Search Results (326)

Akkermansia muciniphila is regarded as a beneficial mucin-degrading commensal, yet in Japanese populations the genus Akkermansia occurs at low abundance and is frequently undetectable. We examined its dietary correlates and its relationship with physical function in 786 community-dwelling older Japanese adults (age ≥ 65 years). Dietary patterns were derived from 31 food groups by principal component analysis (PCA) with varimax rotation, and Akkermansia was quantified by 16S rRNA gene sequencing and expressed as centered log-ratio (CLR)-transformed z-scores. Akkermansia was undetectable in 356 participants (45.3%) and of low abundance among carriers (median ≈ 0.35%). A noodle-based dietary pattern (principal component [PC] 4) was inversely associated with Akkermansia after adjustment for age, sex, body mass index, and lifestyle/comorbidity covariates (β = −0.125; p = 0.0007), with a dose–response across quartiles (p for trend = 0.004). In a two-part model, this association was confined to detection (adjusted odds ratio per 1 standard deviation = 0.80; 95% confidence interval, 0.69–0.93) and was absent among carriers (p = 0.25), indicating that diet was related to whether Akkermansia was present rather than to its abundance. Akkermansia was unrelated to grip strength, muscle mass, and frailty; weak carrier-restricted associations did not survive false discovery rate correction (p = 0.056) and are considered exploratory. These findings provide limited evidence for a role of Akkermansia in physical function in this low-abundance population. Full article

Monitoring poultry microbiota and antimicrobial resistance genes is important, as they can reflect flock health, farm conditions, and the level of antimicrobial resistance. Although shotgun metagenomics has been widely applied worldwide to investigate poultry microbiota and antimicrobial resistance, comparable baseline datasets describing the cloacal microbiota and resistome of poultry in Kazakhstan are scarce. In this study, taxonomic and resistome profiles were characterized in pooled metagenomes of the cloacal microbiota of chickens sampled from household and industrial poultry farms in Kazakhstan. Cloacal swabs were collected from laying hens, pooled at the house level, and analyzed using high-throughput metagenomic sequencing. Taxonomic profiles were generated at the genus level, and antimicrobial resistance gene signals were summarized by drug class. Compositional patterns were assessed using CLR/Aitchison ordination, the Mantel test, and Procrustes analysis. The pooled samples exhibited heterogeneous microbiota profiles at the genus level and included taxa of veterinary interest, such as Chlamydia, Avibacterium, and Gallibacterium spp. Resistome profiling revealed a broad but uneven distribution of antimicrobial resistance signals, including those associated with tetracyclines, fluoroquinolones, aminoglycosides, and beta-lactams. Taxonomic and resistome profiles showed preliminary alignment at the matrix level, indicating that resistome variations are partially linked to microbial community structure. Full article

Background: Diet and the gut microbiota are each associated with hepatic steatosis, but their joint variation and shared explanatory contribution are rarely quantified in older Asian community-dwelling populations. Methods: In 701 non-heavy-drinking Kyotango longevity cohort adults, habitual diet (BDHQ; 31 food groups) and stool 16S rRNA microbiome (47 genera; CLR-transformed) were related to the fatty liver index (FLI) by canonical correlation analysis (CCA), reduced-rank regression (RRR), and bootstrap mediation; FIB-4 was a secondary exploratory outcome. Results: Four dietary patterns and four microbial clusters emerged. CCA revealed multivariate diet-microbiota co-variation (four significant canonical correlations; r = 0.40–0.46; all p < 0.05). Combined RRR (n = 697 with complete FLI data) explained 11.1% of FLI variance in-sample (permutation p = 0.006), although cross-validated R² was negative, reframing the model as hypothesis-generating rather than predictive. Bootstrap mediation suggested that 12.6% of the diet-on-FLI effect was carried by the microbiota (95% bootstrap CI excluding zero). Of 1457 FDR-corrected food-genus pairs, one was significant (fruits × Eubacterium eligens; r = +0.202, q = 1.0 × 10⁻⁴). Conclusions: In this cross-sectional, hypothesis-generating analysis with no individual-level predictive utility, dietary patterns and gut microbial composition co-vary and jointly relate to FLI. The findings describe population-level covariance patterns for future prospective diet-microbiome intervention testing; external validation in independent cohorts is essential. Full article

High-altitude environments, characterized by hypoxia, intense ultraviolet radiation, and low temperatures, pose major challenges to livestock survival. In recent years, researchers have gradually uncovered adaptive mechanisms in livestock across different altitudes using whole-genome resequencing. Previous studies of goat altitude adaptation have been limited by small breed numbers and low sequencing depth, hindering comprehensive exploration of adaptive mechanisms across different altitudes. This study analyzed whole-genome resequencing data from 151 individuals across 17 goat breeds representing three distinct altitude gradients (high, middle, and low). Using both SNPs and structural variations (SVs), we characterized population relationships, gene flow, and the SV landscape, including QTL–SV associations and transposable element interactions. Selective sweep analyses using F_ST, θπ ratio, XP-CLR, XP-EHH, and LFMM identified several candidate genes associated with altitude adaptation, including ABCC4, RPS6, DSG4, and LY9, which were significantly enriched in pathways related to hypoxia response, oxidative stress, energy metabolism, angiogenesis, and nervous system regulation. Notably, ABCC4 showed ABCC4 showed recurrent candidate selection signals in both SNP and SV analyses, suggesting its potential involvement in altitude adaptation. These findings provide multi-level genomic evidence for goat adaptation to high-altitude stress and provide important insights into the adaptive evolution of goats. Full article

Background: The gut microbiota is an emerging factor in athletic performance. Methodological limitations persist in longitudinal microbiome studies, particularly regarding the compositional nature of microbiota data and the lack of standardized performance metrics in team sports. The present study applies a Compositional Data Analysis (CoDA) framework with ANOVA-Simultaneous Component Analysis (ASCA) to investigate longitudinal gut microbiota dynamics in elite volleyball athletes and introduces a novel Technical Performance Level (TPL) metric for objective performance quantification. Methods: Seven elite male volleyball athletes from the Italian SuperLega Championship were monitored across four sampling timepoints (T0, T1, T2, T3) during the Regular Season, Rest Period, and Tournament Period. Fecal samples underwent 16S rRNA metabarcoding. CLR-transformed data were analyzed at the Phylum, Family, and Genus level using ASCA with Season, Player, and their interaction. Statistical significance was assessed using the Freedman-Lane permutation test (10,000 iterations). Within-subject associations between TPL and CLR-transformed microbial features were assessed by repeated-measures correlation. Results: Season and Player effects were statistically significant at all taxonomic levels (p ≤ 0.0004), whereas the Season × Player interaction was significant at the Family and Genus levels (p ≤ 0.002) but not at the Phylum level (p = 0.108). The Player effect represented the largest source of variance at the Family (51.31%) and Genus (50.75%) levels. At the Phylum level, the Season × Player interaction accounted for the largest share of variance (36.23%), although it did not reach significance. Within-subject correlation analyses revealed no statistically significant association between microbial features and TPL at any taxonomic level, and none remained significant after correction for multiple comparisons. Conclusions: The application of a compositional data analysis framework modifies the interpretation of gut microbiota dynamics in elite volleyball athletes compared to standard approaches. Individual athlete identity accounted for most of the compositional variance in the microbiota, whereas exploratory within-subject analyses did not reveal statistically significant associations between taxa and TPL. Given the small cohort, any observed trends should be interpreted as exploratory and hypothesis-generating until confirmed in adequately powered cohorts. Longitudinal monitoring of gut microbiota could support individualized surveillance of gut health in elite sport. Full article

Mint (Mentha spp.) teas are widely consumed as functional herbal beverages and represent important dietary sources of bioactive volatile compounds. The present study comparatively evaluated the chemical composition and antimicrobial activity of essential oils (EOs) obtained from commercially available Mentha tea products purchased in Estonia, Lithuania, and Norway. In 10 EO samples studied, 85 compounds were identified by GC–MS. Carvone was the dominant constituent in the carvone chemotype (up to 58.1%), whereas menthol reached 30.7% in the menthol chemotype. Hierarchical clustering of CLR-transformed GC–MS data revealed three well-defined chemotypes among the analyzed Mentha EOs: carvone chemotype (three samples), menthol chemotype (five samples) and mixed, carvone-menthol chemotype (two samples). The antimicrobial activity of EOs was evaluated against 17 clinical antibiotic-sensitive and antibiotic-resistant microorganisms by the agar well diffusion method. The strongest antimicrobial activity was observed against resistant clinical Staphylococcus aureus strains, with MIC values as low as 0.10 mg/mL. EOs with a high carvone content consistently showed higher activity, especially against S. aureus, including resistant phenotypes. In contrast, menthol-chemotype EOs were associated with reduced antibacterial effectiveness. The obtained results demonstrate that the investigated commercial mint teas differ considerably in their phytochemical composition and biological properties, which may contribute to differences in their potential nutraceutical relevance as functional plant-derived beverages. Full article

C-type lectin receptors (CLRs) play central roles in sensing fungal pathogens and coordinating Syk-CARD9-dependent inflammatory responses. While Dectin-3 contributes to antifungal immunity against several clinically relevant fungi, its role in host defense against Paracoccidioides brasiliensis remains unknown. Here, we investigated the impact of Dectin-3 deficiency using Clec4d⁻/⁻ mice and primary phagocytes during experimental Paracoccidioidomycosis. Dectin-3-deficient macrophages and dendritic cells displayed unaltered cytokine production, phagocytic capacity, fungicidal activity, and maturation following P. brasiliensis challenge. Consistently, the absence of Dectin-3 did not impact survival or pulmonary fungal burden during long-term systemic infection. These findings are consistent with functional redundancy among CLRs, potentially involving Dectin-1, Dectin-2, or other Syk-coupled receptors rendering Dectin 3 dispensable for immunity to systemic experimental P. brasiliensis infection. Full article

Remote sensing scene classification remains challenging due to substantial object-scale variations, complex background interference, and high inter-class similarity. To address these issues, a lightweight classification framework, termed MDC-MobileNetV3, is proposed based on the MobileNetV3-Large backbone. The framework integrates a Multi-Scale Feature Extraction (MSFE) module for capturing spatial information at different receptive fields, a Dynamic Feature Weighted Fusion (DFWF) mechanism for adaptive feature recalibration, and the hierarchical CBAM attention strategy to enhance discriminative region representation. The model achieved high classification accuracies of 99.52%, 91.54%, 96.48%, 97.35%, 92.43%, and 99.72% on the UC Merced, WHU-RS19, NWPU-Resisc45, AID, CLRS, and PatternNet benchmark datasets, respectively, validating the effectiveness of the proposed framework, while maintaining a lightweight architecture with approximately 4.35 M parameters. In addition, Grad-CAM visualizations indicate that the model effectively focuses on semantically meaningful regions and suppresses irrelevant background information. The results confirm that the proposed framework provides a favorable trade-off between classification accuracy, model lightweight design, and model interpretability for remote sensing scene understanding. Full article

In this study, we examined the mineralogy, geochemistry and origin of Fe–Mn mineralization occurring as lenses within siliceous mudstones of the Middle Eocene Maden Complex (Durmuştepe, Elazığ, Türkiye) using multivariate statistical methods and Compositional Data Analysis (CoDA). Major-oxide, trace-element and REE analyses were performed on 21 samples (n = 21), comprising 11 ore and 10 host-rock samples. Ore microscopy, discrimination diagrams, PAAS-normalized REE patterns, Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), factor analysis, Kendall correlation and CLR-transformed CoDA-PCA were applied. The ore consists of pyrolusite, braunite, manganite, magnetite and hematite. Massive and felty textures indicate rapid precipitation linked to abrupt physicochemical changes near the vents. Fe/Mn ≈ 1.62 and Co/Ni = 0.03–0.04 support an exhalative origin; Ce/Ce* = 0.10–0.15 (mean 0.11) records a pronounced negative Ce anomaly; and Eu/Eu* = 1.06–1.18 suggests vent-fluid temperatures below ~250 °C. CoDA-PCA shows that Ce is decoupled from the other lanthanides; HCA separates ore from host-rock populations; and factor analysis indicates that Ti–Al–K detrital input diluted ore accumulation. Integrating these results, we interpret Durmuştepe as a proximal volcano-sedimentary hydrothermal-exhalative Fe–Mn system that formed during magmatism in the Maden marginal basin under oxic seawater mixing and contemporaneous detrital sedimentation. The multivariate workflow also provides a reproducible approach for source characterization in similar mixed-origin deposits. Full article

Speech-based depression screening is a promising non-invasive approach, but short spontaneous-speech segments often contain weak acoustic cues, fragmented semantic context, and overlapping representations between depressed and non-depressed participants. This study addresses this problem by proposing a preliminary short-segment acoustic screening framework that integrates multi-scale feature enhancement and reference-enhanced contrastive calibration. Based on the self-supervised pretrained Wav2Vec 2.0 model, a Multi-Scale Convolution (MSC) module captures short-term vocal fluctuations and longer-range prosodic patterns. A Reference-Enhanced Contrastive Learning (ReCLR) mechanism further calibrates latent acoustic representations using external emotion-reference features. Whisper-based transcription features are also incorporated to test whether short-segment semantic information can complement acoustic cues. In the participant-independent DAIC-WOZ evaluation, the proposed framework achieved an accuracy of 0.7021, a specificity of 0.8485 for non-depressed participants, and a sensitivity of 0.3571 for depressed participants. These results indicate that multi-scale acoustic enhancement and emotion-reference contrastive calibration may improve specificity-oriented short-segment screening, but the limited depressed-class sensitivity shows that the framework remains preliminary and requires stronger threshold calibration, training stability, and external validation before practical deployment. Full article

Background/Objectives: Inflammation plays a central role in the pathophysiology of ST-elevation myocardial infarction (STEMI) and may influence myocardial injury, ventricular dysfunction and clinical outcomes. Simple inflammatory biomarkers derived from routine laboratory tests have been proposed as potential prognostic indicators in patients undergoing primary percutaneous coronary intervention (PCI). Objective: This study aimed to evaluate the association between admission inflammatory biomarkers, echocardiographic markers of ventricular dysfunction and in-hospital mortality in patients with STEMI treated with primary PCI. Methods: We conducted a retrospective observational study including 600 consecutive patients admitted with STEMI and treated with primary PCI between January 2021 and August 2025. Inflammatory biomarkers measured at admission included C-reactive protein (CRP); neutrophil-to-lymphocyte ratio (NLR); platelet-to-lymphocyte ratio (PLR); systemic immune-inflammation index (SII) and C-reactive protein-to-lymphocyte ratio (CLR). Echocardiographic parameters and clinical outcomes were recorded. Multivariable logistic regression analysis was performed to identify independent predictors of in-hospital mortality. Results: In-hospital mortality occurred in 54 patients (9.0%). Patients with reduced left ventricular ejection fraction (LVEF ≤ 40%) had significantly higher CRP and CLR levels (p < 0.01). Inflammatory biomarkers were associated with markers of ventricular dysfunction but were not independent predictors of mortality. Age, LVEF < 40% and the number of residual coronary lesions independently predicted in-hospital death. Conclusions: In STEMI patients undergoing primary PCI, early mortality is mainly determined by age; ventricular dysfunction and residual coronary disease burden, while inflammatory biomarkers primarily reflect the severity of myocardial injury rather than independently predicting short-term mortality. Full article

Early stage mineral exploration requires the reliable identification of anomalous gold zones from drillhole geochemistry in data-limited environments. This study applies a machine learning (ML) classification framework to detect anomalous gold zones (Au ≥ 0.68 ppm; 90th percentile) from bulk XRF multielement drillhole geochemistry in a Paleoproterozoic Birimian greenstone belt sulphide-hosted orogenic gold system, West African Craton. A total of 53,126 one-metre diamond core samples from 301 drillholes were preprocessed within a compositional data analysis (CoDA) framework, with Au being explicitly excluded from the centred log-ratio (CLR) transformation to eliminate target–predictor circularity. After Minimum Covariance Determinant (MCD) outlier filtering, 40,385 samples were retained to construct a 19-feature matrix of 10 CLR-transformed elements, 1 rock-type feature, and 8 sulphide–lithology interaction features. Drillhole-based block cross-validation (DH-block CV), validated by an experimental along-hole variogram (practical autocorrelation range ≈ 20 m), ensured spatially honest performance estimates. Four nonlinear classifiers—Random Forest (RF), XGBoost, LightGBM, and Multi-Layer Perceptron (MLP)—were benchmarked against a Logistic Regression (LR) linear baseline. All nonlinear classifiers achieved validation AUC of 0.936–0.938, outperforming LR (AUC = 0.931) with F1-score improvements of +0.09 to +0.11 and precision gains of up to +35 percentage points—directly reducing wasted drill holes in applied exploration. MLP recorded the highest F1-score (0.666) and precision (0.765), and XGBoost the highest recall (0.787). Permutation importance identified S-Ti (ΔAUC = 0.028), S-Fe (0.021), and S-Al (0.013) as the top-ranked features, confirming that sulphide enrichment relative to lithological background is the primary discriminating signal. Partial dependence analysis revealed a threshold-driven non-monotonic Fe dependence at CLR(Fe) ≈ 3, marking the transition from lithological dilutant to sulphide co-indicator—a nonlinear pattern inaccessible to linear classifiers. Full article

Accurate pest recognition is an important foundation for intelligent plant protection, precision pesticide application, and sustainable agricultural management. However, in real field environments, pest targets are often small in scale, severely occluded, and embedded in complex backgrounds, which limits the performance of existing supervised learning methods under low-annotation and cross-scenario conditions. To address these issues, a multimodal self-supervised pretraining framework is proposed for pest recognition, in which field pest images and environmental sensor data are integrated to construct pest representations with environmental awareness. In this framework, image features, including pest morphology, leaf texture, and damaged regions, are first extracted through a visual encoding branch, while temporal variation features of ecological factors, including temperature, humidity, illumination, soil moisture, rainfall, and wind speed, are modeled through an environmental encoding branch. On this basis, a cross-modal contrastive consistency module is designed to align visual and environmental representations, a temporal consistency self-supervised module is introduced to characterize the continuous evolutionary relationship between pest occurrence and environmental changes, and a multimodal collaborative representation fusion module is constructed to adaptively integrate information from different modalities. The experimental results show that the proposed method achieves favorable performance in the pest recognition task, with Accuracy, Precision, Recall, and F1-score reaching $94.37\%$, $93.96\%$, $93.42\%$, and $93.69\%$, respectively, outperforming ConvNeXtV2-T, ViT-B/16, Swin-T, SimCLR, MAE, and the conventional Image + Sensor fusion method. The ablation experiments further show that, after removing the cross-modal contrastive consistency module, the temporal consistency self-supervised module, and the multimodal collaborative fusion module, the F1-score decreases to $91.00\%$, $91.36\%$, and $90.49\%$, respectively, thereby demonstrating the contribution of each module. This study provides a viable multimodal self-supervised learning approach for AI-driven intelligent pest recognition, early warning, and precision control in agriculture. Full article

Background/Objectives: Structural variations are important forms of genomic variation and are closely related to genomic diversity and many human diseases. Long-read sequencing has improved the ability to detect structural variations in complex genomic regions, but existing methods still mainly rely on manually designed heuristic rules and often have difficulty jointly modeling local SV signatures and cross-subsegment contextual modeling. To address this problem, we propose CMSV, a structural variation detection and genotyping method for long-read sequencing data. Methods: CMSV extracts multi-channel position-level features from alignment results and combines a multi-scale convolutional encoder with stacked Mamba modules for window-level candidate region detection. Candidate variants are then integrated and optimized through DBSCAN-based density clustering and length-based clustering. Genotypes are inferred based on variant-supporting reads and reference-genome-supporting reads. CMSV is designed to support several major structural variation types, including DEL, INS, DUP, INV, and TRA/BND. In our real-data benchmarks, the strongest validation is provided for DEL and INS, while DUP, INV, and TRA/BND are further evaluated using simulated multi-type datasets. Results: Experiments on real HG002 DEL/INS benchmarks, simulated multi-type datasets, and family-based datasets show that CMSV is competitive across PacBio CCS, PacBio CLR, and ONT platforms within the corresponding evaluation settings. Additional held-out chromosome evaluation on GRCh38 chr13–chr22 was further conducted to assess chromosome-level generalization beyond the full HG002 benchmark. CMSV shows stable performance in DEL/INS detection and genotyping on real-data benchmarks, while simulated multi-type evaluations further support its ability to detect DUP, INV, and TRA/BND. The results also show that CMSV can effectively model complex variant signals and maintain good family-level consistency in trio-based evaluation. Conclusions: CMSV provides an effective deep learning framework for long-read structural variation detection and genotyping across sequencing platforms and coverage levels. Full article

The calcitonin gene-related peptide (CGRP) is a 37-amino acid neuropeptide belonging to the calcitonin family, discovered as a product of alternative splicing of the calcitonin gene. CGRP has emerged as a pleiotropic signaling molecule with widespread distribution in the central and peripheral nervous systems, particularly within primary sensory neurons. This narrative review synthesizes current knowledge on the CGRP system, integrating recent advances in its molecular structure, gene organization, and post-translational processing with high-resolution structural insights into its heterodimeric receptor complex (CLR-RAMP1) obtained through cryo-electron microscopy. We also include long-term safety data on anti-CGRP monoclonal antibodies, emerging cardiovascular risk signals, and novel therapeutic applications in vestibular migraine and pediatric populations. The intracellular signaling cascades activated by CGRP, including the canonical cAMP-PKA pathway, MAP kinase activation, and context-dependent calcium signaling, are discussed in relation to its diverse physiological functions. These encompass vasodilation, nociception modulation, neurogenic inflammation, gastrointestinal motility, bone metabolism, tissue regeneration, and energy homeostasis. The central role of CGRP in migraine pathophysiology is examined to understand the development of targeted therapies. The current pharmacological landscape is reviewed, including the evolution of small-molecule CGRP receptor antagonists (gepants) through three generations and the four approved monoclonal antibodies targeting CGRP or its receptor, with comparative analysis of their efficacy, safety profiles, and clinical positioning. Beyond migraine, emerging and predominantly preclinical roles of the CGRP system are discussed in chronic pain, osteoarthritis, cardiovascular diseases, sepsis, cancer (particularly bone metastases and tumor microenvironment immunomodulation), and neurodegenerative disorders such as Alzheimer’s disease. In these areas, the available evidence remains heterogeneous and, in most cases, is not yet sufficient to support clinical translation. Finally, future directions are discussed, including the development of stable CGRP analogs, allosteric modulators, and the potential expansion of therapeutic applications into oncology, intensive care medicine, and neuroprotection. Full article