Objective: Exercise-induced fatigue (EIF) is closely associated with impaired glycometabolism in skeletal muscle. This study investigated the protective effects of Ganoderic acid A (GAA) on glycometabolism in EIF mice and explored its underlying mechanisms. Methods: Sixty KM mice were divided into five groups: a blank control (BC), a model control (MC), and three GAA-treated groups (20, 40, and 60 mg/kg/d). After a 7-week intervention, exhaustive treadmill tests and biochemical analyses were conducted to assess fatigue resistance, metabolic parameters, and molecular pathways. Results: GAA administration significantly prolonged the exhaustive running time (p < 0.01), reduced serum levels of blood urea nitrogen (BUN), creatine kinase (CK), lactate dehydrogenase (LDH), and lactate (LD) (p < 0.05), and increased glycogen content in the liver and gastrocnemius muscle. Mechanistically, GAA activated AMPK phosphorylation, upregulated PGC-1α and GLUT4 expression, and enhanced succinate dehydrogenase (SDH) and Ca²⁺-Mg²⁺-ATPase activities. Conclusion: The results demonstrate that GAA alleviates EIF by enhancing energy metabolism through the AMPK/PGC-1α/GLUT4 pathway. These findings highlight GAA as a promising natural supplement for combating exercise-induced fatigue by glycometabolic.

Management of Eptifibatide Induced Severe Thrombocytopenia After Percutaneous Coronary InterventionBoldt, Christopher a; Pham, Caitlynn a,d; Eisenmenger, Elizabeth b; Oyenubi, Olamide c; Bushan, Sita ba Baylor College of Medicine, Department of Medicineb Baylor College of Medicine, Department of Medicine – Hematology and Oncologyc Baylor College of Medicine, Department of Medicine – Cardiologyd Correspondence: Caitlynn Pham, ctpham@bcm.eduKey Clinical Message: The purpose of this study is to provide readers with a case in which eptifibatide, an anti-platelet commonly used after percutaneous coronary intervention, caused severe drug-induced thrombocytopenia. We highlight the treatment options and outcomes for this patient to further provide guidance for clinicians who find themselves in similar situations.

A document by Javad Salimi. Click on the document to view its contents.

Ongoing glacial isostatic adjustment (GIA) is detectable in geodetic height time series and changes in the temporal gravity field. Global GIA models are often used to remove these signals from data but quantifying the errors in such models is difficult due to insufficient knowledge of Earth rheology and past ice history on Earth. Here we de- scribe how estimates of Earth’s temporal gravity field and observed height time series can be used to quantify errors in GIA models. We tested several GIA models in Fennoscandia, Laurentia and Greenland and found spatial coherence in the pattern of errors, with RMS velocity errors of ∼1 mm/yr, ∼2 mm/yr and ∼5 mm/yr, respectively. Surprising, there are substantial similarities in the errors of the models tested. Our diagnostic tool can be used to identify regions where ice histories and/or Earth rheology parameters are deficient in the GIA models.

The inner core is seismically anisotropic, with PKIKP waves traversing the inner core parallel to the rotation axis faster than those in the equatorial plane. This anisotropy increases with depth into the inner core and likely results from alignment of iron crystals deformed during inner core growth. Using previously calculated elastic properties of iron, we seek to determine the most likely iron-light element alloy (FeC, FeO, FeS, or FeSi). For each FeX alloy, we interpolate elastic tensors across the pressure and temperature range of the inner core and model the anisotropy resulting from flow during core growth. Lastly, we compare predicted PKIKP travel times with observations to determine the best fitting alloy. We find that iron-sulphur fits better than pure iron, but also that no light element alloy matches the observed anisotropy better than iron-nickel alloy. Future studies of core compositions must include nickel to explain seismic observations.

Background and Purpose The purpose of our research was to investigate the therapeutic effect of Panax notoginseng polysaccharides (PNP) in ameliorating colitis and revealing its protective mechanism of the gut vascular barrier. Experimental Approach PNP was extracted using a process of pure water extraction and then precipitated by ethanol. Next, the structural properties of PNP were analyzed, including the purity of the polysaccharides, monosaccharide composition, and molecular weight. Utilizing the DSS to induce acute colitis model, ELISA assessed the levels of inflammation-related factors in serum. The types and functional expressions of immune cells were examined using flow cytometry. Additionally, immunofluorescence was applied to evaluate the integrity of the multiple gut barrier. The gut microbiota was assessed, and its mechanism of action was investigated through 16S rRNA sequencing and FISH techniques. Transplanting PNP feces into DSS-induced mice to determine the contribution of intestinal flora to PNP efficacy. Key Results Pharmacological experiments indicate that PNP can ameliorate intestinal inflammatory reactions, reduce bacterial tissue invasion, as well as maintain the integrity of the multiple gut barrier, including epithelial barrier, mucus barrier, and vascular barrier. PNP-FMT obtained similar efficacy results. Mechanically, PNP treatment modulated the gut microbiota and increased ILC3-secreted IL-22 to repair the gut vascular barrier. Conclusions and Implications The findings indicate that PNP effectively repairs intestinal epithelial and vascular barrier damage by enhancing the function of ILC3 through microbial mediation.

AbstractAn 80-year-old male patient was admitted to our hospital with a one-year history of recurrent reflux and vomiting, accompanied by mild fatigue and decreased appetite. Despite initial treatment with gastric motility agents, his symptoms showed limited improvement. Comprehensive diagnostic evaluations, including imaging and laboratory tests, ruled out common gastrointestinal disorders (such as peptic ulcer disease, gastroesophageal reflux disease, and functional dyspepsia), intracranial pathologies, and other systemic conditions. Based on his clinical presentation and a history of chronic glucocorticoid use, glucocorticoid-induced adrenal insufficiency was suspected as the underlying cause. Remarkably, his symptoms resolved completely following a 10-day course of hydrocortisone replacement therapy.

The expansion of human society exerts complex impacts on nature reserves, where intertwined resource, socio-economic, and governance factors disrupt the balance between land development and ecological preservation.Resolving conflicts between conservation needs and human land use demands remains critical.This study employs the gravity model to establish an interaction model between nature reserves and surrounding land use, examining these dynamics through the lenses of resilience and vulnerability. By delineating a conflict-coordination system between nature reserves and surrounding land use, this model elucidates the intricate relationship between the resilience and vulnerability of nature reserves in the context of their surroundings. Employing the network structure, the gravity model assesses the impact of various land uses on nature reserves to derive impact standard values. Key findings include: (1) The size of surrounding land use and its proximity to the nature reserve are primary factors influencing the reserve’s vulnerability and resilience; (2) With the exception of green spaces and square areas (37.76), all urban development land exert a greater impact on the resilience of Wuyishan National Nature Reserve than on its vulnerability, indicating that urban development inevitably poses ecological threats to the reserve; (3) The consistent influence of different urban development land on the reserve is quantified (Balance coefficient k value). These findings establish benchmark values for the development benefits of different land uses around nature reserves, offering a scientific basis for local governments to devise more informed land-use plans and conservation policies.

A document by Jie Du. Click on the document to view its contents.

A document by Brent McGrath. Click on the document to view its contents.

Duración:  50 minutosGrado sugerido:  7mo.Área temática:  Robótica y Tecnología.Objetivo general:  Los estudiantes diseñarán un filtro de agua funcional utilizando materiales de uso diario, aplicando el pensamiento científico y de ingeniería para resolver un problema real: el acceso al agua potable.

This comparative review examines Yemeni Jewish culture through a synthesis of academic and non-academic sources, focusing on its historical roots, cultural practices, and contemporary preservation challenges. By analyzing diverse perspectives, the study highlights the unique contributions of Yemeni Jews to Yemen's cultural and social fabric, while identifying gaps in existing scholarship.

Mammalian species are key in maintaining a healthy ecosystem. The tropical rainforest in Borneo is characterized by its rich biodiversity and rugged interior, which houses various forest types from the lowland dipterocarps forest to the montane and ericaceous forests above 1,500 m. Using the data obtained from 81 camera trap stations set up from April 2023 to September 2024, we investigated the diversity of mammalian species across the spatial and temporal dimensions. We detected 35 species of mammals, from 6 orders and 15 families. We reported a bimodal peak for the average number of species per station, at 800-899 m and 1700-1799 m. The pig-tailed macaque Macaca nemestrina is the most abundant species in the study site, followed by the red muntjacs Muntiacus muntjak. Temporally, all the individual species’ activity patterns followed the previous studies, except for the mousedeer Tragulus spp., which is predominantly nocturnal. While other studies in Borneo observed more diurnality in mousedeer, we think the difference may be a co-existence strategy to reduce predation risk. We also reported evidence of spatial partitioning between the two species of muntjacs based on elevation. In conclusion, our results offer baseline knowledge on the spatial and temporal distribution pattern of non-volant mammals in a high-altitude protected area.

Rapid and accurate brain tumor detection from MRI sequences is essential for timely diagnosis and intervention. This work presents a lightweight deep learning model that balances performance and computational efficiency. Methods: We propose Next-Gen ST-CNN, a spatio-temporal convolutional neural network that efficiently captures both intra-slice spatial features and inter-slice temporal dependencies. The model employs a novel decomposition of 3D convolutions to significantly reduce complexity while preserving diagnostic features. Training and evaluation were performed on a standard brain tumor MRI benchmark dataset, covering glioma, meningioma, pituitary, and non-tumor cases. Results: The proposed model achieves a test accuracy of 98.63%, along with macro precision, recall, and F1 scores ranging from 97% to 100%. It records a low test loss of 0.069 and a fast training time of 367.2 seconds. With only 2.17 million parameters, the model is compact yet powerful. Conclusion: Next-Gen ST-CNN effectively integrates spatial and temporal analysis while maintaining a lightweight architecture, making it suitable for real-time deployment. Significance: The proposed approach offers a compelling solution for next-generation computer-aided diagnosis in neurooncology, enabling fast, reliable and accessible brain tumor screening using minimal resources.

Quality healthcare is a major challenge around the world, particularly in developing countries, and this concern applies to dermatology as well. Many communities in such environments lack adequate medical infrastructure leaving behind patients in dire need to get treatments.  Many people with skin conditions either do not receive treatment or wait so long that their symptoms worsen, which drastically reduces their quality of life. Artificial intelligence (AI) could be a viable solution for this concern.AI-based tools, particularly picture recognition algorithms, can detect common dermatological illnesses. AI-powered smartphone apps can also allow patients to upload photos of their skin problems and receive treatments. Moreover, AI-powered telemedicine solutions can eliminate the need for city-based specialists by enabling general practitioners in remote areas to get prompt support. This model has the potential to be scaled and replicated in resource-limited areas, effectively addressing the dermatological care gap through digital health technologies. The focus should be placed on the cost and accessibility of these technologies to bridge the gap between underserved populations and their necessary treatments.

Subject: Comments on “Impact of Contact Force Sensing Technology on Atrial Fibrillation Ablation: A Meta-Analysis”

Motors play a crucial role in energy conversion and are essential components of mechatronic systems. However, diagnosing faults in rolling bearings during motor operation presents significant challenges, making it difficult to achieve high accuracy in fault identification. To address these challenges, this paper introduces a novel intelligent diagnostic method based on an enhanced multi-kernel extreme learning machine (ELM) model. While the ELM model is widely used for diagnosing motor rolling bearing faults, it often struggles to classify complex vibration data. To improve its performance, this study proposes a multi-kernel ELM (MKELM) model that integrates three traditional kernel functions: Gaussian, polynomial, and perceptron kernels. Additionally, to overcome the challenges posed by the numerous parameters and the risk of local optima in the MKELM model, the kernel parameters were optimized using the Grey Wolf Optimization (GWO) algorithm, resulting in the GWO-MKELM algorithm. Finally, the GWO-MKELM algorithm was applied to diagnose motor rolling bearing faults. Experimental results show that this method achieves a 99.6% accuracy rate and effectively identifies various types of bearing faults.

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning

A document by Jalal Khawaldeh. Click on the document to view its contents.

Supply chain disruptions, exacerbated by global uncertainties, demand agile planning tools. This paper presents a machine learning (ML)-enhanced demand sensing model integrated into SAP Integrated Business Planning (IBP) to improve supply chain resilience in automotive manufacturing. We propose a hybrid ML architecture combining Long Short-Term Memory (LSTM) networks for temporal pattern recognition and XGBoost for feature importance analysis. Deployed in a tier-1 automotive supplier, the model reduced stockouts by 30% while maintaining 98% service levels. The study highlights technical implementation steps, quantifies performance gains, and provides actionable insights for scaling ML-driven planning in SAP IBP.

Grassland ecosystems in arid regions face mounting stress from intensified climate variability and anthropogenic disturbance. Despite the predictive capabilities of machine learning models, their lack of interpretability challenges the transparency of resilience drivers. This study integrates temporal autocorrelation (TAC) metrics with explainable machine learning (ML) to assess grassland resilience dynamics in an arid ecosystem from 2001 to 2023. Results reveal spatial divergence, with reduced resilience in radiation-dominated arid zones and stronger recovery in hydrothermally stable areas. The model identifies temperature variability and vegetation activity as dominant contributors to resilience trends, exhibiting marked heterogeneity across grassland types. By quantifying both structural and dynamic aspects of resilience, this framework enhances interpretability and diagnostic precision, offering a practical tool for identifying degradation risks and ecological tipping points. Findings support region-specific adaptation strategies and provide a robust foundation for sustainable grassland governance in the face of accelerating global change.

The Nowadays Science of Physics states that when an external Force is exerted on any Massive Body it causes an Acceleration of this Massive Body according to Newton's Second Law of Motion, F=ma. From the above follows that, when an external Force is exerted on an Uncharged (Not Electrically Charged) Body, since that Body Accelerates according to Newton's Second Law of Motion, F=ma, then, this also implies, as will be presented also in this paper, that that Body acquires a Kinetic Energy equal to: mv 2 /2, according to the Newtonian Physics. But, if that Uncharged Body Accelerates according to Newton's Second Law of Motion, F=ma, this also implies, as will be also presented in this paper, that all the Work done by this external Force, is already manifested and embedded in the Kinetic Energy that that Uncharged Body acquired, because of the above-mentioned external Force which was exerted on it. However, if an external Force is exerted on an Electrically Charged Body, what was presented above might need some modifications.

Bergmann’s rule suggests that animals in colder climates tend to be larger, but does this pattern hold across all vertebrates? While traditionally thought to apply more strongly to endotherms, evidence remains inconsistent across species and ecosystems. We analysed body size trends across latitude in major terrestrial vertebrate—amphibians, reptiles, birds, and mammals—to assess the rule’s validity. Birds and mammals followed Bergmann’s rule, while amphibians and reptiles showed no consistent pattern. In addition, we observed a stronger support for Bergmann’s rule in endotherms compared to ectotherms at the order/family level, but a slightly higher support at the species level. Contrary to expectation, however, Bergmann’s rule is not stronger at the population level than at the species level. These results highlight that Bergmann’s rule is context-dependent, shaped by factors like body temperature regulation and evolutionary history, and underscore the need to consider these differences when predicting species’ vulnerability to climate change.

Communities can buffer environmental change through the diverse responses of their species, often leading to greater emergent stability than expected from individual species. Metrics such as response dissimilarity and divergence capture this response diversity in fluctuating environments. Here, we test whether diverse species responses also stabilise community properties under pulse disturbance. Combining model simulations of multi-species communities with empirical data from a meta-analysis, we find that community stability was consistently determined by the mean species response, regardless of interaction strength. Contrastingly, response dissimilarity and divergence were only related to stability in the absence of interspecific interactions. While response diversity increases stability under fluctuating conditions, pulse disturbances often cause negative responses in most species, and stability is highest when species uniformly exhibit strong resistance or fast recovery. These results highlight that the role of response diversity in promoting community stability depends on the disturbance regime and is shaped by species interactions.