Shifting vulnerabilities in suicide mortality from the COVID-19 crisis to the socioeconomic aftermath in Spain (2016-2024): A Bayesian triple-interaction analysis.

Automated microplastic quantification is currently compromised by morphological mimicry: air bubbles, organic biofilms, and sediment create high false-positive rates in standard Convolutional Neural Networks (CNNs).This study introduces AquaEye, a containerized computer vision framework that mitigates artifact misclassification by fusing Bayesian Deep Learning with ISO-standard morphometrics.Unlike deterministic U-Net implementations, we deploy a Monte Carlo Dropout inference pipeline to estimate epistemic uncertainty, enabling the suppression of predictions where model variance exceeds a safety threshold.To enforce physical validity, a post-processing geometric gate rejects candidates based on Circularity (4A/P 2 ) and Solidity, filtering non-polymer structures that bypass the neural filter.The system, deployed via a Dockerized microservices architecture, ensures reproducibil-ity often absent in -lab-bench scripts.Experimental validation confirms that AquaEye statistically decouples true microplastic instances from background noise, offering a robust alternative to manual microscopy for highthroughput environmental monitoring.

The genus Nyctinomops Miller (1902) comprises small to medium sized, large-eared, free-tailed aerial insectivorous bats widely distributed from southern United States to northern Argentina. Five species are currently recognized for the genus: Nyctinomops aurispinosus (Peale, 1848), N. femorosaccus (Merriam, 1889), N. macrotis (Gray, 1839), N. mbopicuare (Barquez et al., 2023) and N. laticaudatus (Geoffroy St.-Hilaire, 1805). At least six subspecies have been historically described for N. laticaudatus including N. l. ferrugineus (Goodwin, 1954), N. l. yucatanicus (Miller, 1902), N. l. europs (Allen, 1899), N. l. macarenensis (Barriga-Bonilla, 1965), and N. l. gracilis (Wagner, 1843) and comprehensive character-based analyses are largely missing to test the variation within these putative populational/species complexes. Here we use three mitochondrial markers (Cytochrome b, Cytochrome oxidase I, and Nicotinamide adenine dinucleotide dehydrogenase subunit 1), and two nuclear markers (Signal transducer and activator of transcription 5 A and the Recombination activating gene 2) to assess the phylogenetic relationships within Nyctinomops including a dense geographic sample of N. laticaudatus, comprising most of its known geographic distribution. Bayesian analyses recovered a novel phylogenetic hypothesis for the internal relationships of Nyctinomops supporting a non-monophyletic arrangement for N. laticaudatus (stricto sensu) comprising three independent lineages, one corresponding to the subspecies N. l. yucatanicus from USA, Mexico, and Central America, another including the nominate form N. laticaudatus (hereafter N. laticaudatus stricto sensu), and a third lineage with individuals from the Amazon basin. Based on our molecular data the recently described N. mbopicuare is nested in N. laticaudatus s.s. clade and may be a junior synonymous or a subspecies of N. laticaudatus.

Bayesian Mendelian randomization methods for index trait bias correction in subsequent trait genome-wide association studies.

Symptom and Bayesian network analyses of positive and negative symptoms in psychotic-like experiences: A multicenter cross-sectional study of Chinese.

Plant-specific human reliability analysis (HRA) tailors human error probabilities (HEPs) to a facility's unique contexts. The diversity of digital human-machine interfaces and operational cultures necessitates methods grounded in empirical data rather than generic databases to ensure true plant-specificity. Advancing beyond prior fragmented data applications, this study establishes a systematic pipeline integrating the HuREX (Human Reliability data Extraction) database with the EMBRACE (EMpirical data-Based crew Reliability Assessment and Cognitive Error analysis) method. By sharing a unified task taxonomy, this framework directly translates plant-specific simulator data into coherent HEP estimates. In an application of this framework to the APR1400 plant, time-dependent failure probabilities were derived using Bayesian inference on 30 simulator performance records. Concurrently, nominal primitive error probabilities (NPEPs) were estimated via logistic regression from 44,585 task records. Due to current data scarcity, performance shaping factor (PSF) multipliers were determined through structured expert elicitation. This data-method integration provides a replicable foundation for generating context-sensitive HEPs in digitalized control rooms, highlighting both empirical strengths and current data limitations. • A plant-specific HRA approach is developed by integrating HuREX with EMBRACE. • HuREX data support the estimation of the time and cognitive failure probabilities. • The proposed approach is applied to estimate HEPs for the APR1400 plant. • Limitations such as reliance on expert judgment are discussed.

Modeling Continuous Direct Compression processes with inherent delay using SINDYc and Bayesian inference

Climate change has intensified the need to reduce greenhouse gas emissions, particularly methane (CH4) from enteric fermentation. Genetic selection has emerged as a promising mitigation strategy; however, studies on Bos taurus indicus, especially Nellore cattle, remain limited. This study aimed to estimate heritabilities and genetic correlations for CH4 emission traits and their relationships with feeding behaviour, feed efficiency, and performance, as well as to evaluate the direct and correlated responses to selection for lower CH4 emissions. Data were from 2418 Nellore cattle evaluated in feed efficiency trials. Traits included dry matter intake (DMI), feeding time per day (FTd), feed events per day (FEd), and feeding rate (FR), residual feed intake (RFI), average daily gain (ADG), and mid-test body weight (MBW). Methane emissions were measured in 1153 animals using the SF6 tracer technique, providing daily CH4 emission (g/day), CH4 per unit of DMI (CH4DMI, g/day), and residual CH4 (CH4res). Variance components were estimated using the single-step genomic BLUP (ssGBLUP) method through Bayesian inference. Heritability estimates were moderate for CH4 (0.25), CH4DMI (0.14), CH4res (0.14), and performance traits such as DMI (0.35), ADG (0.36), and MBW (0.40). Higher estimates were observed for feeding behaviour traits FTd (0.49) and FR (0.42). Genetic correlations between CH4 and production traits were high, particularly with DMI (0.79), ADG (0.90), and MBW (0.91), indicating that selection for reduced CH4 emissions may affect growth. Direct selection for CH4 led to a modest annual reduction in emissions but also a correlated decline in MBW. These results demonstrate that while CH4 emissions are heritable, their strong genetic association with productivity traits indicates that isolated selection for reduced emissions may lead to undesirable outcomes in feed intake and performance. Therefore, strategies aiming to reduce CH4 emissions should consider the genetic relationships with growth and efficiency traits to avoid compromising animal productivity.

Pollutant fate of groundwater LNAPLs under unknown pollution source information by stochastic methods.

No Clear Association Between Intravenous Fluid Administration and Short-Term Worsening of Left Ventricular Function in Septic Patients with, or without, Heart Failure: A Bayesian Analysis.

Minimal evidence of adaptation deficits in children with dyslexia: An EEG study with controlled expectations.

An Evaluation of Artificial Intelligence Chatbots as Alternatives to Specialized Software in Teaching Bayesian Pharmacokinetic Analysis.

Introduction Genetic diversity and population structure are critical for understanding species distribution, environmental adaptability, and responses to anthropogenic disturbances. Dendrocalamus longispathus , a commercially and ecologically important bamboo species in North East India, remains understudied in terms of its population genetics. This study aims to assess the genetic diversity and structure of this species across its natural range in Mizoram and Tripura. Methods Leaf samples were collected from 12 populations and analyzed using 13 polymorphic simple sequence repeat (SSR) markers. Genetic diversity parameters, including number of alleles, allelic richness, heterozygosity, and inbreeding coefficients, were estimated. Population structure was examined using Bayesian analysis, Neighbor-Joining method based genetic clustering, and principal coordinate analysis (PCoA). Results The analysis revealed moderate genetic diversity, with mean number of alleles (Na = 2.84), effective alleles (Ne = 1.87), and expected heterozygosity (He = 0.362). Observed heterozygosity (Ho = 0.234) was lower than expected, indicating inbreeding, supported by a positive inbreeding coefficient (F IS = 0.306). Populations from Mizoram exhibited higher allelic richness compared to those from Tripura. AMOVA showed that 70% of genetic variation occurred within populations and 30% among populations (F ST = 0.297), indicating very high levels of genetic differentiation with low gene flow (Nm = 0.86). Bayesian analysis identified three genetic clusters (K = 3), with most populations clearly structured except one admixed population. Overall, pattern of genetic clustering between population was largely aligned with geographic proximity. Discussion The findings indicate moderate genetic diversity, very high genetic differentiation, and robust genetic structure in D. longispathus populations, with evidence of limited gene flow and inbreeding. These results highlight the need for targeted conservation strategies and the use of genetically diverse populations for plantation and sustainable management programs.

Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors

This article introduces a pyramid-based multiresolution Bayesian framework for high-resolution behavioral analysis from sparse data. By restricting covariance modeling to the coarsest layer of a parameter pyramid while using a difference pyramid for fine-scale refinement, the framework overcomes major computational and statistical challenges of traditional hierarchical Bayesian models with covariance (HBMc). The framework is evaluated against three central claims: (1) scalability through dramatically reduced computational cost, (2) precision under sparsity even with very few trials per block, and (3) improved interpretability via complementary information across resolution layers. Three Bayesian variants-Bayesian inference procedure (BIP; independent parameters), hierarchical Bayesian model with variance only (HBMv), and HBMc-were implemented in PyMC. The best-performing model, PyramidHBMc (HBMc at the top layer combined with HBMv refinement), consistently achieved the best performance (Watanabe-Akaike information criterion weight = 1.0), with the lowest root mean square error and standard deviation, reducing errors and variability by up to 74.1% and 78.5% relative to BIP across four datasets spanning one-dimensional temporal and two-dimensional spatial functions. These results directly support the three central claims and demonstrate the framework's broad applicability in perceptual and cognitive science.

Making footprints move: Temporal disaggregation of building footprint data using Sentinel-2 imagery and Bayesian deep learning

A weighted Bayesian estimation method for process uncertainty in crowdsourced data for pavement performance prediction

Parameterisation of epidemiological models from small field experiments: A case study of banana bunchy top virus transmission.

The nonlinear impact of road safety policy implementation on the severity of road traffic crashes: A fusion of deep learning and Bayesian random parameter methods.

In late 2024, a major human metapneumovirus (HMPV) outbreak in China caused widespread pneumonia and thousands of hospitalizations, drawing global attention to this virus. In Senegal, a concomitant rise in HMPV detections was observed in both community and hospital settings. This study aimed to describe the epidemiology, genomic diversity, and the evolutionary dynamics of HMPV strains circulating in Senegal over 7 years of integrated community- and hospital-based surveillance (2018-2024). Nasopharyngeal samples were tested using multiplex real-time reverse transcription polymerase chain reaction. A subset of HMPV isolates underwent sequencing, followed by phylogenetic and Bayesian evolutionary analyses. HMPV was detected in 3.3% (n = 19,204) of tested specimens. The highest positivity rate was observed among infants aged ≤ 11 months (31.2%), with decreasing rates in older age groups. Following COVID-19 related disruptions, HMPV circulation re-established a seasonal pattern after 2022, peaking during the rainy season. Genomic analyses revealed sustained cocirculation of sublineages A2b1, A2b2, B1, and B2, with periodic lineage replacement every 1-3 years. Genetic variability was predominantly observed in the G glycoprotein, with evidence of positive selection at codons 102, 153, 167, 169, 179, 181, and 202. Estimated evolutionary rates were 9.30 × 10-4 substitutions per site per year for HMPV-A and 9.86 × 10-4 substitutions per site per year for HMPV-B. Phylogeographic analyses indicate that HMPV epidemics in Senegal are driven by repeated introductions and ongoing exchange with other regions. Our findings indicate sustained circulation and dynamic turnover of pre-existing sublineages rather than the emergence of a novel variant within the population, especially in infants aged ≤ 11 months.