Search Results (5,439)

Suicide is a major public health concern worldwide, and identifying the spatial patterns associated with its occurrence is essential for designing effective preventive strategies. This study aimed to identify and characterize suicide locations in two provinces of Castilla-La Mancha, Spain, using a descriptive and retrospective analysis of 421 cases recorded by the Institutes of Legal Medicine and Forensic Sciences of Toledo and Albacete between 2020 and 2024. Locations were classified as critical or non-critical based on recurrence and public accessibility, and logistic regression was used to explore predictors of suicide in public settings. Results showed that 82% of cases involved men, yielding a 5:1 male-to-female ratio that exceeds the national average; the mean age was 56.6 years, and hanging was the most frequent method (56.1%). Most suicides occurred in private environments, and only one location met the criteria for a critical site. These findings indicate that spatial clustering plays a minimal role in the regional suicide burden and that prevention efforts should prioritize means restriction and early detection in private settings, along with broader measures for dispersed public cases rather than hotspot-focused interventions. The study underscores the importance of systematically incorporating spatial information into forensic records to improve regional suicide surveillance and inform more targeted, context-sensitive prevention policies. Full article

Background/Objectives: Among survivors of cancer diagnosed in childhood, adolescence, or young adulthood (C-AYAs), cardiotoxic therapies combined with acquired cardiovascular risk factors (CVRFs) increase the risk for cardiovascular events. To our knowledge, no prior analysis has examined CVRFs among C-AYAs < 20 years old or compared CVRFs among younger and older C-AYAs. Methods: In this single-center study, individuals diagnosed with cancer at ≤39 years, treated with anthracycline-based chemotherapy (2010–2023), and with a post-treatment lipid panel and ≥2 post-treatment ambulatory blood pressure measurements were included. The CVRF prevalence was assessed among C-AYAs < 20 and ≥20 years old, using age-appropriate AAP and ACC/AHA guidelines. These prevalences were compared with the ICD-9/10 code prevalence. The prescription of medications with antihypertensive effects (MAHEs) and lipid-lowering therapy was assessed. Results: Among 276 C-AYAs, the median age was 28.1 years (IQR 18.1–38.3) at dyslipidemia screening and 29.3 (IQR 20.0–38.7) at hypertension screening. Dyslipidemia was present in 52.9% (146/276) and hypertension in 56.2% (155/276) of C-AYAs. C-AYAs < 20 years old had a high prevalence of dyslipidemia, 51.7% (45/87), and hypertension, 31.9% (29/91). CVRFs were frequently underdiagnosed, particularly dyslipidemia, among C-AYAs < 20 years old, with only 12.6% (11/87) having a diagnosis via the ICD code. C-AYAs < 20 years old with diagnoses of dyslipidemia and hypertension were significantly less likely to receive lipid-lowering therapy (2.2% vs. 14.9%) and trended toward less MAHEs (13.8% vs. 31.0%) compared to C-AYAs ≥ 20. Conclusions: Among C-AYAs treated with anthracyclines, dyslipidemia and hypertension were highly prevalent even at a young age (<20 years). Younger survivors with dyslipidemia and hypertension were less frequently prescribed lipid-lowering therapy or MAHEs. Full article

Alcoholic liver disease (ALD) is a spectrum of alcohol-induced disorders and represents a major global health challenge. B-cell receptor-associated protein 31 (BAP31) is an endoplasmic reticulum-resident chaperone involved in protein transport, apoptosis, cancer biology, and lipid metabolism. To explore its role in ALD, we used hepatocyte-specific BAP31 knockout mice (BAP31-LKO) and wild-type (WT) littermates exposed to ethanol to assess BAP31′s biochemical and metabolic impact. Following ethanol exposure, BAP31-LKO mice exhibited elevated serum alanine transaminase (23.2%, p < 0.05) and aspartate transaminase (31.4%, p < 0.05) levels compared to WT mice. Increased malondialdehyde (8.5%, p < 0.05) and reduced superoxide dismutase (22.8%, p < 0.05) in BAP31-LKO mice indicate exacerbated liver injury. Furthermore, BAP31 deficiency increased triglyceride (35.7%, p < 0.05) and free fatty acid (16.2%, p < 0.05) accumulation following ethanol treatment, while the expression of fatty acid oxidation-related genes, including Pparα, Cd36, Fatp2, Cpt2, and Acox1, was reduced in BAP31-LKO mice. The mRNA levels of Xbp1, Xbp1s, and Chop, as well as protein levels of p-eIF2α, IRE1α, GRP78, and CHOP, were increased in BAP31-LKO mice compared to WT controls, indicating aggravated ethanol-induced ER stress. Hepatic glycogen content was also reduced in BAP31-LKO mice, along with reduced Ppp1r3c expression, demonstrating impaired glycogen synthesis. Consistently, BAP31 knockdown amplified ethanol-induced lipid accumulation, inflammation, impaired glycogen storage, ER stress, and suppression of Pparα signaling in HepG2 cells. Together, these findings demonstrate that BAP31 deficiency exacerbates ethanol-induced liver steatosis, inflammation, and liver injury by impairing fatty acid oxidation and glycogen synthesis, and by amplifying ER stress responses. Full article

Natural humus, characterized by its high organic carbon content and high degree of humification, is widely used in soil improvement. However, the impact of natural humus on the multifunctionality of saline–alkali soils and its relationship with soil microbial diversity remain poorly understood. This study conducted experiments with varying concentrations of natural humus to explore changes in soil multifunctionality and its driving factors. The results indicate that the addition of natural humus increases soil organic matter (by 23.5–45.73%) and alkali-hydrolyzable nitrogen (by 40–81.57%), while reducing electrical conductivity (by 1.8–35.9%). These changes enhance soil microbial diversity and improve soil multifunctionality. As natural humus is a high C/N material, nitrogen limitation in soil microorganisms may occur with increasing humus addition. However, the increase in K-strategy fungi (which are more efficient in resource utilization) helps maintain a relatively high level of soil multifunctionality. At the maximum application rate (30 t/ha), soil multifunctionality reached its peak value of 0.41. These findings highlight the significant role of natural humus in improving saline–alkali soils and enhancing soil functions, particularly through its effects on microbial communities, especially K-strategy fungi. Full article

Based on the COAWST (Coupled Ocean–Atmosphere–Wave–Sediment Transport) model, this study developed an atmospheric refractivity forecasting model incorporating ensemble bias correction by combining five bias correction algorithms with the Bayesian Model Averaging (BMA) method. Hindcast tests conducted over the Yellow Sea and Bohai Sea regions demonstrated that the ensemble bias correction enhanced both forecasting accuracy and adaptability. On the one hand, the corrected forecasting outperformed the original COAWST model in terms of mean error (ME), root mean square error (RMSE), and correlation coefficient (CC), with the RMSE reduced by approximately 20% below 3000 m altitude. On the other hand, the corrected forecasting reduced the uncertainty associated with the performance of different algorithms. In particular, during typhoon events, the corrected forecasting maintained stable bias characteristics across different height layers through dynamic weight adjustment. Throughout the hindcast period, the ME of the corrected forecasting was lower than that of any single bias correction algorithm. Moreover, compared with other ensemble methods, the corrected forecasting developed in this study achieved more flexible weight allocation through Bayesian optimization, resulting in lower ME. In addition, the corrected forecasting maintained an improvement of approximately 28% in bias reduction even at a 72 h forecasting lead time, demonstrating their robustness and reliability under complex weather conditions. Full article

To optimize the flow stability and improve application accuracy of the PWM intermittent variable-rate spraying system, which suffers from insufficient flow stability and response delays during changes in travel speed, this study proposes an intelligent control method based on an improved Adaptive Neural Fuzzy Inference System (ANFIS). Flow characteristic data of the solenoid valve were collected under four pressure conditions (0.2–0.5 MPa), drive frequencies (5–20 Hz), and duty cycles (10–90%) using an indoor test system. An ANFIS controller architecture was constructed with target flow rate and actual travel speed as input variables and PWM frequency-duty cycle combinations as output variables. This controller enhances the traditional single-output mode of ANFIS by achieving multi-output collaborative optimization through shared premise parameters, thereby strengthening the system’s nonlinear modeling and control capabilities. To validate the system’s practical performance, a field simulation test platform based on a spraying robot was constructed. By analyzing preset prescription map information, the system achieved precise variable-rate spraying operations during movement. Test results demonstrate that the steady-state error remains within 5.03% under various speed-varying conditions. This research provides a high-precision intelligent control solution for variable-rate spraying systems, holding significant implications for reducing pesticide application rates and advancing precision agriculture. Full article

This study presents an integrated stochastic framework for assessing and forecasting drought dynamics in the western Bhagirathi–Hooghly River Basin, encompassing the districts of Bankura, Birbhum, Burdwan, Medinipur, and Purulia. Employing multiple probabilistic and statistical techniques, including the gamma-based standardized precipitation index (SPI), effective drought index (EDI), rainfall anomaly index (RAI), and the auto-regressive integrated moving average (ARIMA) model, the research quantifies spatio-temporal variability and projects drought risk under non-stationary climatic conditions. The analysis of century-long rainfall records (1905–2023), coupled with LANDSAT-derived vegetation and moisture indices, reveals escalating drought frequency and severity, particularly in Purulia, where recurrent droughts occur at roughly four-year intervals. Stochastic evaluation of rainfall anomalies and SPI distributions indicates significant inter-annual variability and complex temporal dependencies across all districts. ARIMA-based forecasts (2025–2045) suggest persistent negative SPI trends, with Bankura and Purulia exhibiting heightened drought probability and reduced predictability at longer timescales. The integration of remote sensing and time-series modelling enhances the robustness of drought prediction by combining climatic stochasticity with land-surface responses. The findings demonstrate that a hybrid stochastic modelling approach effectively captures uncertainty in drought evolution and supports climate-resilient water resource management. This research contributes a novel, region-specific stochastic framework that advances risk-based drought assessment, aligning with the broader goal of developing adaptive and probabilistic environmental management strategies under changing climatic regimes. Full article

Objective: Identifying biomarkers that predict metastatic potential or guide treatment selection is critical for improving breast cancer (BC) management. Previously, we established the Mitotic Network Activity Index (MNAI) as a prognostic marker in BC. Here, we bioinformatically and experimentally evaluated MNAI as a biomarker for metastasis risk and therapeutic response. Methods: We used Kaplan–Meier and Cox proportional hazard regression analyses to assess the association between MNAI and distant metastasis-free survival (DMFS) across 14 published BC datasets. A total of 16 publicly available clinical trial datasets, including the I-SPY trials, were used to evaluate the predictive value of MNAI for treatment response. Additionally, wound-healing and transmembrane assays were conducted to determine the effects of PLK1, CHEK1, and BUB1 inhibition on BC cell migration and invasion. Results: High MNAI levels were strongly associated with shorter DMFS. Multivariate analysis further confirmed MNAI as an independent risk factor for DMFS, beyond estrogen receptor status and PAM50-based molecular subtypes. Functionally, pharmacologic disruption of the mitotic network using PLK1, CHEK1, or BUB1 inhibitors significantly reduced cell migration and invasion in MDA-MB-231 and BT-549 BC cell lines. Moreover, BC cells with high MNAI increased sensitivity to microtubule-targeting agents such as docetaxel, paclitaxel, and ixabepilone but increased resistance to tamoxifen, AKT1/2 inhibitors, and mTOR inhibitors. Consistent with these findings, analysis of 16 clinical trial cohorts revealed that patients with high MNAI achieved higher pathological complete response rates to taxane-containing and ixabepilone-based therapies. Conclusions: Our findings demonstrate the MNAI as a clinically actionable biomarker that can refine risk stratification and guide the selection of targeted or chemotherapy regimens, advancing precision medicine in BC management. Full article

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative diseases that share clinical and pathological features, as well as genetic causes. A G₄C₂ repeat expansion in chromosome 9 open reading frame 72 (C9orf72) is the most common genetic cause of ALS and FTD, collectively referred to as c9ALS/FTD. Assembly modulation is a new therapeutic approach which appears to target allosteric sites on aberrant forms of multi-protein complexes and restore them to the healthy state. Recent findings demonstrate that tetrahydroisoquinolone (THIQ)-based protein assembly modulators can ameliorate ALS/FTD-associated phenotypes in cellular and animal models. In the present study, we investigated the effects of PAV-615, a novel and advanced THIQ-based modulator, in a c9ALS/FTD mouse model expressing 149 G₄C₂ repeat expansions (referred to as 149R mouse model). Specifically, PAV-615 was administered to 5-month-old 149R mice via intraperitoneal injection for one month. Motor function was evaluated using the hang wire test, while anxiety-like behavior and hyperactivity were assessed using the open-field test. Pathological markers, including dipeptide repeat (DPR) proteins, phosphorylated TAR DNA-binding protein 43 (pTDP-43) and ataxin 2-positive stress granules, were quantified by Meso Scale Discovery and immunohistochemistry assays. Compared with vehicle-treated controls, PAV-615 significantly improved motor performance and modestly reduced anxiety-like behavior and hyperactivity in 149R mice. Moreover, PAV-615 treatment significantly decreased cortical DPR, pTDP-43 and ataxin 2-positive stress granule burdens. These results support assembly modulation as a promising therapeutic approach treatment of ALS/FTD. Full article

The investigation of topological structures and phases in photonics has created unprecedented opportunities for developing advanced on-chip terahertz waveguide devices. Topological waveguides, which exhibit reduced backscattering and improved turning characteristics, provide a potential route toward more compact and robust on-chip photonic systems. Unlike conventional waveguides, the mode fields in topological waveguides are localized at the domain wall interface and decay into the bulk, making their bending loss sensitive to both the bulk thickness and the photonic band gap. However, a comprehensive analysis that simultaneously considers the bulk thickness, photonic band gap, and bending loss remains lacking. In this paper, we comprehensively studied the relationship between the bending loss in valley Hall photonic crystal waveguides and both the bulk thickness and photonic band gap width, using an all-silicon terahertz platform. The results provide guidance and a reference for the routing and design of terahertz photonic systems. Full article

As an important marginal sea in the western Pacific, sea-level changes in the South China Sea not only respond to global warming but are also regulated by regional ocean dynamics and climate modes, exerting profound impacts on the socioeconomic development and engineering safety of coastal regions. To address the widespread issues of low accuracy and robustness in existing sea-level prediction models when handling nonlinear, multi-scale sequences, as well as the complexity of sea-level change mechanisms in the South China Sea, this study constructs a hybrid model combining Singular Spectrum Analysis and Long Short-Term Memory neural networks (SSA-LSTM). The coral skeletal oxygen isotope ratio (δ¹⁸O) used in this study is a key indicator for characterizing the marine environment, defined as the per mille difference in the ¹⁸O/¹⁶O ratio of a sample relative to a standard. Based on coral δ¹⁸O data from the South China Sea, the sea level from 1850 to 2015 is reconstructed. SSA is then applied to decompose the sea-level data into trend and periodic components. The trend component, accounting for 37.03%, and components 2 to 11, containing major periodic information, are extracted to reconstruct the sea-level series. The reconstructed series retains 95.89% of the original information. The trend component is modeled through curve fitting, while the periodic components are modeled using an LSTM neural network. Optimal hyperparameters for the LSTM are determined through parameter sensitivity analysis. An integrated SSA-LSTM model is constructed to predict sea level in the South China Sea, and its predictions are compared with those from a Singular Spectrum Analysis-Autoregressive Integrated Moving Average (SSA-ARIMA) model. The results indicate that from 1850 to 2015, sea level in the South China Sea exhibits periodic fluctuations with a significant overall upward trend. Specifically, the growth rate from 1921 to 1940 reaches 5.49 mm/yr. Predictions from the SSA-LSTM model are significantly higher than those from the SSA-ARIMA model. The SSA-LSTM model projects that from 2016 to 2035, sea level in the South China Sea will continue to rise at a fluctuating rate of 0.75 mm/yr, with a cumulative rise of approximately 15 mm. This study provides a novel methodology for investigating the mechanisms of sea-level change in the South China Sea and offers a scientific basis for coastal risk management. Full article

Wind power is a major source of renewable energy, yet turbine performance is strongly influenced by atmospheric conditions and surrounding terrain. Several meteorological phenomena can hinder energy production, disrupt operations, and accelerate structural deterioration. This paper reviews three key atmospheric hazards affecting wind turbine systems: lightning, icing, and rain. For each phenomenon, the formation mechanisms, operational effects, and mitigation approaches are examined, with offshore-specific processes and conditions integrated directly into each hazard discussion. Building on this foundation, the review then analyses interactions between the hazards, their combined implications for turbine performance and maintenance, and the associated economic impacts. Comparisons of material behaviour across lightning, icing, and rain-erosion conditions are also incorporated. Finally, future research directions are proposed. Full article

To address the limitations of the traditional Coati Optimization Algorithm (COA), such as insufficient global exploration, poor population cooperation, and low convergence efficiency in global optimization and photovoltaic (PV) model parameter identification, this paper proposes a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA). MECOA improves performance through three core strategies: (1) Elite-guided search, which replaces the single global best solution with an elite pool of three top individuals and incorporates the heavy-tailed property of Lévy flights to balance large-step exploration and small-step exploitation; (2) Horizontal crossover, which simulates biological gene recombination to promote information sharing among individuals and enhance cooperative search efficiency; and (3) Precise elimination, which discards 20% of low-fitness individuals in each generation and generates new individuals around the best solution to improve population quality. Experiments on the CEC2017 (30/50/100-dimensional) and CEC2022 (20-dimensional) benchmark suites demonstrate that MECOA achieves superior performance. On CEC2017, MECOA ranks first with an average rank of 1.87, 2.07, 1.83, outperforming the second-best LSHADE (2.03, 2.43 and 2.63) and the original COA (9.93, 9.93 and 9.96). On CEC2022, MECOA also maintains the leading position with an average rank of 1.58, far surpassing COA (8.92). Statistical analysis using the Wilcoxon rank-sum test (significance level 0.05) confirms the superiority of MECOA. Furthermore, MECOA is applied to parameter identification of single-diode (SDM) and double-diode (DDM) PV models. Experiments based on real measurement data show that the SDM model achieves an RMSE of 9.8610 × 10⁻⁴, which is only 1/20 of that of COA. For the DDM model, the fitted curves almost perfectly overlap with the experimental data, with a total integrated absolute error (IAE) of only 0.021555 A. These results fully validate the effectiveness and reliability of MECOA in solving complex engineering optimization problems, providing a robust and efficient solution for accurate modeling and optimization of PV systems. Full article

Smart phones have become an integral part of our lives in modern society, as we carry and use them throughout a day. However, this “body part” may maliciously collect and leak our personal information without our knowledge. When we install mobile applications on our smart phones and grant their permission requests, these apps can use sensors embedded in the smart phones and the stored data to gather and infer our personal information, preferences, and habits. In this paper, we present our preliminary results on quantifying the privacy risk of mobile applications by assessing whether requested permissions are necessary based on app descriptions through textual entailment decided by language models (LMs). We observe that despite incorporating various improvements of LMs proposed in the literature for natural language processing (NLP) tasks, the performance of the trained model remains far from ideal. Full article

The performance of ultra-high-purity copper sputtering targets is critical for nanoscale integrated circuit fabrication, yet challenges such as dynamic recovery and recrystallization hinder grain refinement and texture control. In the present work, cryogenic deformation was introduced to address these issues. Through electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and mechanical testing, the microstructure, texture, and mechanical properties of 6N ultra-high-purity copper processed by room-temperature rolling (RTR) and cryorolling (CR) were comparatively investigated. Results reveal that RTR deformation is dominated by slip mechanisms; the RTR sample with 90% reduction exhibits obvious dynamic recrystallization (DRX) and forms a bimodal structure dominated by Copper ({112}⟨111⟩) and S ({123}⟨634⟩) textures. In contrast, CR suppresses thermal activation processes, enabling deformation mechanisms suggestive of twinning activity, leading to ultrafine fibrous structures, while shifting texture components toward Brass ({110}⟨112⟩) and S. Compared to RTR-processed samples, CR-processed samples possess superior mechanical performance. The CR sample with 90% reduction exhibits: a microhardness of 164.60 HV, a yield strength of 385.61 MPa, and a tensile strength of 648.02 MPa, which are, respectively, 33.2%, 91.7%, and 84.6% higher than those of RTR counterparts. Williamson–Hall analysis confirms that the CR sample with 90% reduction achieves finer substructure sizes (~133 nm) and higher stored energy (~22 J·mol⁻¹) by suppressing dynamic recovery, providing a robust driving force for subsequent annealing. This work demonstrates that cryorolling optimizes microstructure and texture through twin-dislocation synergy, providing a fundamental basis for the development of advanced sputtering targets. Full article