Search Results (7,401)

Ash dieback is an often-fatal disease caused by the fungus Hymenoscyphus fraxineus (T. Kowalski) Baral, Queloz & Hosoya. It emerged in Europe during the 1990s and poses a substantial threat to ash populations. In Hungary, symptoms were first detected on common ash (Fraxinus excelsior L.) in 2008. The disease also severely impacts another native species, the narrow-leaved ash (Fraxinus angustifolia Vahl). An effective strategy for counteracting ash decline is to identify and utilize sources of tolerance. We are monitoring the health status of the selected trees that demonstrate low susceptibility (plus trees) and conducting molecular genetic studies to enable their genetic characterization and individual identification using 16 nuclear microsatellite (nSSR) markers. The PCoA (Principal Coordinates Analysis) separated the eight assessed groups into two distinct clusters based on the taxonomic traits. Based on the Structure analysis results, K = 2 was the most probable cluster number. Hybridization was also indicated in the case of several individuals across various groups. We intend to incorporate the results in the establishment of seed orchards using the selected plus trees, considering the taxonomical, geographical, and genetic distinctiveness of the different groups. Full article

Crop residues can harbor pathogens, making winter sanitation essential for sustainable viticulture. The grass–sheep–grape system could improve vineyard health through microbial optimization. To evaluate this, we assessed the effects of sheep feeding on fallen leaves on the occurrence of grape diseases through greenhouse experiments and used high-throughput-sequencing to compare microbial communities in grape fallen leaves and sheep feces, aiming to determine whether winter grazing reduces residue-borne pathogens. The results revealed that sheep grazing in vineyards significantly reduces the occurrence of grape leaf and cluster diseases, as well as a fundamental difference in microbial structures between leaves and feces, with no fungal taxa detected in the feces. The number of shared bacterial OTUs was minimal, while feces contained significantly more unique bacterial OTUs than fallen leaves. Additionally, bacterial diversity was significantly higher in feces than in fallen leaves. Sheep feces harbored a substantial number of highly efficient cellulose-degrading anaerobic bacteria, which may enhance organic matter conversion efficiency, and promote nutrient cycling in vineyards. Moreover, the grazing process directly reduced several pathogenic fungi associated with grape leaf, fruit, and root diseases. Functional analysis further indicated that fecal bacterial communities were primarily enriched in core metabolic and genetic processing functions, while leaf microbes were more involved in microbial interactions and secondary metabolism. More importantly, no function guilds of plant pathogenic fungi were present in feces. Overall, winter sheep grazing in vineyards can remove fallen leaves, not only reducing the risk of pathogen transmission but also potentially introducing beneficial bacterial communities. This study provides a feasible strategy for organic vineyard management in winter, and offers important insights for promoting sustainable vineyard production. Full article

This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes. Full article

Bovine leukemia virus (BLV) is an oncogenic deltaretrovirus that infects B cells, and its possible presence in humans has garnered increasing attention. This study included 58 participants: 11 with B-cell precursor acute lymphoblastic leukemia (B-ALL, cases) and 47 healthy individuals (controls). Researchers assessed anti-gp51 antibodies and BLV proviral DNA in bone marrow and blood samples. Seropositivity was observed only in the B-ALL group (18.2%; 2/11), while all controls were seronegative. Quantitative PCR targeting the pol gene detected proviral DNA in 74.1% of samples, with similar detection rates between cases and controls. Although proviral load was higher in controls, this difference did not reach statistical significance. Conventional and nested PCR for other viral genes revealed a differential pattern: amplification of the tax gene was significantly associated with B-ALL, whereas gag and env were not. Bayesian Chow–Liu network analyses identified dependencies among viral genes and suggested that contextual factors, such as fieldwork, may influence the association between molecular positivity and B-ALL. Sequence analyses showed that the detected BLV strains clustered with previously reported bovine and human sequences from Colombia, all within genotype 1. These findings support human exposure to BLV and raise important questions about its persistence and potential connections to hematological diseases in humans. Full article

Industrial Quality Inspection (IQI) is a pivotal part of intelligent manufacturing, critical to ensuring product quality. Deep learning-based methods have attracted growing attention for their excellent feature extraction ability, outperforming traditional detection approaches. However, existing methods still face issues of insufficient efficiency and poor transferability, and this paper proposes a Fine-tuned Lightweight Faster RCNN (FLF-RCNN) framework designed to address key challenges in IQI, including the trade-off between accuracy and computational efficiency, and the insufficient adaptability of preset anchor box ratios. FLF-RCNN introduces a lightweight backbone network, LSNet, which enhances the receptive field through architectural optimization. Specifically, it uses a collaborative mechanism that combines large kernel convolutions for extracting contextual information and small kernel convolutions for capturing fine-grained details. This mechanism enables the model to efficiently and precisely represent defects. To enhance generalization in data-scarce industrial scenarios, the framework leverages transfer learning with pretrained weights. Furthermore, an Adaptive Anchor Box-Adjustment Module (AAB-AM) based on K-means clustering is introduced to improve detection across varied defect scales. Extensive experiments conducted on the Tianchi dataset show that FLF-RCNN achieves a mAP50 of 43.6%, outperforming detectors using MobileNet and EfficientNet backbones and surpassing the baseline Faster R-CNN by 7.9% in mAP50. Meanwhile, the proposed method reduces computational complexity by approximately 40%, reaching 98.65 GFLOPs, and decreases parameter count by around 30% to 28.2M. These results demonstrate that FLF-RCNN offers a feasibility and practical solution for IQI, achieving a superior accuracy-efficiency balance within the two-stage detection paradigm. Full article

Mycoplasma genitalium (MG) is a cell wall–deficient bacterial pathogen associated with several sexually transmitted infections (STIs), including nongonococcal urethritis, cervicitis, and pelvic inflammatory disease. In the context of increasing antibiotic resistance and the challenges in clinical management, molecular epidemiological data are crucial for supporting surveillance strategies. This study aimed to assess the prevalence and genetic diversity of M. genitalium infections in a tertiary care hospital located in Northeastern Italy. In 2023, 2524 subjects (1622 men and 902 women) were screened using real-time multiplex PCR for the detection of major urogenital pathogens. M. genitalium-positive samples were molecularly characterized using a locus-typing approach based on sequence polymorphisms in the mgpB gene and the MG309 locus, enabling enhanced strain discrimination. Results revealed an overall positivity rate of 7.4% (118 cases), with a significantly higher prevalence in men (10.2%) than in women (2.6%), and the highest detection rate found in rectal swab specimens. Coinfections were detected in 48% of M. genitalium-positive subjects, most commonly involving Ureaplasma urealyticum (24%) and Metamycoplasma hominis (14%). Molecular typing on 22 M. genitalium-positive samples revealed significant locus-specific genetic heterogeneity, alongside the presence of a dominant cluster of 14 isolates with closely related allele profiles, suggesting the circulation of predominant local M. genitalium alleles within the analysed population. Full article

Equine trypanosomosis remains an important veterinary concern in regions where horses play a significant economic and cultural role. In Kazakhstan, comprehensive nationwide data on the seroepidemiological status of equine trypanosomes are limited. The aim of this study was to assess the serological distribution of equine trypanosomosis across all administrative regions of Kazakhstan using complement fixation testing (CFT). A total of 6065 equine serum samples were collected from seventeen regions between 2023 and 2025. Antibodies against members of the Trypanozoon subgenus were detected using a WOAH-recommended CFT protocol. Overall seropositivity was 4.73%, with substantial regional variation ranging from 0% to 16.52%. Statistically significant differences in seroprevalence were observed between regions (p < 0.001), and mixed-effects modelling indicated considerable regional clustering. PCR testing of seropositive samples did not confirm the presence of Trypanosoma equiperdum, while one sample tested positive for Trypanosoma evansi. These findings suggest that CFT seropositivity reflects exposure to equine trypanosomes rather than confirmed dourine infection. Given the inability of CFT to reliably distinguish between T. equiperdum and T. evansi, species-level attribution remains uncertain. This study provides the first nationwide overview of serological reactivity to equine trypanosomes in Kazakhstan. The results highlight regional heterogeneity in antibody detection and underscore the need for expanded molecular surveillance and improved species-specific diagnostic tools to clarify the epidemiological status of equine trypanosomosis in the country. Full article

Eleutherococcus senticosus, a medicinally important woody plant, is widely used in pharmaceuticals and functional foods due to its bioactive compounds. Its wild populations are facing severe threats due to over-harvesting. To inform scientific conservation and sustainable utilization strategies, this study aimed to comprehensively assess its genetic background. We developed 13 highly polymorphic expressed sequence tag-simple sequence repeat (EST-SSR) markers from full-length transcriptome data, with an average polymorphism information content (PIC) of 0.52. Using these markers, we systematically evaluated the genetic diversity of 405 individuals from 22 natural populations across Northeast China. The results indicate that E. senticosus maintains moderate genetic diversity at the species level (mean expected heterozygosity He = 0.43), but substantial variation exists among populations. The Linjiang population showed the highest diversity (He = 0.58), whereas peripheral populations such as Tonghua (He = 0.31) and Huinan (He = 0.32) exhibited lower diversity. Analysis of molecular variance (AMOVA) revealed that genetic variation primarily resided within populations (66.3%), but moderate differentiation among populations was also detected (Fst = 0.21). Both structure analysis and clustering consistently divided all populations into two major genetic lineages. Frequent gene flow (e.g., Nm > 10 between Raohe and Hulin) and high genetic homogeneity were observed among populations in the core distribution area (e.g., Raohe, Jixi, Hulin), whereas several peripheral populations displayed significant genetic distinctiveness and isolation. This study provides the first macro-scale insight into the population genetic structure of E. senticosus, offering crucial molecular tools and a scientific basis for in situ and ex situ conservation, core collection establishment, and future genetic improvement of this species. Full article

Human–AI collaboration (HAIC) increasingly mediates high-risk decisions in public and private sectors, yet many documented AI harms arise not only from model error but from breakdowns in joint human–AI work: miscalibrated reliance, impaired contestability, misallocated agency, and governance opacity. Conventional explainable AI (XAI) approaches, often delivered as static one-shot artifacts, are poorly matched to these sociotechnical dynamics. This paper is a position paper arguing that explainability should be reframed as a harm-mitigation infrastructure for HAIC: an interactive, iterative capability that supports ongoing sensemaking, safe handoffs of control, governance stakeholder roles and institutional accountability. We introduce co-explainers as a conceptual framework for interactive XAI, in which explanations are co-produced through structured dialogue, feedback, and governance-aware escalation (explain → feedback → update → govern). To ground this position, we synthesize prior harm taxonomies into six HAIC-oriented harm clusters and use them as heuristic design lenses to derive cluster-specific explainability requirements, including uncertainty communication, provenance and logging, contrastive “why/why-not” and counterfactual querying, role-sensitive justification, and recourse-oriented interaction protocols. We emphasize that co-explainers do not “mitigate” sociotechnical harms in isolation; rather, they provide an interface layer that makes harms more detectable, decisions more contestable, and accountability handoffs more operational under realistic constraints such as sealed models, dynamic updates, and value pluralism. We conclude with an agenda for evaluating co-explainers and aligning interactive XAI with governance frameworks in real-world HAIC deployments. Full article

Background: Conventional omics analysis often treats outliers as noise, yet they may harbor critical biological insights. Objetive: This study proposes a paradigm shift: actively investigating outliers to discover biologically relevant subtypes within metabolic–inflammatory syndromes. Methods: We applied a comprehensive analytical framework for outlier detection based on a multi-algorithm consensus (IQR, MAD, Isolation Forest) to a clinical cohort of diabetic neuropathy (n = 93) and an in vitro 3T3-L1 adipocyte model (n = 39). The identified outliers were characterized using robust PCA, co-expression networks, unsupervised clustering, and Random Forest predictive modeling. Results: In the clinical cohort, an outlier subgroup (47.3%) exhibited an extreme immune–metabolic phenotype characterized by hyperactivation of Th1/Th17 pathways (elevated T-bet and IL-17; p < 0.001), hypertriglyceridemia, and network reconfiguration (TGFβ and STAT4 hubs). In the cellular model, outlier samples (12.8%) showed autonomous pro-inflammatory behavior characterized by IL-6 overproduction (p = 0.002) and IL-10 suppression. Conclusions: Multivariate analysis confirmed spatial segregation of these profiles. Systematic outlier investigation revealed discrete pathophysiological subtypes invisible to mean-focused analyses, demonstrating that extreme values encapsulate potent biological signals. This framework offers a generalizable approach for uncovering clinical heterogeneity and identifying therapeutic targets in complex diseases. Full article

Avian Infectious Bronchitis Virus (IBV) is a highly contagious Gammacoronavirus that poses a significant threat to the global poultry industry. Despite its worldwide prevalence, a critical knowledge gap exists regarding the genetic diversity of IBV in Central Asia, particularly in Uzbekistan. This study is the first comprehensive molecular characterization of IBV in Uzbekistan. This study also provides a unique and informative bioinformatic analysis of the detected strains. Three IBV strains were isolated and identified from chickens suspected of IBV infection. The isolates were identified and subjected to S1 gene sequencing, phylogenetic analysis, recombination screening, selective pressure mapping, and in silico structural and antigenic profiling. Phylogenetic inference revealed that the isolates clustered within the established genotypes GI-1, GI-13, and GI-23. Comparative alignments revealed distinct nucleotide and amino acid substitutions relative to global reference strains. The evolutionary patterns are consistent with a predominantly clonal mode of evolution. Structural modeling and B-cell epitope prediction revealed pronounced antigenic and topological divergence among the Uzbek isolates. Genotype-specific substitutions, particularly in solvent-exposed regions of the spike protein, were associated with altered epitope profiles, implying potential impacts on vaccine cross-protection. These findings contribute to current knowledge of IBV molecular characterization and provide the first reference framework for the Central Asian region. The study highlights the importance of continuous molecular surveillance, region-specific vaccination strategies, and integrated genomic monitoring for novel IBV variants. Full article

This study examines the social burden and systemic infrastructure vulnerabilities associated with fatal road traffic incidents in Medellín, Colombia, over the period 2008–2025. Using official records from the Secretaría de Movilidad de Medellín, the analysis quantifies impact through Years of Potential Life Lost (YPLL) and high-resolution spatiotemporal clustering, moving beyond simple fatality counts or economic valuation alone. Phase I applies an age-group proportional allocation method to estimate YPLL for 2762 fatal road traffic incidents, while Phase II employs a spatiotemporal geostatistical framework (ICCE-T) to detect statistically significant concentration clusters. Results indicate that these incidents generated 100,851 years of potential life lost, with individuals aged 15–35 accounting for 64.7% of total YPLL, and the 20–25 age group alone contributing 21.5% of the overall burden. Spatial analysis reveals persistent clustering along key urban corridors, particularly in Comuna 10 (La Candelaria), identifying recurrent nodes of elevated systemic vulnerability. By integrating epidemiological measurement with spatiotemporal analysis, the study provides a decision-oriented analytical framework to support resilient, evidence-based urban mobility interventions and guide strategic public investment under the Safe System approach. Full article

Background: Spatially resolved transcriptomics (SRT) enables simultaneous measurement of gene expression and spatial location, but the existing domain detection methods are limited by over-reliance on spot-to-spot proximity, rigid pre-alignment requirements for multi-slice datasets, and inadequate mitigation of batch effects. This study aims to develop a unified method for accurate spatial domain identification across both single-slice and multi-slice SRT datasets. Methods: We propose a novel method named spatially resolved transcriptomics heterogeneous graph contrastive learning (stHGCL), which integrates a spot–gene heterogeneous graph, a dual-stage encoder (comprising LightGCN and GCN), and a neighborhood-driven contrastive learning module. The heterogeneous graph captures high-order structural information through spot–gene connections mediated by shared genes; the dual-stage encoder refines spot embeddings by fusing gene expression and spatial location; contrastive learning enhances intra-cluster compactness and mitigates batch effects. Results: stHGCL was validated on seven benchmark datasets from platforms including 10x Visium, BaristaSeq, STARmapSeq, Slide-seq, and Stereo-seq. It outperformed nine single-slice and eight multi-slice state-of-the-art methods. It achieved the highest mean Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) scores and could accurately delineate complex spatial domains with distinct boundaries, and even achieved cross-slice spatial domain detection for unaligned multi-slice datasets. Ablation studies confirmed the effectiveness of its main modules. Conclusions: stHGCL effectively captures high-order structural and spatial information and mitigates batch effects. It provides a robust scalable solution for unified spatial domain detection in SRT, facilitating insights into the spatial domains across both single-slice and multi-slice experimental paradigms. Full article

Pear scab, caused by Venturia pyrina, poses a threat to pear cultivation, with particularly severe consequences for Portugal’s high-value Rocha pear industry. Despite its economic impact, few biological control agents are currently available. In this work, the phenotypic and genomic characterization of Pseudomonas capeferrum NFX1 is performed and its role as an effective biocontrol agent against V. pyrina is reported. Detailed genomic analysis revealed that strain NFX1 and other members of the Pseudomonas capeferrum species contain key biosynthetic gene clusters involved in pathogen antagonism, including the cyclic lipopeptide putisolvin. Phenotypic assays showed that strain NFX1 significantly inhibited V. pyrina growth, spore germination, and reduced pear scab lesion severity and fungal colonization in detached leaf assays. Moreover, strain NFX1 reprogrammed the Rocha pear leaf transcriptome to be consistent with a priming state and induced systemic resistance. A novel image-based method quantifying lesion darkening as a proxy for pear scab severity in detached leaves and a qPCR assay targeting the V. pyrina ef1-α gene and optimized for fungal DNA detection in infected pear leaves were also developed, thereby establishing a laboratory workflow specifically tailored to biocontrol evaluation against V. pyrina. Ultimately, the obtained results demonstrated the potential of P. capeferrum NFX1 for sustainable pear scab control. Full article

In this paper, the authors developed a data-driven model to classify radar measurements into precipitation (P) and non-precipitation (NP) echoes using the Random Forest machine learning algorithm. Dual-polarimetric radar variables and their local variability exhibit distinctive characteristics between P and NP echoes. The authors found that using larger search window sizes generally improves classification accuracy, though it involves a trade-off: while it helps eliminate small clusters of NP echoes, it may also suppress weak precipitation signals near storm edges. Incorporating multiscale local variability estimates computed with varying window sizes further enhances classification performance by capturing spatial-scale-dependent features characteristic of P and NP echoes. The main model uses radar variables obtained from a single scan and demonstrates consistent performance across all distances from the radar. This consistency allows reliable use of the model out to 230 km—the maximum range at which dual-polarimetric variables are used for rainfall estimation from NEXRAD radars—without significant degradation in accuracy due to range effects. Supplementing the model with independent information from GOES-16 infrared channel products further improves classification by helping to eliminate localized NP echoes remaining after the main model, particularly those caused by wind turbines that mimic precipitation in dual-polarimetric signatures. This is based on the tendency of water vapor and/or raindrops to absorb terrestrial radiation, thereby lowering brightness temperatures. A practical challenge remains near the radar, where the sampling volume is small and signal processing (e.g., sidelobe impact and ground clutter suppression) can distort radar measurements. The under-detection of precipitation in these regions is likely due to such corrupted data. This issue may be mitigated by adopting a hybrid scan strategy—such as a Constant Altitude Plan Position Indicator (CAPPI)—specifically for regions close to the radar. Full article