS Comput Biol. 2024 May 6;20(5):e1012046. doi: 10.1371/journal.pcbi.1012046. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible-ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (e.g., food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project's documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709820/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709820</a> | DOI:<a href=https://doi.org/10.1371/journal.pcbi.1012046>10.1371/journal.pcbi.1012046</a></p></div>]]></content:encoded>
  215.      <guid isPermaLink="false">pubmed:38709820</guid>
  216.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  217.      <dc:creator>Héctor M Sánchez C</dc:creator>
  218.      <dc:creator>David L Smith</dc:creator>
  219.      <dc:creator>John M Marshall</dc:creator>
  220.      <dc:date>2024-05-06</dc:date>
  221.      <dc:source>PLoS computational biology</dc:source>
  222.      <dc:title>MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations</dc:title>
  223.      <dc:identifier>pmid:38709820</dc:identifier>
  224.      <dc:identifier>doi:10.1371/journal.pcbi.1012046</dc:identifier>
  225.    </item>
  226.    <item>
  227.      <title>CO2 Response Screen in Grass Brachypodium Reveals Key Role of a MAP Kinase in CO2-Triggered Stomatal Closure</title>
  228.      <link>https://pubmed.ncbi.nlm.nih.gov/38709683/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  229.      <description>Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing...</description>
  230.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Plant Physiol. 2024 May 6:kiae262. doi: 10.1093/plphys/kiae262. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Plants respond to increased CO2 concentrations through stomatal closure, which can contribute to increased water use efficiency. Grasses display faster stomatal responses than eudicots due to dumbbell-shaped guard cells flanked by subsidiary cells working in opposition. However, forward genetic screening for stomatal CO2 signal transduction mutants in grasses has yet to be reported. The grass model Brachypodium distachyon is closely related to agronomically important cereal crops, sharing largely collinear genomes. To gain insights into CO2 control mechanisms of stomatal movements in grasses, we developed an unbiased forward genetic screen with an EMS-mutagenized Brachypodium distachyon M5 generation population using infrared imaging to identify plants with altered leaf temperatures at elevated CO2. Among isolated mutants, a "chill1" mutant exhibited cooler leaf temperatures than wildtype Bd21-3 parent control plants after exposure to increased [CO2]. chill1 plants showed strongly impaired high CO2-induced stomatal closure despite retaining a robust abscisic acid-induced stomatal closing response. Through bulked segregant whole-genome-sequencing analyses followed by analyses of further backcrossed F4 generation plants and generation and characterization of sodium-azide and CRISPR-cas9 mutants, chill1 was mapped to a protein kinase, Mitogen-Activated Protein Kinase 5 (BdMPK5). The chill1 mutation impaired BdMPK5 protein-mediated CO2/HCO3- sensing together with the High Temperature 1 (HT1) Raf-like kinase in vitro. Furthermore, AlphaFold2-directed structural modeling predicted that the identified BdMPK5-D90N chill1 mutant residue is located at the interface of BdMPK5 with the BdHT1 Raf-like kinase. BdMPK5 is a key signaling component that mediates CO2-induced stomatal movements and is proposed to function as a component of the primary CO2 sensor in grasses.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709683/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709683</a> | DOI:<a href=https://doi.org/10.1093/plphys/kiae262>10.1093/plphys/kiae262</a></p></div>]]></content:encoded>
  231.      <guid isPermaLink="false">pubmed:38709683</guid>
  232.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  233.      <dc:creator>Bryn N K Lopez</dc:creator>
  234.      <dc:creator>Paulo H O Ceciliato</dc:creator>
  235.      <dc:creator>Yohei Takahashi</dc:creator>
  236.      <dc:creator>Felipe J Rangel</dc:creator>
  237.      <dc:creator>Evana A Salem</dc:creator>
  238.      <dc:creator>Klara Kernig</dc:creator>
  239.      <dc:creator>Kelly Chow</dc:creator>
  240.      <dc:creator>Li Zhang</dc:creator>
  241.      <dc:creator>Morgana A Sidhom</dc:creator>
  242.      <dc:creator>Christian G Seitz</dc:creator>
  243.      <dc:creator>Tingwen Zheng</dc:creator>
  244.      <dc:creator>Richard Sibout</dc:creator>
  245.      <dc:creator>Debbie L Laudencia-Chingcuanco</dc:creator>
  246.      <dc:creator>Daniel P Woods</dc:creator>
  247.      <dc:creator>James Andrew McCammon</dc:creator>
  248.      <dc:creator>John P Vogel</dc:creator>
  249.      <dc:creator>Julian I Schroeder</dc:creator>
  250.      <dc:date>2024-05-06</dc:date>
  251.      <dc:source>Plant physiology</dc:source>
  252.      <dc:title>CO2 Response Screen in Grass Brachypodium Reveals Key Role of a MAP Kinase in CO2-Triggered Stomatal Closure</dc:title>
  253.      <dc:identifier>pmid:38709683</dc:identifier>
  254.      <dc:identifier>doi:10.1093/plphys/kiae262</dc:identifier>
  255.    </item>
  256.    <item>
  257.      <title>Design Principles and Routes for Calcium Alkoxyaluminate Electrolytes</title>
  258.      <link>https://pubmed.ncbi.nlm.nih.gov/38709010/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  259.      <description>Multivalent-ion battery technologies are increasingly attractive options for meeting diverse energy storage needs. Calcium ion batteries (CIB) are particularly appealing candidates for their earthly abundance, high theoretical volumetric energy density, and relative safety advantages. At present, only a few Ca-ion electrolyte systems are reported to reversibly plate at room temperature: for example, aluminates and borates, including Ca[TPFA](2), where [TPFA]^(-) = [Al(OC(CF(3))(3))(4)]^(-) and...</description>
  260.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">J Phys Chem Lett. 2024 May 6:5096-5102. doi: 10.1021/acs.jpclett.4c00969. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Multivalent-ion battery technologies are increasingly attractive options for meeting diverse energy storage needs. Calcium ion batteries (CIB) are particularly appealing candidates for their earthly abundance, high theoretical volumetric energy density, and relative safety advantages. At present, only a few Ca-ion electrolyte systems are reported to reversibly plate at room temperature: for example, aluminates and borates, including Ca[TPFA]<sub>2</sub>, where [TPFA]<sup>-</sup> = [Al(OC(CF<sub>3</sub>)<sub>3</sub>)<sub>4</sub>]<sup>-</sup> and Ca[B(hfip)<sub>4</sub>]<sub>2</sub>, [B(hfip)<sub>4</sub>]<sub>2</sub><sup>-</sup> = [B(OCH(CF<sub>3</sub>)<sub>2</sub>)<sub>4</sub>]<sup>-</sup>. Analyzing the structure of these salts reveals a common theme: the prevalent use of a weakly coordinating anion (WCA) consisting of a tetracoordinate aluminum/boron (Al/B) center with fluorinated alkoxides. Leveraging the concept of theory-aided design, we report an innovative, one-pot synthesis of two new calcium-ion electrolyte salts (Ca[Al(tftb)<sub>4</sub>]<sub>2</sub>, Ca[Al(hftb)<sub>4</sub>]<sub>2</sub>) and two reported salts (Ca[Al(hfip)<sub>4</sub>]<sub>2</sub> and Ca[TPFA]<sub>2</sub>) where hfip = (-OCH(CF<sub>3</sub>)<sub>2</sub>), tftb = (-OC(CF<sub>3</sub>)(Me)<sub>2</sub>), hftb = (-OC(CF<sub>3</sub>)<sub>2</sub>(Me)), [TPFA]<sup>-</sup> = [Al(OC(CF<sub>3</sub>)<sub>3</sub>)<sub>4</sub>]<sup>-</sup>. We also reveal the dependence of Coulombic efficiency on their inherent propensity for cation-anion coordination.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38709010/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38709010</a> | DOI:<a href=https://doi.org/10.1021/acs.jpclett.4c00969>10.1021/acs.jpclett.4c00969</a></p></div>]]></content:encoded>
  261.      <guid isPermaLink="false">pubmed:38709010</guid>
  262.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  263.      <dc:creator>Noel J Leon</dc:creator>
  264.      <dc:creator>Stefan Ilic</dc:creator>
  265.      <dc:creator>Xiaowei Xie</dc:creator>
  266.      <dc:creator>Heonjae Jeong</dc:creator>
  267.      <dc:creator>Zhenzhen Yang</dc:creator>
  268.      <dc:creator>Bingning Wang</dc:creator>
  269.      <dc:creator>Evan Walter Clark Spotte-Smith</dc:creator>
  270.      <dc:creator>Charlotte Stern</dc:creator>
  271.      <dc:creator>Nathan Hahn</dc:creator>
  272.      <dc:creator>Kevin Zavadil</dc:creator>
  273.      <dc:creator>Lei Cheng</dc:creator>
  274.      <dc:creator>Kristin A Persson</dc:creator>
  275.      <dc:creator>Justin G Connell</dc:creator>
  276.      <dc:creator>Chen Liao</dc:creator>
  277.      <dc:date>2024-05-06</dc:date>
  278.      <dc:source>The journal of physical chemistry letters</dc:source>
  279.      <dc:title>Design Principles and Routes for Calcium Alkoxyaluminate Electrolytes</dc:title>
  280.      <dc:identifier>pmid:38709010</dc:identifier>
  281.      <dc:identifier>doi:10.1021/acs.jpclett.4c00969</dc:identifier>
  282.    </item>
  283.    <item>
  284.      <title>Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents</title>
  285.      <link>https://pubmed.ncbi.nlm.nih.gov/38708262/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  286.      <description>Carbon dioxide (CO(2)) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO(2) capture. Chemically reactive DESs, which form chemical bonds with the CO(2), are superior to nonreactive, physically based DESs for CO(2) absorption. However, there are no accurate computational models that...</description>
  287.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">ACS Omega. 2024 Apr 19;9(17):19548-19559. doi: 10.1021/acsomega.4c01175. eCollection 2024 Apr 30.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Carbon dioxide (CO<sub>2</sub>) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO<sub>2</sub> capture. Chemically reactive DESs, which form chemical bonds with the CO<sub>2</sub>, are superior to nonreactive, physically based DESs for CO<sub>2</sub> absorption. However, there are no accurate computational models that provide accurate predictions of the CO<sub>2</sub> solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO<sub>2</sub> in chemically reactive DESs. As training data, we collected 214 data points for the CO<sub>2</sub> solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO<sub>2</sub> solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO<sub>2</sub> solubility precisely and thus accelerate the design and application of chemically reactive DESs.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38708262/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38708262</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11064036/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11064036</a> | DOI:<a href=https://doi.org/10.1021/acsomega.4c01175>10.1021/acsomega.4c01175</a></p></div>]]></content:encoded>
  288.      <guid isPermaLink="false">pubmed:38708262</guid>
  289.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  290.      <dc:creator>Mood Mohan</dc:creator>
  291.      <dc:creator>Omar N Demerdash</dc:creator>
  292.      <dc:creator>Blake A Simmons</dc:creator>
  293.      <dc:creator>Seema Singh</dc:creator>
  294.      <dc:creator>Michelle K Kidder</dc:creator>
  295.      <dc:creator>Jeremy C Smith</dc:creator>
  296.      <dc:date>2024-05-06</dc:date>
  297.      <dc:source>ACS omega</dc:source>
  298.      <dc:title>Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents</dc:title>
  299.      <dc:identifier>pmid:38708262</dc:identifier>
  300.      <dc:identifier>pmc:PMC11064036</dc:identifier>
  301.      <dc:identifier>doi:10.1021/acsomega.4c01175</dc:identifier>
  302.    </item>
  303.    <item>
  304.      <title>Testing of putative antiseizure medications in a preclinical Dravet syndrome zebrafish model</title>
  305.      <link>https://pubmed.ncbi.nlm.nih.gov/38707709/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  306.      <description>Dravet syndrome is a severe genetic epilepsy primarily caused by de novo mutations in a voltage-activated sodium channel gene (SCN1A). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications. Preclinical Dravet syndrome animal models are a valuable tool to identify candidate anti-seizure medications for these patients. Among these, scn1lab mutant zebrafish, exhibiting spontaneous seizure-like activity, are particularly amenable to large-scale drug...</description>
  307.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Brain Commun. 2024 Apr 16;6(3):fcae135. doi: 10.1093/braincomms/fcae135. eCollection 2024.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Dravet syndrome is a severe genetic epilepsy primarily caused by <i>de novo</i> mutations in a voltage-activated sodium channel gene (<i>SCN1A</i>). Patients face life-threatening seizures that are largely resistant to available anti-seizure medications. Preclinical Dravet syndrome animal models are a valuable tool to identify candidate anti-seizure medications for these patients. Among these, <i>scn1lab</i> mutant zebrafish, exhibiting spontaneous seizure-like activity, are particularly amenable to large-scale drug screening. Thus far, we have screened more than 3000 drug candidates in <i>scn1lab</i> zebrafish mutants, identifying valproate, stiripentol, and fenfluramine e.g. Food and Drug Administration-approved drugs, with clinical application in the Dravet syndrome population. Successful phenotypic screening in <i>scn1lab</i> mutant zebrafish is rigorous and consists of two stages: (i) a locomotion-based assay measuring high-velocity convulsive swim behaviour and (ii) an electrophysiology-based assay, using <i>in vivo</i> local field potential recordings, to quantify electrographic seizure-like events. Historically, nearly 90% of drug candidates fail during translation from preclinical models to the clinic. With such a high failure rate, it becomes necessary to address issues of replication and false positive identification. Leveraging our <i>scn1lab</i> zebrafish assays is one approach to address these problems. Here, we curated a list of nine anti-seizure drug candidates recently identified by other groups using preclinical Dravet syndrome models: 1-Ethyl-2-benzimidazolinone, AA43279, chlorzoxazone, donepezil, lisuride, mifepristone, pargyline, soticlestat and vorinostat. First-stage locomotion-based assays in <i>scn1lab</i> mutant zebrafish identified only 1-Ethyl-2-benzimidazolinone, chlorzoxazone and lisuride. However, second-stage local field potential recording assays did not show significant suppression of spontaneous electrographic seizure activity for any of the nine anti-seizure drug candidates. Surprisingly, soticlestat induced frank electrographic seizure-like discharges in wild-type control zebrafish. Taken together, our results failed to replicate clear anti-seizure efficacy for these drug candidates highlighting a necessity for strict scientific standards in preclinical identification of anti-seizure medications.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38707709/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38707709</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11069116/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11069116</a> | DOI:<a href=https://doi.org/10.1093/braincomms/fcae135>10.1093/braincomms/fcae135</a></p></div>]]></content:encoded>
  308.      <guid isPermaLink="false">pubmed:38707709</guid>
  309.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  310.      <dc:creator>Paige A Whyte-Fagundes</dc:creator>
  311.      <dc:creator>Anjelica Vance</dc:creator>
  312.      <dc:creator>Aloe Carroll</dc:creator>
  313.      <dc:creator>Francisco Figueroa</dc:creator>
  314.      <dc:creator>Catherine Manukyan</dc:creator>
  315.      <dc:creator>Scott C Baraban</dc:creator>
  316.      <dc:date>2024-05-06</dc:date>
  317.      <dc:source>Brain communications</dc:source>
  318.      <dc:title>Testing of putative antiseizure medications in a preclinical Dravet syndrome zebrafish model</dc:title>
  319.      <dc:identifier>pmid:38707709</dc:identifier>
  320.      <dc:identifier>pmc:PMC11069116</dc:identifier>
  321.      <dc:identifier>doi:10.1093/braincomms/fcae135</dc:identifier>
  322.    </item>
  323.    <item>
  324.      <title>Examining the relation between bilingualism and age of symptom onset in frontotemporal dementia</title>
  325.      <link>https://pubmed.ncbi.nlm.nih.gov/38707508/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  326.      <description>Bilingualism is thought to confer advantages in executive functioning, thereby contributing to cognitive reserve and a later age of dementia symptom onset. While the relation between bilingualism and age of onset has been explored in Alzheimer's dementia, there are few studies examining bilingualism as a contributor to cognitive reserve in frontotemporal dementia (FTD). In line with previous findings, we hypothesized that bilinguals with behavioral variant FTD would be older at symptom onset...</description>
  327.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Biling (Camb Engl). 2024 Mar;27(2):274-286. doi: 10.1017/s1366728923000226. Epub 2023 Mar 9.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Bilingualism is thought to confer advantages in executive functioning, thereby contributing to cognitive reserve and a later age of dementia symptom onset. While the relation between bilingualism and age of onset has been explored in Alzheimer's dementia, there are few studies examining bilingualism as a contributor to cognitive reserve in frontotemporal dementia (FTD). In line with previous findings, we hypothesized that bilinguals with behavioral variant FTD would be older at symptom onset compared to monolinguals, but that no such effect would be found in patients with nonfluent/agrammatic variant primary progressive aphasia (PPA) or semantic variant PPA. Contrary to our hypothesis, we found no significant difference in age at symptom onset between monolingual and bilingual speakers within any of the FTD variants, and there were no notable differences on neuropsychological measures. Overall, our results do not support a protective effect of bilingualism in patients with FTD-spectrum disease in a U.S. based cohort.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38707508/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38707508</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11065430/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11065430</a> | DOI:<a href=https://doi.org/10.1017/s1366728923000226>10.1017/s1366728923000226</a></p></div>]]></content:encoded>
  328.      <guid isPermaLink="false">pubmed:38707508</guid>
  329.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  330.      <dc:creator>Jessica de Leon</dc:creator>
  331.      <dc:creator>Stephanie Grasso</dc:creator>
  332.      <dc:creator>Isabel Elaine Allen</dc:creator>
  333.      <dc:creator>Danielle P Escueta</dc:creator>
  334.      <dc:creator>Yvette Vega</dc:creator>
  335.      <dc:creator>Malihe Eshghavi</dc:creator>
  336.      <dc:creator>Christa Watson</dc:creator>
  337.      <dc:creator>Nina Dronkers</dc:creator>
  338.      <dc:creator>Maria Luisa Gorno-Tempini</dc:creator>
  339.      <dc:creator>Maya L Henry</dc:creator>
  340.      <dc:date>2024-05-06</dc:date>
  341.      <dc:source>Bilingualism (Cambridge, England)</dc:source>
  342.      <dc:title>Examining the relation between bilingualism and age of symptom onset in frontotemporal dementia</dc:title>
  343.      <dc:identifier>pmid:38707508</dc:identifier>
  344.      <dc:identifier>pmc:PMC11065430</dc:identifier>
  345.      <dc:identifier>doi:10.1017/s1366728923000226</dc:identifier>
  346.    </item>
  347.    <item>
  348.      <title>The BioCascade Impactor: A novel device for direct collection of size-fractionated bioaerosols into liquid medium</title>
  349.      <link>https://pubmed.ncbi.nlm.nih.gov/38706712/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  350.      <description>The ability to collect size-fractionated airborne particles that contain viable bacteria and fungi directly into liquid medium while also maintaining their viability is critical for assessing exposure risks. In this study, we present the BioCascade impactor, a novel device designed to collect airborne particles into liquid based on their aerodynamic diameter in three sequential stages (&gt;9.74 μm, 3.94-9.74 μm, and 1.38-3.94 μm when operated at 8.5 L/min). Aerosol samples containing microorganisms...</description>
  351.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Aerosol Sci Technol. 2024;58(3):264-275. doi: 10.1080/02786826.2024.2301941. Epub 2024 Jan 25.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">The ability to collect size-fractionated airborne particles that contain viable bacteria and fungi directly into liquid medium while also maintaining their viability is critical for assessing exposure risks. In this study, we present the BioCascade impactor, a novel device designed to collect airborne particles into liquid based on their aerodynamic diameter in three sequential stages (&gt;9.74 μm, 3.94-9.74 μm, and 1.38-3.94 μm when operated at 8.5 L/min). Aerosol samples containing microorganisms - either <i>Saccharomyces kudriavzevii</i> or <i>Micrococcus luteus</i>, were used to evaluate the performance of the BioCascade (BC) paired with either the VIable Virus Aerosol Sampler (VIVAS) or a gelatin filter (GF) as stage 4 to collect particles &lt;1.38 μm. Stages 2 and 3 collected the largest fractions of viable <i>S. kudriavzevii</i> when paired with VIVAS (0.468) and GF (0.519), respectively. Stage 3 collected the largest fraction of viable <i>M. luteus</i> particles in both BC+VIVAS (0.791) and BC+GF (0.950) configurations. The distribution function of viable microorganisms was consistent with the size distributions measured by the Aerodynamic Particle Sizer. Testing with both bioaerosol species confirmed no internal loss and no re-aerosolization occurred within the BC. Irrespective of the bioaerosol tested, stages 1, 3 and 4 maintained ≥80% of viability, while stage 2 maintained only 37% and 73% of viable <i>S. kudriavzevii</i> and <i>M. luteus</i>, respectively. The low viability that occurred in stage 2 warrants further investigation. Our work shows that the BC can efficiently size-classify and collect bioaerosols without re-aerosolization and effectively maintain the viability of collected microorganisms.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38706712/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38706712</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11067687/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11067687</a> | DOI:<a href=https://doi.org/10.1080/02786826.2024.2301941>10.1080/02786826.2024.2301941</a></p></div>]]></content:encoded>
  352.      <guid isPermaLink="false">pubmed:38706712</guid>
  353.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  354.      <dc:creator>Yuqiao Chen</dc:creator>
  355.      <dc:creator>Jiayi Chen</dc:creator>
  356.      <dc:creator>Sripriya Nannu Shankar</dc:creator>
  357.      <dc:creator>Stavros Amanatidis</dc:creator>
  358.      <dc:creator>Arantzazu Eiguren-Fernandez</dc:creator>
  359.      <dc:creator>Nathan Kreisberg</dc:creator>
  360.      <dc:creator>Steven Spielman</dc:creator>
  361.      <dc:creator>John A Lednicky</dc:creator>
  362.      <dc:creator>Chang-Yu Wu</dc:creator>
  363.      <dc:date>2024-05-06</dc:date>
  364.      <dc:source>Aerosol science and technology : the journal of the American Association for Aerosol Research</dc:source>
  365.      <dc:title>The BioCascade Impactor: A novel device for direct collection of size-fractionated bioaerosols into liquid medium</dc:title>
  366.      <dc:identifier>pmid:38706712</dc:identifier>
  367.      <dc:identifier>pmc:PMC11067687</dc:identifier>
  368.      <dc:identifier>doi:10.1080/02786826.2024.2301941</dc:identifier>
  369.    </item>
  370.    <item>
  371.      <title>Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes</title>
  372.      <link>https://pubmed.ncbi.nlm.nih.gov/38706320/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  373.      <description>The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78...</description>
  374.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Brief Bioinform. 2024 Mar 27;25(3):bbae206. doi: 10.1093/bib/bbae206.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38706320/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38706320</a> | DOI:<a href=https://doi.org/10.1093/bib/bbae206>10.1093/bib/bbae206</a></p></div>]]></content:encoded>
  375.      <guid isPermaLink="false">pubmed:38706320</guid>
  376.      <pubDate>Mon, 06 May 2024 06:00:00 -0400</pubDate>
  377.      <dc:creator>Kaixin Hu</dc:creator>
  378.      <dc:creator>Fernando Meyer</dc:creator>
  379.      <dc:creator>Zhi-Luo Deng</dc:creator>
  380.      <dc:creator>Ehsaneddin Asgari</dc:creator>
  381.      <dc:creator>Tzu-Hao Kuo</dc:creator>
  382.      <dc:creator>Philipp C Münch</dc:creator>
  383.      <dc:creator>Alice C McHardy</dc:creator>
  384.      <dc:date>2024-05-06</dc:date>
  385.      <dc:source>Briefings in bioinformatics</dc:source>
  386.      <dc:title>Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes</dc:title>
  387.      <dc:identifier>pmid:38706320</dc:identifier>
  388.      <dc:identifier>doi:10.1093/bib/bbae206</dc:identifier>
  389.    </item>
  390.    <item>
  391.      <title>Macroevolution of the plant-hummingbird pollination system</title>
  392.      <link>https://pubmed.ncbi.nlm.nih.gov/38705863/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  393.      <description>Plant-hummingbird interactions are considered a classic example of coevolution, a process in which mutually dependent species influence each other's evolution. Plants depend on hummingbirds for pollination, whereas hummingbirds rely on nectar for food. As a step towards understanding coevolution, this review focuses on the macroevolutionary consequences of plant-hummingbird interactions, a relatively underexplored area in the current literature. We synthesize prior studies, illustrating the...</description>
  394.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Biol Rev Camb Philos Soc. 2024 May 5. doi: 10.1111/brv.13094. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Plant-hummingbird interactions are considered a classic example of coevolution, a process in which mutually dependent species influence each other's evolution. Plants depend on hummingbirds for pollination, whereas hummingbirds rely on nectar for food. As a step towards understanding coevolution, this review focuses on the macroevolutionary consequences of plant-hummingbird interactions, a relatively underexplored area in the current literature. We synthesize prior studies, illustrating the origins and dynamics of hummingbird pollination across different angiosperm clades previously pollinated by insects (mostly bees), bats, and passerine birds. In some cases, the crown age of hummingbirds pre-dates the plants they pollinate. In other cases, plant groups transitioned to hummingbird pollination early in the establishment of this bird group in the Americas, with the build-up of both diversities coinciding temporally, and hence suggesting co-diversification. Determining what triggers shifts to and away from hummingbird pollination remains a major open challenge. The impact of hummingbirds on plant diversification is complex, with many tropical plant lineages experiencing increased diversification after acquiring flowers that attract hummingbirds, and others experiencing no change or even a decrease in diversification rates. This mixed evidence suggests that other extrinsic or intrinsic factors, such as local climate and isolation, are important covariables driving the diversification of plants adapted to hummingbird pollination. To guide future studies, we discuss the mechanisms and contexts under which hummingbirds, as a clade and as individual species (e.g. traits, foraging behaviour, degree of specialization), could influence plant evolution. We conclude by commenting on how macroevolutionary signals of the mutualism could relate to coevolution, highlighting the unbalanced focus on the plant side of the interaction, and advocating for the use of species-level interaction data in macroevolutionary studies.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38705863/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38705863</a> | DOI:<a href=https://doi.org/10.1111/brv.13094>10.1111/brv.13094</a></p></div>]]></content:encoded>
  395.      <guid isPermaLink="false">pubmed:38705863</guid>
  396.      <pubDate>Sun, 05 May 2024 06:00:00 -0400</pubDate>
  397.      <dc:creator>Elisa Barreto</dc:creator>
  398.      <dc:creator>Mannfred M A Boehm</dc:creator>
  399.      <dc:creator>Ezgi Ogutcen</dc:creator>
  400.      <dc:creator>Stefan Abrahamczyk</dc:creator>
  401.      <dc:creator>Michael Kessler</dc:creator>
  402.      <dc:creator>Jordi Bascompte</dc:creator>
  403.      <dc:creator>Agnes S Dellinger</dc:creator>
  404.      <dc:creator>Carolina Bello</dc:creator>
  405.      <dc:creator>D Matthias Dehling</dc:creator>
  406.      <dc:creator>François Duchenne</dc:creator>
  407.      <dc:creator>Miriam Kaehler</dc:creator>
  408.      <dc:creator>Laura P Lagomarsino</dc:creator>
  409.      <dc:creator>Lúcia G Lohmann</dc:creator>
  410.      <dc:creator>María A Maglianesi</dc:creator>
  411.      <dc:creator>Hélène Morlon</dc:creator>
  412.      <dc:creator>Nathan Muchhala</dc:creator>
  413.      <dc:creator>Juan Francisco Ornelas</dc:creator>
  414.      <dc:creator>Mathieu Perret</dc:creator>
  415.      <dc:creator>Nelson R Salinas</dc:creator>
  416.      <dc:creator>Stacey D Smith</dc:creator>
  417.      <dc:creator>Jana C Vamosi</dc:creator>
  418.      <dc:creator>Isabela G Varassin</dc:creator>
  419.      <dc:creator>Catherine H Graham</dc:creator>
  420.      <dc:date>2024-05-05</dc:date>
  421.      <dc:source>Biological reviews of the Cambridge Philosophical Society</dc:source>
  422.      <dc:title>Macroevolution of the plant-hummingbird pollination system</dc:title>
  423.      <dc:identifier>pmid:38705863</dc:identifier>
  424.      <dc:identifier>doi:10.1111/brv.13094</dc:identifier>
  425.    </item>
  426.    <item>
  427.      <title>Ambient Environment and the Epidemiology of Preterm Birth</title>
  428.      <link>https://pubmed.ncbi.nlm.nih.gov/38705646/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  429.      <description>Preterm birth (PTB) is associated with substantial mortality and morbidity. We describe environmental factors that may influence PTB risks. We focus on exposures associated with an individual's ambient environment, such as air pollutants, water contaminants, extreme heat, and proximities to point sources (oil/gas development or waste sites) and greenspace. These exposures may further vary by other PTB risk factors such as social constructs and stress. Future examinations of risks associated with...</description>
  430.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Clin Perinatol. 2024 Jun;51(2):361-377. doi: 10.1016/j.clp.2024.02.004. Epub 2024 Mar 13.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Preterm birth (PTB) is associated with substantial mortality and morbidity. We describe environmental factors that may influence PTB risks. We focus on exposures associated with an individual's ambient environment, such as air pollutants, water contaminants, extreme heat, and proximities to point sources (oil/gas development or waste sites) and greenspace. These exposures may further vary by other PTB risk factors such as social constructs and stress. Future examinations of risks associated with ambient environment exposures would benefit from consideration toward multiple exposures - the exposome - and factors that modify risk including variations associated with the structural genome, epigenome, social stressors, and diet.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38705646/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38705646</a> | DOI:<a href=https://doi.org/10.1016/j.clp.2024.02.004>10.1016/j.clp.2024.02.004</a></p></div>]]></content:encoded>
  431.      <guid isPermaLink="false">pubmed:38705646</guid>
  432.      <pubDate>Sun, 05 May 2024 06:00:00 -0400</pubDate>
  433.      <dc:creator>Gary M Shaw</dc:creator>
  434.      <dc:creator>David J X Gonzalez</dc:creator>
  435.      <dc:creator>Dana E Goin</dc:creator>
  436.      <dc:creator>Kari A Weber</dc:creator>
  437.      <dc:creator>Amy M Padula</dc:creator>
  438.      <dc:date>2024-05-05</dc:date>
  439.      <dc:source>Clinics in perinatology</dc:source>
  440.      <dc:title>Ambient Environment and the Epidemiology of Preterm Birth</dc:title>
  441.      <dc:identifier>pmid:38705646</dc:identifier>
  442.      <dc:identifier>doi:10.1016/j.clp.2024.02.004</dc:identifier>
  443.    </item>
  444.    <item>
  445.      <title>Self-Powered Autonomous Electrostatic Dust Removal for Solar Panels by an Electret Generator</title>
  446.      <link>https://pubmed.ncbi.nlm.nih.gov/38704732/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  447.      <description>Solar panels often suffer from dust accumulation, significantly reducing their output, especially in desert regions where many of the world's largest solar plants are located. Here, an autonomous dust removal system for solar panels, powered by a wind-driven rotary electret generator is proposed. The generator applies a high voltage between one solar panel's output electrode and an upper mesh electrode to generate a strong electrostatic field. It is discovered that dust particles on the...</description>
  448.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Adv Sci (Weinh). 2024 May 5:e2401689. doi: 10.1002/advs.202401689. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Solar panels often suffer from dust accumulation, significantly reducing their output, especially in desert regions where many of the world's largest solar plants are located. Here, an autonomous dust removal system for solar panels, powered by a wind-driven rotary electret generator is proposed. The generator applies a high voltage between one solar panel's output electrode and an upper mesh electrode to generate a strong electrostatic field. It is discovered that dust particles on the insulative glass cover of the panel can be charged under the high electrical field, assisted by adsorbed water, even in low-humidity environments. The charged particles are subsequently repelled from the solar panel with the significant Coulomb force. Two panels covered with sand dust are cleaned in only 6.6 min by a 15 cm diameter rotary electret generator at 1.6 m s<sup>-1</sup> wind speed. Experimental results manifest that the system can work effectively in a wide range of environmental conditions, and doesn't impact the panel performance for long-term operation. This autonomous system, with its high dust removal efficiency, simplicity, and low cost, holds great potential in practical applications.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38704732/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38704732</a> | DOI:<a href=https://doi.org/10.1002/advs.202401689>10.1002/advs.202401689</a></p></div>]]></content:encoded>
  449.      <guid isPermaLink="false">pubmed:38704732</guid>
  450.      <pubDate>Sun, 05 May 2024 06:00:00 -0400</pubDate>
  451.      <dc:creator>Rong Ding</dc:creator>
  452.      <dc:creator>Zeyuan Cao</dc:creator>
  453.      <dc:creator>Junchi Teng</dc:creator>
  454.      <dc:creator>Yujia Cao</dc:creator>
  455.      <dc:creator>Xiaoyu Qian</dc:creator>
  456.      <dc:creator>Wei Yue</dc:creator>
  457.      <dc:creator>Xiangzhu Yuan</dc:creator>
  458.      <dc:creator>Kang Deng</dc:creator>
  459.      <dc:creator>Zibo Wu</dc:creator>
  460.      <dc:creator>Shuiqing Li</dc:creator>
  461.      <dc:creator>Liwei Lin</dc:creator>
  462.      <dc:creator>Xiongying Ye</dc:creator>
  463.      <dc:date>2024-05-05</dc:date>
  464.      <dc:source>Advanced science (Weinheim, Baden-Wurttemberg, Germany)</dc:source>
  465.      <dc:title>Self-Powered Autonomous Electrostatic Dust Removal for Solar Panels by an Electret Generator</dc:title>
  466.      <dc:identifier>pmid:38704732</dc:identifier>
  467.      <dc:identifier>doi:10.1002/advs.202401689</dc:identifier>
  468.    </item>
  469.    <item>
  470.      <title>Why are triploid quaking aspen (Populus tremuloides) common?</title>
  471.      <link>https://pubmed.ncbi.nlm.nih.gov/38704729/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  472.      <description>CONCLUSIONS: The coexistence of diploids and triploids in quaking aspen is statistically likely and promoted by the existence of commonly observed, long-lived triploid clones. However, other mechanisms not captured by the model related to environmental variation could also occur. Further empirical data or more complex but difficult-to-parameterize models are needed to gain further insight.</description>
  473.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Am J Bot. 2024 May 5:e16325. doi: 10.1002/ajb2.16325. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">PREMISE: Quaking aspen is a clonal tree species that has mixed ploidy, often with high relative abundance of both diploids and triploids but no haploids or tetraploids. Triploids typically have low fertility, leaving their occurrence apparently unlikely from an evolutionary perspective, unless they provide a "triploid bridge" to generating higher-fitness tetraploids-which are not observed in this species. This study focused on how triploidy can be maintained in quaking aspen.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">METHODS: A computational model was used to simulate gamete production, sexual reproduction, asexual reproduction, parent survival, and offspring survival in a population. All parameters were assumed to be cytotype-dependent and environment-independent. Sampling methods were used to identify parameter combinations consistent with observed cytotype frequencies.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">RESULTS: Many processes and parameter values were sufficient to yield a moderate frequency of triploids, and very few were necessary. The most plausible route involved higher triploid survival at the parent or offspring stage and limited unreduced gamete production by either diploid or triploid parents. Triploid fertility was helpful but not necessary.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">CONCLUSIONS: The coexistence of diploids and triploids in quaking aspen is statistically likely and promoted by the existence of commonly observed, long-lived triploid clones. However, other mechanisms not captured by the model related to environmental variation could also occur. Further empirical data or more complex but difficult-to-parameterize models are needed to gain further insight.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38704729/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38704729</a> | DOI:<a href=https://doi.org/10.1002/ajb2.16325>10.1002/ajb2.16325</a></p></div>]]></content:encoded>
  474.      <guid isPermaLink="false">pubmed:38704729</guid>
  475.      <pubDate>Sun, 05 May 2024 06:00:00 -0400</pubDate>
  476.      <dc:creator>Benjamin Wong Blonder</dc:creator>
  477.      <dc:date>2024-05-05</dc:date>
  478.      <dc:source>American journal of botany</dc:source>
  479.      <dc:title>Why are triploid quaking aspen (Populus tremuloides) common?</dc:title>
  480.      <dc:identifier>pmid:38704729</dc:identifier>
  481.      <dc:identifier>doi:10.1002/ajb2.16325</dc:identifier>
  482.    </item>
  483.    <item>
  484.      <title>Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease</title>
  485.      <link>https://pubmed.ncbi.nlm.nih.gov/38704445/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  486.      <description>Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact...</description>
  487.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Commun Biol. 2024 May 4;7(1):528. doi: 10.1038/s42003-024-06217-2.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38704445/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38704445</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11069569/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11069569</a> | DOI:<a href=https://doi.org/10.1038/s42003-024-06217-2>10.1038/s42003-024-06217-2</a></p></div>]]></content:encoded>
  488.      <guid isPermaLink="false">pubmed:38704445</guid>
  489.      <pubDate>Sat, 04 May 2024 06:00:00 -0400</pubDate>
  490.      <dc:creator>Lazaro M Sanchez-Rodriguez</dc:creator>
  491.      <dc:creator>Gleb Bezgin</dc:creator>
  492.      <dc:creator>Felix Carbonell</dc:creator>
  493.      <dc:creator>Joseph Therriault</dc:creator>
  494.      <dc:creator>Jaime Fernandez-Arias</dc:creator>
  495.      <dc:creator>Stijn Servaes</dc:creator>
  496.      <dc:creator>Nesrine Rahmouni</dc:creator>
  497.      <dc:creator>Cécile Tissot</dc:creator>
  498.      <dc:creator>Jenna Stevenson</dc:creator>
  499.      <dc:creator>Thomas K Karikari</dc:creator>
  500.      <dc:creator>Nicholas J Ashton</dc:creator>
  501.      <dc:creator>Andréa L Benedet</dc:creator>
  502.      <dc:creator>Henrik Zetterberg</dc:creator>
  503.      <dc:creator>Kaj Blennow</dc:creator>
  504.      <dc:creator>Gallen Triana-Baltzer</dc:creator>
  505.      <dc:creator>Hartmuth C Kolb</dc:creator>
  506.      <dc:creator>Pedro Rosa-Neto</dc:creator>
  507.      <dc:creator>Yasser Iturria-Medina</dc:creator>
  508.      <dc:date>2024-05-04</dc:date>
  509.      <dc:source>Communications biology</dc:source>
  510.      <dc:title>Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease</dc:title>
  511.      <dc:identifier>pmid:38704445</dc:identifier>
  512.      <dc:identifier>pmc:PMC11069569</dc:identifier>
  513.      <dc:identifier>doi:10.1038/s42003-024-06217-2</dc:identifier>
  514.    </item>
  515.    <item>
  516.      <title>Increased COVID-19 mortality among immigrants compared with US-born individuals: a cross-sectional analysis of 2020 mortality data</title>
  517.      <link>https://pubmed.ncbi.nlm.nih.gov/38703491/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  518.      <description>CONCLUSIONS: Immigrant individuals experienced greater mortality due to COVID-19 compared with their US-born counterparts. As COVID-19 becomes more endemic, greater clinical and public health efforts are needed to reduce disparities in mortality among immigrants compared with their US-born counterparts.</description>
  519.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Public Health. 2024 May 3;231:173-178. doi: 10.1016/j.puhe.2024.03.016. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">OBJECTIVE: Multiple studies have shown that racially minoritized groups had disproportionate COVID-19 mortality relative to non-Hispanic White individuals. However, there is little known regarding mortality by immigrant status nationally in the United States, despite being another vulnerable population.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">STUDY DESIGN: This was an observational cross-sectional study using mortality vital statistics system data to calculate proportionate mortality ratios (PMRs) and mortality rates due to COVID-19 as the underlying cause.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">METHODS: Rates were compared by decedents' identified race, ethnicity (Hispanic vs non-Hispanic), and immigrant (immigrants vs US born) status. Asian race was further disaggregated into "Asian Indian," "Chinese," "Filipino," "Japanese," "Korean," and "Vietnamese."</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">RESULTS: Of the over 3.4 million people who died in 2020, 10.4% of all deaths were attributed to COVID-19 as the underlying cause (n = 351,530). More than double (18.9%, n = 81,815) the percentage of immigrants who died of COVID-19 compared with US-born decedents (9.1%, n = 269,715). PMRs due to COVID-19 were higher among immigrants compared with US-born individuals for non-Hispanic White, non-Hispanic Black, Hispanic, and most disaggregated Asian groups. Among disaggregated Asian immigrants, age- and sex-adjusted PMR due to COVID-19 ranged from 1.58 times greater mortality among Filipino immigrants (95% confidence interval [CI]: 1.53, 1.64) to 0.77 times greater mortality among Japanese immigrants (95% CI: 0.68, 0.86). Age-adjusted mortality rates were also higher among immigrant individuals compared with US-born people.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">CONCLUSIONS: Immigrant individuals experienced greater mortality due to COVID-19 compared with their US-born counterparts. As COVID-19 becomes more endemic, greater clinical and public health efforts are needed to reduce disparities in mortality among immigrants compared with their US-born counterparts.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38703491/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38703491</a> | DOI:<a href=https://doi.org/10.1016/j.puhe.2024.03.016>10.1016/j.puhe.2024.03.016</a></p></div>]]></content:encoded>
  520.      <guid isPermaLink="false">pubmed:38703491</guid>
  521.      <pubDate>Sat, 04 May 2024 06:00:00 -0400</pubDate>
  522.      <dc:creator>A M Bacong</dc:creator>
  523.      <dc:creator>R Chu</dc:creator>
  524.      <dc:creator>A Le</dc:creator>
  525.      <dc:creator>V Bui</dc:creator>
  526.      <dc:creator>N E Wang</dc:creator>
  527.      <dc:creator>L P Palaniappan</dc:creator>
  528.      <dc:date>2024-05-04</dc:date>
  529.      <dc:source>Public health</dc:source>
  530.      <dc:title>Increased COVID-19 mortality among immigrants compared with US-born individuals: a cross-sectional analysis of 2020 mortality data</dc:title>
  531.      <dc:identifier>pmid:38703491</dc:identifier>
  532.      <dc:identifier>doi:10.1016/j.puhe.2024.03.016</dc:identifier>
  533.    </item>
  534.    <item>
  535.      <title>Comparing niraparib versus platinum-taxane doublet chemotherapy as neoadjuvant treatment in patients with newly diagnosed homologous recombination-deficient stage III/IV ovarian cancer: study protocol for cohort C of the open-label, phase 2, randomized controlled multicenter OPAL trial</title>
  536.      <link>https://pubmed.ncbi.nlm.nih.gov/38702828/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  537.      <description>BACKGROUND: Maintenance therapy with niraparib, a poly(ADP-ribose) polymerase inhibitor, has been shown to extend progression-free survival in patients with newly diagnosed advanced ovarian cancer who responded to first-line platinum-based chemotherapy, regardless of biomarker status. However, there are limited data on niraparib's efficacy and safety in the neoadjuvant setting. The objective of Cohort C of the OPAL trial (OPAL-C) is to evaluate the efficacy, safety, and tolerability of...</description>
  538.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Trials. 2024 May 4;25(1):301. doi: 10.1186/s13063-024-08142-5.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">BACKGROUND: Maintenance therapy with niraparib, a poly(ADP-ribose) polymerase inhibitor, has been shown to extend progression-free survival in patients with newly diagnosed advanced ovarian cancer who responded to first-line platinum-based chemotherapy, regardless of biomarker status. However, there are limited data on niraparib's efficacy and safety in the neoadjuvant setting. The objective of Cohort C of the OPAL trial (OPAL-C) is to evaluate the efficacy, safety, and tolerability of neoadjuvant niraparib treatment compared with neoadjuvant platinum-taxane doublet chemotherapy in patients with newly diagnosed stage III/IV ovarian cancer with confirmed homologous recombination-deficient tumors.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">METHODS: OPAL is an ongoing global, multicenter, randomized, open-label, phase 2 trial. In OPAL-C, patients will be randomized 1:1 to receive three 21-day cycles of either neoadjuvant niraparib or platinum-taxane doublet neoadjuvant chemotherapy per standard of care. Patients with a complete or partial response per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) will then undergo interval debulking surgery; patients with stable disease may proceed to interval debulking surgery or alternative therapy at the investigator's discretion. Patients with disease progression will exit the study treatment and proceed to alternative therapy at the investigator's discretion. After interval debulking surgery, all patients will receive up to three 21-day cycles of platinum-taxane doublet chemotherapy followed by niraparib maintenance therapy for up to 36 months. Adult patients with newly diagnosed stage III/IV ovarian cancer eligible to receive neoadjuvant platinum-taxane doublet chemotherapy followed by interval debulking surgery may be enrolled. Patients must have tumors that are homologous recombination-deficient. The primary endpoint is the pre-interval debulking surgery unconfirmed overall response rate, defined as the investigator-assessed percentage of patients with unconfirmed complete or partial response on study treatment before interval debulking surgery per RECIST v1.1.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">DISCUSSION: OPAL-C explores the use of niraparib in the neoadjuvant setting as an alternative to neoadjuvant platinum-taxane doublet chemotherapy to improve postsurgical residual disease outcomes for patients with ovarian cancer with homologous recombination-deficient tumors. Positive findings from this approach could significantly impact preoperative ovarian cancer therapy, particularly for patients who are ineligible for primary debulking surgery.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">TRIAL REGISTRATION: ClinicalTrials.gov NCT03574779. Registered on February 28, 2022.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38702828/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38702828</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11069300/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11069300</a> | DOI:<a href=https://doi.org/10.1186/s13063-024-08142-5>10.1186/s13063-024-08142-5</a></p></div>]]></content:encoded>
  539.      <guid isPermaLink="false">pubmed:38702828</guid>
  540.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  541.      <dc:creator>Jimmy Belotte</dc:creator>
  542.      <dc:creator>Brunella Felicetti</dc:creator>
  543.      <dc:creator>Amanda J Baines</dc:creator>
  544.      <dc:creator>Ahmed YoussefAgha</dc:creator>
  545.      <dc:creator>Luis Rojas-Espaillat</dc:creator>
  546.      <dc:creator>Ana Godoy Ortiz</dc:creator>
  547.      <dc:creator>Diane Provencher</dc:creator>
  548.      <dc:creator>Raúl Márquez Vázquez</dc:creator>
  549.      <dc:creator>Lucia González Cortijo</dc:creator>
  550.      <dc:creator>Xing Zeng</dc:creator>
  551.      <dc:date>2024-05-03</dc:date>
  552.      <dc:source>Trials</dc:source>
  553.      <dc:title>Comparing niraparib versus platinum-taxane doublet chemotherapy as neoadjuvant treatment in patients with newly diagnosed homologous recombination-deficient stage III/IV ovarian cancer: study protocol for cohort C of the open-label, phase 2, randomized controlled multicenter OPAL trial</dc:title>
  554.      <dc:identifier>pmid:38702828</dc:identifier>
  555.      <dc:identifier>pmc:PMC11069300</dc:identifier>
  556.      <dc:identifier>doi:10.1186/s13063-024-08142-5</dc:identifier>
  557.    </item>
  558.    <item>
  559.      <title>Racialized economic segregation and inequities in treatment initiation and survival among patients with metastatic breast cancer</title>
  560.      <link>https://pubmed.ncbi.nlm.nih.gov/38702585/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  561.      <description>CONCLUSION: Racialized economic segregation is a social determinant of health associated with treatment and survival inequities in mBC. Public investments directly addressing racialized economic segregation and other forms of structural racism are needed to reduce inequities in cancer care and outcomes.</description>
  562.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Breast Cancer Res Treat. 2024 May 3. doi: 10.1007/s10549-024-07319-5. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">PURPOSE: Racialized economic segregation, a form of structural racism, may drive persistent inequities among patients with breast cancer. We examined whether a composite area-level index of racialized economic segregation was associated with real-world treatment and survival in metastatic breast cancer (mBC).</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">METHODS: We conducted a retrospective cohort study among adult women with mBC using a US nationwide electronic health record-derived de-identified database (2011-2022). Population-weighted quintiles of the index of concentration at the extremes were estimated using census tract data. To identify inequities in time to treatment initiation (TTI) and overall survival (OS), we employed Kaplan-Meier methods and estimated hazard ratios (HR) adjusted for clinical factors.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">RESULTS: The cohort included 27,459 patients. Compared with patients from the most privileged areas, those from the least privileged areas were disproportionately Black (36.9% vs. 2.6%) or Latinx (13.2% vs. 2.6%) and increasingly diagnosed with de novo mBC (33.6% vs. 28.9%). Those from the least privileged areas had longer median TTI than those from the most privileged areas (38 vs 31 days) and shorter median OS (29.7 vs 39.2 months). Multivariable-adjusted HR indicated less timely treatment initiation (HR 0.87, 95% CI 0.83, 0.91, p &lt; 0.01) and worse OS (HR 1.19, 95% CI 1.13, 1.25, p &lt; 0.01) among those from the least privileged areas compared to the most privileged areas.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">CONCLUSION: Racialized economic segregation is a social determinant of health associated with treatment and survival inequities in mBC. Public investments directly addressing racialized economic segregation and other forms of structural racism are needed to reduce inequities in cancer care and outcomes.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38702585/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38702585</a> | DOI:<a href=https://doi.org/10.1007/s10549-024-07319-5>10.1007/s10549-024-07319-5</a></p></div>]]></content:encoded>
  563.      <guid isPermaLink="false">pubmed:38702585</guid>
  564.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  565.      <dc:creator>Harlan Pittell</dc:creator>
  566.      <dc:creator>Gregory S Calip</dc:creator>
  567.      <dc:creator>Amy Pierre</dc:creator>
  568.      <dc:creator>Cleo A Ryals</dc:creator>
  569.      <dc:creator>Jenny S Guadamuz</dc:creator>
  570.      <dc:date>2024-05-03</dc:date>
  571.      <dc:source>Breast cancer research and treatment</dc:source>
  572.      <dc:title>Racialized economic segregation and inequities in treatment initiation and survival among patients with metastatic breast cancer</dc:title>
  573.      <dc:identifier>pmid:38702585</dc:identifier>
  574.      <dc:identifier>doi:10.1007/s10549-024-07319-5</dc:identifier>
  575.    </item>
  576.    <item>
  577.      <title>Author Correction: Stepwise activation of a metabotropic glutamate receptor</title>
  578.      <link>https://pubmed.ncbi.nlm.nih.gov/38702522/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  579.      <description>No abstract</description>
  580.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Nature. 2024 May 3. doi: 10.1038/s41586-024-07470-5. Online ahead of print.</p><p><b>NO ABSTRACT</b></p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38702522/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38702522</a> | DOI:<a href=https://doi.org/10.1038/s41586-024-07470-5>10.1038/s41586-024-07470-5</a></p></div>]]></content:encoded>
  581.      <guid isPermaLink="false">pubmed:38702522</guid>
  582.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  583.      <dc:creator>Kaavya Krishna Kumar</dc:creator>
  584.      <dc:creator>Haoqing Wang</dc:creator>
  585.      <dc:creator>Chris Habrian</dc:creator>
  586.      <dc:creator>Naomi R Latorraca</dc:creator>
  587.      <dc:creator>Jun Xu</dc:creator>
  588.      <dc:creator>Evan S O'Brien</dc:creator>
  589.      <dc:creator>Chensong Zhang</dc:creator>
  590.      <dc:creator>Elizabeth Montabana</dc:creator>
  591.      <dc:creator>Antoine Koehl</dc:creator>
  592.      <dc:creator>Susan Marqusee</dc:creator>
  593.      <dc:creator>Ehud Y Isacoff</dc:creator>
  594.      <dc:creator>Brian K Kobilka</dc:creator>
  595.      <dc:date>2024-05-03</dc:date>
  596.      <dc:source>Nature</dc:source>
  597.      <dc:title>Author Correction: Stepwise activation of a metabotropic glutamate receptor</dc:title>
  598.      <dc:identifier>pmid:38702522</dc:identifier>
  599.      <dc:identifier>doi:10.1038/s41586-024-07470-5</dc:identifier>
  600.    </item>
  601.    <item>
  602.      <title>Lateral palatal foramina are not widespread in Artiodactyla and imply baleen in extinct mysticetes</title>
  603.      <link>https://pubmed.ncbi.nlm.nih.gov/38702346/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  604.      <description>No abstract</description>
  605.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Sci Rep. 2024 May 3;14(1):10174. doi: 10.1038/s41598-024-60673-8.</p><p><b>NO ABSTRACT</b></p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38702346/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38702346</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11068900/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11068900</a> | DOI:<a href=https://doi.org/10.1038/s41598-024-60673-8>10.1038/s41598-024-60673-8</a></p></div>]]></content:encoded>
  606.      <guid isPermaLink="false">pubmed:38702346</guid>
  607.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  608.      <dc:creator>Eric G Ekdale</dc:creator>
  609.      <dc:creator>Joseph J El Adli</dc:creator>
  610.      <dc:creator>Michael R McGowen</dc:creator>
  611.      <dc:creator>Thomas A Deméré</dc:creator>
  612.      <dc:creator>Agnese Lanzetti</dc:creator>
  613.      <dc:creator>Annalisa Berta</dc:creator>
  614.      <dc:creator>Mark S Springer</dc:creator>
  615.      <dc:creator>Robert W Boessenecker</dc:creator>
  616.      <dc:creator>John Gatesy</dc:creator>
  617.      <dc:date>2024-05-03</dc:date>
  618.      <dc:source>Scientific reports</dc:source>
  619.      <dc:title>Lateral palatal foramina are not widespread in Artiodactyla and imply baleen in extinct mysticetes</dc:title>
  620.      <dc:identifier>pmid:38702346</dc:identifier>
  621.      <dc:identifier>pmc:PMC11068900</dc:identifier>
  622.      <dc:identifier>doi:10.1038/s41598-024-60673-8</dc:identifier>
  623.    </item>
  624.    <item>
  625.      <title>Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling</title>
  626.      <link>https://pubmed.ncbi.nlm.nih.gov/38701930/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  627.      <description>Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g. from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g....</description>
  628.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Sci Total Environ. 2024 May 1:172827. doi: 10.1016/j.scitotenv.2024.172827. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g. from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g. agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1μg/L, were used to predict concentrations of chemicals downstream. Using this approach, &gt;90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38701930/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38701930</a> | DOI:<a href=https://doi.org/10.1016/j.scitotenv.2024.172827>10.1016/j.scitotenv.2024.172827</a></p></div>]]></content:encoded>
  629.      <guid isPermaLink="false">pubmed:38701930</guid>
  630.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  631.      <dc:creator>Kajetan Chrapkiewicz</dc:creator>
  632.      <dc:creator>Alex G Lipp</dc:creator>
  633.      <dc:creator>Leon P Barron</dc:creator>
  634.      <dc:creator>Richard Barnes</dc:creator>
  635.      <dc:creator>Gareth G Roberts</dc:creator>
  636.      <dc:date>2024-05-03</dc:date>
  637.      <dc:source>The Science of the total environment</dc:source>
  638.      <dc:title>Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling</dc:title>
  639.      <dc:identifier>pmid:38701930</dc:identifier>
  640.      <dc:identifier>doi:10.1016/j.scitotenv.2024.172827</dc:identifier>
  641.    </item>
  642.    <item>
  643.      <title>Novel Measurement of the Neutron Magnetic Form Factor from A=3 Mirror Nuclei</title>
  644.      <link>https://pubmed.ncbi.nlm.nih.gov/38701469/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  645.      <description>The electromagnetic form factors of the proton and neutron encode information on the spatial structure of their charge and magnetization distributions. While measurements of the proton are relatively straightforward, the lack of a free neutron target makes measurements of the neutron's electromagnetic structure more challenging and more sensitive to experimental or model-dependent uncertainties. Various experiments have attempted to extract the neutron form factors from scattering from the...</description>
  646.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Phys Rev Lett. 2024 Apr 19;132(16):162501. doi: 10.1103/PhysRevLett.132.162501.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">The electromagnetic form factors of the proton and neutron encode information on the spatial structure of their charge and magnetization distributions. While measurements of the proton are relatively straightforward, the lack of a free neutron target makes measurements of the neutron's electromagnetic structure more challenging and more sensitive to experimental or model-dependent uncertainties. Various experiments have attempted to extract the neutron form factors from scattering from the neutron in deuterium, with different techniques providing different, and sometimes large, systematic uncertainties. We present results from a novel measurement of the neutron magnetic form factor using quasielastic scattering from the mirror nuclei ^{3}H and ^{3}He, where the nuclear effects are larger than for deuterium but expected to largely cancel in the cross-section ratios. We extracted values of the neutron magnetic form factor for low-to-modest momentum transfer, 0.6&lt;Q^{2}&lt;2.9 GeV^{2}, where existing measurements give inconsistent results. The precision and Q^{2} range of these data allow for a better understanding of the current world's data and suggest a path toward further improvement of our overall understanding of the neutron's magnetic form factor.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38701469/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38701469</a> | DOI:<a href=https://doi.org/10.1103/PhysRevLett.132.162501>10.1103/PhysRevLett.132.162501</a></p></div>]]></content:encoded>
  647.      <guid isPermaLink="false">pubmed:38701469</guid>
  648.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  649.      <dc:creator>S N Santiesteban</dc:creator>
  650.      <dc:creator>S Li</dc:creator>
  651.      <dc:creator>D Abrams</dc:creator>
  652.      <dc:creator>S Alsalmi</dc:creator>
  653.      <dc:creator>D Androic</dc:creator>
  654.      <dc:creator>K Aniol</dc:creator>
  655.      <dc:creator>J Arrington</dc:creator>
  656.      <dc:creator>T Averett</dc:creator>
  657.      <dc:creator>C Ayerbe Gayoso</dc:creator>
  658.      <dc:creator>J Bane</dc:creator>
  659.      <dc:creator>S Barcus</dc:creator>
  660.      <dc:creator>J Barrow</dc:creator>
  661.      <dc:creator>A Beck</dc:creator>
  662.      <dc:creator>V Bellini</dc:creator>
  663.      <dc:creator>H Bhatt</dc:creator>
  664.      <dc:creator>D Bhetuwal</dc:creator>
  665.      <dc:creator>D Biswas</dc:creator>
  666.      <dc:creator>A Camsonne</dc:creator>
  667.      <dc:creator>J Castellanos</dc:creator>
  668.      <dc:creator>J Chen</dc:creator>
  669.      <dc:creator>J-P Chen</dc:creator>
  670.      <dc:creator>D Chrisman</dc:creator>
  671.      <dc:creator>M E Christy</dc:creator>
  672.      <dc:creator>C Clarke</dc:creator>
  673.      <dc:creator>S Covrig</dc:creator>
  674.      <dc:creator>R Cruz-Torres</dc:creator>
  675.      <dc:creator>D Day</dc:creator>
  676.      <dc:creator>D Dutta</dc:creator>
  677.      <dc:creator>E Fuchey</dc:creator>
  678.      <dc:creator>C Gal</dc:creator>
  679.      <dc:creator>F Garibaldi</dc:creator>
  680.      <dc:creator>T N Gautam</dc:creator>
  681.      <dc:creator>T Gogami</dc:creator>
  682.      <dc:creator>J Gomez</dc:creator>
  683.      <dc:creator>P Guèye</dc:creator>
  684.      <dc:creator>T J Hague</dc:creator>
  685.      <dc:creator>J O Hansen</dc:creator>
  686.      <dc:creator>F Hauenstein</dc:creator>
  687.      <dc:creator>W Henry</dc:creator>
  688.      <dc:creator>D W Higinbotham</dc:creator>
  689.      <dc:creator>R J Holt</dc:creator>
  690.      <dc:creator>C Hyde</dc:creator>
  691.      <dc:creator>K Itabashi</dc:creator>
  692.      <dc:creator>M Kaneta</dc:creator>
  693.      <dc:creator>A Karki</dc:creator>
  694.      <dc:creator>A T Katramatou</dc:creator>
  695.      <dc:creator>C E Keppel</dc:creator>
  696.      <dc:creator>P M King</dc:creator>
  697.      <dc:creator>L Kurbany</dc:creator>
  698.      <dc:creator>T Kutz</dc:creator>
  699.      <dc:creator>N Lashley-Colthirst</dc:creator>
  700.      <dc:creator>W B Li</dc:creator>
  701.      <dc:creator>H Liu</dc:creator>
  702.      <dc:creator>N Liyanage</dc:creator>
  703.      <dc:creator>E Long</dc:creator>
  704.      <dc:creator>A Lovato</dc:creator>
  705.      <dc:creator>J Mammei</dc:creator>
  706.      <dc:creator>P Markowitz</dc:creator>
  707.      <dc:creator>R E McClellan</dc:creator>
  708.      <dc:creator>F Meddi</dc:creator>
  709.      <dc:creator>D Meekins</dc:creator>
  710.      <dc:creator>R Michaels</dc:creator>
  711.      <dc:creator>M Mihovilovič</dc:creator>
  712.      <dc:creator>A Moyer</dc:creator>
  713.      <dc:creator>S Nagao</dc:creator>
  714.      <dc:creator>D Nguyen</dc:creator>
  715.      <dc:creator>M Nycz</dc:creator>
  716.      <dc:creator>M Olson</dc:creator>
  717.      <dc:creator>L Ou</dc:creator>
  718.      <dc:creator>V Owen</dc:creator>
  719.      <dc:creator>C Palatchi</dc:creator>
  720.      <dc:creator>B Pandey</dc:creator>
  721.      <dc:creator>A Papadopoulou</dc:creator>
  722.      <dc:creator>S Park</dc:creator>
  723.      <dc:creator>T Petkovic</dc:creator>
  724.      <dc:creator>S Premathilake</dc:creator>
  725.      <dc:creator>V Punjabi</dc:creator>
  726.      <dc:creator>R D Ransome</dc:creator>
  727.      <dc:creator>P E Reimer</dc:creator>
  728.      <dc:creator>J Reinhold</dc:creator>
  729.      <dc:creator>S Riordan</dc:creator>
  730.      <dc:creator>N Rocco</dc:creator>
  731.      <dc:creator>V M Rodriguez</dc:creator>
  732.      <dc:creator>A Schmidt</dc:creator>
  733.      <dc:creator>B Schmookler</dc:creator>
  734.      <dc:creator>E P Segarra</dc:creator>
  735.      <dc:creator>A Shahinyan</dc:creator>
  736.      <dc:creator>S Širca</dc:creator>
  737.      <dc:creator>K Slifer</dc:creator>
  738.      <dc:creator>P Solvignon</dc:creator>
  739.      <dc:creator>T Su</dc:creator>
  740.      <dc:creator>R Suleiman</dc:creator>
  741.      <dc:creator>L Tang</dc:creator>
  742.      <dc:creator>Y Tian</dc:creator>
  743.      <dc:creator>W Tireman</dc:creator>
  744.      <dc:creator>F Tortorici</dc:creator>
  745.      <dc:creator>Y Toyama</dc:creator>
  746.      <dc:creator>K Uehara</dc:creator>
  747.      <dc:creator>G M Urciuoli</dc:creator>
  748.      <dc:creator>D Votaw</dc:creator>
  749.      <dc:creator>J Williamson</dc:creator>
  750.      <dc:creator>B Wojtsekhowski</dc:creator>
  751.      <dc:creator>S Wood</dc:creator>
  752.      <dc:creator>Z H Ye</dc:creator>
  753.      <dc:creator>J Zhang</dc:creator>
  754.      <dc:creator>X Zheng</dc:creator>
  755.      <dc:creator>Jefferson Lab Hall A Collaboration</dc:creator>
  756.      <dc:date>2024-05-03</dc:date>
  757.      <dc:source>Physical review letters</dc:source>
  758.      <dc:title>Novel Measurement of the Neutron Magnetic Form Factor from A=3 Mirror Nuclei</dc:title>
  759.      <dc:identifier>pmid:38701469</dc:identifier>
  760.      <dc:identifier>doi:10.1103/PhysRevLett.132.162501</dc:identifier>
  761.    </item>
  762.    <item>
  763.      <title>First Measurement of the |t| Dependence of Incoherent J/ψ Photonuclear Production</title>
  764.      <link>https://pubmed.ncbi.nlm.nih.gov/38701458/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  765.      <description>The first measurement of the cross section for incoherent photonuclear production of J/ψ vector mesons as a function of the Mandelstam |t| variable is presented. The measurement was carried out with the ALICE detector at midrapidity, |y|&lt;0.8, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02 TeV. This rapidity interval corresponds to a Bjorken-x range (0.3-1.4)×10^{-3}. Cross sections are given in five |t| intervals in the range...</description>
  766.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Phys Rev Lett. 2024 Apr 19;132(16):162302. doi: 10.1103/PhysRevLett.132.162302.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">The first measurement of the cross section for incoherent photonuclear production of J/ψ vector mesons as a function of the Mandelstam |t| variable is presented. The measurement was carried out with the ALICE detector at midrapidity, |y|&lt;0.8, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02 TeV. This rapidity interval corresponds to a Bjorken-x range (0.3-1.4)×10^{-3}. Cross sections are given in five |t| intervals in the range 0.04&lt;|t|&lt;1 GeV^{2} and compared to the predictions by different models. Models that ignore quantum fluctuations of the gluon density in the colliding hadron predict a |t| dependence of the cross section much steeper than in data. The inclusion of such fluctuations in the same models provides a better description of the data.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38701458/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38701458</a> | DOI:<a href=https://doi.org/10.1103/PhysRevLett.132.162302>10.1103/PhysRevLett.132.162302</a></p></div>]]></content:encoded>
  767.      <guid isPermaLink="false">pubmed:38701458</guid>
  768.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  769.      <dc:creator>S Acharya</dc:creator>
  770.      <dc:creator>D Adamová</dc:creator>
  771.      <dc:creator>A Adler</dc:creator>
  772.      <dc:creator>G Aglieri Rinella</dc:creator>
  773.      <dc:creator>M Agnello</dc:creator>
  774.      <dc:creator>N Agrawal</dc:creator>
  775.      <dc:creator>Z Ahammed</dc:creator>
  776.      <dc:creator>S Ahmad</dc:creator>
  777.      <dc:creator>S U Ahn</dc:creator>
  778.      <dc:creator>I Ahuja</dc:creator>
  779.      <dc:creator>A Akindinov</dc:creator>
  780.      <dc:creator>M Al-Turany</dc:creator>
  781.      <dc:creator>D Aleksandrov</dc:creator>
  782.      <dc:creator>B Alessandro</dc:creator>
  783.      <dc:creator>H M Alfanda</dc:creator>
  784.      <dc:creator>R Alfaro Molina</dc:creator>
  785.      <dc:creator>B Ali</dc:creator>
  786.      <dc:creator>A Alici</dc:creator>
  787.      <dc:creator>N Alizadehvandchali</dc:creator>
  788.      <dc:creator>A Alkin</dc:creator>
  789.      <dc:creator>J Alme</dc:creator>
  790.      <dc:creator>G Alocco</dc:creator>
  791.      <dc:creator>T Alt</dc:creator>
  792.      <dc:creator>A R Altamura</dc:creator>
  793.      <dc:creator>I Altsybeev</dc:creator>
  794.      <dc:creator>J R Alvarado</dc:creator>
  795.      <dc:creator>M N Anaam</dc:creator>
  796.      <dc:creator>C Andrei</dc:creator>
  797.      <dc:creator>A Andronic</dc:creator>
  798.      <dc:creator>V Anguelov</dc:creator>
  799.      <dc:creator>F Antinori</dc:creator>
  800.      <dc:creator>P Antonioli</dc:creator>
  801.      <dc:creator>N Apadula</dc:creator>
  802.      <dc:creator>L Aphecetche</dc:creator>
  803.      <dc:creator>H Appelshäuser</dc:creator>
  804.      <dc:creator>C Arata</dc:creator>
  805.      <dc:creator>S Arcelli</dc:creator>
  806.      <dc:creator>M Aresti</dc:creator>
  807.      <dc:creator>R Arnaldi</dc:creator>
  808.      <dc:creator>J G M C A Arneiro</dc:creator>
  809.      <dc:creator>I C Arsene</dc:creator>
  810.      <dc:creator>M Arslandok</dc:creator>
  811.      <dc:creator>A Augustinus</dc:creator>
  812.      <dc:creator>R Averbeck</dc:creator>
  813.      <dc:creator>M D Azmi</dc:creator>
  814.      <dc:creator>H Baba</dc:creator>
  815.      <dc:creator>A Badalà</dc:creator>
  816.      <dc:creator>J Bae</dc:creator>
  817.      <dc:creator>Y W Baek</dc:creator>
  818.      <dc:creator>X Bai</dc:creator>
  819.      <dc:creator>R Bailhache</dc:creator>
  820.      <dc:creator>Y Bailung</dc:creator>
  821.      <dc:creator>A Balbino</dc:creator>
  822.      <dc:creator>A Baldisseri</dc:creator>
  823.      <dc:creator>B Balis</dc:creator>
  824.      <dc:creator>D Banerjee</dc:creator>
  825.      <dc:creator>Z Banoo</dc:creator>
  826.      <dc:creator>R Barbera</dc:creator>
  827.      <dc:creator>F Barile</dc:creator>
  828.      <dc:creator>L Barioglio</dc:creator>
  829.      <dc:creator>M Barlou</dc:creator>
  830.      <dc:creator>G G Barnaföldi</dc:creator>
  831.      <dc:creator>L S Barnby</dc:creator>
  832.      <dc:creator>V Barret</dc:creator>
  833.      <dc:creator>L Barreto</dc:creator>
  834.      <dc:creator>C Bartels</dc:creator>
  835.      <dc:creator>K Barth</dc:creator>
  836.      <dc:creator>E Bartsch</dc:creator>
  837.      <dc:creator>N Bastid</dc:creator>
  838.      <dc:creator>S Basu</dc:creator>
  839.      <dc:creator>G Batigne</dc:creator>
  840.      <dc:creator>D Battistini</dc:creator>
  841.      <dc:creator>B Batyunya</dc:creator>
  842.      <dc:creator>D Bauri</dc:creator>
  843.      <dc:creator>J L Bazo Alba</dc:creator>
  844.      <dc:creator>I G Bearden</dc:creator>
  845.      <dc:creator>C Beattie</dc:creator>
  846.      <dc:creator>P Becht</dc:creator>
  847.      <dc:creator>D Behera</dc:creator>
  848.      <dc:creator>I Belikov</dc:creator>
  849.      <dc:creator>A D C Bell Hechavarria</dc:creator>
  850.      <dc:creator>F Bellini</dc:creator>
  851.      <dc:creator>R Bellwied</dc:creator>
  852.      <dc:creator>S Belokurova</dc:creator>
  853.      <dc:creator>G Bencedi</dc:creator>
  854.      <dc:creator>S Beole</dc:creator>
  855.      <dc:creator>A Bercuci</dc:creator>
  856.      <dc:creator>Y Berdnikov</dc:creator>
  857.      <dc:creator>A Berdnikova</dc:creator>
  858.      <dc:creator>L Bergmann</dc:creator>
  859.      <dc:creator>M G Besoiu</dc:creator>
  860.      <dc:creator>L Betev</dc:creator>
  861.      <dc:creator>P P Bhaduri</dc:creator>
  862.      <dc:creator>A Bhasin</dc:creator>
  863.      <dc:creator>M A Bhat</dc:creator>
  864.      <dc:creator>B Bhattacharjee</dc:creator>
  865.      <dc:creator>L Bianchi</dc:creator>
  866.      <dc:creator>N Bianchi</dc:creator>
  867.      <dc:creator>J Bielčík</dc:creator>
  868.      <dc:creator>J Bielčíková</dc:creator>
  869.      <dc:creator>J Biernat</dc:creator>
  870.      <dc:creator>A P Bigot</dc:creator>
  871.      <dc:creator>A Bilandzic</dc:creator>
  872.      <dc:creator>G Biro</dc:creator>
  873.      <dc:creator>S Biswas</dc:creator>
  874.      <dc:creator>N Bize</dc:creator>
  875.      <dc:creator>J T Blair</dc:creator>
  876.      <dc:creator>D Blau</dc:creator>
  877.      <dc:creator>M B Blidaru</dc:creator>
  878.      <dc:creator>N Bluhme</dc:creator>
  879.      <dc:creator>C Blume</dc:creator>
  880.      <dc:creator>G Boca</dc:creator>
  881.      <dc:creator>F Bock</dc:creator>
  882.      <dc:creator>T Bodova</dc:creator>
  883.      <dc:creator>A Bogdanov</dc:creator>
  884.      <dc:creator>S Boi</dc:creator>
  885.      <dc:creator>J Bok</dc:creator>
  886.      <dc:creator>L Boldizsár</dc:creator>
  887.      <dc:creator>M Bombara</dc:creator>
  888.      <dc:creator>P M Bond</dc:creator>
  889.      <dc:creator>G Bonomi</dc:creator>
  890.      <dc:creator>H Borel</dc:creator>
  891.      <dc:creator>A Borissov</dc:creator>
  892.      <dc:creator>A G Borquez Carcamo</dc:creator>
  893.      <dc:creator>H Bossi</dc:creator>
  894.      <dc:creator>E Botta</dc:creator>
  895.      <dc:creator>Y E M Bouziani</dc:creator>
  896.      <dc:creator>L Bratrud</dc:creator>
  897.      <dc:creator>P Braun-Munzinger</dc:creator>
  898.      <dc:creator>M Bregant</dc:creator>
  899.      <dc:creator>M Broz</dc:creator>
  900.      <dc:creator>G E Bruno</dc:creator>
  901.      <dc:creator>M D Buckland</dc:creator>
  902.      <dc:creator>D Budnikov</dc:creator>
  903.      <dc:creator>H Buesching</dc:creator>
  904.      <dc:creator>S Bufalino</dc:creator>
  905.      <dc:creator>P Buhler</dc:creator>
  906.      <dc:creator>N Burmasov</dc:creator>
  907.      <dc:creator>Z Buthelezi</dc:creator>
  908.      <dc:creator>A Bylinkin</dc:creator>
  909.      <dc:creator>S A Bysiak</dc:creator>
  910.      <dc:creator>M Cai</dc:creator>
  911.      <dc:creator>H Caines</dc:creator>
  912.      <dc:creator>A Caliva</dc:creator>
  913.      <dc:creator>E Calvo Villar</dc:creator>
  914.      <dc:creator>J M M Camacho</dc:creator>
  915.      <dc:creator>P Camerini</dc:creator>
  916.      <dc:creator>F D M Canedo</dc:creator>
  917.      <dc:creator>S L Cantway</dc:creator>
  918.      <dc:creator>M Carabas</dc:creator>
  919.      <dc:creator>A A Carballo</dc:creator>
  920.      <dc:creator>F Carnesecchi</dc:creator>
  921.      <dc:creator>R Caron</dc:creator>
  922.      <dc:creator>L A D Carvalho</dc:creator>
  923.      <dc:creator>J Castillo Castellanos</dc:creator>
  924.      <dc:creator>F Catalano</dc:creator>
  925.      <dc:creator>C Ceballos Sanchez</dc:creator>
  926.      <dc:creator>I Chakaberia</dc:creator>
  927.      <dc:creator>P Chakraborty</dc:creator>
  928.      <dc:creator>S Chandra</dc:creator>
  929.      <dc:creator>S Chapeland</dc:creator>
  930.      <dc:creator>M Chartier</dc:creator>
  931.      <dc:creator>S Chattopadhyay</dc:creator>
  932.      <dc:creator>S Chattopadhyay</dc:creator>
  933.      <dc:creator>T Cheng</dc:creator>
  934.      <dc:creator>C Cheshkov</dc:creator>
  935.      <dc:creator>B Cheynis</dc:creator>
  936.      <dc:creator>V Chibante Barroso</dc:creator>
  937.      <dc:creator>D D Chinellato</dc:creator>
  938.      <dc:creator>E S Chizzali</dc:creator>
  939.      <dc:creator>J Cho</dc:creator>
  940.      <dc:creator>S Cho</dc:creator>
  941.      <dc:creator>P Chochula</dc:creator>
  942.      <dc:creator>P Christakoglou</dc:creator>
  943.      <dc:creator>C H Christensen</dc:creator>
  944.      <dc:creator>P Christiansen</dc:creator>
  945.      <dc:creator>T Chujo</dc:creator>
  946.      <dc:creator>M Ciacco</dc:creator>
  947.      <dc:creator>C Cicalo</dc:creator>
  948.      <dc:creator>F Cindolo</dc:creator>
  949.      <dc:creator>M R Ciupek</dc:creator>
  950.      <dc:creator>G Clai</dc:creator>
  951.      <dc:creator>F Colamaria</dc:creator>
  952.      <dc:creator>J S Colburn</dc:creator>
  953.      <dc:creator>D Colella</dc:creator>
  954.      <dc:creator>M Colocci</dc:creator>
  955.      <dc:creator>M Concas</dc:creator>
  956.      <dc:creator>G Conesa Balbastre</dc:creator>
  957.      <dc:creator>Z Conesa Del Valle</dc:creator>
  958.      <dc:creator>G Contin</dc:creator>
  959.      <dc:creator>J G Contreras</dc:creator>
  960.      <dc:creator>M L Coquet</dc:creator>
  961.      <dc:creator>P Cortese</dc:creator>
  962.      <dc:creator>M R Cosentino</dc:creator>
  963.      <dc:creator>F Costa</dc:creator>
  964.      <dc:creator>S Costanza</dc:creator>
  965.      <dc:creator>C Cot</dc:creator>
  966.      <dc:creator>J Crkovská</dc:creator>
  967.      <dc:creator>P Crochet</dc:creator>
  968.      <dc:creator>R Cruz-Torres</dc:creator>
  969.      <dc:creator>P Cui</dc:creator>
  970.      <dc:creator>A Dainese</dc:creator>
  971.      <dc:creator>M C Danisch</dc:creator>
  972.      <dc:creator>A Danu</dc:creator>
  973.      <dc:creator>P Das</dc:creator>
  974.      <dc:creator>P Das</dc:creator>
  975.      <dc:creator>S Das</dc:creator>
  976.      <dc:creator>A R Dash</dc:creator>
  977.      <dc:creator>S Dash</dc:creator>
  978.      <dc:creator>A De Caro</dc:creator>
  979.      <dc:creator>G de Cataldo</dc:creator>
  980.      <dc:creator>J de Cuveland</dc:creator>
  981.      <dc:creator>A De Falco</dc:creator>
  982.      <dc:creator>D De Gruttola</dc:creator>
  983.      <dc:creator>N De Marco</dc:creator>
  984.      <dc:creator>C De Martin</dc:creator>
  985.      <dc:creator>S De Pasquale</dc:creator>
  986.      <dc:creator>R Deb</dc:creator>
  987.      <dc:creator>S Deb</dc:creator>
  988.      <dc:creator>R Del Grande</dc:creator>
  989.      <dc:creator>L Dello Stritto</dc:creator>
  990.      <dc:creator>W Deng</dc:creator>
  991.      <dc:creator>P Dhankher</dc:creator>
  992.      <dc:creator>D Di Bari</dc:creator>
  993.      <dc:creator>A Di Mauro</dc:creator>
  994.      <dc:creator>B Diab</dc:creator>
  995.      <dc:creator>R A Diaz</dc:creator>
  996.      <dc:creator>T Dietel</dc:creator>
  997.      <dc:creator>Y Ding</dc:creator>
  998.      <dc:creator>R Divià</dc:creator>
  999.      <dc:creator>D U Dixit</dc:creator>
  1000.      <dc:creator>Ø Djuvsland</dc:creator>
  1001.      <dc:creator>U Dmitrieva</dc:creator>
  1002.      <dc:creator>A Dobrin</dc:creator>
  1003.      <dc:creator>B Dönigus</dc:creator>
  1004.      <dc:creator>J M Dubinski</dc:creator>
  1005.      <dc:creator>A Dubla</dc:creator>
  1006.      <dc:creator>S Dudi</dc:creator>
  1007.      <dc:creator>P Dupieux</dc:creator>
  1008.      <dc:creator>M Durkac</dc:creator>
  1009.      <dc:creator>N Dzalaiova</dc:creator>
  1010.      <dc:creator>T M Eder</dc:creator>
  1011.      <dc:creator>R J Ehlers</dc:creator>
  1012.      <dc:creator>F Eisenhut</dc:creator>
  1013.      <dc:creator>R Ejima</dc:creator>
  1014.      <dc:creator>D Elia</dc:creator>
  1015.      <dc:creator>B Erazmus</dc:creator>
  1016.      <dc:creator>F Ercolessi</dc:creator>
  1017.      <dc:creator>F Erhardt</dc:creator>
  1018.      <dc:creator>M R Ersdal</dc:creator>
  1019.      <dc:creator>B Espagnon</dc:creator>
  1020.      <dc:creator>G Eulisse</dc:creator>
  1021.      <dc:creator>D Evans</dc:creator>
  1022.      <dc:creator>S Evdokimov</dc:creator>
  1023.      <dc:creator>L Fabbietti</dc:creator>
  1024.      <dc:creator>M Faggin</dc:creator>
  1025.      <dc:creator>J Faivre</dc:creator>
  1026.      <dc:creator>F Fan</dc:creator>
  1027.      <dc:creator>W Fan</dc:creator>
  1028.      <dc:creator>A Fantoni</dc:creator>
  1029.      <dc:creator>M Fasel</dc:creator>
  1030.      <dc:creator>P Fecchio</dc:creator>
  1031.      <dc:creator>A Feliciello</dc:creator>
  1032.      <dc:creator>G Feofilov</dc:creator>
  1033.      <dc:creator>A Fernández Téllez</dc:creator>
  1034.      <dc:creator>L Ferrandi</dc:creator>
  1035.      <dc:creator>M B Ferrer</dc:creator>
  1036.      <dc:creator>A Ferrero</dc:creator>
  1037.      <dc:creator>C Ferrero</dc:creator>
  1038.      <dc:creator>A Ferretti</dc:creator>
  1039.      <dc:creator>V J G Feuillard</dc:creator>
  1040.      <dc:creator>V Filova</dc:creator>
  1041.      <dc:creator>D Finogeev</dc:creator>
  1042.      <dc:creator>F M Fionda</dc:creator>
  1043.      <dc:creator>F Flor</dc:creator>
  1044.      <dc:creator>A N Flores</dc:creator>
  1045.      <dc:creator>S Foertsch</dc:creator>
  1046.      <dc:creator>I Fokin</dc:creator>
  1047.      <dc:creator>S Fokin</dc:creator>
  1048.      <dc:creator>E Fragiacomo</dc:creator>
  1049.      <dc:creator>E Frajna</dc:creator>
  1050.      <dc:creator>U Fuchs</dc:creator>
  1051.      <dc:creator>N Funicello</dc:creator>
  1052.      <dc:creator>C Furget</dc:creator>
  1053.      <dc:creator>A Furs</dc:creator>
  1054.      <dc:creator>T Fusayasu</dc:creator>
  1055.      <dc:creator>J J Gaardhøje</dc:creator>
  1056.      <dc:creator>M Gagliardi</dc:creator>
  1057.      <dc:creator>A M Gago</dc:creator>
  1058.      <dc:creator>T Gahlaut</dc:creator>
  1059.      <dc:creator>C D Galvan</dc:creator>
  1060.      <dc:creator>D R Gangadharan</dc:creator>
  1061.      <dc:creator>P Ganoti</dc:creator>
  1062.      <dc:creator>C Garabatos</dc:creator>
  1063.      <dc:creator>A T Garcia</dc:creator>
  1064.      <dc:creator>T García Chávez</dc:creator>
  1065.      <dc:creator>E Garcia-Solis</dc:creator>
  1066.      <dc:creator>C Gargiulo</dc:creator>
  1067.      <dc:creator>K Garner</dc:creator>
  1068.      <dc:creator>P Gasik</dc:creator>
  1069.      <dc:creator>A Gautam</dc:creator>
  1070.      <dc:creator>M B Gay Ducati</dc:creator>
  1071.      <dc:creator>M Germain</dc:creator>
  1072.      <dc:creator>A Ghimouz</dc:creator>
  1073.      <dc:creator>C Ghosh</dc:creator>
  1074.      <dc:creator>M Giacalone</dc:creator>
  1075.      <dc:creator>P Giubellino</dc:creator>
  1076.      <dc:creator>P Giubilato</dc:creator>
  1077.      <dc:creator>A M C Glaenzer</dc:creator>
  1078.      <dc:creator>P Glässel</dc:creator>
  1079.      <dc:creator>E Glimos</dc:creator>
  1080.      <dc:creator>D J Q Goh</dc:creator>
  1081.      <dc:creator>V Gonzalez</dc:creator>
  1082.      <dc:creator>M Gorgon</dc:creator>
  1083.      <dc:creator>K Goswami</dc:creator>
  1084.      <dc:creator>S Gotovac</dc:creator>
  1085.      <dc:creator>V Grabski</dc:creator>
  1086.      <dc:creator>L K Graczykowski</dc:creator>
  1087.      <dc:creator>E Grecka</dc:creator>
  1088.      <dc:creator>A Grelli</dc:creator>
  1089.      <dc:creator>C Grigoras</dc:creator>
  1090.      <dc:creator>V Grigoriev</dc:creator>
  1091.      <dc:creator>S Grigoryan</dc:creator>
  1092.      <dc:creator>F Grosa</dc:creator>
  1093.      <dc:creator>J F Grosse-Oetringhaus</dc:creator>
  1094.      <dc:creator>R Grosso</dc:creator>
  1095.      <dc:creator>D Grund</dc:creator>
  1096.      <dc:creator>G G Guardiano</dc:creator>
  1097.      <dc:creator>R Guernane</dc:creator>
  1098.      <dc:creator>M Guilbaud</dc:creator>
  1099.      <dc:creator>K Gulbrandsen</dc:creator>
  1100.      <dc:creator>T Gündem</dc:creator>
  1101.      <dc:creator>T Gunji</dc:creator>
  1102.      <dc:creator>W Guo</dc:creator>
  1103.      <dc:creator>A Gupta</dc:creator>
  1104.      <dc:creator>R Gupta</dc:creator>
  1105.      <dc:creator>R Gupta</dc:creator>
  1106.      <dc:creator>K Gwizdziel</dc:creator>
  1107.      <dc:creator>L Gyulai</dc:creator>
  1108.      <dc:creator>M K Habib</dc:creator>
  1109.      <dc:creator>C Hadjidakis</dc:creator>
  1110.      <dc:creator>F U Haider</dc:creator>
  1111.      <dc:creator>H Hamagaki</dc:creator>
  1112.      <dc:creator>A Hamdi</dc:creator>
  1113.      <dc:creator>M Hamid</dc:creator>
  1114.      <dc:creator>Y Han</dc:creator>
  1115.      <dc:creator>B G Hanley</dc:creator>
  1116.      <dc:creator>R Hannigan</dc:creator>
  1117.      <dc:creator>J Hansen</dc:creator>
  1118.      <dc:creator>M R Haque</dc:creator>
  1119.      <dc:creator>J W Harris</dc:creator>
  1120.      <dc:creator>A Harton</dc:creator>
  1121.      <dc:creator>H Hassan</dc:creator>
  1122.      <dc:creator>D Hatzifotiadou</dc:creator>
  1123.      <dc:creator>P Hauer</dc:creator>
  1124.      <dc:creator>L B Havener</dc:creator>
  1125.      <dc:creator>S T Heckel</dc:creator>
  1126.      <dc:creator>E Hellbär</dc:creator>
  1127.      <dc:creator>H Helstrup</dc:creator>
  1128.      <dc:creator>M Hemmer</dc:creator>
  1129.      <dc:creator>T Herman</dc:creator>
  1130.      <dc:creator>G Herrera Corral</dc:creator>
  1131.      <dc:creator>F Herrmann</dc:creator>
  1132.      <dc:creator>S Herrmann</dc:creator>
  1133.      <dc:creator>K F Hetland</dc:creator>
  1134.      <dc:creator>B Heybeck</dc:creator>
  1135.      <dc:creator>H Hillemanns</dc:creator>
  1136.      <dc:creator>B Hippolyte</dc:creator>
  1137.      <dc:creator>F W Hoffmann</dc:creator>
  1138.      <dc:creator>B Hofman</dc:creator>
  1139.      <dc:creator>B Hohlweger</dc:creator>
  1140.      <dc:creator>G H Hong</dc:creator>
  1141.      <dc:creator>M Horst</dc:creator>
  1142.      <dc:creator>A Horzyk</dc:creator>
  1143.      <dc:creator>Y Hou</dc:creator>
  1144.      <dc:creator>P Hristov</dc:creator>
  1145.      <dc:creator>C Hughes</dc:creator>
  1146.      <dc:creator>P Huhn</dc:creator>
  1147.      <dc:creator>L M Huhta</dc:creator>
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  1149.      <dc:creator>A Hutson</dc:creator>
  1150.      <dc:creator>D Hutter</dc:creator>
  1151.      <dc:creator>R Ilkaev</dc:creator>
  1152.      <dc:creator>H Ilyas</dc:creator>
  1153.      <dc:creator>M Inaba</dc:creator>
  1154.      <dc:creator>G M Innocenti</dc:creator>
  1155.      <dc:creator>M Ippolitov</dc:creator>
  1156.      <dc:creator>A Isakov</dc:creator>
  1157.      <dc:creator>T Isidori</dc:creator>
  1158.      <dc:creator>M S Islam</dc:creator>
  1159.      <dc:creator>M Ivanov</dc:creator>
  1160.      <dc:creator>M Ivanov</dc:creator>
  1161.      <dc:creator>V Ivanov</dc:creator>
  1162.      <dc:creator>K E Iversen</dc:creator>
  1163.      <dc:creator>M Jablonski</dc:creator>
  1164.      <dc:creator>B Jacak</dc:creator>
  1165.      <dc:creator>N Jacazio</dc:creator>
  1166.      <dc:creator>P M Jacobs</dc:creator>
  1167.      <dc:creator>S Jadlovska</dc:creator>
  1168.      <dc:creator>J Jadlovsky</dc:creator>
  1169.      <dc:creator>S Jaelani</dc:creator>
  1170.      <dc:creator>C Jahnke</dc:creator>
  1171.      <dc:creator>M J Jakubowska</dc:creator>
  1172.      <dc:creator>M A Janik</dc:creator>
  1173.      <dc:creator>T Janson</dc:creator>
  1174.      <dc:creator>M Jercic</dc:creator>
  1175.      <dc:creator>S Ji</dc:creator>
  1176.      <dc:creator>S Jia</dc:creator>
  1177.      <dc:creator>A A P Jimenez</dc:creator>
  1178.      <dc:creator>F Jonas</dc:creator>
  1179.      <dc:creator>D M Jones</dc:creator>
  1180.      <dc:creator>J M Jowett</dc:creator>
  1181.      <dc:creator>J Jung</dc:creator>
  1182.      <dc:creator>M Jung</dc:creator>
  1183.      <dc:creator>A Junique</dc:creator>
  1184.      <dc:creator>A Jusko</dc:creator>
  1185.      <dc:creator>M J Kabus</dc:creator>
  1186.      <dc:creator>J Kaewjai</dc:creator>
  1187.      <dc:creator>P Kalinak</dc:creator>
  1188.      <dc:creator>A S Kalteyer</dc:creator>
  1189.      <dc:creator>A Kalweit</dc:creator>
  1190.      <dc:creator>V Kaplin</dc:creator>
  1191.      <dc:creator>A Karasu Uysal</dc:creator>
  1192.      <dc:creator>D Karatovic</dc:creator>
  1193.      <dc:creator>O Karavichev</dc:creator>
  1194.      <dc:creator>T Karavicheva</dc:creator>
  1195.      <dc:creator>P Karczmarczyk</dc:creator>
  1196.      <dc:creator>E Karpechev</dc:creator>
  1197.      <dc:creator>U Kebschull</dc:creator>
  1198.      <dc:creator>R Keidel</dc:creator>
  1199.      <dc:creator>D L D Keijdener</dc:creator>
  1200.      <dc:creator>M Keil</dc:creator>
  1201.      <dc:creator>B Ketzer</dc:creator>
  1202.      <dc:creator>S S Khade</dc:creator>
  1203.      <dc:creator>A M Khan</dc:creator>
  1204.      <dc:creator>S Khan</dc:creator>
  1205.      <dc:creator>A Khanzadeev</dc:creator>
  1206.      <dc:creator>Y Kharlov</dc:creator>
  1207.      <dc:creator>A Khatun</dc:creator>
  1208.      <dc:creator>A Khuntia</dc:creator>
  1209.      <dc:creator>M B Kidson</dc:creator>
  1210.      <dc:creator>B Kileng</dc:creator>
  1211.      <dc:creator>B Kim</dc:creator>
  1212.      <dc:creator>C Kim</dc:creator>
  1213.      <dc:creator>D J Kim</dc:creator>
  1214.      <dc:creator>E J Kim</dc:creator>
  1215.      <dc:creator>J Kim</dc:creator>
  1216.      <dc:creator>J S Kim</dc:creator>
  1217.      <dc:creator>J Kim</dc:creator>
  1218.      <dc:creator>J Kim</dc:creator>
  1219.      <dc:creator>M Kim</dc:creator>
  1220.      <dc:creator>S Kim</dc:creator>
  1221.      <dc:creator>T Kim</dc:creator>
  1222.      <dc:creator>K Kimura</dc:creator>
  1223.      <dc:creator>S Kirsch</dc:creator>
  1224.      <dc:creator>I Kisel</dc:creator>
  1225.      <dc:creator>S Kiselev</dc:creator>
  1226.      <dc:creator>A Kisiel</dc:creator>
  1227.      <dc:creator>J P Kitowski</dc:creator>
  1228.      <dc:creator>J L Klay</dc:creator>
  1229.      <dc:creator>J Klein</dc:creator>
  1230.      <dc:creator>S Klein</dc:creator>
  1231.      <dc:creator>C Klein-Bösing</dc:creator>
  1232.      <dc:creator>M Kleiner</dc:creator>
  1233.      <dc:creator>T Klemenz</dc:creator>
  1234.      <dc:creator>A Kluge</dc:creator>
  1235.      <dc:creator>A G Knospe</dc:creator>
  1236.      <dc:creator>C Kobdaj</dc:creator>
  1237.      <dc:creator>T Kollegger</dc:creator>
  1238.      <dc:creator>A Kondratyev</dc:creator>
  1239.      <dc:creator>N Kondratyeva</dc:creator>
  1240.      <dc:creator>E Kondratyuk</dc:creator>
  1241.      <dc:creator>J Konig</dc:creator>
  1242.      <dc:creator>S A Konigstorfer</dc:creator>
  1243.      <dc:creator>P J Konopka</dc:creator>
  1244.      <dc:creator>G Kornakov</dc:creator>
  1245.      <dc:creator>M Korwieser</dc:creator>
  1246.      <dc:creator>S D Koryciak</dc:creator>
  1247.      <dc:creator>A Kotliarov</dc:creator>
  1248.      <dc:creator>V Kovalenko</dc:creator>
  1249.      <dc:creator>M Kowalski</dc:creator>
  1250.      <dc:creator>V Kozhuharov</dc:creator>
  1251.      <dc:creator>I Králik</dc:creator>
  1252.      <dc:creator>A Kravčáková</dc:creator>
  1253.      <dc:creator>L Krcal</dc:creator>
  1254.      <dc:creator>M Krivda</dc:creator>
  1255.      <dc:creator>F Krizek</dc:creator>
  1256.      <dc:creator>K Krizkova Gajdosova</dc:creator>
  1257.      <dc:creator>M Kroesen</dc:creator>
  1258.      <dc:creator>M Krüger</dc:creator>
  1259.      <dc:creator>D M Krupova</dc:creator>
  1260.      <dc:creator>E Kryshen</dc:creator>
  1261.      <dc:creator>V Kučera</dc:creator>
  1262.      <dc:creator>C Kuhn</dc:creator>
  1263.      <dc:creator>P G Kuijer</dc:creator>
  1264.      <dc:creator>T Kumaoka</dc:creator>
  1265.      <dc:creator>D Kumar</dc:creator>
  1266.      <dc:creator>L Kumar</dc:creator>
  1267.      <dc:creator>N Kumar</dc:creator>
  1268.      <dc:creator>S Kumar</dc:creator>
  1269.      <dc:creator>S Kundu</dc:creator>
  1270.      <dc:creator>P Kurashvili</dc:creator>
  1271.      <dc:creator>A Kurepin</dc:creator>
  1272.      <dc:creator>A B Kurepin</dc:creator>
  1273.      <dc:creator>A Kuryakin</dc:creator>
  1274.      <dc:creator>S Kushpil</dc:creator>
  1275.      <dc:creator>M J Kweon</dc:creator>
  1276.      <dc:creator>Y Kwon</dc:creator>
  1277.      <dc:creator>S L La Pointe</dc:creator>
  1278.      <dc:creator>P La Rocca</dc:creator>
  1279.      <dc:creator>A Lakrathok</dc:creator>
  1280.      <dc:creator>M Lamanna</dc:creator>
  1281.      <dc:creator>A R Landou</dc:creator>
  1282.      <dc:creator>R Langoy</dc:creator>
  1283.      <dc:creator>P Larionov</dc:creator>
  1284.      <dc:creator>E Laudi</dc:creator>
  1285.      <dc:creator>L Lautner</dc:creator>
  1286.      <dc:creator>R Lavicka</dc:creator>
  1287.      <dc:creator>R Lea</dc:creator>
  1288.      <dc:creator>H Lee</dc:creator>
  1289.      <dc:creator>I Legrand</dc:creator>
  1290.      <dc:creator>G Legras</dc:creator>
  1291.      <dc:creator>J Lehrbach</dc:creator>
  1292.      <dc:creator>T M Lelek</dc:creator>
  1293.      <dc:creator>R C Lemmon</dc:creator>
  1294.      <dc:creator>I León Monzón</dc:creator>
  1295.      <dc:creator>M M Lesch</dc:creator>
  1296.      <dc:creator>E D Lesser</dc:creator>
  1297.      <dc:creator>P Lévai</dc:creator>
  1298.      <dc:creator>X Li</dc:creator>
  1299.      <dc:creator>X L Li</dc:creator>
  1300.      <dc:creator>J Lien</dc:creator>
  1301.      <dc:creator>R Lietava</dc:creator>
  1302.      <dc:creator>I Likmeta</dc:creator>
  1303.      <dc:creator>B Lim</dc:creator>
  1304.      <dc:creator>S H Lim</dc:creator>
  1305.      <dc:creator>V Lindenstruth</dc:creator>
  1306.      <dc:creator>A Lindner</dc:creator>
  1307.      <dc:creator>C Lippmann</dc:creator>
  1308.      <dc:creator>A Liu</dc:creator>
  1309.      <dc:creator>D H Liu</dc:creator>
  1310.      <dc:creator>J Liu</dc:creator>
  1311.      <dc:creator>G S S Liveraro</dc:creator>
  1312.      <dc:creator>I M Lofnes</dc:creator>
  1313.      <dc:creator>C Loizides</dc:creator>
  1314.      <dc:creator>S Lokos</dc:creator>
  1315.      <dc:creator>J Lomker</dc:creator>
  1316.      <dc:creator>P Loncar</dc:creator>
  1317.      <dc:creator>J A Lopez</dc:creator>
  1318.      <dc:creator>X Lopez</dc:creator>
  1319.      <dc:creator>E López Torres</dc:creator>
  1320.      <dc:creator>P Lu</dc:creator>
  1321.      <dc:creator>J R Luhder</dc:creator>
  1322.      <dc:creator>M Lunardon</dc:creator>
  1323.      <dc:creator>G Luparello</dc:creator>
  1324.      <dc:creator>Y G Ma</dc:creator>
  1325.      <dc:creator>M Mager</dc:creator>
  1326.      <dc:creator>A Maire</dc:creator>
  1327.      <dc:creator>E M Majerz</dc:creator>
  1328.      <dc:creator>M V Makariev</dc:creator>
  1329.      <dc:creator>M Malaev</dc:creator>
  1330.      <dc:creator>G Malfattore</dc:creator>
  1331.      <dc:creator>N M Malik</dc:creator>
  1332.      <dc:creator>Q W Malik</dc:creator>
  1333.      <dc:creator>S K Malik</dc:creator>
  1334.      <dc:creator>L Malinina</dc:creator>
  1335.      <dc:creator>D Mallick</dc:creator>
  1336.      <dc:creator>N Mallick</dc:creator>
  1337.      <dc:creator>G Mandaglio</dc:creator>
  1338.      <dc:creator>S K Mandal</dc:creator>
  1339.      <dc:creator>V Manko</dc:creator>
  1340.      <dc:creator>F Manso</dc:creator>
  1341.      <dc:creator>V Manzari</dc:creator>
  1342.      <dc:creator>Y Mao</dc:creator>
  1343.      <dc:creator>R W Marcjan</dc:creator>
  1344.      <dc:creator>G V Margagliotti</dc:creator>
  1345.      <dc:creator>A Margotti</dc:creator>
  1346.      <dc:creator>A Marín</dc:creator>
  1347.      <dc:creator>C Markert</dc:creator>
  1348.      <dc:creator>P Martinengo</dc:creator>
  1349.      <dc:creator>M I Martínez</dc:creator>
  1350.      <dc:creator>G Martínez García</dc:creator>
  1351.      <dc:creator>M P P Martins</dc:creator>
  1352.      <dc:creator>S Masciocchi</dc:creator>
  1353.      <dc:creator>M Masera</dc:creator>
  1354.      <dc:creator>A Masoni</dc:creator>
  1355.      <dc:creator>L Massacrier</dc:creator>
  1356.      <dc:creator>A Mastroserio</dc:creator>
  1357.      <dc:creator>O Matonoha</dc:creator>
  1358.      <dc:creator>S Mattiazzo</dc:creator>
  1359.      <dc:creator>P F T Matuoka</dc:creator>
  1360.      <dc:creator>A Matyja</dc:creator>
  1361.      <dc:creator>C Mayer</dc:creator>
  1362.      <dc:creator>A L Mazuecos</dc:creator>
  1363.      <dc:creator>F Mazzaschi</dc:creator>
  1364.      <dc:creator>M Mazzilli</dc:creator>
  1365.      <dc:creator>J E Mdhluli</dc:creator>
  1366.      <dc:creator>A F Mechler</dc:creator>
  1367.      <dc:creator>Y Melikyan</dc:creator>
  1368.      <dc:creator>A Menchaca-Rocha</dc:creator>
  1369.      <dc:creator>E Meninno</dc:creator>
  1370.      <dc:creator>A S Menon</dc:creator>
  1371.      <dc:creator>M Meres</dc:creator>
  1372.      <dc:creator>S Mhlanga</dc:creator>
  1373.      <dc:creator>Y Miake</dc:creator>
  1374.      <dc:creator>L Micheletti</dc:creator>
  1375.      <dc:creator>L C Migliorin</dc:creator>
  1376.      <dc:creator>D L Mihaylov</dc:creator>
  1377.      <dc:creator>K Mikhaylov</dc:creator>
  1378.      <dc:creator>A N Mishra</dc:creator>
  1379.      <dc:creator>D Miśkowiec</dc:creator>
  1380.      <dc:creator>A Modak</dc:creator>
  1381.      <dc:creator>A P Mohanty</dc:creator>
  1382.      <dc:creator>B Mohanty</dc:creator>
  1383.      <dc:creator>M Mohisin Khan</dc:creator>
  1384.      <dc:creator>M A Molander</dc:creator>
  1385.      <dc:creator>S Monira</dc:creator>
  1386.      <dc:creator>Z Moravcova</dc:creator>
  1387.      <dc:creator>C Mordasini</dc:creator>
  1388.      <dc:creator>D A Moreira De Godoy</dc:creator>
  1389.      <dc:creator>I Morozov</dc:creator>
  1390.      <dc:creator>A Morsch</dc:creator>
  1391.      <dc:creator>T Mrnjavac</dc:creator>
  1392.      <dc:creator>V Muccifora</dc:creator>
  1393.      <dc:creator>S Muhuri</dc:creator>
  1394.      <dc:creator>J D Mulligan</dc:creator>
  1395.      <dc:creator>A Mulliri</dc:creator>
  1396.      <dc:creator>M G Munhoz</dc:creator>
  1397.      <dc:creator>R H Munzer</dc:creator>
  1398.      <dc:creator>H Murakami</dc:creator>
  1399.      <dc:creator>S Murray</dc:creator>
  1400.      <dc:creator>L Musa</dc:creator>
  1401.      <dc:creator>J Musinsky</dc:creator>
  1402.      <dc:creator>J W Myrcha</dc:creator>
  1403.      <dc:creator>B Naik</dc:creator>
  1404.      <dc:creator>A I Nambrath</dc:creator>
  1405.      <dc:creator>B K Nandi</dc:creator>
  1406.      <dc:creator>R Nania</dc:creator>
  1407.      <dc:creator>E Nappi</dc:creator>
  1408.      <dc:creator>A F Nassirpour</dc:creator>
  1409.      <dc:creator>A Nath</dc:creator>
  1410.      <dc:creator>C Nattrass</dc:creator>
  1411.      <dc:creator>M N Naydenov</dc:creator>
  1412.      <dc:creator>A Neagu</dc:creator>
  1413.      <dc:creator>A Negru</dc:creator>
  1414.      <dc:creator>L Nellen</dc:creator>
  1415.      <dc:creator>R Nepeivoda</dc:creator>
  1416.      <dc:creator>S Nese</dc:creator>
  1417.      <dc:creator>G Neskovic</dc:creator>
  1418.      <dc:creator>B S Nielsen</dc:creator>
  1419.      <dc:creator>E G Nielsen</dc:creator>
  1420.      <dc:creator>S Nikolaev</dc:creator>
  1421.      <dc:creator>S Nikulin</dc:creator>
  1422.      <dc:creator>V Nikulin</dc:creator>
  1423.      <dc:creator>F Noferini</dc:creator>
  1424.      <dc:creator>S Noh</dc:creator>
  1425.      <dc:creator>P Nomokonov</dc:creator>
  1426.      <dc:creator>J Norman</dc:creator>
  1427.      <dc:creator>N Novitzky</dc:creator>
  1428.      <dc:creator>P Nowakowski</dc:creator>
  1429.      <dc:creator>A Nyanin</dc:creator>
  1430.      <dc:creator>J Nystrand</dc:creator>
  1431.      <dc:creator>M Ogino</dc:creator>
  1432.      <dc:creator>S Oh</dc:creator>
  1433.      <dc:creator>A Ohlson</dc:creator>
  1434.      <dc:creator>V A Okorokov</dc:creator>
  1435.      <dc:creator>J Oleniacz</dc:creator>
  1436.      <dc:creator>A C Oliveira Da Silva</dc:creator>
  1437.      <dc:creator>M H Oliver</dc:creator>
  1438.      <dc:creator>A Onnerstad</dc:creator>
  1439.      <dc:creator>C Oppedisano</dc:creator>
  1440.      <dc:creator>A Ortiz Velasquez</dc:creator>
  1441.      <dc:creator>J Otwinowski</dc:creator>
  1442.      <dc:creator>M Oya</dc:creator>
  1443.      <dc:creator>K Oyama</dc:creator>
  1444.      <dc:creator>Y Pachmayer</dc:creator>
  1445.      <dc:creator>S Padhan</dc:creator>
  1446.      <dc:creator>D Pagano</dc:creator>
  1447.      <dc:creator>G Paić</dc:creator>
  1448.      <dc:creator>S Paisano-Guzmán</dc:creator>
  1449.      <dc:creator>A Palasciano</dc:creator>
  1450.      <dc:creator>S Panebianco</dc:creator>
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  1470.      <dc:creator>P Pillot</dc:creator>
  1471.      <dc:creator>O Pinazza</dc:creator>
  1472.      <dc:creator>L Pinsky</dc:creator>
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  1474.      <dc:creator>S Pisano</dc:creator>
  1475.      <dc:creator>M Płoskoń</dc:creator>
  1476.      <dc:creator>M Planinic</dc:creator>
  1477.      <dc:creator>F Pliquett</dc:creator>
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  1479.      <dc:creator>B Polichtchouk</dc:creator>
  1480.      <dc:creator>S Politano</dc:creator>
  1481.      <dc:creator>N Poljak</dc:creator>
  1482.      <dc:creator>A Pop</dc:creator>
  1483.      <dc:creator>S Porteboeuf-Houssais</dc:creator>
  1484.      <dc:creator>V Pozdniakov</dc:creator>
  1485.      <dc:creator>I Y Pozos</dc:creator>
  1486.      <dc:creator>K K Pradhan</dc:creator>
  1487.      <dc:creator>S K Prasad</dc:creator>
  1488.      <dc:creator>S Prasad</dc:creator>
  1489.      <dc:creator>R Preghenella</dc:creator>
  1490.      <dc:creator>F Prino</dc:creator>
  1491.      <dc:creator>C A Pruneau</dc:creator>
  1492.      <dc:creator>I Pshenichnov</dc:creator>
  1493.      <dc:creator>M Puccio</dc:creator>
  1494.      <dc:creator>S Pucillo</dc:creator>
  1495.      <dc:creator>Z Pugelova</dc:creator>
  1496.      <dc:creator>S Qiu</dc:creator>
  1497.      <dc:creator>L Quaglia</dc:creator>
  1498.      <dc:creator>R E Quishpe</dc:creator>
  1499.      <dc:creator>S Ragoni</dc:creator>
  1500.      <dc:creator>A Rakotozafindrabe</dc:creator>
  1501.      <dc:creator>L Ramello</dc:creator>
  1502.      <dc:creator>F Rami</dc:creator>
  1503.      <dc:creator>T A Rancien</dc:creator>
  1504.      <dc:creator>M Rasa</dc:creator>
  1505.      <dc:creator>S S Räsänen</dc:creator>
  1506.      <dc:creator>R Rath</dc:creator>
  1507.      <dc:creator>M P Rauch</dc:creator>
  1508.      <dc:creator>I Ravasenga</dc:creator>
  1509.      <dc:creator>K F Read</dc:creator>
  1510.      <dc:creator>C Reckziegel</dc:creator>
  1511.      <dc:creator>A R Redelbach</dc:creator>
  1512.      <dc:creator>K Redlich</dc:creator>
  1513.      <dc:creator>C A Reetz</dc:creator>
  1514.      <dc:creator>H D Regules-Medel</dc:creator>
  1515.      <dc:creator>A Rehman</dc:creator>
  1516.      <dc:creator>F Reidt</dc:creator>
  1517.      <dc:creator>H A Reme-Ness</dc:creator>
  1518.      <dc:creator>Z Rescakova</dc:creator>
  1519.      <dc:creator>K Reygers</dc:creator>
  1520.      <dc:creator>A Riabov</dc:creator>
  1521.      <dc:creator>V Riabov</dc:creator>
  1522.      <dc:creator>R Ricci</dc:creator>
  1523.      <dc:creator>M Richter</dc:creator>
  1524.      <dc:creator>A A Riedel</dc:creator>
  1525.      <dc:creator>W Riegler</dc:creator>
  1526.      <dc:creator>C Ristea</dc:creator>
  1527.      <dc:creator>M V Rodriguez</dc:creator>
  1528.      <dc:creator>M Rodríguez Cahuantzi</dc:creator>
  1529.      <dc:creator>S A Rodríguez Ramírez</dc:creator>
  1530.      <dc:creator>K Røed</dc:creator>
  1531.      <dc:creator>R Rogalev</dc:creator>
  1532.      <dc:creator>E Rogochaya</dc:creator>
  1533.      <dc:creator>T S Rogoschinski</dc:creator>
  1534.      <dc:creator>D Rohr</dc:creator>
  1535.      <dc:creator>D Röhrich</dc:creator>
  1536.      <dc:creator>P F Rojas</dc:creator>
  1537.      <dc:creator>S Rojas Torres</dc:creator>
  1538.      <dc:creator>P S Rokita</dc:creator>
  1539.      <dc:creator>G Romanenko</dc:creator>
  1540.      <dc:creator>F Ronchetti</dc:creator>
  1541.      <dc:creator>A Rosano</dc:creator>
  1542.      <dc:creator>E D Rosas</dc:creator>
  1543.      <dc:creator>K Roslon</dc:creator>
  1544.      <dc:creator>A Rossi</dc:creator>
  1545.      <dc:creator>A Roy</dc:creator>
  1546.      <dc:creator>S Roy</dc:creator>
  1547.      <dc:creator>N Rubini</dc:creator>
  1548.      <dc:creator>D Ruggiano</dc:creator>
  1549.      <dc:creator>R Rui</dc:creator>
  1550.      <dc:creator>P G Russek</dc:creator>
  1551.      <dc:creator>R Russo</dc:creator>
  1552.      <dc:creator>A Rustamov</dc:creator>
  1553.      <dc:creator>E Ryabinkin</dc:creator>
  1554.      <dc:creator>Y Ryabov</dc:creator>
  1555.      <dc:creator>A Rybicki</dc:creator>
  1556.      <dc:creator>H Rytkonen</dc:creator>
  1557.      <dc:creator>J Ryu</dc:creator>
  1558.      <dc:creator>W Rzesa</dc:creator>
  1559.      <dc:creator>O A M Saarimaki</dc:creator>
  1560.      <dc:creator>R Sadek</dc:creator>
  1561.      <dc:creator>S Sadhu</dc:creator>
  1562.      <dc:creator>S Sadovsky</dc:creator>
  1563.      <dc:creator>J Saetre</dc:creator>
  1564.      <dc:creator>K Šafařík</dc:creator>
  1565.      <dc:creator>P Saha</dc:creator>
  1566.      <dc:creator>S K Saha</dc:creator>
  1567.      <dc:creator>S Saha</dc:creator>
  1568.      <dc:creator>B Sahoo</dc:creator>
  1569.      <dc:creator>B Sahoo</dc:creator>
  1570.      <dc:creator>R Sahoo</dc:creator>
  1571.      <dc:creator>S Sahoo</dc:creator>
  1572.      <dc:creator>D Sahu</dc:creator>
  1573.      <dc:creator>P K Sahu</dc:creator>
  1574.      <dc:creator>J Saini</dc:creator>
  1575.      <dc:creator>K Sajdakova</dc:creator>
  1576.      <dc:creator>S Sakai</dc:creator>
  1577.      <dc:creator>M P Salvan</dc:creator>
  1578.      <dc:creator>S Sambyal</dc:creator>
  1579.      <dc:creator>I Sanna</dc:creator>
  1580.      <dc:creator>T B Saramela</dc:creator>
  1581.      <dc:creator>D Sarkar</dc:creator>
  1582.      <dc:creator>N Sarkar</dc:creator>
  1583.      <dc:creator>P Sarma</dc:creator>
  1584.      <dc:creator>V Sarritzu</dc:creator>
  1585.      <dc:creator>V M Sarti</dc:creator>
  1586.      <dc:creator>M H P Sas</dc:creator>
  1587.      <dc:creator>J Schambach</dc:creator>
  1588.      <dc:creator>H S Scheid</dc:creator>
  1589.      <dc:creator>C Schiaua</dc:creator>
  1590.      <dc:creator>R Schicker</dc:creator>
  1591.      <dc:creator>A Schmah</dc:creator>
  1592.      <dc:creator>C Schmidt</dc:creator>
  1593.      <dc:creator>H R Schmidt</dc:creator>
  1594.      <dc:creator>M O Schmidt</dc:creator>
  1595.      <dc:creator>M Schmidt</dc:creator>
  1596.      <dc:creator>N V Schmidt</dc:creator>
  1597.      <dc:creator>A R Schmier</dc:creator>
  1598.      <dc:creator>R Schotter</dc:creator>
  1599.      <dc:creator>A Schröter</dc:creator>
  1600.      <dc:creator>J Schukraft</dc:creator>
  1601.      <dc:creator>K Schweda</dc:creator>
  1602.      <dc:creator>G Scioli</dc:creator>
  1603.      <dc:creator>E Scomparin</dc:creator>
  1604.      <dc:creator>J E Seger</dc:creator>
  1605.      <dc:creator>Y Sekiguchi</dc:creator>
  1606.      <dc:creator>D Sekihata</dc:creator>
  1607.      <dc:creator>M Selina</dc:creator>
  1608.      <dc:creator>I Selyuzhenkov</dc:creator>
  1609.      <dc:creator>S Senyukov</dc:creator>
  1610.      <dc:creator>J J Seo</dc:creator>
  1611.      <dc:creator>D Serebryakov</dc:creator>
  1612.      <dc:creator>L Šerkšnytė</dc:creator>
  1613.      <dc:creator>A Sevcenco</dc:creator>
  1614.      <dc:creator>T J Shaba</dc:creator>
  1615.      <dc:creator>A Shabetai</dc:creator>
  1616.      <dc:creator>R Shahoyan</dc:creator>
  1617.      <dc:creator>A Shangaraev</dc:creator>
  1618.      <dc:creator>A Sharma</dc:creator>
  1619.      <dc:creator>B Sharma</dc:creator>
  1620.      <dc:creator>D Sharma</dc:creator>
  1621.      <dc:creator>H Sharma</dc:creator>
  1622.      <dc:creator>M Sharma</dc:creator>
  1623.      <dc:creator>S Sharma</dc:creator>
  1624.      <dc:creator>S Sharma</dc:creator>
  1625.      <dc:creator>U Sharma</dc:creator>
  1626.      <dc:creator>A Shatat</dc:creator>
  1627.      <dc:creator>O Sheibani</dc:creator>
  1628.      <dc:creator>K Shigaki</dc:creator>
  1629.      <dc:creator>M Shimomura</dc:creator>
  1630.      <dc:creator>J Shin</dc:creator>
  1631.      <dc:creator>S Shirinkin</dc:creator>
  1632.      <dc:creator>Q Shou</dc:creator>
  1633.      <dc:creator>Y Sibiriak</dc:creator>
  1634.      <dc:creator>S Siddhanta</dc:creator>
  1635.      <dc:creator>T Siemiarczuk</dc:creator>
  1636.      <dc:creator>T F Silva</dc:creator>
  1637.      <dc:creator>D Silvermyr</dc:creator>
  1638.      <dc:creator>T Simantathammakul</dc:creator>
  1639.      <dc:creator>R Simeonov</dc:creator>
  1640.      <dc:creator>B Singh</dc:creator>
  1641.      <dc:creator>B Singh</dc:creator>
  1642.      <dc:creator>K Singh</dc:creator>
  1643.      <dc:creator>R Singh</dc:creator>
  1644.      <dc:creator>R Singh</dc:creator>
  1645.      <dc:creator>R Singh</dc:creator>
  1646.      <dc:creator>S Singh</dc:creator>
  1647.      <dc:creator>V K Singh</dc:creator>
  1648.      <dc:creator>V Singhal</dc:creator>
  1649.      <dc:creator>T Sinha</dc:creator>
  1650.      <dc:creator>B Sitar</dc:creator>
  1651.      <dc:creator>M Sitta</dc:creator>
  1652.      <dc:creator>T B Skaali</dc:creator>
  1653.      <dc:creator>G Skorodumovs</dc:creator>
  1654.      <dc:creator>M Slupecki</dc:creator>
  1655.      <dc:creator>N Smirnov</dc:creator>
  1656.      <dc:creator>R J M Snellings</dc:creator>
  1657.      <dc:creator>E H Solheim</dc:creator>
  1658.      <dc:creator>J Song</dc:creator>
  1659.      <dc:creator>A Songmoolnak</dc:creator>
  1660.      <dc:creator>C Sonnabend</dc:creator>
  1661.      <dc:creator>F Soramel</dc:creator>
  1662.      <dc:creator>A B Soto-Hernandez</dc:creator>
  1663.      <dc:creator>R Spijkers</dc:creator>
  1664.      <dc:creator>I Sputowska</dc:creator>
  1665.      <dc:creator>J Staa</dc:creator>
  1666.      <dc:creator>J Stachel</dc:creator>
  1667.      <dc:creator>I Stan</dc:creator>
  1668.      <dc:creator>P J Steffanic</dc:creator>
  1669.      <dc:creator>S F Stiefelmaier</dc:creator>
  1670.      <dc:creator>D Stocco</dc:creator>
  1671.      <dc:creator>I Storehaug</dc:creator>
  1672.      <dc:creator>P Stratmann</dc:creator>
  1673.      <dc:creator>S Strazzi</dc:creator>
  1674.      <dc:creator>C P Stylianidis</dc:creator>
  1675.      <dc:creator>A A P Suaide</dc:creator>
  1676.      <dc:creator>C Suire</dc:creator>
  1677.      <dc:creator>M Sukhanov</dc:creator>
  1678.      <dc:creator>M Suljic</dc:creator>
  1679.      <dc:creator>R Sultanov</dc:creator>
  1680.      <dc:creator>V Sumberia</dc:creator>
  1681.      <dc:creator>S Sumowidagdo</dc:creator>
  1682.      <dc:creator>S Swain</dc:creator>
  1683.      <dc:creator>I Szarka</dc:creator>
  1684.      <dc:creator>M Szymkowski</dc:creator>
  1685.      <dc:creator>S F Taghavi</dc:creator>
  1686.      <dc:creator>G Taillepied</dc:creator>
  1687.      <dc:creator>J Takahashi</dc:creator>
  1688.      <dc:creator>G J Tambave</dc:creator>
  1689.      <dc:creator>S Tang</dc:creator>
  1690.      <dc:creator>Z Tang</dc:creator>
  1691.      <dc:creator>J D Tapia Takaki</dc:creator>
  1692.      <dc:creator>N Tapus</dc:creator>
  1693.      <dc:creator>L A Tarasovicova</dc:creator>
  1694.      <dc:creator>M G Tarzila</dc:creator>
  1695.      <dc:creator>G F Tassielli</dc:creator>
  1696.      <dc:creator>A Tauro</dc:creator>
  1697.      <dc:creator>G Tejeda Muñoz</dc:creator>
  1698.      <dc:creator>A Telesca</dc:creator>
  1699.      <dc:creator>L Terlizzi</dc:creator>
  1700.      <dc:creator>C Terrevoli</dc:creator>
  1701.      <dc:creator>S Thakur</dc:creator>
  1702.      <dc:creator>D Thomas</dc:creator>
  1703.      <dc:creator>A Tikhonov</dc:creator>
  1704.      <dc:creator>A R Timmins</dc:creator>
  1705.      <dc:creator>M Tkacik</dc:creator>
  1706.      <dc:creator>T Tkacik</dc:creator>
  1707.      <dc:creator>A Toia</dc:creator>
  1708.      <dc:creator>R Tokumoto</dc:creator>
  1709.      <dc:creator>K Tomohiro</dc:creator>
  1710.      <dc:creator>N Topilskaya</dc:creator>
  1711.      <dc:creator>M Toppi</dc:creator>
  1712.      <dc:creator>T Tork</dc:creator>
  1713.      <dc:creator>V V Torres</dc:creator>
  1714.      <dc:creator>A G Torres Ramos</dc:creator>
  1715.      <dc:creator>A Trifiró</dc:creator>
  1716.      <dc:creator>A S Triolo</dc:creator>
  1717.      <dc:creator>S Tripathy</dc:creator>
  1718.      <dc:creator>T Tripathy</dc:creator>
  1719.      <dc:creator>S Trogolo</dc:creator>
  1720.      <dc:creator>V Trubnikov</dc:creator>
  1721.      <dc:creator>W H Trzaska</dc:creator>
  1722.      <dc:creator>T P Trzcinski</dc:creator>
  1723.      <dc:creator>A Tumkin</dc:creator>
  1724.      <dc:creator>R Turrisi</dc:creator>
  1725.      <dc:creator>T S Tveter</dc:creator>
  1726.      <dc:creator>K Ullaland</dc:creator>
  1727.      <dc:creator>B Ulukutlu</dc:creator>
  1728.      <dc:creator>A Uras</dc:creator>
  1729.      <dc:creator>M Urioni</dc:creator>
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  1731.      <dc:creator>M Vala</dc:creator>
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  1734.      <dc:creator>M van Leeuwen</dc:creator>
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  1738.      <dc:creator>D Varga</dc:creator>
  1739.      <dc:creator>Z Varga</dc:creator>
  1740.      <dc:creator>M Vasileiou</dc:creator>
  1741.      <dc:creator>A Vasiliev</dc:creator>
  1742.      <dc:creator>O Vázquez Doce</dc:creator>
  1743.      <dc:creator>O Vazquez Rueda</dc:creator>
  1744.      <dc:creator>V Vechernin</dc:creator>
  1745.      <dc:creator>E Vercellin</dc:creator>
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  1799.      <dc:creator>H Yu</dc:creator>
  1800.      <dc:creator>S Yuan</dc:creator>
  1801.      <dc:creator>A Yuncu</dc:creator>
  1802.      <dc:creator>V Zaccolo</dc:creator>
  1803.      <dc:creator>C Zampolli</dc:creator>
  1804.      <dc:creator>F Zanone</dc:creator>
  1805.      <dc:creator>N Zardoshti</dc:creator>
  1806.      <dc:creator>A Zarochentsev</dc:creator>
  1807.      <dc:creator>P Závada</dc:creator>
  1808.      <dc:creator>N Zaviyalov</dc:creator>
  1809.      <dc:creator>M Zhalov</dc:creator>
  1810.      <dc:creator>B Zhang</dc:creator>
  1811.      <dc:creator>C Zhang</dc:creator>
  1812.      <dc:creator>L Zhang</dc:creator>
  1813.      <dc:creator>S Zhang</dc:creator>
  1814.      <dc:creator>X Zhang</dc:creator>
  1815.      <dc:creator>Y Zhang</dc:creator>
  1816.      <dc:creator>Z Zhang</dc:creator>
  1817.      <dc:creator>M Zhao</dc:creator>
  1818.      <dc:creator>V Zherebchevskii</dc:creator>
  1819.      <dc:creator>Y Zhi</dc:creator>
  1820.      <dc:creator>D Zhou</dc:creator>
  1821.      <dc:creator>Y Zhou</dc:creator>
  1822.      <dc:creator>J Zhu</dc:creator>
  1823.      <dc:creator>Y Zhu</dc:creator>
  1824.      <dc:creator>S C Zugravel</dc:creator>
  1825.      <dc:creator>N Zurlo</dc:creator>
  1826.      <dc:creator>ALICE Collaboration</dc:creator>
  1827.      <dc:date>2024-05-03</dc:date>
  1828.      <dc:source>Physical review letters</dc:source>
  1829.      <dc:title>First Measurement of the |t| Dependence of Incoherent J/ψ Photonuclear Production</dc:title>
  1830.      <dc:identifier>pmid:38701458</dc:identifier>
  1831.      <dc:identifier>doi:10.1103/PhysRevLett.132.162302</dc:identifier>
  1832.    </item>
  1833.    <item>
  1834.      <title>Denoising and Extension of Response Functions in the Time Domain</title>
  1835.      <link>https://pubmed.ncbi.nlm.nih.gov/38701446/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1836.      <description>Response functions of quantum systems, such as electron Green's functions, magnetic, or charge susceptibilities, describe the response of a system to an external perturbation. They are the central objects of interest in field theories and quantum computing and measured directly in experiment. Response functions are intrinsically causal. In equilibrium and steady-state systems, they correspond to a positive spectral function in the frequency domain. Since response functions define an inner...</description>
  1837.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Phys Rev Lett. 2024 Apr 19;132(16):160403. doi: 10.1103/PhysRevLett.132.160403.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Response functions of quantum systems, such as electron Green's functions, magnetic, or charge susceptibilities, describe the response of a system to an external perturbation. They are the central objects of interest in field theories and quantum computing and measured directly in experiment. Response functions are intrinsically causal. In equilibrium and steady-state systems, they correspond to a positive spectral function in the frequency domain. Since response functions define an inner product on a Hilbert space and thereby induce a positive definite function, the properties of this function can be used to reduce noise in measured data and, in equilibrium and steady state, to construct positive definite extensions for data known on finite time intervals, which are then guaranteed to correspond to positive spectra.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38701446/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38701446</a> | DOI:<a href=https://doi.org/10.1103/PhysRevLett.132.160403>10.1103/PhysRevLett.132.160403</a></p></div>]]></content:encoded>
  1838.      <guid isPermaLink="false">pubmed:38701446</guid>
  1839.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  1840.      <dc:creator>Alexander F Kemper</dc:creator>
  1841.      <dc:creator>Chao Yang</dc:creator>
  1842.      <dc:creator>Emanuel Gull</dc:creator>
  1843.      <dc:date>2024-05-03</dc:date>
  1844.      <dc:source>Physical review letters</dc:source>
  1845.      <dc:title>Denoising and Extension of Response Functions in the Time Domain</dc:title>
  1846.      <dc:identifier>pmid:38701446</dc:identifier>
  1847.      <dc:identifier>doi:10.1103/PhysRevLett.132.160403</dc:identifier>
  1848.    </item>
  1849.    <item>
  1850.      <title>Perspectives on improving photosynthesis to increase crop yield</title>
  1851.      <link>https://pubmed.ncbi.nlm.nih.gov/38701340/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1852.      <description>Improving photosynthesis, the fundamental process by which plants convert light energy into chemical energy, is a key area of research with great potential for enhancing sustainable agricultural productivity and addressing global food security challenges. This perspective delves into the latest advancements and approaches aimed at optimizing photosynthetic efficiency. Our discussion encompasses the entire process, beginning with light harvesting and its regulation and progressing through the...</description>
  1853.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Plant Cell. 2024 May 3:koae132. doi: 10.1093/plcell/koae132. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Improving photosynthesis, the fundamental process by which plants convert light energy into chemical energy, is a key area of research with great potential for enhancing sustainable agricultural productivity and addressing global food security challenges. This perspective delves into the latest advancements and approaches aimed at optimizing photosynthetic efficiency. Our discussion encompasses the entire process, beginning with light harvesting and its regulation and progressing through the bottleneck of electron transfer. We then delve into the carbon reactions of photosynthesis, focusing on strategies targeting the enzymes of the Calvin-Benson-Bassham (CBB) cycle. Additionally, we explore methods to increase CO2 concentration near the Rubisco, the enzyme responsible for the first step of CBB cycle, drawing inspiration from various photosynthetic organisms, and conclude this section by examining ways to enhance CO2 delivery into leaves. Moving beyond individual processes, we discuss two approaches to identifying key targets for photosynthesis improvement: systems modeling and the study of natural variation. Finally, we revisit some of the strategies mentioned above to provide a holistic view of the improvements, analyzing their impact on nitrogen use efficiency and on canopy photosynthesis.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38701340/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38701340</a> | DOI:<a href=https://doi.org/10.1093/plcell/koae132>10.1093/plcell/koae132</a></p></div>]]></content:encoded>
  1854.      <guid isPermaLink="false">pubmed:38701340</guid>
  1855.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  1856.      <dc:creator>Roberta Croce</dc:creator>
  1857.      <dc:creator>Elizabete Carmo-Silva</dc:creator>
  1858.      <dc:creator>Young B Cho</dc:creator>
  1859.      <dc:creator>Maria Ermakova</dc:creator>
  1860.      <dc:creator>Jeremy Harbinson</dc:creator>
  1861.      <dc:creator>Tracy Lawson</dc:creator>
  1862.      <dc:creator>Alistair J McCormick</dc:creator>
  1863.      <dc:creator>Krishna K Niyogi</dc:creator>
  1864.      <dc:creator>Donald R Ort</dc:creator>
  1865.      <dc:creator>Dhruv Patel-Tupper</dc:creator>
  1866.      <dc:creator>Paolo Pesaresi</dc:creator>
  1867.      <dc:creator>Christine Raines</dc:creator>
  1868.      <dc:creator>Andreas P M Weber</dc:creator>
  1869.      <dc:creator>Xin-Guang Zhu</dc:creator>
  1870.      <dc:date>2024-05-03</dc:date>
  1871.      <dc:source>The Plant cell</dc:source>
  1872.      <dc:title>Perspectives on improving photosynthesis to increase crop yield</dc:title>
  1873.      <dc:identifier>pmid:38701340</dc:identifier>
  1874.      <dc:identifier>doi:10.1093/plcell/koae132</dc:identifier>
  1875.    </item>
  1876.    <item>
  1877.      <title>Agricultural Waste Management by Production of Second-Generation Bioethanol from Sugarcane Bagasse Using Indigenous Yeast Strain</title>
  1878.      <link>https://pubmed.ncbi.nlm.nih.gov/38700667/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1879.      <description>In the wake of rapid industrialization and burgeoning transportation networks, the escalating demand for fossil fuels has accelerated the depletion of finite energy reservoirs, necessitating urgent exploration of sustainable alternatives. To address this, current research is focusing on renewable fuels like second-generation bioethanol from agricultural waste such as sugarcane bagasse. This approach not only circumvents the contentious issue of food-fuel conflicts associated with biofuels but...</description>
  1880.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Curr Microbiol. 2024 May 3;81(6):161. doi: 10.1007/s00284-024-03668-y.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">In the wake of rapid industrialization and burgeoning transportation networks, the escalating demand for fossil fuels has accelerated the depletion of finite energy reservoirs, necessitating urgent exploration of sustainable alternatives. To address this, current research is focusing on renewable fuels like second-generation bioethanol from agricultural waste such as sugarcane bagasse. This approach not only circumvents the contentious issue of food-fuel conflicts associated with biofuels but also tackles agricultural waste management. In the present study indigenous yeast strain, Clavispora lusitaniae QG1 (MN592676), was isolated from rotten grapes to ferment xylose sugars present in the hemicellulose content of sugarcane bagasse. To liberate the xylose sugars, dilute acid pretreatment was performed. The highest reducing sugars yield was 1.2% obtained at a temperature of 121 °C for 15 min, a solid-to-liquid ratio of 1:25 (% w/v), and an acid concentration of 1% dilute acid H<sub>2</sub>SO<sub>4</sub> that was significantly higher (P &lt; 0.001) yield obtained under similar conditions at 100 °C for 1 h. The isolated strain was statistically optimized for fermentation process by Plackett-Burman design to achieve the highest ethanol yield. Liberated xylose sugars were completely utilized by Clavispora lusitaniae QG1 (MN592676) and gave 100% ethanol yield. This study optimizes both fermentation process and pretreatment of sugarcane bagasse to maximize bioethanol yield and demonstrates the ability of isolated strain to effectively utilize xylose as a carbon source. The desirable characteristics depicted by strain Clavispora lusitaniae shows its promising utilization in management of industrial waste like sugarcane bagasse by its conversion into renewable biofuels like bioethanol.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38700667/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38700667</a> | DOI:<a href=https://doi.org/10.1007/s00284-024-03668-y>10.1007/s00284-024-03668-y</a></p></div>]]></content:encoded>
  1881.      <guid isPermaLink="false">pubmed:38700667</guid>
  1882.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  1883.      <dc:creator>Sidra Ali</dc:creator>
  1884.      <dc:creator>Qurrat Ul Ain Rana</dc:creator>
  1885.      <dc:creator>Fatima Riaz</dc:creator>
  1886.      <dc:creator>Abdul Haq</dc:creator>
  1887.      <dc:creator>Wasim Sajjad</dc:creator>
  1888.      <dc:creator>Rahul Gauttam</dc:creator>
  1889.      <dc:creator>Mahwish Ali</dc:creator>
  1890.      <dc:creator>Malik Badshah</dc:creator>
  1891.      <dc:date>2024-05-03</dc:date>
  1892.      <dc:source>Current microbiology</dc:source>
  1893.      <dc:title>Agricultural Waste Management by Production of Second-Generation Bioethanol from Sugarcane Bagasse Using Indigenous Yeast Strain</dc:title>
  1894.      <dc:identifier>pmid:38700667</dc:identifier>
  1895.      <dc:identifier>doi:10.1007/s00284-024-03668-y</dc:identifier>
  1896.    </item>
  1897.    <item>
  1898.      <title>Microbial responses to long-term warming differ across soil microenvironments</title>
  1899.      <link>https://pubmed.ncbi.nlm.nih.gov/38699060/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1900.      <description>Soil carbon loss is likely to increase due to climate warming, but microbiomes and microenvironments may dampen this effect. In a 30-year warming experiment, physical protection within soil aggregates affected the thermal responses of soil microbiomes and carbon dynamics. In this study, we combined metagenomic analysis with physical characterization of soil aggregates to explore mechanisms by which microbial communities respond to climate warming across different soil microenvironments....</description>
  1901.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">ISME Commun. 2024 Apr 6;4(1):ycae051. doi: 10.1093/ismeco/ycae051. eCollection 2024 Jan.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Soil carbon loss is likely to increase due to climate warming, but microbiomes and microenvironments may dampen this effect. In a 30-year warming experiment, physical protection within soil aggregates affected the thermal responses of soil microbiomes and carbon dynamics. In this study, we combined metagenomic analysis with physical characterization of soil aggregates to explore mechanisms by which microbial communities respond to climate warming across different soil microenvironments. Long-term warming decreased the relative abundances of genes involved in degrading labile compounds (e.g. cellulose), but increased those genes involved in degrading recalcitrant compounds (e.g. lignin) across aggregate sizes. These changes were observed in most phyla of bacteria, especially for <i>Acidobacteria</i>, <i>Actinobacteria</i>, <i>Bacteroidetes</i>, <i>Chloroflexi</i>, and <i>Planctomycetes</i>. Microbial community composition was considerably altered by warming, leading to declined diversity for bacteria and fungi but not for archaea. Microbial functional genes, diversity, and community composition differed between macroaggregates and microaggregates, indicating the essential role of physical protection in controlling microbial community dynamics. Our findings suggest that microbes have the capacity to employ various strategies to acclimate or adapt to climate change (e.g. warming, heat stress) by shifting functional gene abundances and community structures in varying microenvironments, as regulated by soil physical protection.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38699060/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38699060</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11065356/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11065356</a> | DOI:<a href=https://doi.org/10.1093/ismeco/ycae051>10.1093/ismeco/ycae051</a></p></div>]]></content:encoded>
  1902.      <guid isPermaLink="false">pubmed:38699060</guid>
  1903.      <pubDate>Fri, 03 May 2024 06:00:00 -0400</pubDate>
  1904.      <dc:creator>Xiao Jun A Liu</dc:creator>
  1905.      <dc:creator>Shun Han</dc:creator>
  1906.      <dc:creator>Serita D Frey</dc:creator>
  1907.      <dc:creator>Jerry M Melillo</dc:creator>
  1908.      <dc:creator>Jizhong Zhou</dc:creator>
  1909.      <dc:creator>Kristen M DeAngelis</dc:creator>
  1910.      <dc:date>2024-05-03</dc:date>
  1911.      <dc:source>ISME communications</dc:source>
  1912.      <dc:title>Microbial responses to long-term warming differ across soil microenvironments</dc:title>
  1913.      <dc:identifier>pmid:38699060</dc:identifier>
  1914.      <dc:identifier>pmc:PMC11065356</dc:identifier>
  1915.      <dc:identifier>doi:10.1093/ismeco/ycae051</dc:identifier>
  1916.    </item>
  1917.    <item>
  1918.      <title>Weyl spin-momentum locking in a chiral topological semimetal</title>
  1919.      <link>https://pubmed.ncbi.nlm.nih.gov/38697958/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1920.      <description>Spin-orbit coupling in noncentrosymmetric crystals leads to spin-momentum locking - a directional relationship between an electron's spin angular momentum and its linear momentum. Isotropic orthogonal Rashba spin-momentum locking has been studied for decades, while its counterpart, isotropic parallel Weyl spin-momentum locking has remained elusive in experiments. Theory predicts that Weyl spin-momentum locking can only be realized in structurally chiral cubic crystals in the vicinity of...</description>
  1921.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Nat Commun. 2024 May 2;15(1):3720. doi: 10.1038/s41467-024-47976-0.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Spin-orbit coupling in noncentrosymmetric crystals leads to spin-momentum locking - a directional relationship between an electron's spin angular momentum and its linear momentum. Isotropic orthogonal Rashba spin-momentum locking has been studied for decades, while its counterpart, isotropic parallel Weyl spin-momentum locking has remained elusive in experiments. Theory predicts that Weyl spin-momentum locking can only be realized in structurally chiral cubic crystals in the vicinity of Kramers-Weyl or multifold fermions. Here, we use spin- and angle-resolved photoemission spectroscopy to evidence Weyl spin-momentum locking of multifold fermions in the chiral topological semimetal PtGa. We find that the electron spin of the Fermi arc surface states is orthogonal to their Fermi surface contour for momenta close to the projection of the bulk multifold fermion at the Γ point, which is consistent with Weyl spin-momentum locking of the latter. The direct measurement of the bulk spin texture of the multifold fermion at the R point also displays Weyl spin-momentum locking. The discovery of Weyl spin-momentum locking may lead to energy-efficient memory devices and Josephson diodes based on chiral topological semimetals.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38697958/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38697958</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11066003/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11066003</a> | DOI:<a href=https://doi.org/10.1038/s41467-024-47976-0>10.1038/s41467-024-47976-0</a></p></div>]]></content:encoded>
  1922.      <guid isPermaLink="false">pubmed:38697958</guid>
  1923.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  1924.      <dc:creator>Jonas A Krieger</dc:creator>
  1925.      <dc:creator>Samuel Stolz</dc:creator>
  1926.      <dc:creator>Iñigo Robredo</dc:creator>
  1927.      <dc:creator>Kaustuv Manna</dc:creator>
  1928.      <dc:creator>Emily C McFarlane</dc:creator>
  1929.      <dc:creator>Mihir Date</dc:creator>
  1930.      <dc:creator>Banabir Pal</dc:creator>
  1931.      <dc:creator>Jiabao Yang</dc:creator>
  1932.      <dc:creator>Eduardo B Guedes</dc:creator>
  1933.      <dc:creator>J Hugo Dil</dc:creator>
  1934.      <dc:creator>Craig M Polley</dc:creator>
  1935.      <dc:creator>Mats Leandersson</dc:creator>
  1936.      <dc:creator>Chandra Shekhar</dc:creator>
  1937.      <dc:creator>Horst Borrmann</dc:creator>
  1938.      <dc:creator>Qun Yang</dc:creator>
  1939.      <dc:creator>Mao Lin</dc:creator>
  1940.      <dc:creator>Vladimir N Strocov</dc:creator>
  1941.      <dc:creator>Marco Caputo</dc:creator>
  1942.      <dc:creator>Matthew D Watson</dc:creator>
  1943.      <dc:creator>Timur K Kim</dc:creator>
  1944.      <dc:creator>Cephise Cacho</dc:creator>
  1945.      <dc:creator>Federico Mazzola</dc:creator>
  1946.      <dc:creator>Jun Fujii</dc:creator>
  1947.      <dc:creator>Ivana Vobornik</dc:creator>
  1948.      <dc:creator>Stuart S P Parkin</dc:creator>
  1949.      <dc:creator>Barry Bradlyn</dc:creator>
  1950.      <dc:creator>Claudia Felser</dc:creator>
  1951.      <dc:creator>Maia G Vergniory</dc:creator>
  1952.      <dc:creator>Niels B M Schröter</dc:creator>
  1953.      <dc:date>2024-05-02</dc:date>
  1954.      <dc:source>Nature communications</dc:source>
  1955.      <dc:title>Weyl spin-momentum locking in a chiral topological semimetal</dc:title>
  1956.      <dc:identifier>pmid:38697958</dc:identifier>
  1957.      <dc:identifier>pmc:PMC11066003</dc:identifier>
  1958.      <dc:identifier>doi:10.1038/s41467-024-47976-0</dc:identifier>
  1959.    </item>
  1960.    <item>
  1961.      <title>Genome-wide patterns of non-coding and protein-coding sequence variation in the major fungal pathogen Aspergillus fumigatus</title>
  1962.      <link>https://pubmed.ncbi.nlm.nih.gov/38696662/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1963.      <description>A. fumigatus is a deadly fungal pathogen, responsible for &gt;400,000 infections/year and high mortality rates. A. fumigatus strains exhibit variation in infection-relevant traits, including in their virulence. However, most A. fumigatus protein-coding genes, including those that modulate its virulence, are shared between A. fumigatus strains and closely related non-pathogenic relatives. We hypothesized that A. fumigatus genes exhibit substantial genetic variation in the non-coding regions...</description>
  1964.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">G3 (Bethesda). 2024 May 2:jkae091. doi: 10.1093/g3journal/jkae091. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">A. fumigatus is a deadly fungal pathogen, responsible for &gt;400,000 infections/year and high mortality rates. A. fumigatus strains exhibit variation in infection-relevant traits, including in their virulence. However, most A. fumigatus protein-coding genes, including those that modulate its virulence, are shared between A. fumigatus strains and closely related non-pathogenic relatives. We hypothesized that A. fumigatus genes exhibit substantial genetic variation in the non-coding regions immediately upstream to the start codons of genes, which could reflect differences in gene regulation between strains. To begin testing this hypothesis, we identified 5,812 single-copy orthologs across the genomes of 263 A. fumigatus strains. In general, A. fumigatus non-coding regions showed higher levels of sequence variation compared to their corresponding protein-coding regions. Focusing on 2,482 genes whose protein-coding sequence identity scores ranged between 75% and 99%, we identified 478 total genes with signatures of positive selection only in their non-coding regions and 65 total genes with signatures only in their protein-coding regions. 28 of the 478 non-coding regions and 5 of the 65 protein-coding regions under selection are associated with genes known to modulate A. fumigatus virulence. Non-coding region variation between A. fumigatus strains included single nucleotide polymorphisms and insertions or deletions of at least a few nucleotides. These results show that non-coding regions of A. fumigatus genes harbor greater sequence variation than protein-coding regions, raising the hypothesis that this variation may contribute to A. fumigatus phenotypic heterogeneity.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696662/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696662</a> | DOI:<a href=https://doi.org/10.1093/g3journal/jkae091>10.1093/g3journal/jkae091</a></p></div>]]></content:encoded>
  1965.      <guid isPermaLink="false">pubmed:38696662</guid>
  1966.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  1967.      <dc:creator>Alec Brown</dc:creator>
  1968.      <dc:creator>Jacob L Steenwyk</dc:creator>
  1969.      <dc:creator>Antonis Rokas</dc:creator>
  1970.      <dc:date>2024-05-02</dc:date>
  1971.      <dc:source>G3 (Bethesda, Md.)</dc:source>
  1972.      <dc:title>Genome-wide patterns of non-coding and protein-coding sequence variation in the major fungal pathogen Aspergillus fumigatus</dc:title>
  1973.      <dc:identifier>pmid:38696662</dc:identifier>
  1974.      <dc:identifier>doi:10.1093/g3journal/jkae091</dc:identifier>
  1975.    </item>
  1976.    <item>
  1977.      <title>Differences in mid-gestational and early postnatal neonatal cytokines and chemokines are associated with patterns of maternal autoantibodies in the context of autism</title>
  1978.      <link>https://pubmed.ncbi.nlm.nih.gov/38696596/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  1979.      <description>Associations between maternal immune dysregulation (including autoimmunity and skewed cytokine/chemokine profiles) and offspring neurodevelopmental disorders such as autism have been reported. In maternal autoantibody-related autism, specific maternally derived autoantibodies can access the fetal compartment to target eight proteins critical for neurodevelopment. We examined the relationship between maternal autoantibodies to the eight maternal autoantibody-related autism proteins and...</description>
  1980.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Cereb Cortex. 2024 May 2;34(13):50-62. doi: 10.1093/cercor/bhae082.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Associations between maternal immune dysregulation (including autoimmunity and skewed cytokine/chemokine profiles) and offspring neurodevelopmental disorders such as autism have been reported. In maternal autoantibody-related autism, specific maternally derived autoantibodies can access the fetal compartment to target eight proteins critical for neurodevelopment. We examined the relationship between maternal autoantibodies to the eight maternal autoantibody-related autism proteins and cytokine/chemokine profiles in the second trimester of pregnancy in mothers of children later diagnosed with autism and their neonates' cytokine/chemokine profiles. Using banked maternal serum samples from 15 to 19 weeks of gestation from the Early Markers for Autism Study and corresponding banked newborn bloodspots, we identified three maternal/offspring groups based on maternal autoantibody status: (1) mothers with autoantibodies to one or more of the eight maternal autoantibody-related autismassociated proteins but not a maternal autoantibody-related autism-specific pattern, (2) mothers with a known maternal autoantibody-related autism pattern, and (3) mothers without autoantibodies to any of the eight maternal autoantibody-related autism proteins. Using a multiplex platform, we measured maternal second trimester and neonatal cytokine/chemokine levels. This combined analysis aimed to determine potential associations between maternal autoantibodies and the maternal and neonatal cytokine/chemokine profiles, each of which has been shown to have implications on offspring neurodevelopment independently.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696596/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696596</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11065110/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11065110</a> | DOI:<a href=https://doi.org/10.1093/cercor/bhae082>10.1093/cercor/bhae082</a></p></div>]]></content:encoded>
  1981.      <guid isPermaLink="false">pubmed:38696596</guid>
  1982.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  1983.      <dc:creator>Janna McLellan</dc:creator>
  1984.      <dc:creator>Lisa A Croen</dc:creator>
  1985.      <dc:creator>Ana-Maria Iosif</dc:creator>
  1986.      <dc:creator>Paul Ashwood</dc:creator>
  1987.      <dc:creator>Cathleen Yoshida</dc:creator>
  1988.      <dc:creator>Kimberly Berger</dc:creator>
  1989.      <dc:creator>Judy Van de Water</dc:creator>
  1990.      <dc:date>2024-05-02</dc:date>
  1991.      <dc:source>Cerebral cortex (New York, N.Y. : 1991)</dc:source>
  1992.      <dc:title>Differences in mid-gestational and early postnatal neonatal cytokines and chemokines are associated with patterns of maternal autoantibodies in the context of autism</dc:title>
  1993.      <dc:identifier>pmid:38696596</dc:identifier>
  1994.      <dc:identifier>pmc:PMC11065110</dc:identifier>
  1995.      <dc:identifier>doi:10.1093/cercor/bhae082</dc:identifier>
  1996.    </item>
  1997.    <item>
  1998.      <title>No evidence for methanotrophic growth of diverse marine methanogens</title>
  1999.      <link>https://pubmed.ncbi.nlm.nih.gov/38696482/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2000.      <description>No abstract</description>
  2001.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Proc Natl Acad Sci U S A. 2024 May 14;121(20):e2404143121. doi: 10.1073/pnas.2404143121. Epub 2024 May 2.</p><p><b>NO ABSTRACT</b></p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696482/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696482</a> | DOI:<a href=https://doi.org/10.1073/pnas.2404143121>10.1073/pnas.2404143121</a></p></div>]]></content:encoded>
  2002.      <guid isPermaLink="false">pubmed:38696482</guid>
  2003.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2004.      <dc:creator>Grayson L Chadwick</dc:creator>
  2005.      <dc:creator>Dipti D Nayak</dc:creator>
  2006.      <dc:creator>Michael Rother</dc:creator>
  2007.      <dc:creator>William W Metcalf</dc:creator>
  2008.      <dc:date>2024-05-02</dc:date>
  2009.      <dc:source>Proceedings of the National Academy of Sciences of the United States of America</dc:source>
  2010.      <dc:title>No evidence for methanotrophic growth of diverse marine methanogens</dc:title>
  2011.      <dc:identifier>pmid:38696482</dc:identifier>
  2012.      <dc:identifier>doi:10.1073/pnas.2404143121</dc:identifier>
  2013.    </item>
  2014.    <item>
  2015.      <title>Exploring HIV risk perception mechanisms among youth in a test-and-treat trial in Kenya and Uganda</title>
  2016.      <link>https://pubmed.ncbi.nlm.nih.gov/38696376/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2017.      <description>Understanding risk perception and risk-taking among youth can inform targeted prevention efforts. Using a health beliefs model-informed framework, we analysed 8 semi-structured, gender-specific focus group discussions with 93 youth 15-24 years old (48% male, 52% female), drawn from the SEARCH trial in rural Kenya and Uganda in 2017-2018, coinciding with the widespread introduction of PrEP. Highly connected social networks and widespread uptake of antiretrovirals shaped youth HIV risk perception....</description>
  2018.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">PLOS Glob Public Health. 2024 May 2;4(5):e0002922. doi: 10.1371/journal.pgph.0002922. eCollection 2024.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Understanding risk perception and risk-taking among youth can inform targeted prevention efforts. Using a health beliefs model-informed framework, we analysed 8 semi-structured, gender-specific focus group discussions with 93 youth 15-24 years old (48% male, 52% female), drawn from the SEARCH trial in rural Kenya and Uganda in 2017-2018, coinciding with the widespread introduction of PrEP. Highly connected social networks and widespread uptake of antiretrovirals shaped youth HIV risk perception. Amid conflicting information about HIV prevention methods, youth felt exposed to multiple HIV risk factors like the high prevalence of HIV, belief that people with HIV(PWH) purposefully infect others, dislike of condoms, and doubts about PrEP efficacy. Young women also reported minimal sexual autonomy in the context of economic disadvantages, the ubiquity of intergenerational and transactional sex, and peer pressure from other women to have many boyfriends. Young men likewise reported vulnerability to intergenerational sex, but also adopted a sexual conquest mentality. Comprehensive sexuality education and economic empowerment, through credible and trusted sources, may moderate risk-taking. Messaging should leverage youth's social networks to spread fact-based, gender- and age-appropriate information. PrEP should be offered alongside other reproductive health services to address both pregnancy concerns and reduce HIV risk.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696376/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696376</a> | PMC:<a href="https://www.ncbi.nlm.nih.gov/pmc/PMC11065277/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">PMC11065277</a> | DOI:<a href=https://doi.org/10.1371/journal.pgph.0002922>10.1371/journal.pgph.0002922</a></p></div>]]></content:encoded>
  2019.      <guid isPermaLink="false">pubmed:38696376</guid>
  2020.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2021.      <dc:creator>Lawrence Owino</dc:creator>
  2022.      <dc:creator>Jason Johnson-Peretz</dc:creator>
  2023.      <dc:creator>Joi Lee</dc:creator>
  2024.      <dc:creator>Monica Getahun</dc:creator>
  2025.      <dc:creator>Dana Coppock-Pector</dc:creator>
  2026.      <dc:creator>Irene Maeri</dc:creator>
  2027.      <dc:creator>Anjeline Onyango</dc:creator>
  2028.      <dc:creator>Craig R Cohen</dc:creator>
  2029.      <dc:creator>Elizabeth A Bukusi</dc:creator>
  2030.      <dc:creator>Jane Kabami</dc:creator>
  2031.      <dc:creator>James Ayieko</dc:creator>
  2032.      <dc:creator>Maya Petersen</dc:creator>
  2033.      <dc:creator>Moses R Kamya</dc:creator>
  2034.      <dc:creator>Edwin Charlebois</dc:creator>
  2035.      <dc:creator>Diane Havlir</dc:creator>
  2036.      <dc:creator>Carol S Camlin</dc:creator>
  2037.      <dc:date>2024-05-02</dc:date>
  2038.      <dc:source>PLOS global public health</dc:source>
  2039.      <dc:title>Exploring HIV risk perception mechanisms among youth in a test-and-treat trial in Kenya and Uganda</dc:title>
  2040.      <dc:identifier>pmid:38696376</dc:identifier>
  2041.      <dc:identifier>pmc:PMC11065277</dc:identifier>
  2042.      <dc:identifier>doi:10.1371/journal.pgph.0002922</dc:identifier>
  2043.    </item>
  2044.    <item>
  2045.      <title>Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California</title>
  2046.      <link>https://pubmed.ncbi.nlm.nih.gov/38696278/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2047.      <description>Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM(2.5)) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically...</description>
  2048.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Environ Sci Technol. 2024 May 2. doi: 10.1021/acs.est.3c07728. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM<sub>2.5</sub>) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM<sub>2.5</sub> intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696278/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696278</a> | DOI:<a href=https://doi.org/10.1021/acs.est.3c07728>10.1021/acs.est.3c07728</a></p></div>]]></content:encoded>
  2049.      <guid isPermaLink="false">pubmed:38696278</guid>
  2050.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2051.      <dc:creator>Fiona Greer</dc:creator>
  2052.      <dc:creator>Ahmad Bin Thaneya</dc:creator>
  2053.      <dc:creator>Arpad Horvath</dc:creator>
  2054.      <dc:date>2024-05-02</dc:date>
  2055.      <dc:source>Environmental science &amp; technology</dc:source>
  2056.      <dc:title>Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California</dc:title>
  2057.      <dc:identifier>pmid:38696278</dc:identifier>
  2058.      <dc:identifier>doi:10.1021/acs.est.3c07728</dc:identifier>
  2059.    </item>
  2060.    <item>
  2061.      <title>Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study</title>
  2062.      <link>https://pubmed.ncbi.nlm.nih.gov/38696234/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2063.      <description>CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.</description>
  2064.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">JMIR Mhealth Uhealth. 2024 May 2;12:e54622. doi: 10.2196/54622.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F<sub>1</sub>-score.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included &lt;20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696234/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696234</a> | DOI:<a href=https://doi.org/10.2196/54622>10.2196/54622</a></p></div>]]></content:encoded>
  2065.      <guid isPermaLink="false">pubmed:38696234</guid>
  2066.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2067.      <dc:creator>Eric Hurwitz</dc:creator>
  2068.      <dc:creator>Zachary Butzin-Dozier</dc:creator>
  2069.      <dc:creator>Hiral Master</dc:creator>
  2070.      <dc:creator>Shawn T O'Neil</dc:creator>
  2071.      <dc:creator>Anita Walden</dc:creator>
  2072.      <dc:creator>Michelle Holko</dc:creator>
  2073.      <dc:creator>Rena C Patel</dc:creator>
  2074.      <dc:creator>Melissa A Haendel</dc:creator>
  2075.      <dc:date>2024-05-02</dc:date>
  2076.      <dc:source>JMIR mHealth and uHealth</dc:source>
  2077.      <dc:title>Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study</dc:title>
  2078.      <dc:identifier>pmid:38696234</dc:identifier>
  2079.      <dc:identifier>doi:10.2196/54622</dc:identifier>
  2080.    </item>
  2081.    <item>
  2082.      <title>Vitamin B&lt;sub&gt;12&lt;/sub&gt; Supplementation in Psychiatric Practice</title>
  2083.      <link>https://pubmed.ncbi.nlm.nih.gov/38696105/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2084.      <description>PURPOSE OF REVIEW: Vitamin B(12) (B12, cobalamin) deficiency has been associated with neuropsychiatric symptoms, suggesting a role for B12 supplementation both as a treatment for psychiatric symptoms due to B12 deficiency and as an augmentation strategy for pharmacological treatments of psychiatric disorders. This critical review discusses the major causes of B12 deficiency, the range of psychiatric and non-psychiatric manifestations of B12 deficiency, the indications for testing B12 levels, and...</description>
  2085.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Curr Psychiatry Rep. 2024 May 2. doi: 10.1007/s11920-024-01505-4. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">PURPOSE OF REVIEW: Vitamin B<sub>12</sub> (B12, cobalamin) deficiency has been associated with neuropsychiatric symptoms, suggesting a role for B12 supplementation both as a treatment for psychiatric symptoms due to B12 deficiency and as an augmentation strategy for pharmacological treatments of psychiatric disorders. This critical review discusses the major causes of B12 deficiency, the range of psychiatric and non-psychiatric manifestations of B12 deficiency, the indications for testing B12 levels, and the evidence for B12 supplementation for major psychiatric disorders.</p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">RECENT FINDINGS: We find that high-quality evidence shows no benefit to routine B12 supplementation for mild depressive symptoms or to prevent depression. There is very limited evidence on the role of B12 supplementation to augment antidepressants. No high-quality evidence to date suggests a role for routine B12 supplementation in any other major psychiatric disorder. No formal guidelines indicate when clinicians should test B12 levels for common psychiatric symptoms, in the absence of major risk factors for deficiency or cardinal symptoms of deficiency. No robust evidence currently supports routine B12 supplementation for major psychiatric disorders. However, psychiatrists should be aware of the important risk factors for B12 deficiency and should be able to identify symptoms of B12 deficiency, which requires prompt testing, medical workup, and treatment. Testing for B12 deficiency should be considered for atypical or severe psychiatric presentations.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38696105/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38696105</a> | DOI:<a href=https://doi.org/10.1007/s11920-024-01505-4>10.1007/s11920-024-01505-4</a></p></div>]]></content:encoded>
  2086.      <guid isPermaLink="false">pubmed:38696105</guid>
  2087.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2088.      <dc:creator>Kevin P Kennedy</dc:creator>
  2089.      <dc:creator>Jeanne L Alexander</dc:creator>
  2090.      <dc:creator>Amir Garakani</dc:creator>
  2091.      <dc:creator>Lawrence S Gross</dc:creator>
  2092.      <dc:creator>David L Mintz</dc:creator>
  2093.      <dc:creator>Tapan Parikh</dc:creator>
  2094.      <dc:creator>Janet H Pine</dc:creator>
  2095.      <dc:creator>Calvin R Sumner</dc:creator>
  2096.      <dc:creator>David A Baron</dc:creator>
  2097.      <dc:date>2024-05-02</dc:date>
  2098.      <dc:source>Current psychiatry reports</dc:source>
  2099.      <dc:title>Vitamin B&lt;sub&gt;12&lt;/sub&gt; Supplementation in Psychiatric Practice</dc:title>
  2100.      <dc:identifier>pmid:38696105</dc:identifier>
  2101.      <dc:identifier>doi:10.1007/s11920-024-01505-4</dc:identifier>
  2102.    </item>
  2103.    <item>
  2104.      <title>Discovery of Borosin Catalytic Strategies and Function through Bioinformatic Profiling</title>
  2105.      <link>https://pubmed.ncbi.nlm.nih.gov/38695893/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2106.      <description>Borosins are ribosomally synthesized and post-translationally modified peptides (RiPPs) containing backbone α-N-methylations. These modifications confer favorable pharmacokinetic properties including increased membrane permeability and resistance to proteolytic degradation. Previous studies have biochemically and bioinformatically explored several borosins, revealing (1) numerous domain architectures and (2) diverse core regions lacking conserved sequence elements. Due to these characteristics,...</description>
  2107.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">ACS Chem Biol. 2024 May 2. doi: 10.1021/acschembio.4c00066. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Borosins are ribosomally synthesized and post-translationally modified peptides (RiPPs) containing backbone α-<i>N</i>-methylations. These modifications confer favorable pharmacokinetic properties including increased membrane permeability and resistance to proteolytic degradation. Previous studies have biochemically and bioinformatically explored several borosins, revealing (1) numerous domain architectures and (2) diverse core regions lacking conserved sequence elements. Due to these characteristics, large-scale computational identification of borosin biosynthetic genes remains challenging and often requires additional, time-intensive manual inspection. This work builds upon previous findings and updates the genome-mining tool RODEO to automatically evaluate borosin biosynthetic gene clusters (BGCs) and identify putative precursor peptides. Using the new RODEO module, we provide an updated analysis of borosin BGCs identified in the NCBI database. From our data set, we bioinformatically predict and experimentally characterize a new fused borosin domain architecture, in which the modified natural product core is encoded N-terminal to the methyltransferase domain. Additionally, we demonstrate that a borosin precursor peptide is a native substrate of shewasin A, a reported aspartyl peptidase with no previously identified substrates. Shewasin A requires post-translational modification of the leader peptide for proteolytic maturation, a feature not previously observed in RiPPs. Overall, this work provides a user-friendly and open-access tool for the analysis of borosin BGCs and we demonstrate its utility to uncover additional biosynthetic strategies within the borosin class of RiPPs.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38695893/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38695893</a> | DOI:<a href=https://doi.org/10.1021/acschembio.4c00066>10.1021/acschembio.4c00066</a></p></div>]]></content:encoded>
  2108.      <guid isPermaLink="false">pubmed:38695893</guid>
  2109.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2110.      <dc:creator>Aileen R Lee</dc:creator>
  2111.      <dc:creator>Riley S Carter</dc:creator>
  2112.      <dc:creator>Aman S Imani</dc:creator>
  2113.      <dc:creator>Shravan R Dommaraju</dc:creator>
  2114.      <dc:creator>Graham A Hudson</dc:creator>
  2115.      <dc:creator>Douglas A Mitchell</dc:creator>
  2116.      <dc:creator>Michael F Freeman</dc:creator>
  2117.      <dc:date>2024-05-02</dc:date>
  2118.      <dc:source>ACS chemical biology</dc:source>
  2119.      <dc:title>Discovery of Borosin Catalytic Strategies and Function through Bioinformatic Profiling</dc:title>
  2120.      <dc:identifier>pmid:38695893</dc:identifier>
  2121.      <dc:identifier>doi:10.1021/acschembio.4c00066</dc:identifier>
  2122.    </item>
  2123.    <item>
  2124.      <title>Is it personal or is it social? The interaction of knowledge domain and statistical evidence in U.S. and Chinese preschoolers' social generalizations</title>
  2125.      <link>https://pubmed.ncbi.nlm.nih.gov/38695795/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2126.      <description>Children make inferences about the social world by observing human actions. However, human actions can be ambiguous: They can be sources of information about personal, idiosyncratic characteristics of individuals or socially shared knowledge. In two cross-cultural studies (N = 420; M(age) = 4.05 years, SD = 0.77, 47% female), we ask if U.S. and Chinese children's inferences about whether an action is personal or social vary by domain, statistical evidence, and culture. We did this with a...</description>
  2127.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">J Exp Psychol Gen. 2024 May 2. doi: 10.1037/xge0001605. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Children make inferences about the social world by observing human actions. However, human actions can be ambiguous: They can be sources of information about personal, idiosyncratic characteristics of individuals or socially shared knowledge. In two cross-cultural studies (<i>N</i> = 420; <i>M</i><sub>age</sub> = 4.05 years, <i>SD</i> = 0.77, 47% female), we ask if U.S. and Chinese children's inferences about whether an action is personal or social vary by domain, statistical evidence, and culture. We did this with a generalization method: Preschoolers learn about one agent's actions and then are asked what they think a new agent will do. Low rates of generalization suggest children inferred something unique to an individual, while high rates suggest that children inferred that the action represented socially shared knowledge. In a mixed between- and within-participant design, children observed agents demonstrate sequences of statistically random (or nonrandom, between participants) actions that were verbally framed as relevant to a particular domain (agent's personal preferences, labels, object functions, or game rules). We found that children's social generalizations about actions were on a continuum: with linguistic conventions (e.g., labels) being the most social, preferences being the most personal, and nonlinguistic conventions (i.e., object functions, game rules) falling somewhere in between. Furthermore, the influence of statistical evidence and cultural variation varied for each domain. These findings highlight how children combine knowledge and evidence to infer social meaning from actions and have implications for rational constructivist accounts of cultural learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38695795/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38695795</a> | DOI:<a href=https://doi.org/10.1037/xge0001605>10.1037/xge0001605</a></p></div>]]></content:encoded>
  2128.      <guid isPermaLink="false">pubmed:38695795</guid>
  2129.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2130.      <dc:creator>Teresa Flanagan</dc:creator>
  2131.      <dc:creator>Xin Alice Zhao</dc:creator>
  2132.      <dc:creator>Fei Xu</dc:creator>
  2133.      <dc:creator>Tamar Kushnir</dc:creator>
  2134.      <dc:date>2024-05-02</dc:date>
  2135.      <dc:source>Journal of experimental psychology. General</dc:source>
  2136.      <dc:title>Is it personal or is it social? The interaction of knowledge domain and statistical evidence in U.S. and Chinese preschoolers' social generalizations</dc:title>
  2137.      <dc:identifier>pmid:38695795</dc:identifier>
  2138.      <dc:identifier>doi:10.1037/xge0001605</dc:identifier>
  2139.    </item>
  2140.    <item>
  2141.      <title>Evaluations are inherently comparative, but are compared to what?</title>
  2142.      <link>https://pubmed.ncbi.nlm.nih.gov/38695794/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2143.      <description>Understanding how objective quantities are translated into subjective evaluations has long been of interest to social scientists, medical professionals, and policymakers with an interest in how people process and act on quantitative information. The theory of decision by sampling proposes a comparative procedure: Values seem larger or smaller based on how they rank in a comparison set, the decision sample. But what values are included in this decision sample? We identify and test four...</description>
  2144.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">J Pers Soc Psychol. 2024 May 2. doi: 10.1037/pspa0000394. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Understanding how objective quantities are translated into subjective evaluations has long been of interest to social scientists, medical professionals, and policymakers with an interest in how people process and act on quantitative information. The theory of decision by sampling proposes a comparative procedure: Values seem larger or smaller based on how they rank in a comparison set, the <i>decision sample.</i> But what values are included in this decision sample? We identify and test four mechanistic accounts, each suggesting that how previously encountered attribute values are processed determines whether they linger in the sample to guide the subjective interpretation, and thus the influence, of newly encountered values. Testing our ideas through studies of loss aversion, delay discounting, and vaccine hesitancy, we find strongest support for one account: Quantities need to be subjectively evaluated-rather than merely encountered-for them to enter the decision sample, alter the subjective interpretation of other values, and then guide decision making. Discussion focuses on how the present findings inform understanding of the nature of the decision sample and identify new research directions for the longstanding question of how comparison standards influence decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38695794/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38695794</a> | DOI:<a href=https://doi.org/10.1037/pspa0000394>10.1037/pspa0000394</a></p></div>]]></content:encoded>
  2145.      <guid isPermaLink="false">pubmed:38695794</guid>
  2146.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2147.      <dc:creator>Minah H Jung</dc:creator>
  2148.      <dc:creator>Clayton R Critcher</dc:creator>
  2149.      <dc:creator>Leif D Nelson</dc:creator>
  2150.      <dc:date>2024-05-02</dc:date>
  2151.      <dc:source>Journal of personality and social psychology</dc:source>
  2152.      <dc:title>Evaluations are inherently comparative, but are compared to what?</dc:title>
  2153.      <dc:identifier>pmid:38695794</dc:identifier>
  2154.      <dc:identifier>doi:10.1037/pspa0000394</dc:identifier>
  2155.    </item>
  2156.    <item>
  2157.      <title>The Hopkins-Oxford Psychedelics Ethics (HOPE) Working Group Consensus Statement</title>
  2158.      <link>https://pubmed.ncbi.nlm.nih.gov/38695382/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2159.      <description>No abstract</description>
  2160.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Am J Bioeth. 2024 May 2:1-7. doi: 10.1080/15265161.2024.2342764. Online ahead of print.</p><p><b>NO ABSTRACT</b></p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38695382/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38695382</a> | DOI:<a href=https://doi.org/10.1080/15265161.2024.2342764>10.1080/15265161.2024.2342764</a></p></div>]]></content:encoded>
  2161.      <guid isPermaLink="false">pubmed:38695382</guid>
  2162.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2163.      <dc:creator>Edward Jacobs</dc:creator>
  2164.      <dc:creator>Brian D Earp</dc:creator>
  2165.      <dc:creator>Paul S Appelbaum</dc:creator>
  2166.      <dc:creator>Lori Bruce</dc:creator>
  2167.      <dc:creator>Ksenia Cassidy</dc:creator>
  2168.      <dc:creator>Yuria Celidwen</dc:creator>
  2169.      <dc:creator>Katherine Cheung</dc:creator>
  2170.      <dc:creator>Sean K Clancy</dc:creator>
  2171.      <dc:creator>Neşe Devenot</dc:creator>
  2172.      <dc:creator>Jules Evans</dc:creator>
  2173.      <dc:creator>Holly Fernandez Lynch</dc:creator>
  2174.      <dc:creator>Phoebe Friesen</dc:creator>
  2175.      <dc:creator>Albert Garcia Romeu</dc:creator>
  2176.      <dc:creator>Neil Gehani</dc:creator>
  2177.      <dc:creator>Molly Maloof</dc:creator>
  2178.      <dc:creator>Olivia Marcus</dc:creator>
  2179.      <dc:creator>Ole Martin Moen</dc:creator>
  2180.      <dc:creator>Mayli Mertens</dc:creator>
  2181.      <dc:creator>Sandeep M Nayak</dc:creator>
  2182.      <dc:creator>Tehseen Noorani</dc:creator>
  2183.      <dc:creator>Kyle Patch</dc:creator>
  2184.      <dc:creator>Sebastian Porsdam-Mann</dc:creator>
  2185.      <dc:creator>Gokul Raj</dc:creator>
  2186.      <dc:creator>Khaleel Rajwani</dc:creator>
  2187.      <dc:creator>Keisha Ray</dc:creator>
  2188.      <dc:creator>William Smith</dc:creator>
  2189.      <dc:creator>Daniel Villiger</dc:creator>
  2190.      <dc:creator>Neil Levy</dc:creator>
  2191.      <dc:creator>Roger Crisp</dc:creator>
  2192.      <dc:creator>Julian Savulescu</dc:creator>
  2193.      <dc:creator>Ilina Singh</dc:creator>
  2194.      <dc:creator>David B Yaden</dc:creator>
  2195.      <dc:date>2024-05-02</dc:date>
  2196.      <dc:source>The American journal of bioethics : AJOB</dc:source>
  2197.      <dc:title>The Hopkins-Oxford Psychedelics Ethics (HOPE) Working Group Consensus Statement</dc:title>
  2198.      <dc:identifier>pmid:38695382</dc:identifier>
  2199.      <dc:identifier>doi:10.1080/15265161.2024.2342764</dc:identifier>
  2200.    </item>
  2201.    <item>
  2202.      <title>Diverse fates of ancient horizontal gene transfers in extremophilic red algae</title>
  2203.      <link>https://pubmed.ncbi.nlm.nih.gov/38695111/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2204.      <description>Horizontal genetic transfer (HGT) is a common phenomenon in eukaryotic genomes. However, the mechanisms by which HGT-derived genes persist and integrate into other pathways remain unclear. This topic is of significant interest because, over time, the stressors that initially favoured the fixation of HGT may diminish or disappear. Despite this, the foreign genes may continue to exist if they become part of a broader stress response or other pathways. The conventional model suggests that the...</description>
  2205.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Environ Microbiol. 2024 May;26(5):e16629. doi: 10.1111/1462-2920.16629.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Horizontal genetic transfer (HGT) is a common phenomenon in eukaryotic genomes. However, the mechanisms by which HGT-derived genes persist and integrate into other pathways remain unclear. This topic is of significant interest because, over time, the stressors that initially favoured the fixation of HGT may diminish or disappear. Despite this, the foreign genes may continue to exist if they become part of a broader stress response or other pathways. The conventional model suggests that the acquisition of HGT equates to adaptation. However, this model may evolve into more complex interactions between gene products, a concept we refer to as the 'Integrated HGT Model' (IHM). To explore this concept further, we studied specialized HGT-derived genes that encode heavy metal detoxification functions. The recruitment of these genes into other pathways could provide clear examples of IHM. In our study, we exposed two anciently diverged species of polyextremophilic red algae from the Galdieria genus to arsenic and mercury stress in laboratory cultures. We then analysed the transcriptome data using differential and coexpression analysis. Our findings revealed that mercury detoxification follows a 'one gene-one function' model, resulting in an indivisible response. In contrast, the arsH gene in the arsenite response pathway demonstrated a complex pattern of duplication, divergence and potential neofunctionalization, consistent with the IHM. Our research sheds light on the fate and integration of ancient HGTs, providing a novel perspective on the ecology of extremophiles.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38695111/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38695111</a> | DOI:<a href=https://doi.org/10.1111/1462-2920.16629>10.1111/1462-2920.16629</a></p></div>]]></content:encoded>
  2206.      <guid isPermaLink="false">pubmed:38695111</guid>
  2207.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2208.      <dc:creator>Julia Van Etten</dc:creator>
  2209.      <dc:creator>Timothy G Stephens</dc:creator>
  2210.      <dc:creator>Erin Chille</dc:creator>
  2211.      <dc:creator>Anna Lipzen</dc:creator>
  2212.      <dc:creator>Daniel Peterson</dc:creator>
  2213.      <dc:creator>Kerrie Barry</dc:creator>
  2214.      <dc:creator>Igor V Grigoriev</dc:creator>
  2215.      <dc:creator>Debashish Bhattacharya</dc:creator>
  2216.      <dc:date>2024-05-02</dc:date>
  2217.      <dc:source>Environmental microbiology</dc:source>
  2218.      <dc:title>Diverse fates of ancient horizontal gene transfers in extremophilic red algae</dc:title>
  2219.      <dc:identifier>pmid:38695111</dc:identifier>
  2220.      <dc:identifier>doi:10.1111/1462-2920.16629</dc:identifier>
  2221.    </item>
  2222.    <item>
  2223.      <title>Clathrin Light Chains negatively regulate plant immunity by hijacking the autophagy pathway</title>
  2224.      <link>https://pubmed.ncbi.nlm.nih.gov/38693694/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2225.      <description>The crosstalk between clathrin-mediated endocytosis (CME) and autophagy pathway has been reported in mammals. However, the interconnection of CME with autophagy has not been established in plants. In this report, we showed that Arabidopsis CLATHRIN LIGHT CHAIN (CLC) subunit 2 and 3 double mutant, clc2-1 clc3-1, phenocopied the Arabidopsis AUTOPHAGY-RELATED GENE (ATG) mutants both in auto-immunity and nutrient sensitivity. Accordingly, the autophagy pathway was significantly compromised in the...</description>
  2226.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Plant Commun. 2024 Apr 30:100937. doi: 10.1016/j.xplc.2024.100937. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">The crosstalk between clathrin-mediated endocytosis (CME) and autophagy pathway has been reported in mammals. However, the interconnection of CME with autophagy has not been established in plants. In this report, we showed that Arabidopsis CLATHRIN LIGHT CHAIN (CLC) subunit 2 and 3 double mutant, clc2-1 clc3-1, phenocopied the Arabidopsis AUTOPHAGY-RELATED GENE (ATG) mutants both in auto-immunity and nutrient sensitivity. Accordingly, the autophagy pathway was significantly compromised in the clc2-1 clc3-1 mutant. Interestingly, we demonstrated with multiple assays that CLC2 directly interacted with ATG8h/ATG8i in a domain-specific manner. As expected, both GFP-ATG8h/GFP-ATG8i and CLC2-GFP were subjected to autophagic degradation and the degradation of GFP-ATG8h was significantly reduced in the clc2-1 clc3-1 mutant. Notably, simultaneously knocking out ATG8h and ATG8i by the CRISPR/CAS9 resulted in an enhanced resistance against Golovinomyces cichoracearum, supporting the functional relevance of the CLC2-ATG8h/8i interactions. In conclusion, our results uncovered a link between the function of CLCs and the autophagy pathway in Arabidopsis.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38693694/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38693694</a> | DOI:<a href=https://doi.org/10.1016/j.xplc.2024.100937>10.1016/j.xplc.2024.100937</a></p></div>]]></content:encoded>
  2227.      <guid isPermaLink="false">pubmed:38693694</guid>
  2228.      <pubDate>Thu, 02 May 2024 06:00:00 -0400</pubDate>
  2229.      <dc:creator>Hu-Jiao Lan</dc:creator>
  2230.      <dc:creator>Jie Ran</dc:creator>
  2231.      <dc:creator>Wen-Xu Wang</dc:creator>
  2232.      <dc:creator>Lei Zhang</dc:creator>
  2233.      <dc:creator>Ni-Ni Wu</dc:creator>
  2234.      <dc:creator>Ya-Ting Zhao</dc:creator>
  2235.      <dc:creator>Min-Jun Huang</dc:creator>
  2236.      <dc:creator>Min Ni</dc:creator>
  2237.      <dc:creator>Feng Liu</dc:creator>
  2238.      <dc:creator>Ninghui Cheng</dc:creator>
  2239.      <dc:creator>Paul A Nakata</dc:creator>
  2240.      <dc:creator>Jianwei Pan</dc:creator>
  2241.      <dc:creator>Steven A Whitham</dc:creator>
  2242.      <dc:creator>Barbara J Baker</dc:creator>
  2243.      <dc:creator>Jian-Zhong Liu</dc:creator>
  2244.      <dc:date>2024-05-02</dc:date>
  2245.      <dc:source>Plant communications</dc:source>
  2246.      <dc:title>Clathrin Light Chains negatively regulate plant immunity by hijacking the autophagy pathway</dc:title>
  2247.      <dc:identifier>pmid:38693694</dc:identifier>
  2248.      <dc:identifier>doi:10.1016/j.xplc.2024.100937</dc:identifier>
  2249.    </item>
  2250.    <item>
  2251.      <title>Genomes of multicellular algal sisters to land plants illuminate signaling network evolution</title>
  2252.      <link>https://pubmed.ncbi.nlm.nih.gov/38693345/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2253.      <description>Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall...</description>
  2254.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Nat Genet. 2024 May 1. doi: 10.1038/s41588-024-01737-3. Online ahead of print.</p><p><b>ABSTRACT</b></p><p xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:p1="http://pubmed.gov/pub-one">Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.</p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38693345/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38693345</a> | DOI:<a href=https://doi.org/10.1038/s41588-024-01737-3>10.1038/s41588-024-01737-3</a></p></div>]]></content:encoded>
  2255.      <guid isPermaLink="false">pubmed:38693345</guid>
  2256.      <pubDate>Wed, 01 May 2024 06:00:00 -0400</pubDate>
  2257.      <dc:creator>Xuehuan Feng</dc:creator>
  2258.      <dc:creator>Jinfang Zheng</dc:creator>
  2259.      <dc:creator>Iker Irisarri</dc:creator>
  2260.      <dc:creator>Huihui Yu</dc:creator>
  2261.      <dc:creator>Bo Zheng</dc:creator>
  2262.      <dc:creator>Zahin Ali</dc:creator>
  2263.      <dc:creator>Sophie de Vries</dc:creator>
  2264.      <dc:creator>Jean Keller</dc:creator>
  2265.      <dc:creator>Janine M R Fürst-Jansen</dc:creator>
  2266.      <dc:creator>Armin Dadras</dc:creator>
  2267.      <dc:creator>Jaccoline M S Zegers</dc:creator>
  2268.      <dc:creator>Tim P Rieseberg</dc:creator>
  2269.      <dc:creator>Amra Dhabalia Ashok</dc:creator>
  2270.      <dc:creator>Tatyana Darienko</dc:creator>
  2271.      <dc:creator>Maaike J Bierenbroodspot</dc:creator>
  2272.      <dc:creator>Lydia Gramzow</dc:creator>
  2273.      <dc:creator>Romy Petroll</dc:creator>
  2274.      <dc:creator>Fabian B Haas</dc:creator>
  2275.      <dc:creator>Noe Fernandez-Pozo</dc:creator>
  2276.      <dc:creator>Orestis Nousias</dc:creator>
  2277.      <dc:creator>Tang Li</dc:creator>
  2278.      <dc:creator>Elisabeth Fitzek</dc:creator>
  2279.      <dc:creator>W Scott Grayburn</dc:creator>
  2280.      <dc:creator>Nina Rittmeier</dc:creator>
  2281.      <dc:creator>Charlotte Permann</dc:creator>
  2282.      <dc:creator>Florian Rümpler</dc:creator>
  2283.      <dc:creator>John M Archibald</dc:creator>
  2284.      <dc:creator>Günter Theißen</dc:creator>
  2285.      <dc:creator>Jeffrey P Mower</dc:creator>
  2286.      <dc:creator>Maike Lorenz</dc:creator>
  2287.      <dc:creator>Henrik Buschmann</dc:creator>
  2288.      <dc:creator>Klaus von Schwartzenberg</dc:creator>
  2289.      <dc:creator>Lori Boston</dc:creator>
  2290.      <dc:creator>Richard D Hayes</dc:creator>
  2291.      <dc:creator>Chris Daum</dc:creator>
  2292.      <dc:creator>Kerrie Barry</dc:creator>
  2293.      <dc:creator>Igor V Grigoriev</dc:creator>
  2294.      <dc:creator>Xiyin Wang</dc:creator>
  2295.      <dc:creator>Fay-Wei Li</dc:creator>
  2296.      <dc:creator>Stefan A Rensing</dc:creator>
  2297.      <dc:creator>Julius Ben Ari</dc:creator>
  2298.      <dc:creator>Noa Keren</dc:creator>
  2299.      <dc:creator>Assaf Mosquna</dc:creator>
  2300.      <dc:creator>Andreas Holzinger</dc:creator>
  2301.      <dc:creator>Pierre-Marc Delaux</dc:creator>
  2302.      <dc:creator>Chi Zhang</dc:creator>
  2303.      <dc:creator>Jinling Huang</dc:creator>
  2304.      <dc:creator>Marek Mutwil</dc:creator>
  2305.      <dc:creator>Jan de Vries</dc:creator>
  2306.      <dc:creator>Yanbin Yin</dc:creator>
  2307.      <dc:date>2024-05-01</dc:date>
  2308.      <dc:source>Nature genetics</dc:source>
  2309.      <dc:title>Genomes of multicellular algal sisters to land plants illuminate signaling network evolution</dc:title>
  2310.      <dc:identifier>pmid:38693345</dc:identifier>
  2311.      <dc:identifier>doi:10.1038/s41588-024-01737-3</dc:identifier>
  2312.    </item>
  2313.    <item>
  2314.      <title>Author Correction: PDK4-dependent hypercatabolism and lactate production of senescent cells promotes cancer malignancy</title>
  2315.      <link>https://pubmed.ncbi.nlm.nih.gov/38693321/?utm_source=Feedvalidator&amp;utm_medium=rss&amp;utm_campaign=None&amp;utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&amp;fc=None&amp;ff=20240507005429&amp;v=2.18.0.post9+e462414</link>
  2316.      <description>No abstract</description>
  2317.      <content:encoded><![CDATA[<div><p style="color: #4aa564;">Nat Metab. 2024 May 1. doi: 10.1038/s42255-024-01054-3. Online ahead of print.</p><p><b>NO ABSTRACT</b></p><p style="color: lightgray">PMID:<a href="https://pubmed.ncbi.nlm.nih.gov/38693321/?utm_source=Feedvalidator&utm_medium=rss&utm_content=1toH0ZWNzwdzzQZ8GDwDjd9sHxXJ9pr6UOGUP4egEIOYmwNMxJ&ff=20240507005429&v=2.18.0.post9+e462414">38693321</a> | DOI:<a href=https://doi.org/10.1038/s42255-024-01054-3>10.1038/s42255-024-01054-3</a></p></div>]]></content:encoded>
  2318.      <guid isPermaLink="false">pubmed:38693321</guid>
  2319.      <pubDate>Wed, 01 May 2024 06:00:00 -0400</pubDate>
  2320.      <dc:creator>Xuefeng Dou</dc:creator>
  2321.      <dc:creator>Qiang Fu</dc:creator>
  2322.      <dc:creator>Qilai Long</dc:creator>
  2323.      <dc:creator>Shuning Liu</dc:creator>
  2324.      <dc:creator>Yejun Zou</dc:creator>
  2325.      <dc:creator>Da Fu</dc:creator>
  2326.      <dc:creator>Qixia Xu</dc:creator>
  2327.      <dc:creator>Zhirui Jiang</dc:creator>
  2328.      <dc:creator>Xiaohui Ren</dc:creator>
  2329.      <dc:creator>Guilong Zhang</dc:creator>
  2330.      <dc:creator>Xiaoling Wei</dc:creator>
  2331.      <dc:creator>Qingfeng Li</dc:creator>
  2332.      <dc:creator>Judith Campisi</dc:creator>
  2333.      <dc:creator>Yuzheng Zhao</dc:creator>
  2334.      <dc:creator>Yu Sun</dc:creator>
  2335.      <dc:date>2024-05-01</dc:date>
  2336.      <dc:source>Nature metabolism</dc:source>
  2337.      <dc:title>Author Correction: PDK4-dependent hypercatabolism and lactate production of senescent cells promotes cancer malignancy</dc:title>
  2338.      <dc:identifier>pmid:38693321</dc:identifier>
  2339.      <dc:identifier>doi:10.1038/s42255-024-01054-3</dc:identifier>
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  2343.  

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