Search Results (8,371)

Alpha helical transmembrane proteins are a specific type of membrane proteins that consist of helices spanning the entire cell membrane. They make up almost a third of all transmembrane (TM) proteins and play a significant role in various cellular activities. The structural prediction of these proteins is crucial in understanding how they behave inside the cell and thus in identifying their functions. Despite their importance, only a small portion of TM proteins have had their structure determined experimentally. Inter-helical residue contact is one of the most successful computational approaches for reducing the TM protein fold search space and generating an acceptable 3D structure. Most current TM protein residue contact predictors use features extracted only from protein sequences to predict residue contacts. However, these features alone deliver a low-accuracy contact map and, as a result, a poor 3D structure. Although there are models that explore leveraging features extracted from protein 3D structures in order to produce a better representative contact model, such an approach remains theoretical, assuming the structure features are available, whereas in reality they are only available in the training data, but not in the test data, whose structure is what needs to be predicted. This presents a brand new transfer learning paradigm: training examples contain two sets of features, but test examples contain only one set of the less informative features. In this work, we propose a novel approach that can train a model with training examples that contain both sequence features and atomic features and apply the model on the test data that contain only sequence features but not atomic features, while still improving contact prediction rather than using sequence features alone. Specifically, our method, AT-TSVM, employs Active Transfer for Transductive Support Vector Machines, which is augmented with transfer, active learning and conventional transductive learning to enhance contact prediction accuracy. Results from a benchmark dataset show that our method can boost contact prediction accuracy by an average of 5 to 6% over the inductive classifier and 2.5 to 4% over the transductive classifier. Full article

This study develops an interpretable and calibrated XGBoost framework for probabilistic slope stability assessment using a 627-case database of circular-mode failures. Six predictors, namely, unit weight (γ), cohesion (c), friction angle (φ), slope angle (β), slope height (H), and pore-pressure ratio (rᵤ), were used to train a gradient-boosted tree model optimized through Bayesian hyperparameter search with five-fold stratified cross-validation. Physically based monotone constraints ensured that failure probability (P_f) decreases as c and φ increase and increases with β, H, and rᵤ. The final model achieved strong performance (AUC = 0.88, Accuracy = 0.80, MCC = 0.61) and reliable calibration, confirmed by a Brier score of 0.14 and ECE/MCE of 0.10/0.19. A 1000-iteration bootstrap quantified both epistemic and aleatoric uncertainties, providing 95% confidence bands for P_f-feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > rᵤ). Predicted probabilities were classified into Low (P_f < 0.01), Medium (0.01 ≤ P_f ≤ 0.10), and High (P_f > 0.10) risk levels according to geotechnical reliability practices. The proposed framework integrates calibration, uncertainty quantification, and interpretability into a comprehensive, auditable workflow, supporting transparent and risk-informed slope management for infrastructure, mining, and renewable energy projects. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Background/Objectives: The incidence of early-onset colorectal cancer (EOCRC) is rising worldwide, although its biological and clinical features remain incompletely understood. Emerging evidence implicates gut microbial dysbiosis as a key driver of EOCRC pathogenesis, acting through complex interactions with host genetics, mucosal immunity, and early-life exposures. This review synthesizes current evidence on EOCRC-specific microbial signatures, delineates host–microbiome interactions, and evaluates how these insights may inform precision prevention, early detection, and therapeutic strategies. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science up to August 2025, using combinations of “early-onset colorectal cancer,” “gut microbiome,” “dysbiosis,” and “host–microbiome interactions.” Both clinical and preclinical studies were included. Extracted data encompassed microbial composition, mechanistic insights, host-related factors, and microbiome-targeted interventions. Evidence was synthesized narratively to highlight consistent patterns, methodological limitations, and translational implications. Results: EOCRC is consistently associated with enrichment of pro-inflammatory and genotoxic taxa (e.g., Fusobacterium nucleatum, colibactin-producing Escherichia coli, enterotoxigenic Bacteroides fragilis) and depletion of short-chain fatty acid–producing commensals. Multi-omics analyses reveal distinct host–microbiome signatures influenced by germline predisposition, mucosal immunity, sex, and early-life exposures. However, substantial methodological heterogeneity persists. Collectively, these data point to candidate microbial biomarkers for early detection and support the rationale for microbiome-targeted preventive and adjunctive therapeutic approaches. Conclusions: EOCRC harbors unique microbial and host–environmental features that distinguish it from late-onset disease. Integrating host determinants with microbiome signatures provides a framework for precision prevention and tailored therapeutic strategies. Future priorities include harmonizing methodologies, validating microbial biomarkers in asymptomatic young adults, and rigorously testing microbiome-targeted interventions in clinical trials. Full article

The use of digital tools in agroecological crop production can help mitigate current farming challenges such as labour shortage and climate change. The aim of this study was to map digital tools used in crop production, assess their impacts across economic, environmental, and social dimensions, and determine their potential as enablers of agroecology. A systematic search and screening process, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology, identified 453 relevant studies. The results showed that most digital tools are applied for crop monitoring (83.4%), with unmanned aerial vehicles (37.7%) and camera sensors (75.2% combined) being the most frequently used technologies. Farm Management Information Systems (57.6%) and Decision Support Systems (25.2%) dominated the tool categories, while platforms for market access, social networking, and collaborative learning were rare. Most tools addressed the first tier of agroecology, which refers to input reduction, highlighting a strong focus on efficiency improvements rather than systemic redesign. Although digital tools demonstrated positive contributions to social, environmental, and economic dimensions, studies concentrated mainly on economic benefits. Future research should investigate the potential role of digital technologies in advancing higher tiers of agroecology, emphasising participatory design, agroecosystem services, and broader coverage of the agricultural value chain. Full article

Sports tracking produces large, unstructured trajectory datasets. The search and retrieval of interesting plays are essential parts of their analysis. Since annotations are sparse, similarity search remains the standard technique. It relies on learned lower-dimensional representations for its computational feasibility. Siamese Networks learn dimensionality reduction from pairwise distances. However, complete training datasets are impractical to compute due to their combinatorial nature and the cost of distance calculations. Sub-sampling sacrifices representation quality for speed, leading to less meaningful search results. We propose the novel sampling technique Pairwise Diverse and Uncertain Gradient (PairDUG), which exploits the model’s gradient signals to select representative and informative pairs for training. The broad experimental study implements the method for large-scale basketball and American football datasets. The results show that PairDUG at least halves the required compute time while maintaining, or even improving, retrieval quality, and outperforms other baseline methods. Furthermore, our evaluation shows that the selected pairs’ gradient signals exhibit greater magnitude, diversity, and stability than those of any other method. This work represents a foundational contribution to pairwise distance learning. Hence, future work transfers the method not only to other sports, such as soccer, but also to complex trajectory datasets outside the sports domain. Full article

Floating offshore wind turbines (FOWTs) face complex aero-hydro-servo-elastic interactions that challenge conventional modeling, monitoring, and control. This review critically examines how artificial intelligence (AI) is being applied across four domains—design and surrogate modeling, structural health monitoring, control and operations, and digital twins—with explicit attention to uncertainty and reliability. Using PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a Scopus search identified 412 records; after filtering for articles, conference papers, and open access, 115 studies were analyzed. We organize the literature into a taxonomy covering classical supervised learning, deep neural surrogates, physics-informed and hybrid models, reinforcement learning, digital twins with online learning, and uncertainty-aware approaches. Neural surrogates accelerate coupled simulations; probabilistic encoders improve structural health monitoring; model predictive control and trust-region reinforcement learning enhance adaptive control; and digital twins integrate reduced-order physics with data-driven calibration for lifecycle management. The corpus reveals progress but also recurring limitations: simulation-heavy validation, inconsistent metrics, and insufficient field-scale evidence. We conclude with a bias-aware synthesis and propose priorities for future work, including shared benchmarks, safe RL with stability guarantees, twin-in-the-loop testing, and uncertainty-to-decision standards that connect model outputs to certification and operational risk. Full article

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory disorder characterized by recurrent nodules, abscesses, and sinus tracts in apocrine gland-bearing areas. Surgery plays a key role in moderate-to-severe disease. As patients increasingly rely on the internet for decision-making, the quality of online information on HS surgery requires critical evaluation. Previous studies have shown poor quality and limited coverage of surgical aspects. This study systematically assesses publicly available websites on the surgical and reconstructive management of HS, quantifies their quality using the modified Ensuring Quality Information for Patients (mEQIP) tool, and identifies areas needing improvement to support informed decisions. Methods: Google, Bing, and Yahoo were searched using five HS surgery-related keywords. The first 50 results per keyword and engine were collected (n = 750), and 214 websites met the inclusion criteria. Sites were categorized by provenance (practitioners, hospitals, healthcare portals, professional societies, encyclopedias) and assessed using the 36-item mEQIP checklist. High quality was defined as ≥23/36 (75th percentile). Comparisons were made by publication era (pre-/post-COVID-19) and source type. Results: The mean mEQIP score was 21.7; only 51 websites (23.8%) met the high-quality threshold. No significant difference emerged between pre- and post-COVID publications. Healthcare portals scored highest (22.8), followed by practitioners (21.5) and hospital sites (21.2); professional societies (19.7) and encyclopedias (17.3) performed worst. Major deficiencies included limited discussion of surgical risks, quality-of-life outcomes, and postoperative care. Conclusions: Online resources on HS surgery are frequently incomplete and omit essential details on risks, recurrence, and reconstructive options. Surgeons should direct patients toward vetted sources, and professional societies should develop accessible, evidence-based patient guidelines. Full article

Purpose: Telehealth (TH) offers promising solutions for enhancing the management of chronic obstructive pulmonary disease (COPD), particularly in resource-limited or remote settings. However, regulatory uncertainty remains a significant barrier to adopting and integrating TH technologies into routine care. This systematic review aims to evaluate the role of regulatory guidelines in implementing and adopting TH solutions for COPD care and to identify key barriers and facilitators shaping these regulatory efforts. Methods: Following PRISMA guidelines, a comprehensive search of five databases up to 18 October 2025 (PubMed, Web of Science, Scopus, CINAHL, and JSTOR) and grey literature was conducted. Studies and governmental reports were included if they examined regulatory frameworks, stakeholder perspectives, or implementation challenges related to TH in COPD care. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) tool. Narrative and data synthesis were employed. Results: From 343 identified records, 33 sources (18 peer-reviewed studies and 15 governmental/organizational reports) met the inclusion criteria. Findings revealed wide disparities in the existence, specificity, and enforcement of TH regulatory guidelines across countries. Developed nations often had more structured yet nonspecific frameworks, while emerging health systems, such as Saudi Arabia, exhibited fragmented but evolving regulatory landscapes. Common barriers included unclear stakeholder roles, inadequate funding, technological limitations, and resistance to organizational change. Conclusions: Clear, inclusive, and context-sensitive regulatory guidelines are essential to support the successful integration of TH in COPD care. Enhanced regulatory clarity can improve patient trust, engagement, and adherence by addressing safety, accountability, and accessibility concerns. Future research should focus on stakeholder-informed policies that reflect the practical realities of healthcare delivery in both developed and emerging systems. Full article

Patient stratification strategies based on digital databases and advanced information technology can predict inpatient rehabilitation outcomes and support safe hospital discharge for patients who underwent joint replacement for hip and knee osteoarthritis. The degree of continuity between surgery and rehabilitation, the perioperative process integration, and the setting where rehabilitation is provided are crucial factors to improve care effectiveness, access, minimize readmissions, and cost increase. The primary aim of this scoping review of the literature is to identify perioperative variables that are predictive of inpatient rehabilitation stay after hip and knee arthroplasty for osteoarthritis. These factors are divided by time of assessment through the perioperative pathway and surgical procedure site. The secondary aim is to explore how different data sources and facilities are linked into a patient-centered perioperative pathway. An electronic search of the literature was performed on PubMed, Embase, and Scopus. No time restrictions were applied. All primary research studies investigating predictive factors of inpatient rehabilitation stay after hip and knee osteoarthritis were included. In total, 25 studies were included in the review. Age, caregiver presence, presence of comorbidities, sex, Body Mass Index, Risk Assessment and Prediction Tool composite score, pre-operative Clinician-Reported Outcome Measures, pre-operative Patient-Reported Outcome Measures, and post-operative Barthel Index of autonomy in the Activities of Daily Living were predictive of some degree of inpatient rehabilitation stay in more than one study. The studies were fairly distributed between retrospective and prospective, with multicentric databases more spread among the latter. Data collection occurred in acute hospitals more than in specialized rehabilitation facilities. Using comprehensive models supported by electronic health records and powerful information technologies, analyzing specific inpatient rehabilitation LOS as distinguished from surgical ward rehabilitation, using institutional registries, and including specific rehabilitation factors in these registries, and promoting vocabulary and federated data sharing can strongly enhance the predictivity of models investigating rehabilitation outcomes and support appropriate discharge from inpatient rehabilitation units. Full article

Objective: This meta-analysis aimed to examine the bidirectional association between PTSD and cardiovascular disease (CVD) by evaluating the following: (1) the risk of increased CVD incidence in individuals with PTSD; and (2) the prevalence of PTSD in patients with cardiovascular disease. Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted of the PubMed/Medline, Scopus, and Web of Science databases without using a temporal publication range. For the PTSD-to-CVD direction, 18 studies were combined. For the CVD-to-PTSD direction, 11 studies that ascertained the incidence or prevalence of PTSD following a CVD event were combined. Results: The findings confirm the bidirectional and clinically significant relationship between CVD and PTSD. Conclusions: These data underscore the need to integrate trauma-informed approaches into cardiovascular care and stress management into psychiatric treatment to stop this pathological cycle. Full article

Background: Oxidative stress (OS) is a key biological mechanism influencing surgical recovery, contributing to impaired healing, infectious complications, cardiovascular events, and mortality. This umbrella review aimed to synthesize evidence from systematic reviews and meta-analyses exclusively focused on the relationship between validated oxidative stress biomarkers and postoperative outcomes. Narrative and non-systematic reviews were excluded. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was conducted on 15 March 2024 and updated on 12 December 2024 to identify systematic reviews and meta-analyses including adult surgical patients, validated oxidative stress biomarkers, and clinical outcomes. Methodological quality was evaluated with AMSTAR 2 and ROBIS. The SANRA checklist was used only to verify that narrative or bibliometric reviews did not meet the inclusion criteria. These non-systematic reviews were excluded from the synthesis and cited solely as contextual references. Findings: From 527 records, ten systematic reviews of moderate to high methodological quality were included, encompassing approximately 230 primary studies. The most frequently reported biomarkers were total antioxidant capacity (TAC), glutathione (GSH), superoxide dismutase (SOD), malondialdehyde (MDA), and 8-hydroxy-2′-deoxyguanosine (8-OHdG). Lower TAC, GSH, and SOD levels were consistently associated with poor recovery and multiorgan dysfunction, whereas elevated MDA and 8-OHdG levels correlated with infectious complications, delayed healing, cardiovascular events, persistent pain, and mortality. Antioxidant-based interventions such as vitamin C, N-acetylcysteine, and propofol showed heterogeneous but promising effects, particularly in high-risk surgical populations. The main limitations were the heterogeneity of biomarkers, variability in perioperative protocols, and partial overlap of primary evidence across reviews. Interpretation: The findings were organized into three main clinical domains: (1) infectious complications and impaired healing; (2) cardiovascular and systemic complications; and (3) predictive and prognostic value of OS biomarkers for perioperative risk assessment. This thematic synthesis integrates evidence across different surgical specialties, highlighting consistent mechanistic patterns and key research gaps to inform future investigations and clinical decision-making. Full article

The nonlocality (capable of associating target dynamics across multiple time moments) and memory properties (able to retain historical trajectories) of fractional calculus serve as the core theoretical approach to resolving the “dynamic information association deficiency” in UAV mobile target search. This paper proposes the Fractional-order Black-winged Kite Algorithm (FOBKA), which transforms the search problem into an adaptability function optimization model aimed at “maximizing target capture probability” based on Bayesian theory. Addressing the limitations of the standard Black-winged Kite Algorithm (BKA), the study incorporates fractional calculus theory for enhancement: A fractional-order operator is embedded in the migration behavior phase, leveraging the memory advantage of fractional-orders to precisely capture the temporal span, spatial position, and velocity evolution of targets, thereby enhancing global detection capability and convergence accuracy. Simultaneously, population individuals are initialized using motion-encoding, and the attack behavior phase combines alternating updates with a Lévy flight mechanism to balance local exploration and global search performance. To validate FOBKA’s superiority, comparative experiments were conducted against eight newly proposed meta-heuristic algorithms across six distinct test scenarios. Experimental data demonstrate that FOBKA significantly outperforms the comparison algorithms in convergence accuracy, operational robustness, and target capture probability. Full article

Background: Social media has emerged as a powerful communication tool for healthcare professionals, including nutritionists and dietitians, particularly since the COVID-19 pandemic. Evidence suggests that their online presence can enhance nutritional literacy and play a crucial role in countering misinformation. Objective: This systematic review aims to investigate how and why Registered Nutritionists and Dietitians (RNDs) use social media in their professional practice, focusing on benefits, challenges, and impact. Methods: A systematic literature search was conducted between 1 January 2019 and 28 February 2024, in PubMed, Scopus, Scholar, and SciELO databases using terms such as ‘nutritionist’, ‘dietitian’, and ‘social media’. Quality was assessed using the MMAT tool. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The included studies were analysed with respect to their content, professional practices, and patterns of social media use. Results: Of the 359 articles identified through the systematic search, 10 cross-sectional studies conducted using questionnaires were included in this review. Sample sizes ranged from 10 to 2542 participants across nine countries. Instagram and Twitter were the most frequently used platforms among RDNs, primarily for sharing evidence-based nutritional information, counselling content, and professional promotion. Reported usage ranged from 37.5% to 100%, with a marked increase during the COVID-19 pandemic, especially among younger professionals. Key enablers included enhanced communication, professional visibility, and cost-effective outreach, while main challenges involved limited digital literacy and difficulties replicating face-to-face counselling online. Although ethical concerns were reported, many RNDs maintained compliance with professional standards, particularly in regions with strict marketing regulations. Conclusions: This systematic review provides evidence that social media is a valuable tool for RNDs, particularly in the context of food and/or nutritional education. RNDs would benefit from training in content creation, knowledge dissemination and ethical digital communication. However, clearer guidelines from professional organisations are also recommended. Full article