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Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for individuals with still‐developing executive functions. In this paper, we combined multiple analytic approaches to investigate the potential role of executive functions in elementary school‐aged children's science learning. We performed an experiment that investigates children's science learning during a water displacement task where a “prediction boost” had previously been observed—children either made an explicit prediction or evaluated an event post hoc (i.e., postdiction). We then considered the relation of executive function measures and learning, which were collected following the main experiment. Via mixed effects regression models, we found that stronger executive function skills (i.e., stronger inhibition and switching scores) were associated with higher accuracy in Postdiction but not in the Prediction Condition. Using a theory‐based Bayesian model, we simulated children's individual performance on the learning task (capturing “belief flexibility”), and compared this “flexibility” to the other measures to understand the relationship between belief revision, executive function, and prediction. Children in the Prediction Condition showed near‐ceiling “belief flexibility” scores, which were significantly higher than among children in the Postdiction Condition. We also found a significant correlation between children's executive function measures to our “belief flexibility” parameter, but only for children in the Postdiction Condition. These results indicate that when children provided responses post hoc, they may have required stronger executive function capacities to navigate the learning task. Additionally, these results suggest that the “prediction boost” in children's science learning could be explained by increased metacognitive flexibility in the belief revision process. [ABSTRACT FROM AUTHOR]

The discipline of ecology seeks to understand how ecosystems, communities, and populations are regulated. A ubiquitous mechanism of population regulation of consumers is that capturing energy and nutrients in sufficient quantities for survival and reproduction becomes more difficult as population density increases. Extensive evidence has revealed that populations of large herbivores are often regulated by density dependence, defined as the reduction in the per‐capita population growth rate that occurs as populations grow large. Diminished body mass of individuals has been repeatedly observed in high‐density populations, implicating compromised nutrition as the primary cause of density dependence. However, there is no general explanation for why these nutritional deficiencies occur. Recent work demonstrated that reduced food intake rates resulting from the functional response of herbivores to depleted plant biomass does not provide a sensible explanation for density dependence because rates of food intake of herbivores are often insensitive to changes in plant biomass. A new model of feedbacks from plant biomass to herbivores shows how reduced nutrition of herbivores can result from increased dilution of nutrients in the plant tissue they consume as populations grow, even when their rate of consumption of plants remains constant. The model contains parameters that can be scaled to body mass, allowing unusually general predictions. The model shows that convex, concave, and linear relationships between the per‐capita growth rate and population density can arise from the effects of depletion of plant biomass by herbivore foraging. The model is the first to explicitly include spatial variance in the nutritional quality of plants as a general driver of herbivore population dynamics. I show how regulation of herbivore abundance by plant nutrients can occur, even when a large fraction of the consumable plant biomass remains uneaten, providing a simple, mechanistic explanation for bottom‐up control of population dynamics of primary consumers in a "green world." [ABSTRACT FROM AUTHOR]

The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid's bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices. [ABSTRACT FROM AUTHOR]

Upon researching predictive models related to West Nile virus disease, it is discovered that there are numerous parameters and extensive information in most models, thus contributing to unnecessary complexity. Another challenge frequently encountered is the lead time, which refers to the period for which predictions are made and often is too short. This paper addresses these issues by introducing a parsimonious method based on ICC curves, offering a logistic distribution model derived from the vector-borne SEIR model. Unlike existing models relying on diverse environmental data, our approach exclusively utilizes historical and present infected human cases (number of new cases). With a year-long lead time, the predictions extend throughout the 12 months, gaining precision as new data emerge. Theoretical conditions are derived to minimize Bayesian loss, enhancing predictive precision. We construct a Bayesian forecasting probability density function using carefully selected prior distributions. Applying these functions, we predict month-specific infections nationwide, rigorously evaluating accuracy with probabilistic metrics. Additionally, HPD credible intervals at 90%, 95%, and 99% levels is performed. Precision assessment is conducted for HPD intervals, measuring the proportion of intervals that does not include actual reported cases for 2020–2022.

Multi-competitor races often feature complicated within-race strategies that are difficult to capture when training data on race outcome level data. Models which do not account for race-level strategy may suffer from confounded inferences and predictions. We develop a generative model for multi-competitor races which explicitly models race-level effects like drafting and separates strategy from competitor ability. The model allows one to simulate full races from any real or created starting position opening new avenues for attributing value to within-race actions and performing counter-factual analyses. This methodology is sufficiently general to apply to any track based multi-competitor races where both tracking data is available and competitor movement is well described by simultaneous forward and lateral movements. We apply this methodology to one-mile horse races using frame-level tracking data provided by the New York Racing Association (NYRA) and the New York Thoroughbred Horsemen's Association (NYTHA) for the Big Data Derby 2022 Kaggle Competition. We demonstrate how this model can yield new inferences, such as the estimation of horse-specific speed profiles and examples of posterior predictive counterfactual simulations to answer questions of interest such as starting lane impacts on race outcomes. [ABSTRACT FROM AUTHOR]

The article is devoted to Bayesian modeling of simple comparative binomial experiments with binary data sets (of “hit” and “miss” format) in sociology and other social sciences. The main methodological foundations of application of Bayesian approach in statistics are briefly reviewed: the use of priors in analysis, features of Bayesian statistical inference, differences in frequency and Bayesian confidence intervals, features of hypothesis testing in Bayesian statistics. A Bayesian model of a comparative binomial experiment has been constructed. It supports comparison of independent and dependent samples of binomial variables, and also allows for differences in sizes of the compared samples. The capabilities of the model, as well as the principles of the Bayesian hypothesis testing, were demonstrated on test data using PyMC and ArviZ, contemporary free packages of the Bayesian modeling and analysis. The use of these tools allows implementing direct tensor operations with the obtained posterior distributions and provides the researcher with an effective way to calculate the effect size when comparing two binomial samples without having to resort to complicated forms of calculating this parameter. The possibilities and limitations of the Bayesian approach are shown in the context of comparative analysis of the results of binomial experiments in social sciences by estimating the probability of hypotheses via finding and comparing the area of intervals of posterior distributions

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous‐time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie‐chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie‐chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions. [ABSTRACT FROM AUTHOR]

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AbstractWhile Bayesian methodology is increasingly favored in behavioral research for its clear probabilistic inference and model structure, its widespread acceptance as a standard meta-analysis approach remains limited. Although some conventional Bayesian hierarchical models are frequently used for analysis, their performance has not been thoroughly examined. This study evaluates two commonly used Bayesian models for meta-analysis of standardized mean difference and identifies significant issues with these models. In response, we introduce a new Bayesian model equipped with novel features that address existing model concerns and a broader limitation of the current Bayesian meta-analysis. Furthermore, we introduce a simple computational approach to construct simultaneous credible intervals for the summary effect and between-study heterogeneity, based on their joint posterior samples. This fully captures the joint uncertainty in these parameters, a task that is challenging or impractical with frequentist models. Through simulation studies rooted in a joint Bayesian/frequentist paradigm, we compare our model’s performance against existing ones under conditions that mirror realistic research scenarios. The results reveal that our new model outperforms others and shows enhanced statistical properties. We also demonstrate the practicality of our models using real-world examples, highlighting how our approach strengthens the robustness of inferences regarding the summary effect. [ABSTRACT FROM AUTHOR]

Background: In dairy cattle, mastitis causes high financial losses and impairs animal well-being. Genetic selection is used to breed cows with reduced mastitis susceptibility. Techniques such as milk cell flow cytometry may improve early mastitis diagnosis. In a highly standardized in vivo infection model, 36 half-sib cows were selected for divergent paternal Bos taurus chromosome 18 haplotypes (Q vs. q) and challenged with Escherichia coli for 24 h or Staphylococcus aureus for 96 h, after which the samples were analyzed at 12 h intervals. Vaginal temperature (VT) was recorded every three minutes. The objective of this study was to compare the differential milk cell count (DMCC), milk parameters (fat %, protein %, lactose %, pH) and VT between favorable (Q) and unfavorable (q) haplotype cows using Bayesian models to evaluate their potential as improved early indicators of differential susceptibility to mastitis. Results: After S. aureus challenge, compared to the Q half-sibship cows, the milk of the q cows exhibited higher PMN levels according to the DMCC (24 h, p < 0.001), a higher SCC (24 h, p < 0.01 and 36 h, p < 0.05), large cells (24 h, p < 0.05) and more dead (36 h, p < 0.001) and live cells (24 h, p < 0.01). The protein % was greater in Q milk than in q milk at 0 h (p = 0.025). In the S. aureus group, Q cows had a greater protein % (60 h, p = 0.048) and fat % (84 h, p = 0.022) than q cows. Initially, the greater VT of S. aureus-challenged q cows (0 and 12–24 h, p < 0.05) reversed to a lower VT in q cows than in Q cows (48–60 h, p < 0.05). Additionally, the following findings emphasized the validity of the model: in the S. aureus group all DMCC subpopulations (24 h-96 h, p < 0.001) and in the E. coli group nearly all DMCC subpopulations (12 h-24 h, p < 0.001) were higher in challenged quarters than in unchallenged quarters. The lactose % was lower in the milk samples of E. coli-challenged quarters than in those of S. aureus-challenged quarters (24 h, p < 0.001). Between 12 and 18 h, the VT was greater in cows challenged with E. coli than in those challenged with S. aureus (3-h interval approach, p < 0.001). Conclusion: This in vivo infection model confirmed specific differences between Q and q cows with respect to the DMCC, milk component analysis results and VT results after S. aureus inoculation but not after E. coli challenge. However, compared with conventional milk cell analysis monitoring, e.g., the global SCC, the DMCC analysis did not provide refined phenotyping of the pathogen response. [ABSTRACT FROM AUTHOR]

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases. [ABSTRACT FROM AUTHOR]

Truth discovery is the fundamental technique for resolving the conflicts between the information provided by different data sources by detecting the true values. Traditional methods assume that each data item has only one true value and therefore cannot deal with the circumstances where one data item has multiple true values (i.e., multi-value truth). In this work, we target at this new challenge and propose a generalized Bayesian framework that comprehensively incorporates the features of multi-value truth for the accurate and efficient multi-source data integration. In particular, we identify three key features of multi-value truth, called source-value mapping, differentiated mutual exclusion, and complicated source dependency, to better solve the problem. In particular, sources and values are aggregated based on their mapping to reduce the problem scale, the exclusive relations between values are quantified to reflect the effect of multi-truth, and a fine-grained copy detection method is proposed to deal with complicated source dependency. The data preference of model is also incorporated for fast parameter configuration. Experimental results on real-world and large-scale synthetic datasets demonstrate the effectiveness of our approach, with less execution time and an average 5% higher F1 compared to the latest method. [ABSTRACT FROM AUTHOR]

AbstractLinear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model. [ABSTRACT FROM AUTHOR]

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence among adults, we use serological surveys (also called the serosurveys) conducted within each country. When the serosurveys are incorporated to estimate world seroprevalence, there are two issues. First, there are countries in which a serological survey has not been conducted. Second, the sample collection dates differ from country to country. We attempt to tackle these problems using the vaccination data, confirmed cases data, and national statistics. We construct Bayesian models to estimate the numbers of people who have antibodies produced by infection or vaccination separately. For the number of people with antibodies due to infection, we develop a hierarchical model for combining the information included in both confirmed cases data and national statistics. At the same time, we propose regression models to estimate missing values in the vaccination data. As of 31st of July 2021, using the proposed methods, we obtain the $ 95\% $ 95% credible interval of the world seroprevalence as $ {[35.5\%,56.8\%]} $ [35.5%,56.8%]. [ABSTRACT FROM AUTHOR]

Conventional farming poses threats to sustainable agriculture in growing food demands and increasing flooding risks. This research introduces a Bayesian Belief Network (BBN) to address these concerns. The model explores tillage adaptation for flood management in soils with varying organic carbon (OC) contents for winter wheat production. Three real soils, emphasizing texture and soil water properties, were sourced from the NETMAP soilscape of the Pang catchment area in Berkshire, United Kingdom. Modified with OC content at four levels (1, 3, 5, 7%), they were modeled alongside relevant variables in a BBN. The Decision Support System for Agrotechnology Transfer (DSSAT) simulated datasets across 48 cropping seasons to parameterize the BBN. The study compared tillage effects on wheat yield, surface runoff, and GHG-CO2 emissions, categorizing model parameters (from lower to higher bands) based on statistical data distribution. Results revealed that NT outperformed CT in the highest parametric category, comparing probabilistic estimates with reduced GHG-CO2 emissions from “7.34 to 7.31%” and cumulative runoff from “8.52 to 8.50%,” while yield increased from “7.46 to 7.56%.” Conversely, CT exhibited increased emissions from “7.34 to 7.36%” and cumulative runoff from “8.52 to 8.55%,” along with reduced yield from “7.46 to 7.35%.” The BBN model effectively captured uncertainties, offering posterior probability distributions reflecting conditional relationships across variables and offered decision choice for NT favoring soil carbon stocks in winter wheat (highest among soils “NT.OC-7%PDPG8,” e.g., 286,634 kg/ha) over CT (lowest in “CT.OC-3.9%PDPG8,” e.g., 5,894 kg/ha). On average, NT released minimum GHG- CO2 emissions to “3,985 kgCO2eqv/ha,” while CT emitted “7,415 kgCO2eqv/ha.” Conversely, NT emitted “8,747 kgCO2eqv/ha” for maximum emissions, while CT emitted “15,356 kgCO2eqv/ha.” NT resulted in lower surface runoff against CT in all soils and limits runoff generations naturally for flood alleviation with the potential for customized improvement. The study recommends the model for extensive assessments of various spatiotemporal conditions. The research findings align with sustainable development goals, e.g., SDG12 and SDG13 for responsible production and climate actions, respectively, as defined by the Agriculture and Food Organization of the United Nations.

Abstract Background Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control. Methods This study utilized a Bayesian space–time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions. Results Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882. Conclusions These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus.

Indecision behavior usually appears in daily life, especially on purchase occasions. This trait in the decision process will influence customers' decision satisfaction. This research proposes a Bayesian model to portray the phenomena of indecision and decision satisfaction. The decision satisfaction is a random variable with the prior density in which the parameter given in the density function is demonstrated as indecision. The empirical data of Google Analytics is used to measure indecision behavior and model calibration. Finally, the conclusion is shown to make an application in actual consumer behavior research. This research can help managers control the tendency of indecision to achieve higher decision satisfaction. In advance, the impact factors of indecision can be investigated to design more incentive and attractive platforms to accelerate the payment process

Manifestations of sickle cell disease (SCD) begin early in childhood and cause morbidity and decreased life expectancy. Hematopoietic stem cell transplantation (HSCT) is curative but associated with risk of mortality attributable to the transplant. This risk should be counterbalanced with SCD morbidity and mortality. A severity score using a Bayesian network model was previously validated to predict the risk of death in adult individuals with SCD. The objective of this study is to calculate the severity scores of participants in a multicenter cohort of Brazilians with SCD, using a previously published Bayesian network-derived score, associated with risk of death and then compare the severity scores between participants with and without an indication for HSCT as defined by the Brazilian Ministry of Health (MoH) criteria. This is an observational, retrospective study. We analyzed 2063 individuals with sickle cell anemia from the Recipient Epidemiology and Donor Evaluation Study-III Brazil SCD cohort and applied a Bayesian network-derived score to compare candidates and non-candidates for HSCT according to the Brazilian MoH transplant criteria. Classical statistical methods were used to analyze data and make comparisons. We compared severity scores between cohort members with (n = 431) and without (n = 1632) HSCT indications according to Brazilian MoH. Scores were not different in adult participants with ≥1 HSCT indication when compared to those with no indication (mean 0.342 versus 0.292; median 0.194 versus 0.183, P = .354) and receiver operating characteristic curves did not demonstrate an obvious threshold to differentiate participants with or without HSCT indications. Severity score may predict risk of death but does not differentiate HSCT candidates. Current indications should be evaluated to ensure that patients with more severe disease who might benefit from HSCT are appropriately identified.

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses. [ABSTRACT FROM AUTHOR]

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R , which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package's basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs. [ABSTRACT FROM AUTHOR]

Multi-source longitudinal data have become increasingly common. This type of data refers to longitudinal datasets collected from multiple sources describing the same set of individuals. Representing distinct features of the individuals, each data source may consist of multiple longitudinal markers of distinct types and measurement frequencies. Motivated by the CHILD cohort study, we develop a model for joint clustering multi-source longitudinal data. The proposed model allows each data source to follow source-specific clustering, and they are aggregated to yield a global clustering. The proposed model is demonstrated through real-data analysis and simulation study. [ABSTRACT FROM AUTHOR]

Background: Large-scale outbreaks of scrub typhus combined with its emergence in new areas as a vector-borne rickettsiosis highlight the ongoing neglect of this disease. This study aims to explore the long-term changes and regional leading factors of scrub typhus in China, with the goal of providing valuable insights for disease prevention and control. Methods: This study utilized a Bayesian space–time hierarchical model (BSTHM) to examine the spatiotemporal heterogeneity of scrub typhus and analyze the relationship between environmental factors and scrub typhus in southern and northern China from 2006 to 2018. Additionally, a GeoDetector model was employed to assess the predominant influences of geographical and socioeconomic factors in both regions. Results: Scrub typhus exhibits a seasonal pattern, typically occurring during the summer and autumn months (June to November), with a peak in October. Geographically, the high-risk regions, or hot spots, are concentrated in the south, while the low-risk regions, or cold spots, are located in the north. Moreover, the distribution of scrub typhus is influenced by environment and socio-economic factors. In the north and south, the dominant factors are the monthly normalized vegetation index (NDVI) and temperature. An increase in NDVI per interquartile range (IQR) leads to a 7.580% decrease in scrub typhus risk in northern China, and a 19.180% increase in the southern. Similarly, of 1 IQR increase in temperature reduces the risk of scrub typhus by 10.720% in the north but increases it by 15.800% in the south. In terms of geographical and socio-economic factors, illiteracy rate and altitude are the key determinants in the respective areas, with q-values of 0.844 and 0.882. Conclusions: These results indicated that appropriate climate, environment, and social conditions would increase the risk of scrub typhus. This study provided helpful suggestions and a basis for reasonably allocating resources and controlling the occurrence of scrub typhus. [ABSTRACT FROM AUTHOR]

Introduction: Parkinson disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson disease, using a Bayesian approach. Methods: The data of this study were downloaded from the open neuro site. These data include resting-state functional magnetic resonance imaging (rs-fMRI) of 11 healthy individuals and 11 Parkinson patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. Results: Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (P>0.05). Conclusion: The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain. [ABSTRACT FROM AUTHOR]

The public procurement sector has a high impact on the environment including pollution from manufacturing. Competition is the primary way that the United States government ensures that the most efficient facilities receive contracts, but whether this competition leads to more, or less, pollution is unknown. We explore the impact of competition for contracts on contractor pollution in sectors that handle chromium, a toxic, yet necessary, component in many goods. We find that competition alone has negligible impact on overall chromium releases among contractors; however, the way chromium is used in the manufacturing process does. Contractors that use chromium as a component report higher releases of chromium waste than contractors that only use chromium in non-incorporative ways. However, when contractors face higher competition for contracts, pollution levels significantly decrease. This suggests that both competition and the use of the metal are important. Findings support a complex association between competition and pollution generation. [ABSTRACT FROM AUTHOR]

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose–response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank‐based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4‐parameter log‐logistic (4PLL) model was used to estimate dose–response curves of dose–candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose–response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia. [ABSTRACT FROM AUTHOR]

Applying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models, all of them are complex and costly. Therefore, reusing the open-sourced pre-trained model is a practical way to bypass this hurdle for developers. Given a specific task, developers can collect massive pre-trained deep neural networks from public sources for reusing. However, testing the performance (e.g., accuracy and robustness) of multiple deep neural networks (DNNs) and recommending which model should be used is challenging regarding the scarcity of labeled data and the demand for domain expertise. In this article, we propose a labeling-free (LaF) model selection approach to overcome the limitations of labeling efforts for automated model reusing. The main idea is to statistically learn a Bayesian model to infer the models' specialty only based on predicted labels. We evaluate LaF using nine benchmark datasets, including image, text, and source code, and 165 DNNs, considering both the accuracy and robustness of models. The experimental results demonstrate that LaF outperforms the baseline methods by up to 0.74 and 0.53 on Spearman's correlation and Kendall's τ, respectively. [ABSTRACT FROM AUTHOR]

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Abstract Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous‐time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie‐chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie‐chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.

IntroductionRoutine immunization programs have focused on increasing vaccination coverage, which is equally important for decreasing vaccine-preventable diseases (VPDs), particularly in low- and lower-middle-income countries (LMICs). We estimated the trends and projections of age-appropriate vaccination coverage at the regional and national levels, as well as place of residence and wealth index in LMICs.MethodsIn total, 174 nationally representative household surveys from 2000 to 2020 from 41 LMICs were included in this study. Bayesian hierarchical regression models were used to estimate trends and projections of age-appropriate vaccination.ResultsThe trend in coverage of age-appropriate Bacillus Calmette-Guérin (BCG), third dose of diphtheria, tetanus, and pertussis (DTP3), third dose of polio (polio3), and measles-containing vaccine (MCV) increased rapidly from 2000 to 2020 in LMICs. Findings indicate substantial increases at the regional and national levels, and by area of residence and socioeconomic status between 2000 and 2030. The largest rise was observed in East Africa, followed by South and Southeast Asia. However, out of the 41 countries, only 10 countries are estimated to achieve 90% coverage of the BCG vaccine by 2030, five of DTP3, three of polio3, and none of MCV. Additionally, by 2030, wider pro-urban and -rich inequalities are expected in several African countries.ConclusionSignificant progress in age-appropriate vaccination coverage has been made in LMICs from 2000 to 2020. Despite this, projections show many countries will not meet the 2030 coverage goals, with persistent urban–rural and socioeconomic disparities. Therefore, LMICs must prioritize underperforming areas and reduce inequalities through stronger health systems and increased community engagement to ensure high coverage and equitable vaccine access.

The relationship between agricultural systems and the development of complex societies in ancient Hawai'i has been debated for decades. To contribute to this debate, we examine a terrace complex representing an extended family agricultural land plot in the Kula dryland field system of East Maui, Hawaiian Islands. Botanical, faunal, soil, and architectural analysis data reveal a variety of household labor practices related to agroforestry ca. a.d. 1400–1820. A pre-human open forest soil substrate was replaced with stone agricultural terracing which was in turn enclosed by upslope-downslope garden walls that parceled the terrace complex into distinct garden areas. These results lead us to conclude that a wide range of specialized upland activities were practiced, including food cultivation, forestry, pig husbandry, and bird-hunting. The net sum of these local activities helped underpin the formative process of larger regional-level agricultural systems which in turn can inform us about polity-level staple and wealth finance systems. [ABSTRACT FROM AUTHOR]

In recent years, comprehensive cancer genomics platforms, such as The Cancer Genome Atlas (TCGA), provide access to an enormous amount of high throughput genomic datasets for each patient, including gene expression, DNA copy number alterations, DNA methylation, and somatic mutation. While the integration of these multi‐omics datasets has the potential to provide novel insights that can lead to personalized medicine, most existing approaches only focus on gene‐level analysis and lack the ability to facilitate biological findings at the pathway‐level. In this article, we propose Bayes‐InGRiD (Bayesian Integrative Genomics Robust iDentification of cancer subgroups), a novel pathway‐guided Bayesian sparse latent factor model for the simultaneous identification of cancer patient subgroups (clustering) and key molecular features (variable selection) within a unified framework, based on the joint analysis of continuous, binary, and count data. By utilizing pathway (gene set) information, Bayes‐InGRiD does not only enhance the accuracy and robustness of cancer patient subgroup and key molecular feature identification, but also promotes biological understanding and interpretation. Finally, to facilitate an efficient posterior sampling, an alternative Gibbs sampler for logistic and negative binomial models is proposed using Pólya‐Gamma mixtures of normal to represent latent variables for binary and count data, which yields a conditionally Gaussian representation of the posterior. The R package "INGRID" implementing the proposed approach is currently available in our research group GitHub webpage (https://dongjunchung.github.io/INGRID/). [ABSTRACT FROM AUTHOR]

• Smart traffic sensors collected naturalistic data on a rural road in Sweden. • We estimated lateral clearance and passing speed from drivers passing cyclists. • In contrast to speed, lateral clearance decreased clearly under reduced visibility. • The presence of oncoming traffic had a similar yet stronger effect than visibility. • We provide a Bayesian model of clearance and speed as a reference for road safety. Introduction: Cycling is popular for its ecological, economic, and health benefits. However, especially in rural areas, cyclists may need to share the road with motorized traffic, which is often perceived as a threat. Overtaking a cyclist is a particularly critical maneuver for drivers as they need to control their lateral clearance and speed when passing the cyclist, possibly in the presence of oncoming vehicles or view-obstructing curves. An overtaking vehicle can destabilize the cyclist when passing with low clearance and high speed. At the same time, the cyclist may get scared and eventually stop cycling. In this work, we investigated how visibility regarding available sight distance—an important factor for infrastructure design and regulation—affects drivers' behavior when overtaking cyclists. Method : Using four roadside-based traffic sensors, we collected naturalistic data that contained kinematics of drivers overtaking cyclists on a rural road in Sweden. We modeled lateral clearance and speed at the passing moment in response to variables such as sight distance and oncoming traffic with a Bayesian multivariate approach. Results : Fitted on 81 maneuvers, the model revealed that drivers reduced lateral clearance under reduced sight distance. Speed was similarly reduced, however, not as clearly. When an oncoming vehicle was present, it had a similar—yet stronger—effect than sight distance. While we found an overall correlation between clearance and speed, some maneuvers were recorded at critically low clearance. Conclusions : Cyclists' safety is endangered when passed by drivers under reduced visibility or close to oncoming traffic. Practical Applications : Decision-making for infrastructure and policymaking should aim at prohibiting overtaking in areas with reduced visibility or close oncoming traffic. The model developed in this study may serve as a reference to vehicle active-safety systems and automated driving. The collected and processed data may support evaluating driver models fitted on less ecologically valid data and simulated active-safety systems. [ABSTRACT FROM AUTHOR]

In the problem of distinguishing between two hypotheses, the stopping time of the universal d-guaranteeing procedure, denoted as , is defined as the observation number at which the posterior probability of one of the hypotheses becomes greater than a given reliability. This article demonstrates that for a wide range of probabilistic models, is almost surely finite. The average value of is analyzed in a special case where is the first exit time of a random walk beyond two-sided parabolic boundaries. Conditions are identified under which the average value of is finite or infinite. The problem of distinguishing between hypotheses and about the mean of a normal random variable with a known variance is also examined. In this case, the output parameter is a realization of the normal random variable. It is shown that the average value of is finite only if , otherwise it is infinite. Monte Carlo simulation data supports the assumption that the unconditional mean value of is infinite. [ABSTRACT FROM AUTHOR]

Background The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. Methods To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. Results We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. Conclusion The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico. [ABSTRACT FROM AUTHOR]

This article is aimed at solving a number of issues related to the problems, risks, and threats arising from the profiling of human activity. In this study, the Bayesian method was used, to determine the quantitative and qualitative characteristics of personal data for ensuring the security of this data by dint of reducing the redundancy of data processed by artificial intelligence (AI). A thought experiment to test the possibility of reducing the redundancy of personal data processed by AI allows us to conclude that using the Bayesian method allows to protect human rights to privacy. With this approach, instead of the method associated with the collection and accumulation of the most sensitive categories of personal data, we proposed a method that is associated with obtaining probabilistic estimates of the values of the parameters of these data by conducting statistical studies of the specified personal data without their collection and accumulation. The probabilistic estimates of the parameters of some sensitive personal data obtained in this way can replace their exact values and can be used by AI in the criteria for filtering personal data subjects, including for the purpose of making a decision. [ABSTRACT FROM AUTHOR]

Introduction: The Clinical Nurse Leader (CNL) care model is a different way of organizing frontline nursing care delivery in contrast to the traditional "staff nurse" model and is increasingly being adopted by health systems across the United States and abroad. However, variability in implementation and outcomes has been noted across health settings. Aim: A recently validated CNL Practice Model provides an explanatory pathway for CNL model integration into practice. The purpose of this study was to identify and compare patterns of empirical correspondence to the CNL Practice Model and predict their influence on implementation success. Methods: We conducted a secondary analysis of a 2015 national‐level study with clinicians and administrators involved with CNL initiatives in their health system. A psychometrically validated CNL Practice Survey was used to collect data measuring the presence (0%–100%) of the five domains of the CNL Practice Model (organizational readiness, CNL structuring, CNL practices, outcomes, and value) and one measure of CNL implementation success. We modeled the complex hierarchical structure of the data using a Bayesian multilevel regression mixed modeling approach. A zero–one‐inflated beta distribution, a mixture of Bernoulli distributions for the minimum and maximum responses and a beta distribution for the responses between the minimum and maximum, was used to fit success ratings in the model. Results: A total of 920 participants responded, 540 (59%) provided success scores. The model captured ratings skewed toward upper bound, while also adequately modeling data between the minimum and maximum values. The Bayesian model converged and gave estimates for all hierarchical parameters, which would likely have failed to converge in a pure maximum likelihood framework. The variability around success score across CNL Practice Model element ratings was greatest at the component level, 0.29 (0.18–0.48), compared to either the domain level, 0.16 (0.01–0.54), or the item level, 0.09 (0.01–0.17). The components most predictive of implementation success were (a) consensus CNL model can close gaps, (b) organization level implementation strategy, and (c) alignment of empirical CNL microsystem level structuring to the model's conceptualization. Conclusions: Findings provide further empirical evidence to support the explanatory pathway proposed by the CNL Practice Model and identified specific organizational readiness and CNL workflow structures that are critical antecedents predictive of CNL practice manifestation and production of expected outcomes. Findings indicate actionable implementation evidence that can be successfully adopted across real‐world healthcare settings to achieve safer and higher quality patient care. Clinical Relevance: CNL integrated care delivery is a frontline nursing care model that is being increasingly adopted by health systems across the United States and abroad. However, variability in CNL implementation and outcomes has been noted across health settings, limiting its evidence base. Findings of this study contribute a better understanding about the variability of CNL practice and outcomes found in the literature and contribute empirical and conceptual clarity about the relationships between modes of CNL implementation and successful adoption in healthcare settings. [ABSTRACT FROM AUTHOR]

Simple Summary: In this research, we examined how temperature, rain, and relative humidity influence the seasonal population of Aedes albopictus, which was estimated using ovitrap data collected in the period 2010–2022 by the monitoring network of Emilia-Romagna region, Italy. The main results suggest that the winter–spring period (January to May) is crucial in determining the size of the first generation and overall seasonal growth of the species. We found that the monthly densities depend on various combinations of meteorological parameters. Background: Aedes albopictus, the Asian tiger mosquito, has become a prevalent pest in Italy, causing severe nuisance and posing a threat of transmission of arboviruses introduced by infected travelers. In this study, we investigated the influence of weather parameters on the seasonal population density of Aedes albopictus. Methods: A Bayesian approach was employed to identify the best meteorological predictors of species trend, using the eggs collected monthly from 2010 to 2022 by the Emilia-Romagna regional monitoring network. Results: The findings show that the winter–spring period (January to May) plays a crucial role in the size of the first generation and seasonal development of the species. Conclusions: A temperate winter and a dry and cold March, followed by a rainy and hot spring and a rainy July, seem to favor the seasonal development of Ae. albopictus. [ABSTRACT FROM AUTHOR]

A selection problem for finding the most probable outcome of a multinomial distribution is considered. We present a sequential procedure for solving a d-posterior variation of the problem. For the procedure the convergence and sample size properties are investigated. The paper is concluded with numerical illustrations of the actually achievable d-posterior reliability, average sample size and the convergence speed of the presented sample size estimate. A numerical example on comparison of classical and d-posterior reliability measures is provided showing that a classical selection procedure cannot be made to fit the d-posterior reliability constrains efficiently in the general case. [ABSTRACT FROM AUTHOR]

Urban flood inundation is spatially uncertain. To quantify this uncertainty, it is necessary to explore the spatial probability of urban flood inundation in different return periods. In this study, an urban flood spatial inundation probability assessment method based on an improved Bayesian model is proposed, which comprises three parts: data reconstruction based on undersampling; optimal Bayesian sample planning; and spatial inundation probability assessment. A case study of the central urban area of Jingdezhen City, China, is presented in this paper. The results indicate that (1) the inundation probabilities generated based on various return periods (20-, 50-, and 100-year return periods) are accurately determined and can provide more detailed inundation information. (2) The adoption of the random undersampling data reconstruction method solves the problem of an imbalanced number of inundations/noninundations during Bayesian modeling and substantially enhances the prediction accuracy compared with the traditional Bayesian modeling approach. (3) A sensitivity analysis reveals that inundation probability is sensitive to the drainage network and elevation rather than soil water retention and distance to river. With an increase in the return period, the inundation probability gradually increases. As the proposed method can quantify flood inundation uncertainty, it is valuable in supporting specific flood risk assessments. [ABSTRACT FROM AUTHOR]

Photon counting LiDAR can capture the 3D information of long-distance targets and has the advantages of high sensitivity and high resolution. However, the noise counts restrict improvements in the photon counting imaging quality. Therefore, how to make full use of the limited signal counts under noise interference to achieve efficient 3D imaging is one of the main problems in current research. To address this problem, in this paper, we proposes a 3D imaging method for undulating terrain depth estimation that combines constant false alarm probability detection with the Bayesian model. First, the new 3D cube data are constructed by adaptive threshold segmentation of the reconstructed histogram. Secondly, the signal photons are extracted in the Bayesian model, and depth estimation is realized from coarse to fine by the sliding-window method. The robustness of the method under intense noise is proven by sufficient undulating terrain simulations and outdoor imaging experiments. These results show that the proposed method is superior to typical existing methods. [ABSTRACT FROM AUTHOR]

The well-known Mondrian-style images, aside from being aesthetically amusing, also reflect the core principles of human vision in their viewing experience. First, when we see a Mondrian-style image consisting only of a grid and primary colors, we may automatically interpret its causal history such that it was generated by recursively partitioning a blank scene. Second, the image we observe is open to many possible ways of partitioning, and their probabilities of dominating the interpretation can be captured by a probabilistic distribution. Moreover, the causal interpretation of a Mondrian-style image can emerge almost spontaneously, not being tailored to any specific task. Using Mondrian-style images as a case study, we demonstrate the generative nature of human vision by showing that a Bayesian model based upon an image-generation task can support a wide range of visual tasks with little retraining. Our model, learned from human-synthesized Mondrian-style images, could predict human performance in the perceptual complexity ranking, capture the transmission stability when images were iteratively passed among participants, and pass a visual Turing test. Our results collectively show that human vision is causal such that we interpret an image from the angle of how it was generated. The success of generalization with little retraining suggests that generative vision constitutes a type of common sense that supports a wide range of tasks of different natures. [ABSTRACT FROM AUTHOR]

ObjectiveThis study aimed to evaluate the spatiotemporal distribution of patients with hepatitis C virus (HCV) and the factors influencing this distribution in Jiangsu Province, China, from 2011 to 2020.MethodsThe incidence of reported HCV in Jiangsu Province from 2011 to 2020 was obtained from the Chinese Information System for Disease Control and Prevention (CISDCP). R and GeoDa software were used to visualize the spatiotemporal distribution and the spatial autocorrelation of HCV. A Bayesian spatiotemporal model was constructed to explore the spatiotemporal distribution of HCV in Jiangsu Province and to further analyze the factors related to HCV.ResultsA total of 31,778 HCV patients were registered in Jiangsu Province. The registered incidence rate of HCV increased from 2.60/100,000 people in 2011 to 4.96/100,000 people in 2020, an increase of 190.77%. Moran's I ranged from 0.099 to 0.354 (P < 0.05) from 2011 to 2019, indicating a positive spatial correlation overall. The relative risk (RR) of the urbanization rate, the most important factor affecting the spread of HCV in Jiangsu Province, was 1.254 (95% confidence interval: 1.141–1.376), while other factors had no significance.ConclusionThe reported HCV incidence rate integrally increased in the whole Jiangsu Province, whereas the spatial aggregation of HCV incidence was gradually weakening. Our study highlighted the importance of health education for the floating population and reasonable allocation of medical resources in the future health work.

Artificial intelligence technology is increasingly being employed within educational contexts, markedly enhancing the dynamics of instruction through the integration of intelligent algorithms. This study endeavors to revitalize the BOPPPS model in blended college English teaching by amalgamating it with the Bayesian knowledge tracking model and a reinforcement learning algorithm. The objective is to establish a refined, blended teaching framework based on the BOPPPS model and to investigate its efficacy along with the variables that influence its outcomes. The findings affirm a positive correlation between the effectiveness of blended English teaching at the university level and the student's cognitive abilities, skillsets, and affective attitudes, with all p-values demonstrating significance at the 0.001 level. Comparative analysis of pre-and post-test data from the blended teaching experiment revealed no initial significant differences between the experimental and control groups across six dimensions of English proficiency and five dimensions of interest in learning English. However, post-experiment results indicated substantial enhancements in the experimental group's overall English scores, listening, reading comprehension, writing, translation, and speaking abilities relative to the control group, with p-values falling below 0.05. Additionally, the p-values for the paired sample tests concerning the dimensions of interest in learning English in both groups were also below 0.05. These results suggest that blended English teaching at the university level not only significantly boosts students’ performance in English but also augments their interest in the language. This study underscores the potential of integrated teaching models that incorporate AI-driven algorithms to significantly enhance educational outcomes.

To be able to better improve musical literacy and artistic aesthetics, this paper presents a study on the application of sentiment analysis for vocal music teaching under natural language processing. Firstly, for the training text that has been labeled with categories, a suitable classification model is built for the unlabeled test text using the model category prediction. The sum of the identified sentiment scores is calculated, and the different results are compared to set a reasonable threshold to determine the sentiment polarity. According to the mutual information statistic value of each feature item, the feature items with a mutual information statistic value higher than the threshold are retained. The weights of each feature term are normalized to the interval to which the range of weights is normalized, and the feature weights are obtained. The optimal classification surface is calculated by converting the Lagrangian optimization method into a problem of finding the pair, which makes the low-dimensional problem transformed into a high-dimensional space and combines the advantages of Bayes’ ability to automatically adjust the weight coefficients in training and the huge training capacity of neural networks to improve the flexibility and robustness of the training model. Feature extraction is performed by convolutional layers, and the dimensionality of the feature vector is reduced by the maximum pooling method to realize an emotional-semantic vocal teaching model. The results show that the proposed method can improve the flexibility and accuracy of the semantic analysis model, and also enhance the training robustness of the network, with the F1 value reaching 91.65%, which can accurately mine the application data of “one core and three integrations” vocal teaching model and improve the teaching model in universities.

Background: More than half of all heart failure (HF) patients have heart failure with reduced ejection fraction (HFrEF) or mildly reduced ejection fraction (HFmrEF). The combination of traditional Chinese medicine injections (TCMIs) with Western medicine treatment (WMT) has been reported to have better efficacy than using WMT alone. However, the positive effects of TCMIs combined with WMT on HFrEF and HFmrEF require more comprehensive and systematic evidence and warrant further investigation. Methods: The NMA searched eight databases, including four English and four Chinese, from database creation to November 10, 2022. We used the Cochrane Risk of Bias tool (ROB 2) to assess the selected studies' quality. OpenBUGS and STATA 17.0 were used for network meta-analysis. Results: The 101 RCTs were included in the systematic review. Studies have shown that when combined with any of the five TCMIs, WMT was more efficient than WMT alone. Shenmai injection (SMI) + WMT may be the best treatment for clinical effectiveness rate (CER) improvement and b-type natriuretic peptide (BNP) reduction. Huangqi injection (HQI) + WMT was the best treatment for improving left ventricular ejection fraction (LVEF). Danhong injection (DHI) + WMT may be the best treatment for lowering left ventricular end-diastolic dimension (LVEDD). Xinmailong injection (XMLI) + WMT was likely the best treatment for increasing the 6-min walking test (6MWT). In addition, XMLI had the lowest incidence of adverse reactions (3.38%). Conclusions: Shenfu injection (SFI), SMI, DHI, XMLI, and HQI combined with WMT have stronger efficiency in treating HFrEF and HFmrEF. However, as all studies were conducted in China, this review is limited by the inevitable selection bias, and further high-quality multicenter randomized controlled trials (RCTs) are required.