Search Results (2,065)

Rhabditis (Rhabditella) axei is a predominantly free-living nematode commonly found in sewage systems and decomposing organic matter. While primarily saprophytic, it has been documented as an opportunistic pathogen in human urinary and gastrointestinal tracts. The Chinese red panda (Ailurus styani), a rare and protected species in China, has not previously been reported as a host for Rhabditis (Rhabditella) spp. infections. This study reports the first documented occurrence of R. axei in red panda feces, unambiguously confirmed through integrative taxonomic approaches combining morphological and molecular analyses. The nematodes exhibited key morphological features consistent with R. axei, including a cylindrical rhabditiform esophagus, sexually dimorphic tail structures, and diagnostic spicule morphology. Molecular analysis based on 18S-ITS-28S rDNA sequencing confirmed their identity, showing >99% sequence similarity to R. axei reference strains (GenBank: PP135624.1, PP135622.1). Phylogenetic reconstruction using 18S rDNA and ITS rDNA sequences placed the isolate within a well-supported R. axei clade, clearly distinguishing it from related species such as R. blumi and R. brassicae. The findings demonstrate the ecological plasticity of R. axei as a facultative parasite capable of infecting non-traditional hosts and further highlight potential zoonotic risks associated with environmental exposure in captive wildlife populations. Our results emphasize the indispensable role of molecular diagnostics in accurately distinguishing morphologically similar nematodes within the Rhabditidae family, while providing essential baseline data for health monitoring in both in situ and ex situ conservation programs for this endangered species. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Triclabendazole (TCBZ) is the gold standard treatment for fascioliasis. However, reports on resistance are increasing, emphasizing the need for alternative therapy. Combining TCBZ with ivermectin (IVM) was found to be effective for treating animal fascioliasis. Building on this, we aimed to evaluate the efficacy of the TCBZ/IVM combination therapy for human fascioliasis. This study enrolled 136 patients with Fasciola from Upper Egypt, and they were divided into the first group (n = 65), who received TCBZ monotherapy, and the second group (n = 71), who received the TCBZ/IVM combination. Assessments were to evaluate treatment response based on clinical, eosinophilic, and radiological parameters. Chronic fasciolosis was diagnosed in 17 patients (12.5%). No differences were observed in age and sex. Significant improvements were noted in all parameters in both groups, with more pronounced effects observed in the second group. A significantly higher complete response, including clinical, eosinophilic, and radiological improvements, was reported in the combined therapy group, with 53.3% compared to 26.2% in the monotherapy group (p < 0.001). A high baseline eosinophilic count was significantly associated with response. The efficacy of the TCBZ/IVM combination for treating human fascioliasis suggested a possible boosting effect, which can benefit regions of TCBZ failure. Further large-scale randomized studies are warranted to confirm these findings. Full article

Due to the challenges in obtaining full-body point clouds and the time-consuming registration of parametric body models, we propose an end-to-end Front Point Cloud Parametric Body Regression Network (FPCR-Net). This network directly regresses the pose and shape parameters of a parametric body model from a single front point cloud of the human body. The network first predicts the label probabilities of corresponding body parts and the back point cloud from the input front point cloud. Then, it extracts equivariant features from both the front and predicted back point clouds, which are concatenated into global point cloud equivariant features. For pose prediction, part-level equivariant feature aggregation is performed using the predicted part label probabilities, and the rotations of each joint in the parametric body model are predicted via a self-attention layer. Shape prediction is achieved by applying mean pooling to part-invariant features and estimating the shape parameters using a self-attention mechanism. Experimental results, both qualitative and quantitative, demonstrate that our method achieves comparable accuracy in reconstructing body models from front point clouds when compared to implicit representation-based methods. Moreover, compared to previous regression-based methods, vertex and joint position errors are reduced by 43.2% and 45.0%, respectively, relative to the baseline. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

Rickettsia, Anaplasma, and Ehrlichia species are emerging tick-borne pathogens that cause zoonotic diseases, including rickettsiosis, anaplasmosis, and ehrlichiosis in both human and animal populations. This study aimed to investigate the prevalence of these pathogens in cattle-associated ticks from Ganzhou City, Jiangxi Province, China. Through molecular characterization using multilocus sequence analysis (16S rRNA, gltA, groEL, and ompA genes), we analyzed 392 Rhipicephalus microplus ticks collected from March to September in 2022. The PCR results showed that eight Rickettsiales bacteria were detected, including two species of Rickettsia (51/392, 13.0%), four species of Anaplasma (52/392, 13.3%), and two species of Ehrlichia (70/392, 17.9%). Notably, the circulation of multiple pathogen species within R. microplus populations demonstrates significant microbial diversity in this region. Further consideration and investigation should be given to the possible occurrence of rickettsiosis, ehrlichiosis, and anaplasmosis in humans and domestic animals. Our study provides critical baseline data for developing targeted surveillance strategies and informing public health interventions against tick-borne diseases in southeastern China. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

Croatian agricultural legislation acknowledges the significance of radionuclides as pollutants in agricultural lands; however, it lacks specific thresholds or reference values for contamination levels, in contrast to other contaminants. This absence highlights the necessity for a comprehensive assessment of radionuclides across various agricultural systems in Croatia. This study investigates the transfer of radionuclides ¹³⁷Cs and ⁴⁰K from soil to agricultural crops throughout Croatia and estimates the consequent annual ingestion dose for the population. The samples collected comprised food crops and animal feed, with corresponding soil samples analyzed to calculate transfer factors. Activity concentrations of ¹³⁷Cs exhibited regional and crop-type variability, reflecting the uneven distribution of fallout and differing soil properties. Transfer factors were found to range from 0.003 to 0.06 for ¹³⁷Cs and from 0.15 to 3.1 for ⁴⁰K, with the highest uptake occurring in kidney beans. The total estimated annual effective ingestion dose was calculated to be a maximum of 0.748 mSv/year for children aged 2–7, predominantly attributable to ⁴⁰K. Given the homeostatic regulation of potassium in the human body, the dose associated with ¹³⁷Cs poses a more significant radiological concern. These findings underscore the need for radionuclide-specific agricultural legislation in Croatia and offer a baseline for recommending reference values and informing future regulations regarding agricultural soil contamination. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Humanoid robots have attracted considerable attention for their anthropomorphic structure, extended workspace, and versatile capabilities. This paper presents a novel humanoid upper-body robotic system comprising a pair of 8-degree-of-freedom (DOF) arms, a 3-DOF head, and a 3-DOF torso—yielding a 22-DOF architecture inspired by human biomechanics and implemented via standardized hollow joint modules. To overcome the critical reliance of zeroing neural network (ZNN)-based trajectory tracking on the Jacobian matrix derivative, we propose an integration-enhanced matrix derivative observer (IEMDO) that incorporates nonlinear feedback and integral correction. The observer is theoretically proven to ensure asymptotic convergence and enables accurate, real-time estimation of matrix derivatives, addressing a fundamental limitation in conventional ZNN solvers. Workspace analysis reveals that the proposed design achieves an 87.7% larger total workspace and a remarkable 3.683-fold expansion in common workspace compared to conventional dual-arm baselines. Furthermore, the observer demonstrates high estimation accuracy for high-dimensional matrices and strong robustness to noise. When integrated into the ZNN controller, the IEMDO achieves high-precision trajectory tracking in both simulation and real-world experiments. The proposed framework provides a practical and theoretically grounded approach for redundant humanoid arm control. Full article

Obstructive sleep apnea (OSA) is a prevalent disorder linked to metabolic complications such as diabetes and cardiovascular disease. By fragmenting normal sleep architecture, OSA perturbs the growth hormone/insulin-like growth factor (GH/IGF) axis and alters circulating levels of IGF-binding proteins (IGFBPs). A prior clinical observation of elevated IGFBP4 in OSA patients motivated the present investigation in a controlled animal model. Building on the previously reported protocol, OSA was induced in male C57BL/6 mice (9–12 weeks old) through intralingual injection of polytetrafluoroethylene (PTFE), producing tongue hypertrophy, intermittent airway obstruction, and hypoxemia. After 8–10 weeks, the study assessed (1) hypoxia biomarkers—including HIF-1α and VEGF expression—and (2) neurobehavioral outcomes in anxiety and cognition using the open-field and novel object recognition tests. PTFE-treated mice exhibited a significant increase in circulating IGFBP4 versus both baseline and control groups. Hepatic Igfbp4 mRNA was also upregulated. Behaviorally, PTFE mice displayed heightened anxiety-like behavior and impaired novel object recognition, paralleling cognitive deficits reported in human OSA. These findings validate the PTFE-induced model as a tool for studying OSA-related hypoxia and neurocognitive dysfunction, and they underscore IGFBP4 as a promising biomarker and potential mediator of OSA’s systemic effects. Full article

Background: Maternal and newborn health disparities remain a challenge in Puerto Rico, especially in underserved communities. Comienzo Saludable Puerto Rico, sponsored by the U.S. Department of Health and Human Services’ Healthy Start Initiative (HRSA), addresses these gaps through an integrated Networks of Care model known as Cuidado Compartido. Comienzo Saludable Puerto Rico is a maternal, paternal, and child health program aimed at improving the health and well-being of pregnant women, mothers, fathers, newborns, and children in Puerto Rico, particularly those from disadvantaged communities. Methods: This paper presents the Comienzo Saludable Puerto Rico program’s Cuidado Compartido model to integrate a network of healthcare providers and services across hospitals, community organizations, and families. This model aims to improve maternal and newborn/child health outcomes by focusing on the importance of integrated, hospital-community-based care networks. Results: Participants experienced significant improvements in key birth outcomes: low birth weight prevalence declined by 27.2% compared to the community baseline, premature birth rates decreased by 30.9%, and infant mortality dropped by 75%, reaching 0% by 2021 and remaining there through 2023. These results were complemented by increases in maternal mental health screening, paternal involvement, and breastfeeding practices. Conclusions: The Cuidado Compartido model demonstrates a scalable, culturally responsive strategy to improve maternal, newborn, and child health outcomes. It offers critical insights for implementation in other high-need contexts. Full article

Coyaima is a town in the department of Tolima, Colombia, that was prioritized in a pilot program under Colombia’s National Plan for the Control of the Taeniasis/Cysticercosis Complex, focusing on this neglected health issue. The project engaged local indigenous communities, promoting education and outreach within the One Health framework. The study included 444 randomly selected volunteers, who filled a Knowledge, Attitudes, and Practices (KAP) survey on the taeniasis/cysticercosis complex. The baseline study found no Taenia spp. eggs via microscopy on 383 stool samples examined, and no T. solium DNA was detected on human stool and soil samples by Copro-qPCR. However, seroprevalence was 8.5% for human cysticercosis and 14% for porcine cysticercosis, as detected by in-house ELISA testing for T. solium. Moreover, 57.9% of participants who provided a stool sample were positive for at least one parasite. Following the sampling and characterization activities, local health workers implemented mass treatment with Niclosamide, based on evidence of ongoing transmission, high porcine seroprevalence, poor basic sanitation, and the presence of free-roaming pigs reported in the KAP survey. These findings provide scientific evidence to apply national public health policies for controlling taeniasis/cysticercosis complex in Coyaima. Full article

Human exploitation has been a major driver of marine turtle population declines, particularly affecting naturally scarce species such as the pantropical hawksbill turtle. Although hawksbill sea turtles have been documented in the Gulf of California since the early 20th century, data on their historical demography during periods of high exploitation in this region are nonexistent. We investigated the size structure of hawksbill turtles from the Western Central Gulf of California by examining a unique sample of decorative taxidermies, corresponding to 31 specimens captured during fishing operations near Santa Rosalía, Baja California Sur, Mexico, between 1980 and 1990. An analysis of the curved carapace measures revealed a length range (nuchal notch to posterior of supracaudals) of 29.5–59.5 cm (mean = 38.75 ± 6.67 cm) and a width range of 25.0–51.5 cm (mean = 33.63 ± 5.66 cm), with 87% of specimens having lengths between 30 and 45 cm. Based on the carapace length measurements, we estimated the ages to be between 7 and 20 years, indicating that the population included juveniles. Our findings provide baseline data for an understudied period and region, suggesting that this area previously served as an important juvenile habitat. These results contribute essential historical demographic information for conservation planning. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article