Search Results (188)

Background/Objectives: Human physiological evidence for functional clothing is limited, and garment changes may reflect ordinary material or measurement effects. We described surface-temperature and heart rate variability (HRV) observations before and after wearing black-silica-containing clothing and quantified paired changes and between-participant dispersion. Methods: Ten adults enrolled as healthy volunteers completed this single-center, non-randomized, unblinded, uncontrolled, fixed-order, single-group before–after pilot protocol. No physically matched control textile was used. No directional hypothesis or single primary outcome was prospectively specified. Exploratory domains comprised abdominal and leg surface temperature and eight RR interval (RRI)-derived HRV indices. All participants were analyzed; a post hoc n = 9 quality-control sensitivity analysis excluded one participant with a short post-wearing RRI segment. Effect estimates, 95% confidence intervals (CIs), and Holm-adjusted p values were reported. Between-participant dispersion was secondary and exploratory. Results: Leg surface temperature showed a modest increase of 0.668 °C (95% CI −0.001 to 1.336; raw p = 0.050; Holm p = 0.100); abdominal temperature changed by 0.007 °C (95% CI −0.447 to 0.462). No paired HRV outcome retained support after correction (all Holm p ≥ 0.797). In the secondary dispersion analysis, total power had an after/before log-scale SD ratio of 0.612 (bootstrap 95% CI 0.347 to 0.861; Holm p = 0.031); no dispersion outcome retained support in the n = 9 sensitivity analysis. Conclusions: This small uncontrolled pilot provides hypothesis-generating observations but cannot isolate an effect attributable specifically to black silica or demonstrate autonomic benefit, therapeutic action, or product efficacy. Confirmation requires an adequately powered, randomized, participant-blinded crossover study using physically matched garments and standardized measurement conditions. Full article

Sandy littorals are highly dynamic coastal ecosystems but remain poorly studied as wintering habitats for waterbirds in Mediterranean regions. We analysed mid-winter International Waterbird Census data collected along the 100 km sandy littoral of the province of Venice, northern Adriatic Sea (Italy), between 1994 and 2022. The dataset included 28 winter censuses, 70 species, and 150,083 individuals. The assemblage was rich but highly uneven: the Yellow-legged Gull Larus michahellis, Dunlin Calidris alpina, Black-headed Gull Chroicocephalus ridibundus, and Great Cormorant Phalacrocorax carbo accounted for most individuals. Total abundance was stable, despite strong interannual variability, whereas species richness increased significantly. Eco-functional guilds showed contrasting trends: benthic feeders and diving piscivores increased moderately, omnivores declined moderately, whereas the trends of the remaining four guilds were classified as uncertain. The Community Temperature Index did not show a significant directional trend, either for the whole assemblage or after excluding dominant species, suggesting that the observed changes cannot be interpreted as progressive thermophilisation. Overall, the Venetian sandy littoral supports a diverse and functionally heterogeneous wintering bird assemblage, whose long-term changes reflect shifts in community structure and the changing use of open-coast, nearshore, and artificial roosting habitats. The results provide a long-term evidence base for identifying conservation-relevant shoreline sectors, protecting feeding and high-tide roosting habitats, regulating disturbance and beach-management practices, and maintaining continued IWC monitoring of open-coast waterbird assemblages. Full article

Blackleg, caused by Clostridium chauvoei, is a severe infectious disease of cattle typically characterized by an acute or peracute course with rapid progression to death within 48 h after the onset of clinical signs. However, well-described reports of chronic or prolonged clinical evolution are rare. This study aimed to describe an outbreak with an atypical, prolonged course and to report the therapeutic management of affected animals. In March 2023, an outbreak occurred in an unvaccinated Nelore herd, with seven cattle showing clinical signs and one dying before veterinary intervention. Due to disease severity, four animals were referred to the Veterinary Hospital (HOVET) of the School of Veterinary Medicine and Animal Science, University of São Paulo (FZEA-USP), while others were managed on-farm. Clinical management included fluid therapy, systemic antimicrobial treatment, surgical debridement of necrotic tissues, drainage, and intensive wound care. Despite treatment, three animals died during hospitalization. One severely affected animal underwent partial amputation of a hind limb and survived, and it was discharged after 116 days of treatment. Diagnosis was confirmed by PCR, clinical examination, and necropsy findings. Overall, the clinical course observed differed from the typical acute presentation, with two animals surviving, suggesting that intensive medical and surgical management may improve outcomes in selected cases. Cardiac lesions were identified in necropsied animals, with exclusive involvement of the left ventricle, a finding that may warrant further investigation. This report highlights atypical manifestations of blackleg and contributes to the limited literature on prolonged cases. Full article

Accurate assessment of the presence and severity of plant diseases is essential for effective crop monitoring and management. This study presents a deep learning-based pipeline for quantifying blackleg crown canker disease severity in canola stems by combining image segmentation and severity prediction tasks. Three architectures (ResUNet, UNet and SegFormer) were compared for the first step of stem segmentation to isolate relevant regions. The disease severity scores of four experts, and their aggregated median, were used to train models which were evaluated for label consistency, ambiguity, and model robustness. Among the three segmentation architectures, SegFormer achieved the best performance (mean IoU = 0.939, F1 score = 0.962), outperforming ResUNet and UNet. There was a high correlation in disease severity scores across expert labels, with the median-trained model achieving correlation coefficients of 0.924–0.963 against individual expert assessors on the evaluation dataset. Confusion matrix analysis further demonstrated reliable classification across severity levels. This work highlights the influence of segmentation quality, label aggregation strategies and data imbalances on downstream prediction tasks. This study uses controlled imaging conditions, but the proposed framework provides a strong foundation for future application in field environments. The framework enhances model interpretability by generating severity scores that align closely with expert assessments to support users such as agronomists and plant breeders for better decision-making and help track disease resistance by providing consistent, objective disease measurements over time. Future work will focus on exploring multi-task learning for greater efficiency, alongside validating the approach in field conditions to enable broader adoption. Full article

Blackleg disease, caused by Leptosphaeria spp. fungi, is a major contributor to significant global yield losses in Brassica napus. Thus, selecting resistant plants using molecular markers linked to resistance loci is a common mitigation strategy. The latter, however, faces a challenge as pathogen virulence can overcome host resistance. This necessitates the identification of superior resistant genotypes through the use of numerous novel molecular markers and simplified detection methods to accelerate breeding programs. Based on our previous work, this study evaluated and verified molecular markers linked to blackleg disease resistance. A crucial finding is the identification of polymorphisms within SilicoDArT-type marker sequences that directly confer resistance. We also provide primer sequences for conventional PCR-based detection of SNP-type and SilicoDArT-type markers and a modified PCR protocol to enhance SNP detection efficiency. These validated markers and optimized PCR conditions are expected to significantly aid plant breeders in developing new, more resistant rapeseed varieties. Full article

Amphibian malformation syndromes significantly impact both conservation efforts and aquaculture, yet their underlying systemic pathophysiological mechanisms remain poorly characterized. This study comprehensively examines the multi-level pathological processes associated with the “short-leg” malformation syndrome in the black-spotted frog (Pelophylax nigromaculatus) using an integrated methodology, encompassing morphological, histopathological, gut microbiome, and hepatic transcriptomic analyses. Affected frogs demonstrated shortened limbs, impaired motor function, and a distinctive metabolic phenotype, including increased body weight despite a shorter body length, accumulation of visceral fat, and shortened intestines. Gut microbiota analysis identified significant compositional shifts, characterized by a decreased Firmicutes-to-Bacteroidota ratio, expansion of pro-inflammatory Proteobacteria, and reduction in beneficial Actinobacteriota, suggesting microbial niche restructuring that likely promotes metabolic and inflammatory disorders. Hepatic transcriptome profiling revealed 2617 differentially expressed genes, demonstrating a clear molecular dichotomy with concurrent up-regulation of immune-related pathways (e.g., neutrophil extracellular trap formation, complement cascades, and inflammatory signaling) and broad suppression of metabolic pathways (e.g., lipid oxidation, nutrient absorption, and PPAR and renin–angiotensin systems). This integrated analysis illustrates that the malformation syndrome represents a systemic pathophysiological state involving dysfunction of the gut–liver axis, characterized by the coexistence of gut microbiota alterations, hepatic metabolic suppression, and immune activation. These findings provide a framework for understanding amphibian malformations and suggest potential strategies to improve health outcomes in aquaculture. Full article

Wildfires can threaten the safety of transmission towers by degrading galvanized coatings and reducing the load-bearing capacity of steel members exposed to elevated temperatures. This study investigates the thermal damage evolution and structural response of transmission tower legs under localized wood-crib fire exposure through a combined experimental and numerical approach. A 1:4 scale tower-leg model was subjected to a single wood-crib fire exposure for approximately 20 min, during which temperature histories, surface damage patterns, and deformation of the fire-exposed members were recorded. The results show that the maximum measured temperature reached 803 °C and decreased approximately linearly with height, leading to distinct damage zones along the tower leg. The galvanized coating exhibited progressive degradation, including oxidation, melting, cracking, and local peeling, while the surface appearance changed from bright silver to black and finally to gray-white with reddish-brown areas in severely heated regions. A temperature-informed elastic–plastic finite element model was then used to interpret the global structural response. The analysis indicates that elevated temperature reduced the stiffness and load-bearing capacity of the fire-exposed side, causing deformation concentration and torsional distortion in diagonal members. The proposed framework provides a practical basis for post-fire damage identification and rapid structural assessment of transmission towers in wildfire-prone regions. Full article

Pectobacterium brasiliense is a highly destructive bacterial plant pathogen with a broad host range, causing soft rot and blackleg diseases. Genomic resources are for understanding the mechanisms of virulence in these necrotrophic bacteria. In this study, we isolated P. brasiliense strain 25ASUB12 from a symptomatic summer squash fruit growing in a field research plot in Mississippi. This is the first documented case of P. brasiliense in Mississippi. We extracted genomic DNA from the bacterial strain and sequenced it using Oxford Nanopore PromethION and Illumina NovaSeq X Plus platforms to produce a chromosome-level genome sequence of strain 25ASUB12. Genome annotation and comparative genomics were conducted to gain further insights into the strain. Results showed that the genome size of this strain was 4.90 megabases (Mb) and comprises several factors related to pathogenicity such as pectate lyases, diverse secretion factors, siderophores, and quorum-sensing genes. The whole genome of P. brasiliense strain 25ASUB12 serves as a genomic tool to conduct further research on diseases caused by this globally important plant pathogen. Full article

Xichuan black-boned chicken is a premium Chinese local breed in Xichuan County, Henan Province, China. However, the flavor characteristics of Xichuan black-boned chicken meat have not been systematically studied. Lipidomics and flavoromics approaches were used to analyze DLMs (differential lipid molecules) and DFCs (differential flavor compounds) in breast muscle (BM, n = 6) and leg muscle (LM, n = 6) of black-boned chicken, to reveal molecular mechanisms affecting meat quality in chicken. Lipidomics analysis reveals that 354 differential lipids are the differential abundance between the two groups, of which 33 are up-regulated and 321 are down-regulated in the BM group. These differential lipids were mostly enriched in glycerolipid metabolism, glycerophospholipid metabolism, and metabolic pathways. Flavoromics results demonstrate that there are 70 differential flavors between the two groups. Of these flavors, 59 are down-regulated and 11 are up-regulated in the BM group. These differential flavor compounds are mainly enriched in insect hormone biosynthesis and terpenoid backbone biosynthesis. Integrated lipidomics and flavoromics analysis shows that TG-type lipids and dodecanenitrile flavors may be the major related pairs. These findings not only enhance the understanding of the mechanism of chicken meat flavor formation but also provide novel perspectives for the improvement of meat quality. Full article

Rapeseed (Brassica napus L.) faces escalating threats from abiotic and biotic stresses, notably blackleg caused by Leptosphaeria maculans. Due to limited chemical control efficacy and stringent GMO regulations, marker-assisted selection (MAS) leveraging natural genetic variation has become an indispensable strategy for crop improvement. This study identified novel molecular markers for blackleg resistance by integrating genome-wide association study (GWAS) results with high-throughput genotyping by Diversity Arrays Technology sequencing. Phenotypic screening across the population demonstrated a wide spectrum of disease severity (scores 0–6), confirming the segregation of key resistance genes. The DArTseq platform identified nearly 104,000 markers, comprising 61% SilicoDArTs and 39% SNPs. Among the 33 most significant markers associated with resistance (p < 0.01), 76% were SilicoDArTs. Transcriptomic data further validated these findings, revealing 13 marker-linked genes expressed during infection, seven of which exhibited significant differential expression. Comprehensive functional annotation of Arabidopsis thaliana orthologs associated these genes with diverse cellular and plant-wide processes, particularly during stress responses. Collectively, these findings emphasize the complex polygenic nature of blackleg resistance and provide robust genomic tools for the accelerated breeding of resilient B. napus cultivars. Full article

Bacterial pathogens secrete effector proteins that suppress plant immune responses and facilitate infection. This study focuses on Pectobacterium brasiliense NJAU180, a bacterial pathogen causing severe blackleg disease in potato plants in Inner Mongolia, China. Using exoproteomic analysis, plant-induced extracellular proteins were identified by comparing culture supernatants from P. brasiliense NJAU180 grown in minimal medium (MM) alone and in the presence of aseptically grown potato plantlets at an early growth stage (OD₆₀₀ ≈ 0.5). The results reveal PurK as a novel plant-induced extracellular protein, and deletion of purK markedly reduces virulence. PurK, N⁵-carboxyaminoimidazole ribonucleotide synthetase, is a key enzyme in de novo purine biosynthesis. Its impact on virulence is distinct from the conventional production of plant cell wall–degrading enzymes: PurK promotes motility by modulating transcription of flagellar genes, acting through its three domains as an integrated unit to infect successfully. Extracellularly detected PurK suppresses callose deposition, a PAMP-triggered immunity (PTI)-like defense, while it also triggers a strong hypersensitive response and upregulates expression of PTI marker genes such as PR2 and WRKY7 when secreted into the host plant. Although PurK interacts specifically with PurE, our data indicate that PurK’s pathogenic effects operate independently of purine biosynthesis. This study reveals a reliable experimental model for more accurate assessment of microbe–plant interactions and highlights new functional roles for PurK in P. brasiliense NJAU180 pathogenesis and identifies potential targets for disease control strategies. Full article

Lyme disease, caused by the Borrelia burgdorferi bacterium and transmitted through black-legged (deer) tick bites, is becoming increasingly prevalent globally. According to data from the Lyme Disease Association, the number of cases has surged by more than 357% over the past 15 years. According to the Infectious Disease Society of America, traditional diagnostic methods are often slow, potentially allowing bacterial proliferation and complicating early management. This study proposes a novel hybrid deep learning framework to classify Lyme disease rashes, addressing the global prevalence of the disease caused by the Borrelia burgdorferi bacterium, which is transmitted through black-legged (deer) tick bites. This study presents a novel hybrid deep learning framework for classifying Lyme disease rashes, utilizing pre-trained models (ResNet50 V2, VGG19, DenseNet201) for initial classification. By combining VGG19 and DenseNet201 architectures, we developed a hybrid model, SkinVisualNet, which achieved an impressive accuracy of 98.83%, precision of 98.45%, recall of 99.09%, and an F1 score of 98.76%. To ensure the robustness and generalizability of the model, 5-fold cross-validation (CV) was performed, generating an average validation accuracy between 98.20% and 98.92%. Incorporating image preprocessing techniques such as gamma correction, contrast stretching and data augmentation led to a 10–13% improvement in model accuracy, significantly enhancing its ability to generalize across various conditions and improving overall performance. To improve model interpretability, we applied Explainable AI methods like LIME, Grad-CAM, CAM++, Score CAM and Smooth Grad to visualize the rash image regions most influential in classification. These techniques enhance both diagnostic transparency and model reliability, helping clinicians better understand the diagnostic decisions. The proposed framework demonstrates a significant advancement in automated Lyme disease detection, providing a robust and explainable AI-based diagnostic tool that can aid clinicians in improving patient outcomes. Full article

Body temperature serves as a crucial indicator of dairy cow health. Traditional rectal temperature (RT) measurement often induces stress responses in animals. Body temperature detection based on infrared thermography (IRT) offers non-invasive and timely advantages, contributing to welfare-oriented farming practices. However, automated detection and temperature extraction from critical cow regions are susceptible to complex illumination, black-and-white fur texture interference, and region of interest (ROI) deformation, resulting in low detection accuracy and poor robustness. To address this, this paper proposes the EDC-YOLO-World-DB framework to enhance detection and temperature extraction performance under complex illumination conditions. First, URetinex-Net and CLAHE methods are employed to enhance low light and overexposed images, respectively, improving structural information and boundary contour clarity. Subsequently, spatial relationship constraints between LU and AA are established using five-class text priors—lower udder (LU), around the anus (AA), rear udder, hind legs, and hind quarters—to strengthen the spatial localisation capability of the model for ROIs. Subsequently, a Dual Bidirectional Feature Pyramid Network architecture incorporating EfficientDynamicConv was introduced at the neck of the model to achieve dynamic weight allocation across modalities, levels, and scales. Task Alignment Metric, Gaussian soft-constrained centroid sampling, and combined IoU (C_IoU + G_IoU) loss were introduced to enhance sample matching quality and regression stability. Results demonstrate detection confidence improvements by 0.08 and 0.02 in low light and overexposed conditions, respectively; compared to two-text input, five-text input increases P, R, and mAP50 by 3.61%, 3.81%, and 1.67%, respectively; Comprehensive improvements yielded P = 88.65%, R = 85.77%, and mAP50 = 89.33%—further surpassing the baseline by 2.79%, 3.01%, and 1.92%, respectively. Temperature extraction experiments demonstrated significantly reduced errors for $T_{Max}$, $T_{Min}$, and $T_{avg}$. Specifically, for the mean error of LU, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 33.5%, and 4.27%, respectively; for AA, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 25.4%, and 11.3%, respectively. This study achieves robust detection of LU and AA alongside precise temperature extraction under complex lighting and deformation conditions, providing a viable solution for non-contact, low-interference dairy cow health monitoring. Full article

The holobiont of the blacklegged tick (Ixodes scapularis) includes maternally inherited rickettsial endosymbionts and environmentally acquired microbes that may influence tick fitness and vector competence. While previous studies have focused on characterizing the microbiota of I. scapularis ticks, less is known about the influence of tick infection status on microbial assemblages. Here, we collected engorged female I. scapularis ticks from hunter-harvested white-tailed deer (Odocoileus virginianus) across 11 counties in Wisconsin during fall 2022. The ticks were maintained in laboratory conditions for oviposition and then frozen for nucleic acid extraction. The infection status of each tick was determined using qPCR, targeting Borrelia spp., Babesia spp., and Powassan virus. Bacterial and fungal communities were characterized through amplicon-based sequencing targeting the 16S rRNA gene and ITS2 region, respectively. Our targeted pathogen testing revealed that 14.1% of the collected ticks were infected with Babesia odocoilei and 23.3% with Borrelia burgdorferi. The microbial community composition of ticks was significantly influenced by infection status and pathogen identity. Notably, Borrelia-infected ticks exhibited distinct microbiota profiles and increased microbial network connectivity. These findings provide new insights into the microbial ecology of deer-fed I. scapularis ticks and highlight the role of infection in shaping both microbiota and mycobiota communities. Full article

Brassica napus L. is one of the world’s most important oilseed crops. Blackleg disease is a serious, yield-limiting factor in the cultivation of oilseed rape. Genetic resistance is primarily conferred by major resistance (R) genes. In this study, we analyzed the expression of the blackleg resistance genes Rlm3, Rlm4, and Rlm7 following inoculation with the Leptosphaeria maculans isolate using the RT-qPCR method. Additionally, we demonstrated and assessed their interactions. The results showed that, while Rlm3 was weakly induced, Rlm4 and Rlm7 displayed variable expression post-inoculation. The correlation between phenotypic and genotypic similarity was low. This suggests that transcriptional responses do not fully explain resistance patterns. Furthermore, significant main effects of the analyzed genes, as well as two- and three-way interactions, were indicated. These results support current knowledge of gene-mediated resistance to blackleg in oilseed rape. Full article