Search Results (280)

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne zoonotic pathogen of significant public health concern in South Korea, where human cases continue to occur at high levels; however, information on the molecular epidemiology and genotype diversity of SFTSV in goats—an increasingly important livestock species—remains limited. In this study, blood samples were collected from 750 clinically healthy goats during nationwide surveillance in 2024. Viral RNA was detected by RT-PCR targeting the S and M genomic segments. Epidemiological characteristics were analyzed according to season, region, farm size, breed, and sex. Positive samples were subjected to sequencing and phylogenetic analysis to determine SFTSV genotypes. SFTSV RNA was detected in 10 of 750 goats (1.3%), with significantly higher detection rates in autumn compared with summer, in southern regions compared with northern regions, and in female goats compared with males, while no significant association was observed with farm size or breed. Phylogenetic analysis showed that goat-derived SFTSV strains belonged to genotypes B2, D, and F; notably, genotypes D and F were identified in goats for the first time in South Korea. These findings indicate that goats are exposed to genetically diverse SFTSV strains circulating in tick populations and exhibit epidemiological patterns consistent with tick ecology and human SFTS incidence, supporting the role of goats as incidental or sentinel hosts. Continuous molecular surveillance of goats, integrated with vector monitoring programs, may enhance understanding of regional SFTSV transmission dynamics and facilitate early detection of emerging genotypes with public health implication. Full article

Cryopreservation of bovine ovarian cortical tissue offers a promising strategy for preserving female fertility and genetic resources, yet outcomes remain variable and influenced by both protocol and tissue size. This study investigated how slow freezing-thawing (SFT) and two vitrification-warming procedures (VW1 and VW2) affect preantral follicle morphology and granulosa cell proliferation in bovine ovarian cortex fragments of two dimensions (1 × 10 × 5 mm and 1 × 10 × 10 mm). Tissue from six cows was processed for histological evaluation and Ki67 immunostaining. Small fragments subjected to SFT showed no significant reduction in the proportion of morphologically normal follicles compared with fresh controls, representing the best overall preservation. In contrast, vitrification decreased morphological integrity, with VW2 performing better than VW1 in both fragment sizes. Small SFT pieces contained more morphologically normal follicles than large ones. Granulosa cell proliferation capacity was largely maintained across cryopreservation protocols, increasing with follicular stage; a size-related difference only appeared on VW2, where small fragments displayed higher Ki67 positivity. These findings underscore the relevance of jointly evaluating cryopreservation protocol and fragment size to optimize bovine ovarian tissue preservation, strengthening the evidence supporting SFT of small fragments as a robust option for safeguarding cortical integrity and improving tissue-based fertility preservation strategies. Full article

Imaging logging serves as a critical technology for identifying and characterizing fractures in unconventional oil and gas reservoirs. Despite significant progress in deep learning for automated fracture recognition in this field, the integration of fracture interpretation with large language models remains insufficient. To address this, this paper constructs a Chinese fracture image–text pair dataset covering multiple scenarios and proposes “FracLogGPT”, a three-stage multimodal large language model with a parameter scale of approximately 7 billion. Using Qwen2.5-VL-7B as the baseline model, this study employs Domain-Adaptive pre-training (DAPT) to tailor the model to geological and logging contexts. Efficient Supervised Fine-Tuning (SFT) is achieved via the LoRA method, while output style alignment is accomplished through Direct Preference Optimization (DPO) combined with expert preference data. Experimental results on an independent test set show that FracLogGPT achieves a Count-F1 of 0.70 for fracture-count classification, with location and morphology consistency accuracies of 0.49 and 0.43, respectively, and higher text-level BLEU and ROUGE-L scores than larger, non-domain-adapted external models evaluated under the same conditions. Comparative experiments across stages validate the effectiveness of the proposed workflow. In summary, “FracLogGPT” achieves automated identification and expert-like description of imaging logging fractures with approximately 7 billion parameters, providing a reusable training pathway and evaluation workflow for intelligent imaging logging interpretation. Full article

The boundary of a submerged floating tunnel (SFT) is flexible, and ignoring the influence of boundary and pipeline connections may reduce its structural performance. Therefore, this study uses rotating springs and linear springs to simulate the flexible boundary. Joints are simplified as shear springs and bending springs. A multi-span SFT model on discrete elastic supports is established, and its seismic response is evaluated using the transfer matrix method and the modal superposition method. The proposed method is validated by comparing it with finite element results, and the vertical mechanical response of the SFT when the cable relaxes or fractures under earthquake action is analyzed. The results indicate a significant deviation between the seismic response of flexible constraints and those modeled as simple hinged or fixed connections, and the lower boundary constraint stiffness is beneficial to the seismic response of the SFT. Introducing flexible joints can effectively reduce the internal force response of the structure, and a bending stiffness ratio of 0.01 to 0.03 for the joints is considered reasonable. In contrast, variations in the shear stiffness of the joints have a relatively small impact on the seismic response. Full article

Environmental and economic concerns due to the increasing use of fossil-based chemicals, especially fuel, may be alleviated by production of renewable fuels based on plant biomass, in particular, waste. Multistep cascades of enzymatic reactions are being increasingly sought to enhance the effectiveness of sustainable, environment-friendly processes. The biochemical transformation of lignocellulosic biomass and oils into fatty acid esters (“biodiesel”) involves biomass pretreatment, followed by polysaccharide hydrolysis and sugar fermentation to alcohol, either sequentially or simultaneously. Subsequent trans-esterification with waste or non-food-based oils is usually carried out in an organic solvent. Biocatalysis in aqueous emulsion offers significant advantages. This study presents a novel “one-pot” emulsion-based process for transforming unmodified cellulose and castor oil into biodiesel via hybridized yeasts with cellulose-coated micro-particles incorporating cellulolytic enzymes and lipases. The resultant consolidated bioprocess of saccharification, fermentation, and transesterification (cSFT) promotes effective substrate channeling and can potentially serve as a model for emulsion-based “one-pot” transformations of cellulose into valuable chemicals. Full article

This study investigates and compares different molecular mutation strategies to optimize their application as genetic algorithm operators in drug design. We evaluated five distinct mutation methods—Graph-Based Genetic Algorithm, Graph-Based Generative Model, SmilesClickChem, SELFIES Token, and SMILES Token Mutation—by assessing their computational efficiency, validity, and impact on molecular complexity and structural conservation. Our results reveal that the Graph-Based Genetic Algorithm achieves the highest molecular validity (96.5%) while maintaining computational efficiency, making it ideal for rapid iterative drug discovery. SmilesClickChem and Graph-Based Generative Model tend to increase molecular complexity, whereas SF-T simplifies molecular structures, suggesting different applications in lead optimization. Additionally, we analyzed mutation-induced changes in pIC50 potency and found that SELFIES Token caused the most substantial shifts in bioactivity, particularly in SRC-targeted molecules. These findings underscore the importance of selecting the appropriate mutation strategy to balance validity, structural diversity, and computational cost in AI-driven drug design. Our insights help refine evolutionary algorithms for molecular generation and optimize candidate selection in early-stage drug discovery. Full article

Background/Objectives: Hypertension is a leading cause of cardiovascular morbidity and mortality, particularly in older adults, pharmacological therapy is effective, but side effects and limited adherence highlight the need for non-pharmacological alternatives. This study investigated the effects of a structured Bodypump (BoP) programme, a choreographed group-based resistance training intervention, on blood pressure (BP) and functional fitness in sedentary older adults with hypertension. Methods: Thirty-two participants (65.4 ± 7.7 years) diagnosed with hypertension were randomly allocated to a BoP group (n = 16) or a control group (CG) (n = 16). The intervention lasted 8-week and consisted of 3 supervised sessions per week. Resting systolic BP (SBP) and diastolic BP (DBP) were measured using a validated automated device (Omron M3 Intellisense, HEM-7051-E), functional capacity was assessed with the Senior Fitness Test (SFT) battery. Results: After the intervention, BoP exhibited significant reductions in SBP (−24.4 ± 4.7 mmHg; p < 0.001) and DBP (−6.4 ± 2.7 mmHg; p = 0.025) compared to CG. BoP improved functional fitness, lower- and upper-body strength, aerobic endurance and agility (p < 0.05), with no changes in the CG. Conclusions: 8-week of BoP programme reduced BP and enhanced physical function in sedentary hypertensive older adults. Given its accessible, motivating and socially engaging format, BoP represents a promising non-pharmacological strategy for hypertension management and functional fitness of healthy ageing. Full article

The advancement of Large Language Models (LLMs) has opened new possibilities for automating bug report generation in software engineering. However, a fundamental limitation remains: the generated reports often fail to maintain both consistent structure and reliable semantic quality. To address this issue, this study proposes a Reinforcement Learning (RL) framework that integrates the CTQRS (Completeness, Traceability, Quality, Reproducibility, Specificity) metric as a reward signal. The proposed method aims to enhance both the structural completeness and semantic coherence of generated reports, enabling the automatic creation of reliable bug reports based on open-source LLMs. The training process consists of three stages: Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and Refinement. In the SFT stage, the model learns the formal structure of bug reports, reducing the loss from 1.9 to 1.3 and achieving initial CTQRS and SBERT scores of 0.46 and 0.68, respectively. In the RL stage, a multi-reward function centered on CTQRS is combined with the Proximal Policy Optimization (PPO) algorithm, increasing the reward value from 0.42 to 0.63 with stable convergence confirmed through the Exponential Moving Average (EMA). During this process, the CTQRS and SBERT scores improved to 0.72 and 0.84, demonstrating that the model simultaneously enhanced structural completeness and semantic consistency. In the final Refinement stage, the outcomes of SFT and RL are integrated, and a critic-based fine-grained feedback adjustment strategy is applied to stabilize the final outputs. The refined reports maintained a reward value of approximately 0.65, achieving peak CTQRS and SBERT scores of 0.76 and 0.85, respectively. Throughout the entire training process, the stability of the reward gradients was preserved, and the adjustments to length rewards and repetition penalties effectively prevented excessive verbosity. Experimental results show that the proposed CTQRS-based reinforcement learning framework improves the structural completeness, contextual accuracy, and evaluation stability of bug reports, thereby quantitatively enhancing the reliability of LLM-based (v.Qwen2.5-7B-Instruct) software quality assurance documentation. Future work will focus on further improving formal precision and evaluation consistency by fine-tuning the number of critic iterations (critic_iters) and adjusting detailed reward weights. Full article

Severe fever with thrombocytopenia syndrome (SFTS), caused by SFTS virus (SFTSV), is an emerging tick-borne disease in East Asia. SFTS monitoring has been carried out since 2010 in mainland China, but no confirmed human cases or infected vectors had been reported from the northern regions of Hebei Province. We intensified surveillance in this area by collecting serum samples from patients with fever of unknown origin (FUO) and ticks from local habitats. Subsequently, all collected samples were screened for SFTSV by qRT-PCR. SFTSV RNA was detected in two patient sera from Chengde (2.2%). In six, positive ticks were detected among the Haemaphysalis verticalis (8.6%) collected from Zhangjiakou; no positive ticks were detected among the ticks collected from Chengde. Complete viral genomes were recovered from positive tick samples via next-generation sequencing and subjected to a suite of bioinformatic analyses. Two complete genomes from Haemaphysalis verticalis formed a distinct clade with an Inner Mongolia strain across L/M/S (bootstrap = 1.0) and separate from genotypes A–F; pairwise p-distances to genotypes A–F were >0.11 across L/M/S, supporting designation of a distinct genotype. We designate this lineage as genotype G; no credible recombination was detected. Based on the L segment, molecular-clock analyses dated the genotype G lineage to the late 13th century, predating the crown age of genotypes A–F (~18th century) by more than 400 years. We provide the first evidence of SFTSV circulation in northern Hebei and identify a novel, deeply divergent lineage. This finding confirms the co-circulation of distinct viral lineages (G and F) within the province and provides critical new insights into the virus’s diversity and evolutionary history. These results expand the known range and genetic diversity of SFTSV, underscoring the need for enhanced surveillance and ecological investigation in emerging regions. It is necessary to strengthen public health education, improve the early diagnosis and treatment ability of medical workers, and provide a scientific basis for targeted public health interventions. Full article

This paper presents a quantum-inspired cross-attention alignment approach for abstractive summarization. The motivation is that current neural summarizers often lose key content and produce summaries that are weakly grounded in the source, especially for long and information-dense scientific articles in low-resource languages. The method itself is model-agnostic and aims to strengthen token-level alignment without introducing additional trainable parameters or inference overhead, by exploiting a Born-rule-based similarity between encoder and decoder states. This general idea is instantiated and tested on the task of summarizing Turkish scientific articles in Mathematics Education, which provides a challenging low-resource test bed with long and dense source texts. Six different fine-tuning variants built upon the mBART-50 model are examined, including SFT, LoRA baselines, and two novel quantum-augmented decoders: the parameter-free SFT + QDA + QKernel and SFT + QDA + QBorn (Born-rule-inspired, learnable classical mapping). Models are trained with five random seeds and evaluated using beam search and sampling schemes. Statistical significance is assessed via bootstrap confidence intervals, Benjamini–Hochberg FDR correction, and Cliff’s δ effect size. Beam search consistently outperforms sampling across all architectures. Our best configuration, SFT + QDA + QKernel, achieves strong results (ROUGE-L: 0.2966, BERTScore-F1: 0.8890) and yields statistically significant, large-effect gains over all baselines. These findings indicate that the proposed parameter-free quantum kernel provides a practical way to improve abstraction quality and faithfulness, particularly in low-resource summarization settings. Full article

To investigate the deterioration pattern of the rheological properties of high-viscosity high-elasticity asphalt (HVEA) under UV and salt freeze–thaw (SFT) cycle environments, two snowmelt salts were used for coupled aging tests, along with temperature sweep, bending beam rheological (BBR), and Fourier-transform infrared spectroscopy (FT-IR) tests. The results showed that both snowmelt salts could enhance the high-temperature rutting resistance of HVEA, in which the enhancement effect of NaCl was more significant. With the increase in salt concentration, the BBR stiffness of HVEA decreased and then increased, while the m-value showed the opposite trend, indicating that the addition of snowmelt salt impaired its low-temperature creep performance. Additionally, UV-SFT aging would exacerbate the degradation of low-temperature crack resistance. The temperature sensitivity of HVEA gradually decreased with the drop of viscosity temperature sensitivity (VTS) value; salt corrosion further significantly reduced its temperature sensitivity. UV-SFT aging would significantly weaken fatigue performance of HVEA, especially after 15 cycles. FT-IR test showed that UV-SFT resulted in the enhancement of S=O and C=C characteristic peaks, suggesting that the HVEA underwent oxidization and chemical aging, which increased the low-temperature brittleness. Full article

The sonic log is a key tool for assessing the mechanical properties of rocks, identifying structural features, calibrating seismic data, and monitoring well integrity. However, sonic data are often incomplete due to time and cost constraints, tool failures, or unreliable measurements. Traditional approaches to generate synthetic sonic logs usually rely on empirical relationships or statistical methods. In this study, we applied an artificial intelligence approach in which a deep neural network was trained with real data from an oilfield in Mexico to reconstruct sonic logs based on their relationships with other geophysical well logs. Three models, each using different input logs, were trained to predict the sonic response. The models were validated on wells excluded from training, and performance was evaluated using the root mean square error (RMSE) and mean absolute percentage error (MAPE), showing satisfactory accuracy. The models achieved RMSE values between 1.4 and 1.7 [$\mathsf{\mu }$s/ft] and MAPE values between 2.1 and 2.6% on independent test wells, confirming robust predictive performance. We also generated synthetic sonic logs for wells where no sonic data were originally acquired, demonstrating the practical value of the proposed method. This work integrates convolutional (CNN) and recurrent (GRU) layers in a single deep-learning architecture, trained under strict well-level validation. The workflow is demonstrated on wells from the Tabasco Basin, representing a field-scale deployment not previously reported in similar studies. Full article

Urban recreational spaces (URSs) are pivotal for enhancing resident well-being, making the accurate assessment of public perceptions crucial for quality optimization. Compared to traditional surveys, social media data provide a scalable means for multi-dimensional perception assessment. However, existing studies predominantly rely on single-modal data, which limits the comprehensive capturing of complex perceptions and lacks interpretability. To address these gaps, this study employs cutting-edge large vision–language models (LVLMs) and develops an interpretable model, Qwen2.5-VL-7B-SFT, through supervised fine-tuning on a manually annotated dataset. The model integrates visual-linguistic features to assess four perceptual dimensions of URSs: esthetics, attractiveness, cultural significance, and restorativeness. Crucially, we generate textual evidence for our judgments by identifying the key spatial elements and emotional characteristics associated with specific perceptions. By integrating multi-source built environment data with Optuna-optimized machine learning and SHAP analysis, we further decipher the nonlinear relationships between built environment variables and perceptual outcomes. The results are as follows: (1) Interpretable LVLMs are highly effective for urban spatial perception research. (2) URSs within Beijing’s Third Ring Road fall into four typologies, historical heritage, commercial entertainment, ecological-natural, and cultural spaces, with significant correlations observed between physical elements and emotional responses. (3) Historical heritage accessibility and POI density are identified as key predictors of public perception. Positive perception significantly improves when a block’s POI functional density exceeds 4000 units/km² or when its 500 m radius encompasses more than four historical heritage sites. Our methodology enables precise quantification of multidimensional URS perceptions, links built environment elements to perceptual mechanisms, and provides actionable insights for urban planning. Full article

Severe Fever with Thrombocytopenia Syndrome (SFTS) is an emerging tick-borne infectious disease that poses a significant public health threat. SFTS virus (SFTSV) has a broad host range, including humans, cats, and natural reservoir species. Therefore, cultured cell lines derived from different mammalian species are useful for understanding the susceptibility of SFTSV in hosts. In this study, we evaluated pathogenicity and infectivity, focusing on cytopathic effect (CPE) induction and growth kinetics of SFTSV in several mammalian cell lines, including our original tiger-derived TLT, wild deer–derived DFKT and DFLT, and hedgehog-derived HHoVT. Following SFTSV infection, TLT, CRFK (cat), FCWF-4 (cat), and CPK (porcine) cells exhibited CPE, whereas Vero E6 (monkey), A549 (human), BHK-21 (hamster), DFKT, DFLT, and HHoVT cells did not. Infectious viral yields in the supernatants of TLT, CRFK, FCWF-4, Vero E6, and BHK-21 were higher than those of CPK, A549, DFLT, and DFKT. SFTSV infection in hedgehog-derived HHoVT cells was very limited. These observations suggest that features such as viral CPE and virus yield following SFTSV infection depend on cell type. It is noteworthy that TLT formed clear plaques that were easy to count, indicating that TLT cells are useful for the titration of infectious SFTSV by plaque-forming assay. Our results provide useful information and tools for further elucidating the mechanisms of SFTSV infectivity, proliferation, and pathogenicity using in vitro models. Full article

Background: The functional capacity of older adults is a critical determinant of autonomy and quality of life, particularly in low-income populations from remote regions with limited access to health services. This study aimed to characterize the functional fitness (FF) of community-dwelling older adults in the interior of Amazonas, Brazil, stratified by sex and age group. Methods: A cross-sectional study was conducted with 807 older adults (471 females), aged ≥ 60 years, from four municipalities in northern Brazil. The FF was assessed using the Senior Fitness Test (SFT), including measures of strength (30-s Chair Stand Test—CST; 30-s Arm Curl Test—ACT), flexibility (Chair Sit and Reach Test-CSAR, Back Scratch Test-BST), balance/agility (8-Foot Up-and-Go Test—FUG), and aerobic endurance (6-min walk test—6MWT). Descriptive statistics, confidence intervals, and age- and sex-specific percentiles were calculated. Results: Results indicated a progressive decline in FF with advancing age. Males outperformed females in strength and endurance tests, whereas females exhibited better flexibility. Notable reductions in performance were observed after age 75, particularly in CST, ACT, FUG, and 6MWT. Overall, the functional profiles of this population were below international norms, especially among females and individuals aged ≥ 80. The prevalence of overweight was 39.3%, with socioeconomic vulnerability affecting over 90% of participants. Conclusions: Older adults in low-income, remote Amazonian Brazilian communities demonstrate marked functional decline with ageing, influenced by socioeconomic and environmental constraints. These findings highlight the urgency of implementing accessible, community-based interventions focused on physical activity, strength, mobility, and endurance to support healthy ageing in underserved regions. Full article