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Scrub typhus, a vector-borne bacterial infection, is an important but neglected disease globally. Accurately characterizing the burden is challenging because of nonspecific symptoms and limited diagnostics. Prior seroepidemiology studies have struggled to find consensus cutoffs that permit comparisons of estimates across contexts and time. In this study, we present a novel approach that does not require a cutoff and instead uses information about antibody kinetics after infection to estimate seroincidence. We use data from three cohorts of scrub typhus patients in Chiang Rai, Thailand, and Vellore, India, to characterize antibody kinetics after infection and two population serosurveys in the Kathmandu Valley, Nepal, and Tamil Nadu, India, to estimate seroincidence. The samples were tested for IgM and IgG responses to Orientia tsutsugamushi-derived recombinant 56-kDa antigen using commercial enzyme-linked immunosorbent assay kits. We used Bayesian hierarchical models to characterize antibody responses after scrub typhus infection and used the joint distributions of the peak antibody titers and decay rates to estimate population-level incidence rates in the cross-sectional serosurveys. Median responses persisted above an optical density (OD) of 1.8 for 23.6 months for IgG and an OD of 1 for 4.5 months for IgM. Among 18- to 29-year-olds, the seroincidence was 10 per 1,000 person-years (95% CI, 5-19) in Tamil Nadu, India, and 14 per 1,000 person-years (95% CI: 10-20) in the Kathmandu Valley, Nepal. When seroincidence was calculated with antibody decay ignored, the disease burden was underestimated by more than 50%. The approach can be deployed prospectively, coupled with existing serosurveys, or leverage banked samples to efficiently generate scrub typhus seroincidence estimates.

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete-pooling, no-pooling, or partial-pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular MPT models in psychology (e.g., process-dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBOs gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

A view that has been gaining prevalence over the past decade is that the human conceptual system is malleable, dynamic, context-dependent, and task-dependent, that is, flexible. Within the flexible conceptual representation framework, conceptual representations are constructed ad hoc, forming a different, idiosyncratic instantiation upon each occurrence. In this review, we scrutinize the neurocognitive literature to better understand the nature of this flexibility. First, we identify some key characteristics of these representations. Next, we consider how these flexible representations are constructed by addressing some of the open questions in this framework: We review the age-old question of how to reconcile flexibility with the apparent need for shareable stable definitions to anchor meaning and come to mutual understanding, as well as some newer questions we find critical, namely, the nature of relations among flexible representations, the role of feature saliency in activation, and the viability of all-or-none feature activations. We suggest replacing the debate about the existence of a definitional stable core that is obligatorily activated with a question of the degree and probability of activation of the information constituting a conceptual representation. We rely on published works to suggest that (1) prior featural salience matters, (2) feature activation may be graded, and (3) Bayesian updating of prior information according to current demands offers a viable account of how flexible representations are constructed. This proposal provides a theoretical mechanism for incorporating a changing momentary context into a constructed representation, while still preserving some of the concept's constituent meaning.

The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Researchers and clinicians often rely on molecular assays like PCR to identify and monitor viral infections, instead of the resource-prohibitive gold standard of viral culture. However, it remains unclear when (if ever) PCR measurements of viral load are reliable indicators of replicating or infectious virus. The recent popularity of PCR protocols targeting subgenomic RNA for SARS-CoV-2 has caused further confusion, as the relationships between subgenomic RNA and standard total RNA assays are incompletely characterized and opinions differ on which RNA type better predicts culture outcomes. Here, we explore these issues by comparing total RNA, subgenomic RNA, and viral culture results from 24 studies of SARS-CoV-2 in non-human primates (including 2167 samples from 174 individuals) using custom-developed Bayesian statistical models. On out-of-sample data, our best models predict subgenomic RNA positivity from total RNA data with 91% accuracy, and they predict culture positivity with 85% accuracy. Further analyses of individual time series indicate that many apparent prediction errors may arise from issues with assay sensitivity or sample processing, suggesting true accuracy may be higher than these estimates. Total RNA and subgenomic RNA showed equivalent performance as predictors of culture positivity. Multiple cofactors (including exposure conditions, host traits, and assay protocols) influence culture predictions, yielding insights into biological and methodological sources of variation in assay outcomes-and indicating that no single threshold value applies across study designs. We also show that our model can accurately predict when an individual is no longer infectious, illustrating the potential for future models trained on human data to guide clinical decisions on case isolation. Our work shows that meta-analysis of in vivo data can overcome longstanding challenges arising from limited sample sizes and can yield robust insights beyond those attainable from individual studies. Our analytical pipeline offers a framework to develop similar predictive tools in other virus-host systems, including models trained on human data, which could support laboratory analyses, medical decisions, and public health guidelines.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

The entomopathogenic fungus Conoideocrella luteorostrata has recently been implicated in natural epizootics among exotic elongate hemlock scale (EHS) insects in Christmas tree farms in the eastern United States. Since 1913, C. luteorostrata has been reported from various plant feeding Hemiptera in the southeastern United States, but comprehensive morphological and phylogenetic studies of U.S. populations are lacking. The recovery of multiple strains of C. luteorostrata from mycosed EHS in North Carolina provided an opportunity to conduct pathogenicity assays and morphological and phylogenetic studies to investigate genus- and species-level boundaries among the Clavicipitaceae. Pathogenicity assays confirmed that C. luteorostrata causes mortality of EHS crawlers, an essential first step in developing this fungus as a biocontrol. Morphological studies revealed that conidia aligned with previous measurements of the Paecilomyces-like asexual state of C. luteorostrata, with conidiophore morphology consistent with historical observations. Additionally, a Hirsutella-like synanamorph was observed in select C. luteorostrata strains. In both a four-locus, 54-taxon Clavicipitaceae-wide phylogenetic analysis including D1-D2 domains of the nuclear 28S rRNA region (28S), elongation factor 1 alpha (EF1-α), DNA-directed RNA polymerase II subunit 1 (RPB1), and DNA-directed RNA polymerase II subunit 2 (RPB2) and a two-locus, 38-taxon (28S and EF1-α) phylogenetic analysis, all three Conoideocrella species were resolved as strongly supported monophyletic lineages across all loci and both methods (maximum likelihood and Bayesian inference) of phylogenetic inference except for 28S for C. tenuis. Despite the strong support for individual Conoideocrella species, none of the analyses supported the monophyly of Conoideocrella with the inclusion of Dussiella. Due to the paucity of RPB1 and RPB2 sequence data, EF1-α provided superior delimitation of intraspecies groupings for Conoideocrella and should be used in future studies. Further development of C. luteorostrata as a biocontrol against EHS will require additional surveys across diverse Hemiptera and expanded pathogenicity testing to clarify host range and efficacy of this fungus.

BackgroundPre-exposure prophylaxis (PrEP), condom use, post-exposure prophylaxis (PEP), and sexual partner reduction help to prevent HIV acquisition but have low uptake among young people. We aimed to assess the efficacy of automated text messaging and monitoring, online peer support, and strengths-based telehealth coaching to improve uptake of and adherence to PrEP, condom use, and PEP among adolescents aged 12-24 years at risk of HIV acquisition in Los Angeles, CA, USA, and New Orleans, LA, USA.MethodsWe conducted a four-arm randomised controlled factorial trial, assessing interventions designed to support uptake and adherence of HIV prevention options (ie, PrEP, PEP, condom use, and sexual partner reduction). We recruited young people aged 12-24 years who were at risk of HIV acquisition from 13 community-based organisations, adolescent medicine clinics, and organisations serving people who are unstably housed, people who were previously incarcerated, and other vulnerable young people, and through dating apps, peer referrals, and social venues and events in Los Angeles, CA, USA, and New Orleans, LA, USA. Young people who tested seronegative and reported being gay, bisexual, or other men who have sex with men, transgender men or women, or gender diverse (eg. non-binary or genderqueer) were eligible for inclusion. Participants were randomly assigned to one of four intervention groups in a factorial design: automated text messaging and monitoring (AMMI) only, AMMI plus peer support via private social media, AMMI plus strengths-based telehealth coaching by near-peer paraprofessionals, or AMMI plus peer support and coaching. Assignment was further stratified by race or ethnicity and sexual orientation within each interviewer's group of participants. Participants were masked to intervention assignment until after baseline interviews when offered their randomly assigned intervention, and interviewers were masked throughout the study. Interventions were available throughout the 24-month follow-up period, and participants completed baseline and follow-up assessments, including rapid diagnostic tests for sexually transmitted infections, HIV, and substance use, at 4-month intervals over 24 months. The primary outcomes were uptake and adherence to HIV prevention options over 24 months, measured by self-reported PrEP use and adherence, consistent condom use with all partners, PEP prescription and adherence, and number of sexual partners in participants with at least one follow-up. We used Bayesian generalised linear modelling to assess changes in outcomes over time comparing the four study groups. This study is registered with ClinicalTrials.gov (NCT03134833) and is completed.FindingsWe screened 2314 adolescents beginning May 1, 2017, to enrol 1037 participants (45%) aged 16-24 years between May 6, 2017, and Aug 30, 2019, of whom 895 (86%) had follow-up assessments and were included in the analytical sample (313 assigned to AMMI only, 205 assigned to AMMI plus peer support, 196 assigned to AMMI plus coaching, and 181 assigned to AMMI plus peer support and coaching). Follow-up was completed on Nov 8, 2021. Participants were diverse in race and ethnicity (362 [40%] Black or African American, 257 [29%] Latinx or Hispanic, 184 [21%] White, and 53 [6%] Asian or Pacific Islander) and other sociodemographic factors. At baseline, 591 (66%) participants reported anal sex without a condom in the past 12 months. PrEP use matched that in young people nationally, with 101 (11%) participants reporting current PrEP use at baseline, increasing at 4 months to 132 (15%) and continuing to increase in the AMMI plus peer support and coaching group (odds ratio 2·31, 95% CI 1·28-4·14 vs AMMI control). There was no evidence for intervention effect on condom use, PEP use (ie, prescription or adherence), PrEP adherence, or sexual partner numbers. No unanticipated or study-related adverse events occurred.InterpretationResults are consistent with hypothesised synergistic intervention effects of evidence-based functions of informational, motivational, and reminder messaging; peer support for HIV prevention; and strengths-based, goal-focused, and problem-solving telehealth coaching delivered by near-peer paraprofessionals. These core functions could be flexibly scaled via combinations of technology platforms and front-line or telehealth HIV prevention workers.FundingAdolescent Medicine Trials Network for HIV/AIDS Interventions, US National Institutes of Health.

OBJECTIVE: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. METHODS: Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. RESULTS: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). CONCLUSION: The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. SIGNIFICANCE: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

PurposeOur preclinical studies showed that lycopene enhanced the anti-prostate cancer efficacy of docetaxel in animal models. A phase I trial (NCT0149519) was conducted to identify an optimum dose of synthetic lycopene in combination with docetaxel (and androgen blockade [androgen deprivation therapy, ADT]), and to evaluate its effect on the safety and pharmacokinetics of docetaxel in men with metastatic prostate cancer.MethodsSubjects were treated with 21-day cycles of 75 mg/m2 docetaxel (and ADT), plus lycopene at 30, 90 or 150 mg/day. A Bayesian model averaging continual reassessment method was used to guide dose escalation. Pharmacokinetics of docetaxel and multiple correlative studies were carried out.ResultsTwenty-four participants were enrolled, 18 in a dose escalation cohort to define the maximum tolerated dose (MTD), and six in a pharmacokinetic cohort. Docetaxel/ADT plus 150 mg/day synthetic lycopene resulted in dose-limiting toxicity (pulmonary embolus) in one out of 12 participants with an estimated probability of .106 and thus was chosen as the MTD. Lycopene increased the AUCinf and Cmax of plasma docetaxel by 9.5% and 15.1%, respectively. Correlative studies showed dose-related changes in circulating endothelial cells and vascular endothelial growth factor A, and reduction in insulin-like growth factor 1R phosphorylation, associated with lycopene therapy.ConclusionsThe combination of docetaxel/ADT and synthetic lycopene has low toxicity and favourable pharmacokinetics. The effects of lycopene on biomarkers provide additional support for the toxicity-dependent MTD definition.HighlightsThe maximum tolerated dose was identified as 150 mg/day of lycopene in combination with docetaxel/ADT for the treatment of metastatic prostate cancer patients. Small increases in plasma exposure to docetaxel were observed with lycopene co-administration. Mechanistically significant effects were seen on angiogenesis and insulin-like growth factor 1 signalling by lycopene co-administration with docetaxel/ADT.

The transmission of antimicrobial resistant bacteria in the urban environment is poorly understood. We utilized genomic sequencing and phylogenetics to characterize the transmission dynamics of antimicrobial resistant Escherichia coli (AMR-Ec) cultured from putative canine (caninep) and human feces present on urban sidewalks in San Francisco, California. We isolated a total of fifty-six AMR-Ec isolates from human (n = 20) and caninep (n = 36) fecal samples from the Tenderloin and South of Market (SoMa) neighborhoods of San Francisco. We then analyzed phenotypic and genotypic antimicrobial resistance (AMR) of the isolates, as well as clonal relationships based on cgMLST and single nucleotide polymorphisms (SNPs) of the core genomes. Using Bayesian inference, we reconstructed the transmission dynamics between humans and caninesp from multiple local outbreak clusters using the marginal structured coalescent approximation (MASCOT). Our results provide evidence for multiple sharing events of AMR-Ec between humans and caninesp. In particular, we found one instance of likely transmission from caninesp to humans as well as an additional local outbreak cluster consisting of one caninep and one human sample. Based on this analysis, it appears that non-human feces act as an important reservoir of clinically relevant AMR-Ec within the urban environment for this study population. This work showcases the utility of genomic epidemiology to reconstruct potential pathways by which antimicrobial resistance spreads.

While the importance of human milk in shaping infant immune function is well established, the impact of at-the-nipple (ATN) breastfeeding on maternal immune status has been understudied. Since lactation evolved to support infant survival and boost maternal fitness, we predict that ATN breastfeeding will confer benefits on maternal immune function. We measure the absolute and relative frequency of different infant feeding methods (ATN breastfeeding, pumping, donated milk, other supplementation) used by postpartum women in Seattle, WA (USA). We implement Bayesian modeling to estimate the effects of ATN breastfeeding on diurnal change in secretion rate of pro-inflammatory salivary cytokines and C-reactive protein (CRP). Our results show that most mothers in our sample used a variety of infant feeding methods, with pumping as the most common alternative to ATN breastfeeding. We find that ATN breastfeeding is associated with non-linear effects on diurnal IL-8 and CRP. Furthermore, we find that women who report zero versus ubiquitous ATN breastfeeding exhibit opposing diurnal patterns in CRP secretion rate. This study provides evidence that variation in maternal lactation practices corresponds to differences in maternal immune responses, highlighting how measuring lactation as a continuous variable can further enhance understanding of postpartum maternal physiology.

PURPOSE: The neutralizing peptibody trebananib prevents angiopoietin-1 and angiopoietin-2 from binding with Tie2 receptors, inhibiting angiogenesis and proliferation. Trebananib was combined with paclitaxel±trastuzumab in the I-SPY2 breast cancer trial. PATIENTS AND METHODS: I-SPY2, a phase II neoadjuvant trial, adaptively randomizes patients with high-risk, early-stage breast cancer to one of several experimental therapies or control based on receptor subtypes as defined by hormone receptor (HR) and HER2 status and MammaPrint risk (MP1, MP2). The primary endpoint is pathologic complete response (pCR). A therapy graduates if/when it achieves 85% Bayesian probability of success in a phase III trial within a given subtype. Patients received weekly paclitaxel (plus trastuzumab if HER2-positive) without (control) or with weekly intravenous trebananib, followed by doxorubicin/cyclophosphamide and surgery. Pathway-specific biomarkers were assessed for response prediction. RESULTS: There were 134 participants randomized to trebananib and 133 to control. Although trebananib did not graduate in any signature [phase III probabilities: Hazard ratio (HR)-negative (78%), HR-negative/HER2-positive (74%), HR-negative/HER2-negative (77%), and MP2 (79%)], it demonstrated high probability of superior pCR rates over control (92%-99%) among these subtypes. Trebananib improved 3-year event-free survival (HR 0.67), with no significant increase in adverse events. Activation levels of the Tie2 receptor and downstream signaling partners predicted trebananib response in HER2-positive disease; high expression of a CD8 T-cell gene signature predicted response in HR-negative/HER2-negative disease. CONCLUSIONS: The angiopoietin (Ang)/Tie2 axis inhibitor trebananib combined with standard neoadjuvant therapy increased estimated pCR rates across HR-negative and MP2 subtypes, with probabilities of superiority >90%. Further study of Ang/Tie2 receptor axis inhibitors in validated, biomarker-predicted sensitive subtypes is warranted.

MOTIVATION: Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. RESULTS: We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. AVAILABILITY AND IMPLEMENTATION: We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

BACKGROUND: Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk. METHODS: Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area. RESULTS: Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods. CONCLUSIONS: Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Recent studies have demonstrated regional differences in marine ecosystem C:N:P with implications for carbon and nutrient cycles. Due to strong co-variance, temperature and nutrient stress explain variability in C:N:P equally well. A reductionistic approach can link changes in individual environmental drivers with changes in biochemical traits and cell C:N:P. Thus, we quantified effects of temperature and nutrient stress on Synechococcus chemistry using laboratory chemostats, chemical analyses, and data-independent acquisition mass spectrometry proteomics. Nutrient supply accounted for most C:N:Pcell variability and induced tradeoffs between nutrient acquisition and ribosomal proteins. High temperature prompted heat-shock, whereas thermal effects via the translation-compensation hypothesis were only seen under P-stress. A Nonparametric Bayesian Local Clustering algorithm suggested that changes in lipopolysaccharides, peptidoglycans, and C-rich compatible solutes may also contribute to C:N:P regulation. Physiological responses match field-based trends in ecosystem stoichiometry and suggest a hierarchical environmental regulation of current and future ocean C:N:P.

PURPOSE: Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model improves estimation of local macular ganglion cell complex (GCC) rates of change compared to simple linear regression (SLR) and a conditional autoregressive (CAR) model. METHODS: We analyzed GCC thickness measurements within 49 macular superpixels in 111 eyes (111 patients) with four or more macular optical coherence tomography scans and two or more years of follow-up. We compared superpixel-patient-specific estimates and their posterior variances derived from the latest version of a recently developed Bayesian HSL model, CAR, and SLR. We performed a simulation study to compare the accuracy of intercept and slope estimates in individual superpixels. RESULTS: HSL identified a significantly higher proportion of significant negative slopes in 13/49 superpixels and a significantly lower proportion of significant positive slopes in 21/49 superpixels than SLR. In the simulation study, the median (tenth, ninetieth percentile) ratio of mean squared error of SLR [CAR] over HSL for intercepts and slopes were 1.91 (1.23, 2.75) [1.51 (1.05, 2.20)] and 3.25 (1.40, 10.14) [2.36 (1.17, 5.56)], respectively. CONCLUSIONS: A novel Bayesian HSL model improves estimation accuracy of patient-specific local GCC rates of change. The proposed model is more than twice as efficient as SLR for estimating superpixel-patient slopes and identifies a higher proportion of deteriorating superpixels than SLR while minimizing false-positive detection rates. TRANSLATIONAL RELEVANCE: The proposed HSL model can be used to model macular structural measurements to detect individual glaucoma progression earlier and more efficiently in clinical and research settings.

There is a dearth of statistical models that adequately capture the total signal attributed to whole-brain imaging features. The total signal is often widely distributed across the brain, with individual imaging features exhibiting small effect sizes for predicting neurobehavioral phenotypes. The challenge of capturing the total signal is compounded by the distribution of neurobehavioral data, particularly responses to psychological questionnaires, which often feature zero-inflated, highly skewed outcomes. To close this gap, we have developed a novel Variational Bayes algorithm that characterizes the total signal captured by whole-brain imaging features for zero-inflated outcomes. Our zero-inflated variance (ZIV) estimator estimates the fraction of variance explained (FVE) and the proportion of non-null effects (PNN) from large-scale imaging data. In simulations, ZIV demonstrates superior performance over other linear models. When applied to data from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study, we found that whole-brain imaging features contribute to a larger FVE for externalizing behaviors compared to internalizing behaviors. Moreover, focusing on features contributing to the PNN, ZIV estimator localized key neurocircuitry associated with neurobehavioral traits. To the best of our knowledge, the ZIV estimator is the first specialized method for analyzing zero-inflated neuroimaging data, enhancing future studies on brain-behavior relationships and improving the understanding of neurobehavioral disorders.

Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

ObjectivesSurveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.MethodsThis cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.ResultsWe developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.ConclusionsThe combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Current models of early human subsistence economies suggest a focus on large mammal hunting. To evaluate this hypothesis, we examine human bone stable isotope chemistry of 24 individuals from the early Holocene sites of Wilamaya Patjxa (9.0-8.7 cal. ka) and Soro Mikaya Patjxa (8.0-6.5 cal. ka) located at 3800 meters above sea level on the Andean Altiplano, Peru. Contrary to expectation, Bayesian mixing models based on the isotope chemistry reveal that plants dominated the diet, comprising 70-95% of the average diet. Paleoethnobotanical data further show that tubers may have been the most prominent subsistence resource. These findings update our understanding of earliest forager economies and the pathway to agricultural economies in the Andean highlands. The findings furthermore suggest that the initial subsistence economies of early human populations adapting to new landscapes may have been more plant oriented than current models suggest.

After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes' theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source 'none of the above', which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient's breath), and three sources of unpleasant odours in a meat processing plant. The 18th century cleric Thomas Bayes gave his name to an elegant statement of the probability of an event – in this case identification of an odour – given some evidence: output from a number of metal oxide semiconductor sensors. Knowing the distributions of outputs for target odours, we assign the probabilities of an unknown odour. The greatest probability wins! (Image credit: E-nose Pty Ltd and D. B. Hibbert.) This article belongs to the 10th Anniversary Collection of RACI and AAS Award papers. [ABSTRACT FROM AUTHOR]

Likelihood ratios compare two values (i.e., case rates) in order to illustrate the magnitude of the difference between the two. This ratio increases the confidence one can have in a diagnostic test from a different vantage point than that of sensitivity and specificity. The calculations of likelihood ratios are presented along with a simplified approach. Likelihood ratios are another tool the toxicologist should employ in their understanding of statistics and probability. [ABSTRACT FROM AUTHOR]

Optimizing structural designs is crucial today, with additive manufacturing, particularly selective laser melting, gaining prominence. Thorough mechanical characterization of new materials remains vital. This paper investigates fatigue crack growth behavior in SLM 316L specimens under cyclic loading conditions. The study addresses result uncertainties by using CT specimens aligned along three building directions per ASTM E647 standards and a constant loading ratio (R = 0.1), necessitating mean value and confidence interval predictions. Departing from linear prediction models, innovative Bootstrap Polynomial and Power Regression Models and Bayesian Nonlinear Regression Model updated posterior distribution by Markov Chain Monte Carlo are employed. These approaches leverage bootstrapping to construct confidence intervals, offering robustness and flexibility in handling non‐normal data behavior and limited sample sizes. They provide tailored fits to data curvature, revealing limitations of linear prediction models in capturing observed nonlinear behavior, enhancing reliability in additive manufacturing applications, and advancing material science and engineering. Highlights: Fatigue crack growth in SLM316L is evaluated.Robust nonlinear regression techniques are utilized.Distribution approximation is done using Kernel estimator.Confidence intervals are estimated employing Bootstrap and Bayesian regression models. [ABSTRACT FROM AUTHOR]

BACKGROUND: The efficacy of simvastatin in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear. METHODS: In an ongoing international, multifactorial, adaptive platform, randomized, controlled trial, we evaluated simvastatin (80 mg daily) as compared with no statin (control) in critically ill patients with Covid-19 who were not receiving statins at baseline. The primary outcome was respiratory and cardiovascular organ support-free days, assessed on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support through day 21 in survivors; the analyis used a Bayesian hierarchical ordinal model. The adaptive design included prespecified statistical stopping criteria for superiority (>99% posterior probability that the odds ratio was >1) and futility (>95% posterior probability that the odds ratio was

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

The Staphylococcus aureus Network Adaptive Platform (SNAP) trial is a multifactorial Bayesian adaptive platform trial that aims to improve the way that S. aureus bloodstream infection, a globally common and severe infectious disease, is treated. In a world first, the SNAP trial will simultaneously investigate the effects of multiple intervention modalities within multiple groups of participants with different forms of S. aureus bloodstream infection. Here, we formalise the trial structure, modelling approach, and decision rules that will be used for the SNAP trial. By summarising the statistical principles governing the design, our hope is that the SNAP trial will serve as an adaptable template that can be used to improve comparative effectiveness research efficiency in other disease areas.Trial registration NCT05137119 . Registered on 30 November 2021.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lins concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

BackgroundFindings on the associations between prenatal PFAS exposures and offspring adiposity are inconsistent. Whether such associations may extend to adolescence is especially understudied.ObjectivesWe investigated associations of prenatal PFAS exposures with offspring adiposity and body composition at 16-20 years of age.MethodsWe studied 545 mother-child pairs in the prospective prebirth cohort Project Viva (Boston, Massachusetts). We measured six PFAS (PFOA, PFOS, PFNA, PFHxS, EtFOSAA, and MeFOSAA) in maternal early pregnancy (median age=9.6wk, range: 5.7-19.6 wk) plasma samples. At the late adolescence visit (median age=17.4 y, range: 15.9-20.0 y), we obtained anthropometric measures and assessed body composition using bioelectrical impedance analysis and dual-energy X-ray absorptiometry. We examined associations of individual PFAS with obesity [i.e., age- and sex-specific body mass index (BMI) ≥95th percentile] and adiposity and body composition using multivariable Poisson and linear regression models, respectively. We assessed PFAS mixture effects using Bayesian kernel machine regression (BKMR) and quantile g-computation. We used fractional-polynomial models to assess BMI trajectories (at 3-20 years of age) by prenatal PFAS levels.ResultsThirteen percent (n=73) of the children had obesity in late adolescence. After multivariable adjustment, higher prenatal PFAS concentrations were associated with higher obesity risk [e.g., 1.59 (95% CI: 1.19, 2.12), 1.24 (95% CI: 0.98, 1.57), and 1.49 (95% CI: 1.11, 1.99) times the obesity risk per doubling of PFOS, PFOA, and PFNA, respectively]. BKMR showed an interaction between PFOA and PFOS, where the positive association between PFOS and obesity was stronger when PFOA levels were lower. Each quartile increment of the PFAS mixture was associated with 1.52 (95% CI: 1.03, 2.25) times the obesity risk and 0.52 (95% CI: -0.02, 1.06) kg/m2 higher BMI. Children with higher prenatal PFOS, EtFOSAA, and MeFOSAA concentrations had higher rates of BMI increase starting from 9-11 years of age.DiscussionPrenatal PFAS exposures may have obesogenic effects into late adolescence. https://doi.org/10.1289/EHP12597.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithiums possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P

Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.

Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism. Inference proceeds via maximum likelihood or Bayesian methods, including multiple imputation without auxiliary variables. Such IL approaches are valid under a missing at random (MAR) assumption. Rabe-Hesketh and Skrondal (Ignoring non-ignorable missingness. Presidential Address at the International Meeting of the Psychometric Society, Beijing, China, 2015; Psychometrika, 2023) consider a violation of MAR where a variable A can affect missingness of another variable B also when A is not observed. They show that this case can be handled by discarding more data before proceeding with IL approaches. This data-deletion approach is similar to the sequential estimation of Mohan et al. (in: Advances in neural information processing systems, 2013) based on their ordered factorization theorem but is preferable for parametric models. Which kind of data-deletion or ordered factorization to employ depends on the nature of the MAR violation. In this article, we therefore propose two diagnostic tests, a likelihood-ratio test for a heteroscedastic regression model and a kernel conditional independence test. We also develop a test-based estimator that first uses diagnostic tests to determine which MAR violation appears to be present and then proceeds with the corresponding data-deletion estimator. Simulations show that the test-based estimator outperforms IL when the missing data problem is severe and performs similarly otherwise.

Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.

Determining if ecological communities are saturated (have a limit to the number of species they can support) has important implications for understanding community assembly, species invasions, and climate change. However, previous studies have generally been limited to short time frames that overlook extinction debt and have not explicitly considered how functional trait diversity may mediate patterns of community saturation. Here, we combine data from biodiversity surveys with functional and phylogenetic data to explore if the colonisation events after the Great American Biotic Interchange (closure of the Panamanian Isthmus) resulted in increases in species richness of communities of the snake family Dipsadidae. We determined the number and the direction of dispersal events between Central and South America by estimating ancestral areas based on a Bayesian time-calibrated phylogenetic analysis. We then evaluated whether variation in community saturation was mediated by the functional similarity of six traits for the resident and colonizing snakes and/or local environmental conditions. We found that colonised communities did not support more species than those that were not colonised. Moreover, we did not find an association between the functional diversity across sites and whether they were colonised by members from the lineages dispersing across the Isthmus or not. Instead, variation in species richness was predicted best by covariates such as time since colonisation and local environment. Taken together, our results suggest that snake communities of the Dipsadidae across the neotropics are saturated. Moreover, our research highlights two important factors to consider in studies of community saturation: extinction debt and the functional differences and similarities in species' ecological roles.

Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original $N-1$ internal node heights into a space of one height parameter and $N-2$ ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest.

Analyses of genome sequence data have revealed pervasive interspecific gene flow and enriched our understanding of the role of gene flow in speciation and adaptation. Inference of gene flow using genomic data requires powerful statistical methods. Yet current likelihood-based methods involve heavy computation and are feasible for small datasets only. Here, we implement the multispecies-coalescent-with-migration model in the Bayesian program bpp, which can be used to test for gene flow and estimate migration rates, as well as species divergence times and population sizes. We develop Markov chain Monte Carlo algorithms for efficient sampling from the posterior, enabling the analysis of genome-scale datasets with thousands of loci. Implementation of both introgression and migration models in the same program allows us to test whether gene flow occurred continuously over time or in pulses. Analyses of genomic data from Anopheles mosquitoes demonstrate rich information in typical genomic datasets about the mode and rate of gene flow.

Forensic pattern analysis requires examiners to compare the patterns of items such as fingerprints or tool marks to assess whether they have a common source. This article uses signal detection theory to model examiners' reported conclusions (e.g., identification, inconclusive, or exclusion), focusing on the connection between the examiner's decision threshold and the probative value of the forensic evidence. It uses a Bayesian network model to explore how shifts in decision thresholds may affect rates and ratios of true and false convictions in a hypothetical legal system. It demonstrates that small shifts in decision thresholds, which may arise from contextual bias, can dramatically affect the value of forensic pattern-matching evidence and its utility in the legal system.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality. Colonization by MRSA increases the risk of infection and transmission, underscoring the importance of decolonization efforts. However, success of these decolonization protocols varies, raising the possibility that some MRSA strains may be more persistent than others. Here, we studied how the persistence of MRSA colonization correlates with genomic presence of antibiotic resistance genes. Our analysis using a Bayesian mixed effects survival model found that genetic determinants of high-level resistance to mupirocin was strongly associated with failure of the decolonization protocol. However, we did not see a similar effect with genetic resistance to chlorhexidine or other antibiotics. Including strain-specific random effects improved the predictive performance, indicating that some strain characteristics other than resistance also contributed to persistence. Study subject-specific random effects did not improve the model. Our results highlight the need to consider the properties of the colonizing MRSA strain when deciding which treatments to include in the decolonization protocol.

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Objective To screen the risk factors of hydrocephalus after decompressive craniectomy in patients with cerebral hernia after traumatic brain injury (TBI), and construct a Bayesian network model based on the risk factors. Methods A total of 77 patients with cerebral hernia after TBI who underwent decompressive craniotomy in Nanjing Tongren Hospital Affiliated to Southeast University from March 2020 to January 2022 were included. They were divided into hydrocephalus group (n = 25) and non - hydrocephalus group (n = 52) according to whether hydrocephalus was complicated after surgery. The risk factors of hydrocephalus after decompressive craniectomy in patients with cerebral hernia after TBI were analyzed by univariate and multivariate Logistic regression analyses. The Bayesian network model was constructed based on the risk factors, and the receiver operating characteristic (ROC) curve and calibration curve were drawn and Hosmer-Lemeshow goodness-of-fit test was conducted. Results In hydrocephalus group, the Glasgow Coma Scale (GCS) score at admission (t = 2.178, P = 0.032), the ratio of cerebrospinal fluid replacement after lumbar puncture (χ2 = 8.675, P = 0.003), and the level of β2 -microglobulin after operation (t = 11.146, P = 0.000) were lower than those in non-hydrocephalus group, while subarachnoid hemorrhage (χ2 = 5.901, P = 0.015), bilateral operation (χ2 = 6.441, P = 0.011), the ratio of dural unstitched during operation (χ2 = 9.759, P = 0.002), postoperative intraventricular hemorrhage (χ2 = 8.938, P = 0.003), postoperative midline displacement > 10 mm (χ2 = 7.589, P = 0.006), and intracranial infection (χ2 = 4.519, P = 0.034), as well as postoperative coma time (t = 2.709, P = 0.008) were higher than those in non - hydrocephalus group. Logistic regression analysis showed that subarachnoid hemorrhage (OR = 1.885, 95%CI: 1.432-2.240; P = 0.012), dural unstitched during operation (OR = 1.468, 95%CI: 1.215-1.930; P = 0.006), long postoperative coma time (OR = 1.574, 95%CI: 1.358-1.926; P = 0.007), postoperative intraventricular hemorrhage (OR = 1.550, 95%CI: 1.254-1.768; P = 0.010), the level of β2- microglobulin increased after operation (OR = 1.622, 95%CI: 1.165-1.840; P = 0.004) were risk factors for hydrocephalus after decompressive craniectomy in patients with cerebral hernia after TBI. Based on these 5 factors, the Bayesian network model was constructed, and the area under ROC curve was 0.886 (95%CI: 0.823-0.925, P = 0.000). The calibration curve showed that there was a good consistency between the predicted probability and the actual probability, while the Hosmer-Lemeshow goodness-of-fit test showed no significant difference (χ2 = 8.760, P = 0.232), which indicated that the model had good discrimination, calibration and accuracy. Conclusions Subarachnoid hemorrhage, dural unstitched during operation, long postoperative coma time, postoperative intra ventricular hemorrhage, and elevated β2 - microglobulin level are the risk factors for hydrocephalus after decompressive craniectomy in patients with cerebral hernia after TBI.

This paper presents a probabilistic method to predict fatigue crack growth for surface flaws in pipelines using a particle filtering method based on Bayes theorem. The random response of the fatigue behavior is updated continuously as measured data are accumulated by the particle filtering method. Fatigue crack growth is then predicted through an iterative process in which particles with a high probability are reproduced more during the update process, and particles with a lower probability are removed through a resampling procedure. The effectiveness of the particle filtering method was confirmed by controlling the depth and length direction of the cracks in the pipeline and predicting crack growth in one- and two-dimensional cases. In addition, the fatigue crack growth and remaining service life with a 90% confidence interval were predicted based on the findings of previous studies, and the relationship between the fatigue crack growth rate and the crack size was explained through the Paris' law, which represents fatigue crack growth. Finally, the applicability of the particle filtering method under different diameters, aspect ratios, and materials was investigated by considering the negative correlation between the Paris' law parameters. [ABSTRACT FROM AUTHOR]