Search Results (3,707)

Obesity is a major global health challenge characterized by chronic low-grade inflammation, oxidative stress, and an increased risk of cardiometabolic disorders. Although sex-related differences in inflammatory and redox biomarkers have been reported in obese populations, the molecular mechanisms underlying this heterogeneity remain incompletely understood. In this study, we applied a proteomics-based approach to investigate urinary extracellular vesicles from 45 obese individuals (BMI 30–40 kg/m²; age 50–70 years) in order to identify molecular signatures associated with metabolic dysregulation. Shotgun proteomics analysis performed by nanoLC–MS/MS enabled the identification of 3822 proteins. Hierarchical clustering of proteomic profiles revealed two distinct molecular groups, predominantly enriched in males (Group I) and females (Group II). Label-free quantitative analysis identified 466 differentially abundant proteins between the two clusters. Functional enrichment analysis highlighted pathways associated with immune response, metabolic regulation, and redox homeostasis, including glycolysis/gluconeogenesis, lysosome activity, leukocyte transendothelial migration, and glutathione, cysteine and methionine metabolism. Notably, proteins related to ferroptosis were enriched, suggesting the involvement of iron-dependent oxidative cell death mechanisms in the metabolic imbalance observed in a subset of subjects. Furthermore, the non-enzymatic glycosylation of urinary proteins was significantly higher in Group I compared with Group II (p = 0.0002), indicating increased formation of advanced glycation products in individuals with a more pronounced pro-oxidant state. Preliminary follow-up data suggested a higher incidence of pathological events, including cardiovascular complications, among individuals belonging to Group I. Overall, these findings demonstrate that urinary proteomic profiling can identify distinct molecular phenotypes among obese individuals and highlight oxidative stress, ferroptosis, and protein glycation as potential determinants of metabolic vulnerability, supporting the use of non-invasive proteomic approaches for improved risk stratification in obesity. Full article

Field-service operations for utility companies require routing technicians across multiple depots while guaranteeing same-day response to critical infrastructure customers, a constraint that standard multi-depot routing methods cannot structurally enforce. We introduce the MDVRP with Priority Requests (MDVRP-PR), formalised as a lexicographic optimisation problem that guarantees service to priority customers before maximising coverage and minimising route duration. A three-stage pipeline is proposed: hybrid DBSCAN-Hierarchical clustering for topology-aware depot assignment, an Enhanced Max-Min Ant System (MMAS) with priority-driven construction, lexicographic solution selection, and repair, and a Boundary Relocate post-optimisation stage with global cross-depot recovery. The approach is evaluated on a real-world applied case study from Algérie Télécom (Guelma, Algeria), comprising a single four-depot field-service instance scaled to three sizes (55, 90, and 150 customers) and assessed over 2135 controlled runs. On this case study, the proposed clustering method outperforms the MDVRP-adapted Sweep baseline by $22.9$ percentage points on the largest instance (n = 150; Friedman p < 0.001). The priority mechanisms sustain $100\%$ feasibility across all configurations, compared to complete collapse without them ($0/10$ seeds at $\ge 40\%$ priority), at a route-time overhead below $5\%$. Relative to the company’s current manual practice, the framework improves customer coverage by $16.1$ percentage points within 28 s, confirming its practical utility for daily deployment in this capacity-constrained, priority-sensitive routing context. Full article

In tennis, where performance is governed by complex kinetic chain interactions, objective skill classification is vital for coaching and talent identification. This study presents a hierarchical deep learning framework leveraging synchronized bilateral Inertial Measurement Unit (IMU) data from 39 participants (11 elite, 28 amateur). The proposed system successfully distinguishes expertise levels across a total of 4594 strokes, including augmented samples.. A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture was developed to autonomously extract spatiotemporal features from the raw kinematic signals of forehand, backhand, service, and volley strokes. The proposed model achieved an accuracy of 95.54%, significantly outperforming both traditional machine learning and state-of-the-art deep learning benchmarks. Qualitative t-distributed Stochastic Neighbor Embedding (t-SNE) analyses revealed that elite athletes form highly homogeneous clusters in the feature space. Furthermore, quantitative Asymmetry Index assessments confirmed that professionals exhibit superior bilateral coordination stability. These findings demonstrate that the proposed end-to-end system offers a robust, field-applicable solution for identifying technical excellence. It provides coaches with reliable digital biomarkers, thereby overcoming the limitations of subjective visual observation. Full article

Background: Acetification is a complex process driven by acetic acid bacteria (AAB), in which high ethanol and acidity levels require strong microbial metabolic adaptation. Although the microbiota involved in vinegar production has been described, the functional mechanisms that enable these bacteria to maintain metabolic activity remain poorly understood. In this study, the functional dynamics of AAB during Verdejo vinegar acetification were analyzed using a quantitative metaproteomic approach. Methods: Acetification was performed in submerged culture under semi-continuous conditions, and samples were collected at four stages of the cycle (S1–S4). Results: LC-MS/MS analysis led to the identification of 1626 proteins, of which 1409 were assigned to the Acetobacteraceae family. Komagataeibacter europaeus was the dominant species (73.7%). Hierarchical clustering revealed four protein abundance patterns, and differential analysis identified 350 proteins with increased abundance and 169 with decreased abundance, with the greatest changes observed between S1 and S4. Functional annotation and protein–protein interaction analyses indicated that the main metabolic adaptations involve pathways related to energy metabolism, amino acid biosynthesis, membrane-associated functions, cellular homeostasis, and acid stress response. Conclusions: Overall, the results show that K. europaeus concentrates most of the metabolic activity during acetification and that proteome reorganization reflects key molecular strategies for adaptation and survival under high-acidity conditions. Full article

Myrtus communis L. (common myrtle) is an economically valuable Mediterranean shrub with diverse applications in food, pharmaceutical, and ornamental sectors. However, the biochemical diversity of myrtle genotypes from Mediterranean environments remains insufficiently characterized, particularly regarding the relationship between primary and secondary metabolism and stress adaptation. This study investigated the biochemical and aroma profiles of six myrtle genotypes selected from natural populations in Antalya, Turkey, to identify chemotypic diversity and elucidate metabolic diversity observed in Mediterranean genotypes. Volatile compounds were analyzed using HS-SPME/GC-MS, while sugars and organic acids were quantified by HPLC. Multivariate statistical analyses (PCA, hierarchical clustering) were employed to evaluate metabolic relationships and genotype classification. Descriptive analysis suggested three potential chemotypic patterns: (i) 1,8-cineole-type (G34, G36) with G29 showing a transitional profile, (ii) α-Pinene-type (G15, G37), and (iii) Ester-aldehyde type (G9). These groupings are based on single volatile measurements and should be considered preliminary patterns pending validation through replicate analyses. Significant genotypic variation was observed for primary metabolites (sugars and organic acids) (p < 0.001, η² > 0.90), as evaluated by ANOVA with triplicate biological replicates. Volatile compound differences were evaluated as descriptive exploratory patterns only. Hierarchical clustering revealed three metabolic strategies: balanced metabolism integrating diverse volatile and primary metabolite profiles (Cluster 1: G9, G15, G37), terpene-rich volatile defense with enhanced organic acid metabolism (Cluster 2: G29, G36), and specialized 1,8-cineole-dominant biosynthesis (Cluster 3: G34). These findings highlight substantial metabolic diversity and provide a basis for germplasm evaluation and selection and potential applications. Full article

In the Rhône-Mediterranean-Corsica (RMC) basin (130,000 km², 14 million inhabitants), groundwater intended for human consumption has been monitored for decades. These data, stored in the SISE-EAUX database, were cross-referenced with information from the CORINE Land Cover (CLC) database, which describes human land use, in order to identify potential relationships between pollutant pressure and water quality at the basin scale, as well as the mechanisms specific to each geographical area. Data processing was carried out in three stages. The 27,741 water samples from 2825 abstraction points were assigned to the 224 groundwater bodies (GWBs), and average values for each physicochemical and bacteriological parameter were calculated for each GWB. At the same time, the percentage of surface area covered by each land use type was also extracted at the scale of each GWB. This information was subjected to statistical processing, first separately and then jointly, using principal component analysis (PCA) and hierarchical clustering of parameters. A redundancy in the information carried by the quality parameters, previously observed at the scale of administrative regions (four to five times smaller), is confirmed at this new analysis scale, paving the way for data consolidation and a more synthetic representation. Fecal contamination primarily concerns areas with crystalline lithology and, secondarily, a few karst sectors, with other livestock farming regions being less contaminated. Higher nitrate concentrations are observed in cereal-growing regions and areas of intensive row cropping, while metal concentrations are lower in the drier Mediterranean climate zone than under the more humid continental climate. Structuring factors, notably altitude and climate, emerge at the RMC basin analysis scale, which was not the case at the scale of administrative regions. These structuring factors influence land use, soil type, and hydrological regimes alike, which explains the correlations between the information contained in the CLC and SISE-EAUX databases. Full article

Foundation models are reshaping computational pathology by enabling scalable task-agnostic representations of histopathological whole-slide images (WSIs). Unlike earlier task-specific deep learning systems, pathology foundation models (PFMs) leverage massive whole-slide image repositories and self-supervised Vision Transformer architectures to achieve broad generalization and few-shot adaptability. Their evolution reflects a shift from weakly supervised approaches such as Clustering-Constrained Attention Multiple Instance Learning (CLAM) and hierarchical architectures such as Hierarchical Image Pyramid Transformer (HIPT) to large-scale efforts including foundation models, UNI, Virchow, Phikon, CONtrastive learning from Captions for Histopathology (CONCH), GigaPath, H-Optimus, Transformer-Based Pathology Image and Text Alignment Network (TITAN), and the Mayo Clinic Atlas. These models demonstrate impressive performance across diagnostic and prognostic benchmarks while also opening pathways for multimodal integration with genomics and clinical data. Yet significant barriers remain including inconsistent generalization across institutions, interpretability lagging behind clinical needs, and slow integration into routine laboratory workflows. Certain domains of anatomic pathology such as cytopathology, transplant pathology, frozen sections, and rare tumor subtypes remain particularly resistant to current models. Here, we review the development of PFMs, critically evaluate their strengths and limitations, and outline priorities for their safe and effective clinical translation. We argue that the next phase of PFM development will depend on rigorous benchmarking, pathologist-in-the-loop deployment, and multimodal fusion ensuring these models evolve from research tools into clinically robust systems. Full article

Stress is increasingly recognised as a key factor underlying health and behavioural problems in people with intellectual disabilities. However, little is known about chronic stressors embedded in residential care environments. This study aimed to identify chronic stressors in residential care for people with intellectual disabilities from the perspective of stakeholders. A group concept mapping design was used, combining qualitative data generation with quantitative clustering analyses. Direct support workers, family members, and experts by experience generated statements describing situations perceived as stressful in residential care settings. After data cleaning, 125 unique statements were retained. Participants subsequently clustered and rated these statements on frequency, impact, and controllability. Thirty-eight statements were identified as daily stressors with high frequency and impact. Ward’s hierarchical cluster analysis grouped the statements into eight clusters representing broader conditions within residential care environments. Several clusters contained multiple high-frequency, high-impact stressors and were therefore interpreted as potential chronic stressors. These clusters reflected structural characteristics of residential care, including dependence on support staff, limited autonomy, and shared living environments. Identifying chronic stressors provides a framework for studying chronic stress in people with intellectual disabilities and may inform organisational and environmental interventions aimed at reducing exposure to such stressors. Full article

Transitioning to a circular economy (CE) is now a strategic priority for countries to decouple economic growth from environmental degradation. However, in developing contexts, the readiness of environmental resource sectors to adopt CE principles is unknown due to a lack of data and uneven institutional capacity. This study presents the first regional baseline assessment of circularity readiness in Vietnam’s environmental resource sectors, focusing on land, mining, water and waste. A five-dimensional readiness framework (policy, resource management, innovation, business, awareness) was developed and applied across Vietnam’s six ecological–economic regions. A Delphi process with 12 experts was conducted in three rounds to capture and refine expert judgments, supplemented by triangulated proxy indicators (e.g., plastic recycling rates, wastewater treatment coverage). Readiness scores were aggregated at dimension and regional levels and analyzed using radar charts, heatmaps and hierarchical clustering. Results showed significant regional disparities. The Southeast (SE) and Red River Delta (RRD) have high readiness due to clearer policy frameworks, stronger institutions and more dynamic business ecosystems. The Northern Midlands and Mountains (NMM) and Central Highlands (CH) have low readiness due to infrastructural gaps, weak innovation and limited public engagement. The Mekong Delta (MD) and North Central Coast (NCC) have medium readiness, reflecting partial progress but uneven implementation. The study made three contributions: (1) a new context-specific framework for CE readiness in environmental resource sectors; (2) the value of expert-based, proxy-informed methods in data-scarce contexts; and (3) a policy roadmap for different regional readiness levels. Findings suggest that the CE should be integrated into resource planning, regional observatories should be established and CE-related research and development (R&D) should receive investment. Future research should move towards standardized quantitative indicators and predictive models to track how readiness changes under policy interventions. Full article

The rapid growth of the Internet of Things (IoT) has introduced significant cybersecurity challenges in highly distributed, heterogeneous, and privacy-sensitive environments. Traditional centralized intrusion detection approaches and conventional federated learning (FL) frameworks, which rely on single-model aggregation, are often inadequate in the presence of extreme non-IID data and adversarial conditions. This study proposes an Adaptive Multi-Model Hierarchical Federated Learning (AMM-HFL) framework for robust IoT intrusion detection. The framework operates across client, edge, and cloud tiers and introduces a unified integration of similarity-aware clustering, multi-model aggregation, and dynamic client-side model selection. Unlike existing hierarchical FL approaches, AMM-HFL maintains multiple global models, enabling adaptive personalization and improved representation of heterogeneous data distributions. At the edge level, model updates are clustered to isolate anomalous contributions, while the cloud performs meta-aggregation to refine diverse model representations. Experimental evaluation on the IDSIoT2024 dataset demonstrates detection accuracy up to 96.83–97.54% under IID and 95.64–97.52% under non-IID conditions, while maintaining low computational and cryptographic overhead. Full article

Tomato is widely produced in Burkina Faso for its culinary, nutritional, and economic value. Tens of thousands of farmers are involved in its production throughout the country. However, they face significant biotic constraints that limit yields and income. In particular, tomato yellow leaf curl virus (TYLCV), a begomovirus transmitted by whiteflies (Bemisia tabaci), severely affects tomato production. This study evaluated the response of 13 tomato genotypes to tomato yellow leaf curl disease (TYLCD), including eight lines with different Ty resistance gene combinations; three local improved varieties, and two commercial varieties in western and central Burkina Faso. All genotypes developed TYLCD symptoms with considerable variability in genotypic responses. Four genotypes carrying a single gene, namely CLN4279O (Ty2), CLN4270I (Ty1/Ty3), CLN4270F (Ty1/Ty3), and CLN4018G (Ty2), exhibited the best field tolerance, with lower disease incidence and severity across sites. In contrast, genotype CLN4078A carrying two resistance genes (Ty1/Ty3 + Ty2), and the checks PETOMECH and ROMA VF were highly susceptible. Hierarchical clustering grouped the genotypes into four classes based on tolerance level and yield. These findings highlight the variability in resistance expression under field conditions and suggest possible interactions between host genotype, environmental factors, and virus populations. Broader multi-site evaluations, supported by molecular diagnostics to identify endemic TYLCV strains, are needed to refine the selection process. Full article

This study aimed to evaluate genotype-dependent variation in biochemical composition, antioxidant capacity, and aroma profiles of dried Chinese mulberry (Morus spp.) genotypes. Three cultivars, Lvmeiren (green), Zhenzhubai (white), and Yunsang No 2 (red), were analyzed. Organic acids and sugars were determined using HPLC, while total phenolic content, antioxidant capacity (DPPH and FRAP), and total anthocyanins were quantified using spectrophotometric methods. Volatile compounds were analyzed by HS-SPME/GC–MS. Significant differences were observed among genotypes for all measured parameters. Among the studied genotypes, Yunsang No 2 exhibited the highest total phenolic content (379.59 mg GAE g⁻¹ DW), FRAP value (21.51 μmol g⁻¹ DW), and anthocyanin content (37.1 mg L⁻¹). In contrast, Lvmeiren was characterized by markedly higher sucrose (22.57%) and succinic acid (3.69%) contents. Zhenzhubai exhibited the highest glucose (25.82%) and fructose (32.65%) contents, together with elevated citric (2.58%) and malic acid (2.93%) levels. Yunsang No 2 showed markedly higher total phenolics, anthocyanins, and antioxidant capacity, indicating superior nutraceutical potential. Volatile compound analysis revealed aldehydes and alcohols as dominant groups in Lvmeiren and Zhenzhubai, while acids were predominant in Yunsang No 2. Multivariate analyses (PCA and hierarchical clustering) clearly separated genotypes based on biochemical and antioxidant traits. These findings demonstrate that genotype plays a critical role in determining the nutritional quality and aroma profile of dried mulberries and provide valuable insights for breeding, cultivar selection, and functional food applications. Full article

Background: Chronic hemodialysis (HD) patients face symptoms that significantly impact their quality of life and health outcomes. Longitudinal research on the dynamics of symptom severity and the integration of individual patient characteristics into cluster analyses is limited, hindering understanding of cluster evolution over time. Objective: The objective of this study was to characterize and compare symptom clusters across body systems based on frequency and severity at three time points in chronic HD patients. Methods: This prospective longitudinal study collected self-reported data on 23 symptoms using validated measures from 69 chronic HD patients (age range: 24–87 years) at three time points over a year. Symptoms were rated on a 0–10 scale. Symptom progression and clustering were analyzed using heat maps and principal component analysis. Results: Among 69 HD patients, a substantial symptom burden was identified at baseline, with fatigue, overall perceived health, worry or distress, and sleep disturbance reported as the most severe (mean scores > 4.0 on a 0–10 scale). Hierarchical clustering yielded a five-cluster solution; however, longitudinal analysis revealed poor structural stability in patient symptom profiles over 12 months (ARI < 0.70), indicating significant symptomatic reorganization. Gastrointestinal cluster showed a statistically significant reduction in severity over time (β = −0.914, p = 0.003); fatigue and overall perceived health remained a high burden. Subgroup analyses demonstrated that patients using central venous catheters reported significantly higher severity in pain, fatigue, and nausea compared to patients with arteriovenous fistulas, while Diabetes mellitus was uniquely associated with increased dyspnea (p < 0.001). Conclusions: Chronic HD patients experience a dynamic and multidimensional symptom burden, with significant variations in severity, progression, and clustering of symptoms over time. The observed temporal instability of symptom clusters and the heterogeneity of individual trajectories emphasize the importance of routine, longitudinal symptom assessment and flexible, patient-centered management strategies by nephrology nurse specialists, which may support value-based healthcare approaches. Full article

The efficient use of associated petroleum gas (APG) is one of the key challenges facing the oil and gas sector because it is directly related to reducing hydrocarbon losses, lowering emissions, and improving the sustainability of energy systems. The aim of the study is to develop a multi-criteria risk assessment system for APG utilization projects in three BRICS countries, using Russia, China, and India as examples. Methodologically, the study combines expert risk ranking based on the Fishburn method, spatial aggregation across 16 oil and gas clusters, and hierarchical graph modeling, which makes it possible to trace the transition from local technological constraints to the level of architectural strategies. As a result, a unified risk matrix was constructed, including risks of leaks, fluctuations in gas composition, raw material quality requirements, infrastructure constraints, and the energy intensity of processes. The resulting assessments showed that the risk profiles of clusters differ significantly both between countries and within them. For Russian clusters, leaks and infrastructure constraints proved to be more significant; for some Chinese clusters, gas composition and quality were more critical; whereas Indian clusters are characterized by a mixed profile of constraints. It was concluded that projects involving the use of APG require a cluster-oriented approach, and that universal technological solutions that do not account for the territorial structure of risks have limited applicability. Full article

The large-scale integration of renewable energy and electric vehicles introduces grid-connected power fluctuations in microgrids. To address this, this paper proposes a novel two-stage optimization scheduling strategy that balances economic efficiency and grid compatibility. In the first stage, a multi-objective optimization model is formulated to minimize both operating costs and power fluctuations, and the Improved Multi-Objective Grey Wolf Optimization algorithm—incorporating the Bernoulli chaotic map—is employed to solve it efficiently. In the intra-day phase, a rolling tracking strategy based on model predictive control is proposed to address ultra-short-term forecasting errors, and a multi-unit hierarchical error compensation mechanism is designed. This mechanism prioritizes the use of supercapacitors to absorb high-frequency fluctuations, followed by the coordinated use of batteries, electric vehicle clusters, and micro gas turbines to mitigate residual deviations, thereby effectively reducing the operational burden on individual energy storage devices. Finally, a comparative analysis of six simulation cases was conducted using a weighted evaluation metric that integrates average power deviation values and interconnection line power fluctuations. The results confirm that this strategy not only significantly smooths grid-connected power fluctuations but also demonstrates exceptional robustness and adaptability under extreme forecast error scenarios. Full article