1
|
Waseel F, Streftaris G, Rudrusamy B, Dass SC. Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach. Infect Dis Model 2024; 9:527-556. [PMID: 38525308 PMCID: PMC10958481 DOI: 10.1016/j.idm.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 03/26/2024] Open
Abstract
The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57-66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.
Collapse
Affiliation(s)
- Farhad Waseel
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
- Faculty of Mathematics, Kabul University, Kabul, Afghanistan
| | - George Streftaris
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom
- Maxwell Institute for Mathematical Sciences, United Kingdom
| | - Bhuvendhraa Rudrusamy
- School of Engineering and Physical Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
| | - Sarat C. Dass
- School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia
| |
Collapse
|
2
|
Benton DT, Kamper D, Beaton RM, Sobel DM. Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands. Dev Sci 2024; 27:e13464. [PMID: 38059682 DOI: 10.1111/desc.13464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/13/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.
Collapse
Affiliation(s)
- Deon T Benton
- Department of Psychology and Human Development, Vanderbilt University, Nashville, USA
| | - David Kamper
- Department of Psychology, University of California, Los Angeles, USA
| | - Rebecca M Beaton
- Department of Psychology and Human Development, Vanderbilt University, Nashville, USA
| | - David M Sobel
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Los Angeles, USA
| |
Collapse
|
3
|
Blette BS, Halpern SD, Li F, Harhay MO. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials. Stat Methods Med Res 2024; 33:909-927. [PMID: 38567439 DOI: 10.1177/09622802241242323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.
Collapse
Affiliation(s)
- Bryan S Blette
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott D Halpern
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale University, New Haven, CT, USA
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative and Advanced Illness Research) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
4
|
Yu Q, Ma L, Yan X. Modeling occupant injury severities for electric-vehicle-involved crashes using a vehicle-accident bi-layered correlative framework with matched-pair sampling. Accid Anal Prev 2024; 199:107499. [PMID: 38364595 DOI: 10.1016/j.aap.2024.107499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 01/07/2024] [Accepted: 02/05/2024] [Indexed: 02/18/2024]
Abstract
This study seeks to investigate occupant injury severities for electric-vehicle-involved crashes and inspect if electric vehicles lead to more serious injuries than fuel-powered vehicles, which have commonly been neglected in past studies. A Bayesian random slope model is proposed aiming to capture interactions between occupant injury severity levels and electric vehicle variable. The random slope model is developed under a vehicle-accident bi-layered correlative framework, which can account for the interactive effects of vehicles in the same accident. Based on the crash report sampling system (CRSS) 2020 and 2021 database, the extracted observations are formed into inherently matched pairs under certain matching variables including restraint system use, air bag deployed, ejection and rollover. The introduced data structure is able to ensure the standard error of the modeling parameters are not affected by these matching variables. Meanwhile, a comprehensive modeling performance comparison is conducted between the Bayesian random slope model and the Bayesian random intercept model, the Bayesian basic model. According to the empirical results, the bi-layered Bayesian random slope model presents a strong ability in model fitting and analysis, even when the sample size is small and the error structure is complex. Most importantly, occupants in electric vehicles are more likely to suffer serious injuries, especially incapacitating and fatal injuries, in the event of an accident compared to fuel-powered vehicles, which disproving the long-held misconception that green and safety are related.
Collapse
Affiliation(s)
- Qi Yu
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
| | - Lu Ma
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
| | - Xuedong Yan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
| |
Collapse
|
5
|
Rohloff CT, Kohli N, Lock EF. Identifiability and estimability of Bayesian linear and nonlinear crossed random effects models. Br J Math Stat Psychol 2024; 77:375-394. [PMID: 38264951 DOI: 10.1111/bmsp.12334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
Crossed random effects models (CREMs) are particularly useful in longitudinal data applications because they allow researchers to account for the impact of dynamic group membership on individual outcomes. However, no research has determined what data conditions need to be met to sufficiently identify these models, especially the group effects, in a longitudinal context. This is a significant gap in the current literature as future applications to real data may need to consider these conditions to yield accurate and precise model parameter estimates, specifically for the group effects on individual outcomes. Furthermore, there are no existing CREMs that can model intrinsically nonlinear growth. The goals of this study are to develop a Bayesian piecewise CREM to model intrinsically nonlinear growth and evaluate what data conditions are necessary to empirically identify both intrinsically linear and nonlinear longitudinal CREMs. This study includes an applied example that utilizes the piecewise CREM with real data and three simulation studies to assess the data conditions necessary to estimate linear, quadratic, and piecewise CREMs. Results show that the number of repeated measurements collected on groups impacts the ability to recover the group effects. Additionally, functional form complexity impacted data collection requirements for estimating longitudinal CREMs.
Collapse
Affiliation(s)
- Corissa T Rohloff
- Quantitative Methods in Education, Department of Educational Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Nidhi Kohli
- Quantitative Methods in Education, Department of Educational Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Eric F Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| |
Collapse
|
6
|
Nour Eddine S, Brothers T, Wang L, Spratling M, Kuperberg GR. A predictive coding model of the N400. Cognition 2024; 246:105755. [PMID: 38428168 PMCID: PMC10984641 DOI: 10.1016/j.cognition.2024.105755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding - a biologically plausible algorithm for approximating Bayesian inference - offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from the linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provides a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research. More generally, they raise the possibility that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.
Collapse
Affiliation(s)
- Samer Nour Eddine
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America.
| | - Trevor Brothers
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychology, North Carolina A&T, United States of America
| | - Lin Wang
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
| | | | - Gina R Kuperberg
- Department of Psychology and Center for Cognitive Science, Tufts University, United States of America; Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, United States of America
| |
Collapse
|
7
|
Camps J, Berg LA, Wang ZJ, Sebastian R, Riebel LL, Doste R, Zhou X, Sachetto R, Coleman J, Lawson B, Grau V, Burrage K, Bueno-Orovio A, Weber Dos Santos R, Rodriguez B. Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials. Med Image Anal 2024; 94:103108. [PMID: 38447244 DOI: 10.1016/j.media.2024.103108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024]
Abstract
Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.
Collapse
Affiliation(s)
- Julia Camps
- University of Oxford, Oxford, United Kingdom.
| | | | | | | | | | - Ruben Doste
- University of Oxford, Oxford, United Kingdom
| | - Xin Zhou
- University of Oxford, Oxford, United Kingdom
| | - Rafael Sachetto
- Universidade Federal de São João del Rei, São João del Rei, MG, Brazil
| | | | - Brodie Lawson
- Queensland University of Technology, Brisbane, Australia
| | | | - Kevin Burrage
- University of Oxford, Oxford, United Kingdom; Queensland University of Technology, Brisbane, Australia
| | | | | | | |
Collapse
|
8
|
Heins C, Millidge B, Da Costa L, Mann RP, Friston KJ, Couzin ID. Collective behavior from surprise minimization. Proc Natl Acad Sci U S A 2024; 121:e2320239121. [PMID: 38630721 DOI: 10.1073/pnas.2320239121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.
Collapse
Affiliation(s)
- Conor Heins
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany
- Department of Biology, University of Konstanz, Konstanz D-78457, Germany
- VERSES Research Lab, Los Angeles, CA 90016
| | - Beren Millidge
- Medical Research Council Brain Networks Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom
| | - Lancelot Da Costa
- VERSES Research Lab, Los Angeles, CA 90016
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
| | - Richard P Mann
- Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Karl J Friston
- VERSES Research Lab, Los Angeles, CA 90016
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, United Kingdom
| | - Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz D-78457, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz D-78457, Germany
- Department of Biology, University of Konstanz, Konstanz D-78457, Germany
| |
Collapse
|
9
|
Groves T, Cowie NL, Nielsen LK. Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism. ACS Synth Biol 2024; 13:1205-1214. [PMID: 38579163 PMCID: PMC11036490 DOI: 10.1021/acssynbio.3c00662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/12/2024] [Accepted: 03/19/2024] [Indexed: 04/07/2024]
Abstract
This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.
Collapse
Affiliation(s)
- Teddy Groves
- The
Novo Nordisk Foundation Center for Biosustainability, DTU, Kongens
Lyngby 2800, Denmark
| | - Nicholas Luke Cowie
- The
Novo Nordisk Foundation Center for Biosustainability, DTU, Kongens
Lyngby 2800, Denmark
| | - Lars Keld Nielsen
- The
Novo Nordisk Foundation Center for Biosustainability, DTU, Kongens
Lyngby 2800, Denmark
- Australian
Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia 4067, Australia
| |
Collapse
|
10
|
Kravets VG, Clark TK. An experimentally informed computational model of neurovestibular adaptation to altered gravity. Exp Physiol 2024. [PMID: 38625533 DOI: 10.1113/ep091817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 03/27/2024] [Indexed: 04/17/2024]
Abstract
Transitions to altered gravity environments result in acute sensorimotor impairment for astronauts, leading to serious mission and safety risks in the crucial first moments in a new setting. Our understanding of the time course and severity of impairment in the early stages of adaptation remains limited and confounded by unmonitored head movements, which are likely to impact the rate of adaptation. Here, we aimed to address this gap by using a human centrifuge to simulate the first hour of hypergravity (1.5g) exposure and the subsequent 1g readaptation period, with precisely controlled head tilt activity. We quantified head tilt overestimation via subjective visual vertical and found ∼30% tilt overestimation that did not decrease over the course of 1 h of exposure to the simulated gravity environment. These findings extended the floor of the vestibular adaptation window (with controlled vestibular cueing) to 1 h of exposure to altered gravity. We then used the empirical data to inform a computational model of neurovestibular adaptation to changes in the magnitude of gravity, which can offer insight into the adaptation process and, with further tuning, can be used to predict the temporal dynamics of vestibular-mediated misperceptions in altered gravity.
Collapse
Affiliation(s)
- Victoria G Kravets
- Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder, Colorado, USA
| | - Torin K Clark
- Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder, Colorado, USA
| |
Collapse
|
11
|
Rustand D, van Niekerk J, Krainski ET, Rue H, Proust-Lima C. Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations. Biostatistics 2024; 25:429-448. [PMID: 37531620 PMCID: PMC11017128 DOI: 10.1093/biostatistics/kxad019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/04/2023] Open
Abstract
Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.
Collapse
Affiliation(s)
- Denis Rustand
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Janet van Niekerk
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Elias Teixeira Krainski
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Håvard Rue
- Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Cécile Proust-Lima
- Bordeaux Population Health Center, Inserm, UMR1219, Univ. Bordeaux, F-33000 Bordeaux, France
| |
Collapse
|
12
|
Castelletti F. Learning Bayesian Networks: A Copula Approach for Mixed-Type Data. Psychometrika 2024:10.1007/s11336-024-09969-2. [PMID: 38609693 DOI: 10.1007/s11336-024-09969-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 03/14/2024] [Indexed: 04/14/2024]
Abstract
Estimating dependence relationships between variables is a crucial issue in many applied domains and in particular psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper, we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e., possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students. R code implementing the proposed methodology is available at https://github.com/FedeCastelletti/bayes_networks_mixed_data .
Collapse
Affiliation(s)
- Federico Castelletti
- Department of Statistical Sciences, Universitá Cattolica del Sacro Cuore, Milan, Italy.
| |
Collapse
|
13
|
Callegaro A, Luo Y, Karkada N, Zahaf T. Dynamic borrowing of historical controls adjusting for covariates in vaccine efficacy clinical trials. Pharm Stat 2024. [PMID: 38591424 DOI: 10.1002/pst.2384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/22/2023] [Accepted: 03/07/2024] [Indexed: 04/10/2024]
Abstract
Traditional vaccine efficacy trials usually use fixed designs and often require large sample sizes. Recruiting a large number of subjects can make the trial expensive, long, and difficult to conduct. A possible approach to reduce the sample size and speed up the development is to use historical controls. In this paper, we extend the robust mixture prior (RMP) approach (a well established approach for Bayesian dynamic borrowing of historical controls) to adjust for covariates. The adjustment is done using classical methods from causal inference: inverse probability of treatment weighting, G-computation and double-robust estimation. We evaluate these covariate-adjusted RMP approaches using a comprehensive simulation study and demonstrate their use by performing a retrospective analysis of a prophylactic human papillomavirus vaccine efficacy trial. Adjusting for covariates reduces the drift between current and historical controls, with a beneficial effect on bias, control of type I error and power.
Collapse
Affiliation(s)
| | - Yongyi Luo
- Institute of Mathematics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | | | - Toufik Zahaf
- Department of Biostatistics, GSK, Rixensart, Belgium
| |
Collapse
|
14
|
Wang M, Li Z, Lu J, Zhang L, Li Y, Zhang L. Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data. BMC Med Res Methodol 2024; 24:86. [PMID: 38589783 PMCID: PMC11003030 DOI: 10.1186/s12874-024-02201-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 03/10/2024] [Indexed: 04/10/2024] Open
Abstract
Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.
Collapse
Affiliation(s)
- Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Zheng Li
- Novartis Pharmaceuticals, East Hanover, NJ, USA
| | - Jun Lu
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Yimei Li
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Liangliang Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| |
Collapse
|
15
|
Onaka Y, Sakai R, Fukunaga TM, Ikemoto K, Isobe H. Bayesian Inference for Model Analyses of Supramolecular Complexes: A Case Study with Nanocarbon Hosts. Angew Chem Int Ed Engl 2024:e202405388. [PMID: 38580617 DOI: 10.1002/anie.202405388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 04/07/2024]
Abstract
A 126π-electron nanobowl molecule, phenine tridehydrosumanene, was synthesized in 12 steps through the development of a polygon cyclization strategy that assembled the polygonal precursors via Ni-mediated macrocyclization. The bowl-shaped structure accommodated C70 as a guest at the concave site, and the ball-in-bowl structure was determined by X-ray crystallography. The host-guest equilibrium in solution was studied with titration experiments using isothermal calorimetry, which provided an interesting test case for studying the host-guest stoichiometry. Bayesian inference was introduced for stoichiometric analyses of the equilibrium, and a protocol to estimate the volume of prior probability in the parameter space was developed. The Bayesian protocol functioned as Occam's razor and provided quantitative support for a specific stoichiometry. The method was examined with five host-guest examples comprising nanocarbon hosts, which suggested the versatility of Bayesian inference for studies of supramolecular complexes.
Collapse
Affiliation(s)
| | | | | | | | - Hiroyuki Isobe
- The University of Tokyo, Department of Chemistry, Hongo 7-3-1, 113-0033, Bunkyo-ku, JAPAN
| |
Collapse
|
16
|
Li Y, Barton JP. Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data. Mol Biol Evol 2024; 41:msae060. [PMID: 38507665 PMCID: PMC10986812 DOI: 10.1093/molbev/msae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/02/2024] [Accepted: 03/15/2024] [Indexed: 03/22/2024] Open
Abstract
In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimate linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.
Collapse
Affiliation(s)
- Yunxiao Li
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, USA
| | - John P Barton
- Department of Physics and Astronomy, University of California, Riverside, CA 92521, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| |
Collapse
|
17
|
Pohl AJ, Schofield MR, Edwards WB, Ferber R. Adaptive P-Splines for challenging filtering problems in biomechanics. J Biomech 2024; 167:112074. [PMID: 38614021 DOI: 10.1016/j.jbiomech.2024.112074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/25/2024] [Accepted: 04/02/2024] [Indexed: 04/15/2024]
Abstract
Suppression of noise from recorded signals is a critically important data processing step for biomechanical analyses. While a wide variety of filtering or smoothing spline methods are available, the majority of these are not well suited for the analysis of signals with rapidly changing derivatives such as the processing of motion data for impact-like events. This is because commonly used low-pass filtering approaches or smoothing splines typically assume a single fixed cut-off frequency or regularization penalty which fails to describe rapid changes in the underlying function. To overcome these limitations we examine a class of adaptive penalized splines (APS) that extend commonly used penalized spline smoothers by inferring temporal adaptations in regularization penalty from observed data. Three variations of APS are examined in which temporal variation of spline penalization is described via either a series of independent random variables, an autoregressive process or a smooth cubic spline. Comparing the performance of APS on simulated datasets is promising with APS reducing RMSE by 48%-183% compared to a widely used Butterworth filtering approach. When inferring acceleration from noisy measurements describing the position of a pendulum impacting a barrier we observe between a 13% (independent variables) to 28% (spline) reduction in RMSE when compared to a 4th order Butterworth filter with optimally selected cut-off frequency. In addition to considerable improvement in RMSE, APS can provide estimates of uncertainty for fitted curves and generated quantities such as peak accelerations or durations of stationary periods. As a result, we suggest that researchers should consider the use of APS if features such as impact peaks, rates of loading, or periods of negligible acceleration are of interest.
Collapse
Affiliation(s)
- Andrew J Pohl
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4.
| | | | - W Brent Edwards
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4
| | - Reed Ferber
- Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4; Running Injury Clinic, Calgary, AB, Canada
| |
Collapse
|
18
|
Ferguson AM, Turner G, Orben A. Social uncertainty in the digital world. Trends Cogn Sci 2024; 28:286-289. [PMID: 38448356 PMCID: PMC10993016 DOI: 10.1016/j.tics.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/08/2024]
Abstract
The social world is inherently uncertain. We present a computational framework for thinking about how increasingly popular online environments modulate the social uncertainty we experience, depending on the type of social inferences we make. This framework draws on Bayesian inference, which involves combining multiple informational sources to update our beliefs.
Collapse
Affiliation(s)
- Amanda M Ferguson
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Georgia Turner
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Amy Orben
- Medical Research Council (MRC) Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
19
|
Toussaint B, Heinzle J, Stephan KE. A computationally informed distinction of interoception and exteroception. Neurosci Biobehav Rev 2024; 159:105608. [PMID: 38432449 DOI: 10.1016/j.neubiorev.2024.105608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
While interoception is of major neuroscientific interest, its precise definition and delineation from exteroception continue to be debated. Here, we propose a functional distinction between interoception and exteroception based on computational concepts of sensor-effector loops. Under this view, the classification of sensory inputs as serving interoception or exteroception depends on the sensor-effector loop they feed into, for the control of either bodily (physiological and biochemical) or environmental states. We explain the utility of this perspective by examining the perception of skin temperature, one of the most challenging cases for distinguishing between interoception and exteroception. Specifically, we propose conceptualising thermoception as inference about the thermal state of the body (including the skin), which is directly coupled to thermoregulatory processes. This functional view emphasises the coupling to regulation (control) as a defining property of perception (inference) and connects the definition of interoception to contemporary computational theories of brain-body interactions.
Collapse
Affiliation(s)
- Birte Toussaint
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| |
Collapse
|
20
|
Romero Moreno G, Restocchi V, Fleuriot JD, Anand A, Mercer SW, Guthrie B. Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups. EBioMedicine 2024; 102:105081. [PMID: 38518656 PMCID: PMC10966445 DOI: 10.1016/j.ebiom.2024.105081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/05/2024] [Accepted: 03/09/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. METHODS We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. FINDINGS Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. INTERPRETATION Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. FUNDING National Institute for Health and Care Research.
Collapse
Affiliation(s)
| | | | | | - Atul Anand
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Stewart W Mercer
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Bruce Guthrie
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
21
|
Shaheen H, Melnik R, Singh S. Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease. Stat Anal Data Min 2024; 17:e11679. [PMID: 38646460 PMCID: PMC11031189 DOI: 10.1002/sam.11679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/23/2024] [Indexed: 04/23/2024]
Abstract
The abnormal aggregation of extracellular amyloid-β ( A β ) in senile plaques resulting in calcium C a + 2 dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A β deposition and C a + 2 dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal A β accumulation. Moreover, increasing evidence show a feed-forward loop between A β and C a + 2 levels, i.e. A β disrupts neuronal C a + 2 levels, which in turn affects the formation of A β . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A β and C a + 2 using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between A β and C a + 2 levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A β metabolism or intracellular C a + 2 homeostasis causes the relative growth rate in both C a + 2 and A β , which corresponds to the development of AD. The imbalance of C a + 2 ions causes A β disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of C a + 2 ion transportation and deposition. This suggests that altering the C a + 2 balance or the balance between A β and C a + 2 by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.
Collapse
Affiliation(s)
- Hina Shaheen
- Faculty of Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
| | - Sundeep Singh
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were generated by the Alzheimer’s Disease Metabolomics Consortium (ADMC). As such, the investigators within the ADMC provided data, but did not participate in the analysis or writing of this report. A complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/
| |
Collapse
|
22
|
Goldstein IH, Wakefield J, Minin VM. Incorporating testing volume into estimation of effective reproduction number dynamics. J R Stat Soc Ser A Stat Soc 2024; 187:436-453. [PMID: 38617598 PMCID: PMC11009926 DOI: 10.1093/jrsssa/qnad128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/04/2023] [Accepted: 09/10/2023] [Indexed: 04/16/2024]
Abstract
Branching process inspired models are widely used to estimate the effective reproduction number-a useful summary statistic describing an infectious disease outbreak-using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a new model that incorporates the number of diagnostic tests as a surveillance model covariate. Using simulated data and data from the SARS-CoV-2 pandemic in California, we demonstrate that incorporating tests leads to improved performance over the state of the art.
Collapse
Affiliation(s)
| | - Jon Wakefield
- Departments of Biostatistics and Statistics, University of Washington, Seattle, WA, USA
| | | |
Collapse
|
23
|
Warrier V, Shedge R, Garg PK, Dixit SG, Krishan K, Kanchan T. Applicability of the Suchey-Brooks method for age estimation in an Indian population: A computed tomography-based exploration using Bayesian analysis and machine learning. Med Sci Law 2024; 64:126-137. [PMID: 37491861 DOI: 10.1177/00258024231188799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Age estimation occupies a prominent niche in the identification process. In cases where skeletal remains present for examination, age is often estimated from markers distributed throughout the skeletal framework. Within the pelvis, the pubic symphysis constitutes one of the more commonly utilized skeletal markers for age estimation, with the Suchey-Brooks method comprising one of the more commonly employed methods for pubic symphyseal age estimation. The present study was targeted towards assessing the applicability of the Suchey-Brooks method for pubic symphyseal age estimation, an aspect largely unreported for an Indian population. In order to do so, clinically undertaken pelvic computed tomography scans of individuals were evaluated using the Suchey-Brooks method, and the error associated with the method was established using Bayesian analysis and different machine learning regression models. Amongst different supervised machine learning models, support vector regression and random forest furnished lowest error computations in both sexes. Using both Bayesian analysis and machine learning, lower error computations were observed in females, suggesting that the method demonstrates greater applicability for this sex. Inaccuracy and root mean square error obtained with Bayesian analysis and machine learning illustrates that both statistical modalities furnish comparable error computations for pubic symphyseal age estimation using the Suchey-Brooks method. However, given the numerous advantages associated with machine learning, it is recommended to use the same within medicolegal settings. Error computations obtained with the Suchey-Brooks method, regardless of the statistical modality utilized, indicate that the method should be used in amalgamation with additional markers to garner accurate estimates of age.
Collapse
Affiliation(s)
- Varsha Warrier
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| | - Rutwik Shedge
- School of Forensic Sciences, National Forensic Sciences University, Tripura, India
| | - Pawan Kumar Garg
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, India
| | - Shilpi Gupta Dixit
- Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, India
| | - Kewal Krishan
- Department of Anthropology, UGC Centre of Advanced Study, Panjab University, Chandigarh, India
| | - Tanuj Kanchan
- Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India
| |
Collapse
|
24
|
Mwamula AO, Kwon OG, Kwon C, Kim YS, Kim YH, Lee DW. A Revision of the Phylogeny of Helicotylenchus Steiner, 1945 (Tylenchida: Hoplolaimidae) as Inferred from Ribosomal and Mitochondrial DNA. Plant Pathol J 2024; 40:171-191. [PMID: 38606447 PMCID: PMC11016563 DOI: 10.5423/ppj.oa.01.2024.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/20/2024] [Accepted: 03/03/2024] [Indexed: 04/13/2024]
Abstract
Identification of Helicotylenchus species is very challenging due to phenotypic plasticity and existence of cryptic species complexes. Recently, the use of rDNA barcodes has proven to be useful for identification of Helicotylenchus. Molecular markers are a quick diagnostic tool and are crucial for discriminating related species and resolving cryptic species complexes within this speciose genus. However, DNA barcoding is not an error-free approach. The public databases appear to be marred by incorrect sequences, arising from sequencing errors, mislabeling, and misidentifications. Herein, we provide a comprehensive analysis of the newly obtained, and published DNA sequences of Helicotylenchus, revealing the potential faults in the available DNA barcodes. A total of 97 sequences (25 nearly full-length 18S-rRNA, 12 partial 28S-rRNA, 16 partial internal transcribed spacer [ITS]-rRNA, and 44 partial cytochrome c oxidase subunit I [COI] gene sequences) were newly obtained in the present study. Phylogenetic relationships between species are given as inferred from the analyses of 103 sequences of 18S-rRNA, 469 sequences of 28S-rRNA, 183 sequences of ITS-rRNA, and 63 sequences of COI. Remarks on suggested corrections of published accessions in GenBank database are given. Additionally, COI gene sequences of H. dihystera, H. asiaticus and the contentious H. microlobus are provided herein for the first time. Similar to rDNA gene analyses, the COI sequences support the genetic distinctness and validity of H. microlobus. DNA barcodes from type material are needed for resolving the taxonomic status of the unresolved taxonomic groups within the genus.
Collapse
Affiliation(s)
- Abraham Okki Mwamula
- Research Institute of Invertebrate Vector, Kyungpook National University, Sangju 37224, Korea
| | - Oh-Gyeong Kwon
- Department of Ecological Science, Kyungpook National University, Sangju 37224, Korea
| | - Chanki Kwon
- Department of Plant Protection and Quarantine, Graduate School of Plant Protection and Quarantine, Kyungpook National University, Daegu 41566, Korea
| | - Yi Seul Kim
- Research Institute of Invertebrate Vector, Kyungpook National University, Sangju 37224, Korea
| | - Young Ho Kim
- Research Institute of Invertebrate Vector, Kyungpook National University, Sangju 37224, Korea
- Department of Ecological Science, Kyungpook National University, Sangju 37224, Korea
- Department of Plant Protection and Quarantine, Graduate School of Plant Protection and Quarantine, Kyungpook National University, Daegu 41566, Korea
| | - Dong Woon Lee
- Research Institute of Invertebrate Vector, Kyungpook National University, Sangju 37224, Korea
- Department of Ecological Science, Kyungpook National University, Sangju 37224, Korea
- Department of Plant Protection and Quarantine, Graduate School of Plant Protection and Quarantine, Kyungpook National University, Daegu 41566, Korea
| |
Collapse
|
25
|
Liu S, Zhang Y, Fan S. Adaptive Space-Aware Infotaxis II as a Strategy for Odor Source Localization. Entropy (Basel) 2024; 26:302. [PMID: 38667856 DOI: 10.3390/e26040302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/15/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024]
Abstract
Mobile robot olfaction of toxic and hazardous odor sources is of great significance in anti-terrorism, disaster prevention, and control scenarios. Aiming at the problems of low search efficiency and easily falling into a local optimum of the current odor source localization strategies, the paper proposes the adaptive space-aware Infotaxis II algorithm. To improve the tracking efficiency of robots, a new reward function is designed by considering the space information and emphasizing the exploration behavior of robots. Considering the enhancement in exploratory behavior, an adaptive navigation-updated mechanism is proposed to adjust the movement range of robots in real time through information entropy to avoid an excessive exploration behavior during the search process, which may lead the robot to fall into a local optimum. Subsequently, an improved adaptive cosine salp swarm algorithm is applied to confirm the optimal information adaptive parameter. Comparative simulation experiments between ASAInfotaxis II and the classical search strategies are carried out in 2D and 3D scenarios regarding the search efficiency and search behavior, which show that ASAInfotaxis II is competent to improve the search efficiency to a larger extent and achieves a better balance between exploration and exploitation behaviors.
Collapse
Affiliation(s)
- Shiqi Liu
- Innovation and Research Institute, Hebei University of Technology, Shijiazhuang 050299, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Yan Zhang
- Innovation and Research Institute, Hebei University of Technology, Shijiazhuang 050299, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Shurui Fan
- Innovation and Research Institute, Hebei University of Technology, Shijiazhuang 050299, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
| |
Collapse
|
26
|
Sundar V, Tu B, Guan L, Esvelt K. FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data. bioRxiv 2024:2024.03.26.586797. [PMID: 38586054 PMCID: PMC10996587 DOI: 10.1101/2024.03.26.586797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here, we develop FLIGHTED, a Bayesian method for generating fitness landscapes with calibrated errors from noisy high-throughput experimental data. We apply FLIGHTED to single-step selection assays such as phage display and to a novel high-throughput assay DHARMA that ties fitness to base editing activity. Our results show that FLIGHTED robustly generates fitness landscapes with accurate errors. We demonstrate that FLIGHTED improves model performance and enables the generation of protein fitness datasets of up to 106 variants with DHARMA. FLIGHTED can be used on any high-throughput assay and makes it easy for ML scientists to account for experimental noise when modeling protein fitness.
Collapse
Affiliation(s)
- Vikram Sundar
- Computational and Systems Biology Program, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Boqiang Tu
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Lindsey Guan
- Computational and Systems Biology Program, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| | - Kevin Esvelt
- Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, MA, USA
| |
Collapse
|
27
|
Malekpour SA, Haghverdi L, Sadeghi M. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms. Brief Bioinform 2024; 25:bbae180. [PMID: 38653489 PMCID: PMC11036345 DOI: 10.1093/bib/bbae180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.
Collapse
Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), 19395-5746, Tehran, Iran
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center (BIMSB-MDC) in the Helmholtz Association, Berlin, Germany
| | - Mehdi Sadeghi
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, 1497716316, Tehran, Iran
| |
Collapse
|
28
|
Parino F, Gustani-Buss E, Bedford T, Suchard MA, Trovão NS, Rambaut A, Colizza V, Poletto C, Lemey P. Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation. medRxiv 2024:2024.03.14.24303719. [PMID: 38559244 PMCID: PMC10980132 DOI: 10.1101/2024.03.14.24303719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.
Collapse
Affiliation(s)
- Francesco Parino
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
| | - Emanuele Gustani-Buss
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
- Howard Hughes Medical Institute, Seattle, Washington 98109, USA
| | - Marc A. Suchard
- Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, 90095, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA
| | | | - Andrew Rambaut
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Epidemiologie et de Santé Publique (IPLESP), Paris, France
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121 Padova, Italy
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven – University of Leuven, 3000 Leuven, Belgium
| |
Collapse
|
29
|
Chen K, Zhao Y, Liu M, Lin J, Liu R. DOD-Combo: Bayesian dose finding design in combination trials with meta-analytic-predictive prior. J Biopharm Stat 2024:1-18. [PMID: 38468381 DOI: 10.1080/10543406.2024.2325142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 02/24/2024] [Indexed: 03/13/2024]
Abstract
Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method's superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials.
Collapse
Affiliation(s)
- Kai Chen
- Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, USA
| | - Yunqi Zhao
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, USA
| | - Meizi Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, USA
| | - Rachael Liu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, USA
| |
Collapse
|
30
|
Zhang HL, Li B, Shang J, Wang WW, Zhao FY. Source term estimation for continuous plume dispersion in Fusion Field Trial-07: Bayesian inference probability adjoint inverse method. Sci Total Environ 2024; 915:169802. [PMID: 38215839 DOI: 10.1016/j.scitotenv.2023.169802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 12/09/2023] [Accepted: 12/29/2023] [Indexed: 01/14/2024]
Abstract
In scenarios involving sudden releases of unidentified gases or concealed pollution emergencies, source control emerges as a critical procedure to safeguard residential air quality. Appropriate inverse source tracking methodology depending on diverse measurement data could be utilized to promptly identify pollutant source parameters. In this study, source term estimation (STE) method, i.e., jointly combining probability adjoint method with the Bayesian inference method, has been proposed. General form of the pollutant inverse transport equation was firstly established. Subsequently, the pollution source information, assumed from single continuous point releases during Fusion Field Trials 2007 under an unsteady wind field, was identified using the Bayesian inference probability adjoint inverse method. Metropolis-Hastings Markov Chain Monte Carlo (MH-MCMC) and Differential Evolution Markov Chain Monte Carlo (DE-MCMC) were then compared as sampling methods for Bayesian inference. Results indicated that the DE-MCMC algorithm has superior convergence and could present higher accuracy of pollutant source information than that of MH-MCMC algorithm, particularly for highly nonlinear and multi-modal distribution systems. Furthermore, the integration of Union standard Adjoint Location Probability (UALP) as prior information into the Bayesian inference probability adjoint inverse method effectively narrowed the sampling range, enhancing both the accuracy and robustness of the proposed approach. Finally, the impact of the covariance matrix on the inverse identification accuracy was explored. Overall, this research has provided insights into the future applicability of this Bayesian inference inversion technique for point source identification.
Collapse
Affiliation(s)
- Hong-Liang Zhang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Bin Li
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Jin Shang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Wei-Wei Wang
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China
| | - Fu-Yun Zhao
- School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei Province, PR China; School of Civil Engineering, Hunan University of Technology, Zhuzhou, Hunan Province, PR China.
| |
Collapse
|
31
|
Chen G, O’Malley AJ. Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients. Res Sq 2024:rs.3.rs-4014583. [PMID: 38496605 PMCID: PMC10942573 DOI: 10.21203/rs.3.rs-4014583/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. In such a situation, a patient's own hospital can be thought of as a mediator between the effects of peer hospitals and their outcome. However, as in mediation analyses, there may be interest in allowing the effects of peer units to directly impact patients of other units. To accommodate these possibilities, we develop two hierarchical network autocorrelation models that allow for direct and indirect peer effect pathways between hospitals when modeling individual outcomes of the patients cared for at the hospitals. A Bayesian approach is used for model estimation while a simulation study is used to assess the performance of the models and sensitivity of results to different prior distributions. We construct a United States New England region patient-sharing hospital network and apply our Bayesian hierarchical models to study the diffusion of robotic surgery and hospital peer effects in patient outcomes using a cohort of United States Medicare beneficiaries in 2016 and 2017. The comparative fit of models to the data is assessed using Deviance information criteria tailored to hierarchical models that include peer effects as latent variables.
Collapse
Affiliation(s)
- Guanqing Chen
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, 02215, MA, US
| | - A. James O’Malley
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, 03756, NH, US
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, 03756, NH, US
| |
Collapse
|
32
|
Maggi S, Hock RM, O'Neill M, Buckley M, Moran PM, Bast T, Sami M, Humphries MD. Tracking subjects' strategies in behavioural choice experiments at trial resolution. eLife 2024; 13:e86491. [PMID: 38426402 PMCID: PMC10959529 DOI: 10.7554/elife.86491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 02/23/2024] [Indexed: 03/02/2024] Open
Abstract
Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.
Collapse
Affiliation(s)
- Silvia Maggi
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
| | - Rebecca M Hock
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
| | - Martin O'Neill
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Health & Nutritional Sciences, Atlantic Technological UniversitySligoIreland
| | - Mark Buckley
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Paula M Moran
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Neuroscience, University of NottinghamNottinghamUnited Kingdom
| | - Tobias Bast
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
- Department of Neuroscience, University of NottinghamNottinghamUnited Kingdom
| | - Musa Sami
- Institute of Mental Health, University of NottinghamNottinghamUnited Kingdom
| | - Mark D Humphries
- School of Psychology, University of NottinghamNottinghamUnited Kingdom
| |
Collapse
|
33
|
Bridgen JRE, Lewis JM, Todd S, Taegtmeyer M, Read JM, Jewell CP. A Bayesian approach to identifying the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2. J R Soc Interface 2024; 21:20230525. [PMID: 38442863 PMCID: PMC10914511 DOI: 10.1098/rsif.2023.0525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/31/2024] [Indexed: 03/07/2024] Open
Abstract
Nosocomial infections threaten patient safety, and were widely reported during the COVID-19 pandemic. Effective hospital infection control requires a detailed understanding of the role of different transmission pathways, yet these are poorly quantified. Using patient and staff data from a large UK hospital, we demonstrate a method to infer unobserved epidemiological event times efficiently and disentangle the infectious pressure dynamics by ward. A stochastic individual-level, continuous-time state-transition model was constructed to model transmission of SARS-CoV-2, incorporating a dynamic staff-patient contact network as time-varying parameters. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm was used to estimate transmission rate parameters associated with each possible source of infection, and the unobserved infection and recovery times. We found that the total infectious pressure exerted on an individual in a ward varied over time, as did the primary source of transmission. There was marked heterogeneity between wards; each ward experienced unique infectious pressure over time. Hospital infection control should consider the role of between-ward movement of staff as a key infectious source of nosocomial infection for SARS-CoV-2. With further development, this method could be implemented routinely for real-time monitoring of nosocomial transmission and to evaluate interventions.
Collapse
Affiliation(s)
- Jessica R. E. Bridgen
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Joseph M. Lewis
- Tropical and Infectious Diseases Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, UK
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Stacy Todd
- Tropical and Infectious Diseases Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Miriam Taegtmeyer
- Tropical and Infectious Diseases Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jonathan M. Read
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Chris P. Jewell
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| |
Collapse
|
34
|
Grieve AP. Probability of success and group sequential designs. Pharm Stat 2024; 23:185-203. [PMID: 37916276 DOI: 10.1002/pst.2346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 08/11/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023]
Abstract
In this article, I extend the use of probability of success calculations, previously developed for fixed sample size studies to group sequential designs (GSDs) both for studies planned to be analyzed by standard frequentist techniques or Bayesian approaches. The structure of GSDs lends itself to sequential learning which in turn allows us to consider how knowledge about the result of an interim analysis can influence our assessment of the study's probability of success. In this article, I build on work by Temple and Robertson who introduced the idea of conditional probability of success, an idea which I also treated in a recent monograph.
Collapse
Affiliation(s)
- Andrew P Grieve
- Centre for Excellence in Statistical Innovation, UCB Pharma, Berkshire, UK
| |
Collapse
|
35
|
Ferede MM, Dagne GA, Mwalili SM, Bilchut WH, Engida HA, Karanja SM. Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data. BMC Med Res Methodol 2024; 24:56. [PMID: 38429729 PMCID: PMC10908071 DOI: 10.1186/s12874-024-02164-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 01/29/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. METHODS This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. RESULTS To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. CONCLUSIONS The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.
Collapse
Affiliation(s)
- Melkamu M Ferede
- Department of Statistics, University of Gondar, Gondar, Ethiopia.
| | - Getachew A Dagne
- Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, FL 33612, USA
| | - Samuel M Mwalili
- Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
| | - Workagegnehu H Bilchut
- Department of Internal Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Habtamu A Engida
- Department of Mathematics, Debre Markos University, Debre Markos, Ethiopia
| | - Simon M Karanja
- School of Public Health, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
| |
Collapse
|
36
|
Fabreti LG, Coghill LM, Thomson RC, Höhna S, Brown JM. The Expected Behaviors of Posterior Predictive Tests and Their Unexpected Interpretation. Mol Biol Evol 2024; 41:msae051. [PMID: 38437512 PMCID: PMC10946647 DOI: 10.1093/molbev/msae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 01/09/2024] [Indexed: 03/06/2024] Open
Abstract
Poor fit between models of sequence or trait evolution and empirical data is known to cause biases and lead to spurious conclusions about evolutionary patterns and processes. Bayesian posterior prediction is a flexible and intuitive approach for detecting such cases of poor fit. However, the expected behavior of posterior predictive tests has never been characterized for evolutionary models, which is critical for their proper interpretation. Here, we show that the expected distribution of posterior predictive P-values is generally not uniform, in contrast to frequentist P-values used for hypothesis testing, and extreme posterior predictive P-values often provide more evidence of poor fit than typically appreciated. Posterior prediction assesses model adequacy under highly favorable circumstances, because the model is fitted to the data, which leads to expected distributions that are often concentrated around intermediate values. Nonuniform expected distributions of P-values do not pose a problem for the application of these tests, however, and posterior predictive P-values can be interpreted as the posterior probability that the fitted model would predict a dataset with a test statistic value as extreme as the value calculated from the observed data.
Collapse
Affiliation(s)
- Luiza Guimarães Fabreti
- GeoBio-Center, Ludwig-Maximilians-Universität München, Richard-Wagner-Str. 10, Munich 80333, Germany
- Department of Earth and Environmental Sciences, Paleontology & Geobiology, Ludwig-Maximilians-Universität München, Richard-Wagner-Str. 10, Munich 80333, Germany
| | - Lyndon M Coghill
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
- Present address: Division of Research, Innovation, and Impact & Department of Veterinary Pathobiology, University of Missouri, Columbia, MO 65211, USA
| | - Robert C Thomson
- School of Life Sciences, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
| | - Sebastian Höhna
- GeoBio-Center, Ludwig-Maximilians-Universität München, Richard-Wagner-Str. 10, Munich 80333, Germany
- Department of Earth and Environmental Sciences, Paleontology & Geobiology, Ludwig-Maximilians-Universität München, Richard-Wagner-Str. 10, Munich 80333, Germany
| | - Jeremy M Brown
- Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, LA 70803, USA
| |
Collapse
|
37
|
Zhou Y, Feinman R, Lake BM. Compositional diversity in visual concept learning. Cognition 2024; 244:105711. [PMID: 38224649 DOI: 10.1016/j.cognition.2023.105711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 01/17/2024]
Abstract
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more data and generalizing less flexibly than people do. Here, we study these distinctively human abilities across a range of different types of visual composition, examining how people classify and generate "alien figures" with rich relational structure. We also develop a Bayesian program induction model which searches for the best programs for generating the candidate visual figures, utilizing a large program space containing different compositional mechanisms and abstractions. In few shot classification tasks, we find that people and the program induction model can make a range of meaningful compositional generalizations, with the model providing a strong account of the experimental data as well as interpretable parameters that reveal human assumptions about the factors invariant to category membership (here, to rotation and changing part attachment). In few shot generation tasks, both people and the models are able to construct compelling novel examples, with people behaving in additional structured ways beyond the model capabilities, e.g. making choices that complete a set or reconfigure existing parts in new ways. To capture these additional behavioral patterns, we develop an alternative model based on neuro-symbolic program induction: this model also composes new concepts from existing parts yet, distinctively, it utilizes neural network modules to capture residual statistical structure. Together, our behavioral and computational findings show how people and models can produce a variety of compositional behavior when classifying and generating visual objects.
Collapse
Affiliation(s)
- Yanli Zhou
- Center for Data Science, New York University, United States of America.
| | - Reuben Feinman
- Center for Neural Science, New York University, United States of America.
| | - Brenden M Lake
- Center for Data Science, New York University, United States of America; Department of Psychology, New York University, United States of America.
| |
Collapse
|
38
|
Lee JH, Heo SY, Lee SW. Controlling human causal inference through in silico task design. Cell Rep 2024; 43:113702. [PMID: 38295800 DOI: 10.1016/j.celrep.2024.113702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/01/2023] [Accepted: 01/08/2024] [Indexed: 03/02/2024] Open
Abstract
Learning causal relationships is crucial for survival. The human brain's functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans.
Collapse
Affiliation(s)
- Jee Hang Lee
- Department of Human-Centered AI, Sangmyung University, Seoul, Republic of Korea
| | - Su Yeon Heo
- Program of Brain and Cognitive Engineering, KAIST, Daejeon, Republic of Korea
| | - Sang Wan Lee
- Department of Brain and Cognitive Sciences, KAIST, Daejeon, Republic of Korea; Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea; Program of Brain and Cognitive Engineering, KAIST, Daejeon, Republic of Korea; KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea; KAIST Institute for Artificial Intelligence, Daejeon, Republic of Korea; KAIST Center for Neuroscience-inspired AI, Daejeon, Republic of Korea.
| |
Collapse
|
39
|
Zhang Y, Cameron CJF, Blanchette M. Posterior inference of Hi-C contact frequency through sampling. Front Bioinform 2024; 3:1285828. [PMID: 38455089 PMCID: PMC10919286 DOI: 10.3389/fbinf.2023.1285828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/20/2023] [Indexed: 03/09/2024] Open
Abstract
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
Collapse
Affiliation(s)
- Yanlin Zhang
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Christopher J. F. Cameron
- School of Computer Science, McGill University, Montréal, QC, Canada
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montreal, QC, Canada
| | | |
Collapse
|
40
|
Mallela A, Chen Y, Lin YT, Miller EF, Neumann J, He Z, Nelson KE, Posner RG, Hlavacek WS. Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States. Bull Math Biol 2024; 86:31. [PMID: 38353870 DOI: 10.1007/s11538-024-01258-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.
Collapse
Affiliation(s)
- Abhishek Mallela
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Ye Chen
- Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Yen Ting Lin
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
- Information Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Ely F Miller
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Jacob Neumann
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Zhili He
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Kathryn E Nelson
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Richard G Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - William S Hlavacek
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
| |
Collapse
|
41
|
Chatzimichail T, Hatjimihail AT. A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease. Diagnostics (Basel) 2024; 14:402. [PMID: 38396440 PMCID: PMC10887534 DOI: 10.3390/diagnostics14040402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.
Collapse
|
42
|
Tang J, Liu W, Li X, Peng Y, Zhang Y, Hou S. Linking myosin heavy chain isoform shift to mechanical properties and fracture modes in skeletal muscle tissue. Biomech Model Mechanobiol 2024; 23:103-116. [PMID: 37568047 DOI: 10.1007/s10237-023-01761-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023]
Abstract
Muscle fibers play a crucial role in the mechanical action of skeletal muscle tissue. However, it is unclear how the histological variations affect the mechanical properties of tissues. In this study, the shift of myosin heavy chain (MHC) isoforms is used for the first time to establish a linkage between tissue histological variation and passive mechanical properties. The shift of MHC isoform is found not only to induce significant differences in skeletal muscle passive mechanical properties, but also to lead to differences in strain rate responses. Non-negligible rate dependence is observed even in the conventionally defined quasi-static regime. Fidelity in the estimated constitutive parameters, which can be impacted due to variation in MHC isoforms and hence in rate sensitivity, is enhanced using a Bayesian inference framework. Subsequently, scanning electron microscopy and fluorescence microscopy are used to characterize the fracture morphology of muscle tissues and fibers. The fracture mode of both MHC I and II muscle fibers exhibited shearing of endomysium. Results show that the increase in strain rate only leads to stronger rebounding of the muscle fibers during tissue rupture without changing fracture modes.
Collapse
Affiliation(s)
- Jiabao Tang
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China
| | - Wenyang Liu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China.
| | - Xuhong Li
- The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yun Peng
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Shujuan Hou
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China
| |
Collapse
|
43
|
Arahmane H, Dumazert J, Barat E, Dautremer T, Carrel F, Dufour N, Michel M. Statistical approach for radioactivity detection: A brief review. J Environ Radioact 2024; 272:107358. [PMID: 38142518 DOI: 10.1016/j.jenvrad.2023.107358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/26/2023]
Abstract
Radioactivity detection is a major research and development priority for many practical applications. Amongst the various technical challenges in this field is the need to carry out accurate low-level radioactivity measurements in the presence of a large fluctuations in the natural radiation background, while reducing the false alarm rates. The task becomes even more harder with high detection limits under low signal-to-background ratios. A detection method based on the statistical inference, following either a frequentist or a Bayesian paradigm, adopted to overcome these challenges as well as to ensure a reliable and accurate diagnosis with a competitive tradeoff between sensitivity, specificity and response time. With this respect, several research studies, addressing a range of applications from decommissioning and dismantling to homeland security, have been proposed. Our main goal in this paper is to present a succinct survey of these studies based on a frequentist and Bayesian approaches used to decision-making, uncertainty and risk evaluation, in the context of radioactive detection. In this prospect, a theoretical background of statistical frequentist and Bayesian inferences was presented. Then, a comparative study of both approaches was performed to determine the optimal approach in regards to accuracy and pros/cons. A case of study for low-level radioactivity detection in nuclear decommissioning operations was provided to validate the optimal approach. Results proved the efficiency and usefulness of Bayesian approach against frequentist one with respect to the most challenging scenarios in radiation detection applications.
Collapse
Affiliation(s)
- Hanan Arahmane
- Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France.
| | | | - Eric Barat
- Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France
| | | | | | | | - Maugan Michel
- Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France
| |
Collapse
|
44
|
Abdurrab I, Mahmood T, Sheikh S, Aijaz S, Kashif M, Memon A, Ali I, Peerwani G, Pathan A, Alkhodre AB, Siddiqui MS. Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables-Bayesian Models vs. Machine Learning Models. Healthcare (Basel) 2024; 12:249. [PMID: 38255136 PMCID: PMC10815919 DOI: 10.3390/healthcare12020249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/04/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Length of stay (LoS) prediction is deemed important for a medical institution's operational and logistical efficiency. Sound estimates of a patient's stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.
Collapse
Affiliation(s)
- Ibrahim Abdurrab
- Department of Computer Science, Institute of Business Administration, Karachi 75270, Pakistan;
| | - Tariq Mahmood
- Department of Computer Science, Institute of Business Administration, Karachi 75270, Pakistan;
| | - Sana Sheikh
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Saba Aijaz
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Muhammad Kashif
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Ahson Memon
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Imran Ali
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Ghazal Peerwani
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Asad Pathan
- Department of Clinical Research Cardiology, Tabba Heart Institute, Karachi 75950, Pakistan; (S.S.); (S.A.); (M.K.); (A.M.); (I.A.); (G.P.); (A.P.)
| | - Ahmad B. Alkhodre
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.B.A.); (M.S.S.)
| | - Muhammad Shoaib Siddiqui
- Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; (A.B.A.); (M.S.S.)
| |
Collapse
|
45
|
Pighin S, Filimon F, Tentori K. The impact of problem domain on Bayesian inferences: A systematic investigation. Mem Cognit 2024:10.3758/s13421-023-01497-1. [PMID: 38200204 DOI: 10.3758/s13421-023-01497-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2023] [Indexed: 01/12/2024]
Abstract
Sparse (and occasionally contradictory) evidence exists regarding the impact of domain on probabilistic updating, some of which suggests that Bayesian word problems with medical content may be especially challenging. The present research aims to address this gap in knowledge through three pre-registered online studies, which involved a total of 2,238 participants. Bayesian word problems were related to one of three domains: medical, daily-life, and abstract. In the first two cases, problems presented realistic content and plausible numerical information, while in the latter, problems contained explicitly imaginary elements. Problems across domains were matched in terms of all relevant statistical values and, as much as possible, wording. Studies 1 and 2 utilized the same set of problems, but different response elicitation methods (i.e., an open-ended and a multiple-choice question, respectively). Study 3 involved a larger number of participants per condition and a smaller set of problems to more thoroughly investigate the magnitude of differences between the domains. There was a generally low rate of correct responses (17.2%, 17.4%, and 14.3% in Studies 1, 2, and 3, respectively), consistent with accuracy levels commonly observed in the literature for this specific task with online samples. Nonetheless, a small but significant difference between domains was observed: participants' accuracy did not differ between medical and daily-life problems, while it was significantly higher in corresponding abstract problems. These results suggest that medical problems are not inherently more difficult to solve, but rather that performance is improved with abstract problems for which participants cannot draw from their background knowledge.
Collapse
Affiliation(s)
- Stefania Pighin
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini n. 31, 38068, Rovereto, TN, Italy.
| | - Flavia Filimon
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini n. 31, 38068, Rovereto, TN, Italy
| | - Katya Tentori
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Corso Bettini n. 31, 38068, Rovereto, TN, Italy
- Center for Medical Sciences (CISMed), University of Trento, Trento, Italy
| |
Collapse
|
46
|
Balafrej M, Fagroud M, Sraïri MT. Genetic parameters of sheep growth curve traits reared within rangelands under uncertain paternity. Trop Anim Health Prod 2024; 56:34. [PMID: 38190007 DOI: 10.1007/s11250-023-03882-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 12/22/2023] [Indexed: 01/09/2024]
Abstract
This work aims to improve the selection program of the Timahdit breed through the use of the parameters of the Von Bertalanffy model as selection criteria and the treatment of uncertain paternity found in pastoral systems. A database containing 12,029 animals and a pedigree file integrating the probabilities of the parents with a total of 48,292 animals were used in the analysis. An individual estimation of the parameters of the model studied by the nonlinear regression procedure Proc NLIN of SAS was carried out, followed by the determination of the fixed effects which influence these parameters by means of a general linear model using the GLM procedure of the SAS software. The treatment of uncertain paternity is solved by an R code translating Average Numerator Relationship Matrix Model (ANRM). Then, the variance and (co)variance components were estimated by a Bayesian approach using the BRMS package. The high heritability values obtained, between 0.52 and 0.55 for the parameters studied, suggest good prospects for genetic responses to selection and the maintenance of sustained genetic progress, especially when environmental conditions are unfavorable. The positive correlations between all the parameters studied show that animals with rapid development tend to have lower weight performance. Finally, with optimal selection based on the genetic values associated with these parameters, we can make the desired changes to the growth curve by choosing breeders that achieve high weight performances as quickly as possible, and that would allow improving the feed efficiency for these animals, as well as increasing the profitability of sheep farms.
Collapse
Affiliation(s)
- M Balafrej
- Ministry of Agriculture, Production Chains Development Directorate, Rabat, Morocco.
| | - M Fagroud
- Department of Agronomy and Plant Genetics, National School of Agriculture in Meknes, P.O. Box S/40 50000, Meknes, Morocco
| | - M T Sraïri
- Department of Animal Production and Biotechnology, Hassan II Institute of Agronomy and Veterinary Medicine, P.O. Box 6202, 10101, Rabat, Morocco
| |
Collapse
|
47
|
Li H, Wan B, Fang Y, Li Q, Liu JK, An L. An FPGA implementation of Bayesian inference with spiking neural networks. Front Neurosci 2024; 17:1291051. [PMID: 38249589 PMCID: PMC10796689 DOI: 10.3389/fnins.2023.1291051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Spiking neural networks (SNNs), as brain-inspired neural network models based on spikes, have the advantage of processing information with low complexity and efficient energy consumption. Currently, there is a growing trend to design hardware accelerators for dedicated SNNs to overcome the limitation of running under the traditional von Neumann architecture. Probabilistic sampling is an effective modeling approach for implementing SNNs to simulate the brain to achieve Bayesian inference. However, sampling consumes considerable time. It is highly demanding for specific hardware implementation of SNN sampling models to accelerate inference operations. Hereby, we design a hardware accelerator based on FPGA to speed up the execution of SNN algorithms by parallelization. We use streaming pipelining and array partitioning operations to achieve model operation acceleration with the least possible resource consumption, and combine the Python productivity for Zynq (PYNQ) framework to implement the model migration to the FPGA while increasing the speed of model operations. We verify the functionality and performance of the hardware architecture on the Xilinx Zynq ZCU104. The experimental results show that the hardware accelerator of the SNN sampling model proposed can significantly improve the computing speed while ensuring the accuracy of inference. In addition, Bayesian inference for spiking neural networks through the PYNQ framework can fully optimize the high performance and low power consumption of FPGAs in embedded applications. Taken together, our proposed FPGA implementation of Bayesian inference with SNNs has great potential for a wide range of applications, it can be ideal for implementing complex probabilistic model inference in embedded systems.
Collapse
Affiliation(s)
- Haoran Li
- Guangzhou Institute of Technology, Xidian University, Guangzhou, China
| | - Bo Wan
- School of Computer Science and Technology, Xidian University, Xi'an, China
- Key Laboratory of Smart Human Computer Interaction and Wearable Technology of Shaanxi Province, Xi'an, China
| | - Ying Fang
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
- Digital Fujian Internet-of-Thing Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou, China
| | - Qifeng Li
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jian K. Liu
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Lingling An
- Guangzhou Institute of Technology, Xidian University, Guangzhou, China
- School of Computer Science and Technology, Xidian University, Xi'an, China
| |
Collapse
|
48
|
Zang M, Liu R. Generalized triple outcome decision-making in basket trials. J Biopharm Stat 2024:1-17. [PMID: 38166528 DOI: 10.1080/10543406.2023.2296054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/04/2023] [Indexed: 01/04/2024]
Abstract
Making the go/no-go decision is critical in Phase II (or Ib) clinical trials. The conventional decision-making framework based on a binary hypothesis testing has been gradually replaced by the TODeM (Triple Outcome Decision-Making) which has three zones of outcomes: go, no-go, and consider. The TODeM provides more flexibility in decision-making with considering both of statistical significance and clinical relevance. However, Bayesian methods (e.g. EXNEX, MUCE, etc.) for the information borrowing are still based on the binary decision-making framework. We propose a new decision-making process G-TODeM (Generalized Triple Outcome Decision-Making) to apply those Bayesian methods with information borrowing across different cohorts to the TODeM framework. Essentially, the information borrowed from other cohorts can shrink the consider zone of the inference cohort.
Collapse
Affiliation(s)
- Miao Zang
- Global Statistics & Data Science (GSDS), BeiGene, Beijing, China
| | - Rui Liu
- Global Statistics & Data Science (GSDS), BeiGene, Beijing, China
| |
Collapse
|
49
|
Rigat F. A conservative approach to leveraging external evidence for effective clinical trial design. Pharm Stat 2024; 23:81-90. [PMID: 37751940 DOI: 10.1002/pst.2339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 07/03/2023] [Accepted: 09/03/2023] [Indexed: 09/28/2023]
Abstract
Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is illustrated here, requiring that the operational characteristics of the primary trial outcome are stronger than the prior. This approach is complementary to current Bayesian design methods, in that it insures against prior-data conflict by defining a sample size commensurate to a discrete design prior. This approach is ethical, in that it requires designs appropriate to achieving pre-specified levels of clinical equipoise imbalance. Practical examples are discussed, illustrating design of trials with binary or time to event endpoints. Moderate increases in phase II study sample size are shown to deliver strong levels of overall evidence for go/no-go clinical development decisions. Levels of negative evidence provided by group sequential confirmatory designs are found negligible, highlighting the importance of complementing efficacy boundaries with non-binding futility criteria.
Collapse
Affiliation(s)
- Fabio Rigat
- Oncology Biometrics, AstraZeneca Plc, Cambridge, UK
| |
Collapse
|
50
|
Gao R, Liu Z, Jiang C, Wang Y, Wang S, Wang P. BI-FedGNN: Federated graph neural networks framework based on Bayesian inference. Neural Netw 2024; 169:143-153. [PMID: 37890364 DOI: 10.1016/j.neunet.2023.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/01/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023]
Abstract
The development of the Industrial Internet of Things (IIoT) in recent years has resulted in an increase in the amount of data generated by connected devices, creating new opportunities to enhance the quality of service for machine learning in the IIoT through data sharing. Graph neural networks (GNNs) are the most popular technique in machine learning at the moment because they can learn extremely precise node representations from graph-structured data. Due to privacy issues and legal restrictions of clients in industrial IoT, it is not permissible to directly concentrate vast real-world graph-structured datasets for training on GNNs. To resolve the aforementioned difficulties, this paper proposes a federal graph learning framework based on Bayesian inference (BI-FedGNN) that performs effectively in the presence of noisy graph structure information or missing strong relational edges. BI-FedGNN extends Bayesian Inference (BI) to the process of Federal Graph Learning (FGL), adding random samples with weights and biases to the client-side local model training process, improving the accuracy and generalization ability of FGL in the training process by rendering the graph structure data involved in GNNs training more similar to the graph structure data existing in the real world. Through extensive experimental tests, the results show that BI-FedGNN has about 0.5%-5.0% accuracy improvement over other baselines of federal graph learning. In order to expand the applicability of BI-FedGNN, experiments are carried out on heterogeneous graph datasets, and the results indicate that BI-FedGNN can also have at least 1.4% improvement in classification accuracy.
Collapse
Affiliation(s)
- Rufei Gao
- School of Computer Science and Engineering, Yantai University, Shandong, China.
| | - Zhaowei Liu
- School of Computer Science and Engineering, Yantai University, Shandong, China.
| | - Chenxi Jiang
- School of Computer Science and Engineering, Yantai University, Shandong, China.
| | - Yingjie Wang
- School of Computer Science and Engineering, Yantai University, Shandong, China.
| | - Shenqiang Wang
- Institute of Network Technology (Yantai), Shandong, China.
| | - Pengda Wang
- University of Science and Technology of China, Anhui, China.
| |
Collapse
|