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PubMed2026

A New Species of Eucnemidae (Coleoptera: Elateroidea) with Its Complete Mitogenome and Mitogenome-Based Phylogenetic Analysis.

We describe Microrhagus ziwulingensis Muona & Meng, sp. nov., from China. The genus Microrhagus Dejean, 1833, was previously represented in China by only two species. We sequenced and assembled the complete mitogenome of M. ziwulingensis (GenBank accession OK143440), which encoded 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and a putative control region with a total length of 15,843 bp. Overall, 36 species of Elateroidea were collected as the ingroup (for six of these species, two sequences of the same species submitted by different submitters were used). Eight species of Buprestoidea served as the outgroup. We constructed phylogenetic trees using maximum likelihood (ML) and Bayesian inference (BI) methods based on 13 protein-coding genes (PCGs) from mitochondrial genomes. The phylogenetic trees showed that all families within the superfamily Elateroidea, which was used as the ingroup, formed monophyletic groups. The topology differed from previous studies, showing that Rhagophthalmidae and Lampyridae formed a sister clade, and that Phengodidae + Lycidae, with Cantharidae, formed a sister clade. These discrepancies should be attributed to the use of a single type of molecular marker and the intense shortage of available sampling. This also indicates that mitochondrial genomic research on Eucnemidae, and even on the superfamily Elateroidea, still needs further expansion.

Microrhagus↗
PubMed1991

Information retrieval using a "digital book shelf".

WALT (Washington University's Approach to Lots of Text), is a prototype interface designed to support information retrieval research. The WALT interface serves as a "front end" to a wide array of retrieval engines including those based on Boolean retrieval, latent semantic indexing, term frequency--inverse document frequency, and Bayesian inference techniques. The WALT interface is composed of seven distinct components: a document examination component known as the Document Browsing Area; four navigation components called the Book Shelf, the Book Spine, the Table of Contents, and the Path Clipboard; a term-based information retrieval component called Control Panel; and a relevance feedback component known as the Reader Feedback Panel. WALT's most unique feature may be it's use of "book shelf" and "book spine" metaphors both to facilitate navigation and to provide a histogram-based display showing documents deemed appropriate for answering user queries.

Books↗
PubMed1995

Effective access to distributed heterogeneous medical text databases.

INQUERY is an advanced text information retrieval system developed by the Information Retrieval Laboratory of the University of Massachusetts in Amherst. It is based on Bayesian inference networks, which are probabilistic models for reasoning with multiple sources of uncertain evidence. The evidence, in this case, is the presence or absence of words and/or phrases in a document. Evidence is combined into belief that a document is relevant. The INQUERY retrieval engine has been developed with the support of ARPA, NSF, and industrial funding. It has a number of unique features and has achieved excellent results in the TIPSTER and TREC evaluations. Informatics research and application development using INQUERY has recently begun in the medical domain, including a new ARPA initiative concerned with clinical text. The features that we will focus on in this demonstration are: Automatic processing of natural language queries, including the extraction of phrases and specific medical concepts such as drug doses; Document selection through automatic relevance feedback and routing techniques, including the construction of complex queries using the INQUERY query language; The integration of conventional database techniques with text analysis and retrieval; Automatic thesaurus generation and query expansion using the PhraseFinder system; Distributed database access, including automatic database selection and merging of local searches; this will be demonstrated using a collection of medical databases; Retrieval based on passages, rather than whole documents.

Bayes Theorem↗
PubMed1997

Analysis of aerial survey data on Florida manatee using Markov chain Monte Carlo.

We assess population trends of the Atlantic coast population of Florida manatee, Trichechus manatus latirostris, by reanalyzing aerial survey data collected between 1982 and 1992. To do so, we develop an explicit biological model that accounts for the method by which the manatees are counted, the mammals' movement between surveys, and the behavior of the population total over time. Bayesian inference, enabled by Markov chain Monte Carlo, is used to combine the survey data with the biological model. We compute marginal posterior distributions for all model parameters and predictive distributions for future counts. Several conclusions, such as a decreasing population growth rate and low sighting probabilities, are consistent across different prior specifications.

Animals↗
PubMedDate not supplied

Bayesian predictive approach for inference about proportions.

This paper investigates the Bayesian procedures for comparing proportions. These procedures are especially suitable for accepting (or rejecting) the equivalence of two population proportions. Furthermore the Bayesian predictive probabilities provide a natural and flexible tool in monitoring trials, especially for choosing a sample size and for conducting interim analyses. These methods are illustrated with two examples where antithrombotic treatments are administrated to prevent further occurrences of thromboses.

Bayes Theorem↗
PubMed2025

Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.

The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.

COVID-19↗
PubMed2026

Comparison of phylogenetic metrics of transmission between symptomatic and asymptomatic tuberculosis in individuals who were incarcerated in Brazil in 2008-24: a retrospective genomic epidemiology study.

BACKGROUND: Tuberculosis control efforts have traditionally targeted symptomatic individuals; however, the role of asymptomatic cases in sustaining transmission is increasingly recognised. We aimed to quantify the contribution of asymptomatic tuberculosis to recent transmission using genomic and epidemiological data from a high-transmission setting. METHODS: We conducted a retrospective genomic epidemiology study of Mycobacterium tuberculosis isolates collected in Mato Grosso do Sul, Brazil, between Aug 25, 2008, and March 19, 2024. Available isolates underwent whole-genome sequencing. Demographic, clinical, incarceration history, and laboratory metadata were obtained from surveillance records. From Jan 1, 2017, to March 19, 2024, active case finding was conducted in the state's three largest prisons (all male-only facilities), during which sputum samples were collected from individuals irrespective of symptoms and tested using GeneXpert and culture. Comparisons of transmission between individuals with and without symptoms were restricted to individuals who were incarcerated and were identified through active case finding and for whom high-quality, M tuberculosis lineage 4 genomes were available. Metrics of recent transmission included phylogenetic clustering, time-scaled haplotype density (THD), local branching index (LBI), and transmission probabilities inferred using Bayesian Reconstruction and Evolutionary Analysis of Transmission Histories. FINDINGS: 4448 tuberculosis cases were notified in Mato Grosso do Sul in 2008-24. After excluding cases for which M tuberculosis isolates were not available or had low sequencing quality, who had contaminated cultures or mixed infection, or who were infected with non-lineage 4 M tuberculosis, we included 2362 lineage 4 M tuberculosis isolates with high-quality genome sequences. 1849 (78·3%) of 2362 isolates were part of a genomic cluster. Among 2362 individuals with tuberculosis, 1137 (48·1%) were incarcerated at diagnosis. Of these individuals, 505 were identified through active case finding in three male-only prisons. The median age was 30 years (IQR 25-37); 304 (60·2%) had mixed ethnicity, 90 (17·8%) were White, 56 (11·1%) were Black, 13 (2·6%) were Indigenous, and six (1·2%) were Asian. 277 (54·9%) had symptomatic disease and 228 (45·1%) had asymptomatic tuberculosis. There were no significant differences between symptomatic and asymptomatic individuals in phylogenetic clustering (213 [76·9%] of 277 vs 195 [85·5%] of 228; p=0·37), THD (median 0·39 [IQR 0·06-0·62] vs 0·50 [0·09-0·65]; p=0·12), or LBI (0·00863 [0·00810-0·00988] vs 0·00871 [0·00829-0·01020]; p=0·088). Bayesian transmission trees showed no significant difference in the number of secondary infections inferred from symptomatic compared with asymptomatic individuals (p=0·56). These findings were consistent across genomic clusters and robust to model assumptions. INTERPRETATION: We identified no differences in transmission between individuals who were symptomatic and those who were asymptomatic using multiple genomic measures. In this high-transmission setting, where systematic screening is implemented, our findings indicate that asymptomatic tuberculosis substantially contributes to tuberculosis transmission at the population level. These results suggest that symptom-based case detection alone is likely to be insufficient to interrupt transmission and highlight the importance of expanded screening strategies in high-risk populations. FUNDING: US National Institutes of Health and the Brazilian National Research Council (CNPq).

Humans↗
PubMed1992

Should physicians be bayesian agents?

Because physicians use scientific inference for the generalizations of individual observations and the application of general knowledge to particular situations, the Bayesian probability solution to the problem of induction has been proposed and frequently utilized. Several problems with the Bayesian approach are introduced and discussed. These include: subjectivity, the favoring of a weak hypothesis, the problem of the false hypothesis, the old evidence/new theory problem and the observation that physicians are not currently Bayesians. To the complaint that the prior probability is subjective, Bayesians reply that there will be ultimate convergence, but the rebuttal to this is that there will not be uniform convergence. Secondly, since the Bayesian scheme favors a weak hypothesis, theories turn out to be a gratuitous risk. The problem with the false hypothesis comes out in the denominator of the theorem, revealing that a factor which is not a theory at all is being considered in the reasoning. On the old evidence/new theory problem old evidence cannot confirm a new theory so that the posterior probability will equal the prior probability. Finally, empiric studies have shown that current physicians are not Bayesians. But on consideration of Bayesian inference as a system of inference, it can be reasoned that physicians should be Bayesians. However, the problem of physicians' and patients' own subjectivity continue to plague this system of medical decision making.

Bayes Theorem↗
PubMed1997

A Bayesian analysis of regression models with continuous errors with application to longitudinal studies.

We employ a regression model with errors that follow a continuous autoregressive process to analyse longitudinal studies. In this way, unequally spaced observations do not present a problem in the analysis. We employ a Bayesian approach, where our inferences are based on a direct resampling process that generates values from the posterior distribution of the parameters of the model. We illustrate these Bayesian inferences with an analysis of a longitudinal study that involves the regression of foetal head circumference on menstrual age. Using these same data, we contrast the Bayesian approach with a maximum likelihood technique.

Bayes Theorem↗
PubMed2026

TreeFlow: Probabilistic Modelling and Automatic Differentiation for Phylogenetics.

Probabilistic modelling frameworks are powerful tools for statistical modelling and inference. They are not immediately generalizable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic modelling and automatic differentiation with phylogenetic trees. It embeds phylogenetic trees in the TensorFlow Probability framework, and implements inference algorithms for phylogenetic models given a fixed tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.

Bayesian inference↗
PubMed1996

Population toxicokinetics of tetrachloroethylene.

In assessing the distribution and metabolism of toxic compounds in the body, measurements are not always feasible for ethical or technical reasons. Computer modeling offers a reasonable alternative, but the variability and complexity of biological systems pose unique challenges in model building and adjustment. Recent tools from population pharmacokinetics, Bayesian statistical inference, and physiological modeling can be brought together to solve these problems. As an example, we modeled the distribution and metabolism of tetrachloroethylene (PERC) in humans. We derive statistical distributions for the parameters of a physiological model of PERC, on the basis of data from Monster et al. (1979). The model adequately fits both prior physiological information and experimental data. An estimate of the relationship between PERC exposure and fraction metabolized is obtained. Our median population estimate for the fraction of inhaled tetrachloroethylene that is metabolized, at exposure levels exceeding current occupational standards, is 1.5% [95% confidence interval (0.52%, 4.1%)]. At levels approaching ambient inhalation exposure (0.001 ppm), the median estimate of the fraction metabolized is much higher, at 36% [95% confidence interval (15%, 58%)]. This disproportionality should be taken into account when deriving safe exposure limits for tetrachloroethylene and deserves to be verified by further experiments.

Administration, Inhalation↗
PubMed2025

Core passive and facultative mTOR-mediated mechanisms coordinate mammalian protein synthesis and decay.

The maintenance of cellular homeostasis requires tight regulation of proteome concentration and composition. To achieve this, protein production and elimination must be robustly coordinated. However, the mechanistic basis of this coordination remains unclear. Here, we address this question using quantitative live-cell imaging, computational modeling, transcriptomics, and proteomics approaches. We found that protein decay rates systematically adapt to global alterations of protein synthesis rates. This adaptation is driven by a core passive mechanism supplemented by facultative changes in mechanistic/mammalian target of rapamycin (mTOR) signaling. Passive adaptation hinges on changes in the production rate of the machinery governing protein decay and allows for partial maintenance of the cellular proteome. Sustained changes in mTOR signaling provide an additional layer of adaptation unique to naive pluripotent stem cells, allowing for near-perfect maintenance of proteome composition. Our work unravels the mechanisms protecting the integrity of mammalian proteomes upon variations in protein synthesis rates. A record of this paper's transparent peer review process is included in the supplemental information.

TOR Serine-Threonine Kinases↗
PubMed2026

Molecular epidemiology and phylogeographic architecture of oncogenic intracellular bacteria in cervical cancer patients across Northern China.

BACKGROUND: Oncogenic intracellular bacteria, including Chlamydia trachomatis, Mycoplasma genitalium, and Fusobacterium nucleatum, have emerged as significant contributors to cervical carcinogenesis. Despite growing interest in microbial oncology, the molecular epidemiological landscape and phylogeographic distribution of these pathogens in Northern China remain poorly characterized. This study aimed to determine the prevalence, co-infection patterns, genotypic diversity, and spatial phylogeographic clustering of oncogenic intracellular bacteria among cervical cancer patients across five provinces of Northern China. METHODS: A cross-sectional, multi-center study was conducted between March 2022 and November 2024 across Shaanxi, Heilongjiang, Beijing, Shandong, and Inner Mongolia. Cervical swab specimens were collected from 1247 confirmed cervical cancer patients. Pathogen detection was performed using multiplex real-time polymerase chain reaction, 16S rRNA gene amplicon sequencing, and whole-genome sequencing. Phylogeographic analyses employed maximum likelihood and Bayesian evolutionary inference frameworks. Statistical analyses included multivariate logistic regression and geographic information system-based spatial clustering. RESULTS: The overall prevalence of at least one oncogenic intracellular bacterium was 68.3% (n&#xa0;=&#xa0;852). Chlamydia trachomatis was the most prevalent pathogen detected in 41.2% of participants. Co-infection with two or more bacteria was identified in 29.7% of cases and was independently associated with advanced-stage cervical cancer (adjusted odds ratio&#xa0;=&#xa0;2.87; 95% confidence interval: 1.94 to 4.23; p&#xa0;<&#xa0;0.001). Phylogeographic analysis revealed three distinct molecular clades with evidence of bidirectional gene flow between Shaanxi and Heilongjiang. Whole-genome sequencing identified 14 novel virulence gene variants not previously characterized in Chinese clinical isolates. CONCLUSIONS: Oncogenic intracellular bacteria are highly prevalent and genotypically diverse among cervical cancer patients in Northern China. The identified phylogeographic clustering and novel virulence variants have direct implications for regional screening programs, targeted antimicrobial strategies, and the development of region-specific molecular diagnostic panels.

Cervical cancer↗
PubMed2026

Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc&#x2011;eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P&#x2009;<&#x2009;0.05) and the criterion for strong shared genetic signals (PP.H4&#x2009;>&#x2009;0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8&#x2009;+&#x2009;T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune&#x2011;cell&#x2011;specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

Humans↗
PubMed2025

Navigating Sampling Bias in Discrete Phylogeographic Analysis: Assessing the Performance of an Adjusted Bayes Factor.

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modeling.

Bayes Theorem↗