Search Results (1,771)

This study introduces a real-time, non-intrusive monitoring system designed to support elderly care through vision-based pose estimation and hand gesture recognition. The proposed framework integrates convolutional neural networks (CNNs), temporal modeling using LSTM networks, and symmetry-aware keypoint analysis to enhance the accuracy and reliability of behavior detection under varied real-world conditions. By leveraging the bilateral symmetry of human anatomy, the system improves the robustness of posture and gesture classification, even in the presence of partial occlusion or variable lighting. A total of 21 hand landmarks and 33 body pose points are used to recognize predefined actions and communication gestures, enabling seamless interaction without wearable devices. Experimental evaluations across four distinct lighting environments confirm a consistent accuracy above 90%, with real-time alerts triggered via IoT messaging platforms. The system’s modular architecture, interpretability, and adaptability make it a scalable solution for intelligent elderly individual monitoring, offering a novel application of spatial symmetry and optimized deep learning in healthcare technology. Full article

Wireless body area networks (WBANs) are a pivotal solution for continuous health monitoring, but their energy constraints pose a significant challenge for long-term operation. This paper provides a comprehensive review of state-of-the-art energy-efficient mechanisms, critically evaluating solutions across various network layers. We focus on three key approaches: energy-aware MAC protocols that reduce idle listening and optimize duty cycling; energy-efficient routing protocols that enhance data transmission and network longevity; and emerging energy harvesting techniques that offer a path toward energy-autonomous WBANs. Furthermore, the paper provides a detailed analysis of the inherent trade-offs between energy efficiency and other critical performance metrics, such as latency, reliability, and security. It also explores the transformative potential of emerging technologies, such as AI and blockchain, for dynamic energy management and secure data handling. By synthesizing these findings, this work contributes to the development of sustainable WBAN solutions and outlines clear directions for future research. Full article

Background: Hydroxycarboxylic acid receptor 3 (HCAR3) is a receptor that is mainly expressed in human adipose tissue. It can inhibit lipolysis through the inhibition of adenylyl cyclase; thus, it is closely related to the regulation of lipids in the human body. This makes HCAR3 a compelling target for developing drugs against dyslipidemia. Notably, the reported active compounds for HCAR3 are all carboxylic acids. This observation is in line with the fact that ARG111 has been reported as the key residue to anchor the active compound in a closely related homologous protein—HCAR2. Methods: In this study, we aim to discover new chemicals, through virtual screening, that may bind with HCAR3. As there are several choices for the receptor conformation, cross-docking was conducted and the root-mean-square deviation of the docking pose from the conformation of the crystal ligand was employed to determine the best receptor conformation for screening. Ligands from the ZINC20 database were screened through molecular docking, and 30 candidates were subjected to 100 ns MD simulations. Six stable complexes were further assessed by umbrella sampling to estimate binding affinity. Results: The homology model (HCAR3_homology) was selected as the receptor. Following the protocol determined by the retrospective docking process, prospective docking was conducted to screen the ligands from the ZINC20 database. Subsequently, the top 30 compounds with a good docking score and a good interaction with ARG111 were subjected to 100 ns molecular dynamics (MD) simulations, and their binding stability was analyzed based on the resulting trajectories. Finally, six compounds were chosen for binding free energy calculation using umbrella sampling; all showed negative binding affinities. Conclusions: All six compounds selected for umbrella sampling showed negative binding affinities, suggesting their potential as novel HCAR3 ligands for the development of drugs against dyslipidemia. Full article

Concealed water bodies within surrounding rock formations pose a serious threat to tunnel construction. To address this risk, this study integrates physics-based heat conduction theory with deep learning, unlike existing methods that treat temperature as isolated data points or rely solely on empirical models. The approach introduces three key innovations: (a) analytical temperature–location relationships for water body characterization; (b) pseudo-temporal modeling of spatial sequences and (c) physics-guided neural architecture design. First, a steady-state heat conduction model is established to characterize axial temperature distribution patterns caused by concealed water bodies during excavation. From this, quantitative relationships between temperature anomalies and the location and size of the water bodies are derived. Next, a deep learning model, ST-HydraNet, is proposed to treat tunnel axial temperature data as a pseudo-time series for hazard prediction. Experimental results demonstrate that the model achieves high accuracy (91%) and perfect precision (1.0), significantly outperforming existing methods. These findings show that the proposed framework provides a non-invasive, interpretable, and robust solution for real-time hazard detection, with strong potential for integration into intelligent tunnel safety systems. By enabling earlier and more reliable detection, the model directly enhances construction safety, economic efficiency, and environmental sustainability. Full article

Potentially toxic element pollution caused by coal mining activities, especially the accumulation of cadmium, has become a major threat to the global environment and health. Long-term mining activities in China, a major coal consumer, caused a large accumulation of coal gangue. Gangue weathering and leaching release Cd, which threatens the ecological safety of the surrounding soil and water bodies. Although the government has implemented ecological restoration projects in the mining areas, there is still a lack of systematic evaluation of pollution control of downstream farmlands. For this study, remote sensing analyses of fractional vegetation coverage (FVC), geo-accumulation index (I_geo), and potential ecological risk index (EI) data, as well as the pollution characteristics and ecological risks of Cd, were evaluated for a coal mining area in Jiangxi Province. Coal gangue, restoration cover soil, downstream farmland soil, irrigation water, and sediment samples were used in the analyses. After restoration, the Cd concentration in the mining cover soil (0.23 mg/kg) was significantly lower than that of the coal gangue (1.18 mg/kg), while the Cd concentration in the downstream farmland soil (0.44 mg/kg) was roughly an average of the two. The geo-accumulation index indicates that the farmland soil is mainly unpolluted (with an average I_geo of −0.25). However, some points have reached the level of no pollution to moderate pollution. Coal gangue poses a relatively high ecological risk (with an average EI of 118), while cover soil and farmland soil pose low risks (with an average EI of 22.5 and 39.86, respectively). The restoration project significantly reduced the Cd input in the downstream farmlands. The study revealed the effective blocking of external soil cover on Cd migration, providing a key scientific basis for the optimization of ecological restoration strategy and risk prevention and control in similar mining areas worldwide. Full article

Gestational diabetes mellitus (GDM) is a frequently encountered medical complication during pregnancy that is increasing at a rapid pace globally, posing significant public health concerns. Similarly, there is a rising trend in the number of women who have utilized assisted reproductive technology (ART). Numerous studies have been carried out to investigate the relationship between GDM and ART. This comprehensive systematic review seeks to identify potential biomarkers for the early diagnosis of GDM in pregnancies conceived through ART. We conducted a PubMed search covering the past five years to identify studies that explore biomarkers associated with the development of GDM in pregnancies conceived through ART. The outcome measures included human chorionic gonadotropin (HCG), the body mass index (BMI), the Follicle Stimulating Hormone to Luteinizing Hormone (FSH/LH) ratio, increased hemoglobin A1c levels, fasting insulin concentrations, homeostatic model assessment of insulin resistance (HOMA-IR), triglyceride levels, total cholesterol levels, low-density lipoprotein cholesterol concentrations, low-density lipoprotein/high-density lipoprotein (LDL/HDL), total cholesterol to high-density lipoprotein (TC/HDL), the estradiol/follicle ratio, soluble fms-like tyrosine kinase-1 (sFlt-1), Placental Growth Factor (PLGF), endometrial thickness, and psychological stress. Seventeen studies were included. The identification and development of serum or ultrasound biomarkers for the early detection of GDM in pregnancies conceived through ART pose considerable challenges. These challenges arise from the multifactorial nature of GDM, the methodological variations in ART, and the limited availability of relevant studies. The most promising biomarker identified was the estradiol/follicle ratio. Women with a higher estradiol/follicle ratio exhibited significantly lower rates of GDM. There is a pressing necessity for biomarkers to enable the early detection of GDM in pregnancies conceived through ART. E2 levels, β-hCG, and the E2/F ratio, along with the TC/HDL and LDL/HDL ratios, show potential as reliable biomarkers for identifying GDM. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely impacts motor function, necessitating early detection for effective management. However, current diagnostic methods are expensive and resource-intensive, limiting their accessibility. This study proposes a non-invasive, gait-based screening approach for PD using time-series analysis of video-derived motion data. Gait patterns indicative of PD are analyzed using videos containing walking sequences of PD subjects. The video data are processed via computer vision and human pose estimation techniques to extract key body points. Classification is performed using K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) networks in conjunction with time-series techniques, including Dynamic Time Warping (DTW), Bag of Patterns (BoP), and Symbolic Aggregate Approximation (SAX). KNN classifies based on similarity measures derived from these methods, while LSTM captures complex temporal dependencies. Additionally, Shapelet-based Classification is independently explored for its ability to serve as a self-contained classifier by extracting discriminative motion patterns. On a self-collected dataset (43 instances: 8 PD and 35 healthy), DTW-based classification achieved 88.89% accuracy for both KNN and LSTM. On an external dataset (294 instances: 150 healthy and 144 PD with varying severity), KNN and LSTM achieved 71.19% and 57.63% accuracy, respectively. The proposed approach enhances PD detection through a cost-effective, non-invasive methodology, supporting early diagnosis and disease monitoring. By integrating machine learning with clinical insights, this study demonstrates the potential of AI-driven solutions in advancing PD screening and management. Full article

The food additive sodium benzoate (SB) has been used for decades as an antimicrobial to prevent food spoilage. SB has been deemed to pose no risk to human health when consumed at levels under 5 mg/kg body weight per day; however, when many of the supporting studies were conducted, the importance of the gut microbiome to human health was not yet appreciated. Given SB’s known antimicrobial qualities, it is important to assess the effect of this food additive on the human gut microbiome. The ex vivo SIFR^® (Systemic Intestinal Fermentation Research) technology was used to test the effect of SB on microbial communities from 24 donors, aged infants to older adults. A dose of 3.5 g/L SB elicited a drop in the Pseudomonadota phylum for multiple age groups but did not alter the alpha or beta diversity within any of these groups. This was accompanied by changes in the functional outputs that included an overall rise in butyrate and a drop in propionate production. This higher butyrate correlates with an increase in the abundance of several known butyrate producers in the presence of SB, although the genetic potential for its production in the community did not change. Overall, despite using a dose ten times higher than the accepted daily intake limit, the effect on the gut microbiome was minimal. Full article

The objective of this study was to assess the content of selected elements—fluorine, calcium and inorganic phosphorus—in infusions prepared from selected commercial leaf teas available on the Polish market. A comprehensive analysis was conducted based on tea type and geographical origin. In addition, the Target Hazard Quotient (THQ) was calculated to estimate the non-carcinogenic health risk associated with fluoride intake from tea consumption. Methods: A total of 98 leaf tea samples were analyzed, including 55 black, 27 green, 9 oolong, and 7 white teas. Standardized brewing protocols were applied. Measured parameters included pH, calcium and inorganic phosphorus content, buffer capacity, and titratable acidity. Fluoride concentrations were determined using an ion-selective electrode. Statistical analysis was performed using non-parametric methods (Kruskal–Wallis ANOVA with DSCF post hoc test), and heatmaps were generated to illustrate the distribution of THQ across different models. Results: Black teas exhibited significantly lower pH values and higher titratable acidity, buffer capacity, and inorganic phosphorus levels compared to other tea types, indicating distinct physicochemical properties. Although all THQ values for fluoride remained well below the safety threshold (THQ < 1), the highest values were observed in elderly individuals with low body weight, particularly women consuming green tea, suggesting increased vulnerability in this subgroup. Conclusions: Among the analyzed samples, black teas demonstrated the most distinct chemical profile, characterized by the lowest pH and the highest acidity, buffer capacity, and fluoride and phosphorus content—especially in teas originating from Africa and Central Asia. While fluoride exposure from leaf tea infusions does not appear to pose a direct health risk, older adults, particularly low-weight women, may be more susceptible to potential non-carcinogenic effects and should moderate their intake of high-fluoride teas. Full article

This study examined the spatial and seasonal variations of trihalomethanes (THMs) and estimated the health risks associated with THM exposure in drinking water through various pathways. Water samples were collected from 14 distribution districts connected to the Ulutan Distribution System (UDS) and the Süleyman Bey Distribution System (SDS), which supply drinking water to Zonguldak Province, Türkiye. THMs were measured using the USEPA 551 method. The median total trihalomethanes (TTHMs) ranged from 41 μg/L to 71 μg/L, which is below the Turkish drinking water standard of 100 μg/L. Chloroform (TCM) was the most common trihalomethane in all distribution networks in UDS and SDS. On the other hand, pre-ozonation oxidation after chlorination in SDS disinfection caused the contribution of brominated THMs (62%) to THM formation to be higher than that of TCM (38%). The study on cancer risk reveals that ingestion (96%) poses the greatest risk of the investigated pathways, followed by dermal contact (3.95%), while inhalation has been found to have a negligible effect. The highest and lowest median TTHMs occurred during winter and summer. The findings of the study show that the distribution areas of Kozlu, Ömerli, Topçalı, and Uzunçayır, for both genders, exhibit an unacceptable cancer risk level according to the criteria established by the USEPA (>10⁻⁴). Bromodichloromethane (BDCM) and chlorodibromomethane (DBCM) are the main contributors to cancer risk for males and females in UDS and SDS. The hazard index (HI) data indicated that the HI value remained below one for both UDS and SDS. Sensitivity analysis of THMs demonstrated that exposure frequency (EF) was the primary parameter contributing to the maximum potential impact on the total cancer risk exposure frequency (EF), followed by body weight (BW) and exposure duration (ED). Further, the results provide valuable information for health departments and water management authorities, enabling the formulation of more specific and efficient policies to minimise THM levels in drinking water distribution networks. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

Marker-based motion capture systems using infrared cameras (IR MoCaps) are commonly employed in biomechanical research. However, their high costs pose challenges for many institutions seeking to implement such systems. This study aims to develop a neural network (NN) model to estimate the digitized coordinates of spherical markers and to establish a lower-cost marker-based motion capture system using RGB cameras. Thirteen participants were instructed to walk at self-selected speeds while their movements were recorded with eight RGB cameras. Each participant undertook trials with 24 mm spherical markers attached to 25 body landmarks (marker trials), as well as trials without markers (non-marker trials). To generate training data, virtual markers mimicking spherical markers were randomly inserted into images from the non-marker trials. These images were then used to fine-tune a pre-trained model, resulting in an NN model capable of detecting spherical markers. The digitized coordinates inferred by the NN model were employed to reconstruct the three-dimensional coordinates of the spherical markers, which were subsequently compared with the gold standard. The mean resultant error was determined to be 2.2 mm. These results suggest that the proposed method enables fully automatic marker reconstruction comparable to that of IR MoCap, highlighting its potential for application in motion analysis. Full article

Nuclear factors of activated T cells (NFATs) are pivotal regulatory factors of immune responses, primarily by modulating T cell activity and regulating inflammatory cytokine gene transcription. The grass carp reovirus (GCRV) triggers a serious hemorrhagic condition, posing a significant threat to sustainable grass carp (Ctenopharyngodon idella) aquaculture. However, the precise function of NFAT in the host’s defense against GCRV infection is mostly undefined. This study comprehensively identified and characterized the NFAT genetic family in grass carp, cloned grass carp NFAT1 (CiNFAT1), and investigated its expression and function during GCRV infection. Eight NFAT genes encoding seventeen isoforms have been detected within the grass carp’s genomic sequence, distributed across six different chromosomes. Comparative analysis revealed homology with zebrafish NFATs. CiNFAT1 possesses a 2697 bp open reading frame, encoding 898 amino acids, and contains conserved Rel homology domain (RHD) and NFAT-homology (IPT) domains. Quantitative PCR (qPCR) revealed ubiquitous CiNFAT1 expression in healthy grass carp tissues, with the highest expression in gills and skin and the lowest in liver. Following GCRV challenge in vivo, CiNFAT1 expression in immune tissues (liver, spleen, kidney, gill, intestine) showed dynamic changes over time. In vitro experiments in CIK cells demonstrated that CiNFAT1 expression peaked at 12 h post-GCRV infection. Further functional studies revealed that overexpression of CiNFAT1 significantly reduced GCRV replication at 36 h post-infection. This reduction was accompanied by elevated expression of type I interferon (IFN-I) and interferon regulatory factor 7 (IRF7) at 24 and 36 h, respectively, as well as modulated IL-2, IL-8, and IL-10. Conversely, RNA interference-mediated knockdown of CiNFAT1 enhanced GCRV VP5 and VP7 mRNA levels and suppressed IL-2 and IL-8 expression. These results suggest that CiNFAT1 contributes to anti-GCRV immunity by promoting antiviral and inflammatory cytokine responses, thereby inhibiting viral replication. This study provides a foundational understanding of the NFAT genetic family in grass carp and highlights an important role of CiNFAT1 in mediating the body’s inherent defense mechanism against GCRV infection, offering insights for disease control strategies in aquaculture. Full article

Plastic pollution is one of the leading global problems of modern society. The growing demand for and production of plastic polymers has caused bisphenol A (BPA) and its emergent substitute molecules bisphenol S and F (BPS and BPF) to be present in water, food, and soil worldwide, exposing humans to endocrine disruptors. Exposure to these compounds has been associated with pathologies such as diabetes, obesity, hypertension, and psychiatric disorders. Interestingly, hypovitaminosis D (or low 25(OH)D) is also associated with this class of diseases. Therefore, the present work, for the first time, explores the relationship patterns between urinary bisphenols (BPs) and low 25(OH)D in a large general cohort (NHANES 13–16). Descriptive statistical analyses, comparative analyses, linear regressions, and binomial and multinomial logistic regressions were performed. Descriptive and comparative analysis, and simple linear regressions, showed different trends between BPs, and binomial logistic regressions showed that only BPS is a risk factor of low 25(OH)D, independently of age, BMI, gender, diabetes, dyslipidemia, smoking, and vitamin supplements consumption; odds ratio (95% confidence interval) of 1.10 (1.04–1.17). The different trend patterns observed in urinary bisphenols show that, despite being structurally similar molecules and potential analogs, they may affect the body in different ways. From an integrated perspective, this could represent an even greater potential threat than that posed by BPA alone. Future integrated studies will be required to further explore and clarify this emerging paradigm. Full article

Human pose estimation (HPE) in privacy-sensitive environments such as healthcare facilities and smart homes demands non-visual sensing solutions. Millimeter-wave (mmWave) radar emerges as a promising alternative, yet its development is hindered by the scarcity of high-fidelity datasets with accurate annotations. This paper introduces mmFree-Pose, the first dedicated mmWave radar dataset specifically designed for privacy-preserving HPE. Collected through a novel visual-free framework that synchronizes mmWave radar with VDSuit-Full motion-capture sensors, our dataset covers 10+ actions, from basic gestures to complex falls. Each sample provides (i) raw 3D point clouds with Doppler velocity and intensity, (ii) precise 23-joint skeletal annotations, and (iii) full-body motion sequences in privacy-critical scenarios. Crucially, all data is captured without the use of visual sensors, ensuring fundamental privacy protection by design. Unlike conventional approaches that rely on RGB or depth cameras, our framework eliminates the risk of visual data leakage while maintaining high annotation fidelity. The dataset also incorporates scenarios involving occlusions, different viewing angles, and multiple subject variations to enhance generalization in real-world applications. By providing a high-quality and privacy-compliant dataset, mmFree-Pose bridges the gap between RF sensing and home monitoring applications, where safeguarding personal identity and behavior remains a critical concern. Full article