Adult-onset seizure reflects the burden of acquired brain insults, but established disease-modifying preventive strategies are limited. We evaluated whether semaglutide initiation is associated with a lower incidence of adult-onset seizure compared with sodium-glucose cotransporter 2 inhibitors (SGLT2i) and other glucose-lowering drugs (GLDs) in adults with type 2 diabetes. Using the All of Us Research Program, we emulated a population-based target trial in new-user, active-comparator cohorts from January 2018 to October 2023. We compared semaglutide vs other GLDs and semaglutide vs SGLT2i. Incident epilepsy or seizure was identified from diagnostic codes. Effects were estimated using inverse probability of treatment weighting with weighted Cox models and targeted maximum likelihood estimation (TMLE). Subgroup and sensitivity analyses with multiple outcome definitions and analytic approaches were conducted to assess the robustness of findings. We evaluated mediation through hemoglobin A1c (HbA1c) and body mass index (BMI) using the longitudinal Vansteelandt framework. We analyzed 10,213 patients in the semaglutide (n = 2,586, mean age, 60.1 years; 56.8% female) vs other GLDs cohort (n = 7,627, mean age, 63.7 years; 54.2% female) and 8,605 patients in the semaglutide (n = 2,814, mean age, 60.5 years; 66.2% female) vs SGLT2i cohort (n = 5,791, mean age, 64.4 years; 47.3% female). Semaglutide was associated with a lower risk of adult-onset seizure compared with other GLDs (weighted HR 0.44 [95% CI 0.25-0.79]; 4-year risk difference -1.78% [95% CI -2.58 to -0.98]) and SGLT2i (weighted HR 0.48 [95% CI 0.27-0.85]; 4-year risk difference -1.46% [95% CI -2.41 to -0.51]). TMLE estimated risk differences per 1,000 persons of -14.20 (95% CI -18.33 to -10.07) vs other GLDs and -7.62 (95% CI -11.65 to -3.60) vs SGLT2i, corresponding to numbers needed to treat of 70 and 131, respectively. Mediation was minimal for HbA1c (2.4% vs other GLDs; 6.5% vs SGLT2i) and BMI (0% vs other GLDs; 0.7% vs SGLT2i). Semaglutide initiation was associated with a lower risk of adult-onset seizure compared with SGLT2i and other GLDs in patients with type 2 diabetes, independent of glycemic and weight effects. Residual confounding, low event counts, and shorter follow-up limit causal interpretation. This study provides Class II evidence that semaglutide use was associated with a lower risk of adult-onset seizures compared with other GLDs and SGLT2i.

The chloroplast genome of Amorphophallus allenii, a rare species endemic to limestone habitats, was sequenced to resolve phylogenetic uncertainties within the genus. Using PacBio HiFi sequencing, we performed a de novo assembly and obtained a complete circular genome of 170,657 bp, featuring a typical quadripartite structure. The genome encodes 126 genes, including 81 protein-coding genes, 8 rRNA genes, and 37 tRNA genes. Maximum likelihood phylogenetic analyses based on whole plastome, coding sequence, and protein alignments robustly support the placement of A. allenii within the Continental Asia I clade, with A. muelleri identified as its closest relative. This study provides a reliable genomic resource that clarifies the species’ phylogenetic position and establishes a foundation for investigating evolutionary diversification and biogeographic patterns in the genus Amorphophallus.

Cetaceans and sirenians independently transitioned from land to water, evolving unique and convergent sensory adaptations shaped by aquatic environments. Among sensory receptors, the Transient Receptor Potential (TRP) channel superfamily is central to thermo-, chemo-, and mechanosensation, but its evolutionary history in fully aquatic mammals remains poorly characterized. Here, we investigated the molecular evolution of TRP channels in these lineages. Orthology and phylogenetic relationships were inferred using Maximum Likelihood and Bayesian approaches. Signals of positive selection and molecular convergence were evaluated with codon and amino acid models. Amino acid substitutions, protein structure, and stability were assessed using 3D protein modeling. Our analyses reveal accelerated evolutionary rates in aquatic mammals, including multiple positively selected sites, lineage-specific amino acid substitutions, and convergent evolution across cetaceans and sirenians. Protein-level assessments identified substitutions with potential functional consequences, and evidence of pseudogenization was detected in cetacean PKD1L3, PKD2L1, TRPA1, and TRPM5, in contrast to intact copies in sirenians. These patterns suggest lineage-specific sensory trajectories, including reduced chemosensory repertoires in cetaceans, conservation of taste-related genes in sirenians, and adaptations in somatosensory associated genes that reflect both convergent requirements of fully underwater living and distinct aquatic environments. Overall, our findings advance understanding of the molecular mechanisms underlying sensory evolution during the land-to-water transition in mammals.

Impact of sample size of birds processed per pen in research settings for performance and quality data.

Subgroup analysis provides a useful tool for dealing with the heterogeneity in various situations, such as disease treatments, and has attracted a lot of attention in many areas, such as precision medicine. In this paper, we discuss the subgroup analysis for interval-censored failure time data under a heterogeneous Cox proportional hazards model, and a sieve penalized maximum likelihood estimation procedure is proposed. The proposed method can classify study subjects into different subgroups and determine the number of subgroups, the important predictors, and their estimated effects simultaneously. Also, the theoretical justification of the approach is provided, and an extensive simulation study is conducted to evaluate its empirical performance, which indicates that it works well in practical situations. In addition, the proposed methodology is applied to a set of real data on Alzheimer's Disease that motivated this study.

Recovery centered perinatal care: A retrospective cohort study evaluating a scalable prenatal care model to improve outcomes for birthing people with opioid use disorder and their newborns.

Continuous-time causal distribution learning with identifiability for brain dynamic effective connectivity inference.

Orf virus (ORFV) is the etiologic agent of infectious ecthyma, a common worldwide disease that occasionally causes zoonotic infections. In this study, we aimed to detect and molecularly characterise circulating ORFV strains in goats from different provinces in eastern Türkiye. Skin lesion samples collected during ORFV outbreaks between 2022 and 2024 were analysed. Following DNA extraction, positive samples identified by real-time PCR were analysed using a multi-locus phylogenetic approach targeting the structural F1L, the major envelope protein B2L, and the virulence-related VIR gene regions. Sequencing was performed using the Sanger method, followed by phylogenetic analysis using the Maximum Likelihood method in MEGA X. Phylogenetic analysis revealed high genetic similarity (95.63% to 99.48%) between the Turkish strains and global isolates from countries such as China, India, Iran, and Malaysia. Notably, while most isolates shared 100% similarity, the isolate from one specific province (Tunceli) exhibited significant nucleotide substitutions and a separate clustering pattern, particularly in the VIR gene. The results demonstrate that ORFV strains circulating in Türkiye display close phylogenetic clustering with Asian strains. The study highlights that multi-locus analyses, especially utilising the highly variable VIR gene, are essential for identifying regional genetic heterogeneity and monitoring microbial evolution. These findings provide a pilot reference for future epidemiological surveillance and vaccine development strategies in the region.

One-dimensional discrete models of maximum likelihood degree one

The g formula is a cornerstone method for estimating causal effects of time-varying treatments using longitudinal observational data in the presence of time-varying confounders that are affected by prior treatment. Standard regression techniques often fail in this setting because adjustment for such covariates can distort the very effects under study. The g-formula addresses this problem by expressing the mean potential outcome under specified static or dynamic treatment regimes as a function of the joint distribution of covariates, treatments and outcomes, which can be approximated via parametric or semi-parametric models and simulation. This review presents the g-formula, emphasizing intuitive explanations. After outlining the causal framework and the core identification assumptions-consistency, sequential exchangeability and positivity-the article describes practical parametric g-formula: model specification for covariate and outcome processes, implementation via forward simulation, and the interpretation of marginal causal contrasts between clinically relevant regimes. The g-formula is then situated within the family of g-methods alongside inverse probability weighting and targeted maximum likelihood estimation, highlighting complementary strengths and limitations. A dedicated section discusses concrete applications, including analyses of highly active antiretroviral therapy and AIDS or death, dynamic 'when to start' antiretroviral strategies in HIV and electronic health record-based evaluations of blood pressure treatment targets. Practical guidance on modelling choices, diagnostics and transparent reporting is provided to support applied researchers considering g-formula in clinical and epidemiological investigations.

To compare the efficacy and safety of ticagrelor monotherapy versus aspirin monotherapy in patients with coronary artery disease undergoing coronary revascularization. We conducted a systematic review and meta-analysis of RCTs comparing ticagrelor monotherapy with aspirin monotherapy after PCI or CABG. MEDLINE, Embase, Cochrane Central, Scopus, ClinicalTrials.gov and Google Scholar were searched up to 19th January 2026. The primary outcome was major adverse cardiovascular events (MACE), a composite of all-cause mortality, myocardial infarction and stroke. Secondary outcomes included all-cause mortality, myocardial infarction, stroke, major bleeding, repeat revascularization and stent thrombosis. Risk ratios (RRs) with 95% CIs were pooled using a random-effects model with restricted maximum likelihood estimation and Knapp-Hartung adjustment. Five randomised trials involving 25,994 participants were included, of whom 12,998 received ticagrelor monotherapy and 12,996 received aspirin monotherapy. Ticagrelor monotherapy was associated with a significantly lower risk of MACE than aspirin monotherapy (RR 0.86, 95% CI 0.78 to 0.95; p = 0.012; I² = 0%). All-cause mortality was also reduced with ticagrelor (RR 0.86, 95% CI 0.77 to 0.97; p = 0.023; I² = 0%). No significant differences were observed for myocardial infarction (RR 0.87, 95% CI 0.70 to 1.07; p = 0.138; I² = 0%), stroke (RR 1.01, 95% CI 0.85 to 1.19; p = 0.913; I²=0%), major bleeding (RR 1.00, 95% CI 0.84 to 1.20; p = 0.976; I² = 0%), repeat revascularization (RR 0.89, 95% CI 0.58 to 1.37; p = 0.452; I² = 45.8%) or stent thrombosis (RR 0.88, 95% CI 0.20 to 3.90; p = 0.481; I² = 0%). Ticagrelor monotherapy was associated with a potential reduction in MACE and all-cause mortality compared with aspirin monotherapy after coronary revascularisation, without increasing major bleeding. However, these findings were driven primarily by PCI trials, particularly the GLOBAL LEADERS and GLASSY trial program, and should be interpreted cautiously because PCI and CABG populations were analyzed together. CRD420261286239.

Autonomous driving accelerated evaluation method for independent/dependent variables based on importance sampling.

Common and rare variants that are associated with the risk of developing idiopathic pulmonary fibrosis (IPF) have been identified predominantly in European ancestry populations. To better understand the genetic variants that contribute to IPF in individuals with Asian ancestry, we conducted a genome-wide association study of IPF in East Asian populations. We included 1026 patients with IPF and compared them to 1723 unaffected controls of Japanese and Korean ancestry. Genome-wide association analysis was conducted in the Japanese and Korean ancestry cohorts separately and combined using meta-analysis. Restricted maximum likelihood was used to estimate the SNP-based heritability and local ancestry of chromosome 11 was inferred for each subject. We identified loci on chromosomes 4 (FAM13A; rs7690839), 5 (TERT; rs7734992), 6 (DSP; rs2076295), and 11 (MUC5B; rs35705950) that were significantly associated with risk of IPF. Importantly, the sentinel variants in each of these loci are the same as, or in strong linkage disequilibrium with, the risk variants that have been observed in studies of European ancestry populations. In aggregate, common variants (not including the MUC5B promoter variant) account for approximately 25% of the risk of developing IPF in these East Asian ancestry cohorts. Moreover, local ancestry analysis indicates that the presence of MUC5B promoter variant in the East Asian population is not a result of admixture with European ancestry populations. We conclude that the IPF risk loci in East Asian populations are shared with those of European ancestry populations, although their risk allele frequencies and effect sizes differ. These findings indicate shared genetic risk factors of IPF across ancestries.

Arunachali yak is the first registered yak breed of India and is mainly distributed in the Tawang and West Kameng districts of Arunachal Pradesh. Despite its socio-economic importance, complete mitochondrial genomic information for this breed has remained unavailable. The present study aimed to characterize the complete mitochondrial genome and infer the phylogenetic relationship of Arunachali yak with other bovids. High-quality genomic DNA isolated from Arunachali yak blood was sequenced using the PacBio Sequel II long-read platform. The complete mitochondrial genome was assembled as a circular double-stranded molecule of 16,324 bp comprising 13 protein-coding genes, 22 transfer RNAs, 2 ribosomal RNAs, and an 894 bp control region (D-loop). The mitogenome exhibited AT-rich nucleotide composition with positive AT skew and negative GC skew. Relative synonymous codon usage analysis showed preference for codons ending with A or T at the third codon position. Ka/Ks ratios of all protein-coding genes were <1, indicating strong purifying selection. Phylogenetic analyses using Maximum Likelihood and Bayesian Inference methods produced congruent topologies and clustered Arunachali yak with other domestic yak breeds and wild yak (Bos mutus) within a single monophyletic clade. Arunachali yak showed close maternal affinity with Gannan yak and was positioned within the mt-I yak lineage. This study presents the first complete mitochondrial genome characterization of Arunachali yak and provides new insights into its mitochondrial genome organization and phylogenetic relationships with other bovids. The generated mitogenome represents an important genetic resource for future phylogenetic, comparative genomic, and biodiversity conservation studies in yak populations.

Robust measurement of gender-related beliefs and attitudes is essential for understanding how gender norms operate in higher-education settings. However, little is known about how these constructs are structured among Ghanaian university students or whether widely used instruments retain validity in this context. This study examined the factor structure, reliability, and measurement invariance of adapted versions of the Gender Norm Attitudes Scale (GNAS) and Gender Equitable Men (GEM) Scale among Sociology and Social Work students in Ghana. A cross-sectional survey was conducted among 631 students (98.3% response rate) from two public universities, split for Explorative Factor Analysis (EFA) (n = 339) and Confirmatory Factor Analysis (CFA) (n = 364). EFA used principal axis factoring with oblique rotation on polychoric correlation matrices; parallel analysis guided factor retention. CFA was estimated under maximum likelihood and WLSMV estimators. Reliability was assessed using Cronbach's alpha and McDonald's omega. Measurement invariance was tested across sex, university, programme, and year level using the standard nested sequence of configural, metric, and scalar models, and was evaluated by changes in fit indices (ΔCFI, ΔRMSEA, ΔTLI, and ΔSRMR); differential item functioning (DIF) was assessed using ordinal logistic regression. Data were managed and described using SPSS Version 32; all factor-analytic, reliability, invariance, and DIF analyses were conducted in R (psych and lavaan packages). The GNAS yielded a two-factor, 11-item structure reflecting beliefs supporting men's privileges (α = 0.824, ω = 0.868) and equity for girls and women (α = 0.830, ω = 0.898), with strong fit (CFI = 0.960; TLI = 0.949; RMSEA = 0.057). Polychoric EFA confirmed this, indicating ordinal estimation did not alter the solution. After empirically driven item reduction, the adapted GEM Scale emerged as a modified version of the original instrument, with narrower domain coverage and near-complete loss of the Reproductive Health and Disease Prevention domain; the retained 16-item, four-factor structure covered violence, domestic chores and daily life, sexual relationships, and household authority and obedience, explaining 52.6% of variance with acceptable fit (CFI = 0.888; TLI = 0.865; RMSEA = 0.058), though the Household Authority and Obedience factor rested on only two items and the subscales showing weak Cronbach's alpha values. The students generally rejected inequitable norms, though attitudes toward household authority and domestic roles were more variable. For the GNAS, configural and metric invariance held across sex and university (ΔCFI ≤ 0.005); scalar invariance held across university (ΔCFI = 0.006) and was only partial across sex (ΔCFI = 0.022). For the adapted GEM Scale, metric invariance held across university (ΔCFI = 0.006) but not sex (ΔCFI = 0.016), and scalar invariance was not supported in either grouping. Although preliminary, the adapted GNAS shows acceptable structural validity, adequate-to-strong reliability, and metric invariance across sex and university and scalar invariance across university. In contrast, the adapted GEM Scale should be interpreted cautiously as a substantially modified measure with reduced domain coverage and less stable invariance properties, for which scalar invariance is not yet established. Further validation, confirmation of these invariance results using ordinal (WLSMV) estimators, and exploration of 5- or 7-point response formats are needed before either adapted scale is used confidently in broader research or programme evaluation.

Bacteria of the family Anaplasmataceae are tick-borne pathogens of recognized veterinary and zoonotic relevance, widely distributed among domestic and wild vertebrate hosts. Although increasingly reported in Brazilian wildlife species, molecular data on these agents in xenarthrans remain scarce, particularly in regions undergoing environmental transformation. This study investigated the occurrence of Anaplasma spp. and Ehrlichia spp. in free-ranging giant anteaters (Myrmecophaga tridactyla) and southern tamanduas (Tamandua tetradactyla) from the central-western region of São Paulo State, Brazil. Twenty-six blood samples (21 M. tridactyla and 5 T. tetradactyla) were analyzed using conventional and nested PCR assays targeting the 23S rRNA gene (Anaplasma spp.) and the dsb gene (Ehrlichia spp.), followed by sequencing and phylogenetic inference. An Ehrlichia genotype phylogenetically related to Ehrlichia chaffeensis was detected in two M. tridactyla samples (7.69%), whereas an Anaplasma genotype was detected in one T. tetradactyla sample (3.85%). The Ehrlichia sequences showed high nucleotide identity (99.61-100%) with reference sequences identified as E. chaffeensis and grouped within clades containing E. chaffeensis in Maximum Likelihood analyses. The Anaplasma sequence showed phylogenetic affinity with a clade comprising Anaplasma marginale, Anaplasma centrale, and related genotypes. Because only partial gene fragments were analyzed, species-level assignment was not attempted. These findings provide molecular evidence of the occurrence of Anaplasmataceae-related genotypes in free-ranging xenarthrans inhabiting anthropogenically altered landscapes in southeastern Brazil. Further investigations including expanded sampling, vector identification, and multilocus or genomic approaches are necessary to better characterize these agents and clarify their ecological significance.

Ceratopogonid midges of the genus Forcipomyia (subgenus Lasiohelea) are small hematophagous insects widely distributed across tropical regions. In Australia, developmental stages of Leishmania (Mundinia) macropodum have been observed in Forcipomyia (Lasiohelea), suggesting that biting midges may play a role in Leishmania transmission beyond traditional sand fly vectors. However, in Southeast Asia-where leishmaniasis caused by Mundinia species is an emerging autochthonous disease in humans-fundamental information on Forcipomyia (Lasiohelea) diversity and its association with Leishmania remains limited. An integrated morphological-molecular approach was employed to characterize Forcipomyia (Lasiohelea) midges collected using sweep nets from a leishmaniasis-endemic area in Nakhon Si Thammarat Province, Southern Thailand, near the residence of a patient with locally acquired cutaneous leishmaniasis, during January and June 2025. Morphological examination of mandibular dentition, sensory pits, cibarial armature, and spermathecal structure was combined with mitochondrial cox1 barcoding, Bayesian and maximum likelihood phylogenetic analyses, and species delimitation methods (ASAP and mPTP). Because females of the subgenus Lasiohelea possess a single spermatheca, only single-spermatheca females were selected for species identification. Specimens identified as Lasiohelea were subsequently screened for Leishmania using 18S rRNA-qPCR and ITS1-PCR, followed by nanopore-based ITS1 metabarcoding for species-level identification. Vertebrate blood meal sources were also characterized using vertebrate cox1 metabarcoding. From 264 collected midges, 72 female specimens with a single spermatheca were selected for analysis. Integrated morphological and molecular data identified seven Lasiohelea specimens forming four genetic clusters, comprising F. (L.) parvitas (n = 2) and three lineages closely related to F. (L.) peditata (n = 2), F. (L.) humilavolita (n = 2), and F. (L.) taiwana (n = 1). These were clearly separated from non-Lasiohelea taxa, including F. (Euprojoannisia) fuscimana and two unclassified Ceratopogonidae lineages. Phylogenetic and species delimitation analyses revealed cryptic geneticdiversity despite morphological similarity. Leishmania DNA was detected in six of seven Lasiohelea specimens. Nanopore ITS1 metabarcoding identified autochthonous species (L. (Mundinia) martiniquensisandL. (M.) orientalis) and additional Leishmaniaspecies (L. (Leishmania) amazonensisandL. (L.) major), including mixed-species detections in four specimens. A single engorged specimen contained DNA from red junglefowl (Gallus gallus spadiceus). This study provides the first integrated characterization of Forcipomyia (Lasiohelea) diversity and associated Leishmania detection in Southeast Asia. The results identified several putatively distinct specieswithin Lasiohelea and provide evidence of natural exposure of these midges to multiple Leishmania species, suggesting a complex parasite-midge association andparasite co-circulation in the local environment. Although vector competence was not assessed, these findings suggest that Forcipomyia (Lasiohelea) may be involved in the circulation of Leishmania parasites and may represent promising candidates for further investigation of vector competence. Future studies focusing on host associations, parasite development, and experimental transmission are needed to clarify their epidemiological significance.

Urbanization is one of the major land transformation processes taking place worldwide, particularly in developing countries such as India. In recent decades, secondary cities have expanded rapidly across the globe. In this context, a comprehensive understanding of Land Use Land Cover and Urban Expansion Intensity dynamics is crucial for rational urban planning and policy formulation. This study examined the spatiotemporal pattern of urban expansion using Remote Sensing and GIS techniques. Maximum Likelihood Supervised classification and Urban Expansion Intensity Index have been utilised through satellite imagery of Moradabad district for 2004 and 2024. LULC categories help quantify the growth of various land use classes, whereas Urban Expansion Intensity measures the rate and magnitude of urban growth in Moradabad District. The results reveal a significant increase in built-up area. Particularly in Moradabad tehsil, with a 176% growth over 20 year period, contrasted by decline in agricultural land, water bodies and vegetation cover. The Tehsil-level analysis exposes the uneven growth of built-up area within the district, with very high growth in Moradabad tehsil compared to Bilari, Kanth and Thakurdwara. The findings underscore the need for urban planning, afforestation, river buffer protection and sustainable development and the integration of geospatial monitoring in developmental policies.

There is a growing interest in subject-specific predictions using neural networks, as large-scale biomedical data often exhibit dependency due to high-cardinality categorical features, which have been largely overlooked by traditional neural network frameworks. This article proposes a novel hierarchical likelihood learning framework that captures both nonlinear overall effects and subject-specific effects by incorporating gamma random effects into Poisson neural networks. The global maximizer of the proposed objective function yields maximum likelihood estimators for fixed parameters and best unbiased predictors for random effects. The proposed framework provides a robust end-to-end algorithm for clustered biomedical count data, in the sense that the corresponding estimating equations remain unbiased even when the random-effects distribution is misspecified. To enhance learning efficiency, we introduce an adjustment procedure for the random effects and variance component. Extensive simulation studies and real data analyses demonstrate the practical effectiveness of the proposed method for clustered biomedical count data. The proposed method achieves competitive predictive performance in terms of mean squared Pearson error and mean deviance across various random-effects distributions and real-world datasets.

The log-logistic (LL) distribution is widely used in reliability, actuarial, financial risk, and income distribution studies due to its heavy-tailed structure and flexible hazard function. In this study, the LL distribution is extended to a bivariate form under the Marshall–Olkin (MO) structure, and its dependence properties are addressed through the copula representation. In addition to maximum likelihood estimation (MLE), robust estimation approaches are developed by formulating M- and L-estimators that are more robust to outliers. The copula representation, upper-tail dependence, and the singularity component are theoretically derived, and the effectiveness of the estimators is compared with simulations. The results reveal that the M- and L-estimators provide lower error and more stable deviation than the MLE in small and medium samples. At the same time, the difference decreases in large samples due to the asymptotic efficiency of the MLE. Furthermore, robust estimators increase the stability of the optimization by providing reliable starting values for the MLE. Applications to UEFA Champions League data showed that marginal fit tests did not reject the LL hypothesis, while copula-based tests confirmed model fit. The best results were obtained with the L-estimator based on log-likelihood and AIC criteria. The findings indicate that robust methods are more effective in heavy-tailed and limited-sample datasets, while MLE is more effective in large samples.