Search Results (3,276)

This study predicts sedimentary architectures and facies distribution within the Pliocene prograding prism of the Roussillon Basin (Gulf of Lion, France), developed along an onshore–offshore continuum. Boreholes and outcrops provide facies-scale observations onshore, while seismic data capture basin-scale structures offshore. Forward seismic modeling bridges spatial and scale gaps between these datasets, yielding characteristic synthetic seismic signatures for the sedimentary facies associations observed onshore, used as analogs for offshore deposits. These signatures are then identified in offshore seismic data, allowing seismic profiles to be populated with sedimentary facies without a well tie. Predicted offshore architectures are consistent with shoreline trajectories and facies successions observed onshore. The Roussillon prism records passive margin reconstruction in the Mediterranean Basin following the Messinian Salinity Crisis, through the following three successive depositional profiles marking the onset of infilling: (1) Gilbert deltas, (2) wave- and storm-reworked fan deltas, and (3) a wave-dominated delta. Offshore, transitions in clinoform type modify sedimentary architectures, influenced by inherited Messinian paleotopography. This autogenic control generates spatial variability in accommodation, driving changes in depositional style. Overall, this multi-scale and integrative approach provides a robust framework for predicting offshore sedimentary architectures and can be applied to other deltaic settings with limited land–sea data continuity. Full article

Background: Bioelectrical impedance analysis (BIA) is a widely used field technique for assessing body composition in football. However, its reliance on population-specific regression equations limits its accuracy. Objective: This scoping review aimed to map the scientific literature on BIA applications in professional and semi-professional football, highlighting uses, limitations, and research opportunities. Methods: A comprehensive search was conducted in the scientific databases PubMed, EMBASE, Web of Science, and SPORTDiscus. Identified studies involved the use of BIA in professional and semi-professional football players (≥16 years) in the context of routine training and competition. Results: From 14,624 records, 39 studies met the inclusion criteria and were included. Three main applications were identified: (1) quantitative body composition assessment, (2) qualitative/semi-quantitative analysis (e.g., bioelectrical impedance vector analysis (BIVA)), and (3) muscle health and injury monitoring. Seven specific research areas emerged, including hydration monitoring, cross-method validation of body composition analyses, development of predictive models, sport phenotype identification, tracking training adaptations, performance/load assessment via phase angle, and localized BIA for injury diagnosis and recovery. Conclusions: While quantitative BIA estimates may lack individual-level precision, raw parameter analyses may offer valuable insights into hydration, cellular integrity, and muscle injury status, yet further research is needed to fully realize these applications. Full article

The southeastern Tibet region is characterized by rugged terrain and relative isolation, which has significantly constrained the development of agriculture. However, due to the extremely limited archaeological and historical records available, its important role in the history of agricultural development in Tibet has been overlooked. This study focuses on the Linzhi and Changdu regions of southeastern Tibet, integrating limited archival, historical, and documentary data. By reconstructing historical settlement patterns and population data, this study estimates the arable land area during the Tubo, Yuan, Ming, and Qing dynasties. Using a grid-based model, it reconstructs the distribution patterns of arable land during these periods, aiming to provide a reference for the development of agriculture in Tibet. The research findings indicate the following: (1) During historical periods, settlements in southeastern Tibet were primarily distributed in flat, resource-rich alluvial plains at medium to high altitudes. Settlement types exhibited spatial differentiation: Post stations were primarily situated along major transportation routes that connected river valleys, as well as at high mountain passes. Temples tended to occupy moderately steep slopes, while manors were concentrated in low-lying valleys. (2) During the Tubo, Yuan, Ming, and Qing periods, the total arable land area and cultivation rate in southeastern Tibet were generally low, with total arable land areas of 28,085 hm², 29,449 hm², 25,319 hm², and 24,371 hm², respectively, and cultivation rates of 0.12%, 0.13%, 0.11%, and 0.11%, respectively. (3) Farmland was predominantly distributed along the Yarlung Zangbo, Jinsha, Lancang, and Nu Rivers and their broader tributary valleys. Natural constraints resulted in a highly fragmented farmland distribution. Full article

Background: Cervical intraepithelial neoplasia grade 2 (CIN2) represents a controversial lesion in cervical cancer prevention. Traditionally included in the aggregate CIN2+ endpoint for reasons of diagnostic stability and statistical power, isolated CIN2 has unique biological characteristics: greater interobserver variability, a high probability of spontaneous regression and a lower risk of progression compared to CIN3. Objectives: To critically describe the epidemiology, natural history and management strategies of CIN2, integrating data from clinical and population-based studies and comparing the recommendations of the main international guidelines. Methods: A narrative review was conducted using a search of PubMed and Scopus (1990–January 2025). Prospective and retrospective studies on isolated CIN2, screening and vaccination trials with CIN2+ endpoints, biomarker research, and consensus documents (ASCCP, ESGO, GISCi, Ministry of Health, WHO) were included. Results: Clinical studies have shown a high probability of CIN2 regression (50–70% within two years, >70% in those <25 years), compared to a 10–15% risk of progression, especially in the presence of persistent HPV16. Screening trials and vaccine evaluations with CIN2+ endpoints have documented the efficacy of the HPV test and a dramatic reduction in lesions in vaccinated cohorts, which was also confirmed for isolated CIN2. The most recent guidelines have progressively adopted a risk-based approach, which allows for active surveillance in young women or those seeking to conceive, while the WHO maintains a screen-and-treat model in resource-limited countries. Conclusions: CIN2 is not a lesion to be treated automatically, but rather a paradigmatic model for personalized management. Integrating epidemiological and clinical data, supported by biomarkers, allows for reducing overtreatment without compromising oncological safety. Full article

Advances in geroscience suggest that aging is modulated by molecular pathways that are amenable to dietary and pharmacological intervention. We conducted an integrative critical review of caloric restriction (CR), intermittent fasting (IF), and caloric restriction mimetics (CR-mimetics) to compare shared mechanisms, clinical evidence, limitations, and translational potential. Across modalities, CR and IF consistently activate AMP-activated protein kinase and sirtuins, inhibit mTOR (mechanistic target of rapamycin) signaling, and enhance autophagy, aligning with improvements in insulin sensitivity, lipid profile, low-grade inflammation, and selected epigenetic aging measures in humans. CR-mimetics, such as metformin, resveratrol, rapamycin, and spermidine, partially reproduce these effects; however, long-term safety and efficacy in healthy populations remain incompletely defined. Methodological constraints—short trial duration, selective samples, intermediate (nonclinical) endpoints, and limited adherence monitoring—impede definitive conclusions on hard outcomes (frailty, disability, hospitalization, mortality). We propose the Active Management of Aging and Longevity (AMAL) model, a three-level biomarker-guided framework that integrates personalized diet, chrono-nutrition, exercise, and the selective use of CR-mimetics, along with digital monitoring and decision support. AMAL emphasizes epigenetic clocks, multi-omics profiling, inflammatory and microbiome metrics, and adaptive protocols to enhance adherence and clinical relevance. Overall, CR, IF, and CR mimetics constitute promising, complementary strategies to modulate biological aging; rigorous long-term trials with standardized biomarkers and clinically meaningful endpoints are needed to enable their scalable implementation. Full article

Saudi Arabia’s Vision 2030, unveiled by Crown Prince Mohammed bin Salman on 25 April 2016, aims to diversify the economy beyond oil dependency and engender robust foreign direct investment (FDI). This research investigates the primary economic challenges impeding foreign investors from entering the Saudi market and devises effective strategies to sustain FDI inflows. The study employs a comprehensive methodology, including an extensive literature review spanning from 2016 to 2023, supported by quantitative statistical analysis using a Multiple Linear Regression Model and qualitative insights derived from Delphi interviews with industry experts. The study’s outcomes reveal a significant disparity between the targeted FDI growth from 3.8% to 5.7% of GDP and the existing trajectory, highlighting pressing economic challenges that require immediate attention. Key findings indicate that factors such as population growth, government spending, and trade openness significantly influence FDI dynamics, with a particular emphasis on the positive impact of investment protection agreements. The integration of qualitative methodologies further elucidates the importance of robust legal frameworks and regulatory reforms in fostering an investor-friendly environment. Overall, this research provides strategic recommendations for improving the investment landscape in Saudi Arabia, aligning with the broader goals of Vision 2030, and enhancing the country’s attractiveness as a global investment destination. The commitment to fostering a conducive investment ecosystem serves as a pivotal mechanism for driving economic growth and achieving sustainable development goals within the Kingdom. Full article

African swine fever (ASF) is a highly contagious viral disease with significant impacts on domestic pigs and wild boar populations. This study applies GIS-based spatial analysis to monitor ASF outbreaks in northwestern Italy (Piedmont and Liguria) and identify areas at increased risk. Key factors considered include pig density, wildlife proximity, and environmental conditions. The spatial analysis revealed that central–western municipalities exhibited higher risk due to favorable environmental conditions and dense wild boar populations, while peripheral areas showed a temporal delay in outbreak emergence. Mapping the spreading rate and habitat interfaces allowed the development of a spatial risk model, which was further analyzed using geostatistical techniques to understand disease dynamics. The results demonstrate the effectiveness of geospatial modeling in identifying high-risk zones, characterizing spatio-temporal patterns, and supporting targeted prevention and surveillance strategies. These findings provide actionable insights for ASF management and resource allocation. Future studies may refine these models by integrating additional datasets and environmental variables, enhancing predictive capacity and applicability across different regions. Full article

Background: Type 2 diabetes mellitus (T2DM) is a global health concern driven by aging, lifestyle, and socio-economic disparities. Early detection is key, with tools like FINDRISC, QDScore, and CANRISK providing non-invasive screening. Yet, the combined effects of sociodemographic factors, healthy habits, and perceived quality of life on diabetes risk remain insufficiently studied in working populations. Objectives: To evaluate the association between sociodemographic variables, lifestyle habits (smoking, physical activity, adherence to the Mediterranean diet), and health-related quality of life (HRQoL) with the risk of developing type 2 diabetes, using three validated screening tools in a large cohort of Spanish workers. Methods: A cross-sectional study was conducted among 100,014 Spanish workers aged 18 to 69 years who underwent standardized medical evaluations between January 2021 and December 2023. Diabetes risk was assessed using the FINDRISC, QDScore, and CANRISK tools. Lifestyle variables and HRQoL (measured via the SF-12 questionnaire) were evaluated through validated instruments. Multivariate logistic regression models were used to examine the association of independent variables with moderate-to-high diabetes risk according to each score. Results: Among the strongest predictors, poor adherence to a Mediterranean diet (OR = 1.45, 95% CI: 1.32–1.58) and low physical activity (OR = 1.39, 95% CI: 1.27–1.52) were independently associated with higher diabetes risk. Poor HRQoL was also significant (OR = 1.33, 95% CI: 1.22–1.47). Conclusions: Sociodemographic factors, lifestyle behaviors, and perceived health status are independently associated with increased type 2 diabetes risk in Spanish workers. The integration of HRQoL assessments into occupational health surveillance may enhance early identification of at-risk individuals and guide tailored prevention strategies. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

The synergy between carbon neutrality and urbanization is essential for effective climate governance and socio-ecological intelligent transition. From the perspective of coupled urban dynamic evolution and carbon metabolism systems, this study integrates the Sen-MK trend test and the geographical detector model to explore the spatial–temporal differentiation patterns and driving mechanisms of carbon balance across 337 prefecture-level cities in China from 2012 to 2022. The results reveal a spatial–temporal mismatch between carbon emissions and carbon storage, forming an asymmetric carbon metabolism pattern characterized by “expansion-dominated and shrinkage-dissipative” dynamics. Carbon compensation rates exhibit a west–high to east–low gradient distribution, with hotspots of expansionary cities clustered in the southwest, while shrinking cities display a dispersed pattern from the northwest to the northeast. Based on the four-quadrant carbon balance classification, expansionary cities are mainly located in the “high economic–low ecological” quadrant, whereas shrinking cities concentrate in the “low economic–high ecological” quadrant. Industrial structure and population scale serve as the dual-core drivers of carbon compensation. Expansionary cities are positively regulated by urbanization rates, while shrinking cities are negatively constrained by energy intensity. These findings suggest that differentiated regulation strategies can help optimize carbon governance within national territorial space. Full article

Background: There is an emerging need for new scalable behavioral assays, i.e., assays that are feasible to administer from the comfort of the person’s home, with ease and at higher frequency than clinical visits or visits to laboratory settings can afford us today. This need poses several challenges which we address in this work along with scalable solutions for behavioral data acquisition and analyses aimed at diversifying various populations under study here and to encourage citizen-driven participatory models of research and clinical practices. Methods: Our methods are centered on the biophysical fluctuations unique to the person and on the characterization of behavioral states using standardized biorhythmic time series data (from kinematic, electrocardiographic, voice, and video-based tools) in naturalistic settings, outside a laboratory environment. The methods are illustrated with three representative studies (58 participants, 8–70 years old, 34 males, 24 females). Data is presented across the nervous systems under a proposed functional taxonomy that permits data organization according to nervous systems’ maturation and decline levels. These methods can be applied to various research programs ranging from clinical trials at home, to remote pedagogical settings. They are aimed at creating new standardized biometric scales to screen and diagnose neurological disorders across the human lifespan. Results: Using this remote data collection system under our new unifying statistical platform for individualized behavioral analysis, we characterize the digital ranges of biophysical signals of neurotypical participants and report departure from normative ranges in neurodevelopmental and neurodegenerative disorders. Each study provides parameter spaces with self-emerging clusters whereby data points corresponding to a cluster are probability distribution parameters automatically classifying participants into different continuous Gamma probability distribution families. Non-parametric analysis reveals significant differences in distributions’ shape and scale (p < 0.01). Data reduction is realizable from full probability distribution families to a single parameter, the Gamma scale, amenable to represent each participant within each subclass, and each cluster of similar participants within each cohort. We report on data integration from stochastic analyses that serve to differentiate participants and propose new ways to highly scale our research, education, and clinical practices. Conclusions: This work highlights important methodological and analytical techniques for developing personalized and scalable biometrics across various populations outside a laboratory setting. Full article

Multiple myeloma (MM) is one of the most common hematological malignancies and remains incurable. However, the survival of multiple myeloma patients has significantly increased due to the implementation of novel therapies along with autologous stem cell transplantation, changing the natural history of the disease. Consequently, there is an unmet need for more sensitive response assessment techniques capable of quantifying minimal tumor burden to identify patients at higher risk of early relapse. Multiparameter flow cytometry (MFC) is an essential tool for diagnosing and monitoring patients with various hematological conditions and has recently gained prominence in identifying, characterizing, and monitoring malignant plasma cells. The implementation of Next-Generation Flow (NGF) by EuroFlow aims to overcome the pitfalls of conventional MFC, including lack of standardization and lower sensitivity, by offering standardized and optimized protocols for evaluating response depth. Both MFC and NGF have wide-ranging applications in MM for diagnosis and measurable residual disease (MRD) monitoring. Plasma cell identification and clonality evaluation through MFC and NGF assist in diagnostic workup and are routinely used to assess therapeutic response through MRD analysis. Additionally, flow cytometry is applied for circulating tumor plasma cell (CTPC) enumeration, which has demonstrated significant prognostic value. Immune composition studies through MFC may provide better understanding of disease biology. Furthermore, MFC provides additional information about other bone marrow cell populations, assessing cellularity, immunophenotypic characteristics of plasma cells, and possible hemodilution. This review explores the applications of MFC and NGF in MM, highlighting their roles in diagnosis, response assessment, and prognosis. Beyond their established use in MRD monitoring, flow cytometry-derived immunophenotypic profiles show strong potential as cost-effective prognostic tools. We advocate for future studies to validate and integrate these markers into risk stratification models, complementing cytogenetic analyses and guiding individualized treatment strategies. Full article

Participation in sports and the presence of sports injuries have a lasting impact on youth athletes’ physical, cognitive, and emotional development and sense of self-identity. There is an ongoing growth in participation in sports for youth, as well as growing literature on the epidemiology and outcomes of sports-related injuries. However, there is a paucity of published research regarding the psychological aspects of sports injury, including psychosocial factors, stressors, and responses, from the perspective of young athletes. Key risk factors include the youth’s sex, the types of sports activity, and any previous injuries. Psychosocial models, such as the stress-injury model, help explore such risk factors and their relationship to outcomes of stress. Implications for sports injury outcomes vary within the pediatric population, and the recovery and rehabilitation process requires integrated healthcare to optimize health and mental health outcomes. This review aims to describe the psychosocial factors related to sports injuries in children and adolescents, provide an understanding of sports injury models for youth athletes, and point to recovery and prevention through integrated behavioral health interventions. Based on a literature search, we identified 40 articles most relevant to our aims to explore psychosocial factors and stressors, predisposing and risk factors, and developmental aspects of sports injuries in children and adolescents. Full article

Endometrial carcinoma (EC) is the most common malignancy of the female reproductive tract, with increasing incidence driven by aging populations and obesity. While molecular classification has improved diagnostic precision, the identification of clinically relevant metabolic biomarkers remains incomplete, and targeted therapies are not yet standardized. In this study, we investigated metabolic alterations in four EC cell lines (AN3-CA, EFE-184, HEC-1B and MFE-296) compared to non-malignant controls under normoxic and stress conditions (hypoxia and lactic acidosis) to identify metabolomic differences with potential clinical relevance. Untargeted gas chromatography–mass spectrometry (GC/MS) and targeted liquid chromatography–mass spectrometry (LC/MS) profiling revealed two distinct metabolic subtypes of EC. Cells of metabolic subtype 1 (AN3-CA and EFE-184) exhibited high biosynthetic and energy demands, enhanced cholesterol and hexosyl-ceramides synthesis and increased RNA stability, consistent with classical cancer-associated metabolic reprogramming. Cells of metabolic subtype 2 (HEC-1B and MFE-296) displayed a phospholipid-dominant metabolic profile and greater hypoxia tolerance, suggesting enhanced tumor aggressiveness and metastatic potential. Key metabolic findings were validated via real-time quantitative PCR. This study identifies and characterizes distinct metabolic subtypes of EC within the investigated cancer cell lines, thereby contributing to a better understanding of tumor heterogeneity. The results provide a basis for potential diagnostic differentiation based on specific metabolic profiles and may support the identification of novel therapeutic targets. Further validation in three-dimensional culture models and ultimately patient-derived samples is required to assess clinical relevance and integration with current molecular classifications. Full article

Urban deep excavations conducted near operational tunnels necessitate stringent deformation control. This study investigates the Baiyun Station excavation by employing a three-dimensional finite-element model based on the Hardening Soil Small-strain (HSS) constitutive law, calibrated using Phase I field monitoring data on wall deflection, ground settlement, and tunnel displacement. Material parameters for the HSS model derived from the prior Phase I numerical simulation were held fixed and used to simulate the Phase II excavation, with peak errors of less than 5.8% for wall deflection and less than 2.9% for ground settlement. The model was subsequently applied to evaluate the impacts of Phase II excavation. The key contribution of this study is a monitoring-driven HSS modeling framework that integrates staged excavation simulation with field-based calibration, enabling quantitative assessment of tunnel responses—including settlement troughs, bow-shaped wall deflection patterns, and the distance-decay characteristics of lining displacement—to support structural safety evaluations and protective design measures. The results demonstrate that the predicted deformations and lining stresses in adjacent power and metro tunnels remain within permissible limits, offering practical guidance for excavation control in densely populated urban areas. Full article