Search Results (21,028)

Soil-transmitted helminths (STHs) and Herpes Simplex Virus type 2 (HSV-2) are highly prevalent infections with overlapping distribution, particularly in resource-poor regions. STH/HSV-2 co-infections may impact female reproductive health. However, many aspects of STH/HSV-2 co-infections, including the role of microRNAs (miRNAs) in regulating female genital tract (FGT) immunity and their potential contribution to pathologies such as chronic inflammation, impaired mucosal defense, and reproductive tract cancers remain unclear. In this study we investigated the miRNA expression profiles in murine FGT tissues following single or co-infection with Nippostrongylus brasiliensis (Nb) and HSV-2 and explored predicted miRNA-mRNA targets and pathways. An analysis of miRNA sequencing data was conducted to determine differentially expressed (DE) miRNAs between infected FGT tissues and uninfected controls. Ingenuity Pathway Analysis was conducted to predict the immune-related target genes of the DE miRNAs and reveal enriched canonical pathways, top diseases, and biological functions. Selected representative DE miRNAs were validated using RT-qPCR. Our results showed a total of eight DE miRNAs (mmu-miR-218-5p, mmu-miR-449a-5p, mmu-miR-497a-3p, mmu-miR-144-3p, mmu-miR-33-5p, mmu-miR-451a, mmu-miR-194-5p, and mmu-miR-192-5p) in the comparison of Nb-infected versus uninfected controls; nine DE miRNAs (mmu-miR-451a, mmu-miR-449a-5p, mmu-miR-144-3p, mmu-miR-376a-3p, mmu-miR-192-5p, mmu-miR-218-5p, mmu-miR-205-3p, mmu-miR-103-3p, and mmu-miR-200b-3p) in the comparison of HSV-2-infected versus uninfected controls; and one DE miRNA (mmu-miR-199a-5p) in the comparison of Nb/HSV-2 co-infected versus uninfected controls (p-value < 0.05, |logFC| ≥ 1). Core expression analysis showed that, among other canonical pathways, the DE miRNAs and their predicted mRNA targets were involved in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, natural killer cell signaling, interferon alpha/beta signaling, and ISGylation. Additionally, cancer was predicted as one of the significantly enriched diseases, particularly in the co-infected group. This is the first study to provide insights into the FGT miRNA profiles following Nb and HSV-2 single and co-infection, as well as the predicted genes and pathways they regulate, which may influence host immunity and pathology. This study highlights the role of miRNAs in regulating FGT immunity and pathology in the context of STH/HSV-2 co-infection. Full article

Advanced air defense methods are essential to address the growing complexity of aerial threats. The increasing number of targets necessitates better defensive coordination, and a promising strategy involves the use of interceptors together to protect a specific area. This task fundamentally depends on accurately predicting their kinematic envelopes, or reachable sets. This paper presents a novel approach to determine the boundaries of reachable sets for aerodynamic interceptors, accounting for energy loss from drag, energy gain from thrust, variable acceleration limits, and autopilot dynamics. The devised numerical method approximates reachable sets for nonlinear problems using a constrained model predictive programming concept. Results demonstrate that explicitly accounting for input constraints, such as acceleration limits, significantly impacts the shape and area of the reachable boundaries. Furthermore, a sensitivity analysis was conducted to demonstrate the impact of parameter variations on the reachable set. Revealing the reachable set’s sensitivity to variations in thrust and drag coefficients, this analysis serves as a framework for considering parameter uncertainty and enables the evaluation of these effects prior to embedding the reachability boundaries into an offline database for guidance applications. The resulting boundaries, representing minimum and maximum ranges for various initial parameters, can be stored offline, allowing interceptors to estimate their own or allied platforms’ kinematic capabilities for cooperative strategies. Full article

Natural polymers, polysaccharides, demonstrate piezoelectric behavior suitable for force sensor manufacturing. Carrageenan hydrogel film with α-iron oxide particles can act as a piezoelectric polysaccharide-based force sensor. The mechanical impact on the hydrogel caused by a falling ball shows the impact response time, which is measured in milliseconds. Repeating several experiments in a row shows the dynamics of fatigue, which does not reduce the speed of response to impact. Through the practical experiments, we sought to demonstrate how theoretical knowledge describes the hydrogel we elaborated, which works as a piezoelectric material. In addition to the theoretical basis, which includes the operation of the metal and metal oxide contact junction, the interaction between the metal oxide and the hydrogel surfaces, the paper presents the practical application of this knowledge to the complex hydrogel film. The simple calculations presented in this paper are intended to predict the hydrogel film’s characteristics and explain the results obtained during practical experiments. Carrageenan, as a low-cost and already widely used polysaccharide in various industries, is suitable for the production of low-cost force sensors in combination with iron oxide. Full article

Background: Osteodysplastic syndromes comprise a very diverse group of clinically and genetically heterogeneous disorders characterized by defects in bone and connective tissue development, as well as in bone density. Here, we report the case of a 48-year-old female with a complex medical history characterized by bone dysplasia, hyperostosis, and partial tooth agenesis. Methods: Genetic testing was performed using WES analysis and Sanger sequencing. Molecular modeling analysis and dynamics simulation explored the impact of detected pathogenic variants. Results: The genetic analysis detected multiple pathogenic variants in genes CREB3L1, SLCO2A1, SFRP4, LRP5, and LRP6, each of which has been associated with rare osteodysplastic syndromes. The patient was homozygous for the same rare alleles associated with three of the identified autosomal recessive disorders osteogenesis imperfecta type XVI, primary hypertrophic osteoarthropathy, and metaphyseal dysplasia Pyle type. She also had a variant linked to autosomal dominant endosteal hyperostosis and a variant previously associated with increased risk of osteoporosis and bone fractures. Two of the detected variants are predicted to cause abnormal splicing, while molecular modeling and dynamics simulations analysis suggest that the other three variants probably confer altered local secondary structure and flexibility that may have functionally devastating consequences. Conclusions: Our case highlights the rare coexistence of multiple osteodysplastic syndromes in a single patient that may complicate differential diagnosis. Furthermore, this case emphasizes the necessity for early genetic investigation of such complex cases with overlying phenotypic traits, followed by genetic counseling, facilitating orchestration of clinical interventions and allowing prevention and/or prompt management of manifestations. Full article

This study employed multiple machine learning and hyperparameter optimization techniques to analyze and predict the mechanical properties of self-drilling screw connections in thin-walled steel–ply–bamboo shear walls, leveraging the renewable and eco-friendly nature of bamboo to enhance structural sustainability and reduce environmental impact. The dataset, which included 249 sets of measurement data, was derived from 51 disparate connection specimens fabricated with engineered bamboo—a renewable and low-carbon construction material. Utilizing factor analysis, a ranking table recording the comprehensive score of each connection specimen was established to select the optimal connection type. Eight machine learning models were employed to analyze and predict the mechanical performance of these connection specimens. Through comparison, the most efficient model was selected, and five hyperparameter optimization algorithms were implemented to further enhance its prediction accuracy. The analysis results revealed that the Random Forest (RF) model demonstrated superior classification performance, prediction accuracy, and generalization ability, achieving approximately 61% accuracy on the test set (the highest among all models). In hyperparameter optimization, the RF model processed through Bayesian Optimization (BO) further improved its predictive accuracy to about 67%, outperforming both its non-optimized version and models optimized using the other algorithms. Considering the mechanical performance of connections within TWS composite structures, applying the BO algorithm to the RF model significantly improved the predictive accuracy. This approach enables the identification of the most suitable specimen type based on newly provided mechanical performance parameter sets, providing a data-driven pathway for sustainable bamboo–steel composite structure design. Full article

Background: Homocysteine (Hcy) is a sulfur-containing amino acid crucial for various physiological processes, with elevated levels linked to cardiovascular and neurological adverse conditions. Various factors contribute to high Hcy, and past studies of impact factors relied on traditional statistical methods. Recently, machine learning (ML) techniques have greatly improved and are now widely applied in medical research. This study used four ML methods to identify key factors influencing Hcy in healthy elderly Taiwanese men, comparing their accuracy using multiple linear regression (MLR). The study seeks to improve Hcy prediction accuracy and provide insights into relevant impact factors. Methods: A total of 468 healthy elderly men were studied in terms of 33 parameters using four ML methods: random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net (EN). MLR served as a benchmark. Model performance was assessed using SMAPE, RAE, RRSE, and RMSE. Results: All ML methods demonstrated lower prediction errors than MLR, indicating higher accuracy. By averaging the importance scores from the four ML models, C-reactive protein (CRP) emerged as the leading impact factor for Hcy, followed by GPT, WBC, LDH, eGFR, and sport volume (SV). Conclusions: Machine learning methods outperformed MLR in predicting Hcy levels in healthy elderly Taiwanese men. CRP was identified as the most crucial factor, followed by GPT/ALT, WBC, LDH, and eGFR. Full article

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R²), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance. Full article

Under global warming, persistent extreme heat events (PHEs) in China have increased significantly in both frequency and intensity, posing severe threats to agriculture and socioeconomic development. Combining observational analysis (1961–2019) and numerical simulations, this study investigates the distinct impacts of Northwest Pacific (NWP) and North Atlantic (NA) sea surface temperature (SST) anomalies on PHEs over China. Key findings include the following: (1) PHEs exhibit heterogeneous spatial distribution, with the Yangtze-Huai River Valley as the hotspot showing the highest frequency and intensity. A regime shift occurred post-2000, marked by a threefold increase in extreme indices (+3σ to +4σ). (2) Observational analyses reveal significant but independent correlations between PHEs and SST anomalies in the tropical NWP and mid-high latitude NA. (3) Numerical experiments demonstrate that NWP warming triggers a meridional dipole response (warming in southern China vs. cooling in the north) via the Pacific–Japan teleconnection pattern, characterized by an eastward-retreated and southward-shifted sub-tropical high (WPSH) coupled with an intensified South Asian High (SAH). In contrast, NA warming induces uniform warming across eastern China through a Eurasian Rossby wave train that modulates the WPSH northward. (4) Thermodynamically, NWP forcing dominates via asymmetric vertical motion and advection processes, while NA forcing primarily enhances large-scale subsidence and shortwave radiation. This study elucidates region-specific oceanic drivers of extreme heat, advancing mechanistic understanding for improved heatwave predictability. Full article

Objectives: This study examined the impact of a low-sugar flavored beverage on total fluid intake and hydration biomarkers during intermittent exercise in a hot environment among healthy children. Methods: Twenty-one children (11 girls, 8–10 y) completed a randomized, crossover study with two trials. Each trial involved three bouts of 10 min walking, 5 min rest, 10 min walking, and 35 min rest for a total of 3 h in a hot (29.9 ± 0.6 °C) and dry environment (26 ± 7% relative humidity). Walking intensity was 69 ± 7% of age-predicted maximum heart rate. Participants consumed either plain water (W) or a low-sugar flavored beverage (FB). Body weight, fluid intake, urine samples, and perceptual ratings were collected. Results: Total ad libitum fluid intake was significantly higher with the FB (946 ± 535 mL) than with W (531 ± 267 mL; p < 0.05). This difference was 128% higher for FB compared to W, with 19 out of the 21 children ingesting more fluids in FB versus W. Children rated the FB as more likable across all time points (p < 0.05). Net fluid balance was better with FB at 60, 70, 85, 135, and 145 min (p < 0.05), though not different at the 3 h mark. Urine volume was higher with FB (727 ± 291 mL) than with W (400 ± 293 mL; p < 0.05). Urine osmolality was significantly higher in the W trial at 120 and 180 min (p < 0.05). Conclusions: A flavored, low-sugar beverage enhanced ad libitum fluid intake and improved hydration markers compared to water during exercise in the heat, supporting its potential as a practical rehydration strategy for children. Full article

The sustainable integration of Distributed Energy Resources (DER) with the next-generation distribution networks requires robust, adaptive, and accurate hosting capacity (HC) forecasting. Dynamic Operating Envelopes (DOE) provide real-time constraints for power import/export to the grid, ensuring dynamic DER integration and efficient network operation. However, conventional HC analysis and forecasting approaches struggle to capture temporal dependencies, the impact of DOE constraints on network operation, and uncertainty in DER output. This study introduces a dynamic optimization framework that leverages the benefits of the sensitivity gate of the Sensitivity-Enhanced Recurrent Neural Network (SERNN) forecasting model, Particle Swarm Optimization (PSO), and Bayesian Optimization (BO) for HC forecasting. The PSO determines the optimal weights and biases, and BO fine-tunes hyperparameters of the SERNN forecasting model to minimize the prediction error. This approach dynamically adjusts the import/export of the DER output to the grid by integrating the DOE constraints into the SG-PSO-BO architecture. Performance evaluation on the IEEE-123 test network and a real Australian distribution network demonstrates superior HC forecasting accuracy, with an $R^2$ score of 0.97 and 0.98, Mean Absolute Error (MAE) of 0.21 and 0.16, and Root Mean Square Error (RMSE) of 0.38 and 0.31, respectively. The study shows that the model effectively captures the non-linear and time-sensitive interactions between network parameters, DER variables, and weather information. This study offers valuable insights into advancing dynamic HC forecasting under real-time DOE constraints in sustainable DER integration, contributing to the global transition towards net-zero emissions. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation. Full article

Autonomous vehicles (AVs) hold the potential to revolutionize transportation by improving safety, operational efficiency, and environmental impact. However, ensuring reliability and safety in real-world conditions remains a major challenge. Based on an in-depth examination of 33 peer-reviewed studies (2015–2025), this systematic review organizes advancements across five key domains: fault detection and diagnosis (FDD), collision avoidance and decision making, system reliability and resilience, validation and verification (V&V), and safety evaluation. It integrates both hardware- and software-level perspectives, with a focus on emerging techniques such as Bayesian behavior prediction, uncertainty-aware control, and set-based fault detection to enhance operational robustness. Despite these advances, this review identifies persistent challenges, including limited cross-layer fault modeling, lack of formal verification for learning-based components, and the scarcity of scenario-driven validation datasets. To address these gaps, this paper proposes future directions such as verifiable machine learning, unified fault propagation models, digital twin-based reliability frameworks, and cyber-physical threat modeling. This review offers a comprehensive reference for developing certifiable, context-aware, and fail-operational autonomous driving systems, contributing to the broader goal of ensuring safe and trustworthy AV deployment. Full article