PLoS Computational Biology最新文献

{"title":"Understanding dual process cognition via the minimum description length principle.","authors":"Ted Moskovitz, Kevin J Miller, Maneesh Sahani, Matthew M Botvinick","doi":"10.1371/journal.pcbi.1012383","DOIUrl":"10.1371/journal.pcbi.1012383","url":null,"abstract":"<p><p>Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in domains ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms appear to operate concurrently, one relatively high in computational complexity, the other relatively simple. Why is neural information processing organized in this way? We propose an answer to this question based on the notion of compression. The key insight is that dual-process structure can enhance adaptive behavior by allowing an agent to minimize the description length of its own behavior. We apply a single model based on this observation to findings from research on executive control, reward-based learning, and judgment and decision making, showing that seemingly diverse dual-process phenomena can be understood as domain-specific consequences of a single underlying set of computational principles.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012383"},"PeriodicalIF":3.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11534269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.","authors":"Michele Gentili, Kimberly Glass, Enrico Maiorino, Brian D Hobbs, Zhonghui Xu, Peter J Castaldi, Michael H Cho, Craig P Hersh, Dandi Qiao, Jarrett D Morrow, Vincent J Carey, John Platig, Edwin K Silverman","doi":"10.1371/journal.pcbi.1011079","DOIUrl":"10.1371/journal.pcbi.1011079","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1011079"},"PeriodicalIF":3.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepPL: A deep-learning-based tool for the prediction of bacteriophage lifecycle.","authors":"Yujie Zhang, Mark Mao, Robert Zhang, Yen-Te Liao, Vivian C H Wu","doi":"10.1371/journal.pcbi.1012525","DOIUrl":"10.1371/journal.pcbi.1012525","url":null,"abstract":"<p><p>Bacteriophages (phages) are viruses that infect bacteria and can be classified into two different lifecycles. Virulent phages (or lytic phages) have a lytic cycle that can lyse the bacteria host after their infection. Temperate phages (or lysogenic phages) can integrate their phage genomes into bacterial chromosomes and replicate with bacterial hosts via the lysogenic cycle. Identifying phage lifecycles is a crucial step in developing suitable applications for phages. Compared to the complicated traditional biological experiments, several tools have been designed for predicting phage lifecycle using different algorithms, such as random forest (RF), linear support-vector classifier (SVC), and convolutional neural network (CNN). In this study, we developed a natural language processing (NLP)-based tool-DeepPL-for predicting phage lifecycles via nucleotide sequences. The test results showed that our DeepPL had an accuracy of 94.65% with a sensitivity of 92.24% and a specificity of 95.91%. Moreover, DeepPL had 100% accuracy in lifecycle prediction on the phages we isolated and biologically verified previously in the lab. Additionally, a mock phage community metagenomic dataset was used to test the potential usage of DeepPL in viral metagenomic research. DeepPL displayed a 100% accuracy for individual phage complete genomes and high accuracies ranging from 71.14% to 100% on phage contigs produced by various next-generation sequencing technologies. Overall, our study indicates that DeepPL has a reliable performance on phage lifecycle prediction using the most fundamental nucleotide sequences and can be applied to future phage and metagenomic research.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012525"},"PeriodicalIF":3.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.","authors":"Natchapon Srinak, Porntip Chiewchankaset, Saowalak Kalapanulak, Pornpan Panichnumsin, Treenut Saithong","doi":"10.1371/journal.pcbi.1012533","DOIUrl":"10.1371/journal.pcbi.1012533","url":null,"abstract":"<p><p>The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012533"},"PeriodicalIF":3.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11521316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene signatures for cancer research: A 25-year retrospective and future avenues.","authors":"Wei Liu, Huaqin He, Davide Chicco","doi":"10.1371/journal.pcbi.1012512","DOIUrl":"10.1371/journal.pcbi.1012512","url":null,"abstract":"<p><p>Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012512"},"PeriodicalIF":3.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11482671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal.","authors":"Dmitry R Lyamzin, Andrea Alamia, Mohammad Abdolrahmani, Ryo Aoki, Andrea Benucci","doi":"10.1371/journal.pcbi.1012478","DOIUrl":"10.1371/journal.pcbi.1012478","url":null,"abstract":"<p><p>In natural behaviors, multiple neural signals simultaneously drive activation across overlapping brain networks. Due to limitations in the amount of data that can be acquired in common experimental designs, the determination of these interactions is commonly inferred via modeling approaches, which reduce overfitting by finding appropriate regularizing hyperparameters. However, it is unclear whether these hyperparameters can also be related to any aspect of the underlying biological phenomena and help interpret them. We applied a state-of-the-art regularization procedure-automatic locality determination-to interacting neural activations in the mouse posterior cortex associated with movements of the body and eyes. As expected, regularization significantly improved the determination and interpretability of the response interactions. However, regularizing hyperparameters also changed considerably, and seemingly unpredictably, from animal to animal. We found that these variations were not random; rather, they correlated with the variability in visually evoked responses and with the variability in the state of arousal of the animals measured by pupillometry-both pieces of information that were not included in the modeling framework. These observations could be generalized to another commonly used-but potentially less informative-regularization method, ridge regression. Our findings demonstrate that optimal model hyperparameters can be discovery tools that are informative of factors not a priori included in the model's design.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012478"},"PeriodicalIF":3.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.","authors":"Shaojun Yu, Junjie Wu, Yumeng Shao, Deqiang Qiu, Zhaohui S Qin","doi":"10.1371/journal.pcbi.1012527","DOIUrl":"10.1371/journal.pcbi.1012527","url":null,"abstract":"<p><p>Genome-wide association studies (GWASs) have been widely applied in the neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is likely to be associated with brain anatomy or function which is manifested in MRI brain images. We applied this strategy to a catalog of MRI image and genotype data collected by the Alzheimer's Disease Neuroimaging Initiative (ADNI) consortium. From the results, we identified novel variants that show strong association to brain phenotypes.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012527"},"PeriodicalIF":3.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11508069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ten simple rules to bridge ecology and palaeoecology by publishing outside palaeoecological journals.","authors":"Nick Schafstall, Xavier Benito, Sandra O Brugger, Althea L Davies, Erle Ellis, Sergi Pla-Rabes, Alicja Bonk, M Jane Bunting, Frank M Chambers, Suzette G A Flantua, Tamara L Fletcher, Caroline Greiser, Armand Hernández, Benjamin Gwinneth, Gerbrand Koren, Katarzyna Marcisz, Encarni Montoya, Adolfo Quesada-Román, Amila S Ratnayake, Pierre Sabatier, John P Smol, Nancy Y Suárez-Mozo","doi":"10.1371/journal.pcbi.1012487","DOIUrl":"10.1371/journal.pcbi.1012487","url":null,"abstract":"<p><p>Owing to its specialised methodology, palaeoecology is often regarded as a separate field from ecology, even though it is essential for understanding long-term ecological processes that have shaped the ecosystems that ecologists study and manage. Despite advances in ecological modelling, sample dating, and proxy-based reconstructions facilitating direct comparison of palaeoecological data with neo-ecological data, most of the scientific knowledge derived from palaeoecological studies remains siloed. We surveyed a group of palaeo-researchers with experience in crossing the divide between palaeoecology and neo-ecology, to develop Ten Simple Rules for publishing your palaeoecological research in non-palaeo journals. Our 10 rules are divided into the preparation phase, writing phase, and finalising phase when the article is submitted to the target journal. These rules provide a suite of strategies, including improved networking early in the process, building effective collaborations, transmitting results more efficiently to improve cross-disciplinary accessibility, and integrating concepts and methodologies that appeal to ecologists and a wider readership. Adhering to these Ten Simple Rules can ensure palaeoecologists' findings are more accessible and impactful among ecologists and the wider scientific community. Although this article primarily shows examples of how palaeoecological studies were published in journals for a broader audience, the rules apply to anyone who aims to publish outside specialised journals.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012487"},"PeriodicalIF":3.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11573137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roles and interplay of reinforcement-based and error-based processes during reaching and gait in neurotypical adults and individuals with Parkinson's disease.","authors":"Adam M Roth, John H Buggeln, Joanna E Hoh, Jonathan M Wood, Seth R Sullivan, Truc T Ngo, Jan A Calalo, Rakshith Lokesh, Susanne M Morton, Stephen Grill, John J Jeka, Michael J Carter, Joshua G A Cashaback","doi":"10.1371/journal.pcbi.1012474","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012474","url":null,"abstract":"<p><p>From a game of darts to neurorehabilitation, the ability to explore and fine tune our movements is critical for success. Past work has shown that exploratory motor behaviour in response to reinforcement (reward) feedback is closely linked with the basal ganglia, while movement corrections in response to error feedback is commonly attributed to the cerebellum. While our past work has shown these processes are dissociable during adaptation, it is unknown how they uniquely impact exploratory behaviour. Moreover, converging neuroanatomical evidence shows direct and indirect connections between the basal ganglia and cerebellum, suggesting that there is an interaction between reinforcement-based and error-based neural processes. Here we examine the unique roles and interaction between reinforcement-based and error-based processes on sensorimotor exploration in a neurotypical population. We also recruited individuals with Parkinson's disease to gain mechanistic insight into the role of the basal ganglia and associated reinforcement pathways in sensorimotor exploration. Across three reaching experiments, participants were given either reinforcement feedback, error feedback, or simultaneously both reinforcement & error feedback during a sensorimotor task that encouraged exploration. Our reaching results, a re-analysis of a previous gait experiment, and our model suggests that in isolation, reinforcement-based and error-based processes respectively boost and suppress exploration. When acting in concert, we found that reinforcement-based and error-based processes interact by mutually opposing one another. Finally, we found that those with Parkinson's disease had decreased exploration when receiving reinforcement feedback, supporting the notion that compromised reinforcement-based processes reduces the ability to explore new motor actions. Understanding the unique and interacting roles of reinforcement-based and error-based processes may help to inform neurorehabilitation paradigms where it is important to discover new and successful motor actions.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012474"},"PeriodicalIF":3.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference and design of antibody specificity: From experiments to models and back.","authors":"Jorge Fernandez-de-Cossio-Diaz, Guido Uguzzoni, Kévin Ricard, Francesca Anselmi, Clément Nizak, Andrea Pagnani, Olivier Rivoire","doi":"10.1371/journal.pcbi.1012522","DOIUrl":"10.1371/journal.pcbi.1012522","url":null,"abstract":"<p><p>Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 10","pages":"e1012522"},"PeriodicalIF":3.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11501025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142472903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}