PLoS Computational Biology最新文献

{"title":"Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies","authors":"Adrian J. Green, Lisa Truong, Preethi Thunga, Connor Leong, Melody Hancock, Robyn L. Tanguay, David M. Reif","doi":"10.1371/journal.pcbi.1012423","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012423","url":null,"abstract":"Zebrafish have become an essential model organism in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential “normal” behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the transmission of bacterial agents of sapronotic diseases using an ecosystem-based approach: A first spatially realistic metacommunity model","authors":"Ahmadou Sylla, Christine Chevillon, Ramsès Djidjiou-Demasse, Ousmane Seydi, Carlos A. Vargas Campos, Magdalene Dogbe, Kayla M. Fast, Jennifer L. Pechal, Alex Rakestraw, Matthew E. Scott, Michael W. Sandel, Heather Jordan, Mark Eric Benbow, Jean-François Guégan","doi":"10.1371/journal.pcbi.1012435","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012435","url":null,"abstract":"Pathogens such as bacteria, fungi and viruses are important components of soil and aquatic communities, where they can benefit from decaying and living organic matter, and may opportunistically infect human and animal hosts. One-third of human infectious diseases is constituted by sapronotic disease agents that are natural inhabitants of soil or aquatic ecosystems. They are capable of existing and reproducing in the environment outside of the host for extended periods of time. However, as ecological research on sapronosis is infrequent and epidemiological models are even rarer, very little information is currently available. Their importance is overlooked in medical and veterinary research, as well as the relationships between free environmental forms and those that are pathogenic. Here, using dynamical models in realistic aquatic metacommunity systems, we analyze sapronosis transmission, using the human pathogen <jats:italic>Mycobacterium ulcerans</jats:italic> that is responsible for Buruli ulcer. We show that the persistence of bacilli in aquatic ecosystems is driven by a seasonal upstream supply, and that the attachment and development of cells to aquatic living forms is essential for such pathogen persistence and population dynamics. Our work constitutes the first set of metacommunity models of sapronotic disease transmission, and is highly flexible for adaptation to other types of sapronosis. The importance of sapronotic agents on animal and human disease burden needs better understanding and new models of sapronosis disease ecology to guide the management and prevention of this important group of pathogens.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Antiviral capacity of the early CD8 T-cell response is predictive of natural control of SIV infection: Learning in vivo dynamics using ex vivo data","authors":"Bharadwaj Vemparala, Vincent Madelain, Caroline Passaes, Antoine Millet, Véronique Avettand-Fenoel, Ramsès Djidjou-Demasse, Nathalie Dereuddre-Bosquet, Roger Le Grand, Christine Rouzioux, Bruno Vaslin, Asier Sáez-Cirión, Jérémie Guedj, Narendra M. Dixit","doi":"10.1371/journal.pcbi.1012434","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012434","url":null,"abstract":"While most individuals suffer progressive disease following HIV infection, a small fraction spontaneously controls the infection. Although CD8 T-cells have been implicated in this natural control, their mechanistic roles are yet to be established. Here, we combined mathematical modeling and analysis of previously published data from 16 SIV-infected macaques, of which 12 were natural controllers, to elucidate the role of CD8 T-cells in natural control. For each macaque, we considered, in addition to the canonical <jats:italic>in vivo</jats:italic> plasma viral load and SIV DNA data, longitudinal <jats:italic>ex vivo</jats:italic> measurements of the virus suppressive capacity of CD8 T-cells. Available mathematical models do not allow analysis of such combined <jats:italic>in vivo</jats:italic>-<jats:italic>ex vivo</jats:italic> datasets. We explicitly modeled the <jats:italic>ex vivo</jats:italic> assay, derived analytical approximations that link the <jats:italic>ex vivo</jats:italic> measurements with the <jats:italic>in vivo</jats:italic> effector function of CD8-T cells, and integrated them with an <jats:italic>in vivo</jats:italic> model of virus dynamics, thus developing a new learning framework that enabled the analysis. Our model fit the data well and estimated the recruitment rate and/or maximal killing rate of CD8 T-cells to be up to 2-fold higher in controllers than non-controllers (p = 0.013). Importantly, the cumulative suppressive capacity of CD8 T-cells over the first 4–6 weeks of infection was associated with virus control (Spearman’s ρ = -0.51; p = 0.05). Thus, our analysis identified the early cumulative suppressive capacity of CD8 T-cells as a predictor of natural control. Furthermore, simulating a large virtual population, our model quantified the minimum capacity of this early CD8 T-cell response necessary for long-term control. Our study presents new, quantitative insights into the role of CD8 T-cells in the natural control of HIV infection and has implications for remission strategies.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparison of EEG encoding models using audiovisual stimuli and their unimodal counterparts","authors":"Maansi Desai, Alyssa M. Field, Liberty S. Hamilton","doi":"10.1371/journal.pcbi.1012433","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012433","url":null,"abstract":"Communication in the real world is inherently multimodal. When having a conversation, typically sighted and hearing people use both auditory and visual cues to understand one another. For example, objects may make sounds as they move in space, or we may use the movement of a person’s mouth to better understand what they are saying in a noisy environment. Still, many neuroscience experiments rely on unimodal stimuli to understand encoding of sensory features in the brain. The extent to which visual information may influence encoding of auditory information and vice versa in natural environments is thus unclear. Here, we addressed this question by recording scalp electroencephalography (EEG) in 11 subjects as they listened to and watched movie trailers in audiovisual (AV), visual (V) only, and audio (A) only conditions. We then fit linear encoding models that described the relationship between the brain responses and the acoustic, phonetic, and visual information in the stimuli. We also compared whether auditory and visual feature tuning was the same when stimuli were presented in the original AV format versus when visual or auditory information was removed. In these stimuli, visual and auditory information was relatively uncorrelated, and included spoken narration over a scene as well as animated or live-action characters talking with and without their face visible. For this stimulus, we found that auditory feature tuning was similar in the AV and A-only conditions, and similarly, tuning for visual information was similar when stimuli were presented with the audio present (AV) and when the audio was removed (V only). In a cross prediction analysis, we investigated whether models trained on AV data predicted responses to A or V only test data similarly to models trained on unimodal data. Overall, prediction performance using AV training and V test sets was similar to using V training and V test sets, suggesting that the auditory information has a relatively smaller effect on EEG. In contrast, prediction performance using AV training and A only test set was slightly worse than using matching A only training and A only test sets. This suggests the visual information has a stronger influence on EEG, though this makes no qualitative difference in the derived feature tuning. In effect, our results show that researchers may benefit from the richness of multimodal datasets, which can then be used to answer more than one research question.","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.","authors":"Eslam Abousamra, Marlin Figgins, Trevor Bedford","doi":"10.1371/journal.pcbi.1012443","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012443","url":null,"abstract":"<p><p>Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant Rt. These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ∼0.6% median absolute error and ∼6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing.","authors":"Breanne Sparta, Timothy Hamilton, Gunalan Natesan, Samuel D Aragones, Eric J Deeds","doi":"10.1371/journal.pcbi.1012386","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012386","url":null,"abstract":"<p><p>Effective analysis of single-cell RNA sequencing (scRNA-seq) data requires a rigorous distinction between technical noise and biological variation. In this work, we propose a simple feature selection model, termed \"Differentially Distributed Genes\" or DDGs, where a binomial sampling process for each mRNA species produces a null model of technical variation. Using scRNA-seq data where cell identities have been established a priori, we find that the DDG model of biological variation outperforms existing methods. We demonstrate that DDGs distinguish a validated set of real biologically varying genes, minimize neighborhood distortion, and enable accurate partitioning of cells into their established cell-type groups.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Backtracking: Improved methods for identifying the source of a deliberate release of Bacillus anthracis from the temporal and spatial distribution of cases.","authors":"Joseph Shingleton, David Mustard, Steven Dyke, Hannah Williams, Emma Bennett, Thomas Finnie","doi":"10.1371/journal.pcbi.1010817","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1010817","url":null,"abstract":"<p><p>Reverse epidemiology is a mathematical modelling tool used to ascertain information about the source of a pathogen, given the spatial and temporal distribution of cases, hospitalisations and deaths. In the context of a deliberately released pathogen, such as Bacillus anthracis (the disease-causing organism of anthrax), this can allow responders to quickly identify the location and timing of the release, as well as other factors such as the strength of the release, and the realized wind speed and direction at release. These estimates can then be used to parameterise a predictive mechanistic model, allowing for estimation of the potential scale of the release, and to optimise the distribution of prophylaxis. In this paper we present two novel approaches to reverse epidemiology, and demonstrate their utility in responding to a simulated deliberate release of B. anthracis in ten locations in the UK and compare these to the standard grid-search approach. The two methods-a modified MCMC and a Recurrent Convolutional Neural Network-are able to identify the source location and timing of the release with significantly better accuracy compared to the grid-search approach. Further, the neural network method is able to do inference on new data significantly quicker than either the grid-search or novel MCMC methods, allowing for rapid deployment in time-sensitive outbreaks.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cracking the neural code for word recognition in convolutional neural networks.","authors":"Aakash Agrawal, Stanislas Dehaene","doi":"10.1371/journal.pcbi.1012430","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012430","url":null,"abstract":"<p><p>Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these units are sensitive to specific letter identities and their ordinal position from the left or the right of a word. The transition from retinotopic to ordinal position coding is achieved by a hierarchy of \"space bigram\" unit that detect the position of a letter relative to a blank space and that pool across low- and high-frequency-sensitive units from early layers of the network. The proposed scheme provides a plausible neural code for written words in the VWFA, and leads to predictions for reading behavior, error patterns, and the neurophysiology of reading.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mutant fate in spatially structured populations on graphs: Connecting models to experiments.","authors":"Alia Abbara, Lisa Pagani, Celia García-Pareja, Anne-Florence Bitbol","doi":"10.1371/journal.pcbi.1012424","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012424","url":null,"abstract":"<p><p>In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surveillance strategies for the detection of new pathogen variants across epidemiological contexts.","authors":"Kirstin I Oliveira Roster, Stephen M Kissler, Enoma Omoregie, Jade C Wang, Helly Amin, Steve Di Lonardo, Scott Hughes, Yonatan H Grad","doi":"10.1371/journal.pcbi.1012416","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012416","url":null,"abstract":"<p><p>Surveillance systems that monitor pathogen genome sequences are critical for rapidly detecting the introduction and emergence of pathogen variants. To evaluate how interactions between surveillance capacity, variant properties, and the epidemiological context influence the timeliness of pathogen variant detection, we developed a geographically explicit stochastic compartmental model to simulate the transmission of a novel SARS-CoV-2 variant in New York City. We measured the impact of (1) testing and sequencing volume, (2) geographic targeting of testing, (3) the timing and location of variant emergence, and (4) the relative variant transmissibility on detection speed and on the undetected disease burden. Improvements in detection times and reduction of undetected infections were driven primarily by increases in the number of sequenced samples. The relative transmissibility of the new variant and the epidemic context of variant emergence also influenced detection times, showing that individual surveillance strategies can result in a wide range of detection outcomes, depending on the underlying dynamics of the circulating variants. These findings help contextualize the design, interpretation, and trade-offs of genomic surveillance strategies of pandemic respiratory pathogens.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142140856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}