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This article explores the use of Bayesian neural networks (BNNs) in mortality modeling, specifically in the context of South Africa. BNNs are gaining popularity due to their ability to incorporate prior assumptions and provide uncertainty quantification. The article outlines the steps involved in training a BNN, including assigning priors, defining likelihood models, and using inference methods such as Markov chain Monte Carlo (MCMC) or variational inference. The results show that the BNN performs well in capturing trends and making predictions compared to traditional models, but further exploration and testing are recommended. The authors acknowledge the expertise of professionals in the field. [Extracted from the article]

Bayesian inference is a powerful technique for damage/flaw detection in critical structures. This paper explores the application of Bayesian inference to identify the flaws (voids/inclusions) and its parameters, assuming no information about the flaws is known a priori. Multiple flaws are represented by a parameter vector that contains the locations and the geometric information. In the inverse problem framework, the Scaled Boundary Finite Element Method (SBFEM) with quadtree decomposition is used to solve the forward problem. The flaw parameters are statistically quantified using the Bayesian inference. The likelihood function and prior information update the joint distribution of the number of flaws and their corresponding flaw parameters. The sampling to estimate the posterior distribution of the flaw parameters is based on the trans-dimensional Reversible Jump Markov Chain Monte Carlo (RJMCMC). Also, the impact of additive Gaussian noise on the observed data is investigated. Numerical examples include identifying multiple voids of different shapes such as circle and ellipse and identifying voids and inclusions, using the proposed approach with input sensor data at different noise levels. • RJMCMC based Bayesian framework along with SBFEM for detecting multiple flaws & their parameters is proposed. • Quadtree mesh obviates the need for human intervention during forward problem after Bayesian update. • Unified framework to detect multiple flaws, viz., voids and inclusions. • Confidence interval on proposed parameters of the flaws are obtained. [ABSTRACT FROM AUTHOR]

The article discusses the use of Markov logic to join logical and statistical artificial intelligence (AI), and it mentions how Markov logic has the ability to unify first-order logic and probabilistic graphical models into a single representation. Learning and inference algorithms are addressed, along with open source implementations, Markov chain Monte Carlo, and deep learning methods. Automated reasoning, natural language processing, and machine learning are assessed.

Identifying Coffea arabica cultivars that are more efficient in the use of nitrogen is an important strategy and a necessity in the context of environmental and economic impacts attributed to excessive nitrogen fertilization. Although Coffea arabica breeding data have a multi-trait structure, they are often analyzed under a single trait structure. Thus, the objectives of this study were to use a Bayesian multitrait model, to estimate heritability in the broad sense, and to select arabica coffee cultivars with better genetic potential (desirable agronomic traits) in nitrogen-restricted cultivation. The experiment was carried out in a greenhouse with 20 arabica coffee cultivars grown in a nutrient solution with low-nitrogen content (1.5 mM). The experimental design used was in randomized blocks with three replications. Six agromorphological traits of the arabica coffee breeding program and five nutritional efficiency indices were used. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The agromorphological traits were considered highly heritable, with a credibility interval (95% probability):? H2 = 0.9538 - 5.89E-01. The Bayesian multitrait model presents an adequate strategy for the genetic improvement of arabica coffee grown in low-nitrogen concentrations. Coffee arabica cultivars Icatu Precoce 3282, Icatu Vermelho IAC 4045, Acaiá Cerrado MG 1474, Tupi IAC 1669-33, Catucaí 785/15, Caturra Vermelho and Obatã IAC 1669/20 demonstrated greater potential for cultivation in low-nitrogen concentration. [ABSTRACT FROM AUTHOR]

It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A study published in Gastroenterology Week explores the impact of norovirus transmission on acute kidney injury (AKI) hospitalizations in England. The research indicates that a significant portion of norovirus infections in individuals aged 65 and above are linked to community-acquired AKI hospitalizations, resulting in a substantial economic burden on the healthcare system. The study emphasizes the importance of considering AKI hospitalizations when evaluating future norovirus vaccines and highlights the need for further research in this area. [Extracted from the article]

A study conducted at the University of Southern California (USC) examined the impact of early versus delayed external fixation on the risk of compartment syndrome in patients with tibial plateau fractures. The research found that patients who underwent external fixation within 28.8 hours of admission had a higher risk of compartment syndrome compared to those who had the procedure after this time threshold. While early fixation was associated with a lower risk of compartment syndrome, it was linked to a higher risk of acute respiratory failure/unplanned intubation. The study was peer-reviewed and provides valuable insights into the management of tibial plateau fractures. [Extracted from the article]

The article discusses the use of pathogen genome sequencing in studying infectious disease epidemiology, focusing on the structured coalescent model for phylogeographic analysis. It highlights the challenges of computational costs in analyzing large numbers of genomes and proposes an efficient method for Bayesian inference on migration history, population sizes, and migration rates. The research aims to address current data scales and research questions in pathogen genomic epidemiology studies. [Extracted from the article]

The article explores the genetic basis of Essential Hypertension (EH) in African-derived individuals from quilombo populations in Brazil. Through genomic analysis, researchers identified new loci, variants, and candidate genes related to hypertension. The study aims to facilitate personalized treatments and improve public health for underrepresented populations. However, limitations include restricted SNP coverage and the need for further validation in other populations. [Extracted from the article]

A recent study conducted by researchers at the Mayo Clinic Alix School of Medicine compared the recurrence rates and cost-effectiveness of arthroscopic labral repair and nonoperative management for primary anterior shoulder dislocations in young patients. The study found that arthroscopic Bankart repair (ABR) was more cost-effective and reduced the risk of recurrent dislocations compared to nonoperative management. While ABR had higher upfront costs, it was deemed the optimal strategy in 98.7% of cases. The study emphasizes the importance of individualized treatment decisions and shared decision-making processes in surgical interventions for anterior shoulder instability. [Extracted from the article]

Word meanings change over time, and word senses evolve, emerge or die out in the process. For ancient languages, where the corpora are often small and sparse, modelling such changes accurately proves challenging, and quantifying uncertainty in sense-change estimates consequently becomes important. GASC (Genre-Aware Semantic Change) and DiSC (Diachronic Sense Change) are existing generative models that have been used to analyse sense change for target words from an ancient Greek text corpus, using unsupervised learning without the help of any pre-training. These models represent the senses of a given target word such as "kosmos" (meaning decoration, order or world) as distributions over context words, and sense prevalence as a distribution over senses. The models are fitted using Markov Chain Monte Carlo (MCMC) methods to measure temporal changes in these representations. This paper introduces EDiSC, an Embedded DiSC model, which combines word embeddings with DiSC to provide superior model performance. It is shown empirically that EDiSC offers improved predictive accuracy, ground-truth recovery and uncertainty quantification, as well as better sampling efficiency and scalability properties with MCMC methods. The challenges of fitting these models are also discussed. • Bayesian topic-based sense-change models are enhanced using word embeddings. • Benefits include accuracy, true-model recovery, sampling efficiency and scalability. • Posterior multimodality is solved using carefully constructed MCMC. • Combination of user input and WAIC guides model-selection choices. • Credible sets obtained in unsupervised and supervised settings are comparable. [ABSTRACT FROM AUTHOR]

A novel approach to Bayesian nonparametric spectral analysis of stationary multivariate time series is presented. Starting with a parametric vector-autoregressive model, the parametric likelihood is nonparametrically adjusted in the frequency domain to account for potential deviations from parametric assumptions. A proof of mutual contiguity of the nonparametrically corrected likelihood, the multivariate Whittle likelihood approximation and the exact likelihood for Gaussian time series is given. A multivariate extension of the nonparametric Bernstein-Dirichlet process prior for univariate spectral densities to the space of Hermitian positive definite spectral density matrices is specified directly on the correction matrices. An infinite series representation of this prior is then used to develop a Markov chain Monte Carlo algorithm to sample from the posterior distribution. The code is made publicly available for ease of use and reproducibility. With this novel approach, a generalisation of the multivariate Whittle-likelihood-based method of Meier et al. (2020) as well as an extension of the nonparametrically corrected likelihood for univariate stationary time series of Kirch et al. (2019) to the multivariate case is presented. It is demonstrated that the nonparametrically corrected likelihood combines the efficiencies of a parametric with the robustness of a nonparametric model. Its numerical accuracy is illustrated in a comprehensive simulation study. Its practical advantages are illustrated by a spectral analysis of two environmental time series data sets: a bivariate time series of the Southern Oscillation Index and fish recruitment and a multivariate time series of windspeed data at six locations in California. • A novel Bayesian approach for multivariate spectral analysis is proposed based on the multivariate Whittle likelihood. • The approach capitalises on the strengths of both pure nonparametric and parametric methodologies. • The approach has been successfully applied to meteorological data. • Some asymptotic properties of the approach are investigated. [ABSTRACT FROM AUTHOR]

Researchers at Hebei Medical University conducted a study on the correlation between ALK-positive non-small cell lung cancer targeted therapy and thrombosis. The study compared the incidence of thromboembolism among different treatment options using a systematic review and Bayesian network meta-analysis. Results showed that chemotherapy, brigatinib, and ceritinib had a lower risk of total thromboembolism compared to crizotinib, with similar results for venous thromboembolism. The study concluded that crizotinib significantly increased the risk of thromboembolism and venous thromboembolism compared to other treatments. [Extracted from the article]

Researchers at the University of Macau conducted a systematic review and meta-analysis on the efficacy and safety of direct-acting antiviral regimens for patients with hepatitis C virus genotype 2 (HCV GT2). The study included 31 articles with 2,968 participants and found that Sofosbuvir plus Velpatasvir and Glecaprevir plus Pibrentasvir were the most effective and safe treatments, achieving a sustained virological response 12 weeks after treatment (SVR12) of 100%. The research aimed to provide evidence on the effectiveness of DAAs in managing chronic HCV GT2 infection, with fatigue and headache being the most common adverse events reported. [Extracted from the article]

A recent study conducted at Massachusetts General Hospital explored the impact of APOE e4 and e2 variants on plasma neurofilament light chain levels and cognition in individuals with autosomal dominant Alzheimer's disease (ADAD). The research found that APOE e4 accelerated age-related increases in plasma neurofilament light chain, while APOE e2 attenuated the relationship between higher levels of neurofilament light chain and cognitive decline in ADAD. These findings suggest that neurofilament light chain may serve as a useful biomarker for assessing the efficacy of APOE-modifying drugs in the treatment and prevention of ADAD. [Extracted from the article]

A study conducted by researchers at the University of Science and Technology Beijing explores the analysis of recurrent event data with spatial random effects using a Bayesian approach in the field of health and medicine. The study emphasizes the importance of considering spatial correlations in health and environmental data for risk prediction. The proposed model shows significant improvement in capturing spatial effects compared to models that do not consider spatial correlations, as demonstrated through simulation studies. The research provides valuable insights into the recurrence of cardiovascular diseases by incorporating spatial information. [Extracted from the article]

This patent application discusses a method for improving cancer diagnosis and prediction through targeted sequencing. The inventors address the challenges of accurately identifying cancer-related signals in blood samples and propose a processing system that uses models to generate and filter candidate variants. The system also incorporates noise and predictive cancer models to enhance accuracy. The invention aims to improve targeted sequencing for detecting genetic mutations, particularly in the context of cancer research. [Extracted from the article]

A recent study conducted by researchers at the Fourth Hospital of Hebei Medical University in China examined the correlation between targeted therapy for anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer and the risk of thrombosis. The researchers conducted a systematic review and network meta-analysis of published randomized controlled trials comparing chemotherapy and ALK tyrosine kinase inhibitors (TKIs). The analysis of eight trials showed that chemotherapy, brigatinib, and ceritinib were associated with a lower risk of thromboembolism compared to crizotinib. However, there were no significant differences in the occurrence of arterial thromboembolism among the different treatment options. The study concluded that crizotinib significantly increased the risk of thromboembolism and venous thromboembolism compared to chemotherapy. [Extracted from the article]

A new report discusses research findings on mathematics, specifically the use of variational Bayes (VB) in patient phenotyping for clinical observational studies. The study compares the performance of VB and Markov Chain Monte Carlo (MCMC) approaches using two case studies: the Pima Indian diabetes data and a large real-world dataset of approximately one million diabetes patients. The research concludes that the patient phenotyping composite Bayes model is more easily usable for real-world studies when Monte Carlo is replaced with VB, making it a potentially useful tool for decision support in rare diseases. The full article can be accessed for free online. [Extracted from the article]

A new report discusses research findings on biosensors, specifically focusing on the use of glycofluoroforms for impedimetric biosensing. The researchers from the University of York in the United Kingdom have developed a novel biosensor platform that utilizes site-specific fluorination to introduce protein-glycan selectivity. This platform, coupled with an electrochemical detection method, selectively binds cancer-associated galectin-3 and mitigates against non-specific binding. Bayesian statistical analysis is used to improve the analytical capability and monitor experimental reproducibility. The research provides valuable insights into the potential applications of glycofluoroform biosensors in detecting cancer-associated proteins. [Extracted from the article]

Researchers from Federal University Sao Carlos in Brazil have developed a model to analyze the responses of patients to Transcranial Magnetic Stimulation (TMS) pulses on the motor cortex. The study found that the primary motor cortex consistently showed promising results for eliciting optimal responses, making it a key target for brain mapping using TMS. However, the excitability patterns in this region can vary significantly among patients. The research was funded by various Brazilian funding agencies and has been peer-reviewed. [Extracted from the article]

We consider the weak convergence of the Euler–Maruyama approximation for Schrödinger–Föllmer diffusions, which are solutions of Schrödinger bridge problems and can be used for sampling from given distributions. We show that the distribution of the terminal random variable of the time-discretized process weakly converges to the target one under mild regularity conditions. [ABSTRACT FROM AUTHOR]

A study conducted at Massachusetts General Hospital in Boston, Massachusetts, explored the use of tranexamic acid (TXA) in percutaneous nephrolithotomy (PCNL), a procedure for treating large renal stones. The study analyzed existing evidence through a Bayesian network meta-analysis and found that TXA, whether administered systemically or as part of irrigation fluid, effectively reduced bleeding, postoperative complications, and the risk of transfusion compared to a placebo. The researchers suggested that further studies comparing systemic TXA to TXA in irrigation fluid would be beneficial in determining the optimal route of delivery. This research has been peer-reviewed and published in the Canadian Urological Association Journal. [Extracted from the article]

A recent study conducted by researchers at the University of Leeds in the United Kingdom explores the use of mixture prior distributions and Bayesian models for robust radionuclide image processing in nuclear medicine. The study aims to improve the accuracy and reliability of medical diagnoses and subsequent treatments that involve radionuclide imaging techniques. The researchers propose a novel approach that incorporates different smoothing strategies and automatic model-based estimation of mixture component weightings to create a locally adaptive model. The results demonstrate greater noise reduction without compromising resolution, providing a more realistic model for robust decision-making in nuclear medicine. The study suggests that these methods could be applied to other spatial inverse problems beyond medical imaging applications. [Extracted from the article]

A preprint abstract from biorxiv.org discusses a compartmental modeling approach to analyze radioactive time activity curves for the first pass kinetics of [99mTc]sestamibi in the heart. By reparametrizing the kinetic equations using the Nernst membrane-potential equation, the researchers were able to estimate the sarcolemmal and mitochondrial membrane potentials in the heart non-invasively. The approach was tested on Langendorff perfused rat hearts and then translated to single photon emission planar scintigraphy kinetics in healthy rats, resulting in estimated membrane potentials of Em = -62 +/- 5 mV and DeltaPsi(m) = -151 +/- 5 mV. This research has not yet undergone peer review. [Extracted from the article]

This article provides a brief introduction to the widely applicable Bayesian information criterion (WBIC) for psychological researchers. Model-based inferences are commonly used in psychological research, making model comparison an important scientific procedure. However, calculating the marginal likelihood, a fundamental quantity for model comparison, can be challenging. The WBIC is a tool used to calculate the marginal likelihood in various situations, but its calculation procedure is not well known in the psychology field. The article introduces the WBIC using the Just Another Gibbs Sampler (JAGS) language and provides two real data examples to demonstrate how to define the WBIC's weighted likelihood. The JAGS codes used for analysis are also made available. It is important to note that this preprint has not been peer-reviewed. [Extracted from the article]

A recent study conducted at the Chinese University of Hong Kong explores the individualized disease progression of Alzheimer's disease (AD) using a joint mixed membership model. The researchers aim to address the heterogeneity in disease progression among AD patients by investigating diverse subtypes of neurodegeneration. The study utilizes a Bayesian method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference. The proposed approach shows better performance in AD diagnosis compared to existing joint models, as demonstrated through extensive simulation studies and an application to the Alzheimer's Disease Neuroimaging Initiative study. [Extracted from the article]

A recent report from the University of North Carolina Chapel Hill discusses new research on Information Technology in the context of routine cancer care. The researchers propose a Bayesian longitudinal generalized linear mixed model (BLGLMM) to analyze discrete, multivariate, and potentially high-dimensional repeated measures of patient-reported symptoms. The model integrates low-rank matrix decomposition, a sparse factor model, and random effects to account for the dependence among repeated responses. The research includes simulations and an illustrative example examining factors associated with symptom underascertainment in prostate cancer patients. The study has been peer-reviewed and is published in the Annals of Applied Statistics. [Extracted from the article]

A study conducted by researchers from Khon Kaen University in Vietnam aimed to assess the correlations between environmental, climatic, and socio-economic factors in Central Vietnam and malaria cases. The study found that the risk of Plasmodium falciparum malaria increased with an increase in normalized difference vegetation index (NDVI) and maximum temperature, while the risk of Plasmodium vivax malaria increased with an increase in NDVI and land surface temperature. Spatial analysis showed higher malaria risk in the Central Highlands and southeast parts of Central Vietnam. The researchers concluded that identifying these risk factors and spatial clusters is crucial for designing strategies to eliminate malaria in Vietnam. [Extracted from the article]

A study conducted by Zagazig University in Egypt has introduced a new extended Pham model for modeling cancer data. This model is capable of fitting positive, negative, and approximately symmetric data, and it features four alternative hazard rate shapes. The study compared the new model to traditional Pham and other well-known lifetime models and found that the extended Pham distribution was the best choice for modeling cancer data. The research utilized both classical maximum likelihood estimation and Bayesian estimation methods to determine the model's parameters, with the Bayesian estimates proving to be more effective. [Extracted from the article]

A study conducted by the Harbin Institute of Technology in China examined the effects of the Omicron variant of the coronavirus during the fifth wave of the pandemic in Hong Kong. The researchers used a transmission model to assess the dynamics of the virus in two age groups and explored the impact of booster vaccination campaigns. The study found that younger individuals had a higher transmission advantage, while the mortality rate in older adults was significantly higher. Administering booster doses of vaccines provided substantial protection, particularly against death. The findings of this study can inform future projections and interventions. [Extracted from the article]

Based on the distinctive spatial diffusion characteristics observed in syphilis transmission patterns, this paper introduces a novel reaction–diffusion model for syphilis disease dynamics, incorporating general incidence functions within a heterogeneous environment. We derive the basic reproduction number essential for threshold dynamics and investigate the uniform persistence of the model. We validate the model and estimate its parameters by employing the multi-objective Markov Chain Monte Carlo (MCMC) method, using real syphilis data from the years 2004 to 2018 in China. Furthermore, we explore the impact of spatial heterogeneity and intervention measures on syphilis transmission. Our findings reveal several key insights: (1) In addition to the original high-incidence areas of syphilis, Xinjiang, Guizhou, Hunan and Northeast China have also emerged as high-incidence regions for syphilis in China. (2) The latent syphilis cases represent the highest proportion of newly reported cases, highlighting the critical importance of considering their role in transmission dynamics to avoid underestimation of syphilis outbreaks. (3) Neglecting spatial heterogeneity results in an underestimation of disease prevalence and the number of syphilis-infected individuals, undermining effective disease prevention and control strategies. (4) The initial conditions have minimal impact on the long-term spatial distribution of syphilis-infected individuals in scenarios of varying diffusion rates. This study underscores the significance of spatial dynamics and intervention measures in assessing and managing syphilis transmission, which offers insights for public health policymakers. • Formulating a reaction-diffusion model to understand spatiotemporal syphilis dynamics • Validating the model using real syphilis data • Uncovering new high-incidence regions beyond the original hotspots • Emphasizing the significance of latent stage in syphilis transmission • Testing intervention measures for effective syphilis containment strategies [ABSTRACT FROM AUTHOR]

The effective control of infectious diseases relies on accurate assessment of the impact of interventions, which is often hindered by the complex dynamics of the spread of disease. A Beta-Dirichlet switching state-space transmission model is proposed to track underlying dynamics of disease and evaluate the effectiveness of interventions simultaneously. As time evolves, the switching mechanism introduced in the susceptible-exposed-infected-recovered (SEIR) model is able to capture the timing and magnitude of changes in the transmission rate due to the effectiveness of control measures. The implementation of this model is based on a particle Markov Chain Monte Carlo algorithm, which can estimate the time evolution of SEIR states, switching states, and high-dimensional parameters efficiently. The efficacy of the proposed model and estimation procedure are demonstrated through simulation studies. With a real-world application to British Columbia's COVID-19 outbreak, the proposed switching state-space transmission model quantifies the reduction of transmission rate following interventions. The proposed model provides a promising tool to inform public health policies aimed at studying the underlying dynamics and evaluating the effectiveness of interventions during the spread of the disease. [ABSTRACT FROM AUTHOR]

The article discusses the development of mathematical techniques to detect gerrymandering in electoral district maps. Topics include the mathematicians' organization Metric Geometry and Gerrymandering Group, the use of a Markov chain Monte Carlo (MCMC) technique, and the use of geometric calculations in court cases about gerrymandering.

Traditional correlation filter trackers only rely on a search window to localize the object in each frame. However, such approaches are prone to fail in case of e.g.uncertain motion or distractor, where a simple motion model alone is insufficient for robust tracking. To address this problem, we propose a novel tracking architecture that can utilize target-objectness proposals (TOP) effectively. Firstly, we reformulate the Markov Chains Monte Carlo method as a region proposal mechanism from a new perspective. It integrates learning features into the feature space sampling strategy for global object candidates boxes generator. Secondly, to improve the quality of candidates boxes, we constructe objectness labels to guide the sampling process. With the help of objectness information, this interaction of spatial constraints increases the effectiveness of region proposals. In the end, a unified tracking framework is designed to enable sampling and regression strategies to exploit and complement each other to cope with uncertain motion tracking. The proposed TOP tracker performs favorably against advanced trackers, especially in uncertain motion scenarios, on four benchmark datasets including OTB-2013, OTB-2015, Temple Color-128 and UAV-123. [ABSTRACT FROM AUTHOR]

For linear state space model, the covariance matrix setting errors of process and measurement noise deteriorate the estimation performance of Rauch–Tung–Striebel (RTS) smoother. To address this problem, the Markov Chain Monte Carlo is utilized to sample the state vector and noise covariance matrices simultaneously in this study. The Gibbs sampler is adopted and the corresponding adaptive RTS smoother is designed. Simulation results confirm the performance of proposed smoother. [ABSTRACT FROM AUTHOR]

This paper faces a central theme in applied statistics and information science, which is the assessment of the stochastic structure of rank-size laws in text analysis. We consider the words in a corpus by ranking them on the basis of their frequencies in descending order. The starting point is that the ranked data generated in linguistic contexts can be viewed as the realisations of a discrete states Markov chain, whose stationary distribution behaves according to a discretisation of the best fitted rank-size law. The employed methodological toolkit is Markov Chain Monte Carlo, specifically referring to the Metropolis–Hastings algorithm. The theoretical framework is applied to the rank-size analysis of the hapax legomena occurring in the speeches of the US Presidents. We offer a large number of statistical tests leading to the consistency of our methodological proposal. To pursue our scopes, we also offer arguments supporting that hapaxes are rare ("extreme") events resulting from memory-less-like processes. Moreover, we show that the considered sample has the stochastic structure of a Markov chain of order one. Importantly, we discuss the versatility of the method, which is considered suitable for deducing similar outcomes for other applied science contexts. [ABSTRACT FROM AUTHOR]

A recent study conducted by researchers at the National Institute of Infectious Diseases in Tokyo, Japan, has analyzed the molecular evolution of the fusion genes in the human parainfluenza virus type 4 (HPIV4). The study found that HPIV4 can be classified into two distinct subtypes, 4a and 4b, which diverged around 200 years ago. The researchers also identified conserved genes within each subtype and predicted conformational B-cell epitopes in the fusion protein. This research provides valuable insights into the evolutionary history and genetic characteristics of HPIV4. [Extracted from the article]

The article discusses a new clinical trial evaluating a mobile app designed for guided self-help to improve outcomes in individuals with eating disorders. Topics include the effectiveness of the app in community and outpatient settings, its impact on psychological distress and treatment adherence, and the role of interpersonal skills in treatment success.

A preprint abstract discusses the challenges of inferring the underlying disease state in Alzheimer's Disease (AD) progression. The paper proposes a biophysically motivated Bayesian framework called B-BIND, which models the disease state and continuously infers it from observed quantifications of multiple AD pathological proteins. The framework assigns a latent score, called pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. The paper provides estimation algorithms and applies the methodology to data from the Seattle Alzheimer's Disease Brain Cell Atlas. This research lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases. [Extracted from the article]

Researchers from the University of Kentucky have developed a new method for tracking and predicting the performance degradation of lithium-ion batteries. The study addresses the challenges posed by the complex degradation behaviors of these batteries, which include both gradual degradation and abrupt changes in performance. The researchers used an advanced stochastic model and a Bayesian inference neural network to accurately estimate the degradation rates and scaling parameters of the batteries. The proposed method outperformed other commonly used Bayesian inference methods in terms of accuracy and computational efficiency. The research was supported by the National Science Foundation and has been peer-reviewed. [Extracted from the article]

A recent report from Massachusetts General Hospital discusses the use of group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data in health research. The study focuses on characterizing time-to-event outcomes, such as disease onset or mortality, in terms of a potentially high-dimensional set of risk factors. The researchers propose a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. They develop an R package with a Markov chain Monte Carlo sampler for estimation and apply the method to identify groups of predictive genetic and clinical risk factors for Alzheimer's disease. [Extracted from the article]

The human musculoskeletal system is characterized by redundancy in the sense that the number of muscles exceeds the number of degrees of freedom of the musculoskeletal system. In practice, this means that a given motor task can be performed by activating the muscles in infinitely many different ways. This redundancy is important for the functionality of the system under changing external or internal conditions, including different diseased states. A central problem in biomechanics is how, and based on which principles, the complex of central nervous system and musculoskeletal system selects the normal activation patterns, and how the patterns change under various abnormal conditions including neurodegenerative diseases and aging. This work lays the mathematical foundation for a formalism to address the question, based on Bayesian probabilistic modeling of the musculoskeletal system. Lagrangian dynamics is used to translate observations of the movement of a subject performing a task into a time series of equilibria which constitute the likelihood model. Different prior models corresponding to biologically motivated assumptions about the muscle dynamics and control are introduced. The posterior distributions of muscle activations are derived and explored by using Markov chain Monte Carlo (MCMC) sampling techniques. The different priors can be analyzed by comparing the model predictions with actual observations. • We develop Bayesian methodology to study muscle recruitment problem. • Feasible solutions are explored using Markov chain Monte Carlo. • Longitudinal priors control smoothness of the activation trajectories. • Potential applications include surgery planning and rehabilitation. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at the University of Minnesota has developed a new method for detecting prostate cancer using multiparametric magnetic resonance imaging (mpMRI). The current protocols for estimating the number, size, and location of cancerous lesions in the prostate are highly dependent on the experience of the reader. The researchers propose a Bayesian functional spatial partitioning method that can estimate the boundaries surrounding multiple cancerous lesions, as well as quantify uncertainty in the number of lesions and their spatial parameters. The study demonstrates the effectiveness of this method in detecting prostate cancer lesions using MRI. [Extracted from the article]

A recent study conducted in Brazil aimed to investigate the genetic basis of essential hypertension (EH) in African-derived individuals from partially isolated populations known as quilombo remnants. The study utilized a multi-level computational approach, including genome-wide linkage analysis and family-based association studies, to identify genetic regions, variants, and candidate genes associated with EH. The results revealed 22 regions of interest containing markers co-segregating with the phenotype, as well as 60 EH-related candidate genes and 118 suggestive or significant variants. These findings provide valuable insights into the genetic etiology of EH in quilombo remnant populations. [Extracted from the article]

The patent application by Beauchamp and Haas, assigned to Myriad Women's Health Inc., outlines a method for enhancing the performance of genetic screening tests for fetal chromosomal abnormalities. The method involves analyzing maternal samples containing fetal cell-free DNA and determining statistical significance values for the test chromosome in reference maternal samples. Abnormality classifications are identified for the reference samples, and a relationship between statistical significance values and abnormality classifications is established. Simulated maternal samples are then generated based on this relationship, and abnormality classifications and statistical significance values are determined for these samples. Specificity and sensitivity values are calculated, and a receiver operating characteristic (ROC) curve is generated to select an optimized threshold value for the test. The patent application also describes a computer-based method that involves sequencing maternal samples, using machine learning techniques to determine fetal chromosomal abnormality classifications, and generating ROC curves to compare different classification methods. The goal of this invention is to improve the accuracy and reliability of genetic screening tests for conditions like aneuploidy and microdeletions. [Extracted from the article]

This article provides a brief introduction to the widely applicable Bayesian information criterion (WBIC) for psychological researchers. Model-based inferences are commonly used in psychological research, making model comparison an important scientific procedure. However, calculating the marginal likelihood, a fundamental quantity for model comparison, can be challenging. The WBIC is a tool used to calculate the marginal likelihood in various situations, but its calculation procedure is not well known in the psychology field. The article introduces the WBIC using the Just Another Gibbs Sampler (JAGS) language and provides two real data examples to demonstrate how to define the WBIC's weighted likelihood. The JAGS codes used for analysis are also made available. It is important to note that this preprint has not been peer-reviewed. [Extracted from the article]

A recent study conducted by researchers at the Mayo Clinic Alix School of Medicine in Rochester, Minnesota, compared the cost-effectiveness of different treatment strategies for young, high-level baseball pitchers with partial tears of the medial ulnar collateral ligament (MUCL). The study found that early ulnar collateral ligament reconstruction (UCLR) surgery was more cost-effective and resulted in longer playing careers compared to nonoperative management. Despite the higher upfront costs of surgery, it was determined to be the preferred treatment option for most high-level baseball pitchers with partial MUCL tears. The study provides valuable insights for healthcare professionals and athletes in making treatment decisions. [Extracted from the article]

A new study conducted by researchers at the University of Jeddah in Saudi Arabia explores the impact of environmental indicators on life expectancy (LE) and healthy life expectancy (HLE). The study incorporates HLE into its analysis, which is a novel approach in environmental health research. The findings reveal that economic growth, carbon emissions, and particulate matter 2.5 have harmful effects on both LE and HLE. However, post-Vision 2030 estimations suggest that the environmental impacts in Saudi Arabia have been successfully addressed. The study emphasizes the importance of considering CO2, particulate matter 2.5, and ecological footprints as key environmental indicators in shaping environmental and health policies. [Extracted from the article]

A recent study from Purdue University has developed a new algorithm called SNP-Slice that can analyze mixed infection samples and accurately determine the genetic diversity, co-infection patterns, and genomic relatedness of pathogens. This algorithm is able to reconstruct SNP-haplotypes and individual heterozygosities without the need for reference panels, outperforming other methods in estimating the multiplicity of infections and allele frequencies. The researchers demonstrated the effectiveness of SNP-Slice on malaria and HIV datasets and have made the algorithm and analysis scripts available for use. This research provides a valuable tool for molecular epidemiology and understanding complex infection patterns. [Extracted from the article]