Review
Ecology
Ethan T. Addicott, Eli P. Fenichel, Mark A. Bradford, Malin L. Pinsky, Stephen A. Wood
Summary: Society increasingly calls for accurate predictions of complex ecosystem processes under new conditions to address environmental challenges. However, obtaining process-level knowledge for such predictions doesn't necessarily align with the prevalent use of correlative model selection criteria in ecology. Relying on information criteria may lead researchers to incorrect conclusions about cause-and-effect relationships. Bridging the gap between correlative inference and a process-based understanding of ecological systems is crucial.
FRONTIERS IN ECOLOGY AND THE ENVIRONMENT
(2022)
Article
Engineering, Mechanical
Jiaxin Zhang, Stephanie TerMaath, Michael D. Shields
Summary: This paper proposes a new framework to quantify uncertainties in probability model-form and model parameters resulting from small datasets, and integrates these uncertainties into Sobol' index estimates. Imprecise Sobol' indices are calculated from candidate probability models using an importance sampling reweighting method, providing a measure of confidence in sensitivity estimates and guiding data collection efforts. The approach is demonstrated through examples involving Timoshenko beam parameters and E-glass fiber composite material properties.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Statistics & Probability
Zhibing He, Yunpeng Zhao, Peter Bickel, Charles Weko, Dan Cheng, Jirui Wang
Summary: Statistical network analysis focuses on inferring parameters of observed networks, particularly in social sciences. Zhao and Weko propose the hub model to infer hidden networks from grouping behavior. This article proves the identifiability and consistency of the hub model parameters and generalizes it by introducing a null component. A penalized likelihood approach is also proposed for estimating the unknown hub set.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Fisheries
Sandra Edith de la Fuente, Jorge Homero Rodriguez-Castro, Jose Alberto Ramirez-de Leon, Frida Carmina Caballero-Rico, Jorge Alejandro Rodriguez-Olmeda, Filiberto Toledano-Toledano
Summary: This study used a multimodel approach to describe the growth pattern of the bonnethead shark. The Soriano model with modified growth rate and length at birth was found to be the most suitable, confirming the hypothesis of biphasic growth. Correspondences were identified between growth-phase change sizes and reported sizes for juvenile-adult stage changes in females, as well as onset of reproductive maturity in males and both sexes.
Article
Biology
Gautam Reddy, Michael M. Desai
Summary: Recent research shows that consistent patterns of fitness increase in microbial evolution experiments are mainly driven by diminishing-returns and increasing-costs epistasis. Although the origin of this global epistasis remains unknown, it is found to emerge as a consequence of widespread microscopic epistasis. The specific quantitative relationship between the magnitude of global epistasis and the stochastic effects of microscopic epistasis predicts a universal form of fitness effects distribution when epistasis is prevalent.
Article
Computer Science, Artificial Intelligence
Xinyu Zhou, Yanlin Wu, Hu Peng, Shuixiu Wu, Mingwen Wang
Summary: A new multiple strategies-based differential evolution variant, DIGDE, is proposed to address the issue of strategy selection relying on historical search experience. By utilizing fitness information and spatial information simultaneously to estimate the evolutionary states for each individual and choosing the most appropriate strategy correspondingly, DIGDE achieves competitive performance in terms of result accuracy and convergence rate.
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
(2023)
Article
Biochemical Research Methods
Nathaniel J. Linden, Boris Kramer, Padmini Rangamani
Summary: In this study, a comprehensive framework for Bayesian parameter estimation in systems biology modeling is proposed. This framework enables the estimation of kinetic parameters and the quantification of associated uncertainties. The results highlight the dependence of parameter estimation on data sparsity, noise level, and model structure, and emphasize the importance of uncertainty quantification in systems biology modeling.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Mathematical & Computational Biology
Ozan Cinar, James Umbanhowar, Jason D. Hoeksema, Wolfgang Viechtbauer
Summary: Meta-regression uses regression-type methods to examine the association between effect size estimates and study characteristics in a meta-analysis. Model selection through testing, including univariate models or models with all moderators, is the commonly used approach. Alternative methods like information criteria and multimodel inference may outperform traditional model selection methods, showing higher probabilities of identifying the true model under certain scenarios.
RESEARCH SYNTHESIS METHODS
(2021)
Article
Mathematical & Computational Biology
Vivek Sreejithkumar, Kia Ghods, Tharusha Bandara, Maia Martcheva, Necibe Tuncr
Summary: In this paper, the interaction between HIV and albumin/globulin is studied. By selecting and analyzing mathematical models based on data from SIV-infected monkeys, it is found that the simplest model accurately describes the data and leads to several observations. Structural and practical identifiability analysis helps determine the parameters of the best fitted model.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Mathematics, Applied
Yafei Zhao, Hui Wu, Hua Cheng, Jie Lou
Summary: This paper studies the co-infection dynamic models of HIV and SARS-CoV-2 in four patients from South Africa, and finds that SARS-CoV-2 quickly mutates to become the dominant strain in co-infected patients. Furthermore, systemic inflammation triggered by COVID-19 can reactivate the latent HIV reservoir and temporarily increase viral loads. Therefore, increasing COVID-19 vaccination coverage in countries with a high prevalence of HIV is crucial.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Mathematical & Computational Biology
Sayan Dasgupta, Ying Huang
Summary: This article discusses the use of risk models and multiple vaccine-induced immune response biomarkers to measure the causal association between a vaccine's effects on these biomarkers and clinical endpoints. By selecting markers with high surrogacy, a more concise model can potentially improve the predictive quality of true markers.
Article
Virology
Lele Zhao, Chris Wymant, Francois Blanquart, Tanya Golubchik, Astrid Gall, Margreet Bakker, Daniela Bezemer, Matthew Hall, Swee Hoe Ong, Jan Albert, Norbert Bannert, Jacques Fellay, M. Kate Grabowski, Barbara Gunsenheimer-Bartmeyer, Huldrych F. Gunthard, Pia Kivela, Roger D. Kouyos, Oliver Laeyendecker, Laurence Meyer, Kholoud Porter, Ard van Sighem, Marc van der Valk, Ben Berkhout, Paul Kellam, Marion Cornelissen, Peter Reiss, Christophe Fraser, Luca Ferretti
Summary: This study investigates the relationship between viral load and transmission fitness in HIV-1. The results suggest that higher set-point viral load is associated with increased infectiousness and transmission fitness. This finding has implications for understanding the evolution and spread of HIV-1.
Article
Mathematics
Tian-Tian Wang, Qiang Yang, Xu-Dong Gao
Summary: This paper proposes a dual elite groups-guided mutation strategy called DE/current-to-duelite/1 to solve complex optimization problems in continuous optimization. By guiding the mutation of all individuals using both the elites in the current population and the obsolete parent individuals stored in an archive, DEGGDE achieves a good balance between exploring the complex search space and exploiting the found promising regions, resulting in good optimization performance.
Article
Biology
Thomas O. Richardson, Andrea Coti, Nathalie Stroeymeyt, Laurent Keller
Summary: The relationship between task performance and teamwork in ant colonies is investigated. The study reveals that leadership, specifically the consistency of leaders, influences tandem performance, but 'followership' does not.
COMMUNICATIONS BIOLOGY
(2021)
Article
Mathematics, Applied
Meng Wang, Yafei Zhao, Chen Zhang, Jie Lou
Summary: Understanding the dynamics of SARS-COV-2 infection in vivo is crucial for exploring more effective treatments. This paper presents a series of dynamic models of viral infection in host. The study shows that all models are structurally identifiable and data noise has little effect on the actual identifiability of key parameters. Through numerical simulation, the key factors that may cause cytokine storms are identified, and some qualitative conclusions of the model are obtained.
JOURNAL OF APPLIED ANALYSIS AND COMPUTATION
(2023)