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<?xml version="1.0" encoding="UTF-8" standalone="no"?><feed xmlns="http://www.w3.org/2005/Atom"> <title>PLOS Computational Biology: New Articles</title> <link href="https://journals.plos.org/ploscompbiol/" rel="alternate"/> <author> <name>PLOS</name> <uri>https://journals.plos.org/ploscompbiol/</uri> <email>webmaster@plos.org</email> </author> <subtitle type="text"/> <id>https://journals.plos.org/ploscompbiol/feed/atom</id> <rights>All PLOS articles are Open Access.</rights> <icon>https://journals.plos.org/ploscompbiol/resource/img/favicon.ico</icon> <logo>https://journals.plos.org/ploscompbiol/resource/img/favicon.ico</logo> <updated>2024-05-09T01:02:43Z</updated> <entry> <title>PESSA: A web tool for pathway enrichment score-based survival analysis in cancer</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012024" rel="alternate" title="PESSA: A web tool for pathway enrichment score-based survival analysis in cancer"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012024.PDF" rel="related" title="(PDF) PESSA: A web tool for pathway enrichment score-based survival analysis in cancer" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012024.XML" rel="related" title="(XML) PESSA: A web tool for pathway enrichment score-based survival analysis in cancer" type="text/xml"/> <author> <name>Hong Yang</name> </author> <author> <name>Ying Shi</name> </author> <author> <name>Anqi Lin</name> </author> <author> <name>Chang Qi</name> </author> <author> <name>Zaoqu Liu</name> </author> <author> <name>Quan Cheng</name> </author> <author> <name>Kai Miao</name> </author> <author> <name>Jian Zhang</name> </author> <author> <name>Peng Luo</name> </author> <id>10.1371/journal.pcbi.1012024</id> <updated>2024-05-08T14:00:00Z</updated> <published>2024-05-08T14:00:00Z</published> <content type="html"><p>by Hong Yang, Ying Shi, Anqi Lin, Chang Qi, Zaoqu Liu, Quan Cheng, Kai Miao, Jian Zhang, Peng Luo</p> The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan–Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.</content> </entry> <entry> <title>Group-selection via aggregative propagule-formation enables cooperative multicellularity in an individual based, spatial model</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012107" rel="alternate" title="Group-selection via aggregative propagule-formation enables cooperative multicellularity in an individual based, spatial model"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012107.PDF" rel="related" title="(PDF) Group-selection via aggregative propagule-formation enables cooperative multicellularity in an individual based, spatial model" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012107.XML" rel="related" title="(XML) Group-selection via aggregative propagule-formation enables cooperative multicellularity in an individual based, spatial model" type="text/xml"/> <author> <name>István Oszoli</name> </author> <author> <name>István Zachar</name> </author> <id>10.1371/journal.pcbi.1012107</id> <updated>2024-05-07T14:00:00Z</updated> <published>2024-05-07T14:00:00Z</published> <content type="html"><p>by István Oszoli, István Zachar</p> The emergence of multicellularity is one of the major transitions in evolution that happened multiple times independently. During aggregative multicellularity, genetically potentially unrelated lineages cooperate to form transient multicellular groups. Unlike clonal multicellularity, aggregative multicellular organisms do not rely on kin selection instead other mechanisms maintain cooperation against cheater phenotypes that benefit from cooperators but do not contribute to groups. Spatiality with limited diffusion can facilitate group selection, as interactions among individuals are restricted to local neighbourhoods only. Selection for larger size (e.g. avoiding predation) may facilitate the emergence of aggregation, though it is unknown, whether and how much role such selection played during the evolution of aggregative multicellularity. We have investigated the effect of spatiality and the necessity of predation on the stability of aggregative multicellularity via individual-based modelling on the ecological timescale. We have examined whether aggregation facilitates the survival of cooperators in a temporally heterogeneous environment against cheaters, where only a subset of the population is allowed to periodically colonize a new, resource-rich habitat. Cooperators constitutively produce adhesive molecules to promote aggregation and propagule-formation while cheaters spare this expense to grow faster but cannot aggregate on their own, hence depending on cooperators for long-term survival. We have compared different population-level reproduction modes with and without individual selection (predation) to evaluate the different hypotheses. In a temporally homogeneous environment without propagule-based colonization, cheaters always win. Predation can benefit cooperators, but it is not enough to maintain the necessary cooperator amount in successive dispersals, either randomly or by fragmentation. Aggregation-based propagation however can ensure the adequate ratio of cooperators-to-cheaters in the propagule and is sufficient to do so even without predation. Spatiality combined with temporal heterogeneity helps cooperators via group selection, thus facilitating aggregative multicellularity. External stress selecting for larger size (e.g. predation) may facilitate aggregation, however, according to our results, it is neither necessary nor sufficient for aggregative multicellularity to be maintained when there is effective group-selection.</content> </entry> <entry> <title>Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011787" rel="alternate" title="Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011787.PDF" rel="related" title="(PDF) Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011787.XML" rel="related" title="(XML) Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot" type="text/xml"/> <author> <name>Luke Trinity</name> </author> <author> <name>Ulrike Stege</name> </author> <author> <name>Hosna Jabbari</name> </author> <id>10.1371/journal.pcbi.1011787</id> <updated>2024-05-07T14:00:00Z</updated> <published>2024-05-07T14:00:00Z</published> <content type="html"><p>by Luke Trinity, Ulrike Stege, Hosna Jabbari</p> Understanding and targeting functional RNA structures towards treatment of coronavirus infection can help us to prepare for novel variants of SARS-CoV-2 (the virus causing COVID-19), and any other coronaviruses that could emerge via human-to-human transmission or potential zoonotic (inter-species) events. Leveraging the fact that all coronaviruses use a mechanism known as −1 programmed ribosomal frameshifting (−1 PRF) to replicate, we apply algorithms to predict the most energetically favourable secondary structures (each nucleotide involved in at most one pairing) that may be involved in regulating the −1 PRF event in coronaviruses, especially SARS-CoV-2. We compute previously unknown most stable structure predictions for the frameshift site of coronaviruses via hierarchical folding, a biologically motivated framework where initial non-crossing structure folds first, followed by subsequent, possibly crossing (pseudoknotted), structures. Using mutual information from 181 coronavirus sequences, in conjunction with the algorithm KnotAli, we compute secondary structure predictions for the frameshift site of different coronaviruses. We then utilize the Shapify algorithm to obtain most stable SARS-CoV-2 secondary structure predictions guided by frameshift sequence-specific and genome-wide experimental data. We build on our previous secondary structure investigation of the singular SARS-CoV-2 68 nt frameshift element sequence, by using Shapify to obtain predictions for 132 extended sequences and including covariation information. Previous investigations have not applied hierarchical folding to extended length SARS-CoV-2 frameshift sequences. By doing so, we simulate the effects of ribosome interaction with the frameshift site, providing insight to biological function. We contribute in-depth discussion to contextualize secondary structure dual-graph motifs for SARS-CoV-2, highlighting the energetic stability of the previously identified 3_8 motif alongside the known dominant 3_3 and 3_6 (native-type) −1 PRF structures. Using a combination of thermodynamic methods and sequence covariation, our novel predictions suggest function of the attenuator hairpin via previously unknown pseudoknotted base pairing. While certain initial RNA folding is consistent, other pseudoknotted base pairs form which indicate potential conformational switching between the two structures.</content> </entry> <entry> <title>Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012085" rel="alternate" title="Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012085.PDF" rel="related" title="(PDF) Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012085.XML" rel="related" title="(XML) Differential disruptions in population coding along the dorsal-ventral axis of CA1 in the APP/PS1 mouse model of Aβ pathology" type="text/xml"/> <author> <name>Udaysankar Chockanathan</name> </author> <author> <name>Krishnan Padmanabhan</name> </author> <id>10.1371/journal.pcbi.1012085</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Udaysankar Chockanathan, Krishnan Padmanabhan</p> Alzheimer’s Disease (AD) is characterized by a range of behavioral alterations, including memory loss and psychiatric symptoms. While there is evidence that molecular pathologies, such as amyloid beta (Aβ), contribute to AD, it remains unclear how this histopathology gives rise to such disparate behavioral deficits. One hypothesis is that Aβ exerts differential effects on neuronal circuits across brain regions, depending on the neurophysiology and connectivity of different areas. To test this, we recorded from large neuronal populations in dorsal CA1 (dCA1) and ventral CA1 (vCA1), two hippocampal areas known to be structurally and functionally diverse, in the APP/PS1 mouse model of amyloidosis. Despite similar levels of Aβ pathology, dCA1 and vCA1 showed distinct disruptions in neuronal population activity as animals navigated a virtual reality environment. In dCA1, pairwise correlations and entropy, a measure of the diversity of activity patterns, were decreased in APP/PS1 mice relative to age-matched C57BL/6 controls. However, in vCA1, APP/PS1 mice had increased pair-wise correlations and entropy as compared to age matched controls. Finally, using maximum entropy models, we connected the microscopic features of population activity (correlations) to the macroscopic features of the population code (entropy). We found that the models’ performance increased in predicting dCA1 activity, but decreased in predicting vCA1 activity, in APP/PS1 mice relative to the controls. Taken together, we found that Aβ exerts distinct effects across different hippocampal regions, suggesting that the various behavioral deficits of AD may reflect underlying heterogeneities in neuronal circuits and the different disruptions that Aβ pathology causes in those circuits.</content> </entry> <entry> <title><i>Cooltools</i>: Enabling high-resolution Hi-C analysis in Python</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012067" rel="alternate" title="<i>Cooltools</i>: Enabling high-resolution Hi-C analysis in Python"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012067.PDF" rel="related" title="(PDF) <i>Cooltools</i>: Enabling high-resolution Hi-C analysis in Python" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012067.XML" rel="related" title="(XML) <i>Cooltools</i>: Enabling high-resolution Hi-C analysis in Python" type="text/xml"/> <author> <name>Open2C</name> </author> <author> <name>Nezar Abdennur</name> </author> <author> <name>Sameer Abraham</name> </author> <author> <name>Geoffrey Fudenberg</name> </author> <author> <name>Ilya M. Flyamer</name> </author> <author> <name>Aleksandra A. Galitsyna</name> </author> <author> <name>Anton Goloborodko</name> </author> <author> <name>Maxim Imakaev</name> </author> <author> <name>Betul A. Oksuz</name> </author> <author> <name>Sergey V. Venev</name> </author> <author> <name>Yao Xiao</name> </author> <id>10.1371/journal.pcbi.1012067</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Open2C , Nezar Abdennur, Sameer Abraham, Geoffrey Fudenberg, Ilya M. Flyamer, Aleksandra A. Galitsyna, Anton Goloborodko, Maxim Imakaev, Betul A. Oksuz, Sergey V. Venev, Yao Xiao</p> Chromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers’ time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introduce <i>cooltools</i> (https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data. <i>Cooltools</i> leverages the widely-adopted cooler format which handles storage and access for high-resolution datasets. <i>Cooltools</i> provides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short, <i>cooltools</i> enables the effective use of the latest and largest genome folding datasets.</content> </entry> <entry> <title>Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012058" rel="alternate" title="Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012058.PDF" rel="related" title="(PDF) Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012058.XML" rel="related" title="(XML) Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain" type="text/xml"/> <author> <name>Thirza Dado</name> </author> <author> <name>Paolo Papale</name> </author> <author> <name>Antonio Lozano</name> </author> <author> <name>Lynn Le</name> </author> <author> <name>Feng Wang</name> </author> <author> <name>Marcel van Gerven</name> </author> <author> <name>Pieter Roelfsema</name> </author> <author> <name>Yağmur Güçlütürk</name> </author> <author> <name>Umut Güçlü</name> </author> <id>10.1371/journal.pcbi.1012058</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Thirza Dado, Paolo Papale, Antonio Lozano, Lynn Le, Feng Wang, Marcel van Gerven, Pieter Roelfsema, Yağmur Güçlütürk, Umut Güçlü</p> A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., <i>z</i>- and <i>w</i>-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled <i>w</i> representations outperform both <i>z</i> and CLIP representations in explaining neural responses. Further, <i>w</i>-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.</content> </entry> <entry> <title>Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012053" rel="alternate" title="Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012053.PDF" rel="related" title="(PDF) Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012053.XML" rel="related" title="(XML) Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization" type="text/xml"/> <author> <name>Benjamin Antin</name> </author> <author> <name>Masato Sadahiro</name> </author> <author> <name>Marta Gajowa</name> </author> <author> <name>Marcus A. Triplett</name> </author> <author> <name>Hillel Adesnik</name> </author> <author> <name>Liam Paninski</name> </author> <id>10.1371/journal.pcbi.1012053</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Benjamin Antin, Masato Sadahiro, Marta Gajowa, Marcus A. Triplett, Hillel Adesnik, Liam Paninski</p> 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.</content> </entry> <entry> <title>On the impact of re-mating and residual fertility on the Sterile Insect Technique efficacy: Case study with the medfly, <i>Ceratitis capitata</i></title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012052" rel="alternate" title="On the impact of re-mating and residual fertility on the Sterile Insect Technique efficacy: Case study with the medfly, <i>Ceratitis capitata</i>"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012052.PDF" rel="related" title="(PDF) On the impact of re-mating and residual fertility on the Sterile Insect Technique efficacy: Case study with the medfly, <i>Ceratitis capitata</i>" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012052.XML" rel="related" title="(XML) On the impact of re-mating and residual fertility on the Sterile Insect Technique efficacy: Case study with the medfly, <i>Ceratitis capitata</i>" type="text/xml"/> <author> <name>Yves Dumont</name> </author> <author> <name>Clélia F. Oliva</name> </author> <id>10.1371/journal.pcbi.1012052</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Yves Dumont, Clélia F. Oliva</p> The sterile insect technique (SIT) can be an efficient solution for reducing or eliminating certain insect pest populations. It is widely used in agriculture against fruit flies, including the Mediterranean fruit fly (medfly), <i>Ceratitis capitata</i>. The re-mating tendency of medfly females and the fact that the released sterile males may have some residual fertility could be a challenge for the successful implementation of the SIT. Obtaining the right balance between sterility level and sterile male quality (competitiveness, longevity, etc) is the key to a cost-efficient program. Since field experimental approaches can be impacted by many environmental variables, it is difficult to get a clear understanding on how specific parameters, alone or in combination, may affect the SIT efficiency. The use of models not only helps to gather knowledge, but it allows the simulation of a wide range of scenarios and can be easily adapted to local populations and sterile male production.In this study, we consider single- and double-mated females. We first show that SIT can be successful only if the residual fertility is less than a threshold value that depends on the basic offspring number of the targeted pest population, the re-mating rates, and the parameters of double-mated females. Then, we show how the sterile male release rate is affected by the parameters of double-mated females and the male residual fertility. Different scenarios are explored with continuous and periodic sterile male releases, with and without ginger aromatherapy, which is known to enhance sterile male competitiveness, and also taking into account some biological parameters related to females that have been mated twice, either first by a wild (sterile) male and then a sterile (wild) male, or by two wild males only. Parameter values were chosen for peach as host fruit to reflect what could be expected in the Corsican context, where SIT against the medfly is under consideration.Our results suggest that ginger aromatherapy can be a decisive factor determining the success of SIT against medfly. We also emphasize the importance of estimating the duration of the refractory period between matings depending on whether a wild female has mated with a wild or sterile male. Further, we show the importance of parameters, like the (hatched) eggs deposit rate and the death-rate related to all fertile double-mated females. In general, re-mating is considered to be detrimental to SIT programs. However, our results show that, depending on the parameter values of double-mated females, re-mating may also be beneficial for SIT.Our model can be easily adapted to different contexts and species, for a broader understanding of release strategies and management options.</content> </entry> <entry> <title>MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012046" rel="alternate" title="MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012046.PDF" rel="related" title="(PDF) MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012046.XML" rel="related" title="(XML) MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito populations" type="text/xml"/> <author> <name>Héctor M. Sánchez C.</name> </author> <author> <name>David L. Smith</name> </author> <author> <name>John M. Marshall</name> </author> <id>10.1371/journal.pcbi.1012046</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Héctor M. Sánchez C., David L. Smith, John M. Marshall</p> 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 <i>Aedes aegypti</i> population in a suburban landscape in Queensland, Australia, and ii) an <i>Anopheles gambiae</i> 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.</content> </entry> <entry> <title>Challenges of COVID-19 Case Forecasting in the US, 2020–2021</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011200" rel="alternate" title="Challenges of COVID-19 Case Forecasting in the US, 2020–2021"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011200.PDF" rel="related" title="(PDF) Challenges of COVID-19 Case Forecasting in the US, 2020–2021" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011200.XML" rel="related" title="(XML) Challenges of COVID-19 Case Forecasting in the US, 2020–2021" type="text/xml"/> <author> <name>Velma K. Lopez</name> </author> <author> <name>Estee Y. Cramer</name> </author> <author> <name>Robert Pagano</name> </author> <author> <name>John M. Drake</name> </author> <author> <name>Eamon B. O’Dea</name> </author> <author> <name>Madeline Adee</name> </author> <author> <name>Turgay Ayer</name> </author> <author> <name>Jagpreet Chhatwal</name> </author> <author> <name>Ozden O. Dalgic</name> </author> <author> <name>Mary A. Ladd</name> </author> <author> <name>Benjamin P. Linas</name> </author> <author> <name>Peter P. Mueller</name> </author> <author> <name>Jade Xiao</name> </author> <author> <name>Johannes Bracher</name> </author> <author> <name>Alvaro J. Castro Rivadeneira</name> </author> <author> <name>Aaron Gerding</name> </author> <author> <name>Tilmann Gneiting</name> </author> <author> <name>Yuxin Huang</name> </author> <author> <name>Dasuni Jayawardena</name> </author> <author> <name>Abdul H. Kanji</name> </author> <author> <name>Khoa Le</name> </author> <author> <name>Anja Mühlemann</name> </author> <author> <name>Jarad Niemi</name> </author> <author> <name>Evan L. Ray</name> </author> <author> <name>Ariane Stark</name> </author> <author> <name>Yijin Wang</name> </author> <author> <name>Nutcha Wattanachit</name> </author> <author> <name>Martha W. Zorn</name> </author> <author> <name>Sen Pei</name> </author> <author> <name>Jeffrey Shaman</name> </author> <author> <name>Teresa K. Yamana</name> </author> <author> <name>Samuel R. Tarasewicz</name> </author> <author> <name>Daniel J. Wilson</name> </author> <author> <name>Sid Baccam</name> </author> <author> <name>Heidi Gurung</name> </author> <author> <name>Steve Stage</name> </author> <author> <name>Brad Suchoski</name> </author> <author> <name>Lei Gao</name> </author> <author> <name>Zhiling Gu</name> </author> <author> <name>Myungjin Kim</name> </author> <author> <name>Xinyi Li</name> </author> <author> <name>Guannan Wang</name> </author> <author> <name>Lily Wang</name> </author> <author> <name>Yueying Wang</name> </author> <author> <name>Shan Yu</name> </author> <author> <name>Lauren Gardner</name> </author> <author> <name>Sonia Jindal</name> </author> <author> <name>Maximilian Marshall</name> </author> <author> <name>Kristen Nixon</name> </author> <author> <name>Juan Dent</name> </author> <author> <name>Alison L. Hill</name> </author> <author> <name>Joshua Kaminsky</name> </author> <author> <name>Elizabeth C. Lee</name> </author> <author> <name>Joseph C. Lemaitre</name> </author> <author> <name>Justin Lessler</name> </author> <author> <name>Claire P. Smith</name> </author> <author> <name>Shaun Truelove</name> </author> <author> <name>Matt Kinsey</name> </author> <author> <name>Luke C. Mullany</name> </author> <author> <name>Kaitlin Rainwater-Lovett</name> </author> <author> <name>Lauren Shin</name> </author> <author> <name>Katharine Tallaksen</name> </author> <author> <name>Shelby Wilson</name> </author> <author> <name>Dean Karlen</name> </author> <author> <name>Lauren Castro</name> </author> <author> <name>Geoffrey Fairchild</name> </author> <author> <name>Isaac Michaud</name> </author> <author> <name>Dave Osthus</name> </author> <author> <name>Jiang Bian</name> </author> <author> <name>Wei Cao</name> </author> <author> <name>Zhifeng Gao</name> </author> <author> <name>Juan Lavista Ferres</name> </author> <author> <name>Chaozhuo Li</name> </author> <author> <name>Tie-Yan Liu</name> </author> <author> <name>Xing Xie</name> </author> <author> <name>Shun Zhang</name> </author> <author> <name>Shun Zheng</name> </author> <author> <name>Matteo Chinazzi</name> </author> <author> <name>Jessica T. Davis</name> </author> <author> <name>Kunpeng Mu</name> </author> <author> <name>Ana Pastore y Piontti</name> </author> <author> <name>Alessandro Vespignani</name> </author> <author> <name>Xinyue Xiong</name> </author> <author> <name>Robert Walraven</name> </author> <author> <name>Jinghui Chen</name> </author> <author> <name>Quanquan Gu</name> </author> <author> <name>Lingxiao Wang</name> </author> <author> <name>Pan Xu</name> </author> <author> <name>Weitong Zhang</name> </author> <author> <name>Difan Zou</name> </author> <author> <name>Graham Casey Gibson</name> </author> <author> <name>Daniel Sheldon</name> </author> <author> <name>Ajitesh Srivastava</name> </author> <author> <name>Aniruddha Adiga</name> </author> <author> <name>Benjamin Hurt</name> </author> <author> <name>Gursharn Kaur</name> </author> <author> <name>Bryan Lewis</name> </author> <author> <name>Madhav Marathe</name> </author> <author> <name>Akhil Sai Peddireddy</name> </author> <author> <name>Przemyslaw Porebski</name> </author> <author> <name>Srinivasan Venkatramanan</name> </author> <author> <name>Lijing Wang</name> </author> <author> <name>Pragati V. Prasad</name> </author> <author> <name>Jo W. Walker</name> </author> <author> <name>Alexander E. Webber</name> </author> <author> <name>Rachel B. Slayton</name> </author> <author> <name>Matthew Biggerstaff</name> </author> <author> <name>Nicholas G. Reich</name> </author> <author> <name>Michael A. Johansson</name> </author> <id>10.1371/journal.pcbi.1011200</id> <updated>2024-05-06T14:00:00Z</updated> <published>2024-05-06T14:00:00Z</published> <content type="html"><p>by Velma K. Lopez, Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph C. Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson</p> During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.</content> </entry> <entry> <title>Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012096" rel="alternate" title="Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012096.PDF" rel="related" title="(PDF) Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012096.XML" rel="related" title="(XML) Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation" type="text/xml"/> <author> <name>Phoebe Asplin</name> </author> <author> <name>Matt J. Keeling</name> </author> <author> <name>Rebecca Mancy</name> </author> <author> <name>Edward M. Hill</name> </author> <id>10.1371/journal.pcbi.1012096</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by Phoebe Asplin, Matt J. Keeling, Rebecca Mancy, Edward M. Hill</p>Background <p>Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied.</p> Methods and findings <p>We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, <i>α</i>. Varying <i>α</i> tunes the model from having no symptom propagation (<i>α</i> = 0, as typically assumed) to one where symptoms always propagate (<i>α</i> = 1). For parameters corresponding to three respiratory pathogens—seasonal influenza, pandemic influenza and SARS-CoV-2—we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB).In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation.</p> Conclusions <p>Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.</p></content> </entry> <entry> <title>Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012088" rel="alternate" title="Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012088.PDF" rel="related" title="(PDF) Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012088.XML" rel="related" title="(XML) Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment" type="text/xml"/> <author> <name>Sebastien Benzekry</name> </author> <author> <name>Michalis Mastri</name> </author> <author> <name>Chiara Nicolò</name> </author> <author> <name>John M. L. Ebos</name> </author> <id>10.1371/journal.pcbi.1012088</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by Sebastien Benzekry, Michalis Mastri, Chiara Nicolò, John M. L. Ebos</p> Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (i.e., circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (i.e., CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.</content> </entry> <entry> <title>Visual social information use in collective foraging</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012087" rel="alternate" title="Visual social information use in collective foraging"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012087.PDF" rel="related" title="(PDF) Visual social information use in collective foraging" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012087.XML" rel="related" title="(XML) Visual social information use in collective foraging" type="text/xml"/> <author> <name>David Mezey</name> </author> <author> <name>Dominik Deffner</name> </author> <author> <name>Ralf H. J. M. Kurvers</name> </author> <author> <name>Pawel Romanczuk</name> </author> <id>10.1371/journal.pcbi.1012087</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by David Mezey, Dominik Deffner, Ralf H. J. M. Kurvers, Pawel Romanczuk</p> Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.</content> </entry> <entry> <title>A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012082" rel="alternate" title="A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012082.PDF" rel="related" title="(PDF) A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012082.XML" rel="related" title="(XML) A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms" type="text/xml"/> <author> <name>An Qi Zhang</name> </author> <author> <name>Martin R. Ralph</name> </author> <author> <name>Adam R. Stinchcombe</name> </author> <id>10.1371/journal.pcbi.1012082</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by An Qi Zhang, Martin R. Ralph, Adam R. Stinchcombe</p> Many self-motivated and goal-directed behaviours display highly flexible, approximately 4 hour ultradian (shorter than a day) oscillations. Despite lacking direct correspondence to physical cycles in the environment, these ultradian rhythms may be involved in optimizing functional interactions with the environment and reflect intrinsic neural dynamics. Current evidence supports a role of mesostriatal dopamine (DA) in the expression and propagation of ultradian rhythmicity, however, the biochemical processes underpinning these oscillations remain to be identified. Here, we use a mathematical model to investigate D2 autoreceptor-dependent DA self-regulation as the source of ultradian behavioural rhythms. DA concentration at the midbrain-striatal synapses is governed through a dual-negative feedback-loop structure, which naturally gives rise to rhythmicity. This model shows the propensity of striatal DA to produce an ultradian oscillation characterized by a flexible period that is highly sensitive to parameter variations. Circadian (approximately 24 hour) regulation consolidates the ultradian oscillations and alters their response to the phase-dependent, rapid-resetting effect of a transient excitatory stimulus. Within a circadian framework, the ultradian rhythm orchestrates behavioural activity and enhances responsiveness to an external stimulus. This suggests a role for the circadian-ultradian timekeeping hierarchy in governing organized behaviour and shaping daily experience through coordinating the motivation to engage in recurring, albeit not highly predictable events, such as social interactions.</content> </entry> <entry> <title>A systematic analysis of regression models for protein engineering</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012061" rel="alternate" title="A systematic analysis of regression models for protein engineering"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012061.PDF" rel="related" title="(PDF) A systematic analysis of regression models for protein engineering" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012061.XML" rel="related" title="(XML) A systematic analysis of regression models for protein engineering" type="text/xml"/> <author> <name>Richard Michael</name> </author> <author> <name>Jacob Kæstel-Hansen</name> </author> <author> <name>Peter Mørch Groth</name> </author> <author> <name>Simon Bartels</name> </author> <author> <name>Jesper Salomon</name> </author> <author> <name>Pengfei Tian</name> </author> <author> <name>Nikos S. Hatzakis</name> </author> <author> <name>Wouter Boomsma</name> </author> <id>10.1371/journal.pcbi.1012061</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by Richard Michael, Jacob Kæstel-Hansen, Peter Mørch Groth, Simon Bartels, Jesper Salomon, Pengfei Tian, Nikos S. Hatzakis, Wouter Boomsma</p> To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to <i>predict</i> properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.</content> </entry> <entry> <title>Re-awakening the brain: Forcing transitions in disorders of consciousness by external <i>in silico</i> perturbation</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011350" rel="alternate" title="Re-awakening the brain: Forcing transitions in disorders of consciousness by external <i>in silico</i> perturbation"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011350.PDF" rel="related" title="(PDF) Re-awakening the brain: Forcing transitions in disorders of consciousness by external <i>in silico</i> perturbation" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011350.XML" rel="related" title="(XML) Re-awakening the brain: Forcing transitions in disorders of consciousness by external <i>in silico</i> perturbation" type="text/xml"/> <author> <name>Paulina Clara Dagnino</name> </author> <author> <name>Anira Escrichs</name> </author> <author> <name>Ane López-González</name> </author> <author> <name>Olivia Gosseries</name> </author> <author> <name>Jitka Annen</name> </author> <author> <name>Yonatan Sanz Perl</name> </author> <author> <name>Morten L. Kringelbach</name> </author> <author> <name>Steven Laureys</name> </author> <author> <name>Gustavo Deco</name> </author> <id>10.1371/journal.pcbi.1011350</id> <updated>2024-05-03T14:00:00Z</updated> <published>2024-05-03T14:00:00Z</published> <content type="html"><p>by Paulina Clara Dagnino, Anira Escrichs, Ane López-González, Olivia Gosseries, Jitka Annen, Yonatan Sanz Perl, Morten L. Kringelbach, Steven Laureys, Gustavo Deco</p> A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line <i>in silico</i> probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.</content> </entry> <entry> <title>The quality and complexity of pairwise maximum entropy models for large cortical populations</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012074" rel="alternate" title="The quality and complexity of pairwise maximum entropy models for large cortical populations"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012074.PDF" rel="related" title="(PDF) The quality and complexity of pairwise maximum entropy models for large cortical populations" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012074.XML" rel="related" title="(XML) The quality and complexity of pairwise maximum entropy models for large cortical populations" type="text/xml"/> <author> <name>Valdemar Kargård Olsen</name> </author> <author> <name>Jonathan R. Whitlock</name> </author> <author> <name>Yasser Roudi</name> </author> <id>10.1371/journal.pcbi.1012074</id> <updated>2024-05-02T14:00:00Z</updated> <published>2024-05-02T14:00:00Z</published> <content type="html"><p>by Valdemar Kargård Olsen, Jonathan R. Whitlock, Yasser Roudi</p> We investigate the ability of the pairwise maximum entropy (PME) model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability that multiple neurons are simultaneously active. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while varying the population size, mean firing rate of the chosen population, and the bin size used for binarizing the data. We confirm the previously reported excellent performance of the PME model for small population sizes <i>N</i> < 20. But we also find that larger mean firing rates and bin sizes generally decreases performance. The performance for larger populations were generally not as good. For large populations, pairwise models may be good in terms of predicting third-order correlations and the probability of multiple neurons being active, but still significantly worse than small populations in terms of their improvement over the independent model in KL-divergence. We show that these results are independent of the cortical area and of whether approximate methods or Boltzmann learning are used for inferring the pairwise couplings. We compared the scaling of the inferred couplings with <i>N</i> and find it to be well explained by the Sherrington-Kirkpatrick (SK) model, whose strong coupling regime shows a complex phase with many metastable states. We find that, up to the maximum population size studied here, the fitted PME model remains outside its complex phase. However, the standard deviation of the couplings compared to their mean increases, and the model gets closer to the boundary of the complex phase as the population size grows.</content> </entry> <entry> <title>Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample test</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011999" rel="alternate" title="Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample test"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011999.PDF" rel="related" title="(PDF) Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample test" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011999.XML" rel="related" title="(XML) Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample test" type="text/xml"/> <author> <name>Keiji Ota</name> </author> <author> <name>Laurence T. Maloney</name> </author> <id>10.1371/journal.pcbi.1011999</id> <updated>2024-05-01T14:00:00Z</updated> <published>2024-05-01T14:00:00Z</published> <content type="html"><p>by Keiji Ota, Laurence T. Maloney</p> Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value–step by step–in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) <i>probability density functions</i> and (2) these probability density functions are derived from <i>samples</i>. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample–<i>accuracy</i>, <i>additivity</i> and <i>influence</i>. Influence measures allow us to assess how much weight <i>each point</i> in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual’s use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants’ data than the normative BDT model.</content> </entry> <entry> <title>Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012081" rel="alternate" title="Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012081.PDF" rel="related" title="(PDF) Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012081.XML" rel="related" title="(XML) Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers" type="text/xml"/> <author> <name>Carla Alejandre</name> </author> <author> <name>Jorge Calle-Espinosa</name> </author> <author> <name>Jaime Iranzo</name> </author> <id>10.1371/journal.pcbi.1012081</id> <updated>2024-04-30T14:00:00Z</updated> <published>2024-04-30T14:00:00Z</published> <content type="html"><p>by Carla Alejandre, Jorge Calle-Espinosa, Jaime Iranzo</p> Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.</content> </entry> <entry> <title>Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012084" rel="alternate" title="Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012084.PDF" rel="related" title="(PDF) Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012084.XML" rel="related" title="(XML) Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" type="text/xml"/> <author> <name>H. Robert Frost</name> </author> <id>10.1371/journal.pcbi.1012084</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by H. Robert Frost</p> We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.</content> </entry> <entry> <title>A novel hypergraph model for identifying and prioritizing personalized drivers in cancer</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012068" rel="alternate" title="A novel hypergraph model for identifying and prioritizing personalized drivers in cancer"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012068.PDF" rel="related" title="(PDF) A novel hypergraph model for identifying and prioritizing personalized drivers in cancer" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012068.XML" rel="related" title="(XML) A novel hypergraph model for identifying and prioritizing personalized drivers in cancer" type="text/xml"/> <author> <name>Naiqian Zhang</name> </author> <author> <name>Fubin Ma</name> </author> <author> <name>Dong Guo</name> </author> <author> <name>Yuxuan Pang</name> </author> <author> <name>Chenye Wang</name> </author> <author> <name>Yusen Zhang</name> </author> <author> <name>Xiaoqi Zheng</name> </author> <author> <name>Mingyi Wang</name> </author> <id>10.1371/journal.pcbi.1012068</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Naiqian Zhang, Fubin Ma, Dong Guo, Yuxuan Pang, Chenye Wang, Yusen Zhang, Xiaoqi Zheng, Mingyi Wang</p> Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (<i>LRP1</i>) on tumor cell proliferation.</content> </entry> <entry> <title>Wagers for work: Decomposing the costs of cognitive effort</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012060" rel="alternate" title="Wagers for work: Decomposing the costs of cognitive effort"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012060.PDF" rel="related" title="(PDF) Wagers for work: Decomposing the costs of cognitive effort" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012060.XML" rel="related" title="(XML) Wagers for work: Decomposing the costs of cognitive effort" type="text/xml"/> <author> <name>Sarah L. Master</name> </author> <author> <name>Clayton E. Curtis</name> </author> <author> <name>Peter Dayan</name> </author> <id>10.1371/journal.pcbi.1012060</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Sarah L. Master, Clayton E. Curtis, Peter Dayan</p> Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically, other factors, like memory load, appeared less costly. Other key factors which may drive effort costs, such as error avoidance, had minimal influence on wage requests. Our approach is sensitive to individual differences, and could be used in psychiatric populations to understand the true underlying nature of apparent cognitive deficits.</content> </entry> <entry> <title>Informing policy via dynamic models: Cholera in Haiti</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012032" rel="alternate" title="Informing policy via dynamic models: Cholera in Haiti"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012032.PDF" rel="related" title="(PDF) Informing policy via dynamic models: Cholera in Haiti" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012032.XML" rel="related" title="(XML) Informing policy via dynamic models: Cholera in Haiti" type="text/xml"/> <author> <name>Jesse Wheeler</name> </author> <author> <name>AnnaElaine Rosengart</name> </author> <author> <name>Zhuoxun Jiang</name> </author> <author> <name>Kevin Tan</name> </author> <author> <name>Noah Treutle</name> </author> <author> <name>Edward L. Ionides</name> </author> <id>10.1371/journal.pcbi.1012032</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Jesse Wheeler, AnnaElaine Rosengart, Zhuoxun Jiang, Kevin Tan, Noah Treutle, Edward L. Ionides</p> Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.</content> </entry> <entry> <title>Recurrent neural networks that learn multi-step visual routines with reinforcement learning</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012030" rel="alternate" title="Recurrent neural networks that learn multi-step visual routines with reinforcement learning"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012030.PDF" rel="related" title="(PDF) Recurrent neural networks that learn multi-step visual routines with reinforcement learning" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012030.XML" rel="related" title="(XML) Recurrent neural networks that learn multi-step visual routines with reinforcement learning" type="text/xml"/> <author> <name>Sami Mollard</name> </author> <author> <name>Catherine Wacongne</name> </author> <author> <name>Sander M. Bohte</name> </author> <author> <name>Pieter R. Roelfsema</name> </author> <id>10.1371/journal.pcbi.1012030</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Sami Mollard, Catherine Wacongne, Sander M. Bohte, Pieter R. Roelfsema</p> Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant intermediate results need to be stored by neurons and propagated to the next subproblem, until the overarching goal has been completed. We will here consider visual tasks, which can be decomposed into sequences of elemental visual operations. Experimental evidence suggests that intermediate results of the elemental operations are stored in working memory as an enhancement of neural activity in the visual cortex. The focus of enhanced activity is then available for subsequent operations to act upon. The main question at stake is how the elemental operations and their sequencing can emerge in neural networks that are trained with only rewards, in a reinforcement learning setting. We here propose a new recurrent neural network architecture that can learn composite visual tasks that require the application of successive elemental operations. Specifically, we selected three tasks for which electrophysiological recordings of monkeys’ visual cortex are available. To train the networks, we used RELEARNN, a biologically plausible four-factor Hebbian learning rule, which is local both in time and space. We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operations, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observed in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subsequent subroutines. Our results demonstrate how a biologically plausible learning rule can train a recurrent neural network on multistep visual tasks.</content> </entry> <entry> <title>Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011964" rel="alternate" title="Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011964.PDF" rel="related" title="(PDF) Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011964.XML" rel="related" title="(XML) Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains" type="text/xml"/> <author> <name>Christian Donner</name> </author> <author> <name>Julian Bartram</name> </author> <author> <name>Philipp Hornauer</name> </author> <author> <name>Taehoon Kim</name> </author> <author> <name>Damian Roqueiro</name> </author> <author> <name>Andreas Hierlemann</name> </author> <author> <name>Guillaume Obozinski</name> </author> <author> <name>Manuel Schröter</name> </author> <id>10.1371/journal.pcbi.1011964</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Christian Donner, Julian Bartram, Philipp Hornauer, Taehoon Kim, Damian Roqueiro, Andreas Hierlemann, Guillaume Obozinski, Manuel Schröter</p> Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.</content> </entry> <entry> <title>Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011575" rel="alternate" title="Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011575.PDF" rel="related" title="(PDF) Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011575.XML" rel="related" title="(XML) Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models" type="text/xml"/> <author> <name>Judith A. Bouman</name> </author> <author> <name>Anthony Hauser</name> </author> <author> <name>Simon L. Grimm</name> </author> <author> <name>Martin Wohlfender</name> </author> <author> <name>Samir Bhatt</name> </author> <author> <name>Elizaveta Semenova</name> </author> <author> <name>Andrew Gelman</name> </author> <author> <name>Christian L. Althaus</name> </author> <author> <name>Julien Riou</name> </author> <id>10.1371/journal.pcbi.1011575</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Judith A. Bouman, Anthony Hauser, Simon L. Grimm, Martin Wohlfender, Samir Bhatt, Elizaveta Semenova, Andrew Gelman, Christian L. Althaus, Julien Riou</p> Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.</content> </entry> <entry> <title>UNNT: A novel Utility for comparing Neural Net and Tree-based models</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011504" rel="alternate" title="UNNT: A novel Utility for comparing Neural Net and Tree-based models"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011504.PDF" rel="related" title="(PDF) UNNT: A novel Utility for comparing Neural Net and Tree-based models" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1011504.XML" rel="related" title="(XML) UNNT: A novel Utility for comparing Neural Net and Tree-based models" type="text/xml"/> <author> <name>Vineeth Gutta</name> </author> <author> <name>Satish Ranganathan Ganakammal</name> </author> <author> <name>Sara Jones</name> </author> <author> <name>Matthew Beyers</name> </author> <author> <name>Sunita Chandrasekaran</name> </author> <id>10.1371/journal.pcbi.1011504</id> <updated>2024-04-29T14:00:00Z</updated> <published>2024-04-29T14:00:00Z</published> <content type="html"><p>by Vineeth Gutta, Satish Ranganathan Ganakammal, Sara Jones, Matthew Beyers, Sunita Chandrasekaran</p> The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.</content> </entry> <entry> <title>Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012062" rel="alternate" title="Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012062.PDF" rel="related" title="(PDF) Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012062.XML" rel="related" title="(XML) Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error" type="text/xml"/> <author> <name>Robert Challen</name> </author> <author> <name>Anastasia Chatzilena</name> </author> <author> <name>George Qian</name> </author> <author> <name>Glenda Oben</name> </author> <author> <name>Rachel Kwiatkowska</name> </author> <author> <name>Catherine Hyams</name> </author> <author> <name>Adam Finn</name> </author> <author> <name>Krasimira Tsaneva-Atanasova</name> </author> <author> <name>Leon Danon</name> </author> <id>10.1371/journal.pcbi.1012062</id> <updated>2024-04-26T14:00:00Z</updated> <published>2024-04-26T14:00:00Z</published> <content type="html"><p>by Robert Challen, Anastasia Chatzilena, George Qian, Glenda Oben, Rachel Kwiatkowska, Catherine Hyams, Adam Finn, Krasimira Tsaneva-Atanasova, Leon Danon</p> Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.</content> </entry> <entry> <title>Human decision making balances reward maximization and policy compression</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012057" rel="alternate" title="Human decision making balances reward maximization and policy compression"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012057.PDF" rel="related" title="(PDF) Human decision making balances reward maximization and policy compression" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012057.XML" rel="related" title="(XML) Human decision making balances reward maximization and policy compression" type="text/xml"/> <author> <name>Lucy Lai</name> </author> <author> <name>Samuel J. Gershman</name> </author> <id>10.1371/journal.pcbi.1012057</id> <updated>2024-04-26T14:00:00Z</updated> <published>2024-04-26T14:00:00Z</published> <content type="html"><p>by Lucy Lai, Samuel J. Gershman</p> Policy compression is a computational framework that describes how capacity-limited agents trade reward for simpler action policies to reduce cognitive cost. In this study, we present behavioral evidence that humans prefer simpler policies, as predicted by a capacity-limited reinforcement learning model. Across a set of tasks, we find that people exploit structure in the relationships between states, actions, and rewards to “compress” their policies. In particular, compressed policies are systematically biased towards actions with high marginal probability, thereby discarding some state information. This bias is greater when there is redundancy in the reward-maximizing action policy across states, and increases with memory load. These results could not be explained qualitatively or quantitatively by models that did not make use of policy compression under a capacity limit. We also confirmed the prediction that time pressure should further reduce policy complexity and increase action bias, based on the hypothesis that actions are selected via time-dependent decoding of a compressed code. These findings contribute to a deeper understanding of how humans adapt their decision-making strategies under cognitive resource constraints.</content> </entry> <entry> <title>Spatial transcriptome-guided multi-scale framework connects <i>P</i>. <i>aeruginosa</i> metabolic states to oxidative stress biofilm microenvironment</title> <link href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012031" rel="alternate" title="Spatial transcriptome-guided multi-scale framework connects <i>P</i>. <i>aeruginosa</i> metabolic states to oxidative stress biofilm microenvironment"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012031.PDF" rel="related" title="(PDF) Spatial transcriptome-guided multi-scale framework connects <i>P</i>. <i>aeruginosa</i> metabolic states to oxidative stress biofilm microenvironment" type="application/pdf"/> <link href="https://journals.plos.org/ploscompbiol/article/asset?id=10.1371/journal.pcbi.1012031.XML" rel="related" title="(XML) Spatial transcriptome-guided multi-scale framework connects <i>P</i>. <i>aeruginosa</i> metabolic states to oxidative stress biofilm microenvironment" type="text/xml"/> <author> <name>Tracy J. Kuper</name> </author> <author> <name>Mohammad Mazharul Islam</name> </author> <author> <name>Shayn M. Peirce-Cottler</name> </author> <author> <name>Jason A. Papin</name> </author> <author> <name>Roseanne M Ford</name> </author> <id>10.1371/journal.pcbi.1012031</id> <updated>2024-04-26T14:00:00Z</updated> <published>2024-04-26T14:00:00Z</published> <content type="html"><p>by Tracy J. Kuper, Mohammad Mazharul Islam, Shayn M. Peirce-Cottler, Jason A. Papin, Roseanne M Ford</p> With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate <i>Pseudomonas aeruginosa</i> regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of <i>P</i>. <i>aeruginosa</i> PA14 biofilm spatial transcriptomic data into a <i>P</i>. <i>aeruginosa</i> PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent’s local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm’s spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.</content> </entry></feed>
