PLoS Computational Biology最新文献

{"title":"How peak knee loads are affected by changing the mass of lower-limb body segments during walking.","authors":"Delaney E Miller, Ashley E Brown, Nicholas A Bianco, Rucha Bhise, Scott L Delp, Steven H Collins","doi":"10.1371/journal.pcbi.1012833","DOIUrl":"10.1371/journal.pcbi.1012833","url":null,"abstract":"<p><p>For individuals with knee osteoarthritis, increased knee loading is linked to disease progression and pain. Some approaches to treating osteoarthritis, such as specialized footwear, braces, and powered exoskeletons, also increase the mass of the lower limbs, which could lead to increases in knee loads. Prior studies have investigated the effect of changes in torso mass and total body mass on peak knee contact forces, but the effects of increased leg mass remain unclear. In this study, we created musculoskeletal simulations informed by experimental data to estimate tibiofemoral knee contact force under different lower-limb segment mass conditions. The mass of the foot, shank, and thigh were varied by adding weights to each segment, separately and concurrently, as healthy young adults (N = 10) walked on a treadmill. Kinematics, kinetics, and muscle activity were recorded. Our simulations used an optimal control framework that enforced experimental kinematics while minimizing a combination of net joint moment errors and mismatch between measured and estimated muscle activity. The simulations revealed that adding mass to the lower-limb segments linearly increased early- and late-stance peaks in knee contact force, but that the slope of this relationship was different for each peak and each mass placement location. For each 1% of body weight (BW) added per limb (2% BW total) at the thigh, shank, and foot, early-stance peak knee contact force increased by 1.5%, 2.1%, and 5.9% (r = 0.71), while late-stance peak contact force increased by 1.6%, 0.9% and 3.0% (r = 0.67), respectively. Adding mass to the thigh and shank increases peak contact force at or below the rate of increase in body mass, while adding mass to the foot disproportionately increases peak knee contact force. These detrimental effects should be considered when designing interventions for osteoarthritis.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1012833"},"PeriodicalIF":3.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138472","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":"Identifying COVID-19 peaks using early warning signals.","authors":"Joshua Looker, Kat S Rock, Louise Dyson","doi":"10.1371/journal.pcbi.1013524","DOIUrl":"10.1371/journal.pcbi.1013524","url":null,"abstract":"<p><p>The SARS-CoV-2 (COVID-19) pandemic has had catastrophic effects on public health and economies. Around the world, many countries employed modelling efforts to help guide pharmaceutical and non-pharmaceutical measures designed to reduce the spread of the virus. Modelling efforts for future pandemics could use the theory of early warning signals (EWS), which aims to predict 'critical transitions' in complex dynamical systems. In infectious disease systems, such transitions correspond to (re-)emergence, peaks and troughs in infections which can be indirectly observed through the reported case data. There is increasing evidence that including EWS in modelling can help improve responses to upcoming increases or decreases in case reporting. Here, we present both theoretical and data-driven analyses of the suitability of EWS to predict epidemic transitions in reported case data. We derive analytical statistics for a variety of infectious disease models and show, through stochastic simulations of different modelling scenarios, the applicability of EWS in such contexts. Using the COVID-19 reported case dataset from the United Kingdom, we demonstrate the performance of a range of temporal and spatial statistics to anticipate transitions in the case data. Finally, we also investigate the applicability of using EWS analysis of hospitalisation data to anticipate transitions in the corresponding case data. Together, our findings indicate that EWS analysis could be a vital addition to future modelling analysis for real-world infection data.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013524"},"PeriodicalIF":3.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138492","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":"Mechanistic within-host mathematical model of inhalational anthrax.","authors":"Bevelynn Whaler, Grant Lythe, Joseph J Gillard, Thomas R Laws, Jonathan Carruthers, Thomas Finnie, Carmen Molina-París, Martín López-García","doi":"10.1371/journal.pcbi.1013439","DOIUrl":"10.1371/journal.pcbi.1013439","url":null,"abstract":"<p><p>We present a mathematical model of the dynamics of Bacillus anthracis bacteria within the lymph nodes and blood of a host, following inhalation of an initial dose of spores. We also incorporate the dynamics of protective antigen, which is the binding component of the anthrax toxin produced by the bacteria. The model offers a mechanistic description of the early infection dynamics of inhalational anthrax, while its stochastic nature allows us to study the probabilities of different outcomes (for example, how likely it is that the infection will be cleared for a given inhaled dose of spores) in order to explain dose-response data for inhalational anthrax. The model is calibrated via a Bayesian approach, using in vivo data from New Zealand white rabbit and guinea pig infection studies, enabling within-host parameters to be estimated. We also leverage incubation-period data from the Sverdlovsk 1979 anthrax outbreak to show that the model can accurately describe human time-to-symptoms data under reasonable parameter regimes. Finally, we derive a simple approximate formula for the probability of symptom onset before time t, assuming that the number of inhaled spores has a Poisson distribution.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013439"},"PeriodicalIF":3.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12459799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138576","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":"Category-specific perceptual learning of robust object recognition modelled using deep neural networks.","authors":"Hojin Jang, Frank Tong","doi":"10.1371/journal.pcbi.1013529","DOIUrl":"10.1371/journal.pcbi.1013529","url":null,"abstract":"<p><p>Object recognition in real-world environments requires dealing with considerable ambiguity, yet the human visual system is highly robust to noisy viewing conditions. Here, we investigated the role of perceptual learning in the acquisition of robustness in both humans and deep neural networks (DNNs). Specifically, we sought to determine whether perceptual training with object images in Gaussian noise, drawn from certain animate or inanimate categories, would lead to category-specific or category-general improvements in human robustness. Moreover, might DNNs provide viable models of human perceptual learning? Both before and after training, we evaluated the noise threshold required for accurate recognition using novel object images. Human observers were quite robust to noise before training, but showed additional category-specific improvement after training with only a few hundred noisy object examples. In comparison, standard DNNs initially lacked robustness, then showed both category-general and category-specific learning after training with the same noisy examples. We further evaluated DNN models that were pre-trained with moderately noisy images to match human pre-training accuracy. Notably, these models only showed category-specific improvement, matching the overall pattern of learning exhibited by human observers. A layer-wise analysis of DNN responses revealed that category-general learning effects emerged in the lower layers, whereas category-specific improvements emerged in the higher layers. Our findings provide support for the notion that robustness to noisy visual conditions arises through learning, humans likely acquire robustness from everyday encounters with real-world noise, and additional category-specific improvements exhibited by humans and DNNs involve learning at higher levels of visual representation.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013529"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131773","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":"Utargetome: A targetome prediction tool for modified U1-snRNAs to identify distal-target positions with improved selectivity.","authors":"Paolo Pigini, Federico Manuel Giorgi, Keng Boon Wee","doi":"10.1371/journal.pcbi.1013534","DOIUrl":"10.1371/journal.pcbi.1013534","url":null,"abstract":"<p><p>The endogenous U1 small nuclear RNA (U1-snRNA) plays a crucial role in splicing initiation through base-pairing to donor splice sites (5'-SSs). Likewise, modified U1s that carry a mutation-adapted 5'-terminal sequence have been demonstrated to rescue exon splicing when this is disrupted by genetic mutations within the 5'-SS. Given the base-pairing flexibility of the endogenous U1, the selectivity of modified U1s requires investigation. We developed a computational pipeline (Utargetome) that considers combinations of mismatches and alternative annealing registers to predict the transcriptome-wide binding sites (or targetome) of a U1. The pipeline accuracy was tested by recapitulating well-established alternative annealing registers and specificity for 5'-SSs in the predicted targetome of the human endogenous U1. It was then applied to analyse the targetome of 54 modified U1s that have been demonstrated to restore exon inclusion when affected by 5'-SS pathogenic mutations. While the targetome size was found to be wide-ranging, the off-target load appeared to be reduced for U1s targeting distal sites from the canonical U1-binding position. This feature was predicted also for a large set of 30,204 newly designed U1s targeting 839 5'-SS pathogenic mutations that were expected to affect exon inclusion. Targetome analysis indeed revealed an optimal distal-targeting position at 3 nucleotides downstream from the canonical 5'-SS, for which a modified U1 is likely to have minimal off-targets at 5'-SSs and acceptor splice sites (3'-SSs). Based on these insights, we propose to implement targetome prediction in the design and optimization of therapeutic U1s with improved selectivity.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013534"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12527174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131790","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":"Optimizing therapeutic outcomes with Mechanotherapy and Ultrasound Sonopermeation in solid tumors.","authors":"Marina Koutsi, Triantafyllos Stylianopoulos, Fotios Mpekris","doi":"10.1371/journal.pcbi.1012676","DOIUrl":"10.1371/journal.pcbi.1012676","url":null,"abstract":"<p><p>Mechanical solid stress plays a pivotal role in tumor progression and therapeutic response. Elevated solid stress compresses intratumoral blood vessels, leading to hypoperfusion, and hypoxia, which impair oxygen and drug delivery. These conditions hinder the efficacy of drugs and promote tumor progression and treatment resistance compromising therapeutic outcomes. To enhance treatment efficacy, mechanotherapeutics and ultrasound sonopermeation have been developed to improve tumor perfusion and drug delivery. Mechanotherapy aims to reduce tumor stiffness and mechanical stress within tumors to normal levels leading to decompression of vessels while simultaneously improving perfusion. On the other hand, ultrasound sonopermeation strategy focuses on increasing non-invasively and transiently tumor vessel wall permeability to boost perfusion and thus, improve drug delivery. Within this framework and aiming to replicate published experimental data in silico, we developed a mathematical model designed to derive optimal conditions for the combined use of mechanotherapeutics and sonopermeation, with the goal of optimizing efficacy of nano-immunotherapy. The model incorporates complex interactions among diverse components that are crucial in the multifaceted process of tumor progression. These components encompass a variety of cell populations in tumor, such as tumor cells and immune cells, as well as components of the tumor vasculature including endothelial cells, angiopoietins, and the vascular endothelial growth factor. Seeking initial model verification, we carried out validation of model predictions with published experimental data, wherein a strong correlation was observed between the model predictions and the actual experimental measurements of critical parameters, which are essential to reinforce the overall accuracy of the mathematical framework employed. In addition, a parametric analysis was performed with primary objective to investigate the impact of various critical parameters that influence sonopermeation. Model predictions showed maximal drug delivery and tumor volume reduction at an acoustic pressure range of 0.24-0.27 MPa and mechanical index of 0.17, consistent with values used in clinical trials following sonopermeation treatment. The analysis provided optimal guidelines for the use of sonopermeation in conjunction with mechanotherapy, that contribute to identify optimal conditions for sonopermeation.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1012676"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131766","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 quick tips for protecting health data using de-identification and perturbation of structured datasets.","authors":"Tshikala Eddie Lulamba, Themba Mutemaringa, Nicki Tiffin","doi":"10.1371/journal.pcbi.1013507","DOIUrl":"10.1371/journal.pcbi.1013507","url":null,"abstract":"<p><p>Structured patient data generated within the health data ecosystem are shared both internally for operational use and also externally for research and public health benefit. Protecting individual privacy and health data confidentiality in these contexts relies on data de-identification and anonymisation, although there are no universally accepted standards for these processes and the techniques involved can be technically complex. We present practical recommendations grounded in the principle of data minimisation-avoiding unnecessary granularity and identifying variables that could lead to re-identification when combined with other datasets. We provide practical guidance for anonymising and perturbing structured health data in ways that support compliance with data protection laws, describing technical and operational methods for reducing re-identification risk that include rounding numerical values, replacing precise values with ranges, adding jitter to numeric fields, aggregating data, management of date values and separating sensitive fields from identifying data to prevent linkage leading to re-identification. While some methods require advanced technical knowledge, we focus here on accessible strategies that can be implemented without specialist expertise, recognising the importance of the legal and governance frameworks in which anonymisation occurs. These guidelines support researchers, data managers and institutions in sharing health data responsibly, maintaining data utility while upholding privacy and promoting ethical and legal data stewardship for data-driven health research.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013507"},"PeriodicalIF":3.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131856","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":"Modelling the relative contribution of infection, routine vaccination and supplementary immunisation activities to measles seroconversion in Kenyan Children.","authors":"Caroline Mburu, John Ojal, Rose Selim, Rose Ombati, Donald Akech, Boniface Karia, James Tuju, Antipa Sigilai, Gaby Smits, Pieter van Gageldonk, Fiona van der Klis, Eunice Kagucia, Anthony Scott, Ifedayo Adetifa, Stefan Flasche","doi":"10.1371/journal.pcbi.1013531","DOIUrl":"10.1371/journal.pcbi.1013531","url":null,"abstract":"<p><strong>Background: </strong>Measles outbreaks continue to cause a large burden of disease in Africa including Kenya. We used information from regular serological surveys in Kilifi Health and Demographic Surveillance System (KHDSS) in combination with mathematical modelling to estimate the relative contribution of the vaccination programme to current measles immunity.</p><p><strong>Methods: </strong>We developed a static birth cohort model to track the proportion of children who are either measles naïve or seroconverted due to natural infection or vaccination through first dose of measles-containing vaccine (MCV1), the second dose (MCV2), or supplementary immunisation activities (SIAs). We fitted the model to biennial paediatric serological survey and case notification data and used vaccination coverage estimates from the KHDSS to estimate the relative contributions of vaccination and infection to measles immunity in Kilifi.</p><p><strong>Results: </strong>We estimated that between 2009 and 2021, 60% (95%CI 55-64%) of measles seroconversion in Kilifi was attributable to MCV1, with MCV2 contributing 1.0% (95%CI 0.9-1.1%) since its introduction. Natural infection and SIAs accounted for 24% (95%CI 17-31%) and 16% (95%CI 14-19%), respectively. A hypothetical 10% increase in MCV1 coverage increased the seroconversion attributed to MCV1 to 67% (95%CI 63-71%), with concurrent reductions in seroconversion from natural infection and SIAs to 13% (95%CI 9-18%) and 10% (95%CI 9-12%), respectively. Importantly, this same 10% increase in MCV1, if administered promptly at 9 months, could potentially reduce seroconversion from natural infection further from 24% to 11% (95%CI 07-15%) and reliance on SIAs from 16% to 8% (95% CI 7-10%).</p><p><strong>Conclusion: </strong>Optimizing routine coverage timing and uptake is crucial for reducing SIAs dependence and measles susceptibility. A 10% MCV1 coverage increase could have halved susceptibility and lessened SIA demand, highlighting the potential of minor improvements in coverage to alleviate measles and reduce costly SIAs.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013531"},"PeriodicalIF":3.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12469164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125881","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":"SBMLNetwork: A framework for standards-based visualization of biochemical models.","authors":"Adel Heydarabadipour, Lucian Smith, Joseph L Hellerstein, Herbert M Sauro","doi":"10.1371/journal.pcbi.1013128","DOIUrl":"10.1371/journal.pcbi.1013128","url":null,"abstract":"<p><p>SBMLNetwork is an open-source software library that makes the SBML Layout and Render packages practical for standards-based visualization of biochemical models. Current tools often manage model visualization data in custom-designed, tool-specific formats and store it separately from the model itself, hindering interoperability, reproducibility, and the seamless integration of visualization with model data. SBMLNetwork addresses these limitations by building directly on the SBML Layout and Render specifications, automating the generation of standards-compliant visualization data, offering a modular implementation with broad integration support, and providing a robust API tailored to the needs of systems biology researchers. We illustrate the capabilities of SBMLNetwork across key visualization tasks, including SBGN-compliant visualization, application of predefined style templates, layout arrangement to reflect pathway logic, and integration of model data into network diagrams. These examples demonstrate how SBMLNetwork enables high-level visualization features and seamlessly translate user intent into reproducible outputs that support both structural representation and dynamic data visualization within the SBML model. SBMLNetwork is freely available at https://github.com/sys-bio/SBMLNetwork under the MIT license.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013128"},"PeriodicalIF":3.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125918","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 for building and maintaining sustainable high-performance computing infrastructure for research in resource-limited settings.","authors":"Ronald Galiwango, Christopher J Whalen, Grace Kebirungi, Mugume T Atwine, Rodgers Kimera, Alfred Ssekagiri, Timothy W Kimbowa, Edward Lukyamuzi, Mike Nsubuga, Lloyd Ssentongo, Henry Mutegeki, John M Fonner, Frank Wuerthwein, Ari Berman, Laura B Okalebo, Meghan McCarthy, Victor S Kramer, Mariam Quinones, Phillip Cruz, Darrell Hurt, Maria Y Giovanni, Nicola Mulder, Michael Tartakovsky, Jonathan Kayondo, Daudi Jjingo","doi":"10.1371/journal.pcbi.1013481","DOIUrl":"10.1371/journal.pcbi.1013481","url":null,"abstract":"","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"21 9","pages":"e1013481"},"PeriodicalIF":3.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126037","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}