Search Results (7,953)

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article

Traumatic brain injury (TBI) results in a cascade of neuropathological events, which can significantly disrupt synaptic integrity. This review explores the acute, subacute and chronic phases of synaptic dysfunction and loss in trauma which commence post-TBI, and their contribution to the subsequent neurological sequelae. Central to these disruptions is the loss of dendritic spines and impaired synaptic plasticity, which compromise neuronal connectivity and signal transmission. During the acute phase of TBI, mechanical injury triggers presynaptic glutamate secretion and Ca²⁺ ion-mediated excitotoxic injury, accompanied by cerebral edema, mitochondrial dysfunction and the loss of the mushroom-shaped architecture of the dendritic spines. The subacute phase is marked by continued glutamate excitotoxicity and GABAergic disruption, along with neuroinflammatory pathology and autophagy. In the chronic phase, long-term structural remodeling and reduced synaptic densities are evident. These chronic alterations underlie persistent cognitive and memory deficits, mood disturbances and the development of post-traumatic epilepsy. Understanding the phase-specific progression of TBI-related synaptic dysfunction is essential for targeted interventions. Novel therapeutic strategies primarily focus on how to effectively counter acute excitotoxicity and neuroinflammatory cascades. Future approaches may benefit from boosting synaptic repair and modulating neurotransmitter systems in a phase-specific manner, thereby mitigating the long-term impact of TBI on neuronal function. Full article

Human cytomegalovirus (HCMV) is a complex pathogen that encodes a diverse array of proteins essential for its survival and replication within host organisms. Among these proteins, a noteworthy group comprises four chemokine-like G protein-coupled receptors (cellular GPCRs), which play pivotal roles in the virus’s evasion of the host immune response and the establishment of persistent infections. Of particular interest is pUS28, recognized as one of the most extensively studied viral GPCRs (vGPCRs). This receptor has attracted significant attention for its potential as a target for innovative antiviral therapies aimed at addressing HCMV-related diseases. In contrast, pUS27 has not been as thoroughly characterized, presenting a potentially promising avenue for antiviral intervention. The relative scarcity of research surrounding pUS27 underscores an exciting opportunity for further exploration, as a deeper understanding of its functions and mechanisms may reveal novel strategies for combating HCMV infections. This review seeks to synthesize recent advancements in our understanding of pUS27, elucidating its biological roles, interactions, and potential implications for therapeutic development. We will also highlight critical gaps in the existing literature that warrant further investigation, underscoring the need for a more comprehensive understanding of this understudied receptor. By delving into the complexities of pUS27, we aim to inspire future research initiatives that could lead to the development of novel antiviral treatments, thereby enhancing our overall understanding of HCMV pathogenesis. Importance: The study of vGPCRs is essential for understanding how viruses like HCMV manipulate host cell signaling and evade immune responses. While pUS28 has been extensively studied due to its broad chemokine binding and signaling activity, its lesser-known homolog, pUS27, warrants closer attention. Likely arising from a gene duplication event, pUS27 shares approximately 31% sequence identity with pUS28 and is conserved across HCMV strains, suggesting an important functional role. By focusing on pUS27, we may uncover shared mechanisms that allow therapies to effectively target both pUS28 and pUS27, potentially leading to more potent antiviral treatments. The implications of studying pUS27 are profound, as it could play a pivotal role in improving our approaches to combating HCMV and enhancing our overall understanding of immune evasion strategies. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

Background: The Pharm2Home Initiative’s Community Health Arm adopts a health-equitable approach to chronic disease education and medication therapy management (MTM). We serve senior residents of Solano County, California, who live in affordable housing and have limited financial resources. Aim: This evaluation assesses the uptake of chronic disease management recommendations provided by clinical pharmacists during MTM sessions at community events. Methods: The program engaged clinical pharmacists to provide tailored education and healthcare interventions in senior housing facilities. The goal was to empower seniors to manage their health effectively. The sessions covered various topics, including expired or duplicated medications, incorrect medication use, consultations on medication management, immunizations, and lifestyle adjustments. Results: Over an 18-month period, from January 2022 to August 2023, the program involved 65 participants across ten community health events. These events provided approximately 65 h of direct intervention. Many participants reported significant improvements in understanding their treatment plans and navigating their health needs more confidently. Feedback from 60 seniors after the sessions indicated that 88% felt much better informed about their medications, and 75% expressed that their concerns were addressed extremely well. Conclusions: These outcomes demonstrate the importance of clinical pharmacist-led interventions in improving seniors’ medication use and chronic disease management. The initiative’s approach advocates for integrating clinical pharmacists into community health settings, suggesting a scalable model for enhancing person-centered care. However, further studies are necessary to assess the long-term impacts of these interventions and explore their effectiveness across diverse age groups and more complex conditions. Full article

Climate adaptation convenings have emerged in the last decade to share knowledge and accelerate learning in the field. Convenings provide a wealth of information for understanding what issues are being researched and addressed, for evaluating what practices and key components of convenings are considered particularly valuable to practitioners, and for understanding where there are gaps in our knowledge or trends in learning that should be supported. We analyzed survey and attendance data from online and in-person climate convenings in the U.S. to assess perceived outcomes and future intentions, as well as explored thematic changes in sessions at in-person conferences. We performed descriptive analyses on survey and attendance data and conducted thematic analysis of sessions at in-person conferences. Both online and in-person formats achieved high levels of learning and satisfaction reported by respondents, but with higher connectivity and relationship building at in-person events. Topics addressed in forums showed small but meaningful shifts, as some areas of interest increased (e.g., social justice, nature-based solutions) while others decreased (e.g., decision-making tools, infrastructure) or showed variable responses. These trends and feedback provide a foundation for continuing to grow effective practices to support climate adaptation practitioners with the knowledge and opportunities for connection needed to advance the adaptation field. Full article

This paper proposes a Tibetan judicial event argument extraction method based on machine reading comprehension (MRC) to address the challenges of data scarcity and insufficient model generalization in low-resource language scenarios. Unlike traditional methods, this work models event argument extraction as an MRC task, progressively identifying and extracting various event arguments through a question-guided approach. First, a strategy for constructing event knowledge-enhanced questions tailored to the Tibetan judicial domain is designed. Specifically, interrogative words are formulated for different types of event arguments, and event semantic information is incorporated into questions to effectively disambiguate questions. Second, a deep semantic understanding architecture for Tibetan judicial events based on the CINO (Chinese Minority Pretrained Language Model) is proposed, incorporating a multi-head self-attention mechanism to enhance semantic alignment and global understanding between event sentences and questions. Finally, a two-stage training strategy is proposed for low-resource languages. Training is performed on a general Tibetan machine reading comprehension dataset, followed by task-adaptive fine-tuning on judicial domain data, effectively alleviating the data scarcity issue. Experimental results show that the proposed method achieved an F1-score of 76.59% in the Tibetan judicial event argument extraction task. This research offers new ideas for low-resource language event extraction and is of great significance for promoting intelligent information processing of minority languages. Full article

Scorpions of the genus Tityus are a diverse and medically important group, but many aspects of their natural history, particularly feeding ecology, are poorly documented. A coherent understanding of their natural prey is crucial for interpreting the evolution of their potent venoms. During fieldwork in Quito, Ecuador, we recorded a predation event involving a specimen of Tityus pugilator Pocock, 1898, subduing a scolopendromorph centipede, Otostigmus sp. The centipede was still moving when found, indicating a recent envenomation. This observation adds to the limited knowledge of the genus’s feeding habits both locally and regionally, demonstrating that Tityus can prey on large and dangerous arthropods. This trophic relationship is worth noting as scorpions of this genus have evolved highly potent venoms. Further in-field observations are needed to fully explore this connection between diet and venom evolution in Tityus scorpions. Full article

Human storytellers often orchestrate diverse narrative orders (chronological, flashback) for crafting compelling stories. To equip artificial intelligence systems with such capability, existing methods rely on implicitly learning narrative sequential knowledge, or explicitly modeling narrative order through pairwise event temporal order (e.g., take medicine <after> get ill). However, both suffer from imbalanced narrative order distribution bias and inadequate event temporal understanding, hindering generating high-quality events in the story that balance the logic and narrative order. In this paper, we propose a narrative-order-aware framework, TimeWeaver, which presents an event–order bidirectional pretrained model integrated with temporal mixture-of-experts to orchestrate diverse narrative orders. Specifically, to mitigate imbalanced distribution bias, the temporal mixture-of-experts is devised to route events with various narrative orders to corresponding experts, grasping distinct orders of narrative generation. Then, to enhance event temporal understanding, an event sequence narrative-order-aware model is pretrained with bidirectional reasoning between event and order, encoding the event temporal orders and event correlations. At the fine-tuning stage, reinforcement learning with multi-granular optimal transport reward is designed to boost the quality of generated events. Extensive experimental results on automatic and manual evaluations demonstrate the superiority of our framework in orchestrating diverse narrative orders during story generation. Full article

The LHCB gene family plays a crucial role in light harvesting and photoprotection in plants by encoding key components of the photosystem II antenna complex. The LHCB genes are also involved in salt stress. In this study, we systematically identified and characterized 16 LbaLHCB genes in the economically important medicinal plant Lycium barbarum. Comprehensive bioinformatics analyses revealed that these genes are unevenly distributed across seven chromosomes, with notable gene clustering on chromosome 11. Phylogenetic analysis classified them into seven distinct subfamilies, with the LbaLHCB1 subfamily showing significant expansion through gene duplication events. qRT-PCR and transcriptome analyses revealed tissue-specific expression patterns, with LbaLHCB1.6 exhibiting preferential expression in developing fruits, suggesting its potential involvement in fruit development and quality formation. Under salt stress conditions, the LbaLHCB genes displayed dynamic temporal responses: LbaLHCB1.5 was rapidly induced during early stress (1–3 h), LbaLHCB7 reached peak expression at mid-phase (6–12 h), while LbaLHCB1.2 showed significant downregulation during late stress response (24 h). Promoter analysis identified multiple stress-responsive cis-elements, providing molecular insights into their regulation under abiotic stress. These findings significantly advance our understanding of the LbaLHCB gene family’s structural characteristics and functional diversification in L. barbarum, particularly in relation to photosynthesis regulation and stress adaptation. The study provides valuable genetic resources for future molecular breeding aimed at improving stress tolerance and fruit quality in this important medicinal crop. Full article

Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive memory loss and cognitive decline. Its key pathological hallmarks include extracellular amyloid plaques composed of amyloid beta (Aβ) peptides and intracellular neurofibrillary tangles formed by hyperphosphorylated tau protein. Although numerous studies have investigated the complex pathology of AD, its underlying mechanisms remain incompletely understood. The amyloid cascade hypothesis continues to be the leading model of AD pathogenesis. It suggests that Aβ aggregation is the initial trigger of neurotoxicity, setting off a cascade of pathological events including inflammation, oxidative stress, tau hyperphosphorylation, synaptic dysfunction, and, ultimately, dementia. Molecular dynamics (MD) is a powerful tool in structure-based drug design (SBDD). By simulating biomolecular motions at the atomic level, MD provides unique insights into molecular properties, functions, and inhibition mechanisms—insights often inaccessible through other experimental or computational techniques. When integrated with experimental data, MD further deepens our understanding of molecular interactions and biological processes. Natural compounds, known for their pleiotropic pharmacological activities, favorable safety profiles, and general tolerability (despite occasional side effects), are increasingly explored for their potential in both the treatment and prevention of various diseases, including AD. In this review, we summarize current findings from MD simulations of natural compounds with anti-amyloidogenic potential. This work builds upon our previous publication, which focused on endogenous compounds and repurposed drugs. The review is structured as follows: an overview of the amyloid cascade hypothesis; a discussion of Aβ oligomeric structures and their stabilizing interactions; a section on molecular dynamics, including its challenges and future directions; and a comprehensive analysis of the inhibitory mechanisms of natural compounds, categorized by their shared structural features. Full article

This systematic literature review addresses the critical challenge of ensuring robustness and adaptability in AI-based machine health monitoring (MHM) systems. While the field has seen a surge in research, a significant gap exists in understanding how to effectively manage data scarcity, unknown fault types, and the integration of diverse data streams for real-world industrial applications. The problem is magnified by the rarity of failure events, which leads to imbalanced datasets and hampers the generalizability of predictive models. To synthesize the current state of research and identify key solutions, we followed a rigorous, modified PRISMA methodology. A comprehensive search across Scopus, IEEE Xplore, Web of Science, and Litmaps initially yielded 3235 records. After a multi-stage screening process, a final corpus of 85 peer-reviewed studies was selected. Data were extracted and synthesized based on a thematic framework of 13 core research questions. A bibliometric analysis was also conducted to quantify publication trends and research focus areas. The analysis reveals a rapid increase in research, with publications growing from 1 in 2018 to 35 in 2025. Key findings highlight the adoption of transfer learning and generative AI to combat data scarcity, with multimodal data fusion emerging as a crucial strategy for enhancing diagnostic accuracy. The most active research themes were found to be Predictive Maintenance and Edge Computing, with 12 and 10 references, respectively, while critical areas like standardization remain under-explored. Overall, this review shows that AI benefits machine health monitoring but still faces challenges in reproducibility, benchmarking, and large-scale validation. Its main limitation is the focus on English peer-reviewed studies, excluding industry reports and non-English work. Future research should develop standardized datasets, energy-efficient edge AI, and socio-technical frameworks for trust and transparency. The study offers a structured overview, a roadmap for future work, and underscores the importance of AI in Industry 4.0. Full article

This study examines the potential of satellite internet technologies to enhance crisis management in urban evacuation scenarios in Italy, with a specific focus on the Starlink system as a case study. In emergency situations, traditional mobile and WiFi networks often become inaccessible, significantly impairing timely communication and coordination. Reliable connectivity is therefore imperative for effective rescue operations and public safety. This research analyzes how satellite-based internet can provide robust, uninterrupted connectivity even when conventional infrastructures fail. The study discusses operational advantages such as rapid deployment, broad coverage, and scalability during disasters, as well as key constraints including line-of-sight requirements, environmental sensitivity, and regulatory challenges. Empirical findings from the deployment of Starlink during an actual urban evacuation event in Italy indicate that latency dropped below 200 ms and sustained upload/download speeds averaged approximately 50 Mbps—up to three times faster than ground networks in disrupted zones. By evaluating both benefits and limitations, this paper provides a comprehensive understanding of the integration of satellite internet services within Italian emergency response systems, aiming to improve the performance of urban evacuation strategies. Full article

The Athabasca River flows through the Lower Athabasca Region (LAR) in Alberta, Canada, which is characterized by variable inter-annual weather, long winters and short summers. LAR is important for the extraction of energy resources and industrial activities that lead to environmental concerns, including river pollution and exploitation. This study attempts to forecast the Athabasca River at Fort McMurray and understand the suitability of HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) in cold weather regions, characterized by poorly gauged streams. Daily temperature and precipitation records (1971–2014) were employed in two calibration–validation schemes: (1) a temporally dependent partition (1971–2000 for calibration; 2001–2014 for validation) and (2) a temporally independent partition (alternating years assigned to calibration and validation). The temporally independent approach achieved superior performance, with a Nash–Sutcliffe efficiency of 0.88, outperforming previously developed regional models. HEC-HMS successfully reproduced hydrologic dynamics and peak discharge events under conditions of sparse hydroclimatic data and limited computational inputs, underscoring its robustness for operational forecasting in data-scarce, cold-climate catchments. However, long-term projections may be subject to uncertainty due to the exclusion of anticipated changes in land use and climate forcing. These results substantiate the applicability of HEC-HMS as a cost-effective and reliable tool for hydrological modeling and flow forecasting in support of water resource management, particularly in regions subject to industrial pressures and associated environmental impacts. Full article

Acute myocardial infarction (AMI) remains the leading cause of death worldwide, with myocardial ischemia/reperfusion injury (MIRI) emerging as a significant factor influencing patient outcomes despite timely reperfusion therapy. MIRI refers to paradoxical myocardial damage that occurs upon restoration of coronary blood flow and is driven by complex inflammatory, oxidative, and metabolic mechanisms, which can exacerbate infarct size (IS), contributing to adverse outcomes. This review explores the molecular and cellular pathophysiology of MIRI, emphasizing both its clinical and forensic relevance. The principal mechanisms discussed include oxidative stress and mitochondrial dysfunction, calcium overload and ion homeostasis imbalance, inflammatory responses, with particular focus on the NLRP3 inflammasome and cytokine pathways, and multiple forms of cell death (apoptosis, necroptosis, pyroptosis, and autophagy). Additionally, the authors present original immunohistochemical findings from autopsy cases of patients who suffered ST-segment elevation myocardial infarction (STEMI) and underwent percutaneous coronary intervention (PCI), but subsequently died. These findings underscore that successful reperfusion does not completely prevent delayed complications, like arrhythmias, ventricular fibrillation (VF), and sudden cardiac death (SCD), often caused by secondary MIRI-related mechanisms. Moreover, the case series highlight the diagnostic value of inflammatory markers for pathologists in identifying MIRI as a contributing factor in such fatalities. Finally, immunotherapeutic strategies—including IL-1 and IL-6 inhibitors such as Canakinumab and Tocilizumab—are reviewed for their potential to reduce cardiovascular events and mitigate the effects of MIRI. The review advocates for continued multidisciplinary research aimed at improving our understanding of MIRI, developing effective treatments, and informing forensic investigations of reperfusion-related deaths. Full article