Search Results (2,635)

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

Clarifying the sources of disparity and the systemic structure of influencing factors behind China’s economic resilience is crucial for promoting regional coordinated development and ensuring national security. This study constructs an evaluation index system based on the DPSIR model and employs the entropy method to measure China’s economic resilience from 2008 to 2023, examining its temporal evolution and spatial distribution. A bi-dimensional decomposition method of Gini coefficient is applied to examine disparities from both spatial and structural perspectives. Furthermore, the DEMATEL-ISM model is employed to reveal the systemic structure of influencing factors. The findings reveal that: (1) China’s economic resilience steadily improved during the study period, showing a spatial gradient of “Eastern > Central > Northeastern > Western,” with its geographic center shifting southeastward, reflecting strong spatial dependence. (2) Disparities in economic resilience have generally widened. Inter-regional differences are the main source of spatial disparities, while variations in response dominate the structural disparities. Initially, disparities were mainly due to differences in influence between eastern and western regions, but by the end of the period, disparities in driving forces became the key contributor. (3) Influencing factors follow a four-level, three-stage hierarchical structure. Foreign capital withdrawal risks, innovation investment, technological progress, factor supply, and the output of opening-up constitute deep-level factors influencing economic resilience. This study refines the evaluation framework of economic resilience and provides important references for understanding the disparities in China’s economic resilience and developing targeted improvement strategies. Full article

The fifth annual pan-tumour Early Age Onset Cancer (EAOC) Symposium, held in November 2025 and organized by the Colorectal Cancer Resource & Action Network (CCRAN), convened clinicians, researchers, policymakers, patients, and caregivers to address the rising incidence of cancers in individuals under 50 years. In addition to discussions around diagnostic and therapeutic advances for patients with late-stage disease, content centered on addressing critical gaps along the EAOC care continuum, including (i) diagnostic delays related to limited awareness and suboptimal primary care pathways, (ii) screening eligibility criteria for colorectal cancer (CRC) that no longer reflect current disease epidemiology, and (iii) insufficient age-appropriate infrastructure to meet the EAOC population’s unique unmet needs with respect to psychosocial support, fertility counseling, financial navigation, and survivorship planning. The symposium generated consensus recommendations such as the embedding of EAOC education into medical training curricula to increase the index of suspicion of EAOC in primary care, lowering the CRC screening age to 45 years to match this population’s rising disease incidence, and expanding multidisciplinary adolescent and young adult (AYA) and EAOC programs—including through the use of virtual models—to ensure that patients receive coordinated, comprehensive, equitable and age-appropriate care across the country. Full article

In hepatocellular carcinoma (HepG2), aberrant histone modifications are closely linked to long non-coding RNA (lncRNA) expression. However, existing computational models lack physical interpretability at specific promoter coordinates. To address this, we developed a position-specific statistical scoring model based on adjacent and next-adjacent nucleotide frequencies. We trained two independent, position-specific matrices representing increased and decreased modification states across 600 bp promoter windows centered on the true signal summits. Finally, ten-fold cross-validation revealed that significant energy differences between sequences with increased and decreased histone signals enable excellent classification performance. These results indicted a strong correlation between the total energy of local DNA structures and histone modification signal. Full article

Intercellular communication is crucial for the coordination of skeletal muscle development. However, the intricate signaling networks that regulate chicken myogenesis are not yet fully elucidated. In this study, we utilized CellChat analysis on single-cell and single-nucleus RNA sequencing data to systematically delineate cell–cell communication patterns across five critical developmental stages of chicken skeletal muscle: embryonic day 4 (E4), day 6 (E6), day 12 (E12), day 18 (E18), and post-hatch day 30 (P30). Our findings indicate that communication architectures are highly stage-specific, with mesenchymal cells acting as the predominant signaling hub during the early embryonic stages (E4–E6), whereas fibro-adipogenic progenitors become the principal communicators during mid-to-late embryogenesis (E12–E18). At E4, the communication network was relatively simple, comprising 51 ligand–receptor pairs primarily involving the neural cell adhesion molecule, slit guidance ligand, and midkine (MK) signaling pathways between myogenic progenitors and mesenchymal cells. By E6, the network had expanded significantly, encompassing 6237 ligand–receptor pairs across 51 signaling pathways, which coincided with the emergence of multiple myogenic lineages. Peak communication complexity was observed at E12, characterized by 11,675 ligand–receptor pairs and 61 signaling pathways, reflecting the secondary wave of myogenesis. Comparative analysis across developmental stages revealed key signaling transitions: the pleiotrophin and MK pathways were predominantly active during the early phase of myogenic commitment (E4–E6), whereas the collagen, laminin, and adhesion G protein-coupled receptor L pathways were more prominent during the secondary myogenesis phase (E6–E12). Notably, a significant shift in communication patterns was observed from E12 to E18, marked by a reduction in developmental pathway signaling and an increase in immune-related communications. By P30, the communication network had stabilized into a homeostatic state, centered on interactions among myofibers, stromal cells, and the vascular system. This comprehensive atlas of intercellular communication offers novel insights into the signaling dynamics underpinning chicken skeletal muscle development. Full article

Chronic overlapping pain conditions (COPCs) refer to a set of chronic pain disorders that frequently co-occur and may involve partially overlapping mechanisms. The U.S. National Institutes of Health currently recognizes ten COPCs: fibromyalgia, painful temporomandibular disorders, chronic low back pain, chronic migraine headache, chronic tension-type headache, irritable bowel syndrome, endometriosis, interstitial cystitis/bladder pain syndrome, vulvodynia, and myalgic encephalomyelitis/chronic fatigue syndrome. When multiple COPCs coexist, they are associated with a disproportionate multimorbidity burden, including greater pain, poorer psychological well-being, functional limitations, disability, fatigue, sleep disturbances, diminished quality of life, and increased healthcare utilization. Despite their impact, COPCs remain under-recognized, underdiagnosed, and undertreated. Combining structured literature searches and citation tracking with narrative syntheses, this review examines comorbid relationships, the burden of multimorbidity, and potentially overlapping nociplastic mechanisms. By adopting a multimorbidity-based perspective rather than a one-disease, one-treatment approach, it highlights barriers to care—including limited clinical awareness, under-recognition of additional COPCs, limited mechanistic understanding, and fragmented care—and proposes integrated strategies emphasizing prevention, systematic screening, mechanism-informed assessment, and coordinated, patient-centered multimodal management. Full article

White adipose tissue (WAT) is essential for maintaining energy homeostasis during hibernation by supplying lipolysis-derived fatty acids as a major fuel source. In raccoon dogs (Nyctereutes procyonoides), the activity of brown adipose tissue is diminished, providing a unique model to investigate how WAT supports metabolic homeostasis in a largely non-thermogenic state. Here, we integrated physiological, histological, transcriptomic, and molecular analyses of back-fat and tail-fat depots during autumn fattening and winter sleep. Despite reduced food intake, body weight loss, and mild hypothermia, raccoon dogs maintained systemic glucose and lipid homeostasis. Both WAT depots exhibited adipocyte atrophy and the coordinated suppression of core metabolic and biosynthetic pathways, indicating a shared program of metabolic depression. However, the two depots adopted distinct remodeling strategies. Back-fat showed collagen densification and vascular-associated remodeling, suggesting a structural adaptation that may preserve tissue integrity during winter sleep. In contrast, tail-fat displayed enhanced innate immune signaling and M2 macrophage enrichment, indicating immune niche remodeling that may support tissue protection during prolonged lipid mobilization. Together, these findings reveal that raccoon dogs maintain metabolic homeostasis during shallow hibernation through a non-thermogenic, WAT-centered strategy characterized by shared metabolic depression and depot-specific structural and immunometabolic remodeling. Full article

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture. Full article

Gestational Diabetes Mellitus (GDM) represents a significant public health concern due to its association with adverse maternal and neonatal outcomes, as well as elevated long-term metabolic risks. Its prevalence varies substantially depending on the diagnostic criteria used and the population studied. Women with GDM frequently experience heightened stress, anxiety, and uncertainty, underscoring the need for accessible information, counseling, and ongoing support to navigate glucose monitoring, dietary adjustments, and treatment regimens. Although clinical management has been extensively studied, research has largely focused on metabolic monitoring and therapeutic interventions, often underemphasizing prevention strategies, women’s informational needs, and maternal psychological well-being. Emerging evidence and international guidelines increasingly advocate for integrating these components into structured, woman-centered GDM care plans that actively involve families. Such approaches empower women to engage in self-management, enhance health literacy, support adherence to lifestyle and pharmacological interventions, and promote sustainable behavioral changes. This narrative review presents a comprehensive, holistic model of care across the perinatal continuum, emphasizing early risk identification, preventive strategies, and multidisciplinary coordination. Core elements include individualized antenatal education, empathetic communication, and family engagement, fostering self-efficacy, continuity of care, and integration of medical, educational, and psychosocial interventions. Equipping healthcare professionals with the competencies to deliver this holistic, woman-centered framework is essential to optimize maternal and neonatal outcomes and mitigate the long-term health consequences of GDM. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

Background/Objectives: The role of the endometrial microbiome in reproductive failure remains incompletely understood. This study aimed to describe the composition of the endometrial microbiome in women evaluated for infertility or recurrent miscarriage. Methods: In this single-center descriptive study, endometrial samples were collected from women evaluated for infertility or recurrent miscarriage. Microbiome profiling was performed using 16S rRNA gene next-generation sequencing. Samples were classified as Lactobacillus-dominant when Lactobacillus spp. accounted for ≥90% of the total bacterial community. Alpha diversity was assessed using the Shannon and Simpson indices, while beta diversity was evaluated using Bray–Curtis dissimilarity, principal coordinates analysis (PCoA), PERMANOVA, and PERMDISP. Results: Of the 60 samples, 20 (33.3%) were Lactobacillus-dominant and 40 (66.7%) were non-Lactobacillus-dominant. Across all samples, Firmicutes was the predominant phylum (76.6%). Non-Lactobacillus-dominant samples showed significantly higher alpha diversity than Lactobacillus-dominant samples for both the Shannon and Simpson indices (p = 1.19 × 10⁻⁶ and p = 1.51 × 10⁻⁶, respectively), as well as higher observed taxa richness (p = 0.000017). PCoA based on Bray–Curtis dissimilarity demonstrated clear separation between microbiome profiles, supported by PERMANOVA (pseudo-F = 13.87, R² = 0.193, p = 0.001). PERMDISP showed significantly greater dispersion among non-Lactobacillus-dominant samples (F = 566.94, p < 0.001). Non-Lactobacillus-dominant samples showed greater representation of Enterococcus and Prevotella. Conclusions: In this cohort non-Lactobacillus-dominant communities were more frequent with greater diversity, richness, and compositional heterogeneity than Lactobacillus-dominant communities. These findings highlight the need for larger, standardized studies with appropriate control populations to clarify their clinical significance. Full article

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

In Optical Camera Communication (OCC), precise localization of LED arrays under complex tilt conditions is a core challenge for reliable decoding. This paper proposes an OCC reception scheme for RGB-LED arrays that integrates YOLO-OBB rotated object detection with two-stage geometric correction. The system first employs a YOLOv8n-OBB model to extract a quadrilateral region of interest that tightly encloses the LED array boundary. This effectively suppresses background interference caused by superimposed perspective tilt and in-plane rotation. A coarse-to-fine two-stage correction framework is then applied. The first stage rapidly eliminates the dominant perspective distortion based on the detected bounding-box corners. The second stage performs a refined correction using the actual LED center positions. Two homography matrices are cascaded into a combined transformation, achieving two-stage correction accuracy through a single coordinate mapping. In the corrected image, K-Means clustering constructs a 16 × 16 LED topological grid. A locking strategy is adopted so that subsequent frames skip repeated LED detection and clustering. The steady-state per-frame processing time is reduced to approximately 78.9 ms. Experiments covered 16 cross-combinations of vertical tilt from 0° to 45° (0°, 15°, 30°, 45°) and in-plane rotation from 0° to 40° (0°, 15°, 30°, 40°). The uncorrected scheme and the horizontal-box scheme experienced severe bit errors or complete failure under complicated distortion. The proposed scheme maintained error-free transmission under all 16 tested conditions. The ratios of opposite sides of the corrected LED grid remained stable between 0.997 and 1.004. The system simultaneously achieves high reliability and low-latency real-time processing under complex geometric distortions. Full article

Background: Obesity is a multifactorial metabolic disorder characterized by chronic low-grade inflammation, oxidative stress, mitochondrial dysfunction, lipotoxicity, dysregulated adipogenesis, and alterations in the gut microbiota, which collectively contribute to insulin resistance and cardiometabolic complications. In this context, dietary patterns rich in bioactive compounds have gained relevance as potential strategies to modulate these interconnected pathways. Objective: To assess the potential of the Milpa Diet (a sustainable, plant-dominant Mesoamerican eating pattern centered on the ancient three sisters’ polyculture of maize, beans, and squash, along with chili) as a culturally relevant, multi-axis functional dietary pattern, and to evaluate the molecular mechanisms underlying obesity-associated with metabolic dysfunction. Methods: A scoping review of preclinical and clinical studies was conducted using Medline via PubMed, Scopus, and Web of Science databases. The ChEMBL database was also used to identify chemical structures. The search focused on evidence related to inflammation, oxidative stress, adipogenesis, lipotoxicity, mitochondrial function, and gut microbiota modulation in the context of the main foods of the Milpa Diet, including maize, legumes, chili peppers, nopal, and quelites. Studies were selected based on peer-review status and their relevance to molecular, metabolic, and functional outcomes. Results: The current evidence shows that the core components of the Milpa Diet provide dietary fiber and a broad range of bioactive compounds, such as flavonoids, carotenoids, capsaicinoids, phenolic acids, pigments, and vitamins, which exhibit antioxidant and anti-inflammatory effects. These compounds have been associated with modulation of adipogenesis and lipotoxicity, preservation of mitochondrial function, and favorable regulation of gut microbiota composition and activity, collectively influencing metabolic pathways relevant to obesity. Conclusions: Overall, mechanistic and emerging clinical evidence suggests that the Milpa Diet represents a multi-axis nutritional strategy with potential to mitigate obesity-related metabolic dysfunction through coordinated effects on inflammation, oxidative stress, adipogenesis, lipotoxicity, mitochondrial function, and gut microbiota regulation. Although comprehensive clinical trials evaluating this dietary pattern as an integrated intervention remain limited, current evidence supports its relevance for future translational research, public health strategies, and the development of sustainable dietary models aimed at improving metabolic health. Full article

Engineering education prepares graduates to face complex environmental and societal challenges. This involves the intersection of sustainability and innovation. Integrating these agendas is therefore necessary, and this involves identifying specific elements that have not yet been explored. To examine this relationship, a systematic literature review was conducted using an adapted PRISMA 2020 approach appropriate for a bibliometric and thematic systematic review, through four research questions related to knowledge production, pedagogical methods, innovation outcomes, and reported results. The PRISMA phases were adopted using the SCOPUS and ERIC databases. This yielded three clusters: innovation, sustainability, and engineering education. Student-centered pedagogies have also been identified as an explored opportunity to enhance innovation skills aligned with sustainability objectives. However, this incorporation involves many elements to explore, including the connection between innovation outcomes and sustainability impact. This context involves both development and the relationships among individuals, institutions, and ecosystems. This requires managing diverse visions, languages, and cultures, which highlights several challenges: long-term impacts, mindset development, contextual influences, pedagogical strategies, research–practice alignment, stakeholder communication, and faculty preparation. Overall, the findings show progress but reveal challenges across approaches and contexts. This is because sustainability-driven innovation in engineering education requires coordinated curricular, institutional, and ecosystem-oriented strategies to support learning and strengthen contributions to sustainable futures. Full article