Search Results (23,818)

Circulating microRNAs (miRNAs) are promising minimally invasive biomarkers for cancer risk assessment, yet prospective evidence for breast cancer (BC) remains limited. We conducted a nested case–control study within a prospective cohort to examine whether pre-diagnostic circulating miRNAs are associated with subsequent BC risk and to explore their potential relevance in prospective population-based settings. Baseline serum from 160 women (80 incident BC cases; 80 matched controls) was analyzed, with a median time to diagnosis of 8.9 years. Eight candidate miRNAs were quantified by droplet digital PCR (ddPCR) and normalized to miR-484. Group differences were evaluated by non-parametric tests, and odds ratios for BC were estimated using logistic regression models adjusted for established risk factors, with Bonferroni correction for multiple testing. Cases and controls were comparable at baseline. Among the candidates, lower circulating miR-181 levels showed a suggestive inverse association with BC risk in fully adjusted models, while lower Let7 levels showed only a non-significant, hypothesis-generating inverse trend that did not survive Bonferroni correction. No other miRNA displayed clear associations with BC risk. These findings, while preliminary, support further large-scale prospective investigations specifically designed to assess predictive performance and external validation. employing standardized pre-analytical and analytical protocols, repeated sampling, and independent replication/external validation to clarify the etiologic relevance and potential risk-prediction value of circulating miRNAs for BC. Full article

Background: The effectiveness of prostate-specific antigen (PSA)-based early detection of prostate cancer remains controversial and implementation-dependent. Screening policy changes have substantially altered PSA testing behavior in the United States, yet longitudinal evidence from non-organized European settings is limited. We assessed 12-year changes in awareness and utilization of PSA-based early detection and identified subgroups requiring targeted counseling. Methods: Two cross-sectional survey waves were conducted in 2009 (Study Phase 1) and 2021 (Study Phase 2) among men recruited via general practitioner practices in urban and rural regions of Germany. The survey was developed and reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). Identical questionnaires were used across phases. Endpoints were awareness of PSA-based early detection and prior PSA testing. Univariable and multivariable logistic regression evaluated independent associations with sociodemographic and behavioral factors. To assess sensitivity to compositional differences between survey waves, post-stratified weighted analyses re-aligning Study Phase 2 to the Study Phase 1 distribution of age category, educational attainment, and smoking status were conducted. Results: The analytic cohort comprised 890 men (Study Phase 1, n = 755; Study Phase 2, n = 135). Compared with Study Phase 1, Study Phase 2 participants more frequently were non-smokers (63.0% vs. 48.5%, p < 0.001) and had a university degree (38.5% vs. 30.5%, p = 0.002). In primary multivariable analyses, higher educational attainment (OR 1.71, 95% CI 1.24–2.36) and paternity (OR 1.94, 95% CI 1.25–3.01) were independently associated with greater awareness, whereas increasing age (OR 1.39, 95% CI 1.29–1.50) and higher educational attainment (OR 1.63, 95% CI 1.19–2.24) were independently associated with utilization. Study phase was not independently associated with either endpoint in primary models. In post-stratified sensitivity analyses, study phase was positively associated with utilization, indicating sensitivity of temporal contrasts to population composition. Conclusions: In primary multivariable analyses, we did not detect statistically significant temporal differences in awareness or utilization of PSA-based early detection within this German non-organized setting. The emergence of a study phase effect in weighted sensitivity analyses suggests that apparent time trends may be influenced by compositional differences between survey waves. Persistent social gradients, particularly related to educational attainment, underscore the importance of targeted, evidence-based counseling in opportunistic early detection systems. Larger, prospectively designed studies are needed to distinguish true temporal change from sampling-related effects. Full article

Against the backdrop of sustained growth in energy demand and energy transformation in China, accurately predicting future energy consumption trends is essential to developing science-based energy strategies and ensuring energy security. Traditional grey models suffer from limited prediction accuracy due to irrational background value settings. To address this issue, we introduced a structural optimization by adjusting the parameter count within the background value and employed the Simpson formula to reconstruct it. We proposed a novel three-parameter background value grey model, designated as TPBSVGM(1,1). It utilized the annual consumption data of petroleum, natural gas, and primary electricity and other energy consumption from 2014 to 2023 to construct TPBSVGM(1,1) for energy consumption analysis. To assess the predictive accuracy of TPBSVGM(1,1), this study compared its performance with GM(1,1) and FGM(1,1) in two dimensions: the trends between predicted values and actual values, and error metrics. The results indicate that TPBSVGM(1,1) outperforms the comparative models in energy consumption forecasting. We further used the model to predict annual consumption of the three energy sources from 2024 to 2030, finding that total consumption continues to grow while growth rates decline to varying degrees. It provides reliable data support for China’s energy consumption regulation and energy structure optimization. Full article

Financial data analysis remains a significant challenge due to the inherent stochasticity, non-stationarity, and low signal-to-noise ratio of market data. Conventional methods often struggle to disentangle intrinsic trends from noise and frequently overlook the critical influence of investor sentiment on price dynamics. To address these issues, we propose an adaptive trading model named CBAM-BiLSTM-DDQN, which integrates signal decomposition, multi-source feature fusion, and deep reinforcement learning. First, we construct a comprehensive heterogeneous feature set by combining price signals decomposed via Variational Mode Decomposition (VMD) and investor sentiment indices extracted from financial texts. Subsequently, a Genetic Algorithm (GA) is employed to identify the most significant feature subset, effectively reducing dimensionality and redundancy. Finally, these optimized features are input into a Double Deep Q-Network (DDQN) agent equipped with a Convolutional Block Attention Module (CBAM) and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture complex spatiotemporal dependencies. We evaluated this approach through simulated trading on three major Chinese stock indices—the Shanghai Stock Exchange Composite (SSEC), the Shenzhen Stock Exchange Component (SZSE), and the China Securities 300 (CSI 300). Experimental results demonstrate the superiority of our method over traditional strategies and standard baselines; specifically, the trading agent achieved robust cumulative returns across the SSEC and CSI 300 indices, confirming the model’s exceptional capability in balancing profitability and risk aversion in complex financial environments. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Furthermore, additional experiments on individual stocks in the Chinese A-share market reinforce the robustness and generalization ability of our proposed model, validating its practical potential for diverse trading scenarios. Full article

To better understand how botanical products affect non-target organisms, the present review focuses on the toxicity of botanical pesticides to organisms other than targeted pests, to trace a panorama on the future of sustainable agricultural models worldwide, considering the importance of ecotoxicological studies in the development of new pesticides, including botanical kinds, which are commonly recognized as essentially harmless. The review summarizes published work gathered from digital databases and highlights modern trends in pest management research and the development of novel bioinputs, including a discussion of the world’s current legislation regarding relevant agricultural innovations and field obstacles. Nanotechnology techniques are discussed as major innovations employed in the pest control field, and their employment in improving botanical pesticides is addressed and explored. In this work, we analyze the factors involved in determining the success of botanical products and their importance in the implementation of a more sustainable approach to managing crops. The results indicate a significant lack of studies focused on the effects of botanical products on non-target organisms and an increase in studies with nanoformulations. Full article

Atomic charges are widely used to analyze molecular electronic structure and substituent effects, yet their numerical values and interpretations are inherently dependent on the adopted density partitioning scheme. Here, we adapt the Equivariant Atomic Contribution framework to molecular systems (EAC-qm), enabling prediction of atom-resolved continuous charge densities from which atomic charges are obtained as spatial moments. The predicted densities reproduce reference density functional theory results with high accuracy and preserve global charge conservation. To assess chemical interpretability, we examine charge responses in monosubstituted aromatic systems using Hammett substituent constants as external empirical references. Atomic charges derived from EAC-qm exhibit a strong linear association with Hammett parameters, compared with values obtained from traditional density partitioning approaches applied to the same electronic structures. These correlations indicate that density-derived charges respond systematically to established substituent electronic trends. Beyond scalar charges, atom-resolved dipole moments can be evaluated as first-order moments of the same continuous density representation. Illustrative examples for formaldehyde (H₂CO) and formamide (HCONH₂) show that local dipole vectors provide directional information about intra-atomic polarization that is not captured by point-charge models. Overall, the results suggest that machine-learned continuous electron densities provide a representation-consistent basis for constructing atom-centered electronic descriptors with chemical interpretability. Full article

The application of multi-cycle steam stimulation in heavy oil reservoirs frequently alters reservoir properties, influencing the effectiveness of the stimulation and subsequent development strategies. The inherent heterogeneity of strata, characterized by distinct sedimentary facies rhythms, leads to differential patterns of property evolution. Therefore, understanding facies-controlled property variations during steam stimulation is essential for optimizing recovery strategies. This study integrates 1D core experiments with 3D geological modeling to dynamically simulate the stimulation process, enabling a comprehensive multi-scale analysis. The results show the following: (1) Both sedimentary rhythms exhibit progressive increases in porosity and permeability with successive cycles until reaching stabilization plateaus, with the uniform rhythm stabilizing earlier than the coarsening-upward rhythm. (2) 3D simulations reveal a predominant increasing trend in porosity and permeability after multi-cycle stimulation, albeit with localized reduction zones. (3) Multi-scale analysis indicates that, during the early stage (cycles 1–9), the underwater distributary channel microfacies undergoes more rapid property changes and achieves a greater cumulative increase in porosity and permeability. Conversely, during the later stage (cycles 10–30), the mouth bar microfacies demonstrates faster property alterations and a larger cumulative enhancement. This facies-specific, time-dependent understanding provides critical insights for tailoring steam stimulation strategies in heterogeneous heavy oil reservoirs. Full article

Background: Strongman competitions impose extreme mechanical and metabolic stress on the shoulder girdle, yet quantitative neuromuscular responses under real competition conditions remain poorly characterized. Methods: Ten elite strongmen (Tier 4) and ten age-matched trained controls (Tier 2) completed an official Strongman Champions League competition protocol. Surface EMG was recorded from seven shoulder-girdle muscles during maximal voluntary contraction (MVC) trials performed immediately before and after competition. Normalized RMS amplitudes were expressed as a relative EMG index (% group peak) and analyzed using linear mixed-effects models with Benjamini–Hochberg false discovery rate (FDR) correction. Results: Within-group analyses revealed no generalized pre–post reductions in normalized EMG amplitude in either group after FDR correction. However, the control group demonstrated consistent negative pre–post trends with moderate-to-large effect sizes across several muscles, particularly for mean and median descriptors. In contrast, elite strongmen exhibited smaller and more variable changes without a systematic decline. Difference-in-differences analysis showed that temporal changes generally favored the elite group. After FDR adjustment, a significant interaction was identified for the median lower trapezius amplitude (ΔΔ = 33.76 ± 9.13, pFDR = 0.021), indicating relatively greater preservation of neuromuscular activation in elite strongmen compared with controls. No contrast demonstrated a greater decline in the elite group. Conclusions: Although most effects did not survive correction for multiple testing, the observed effect-size patterns and a significantly lower trapezius interaction suggest greater stability of neuromuscular activation in elite strongmen compared with trained, non-specialized controls. These findings support muscle- and metric-specific fatigue resistance associated with long-term strongman training. Full article

The development of China’s marine food production system is receiving increasing attention, as its developmental level and obstacle factors will profoundly impact the nation’s future food security and nutritional supply. This study establishes a theoretical framework for evaluating the development level of marine food production systems based on three dimensions—resources, benefits, and governance—structured around the logical framework of “exogenous safeguard, endogenous drive, goal oriented”. First, a three-tier coding method based on grounded theory was employed to construct a Chinese marine food production system evaluation framework encompassing 28 specific indicators. Subsequently, a comprehensive weighting of these indicators was achieved by integrating fuzzy comprehensive evaluation with the entropy weighting method. Finally, based on the evaluation results and obstacle degree modeling, a comprehensive assessment study was conducted on 11 coastal provinces and cities, focusing on developmental level investigation and obstacle factor analysis. The results indicate that China’s marine food production system development level exhibits a trend of slow, fluctuating growth overall, maintaining an average annual growth rate of 3.23%. However, significant differentiation characteristics are emerging, with high regional heterogeneity and substantial variation in obstacle factors. Currently, the main constraints hindering the development of the marine food production system are insufficient human resource supply, uneven production resource distribution (higher in the north, lower in the south), and intensified fluctuations in comprehensive output. Finally, this study proposes three strategic recommendations: ecological restoration coupled with strict controls, comprehensive restructuring of the human resource support system, and establishing a multi-scale comprehensive evaluation mechanism. These strategies aim to disrupt the transmission mechanisms of different obstacle factors and accelerate the rapid development of the marine food production system. Full article

Endocrine-disrupting chemical (EDC) exposure from plastics and everyday products is widespread and linked to infertility. We conducted a 3-month uncontrolled feasibility pilot study among five idiopathically infertile couples to assess whether an intensive lifestyle intervention was associated with within-person changes in urinary EDC biomarkers and exploratory changes in reproductive parameters. The intervention was embedded in a film project (“The Plastic Detox”) and integrated personalized education, product substitutions, at-home urine biomonitoring, sperm testing, and weekly coaching. Urine and semen samples were collected at baseline, 6 weeks, and 12 weeks. Linear mixed-effects models were used to estimate biomarker changes. BPA was designated a priori as the primary biomarker endpoint. Directional reductions were observed in urinary bisphenol A (BPA), mono-n-butyl phthalate (MBP), and monobenzyl phthalate (MBzP) over the intervention period. Within-person reductions in products containing ingredients of concern were associated with lower BPA levels. Descriptive upward trends of semen parameters were observed, with the majority of the subfertile men testing >40 million motile sperm/ejaculate after the intervention. Participants had increased environmental health literacy, were more motivated to reduce exposures, and reported improved wellness endpoints. Four couples achieved pregnancy and live birth during follow-up; given the uncontrolled design and small sample size, these outcomes are presented descriptively. Overall, this pilot study demonstrates feasibility and measurable biomarker change, supporting evaluation in larger, controlled trials. Full article

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article

Standard compatibility-based truss models, including the Disturbed Stress Field Model (DSFM), often underestimate the shear strength and deformation capacity of reinforced-concrete (RC) beam-column joints. This study investigates the origin of this bias through a systematic inverse identification framework and derives joint-core constitutive relationships tailored to the highly confined, nonuniform stress states of joints. Inverse analyses show that improving confinement effectiveness alone leads to unrealistic parameter saturation and cannot reproduce the measured energy absorption, indicating that conventional compression-softening formulations remain excessively punitive for joint cores. When confinement activation and softening are identified simultaneously, a clear mechanism shift emerges: unlike panel-based theories that link softening to tensile-cracking measures (principal strain ratio), joint softening is overwhelmingly governed by the principal compressive strain, consistent with crushing-dominated damage accumulation. Based on these trends, unified power-law expressions are proposed for both passive confinement activation and damage-induced softening as functions of principal compressive strain only, adhering to a parsimonious formulation without auxiliary variables such as concrete strength or reinforcement ratio ($R^2\approx 0.89$). The model is validated on an independent database of 113 specimens, including high-strength concrete and exterior joints, eliminating the systematic conservatism of the standard DSFM and improving the mean experimental-to-predicted strength ratio from 0.85 to 1.01 while reducing the coefficient of variation from 34.5% to 13%. The proposed formulation supports more reliable joint shear backbone predictions for seismic assessment of RC frame buildings. Full article

Groundwater is a critical natural and strategic economic resource, and the accurate prediction of groundwater depth dynamics is essential for the rational development and utilization of water resources. However, under the combined influence of climate variability, human activities, and complex hydrogeological conditions, groundwater level time series exhibit strong nonlinear and non-stationary characteristics, posing great challenges to the accurate prediction of groundwater level dynamics. Most existing prediction models rely on sufficient hydro-meteorological and exploitation data that are difficult to obtain in water-scarce regions, or fail to effectively decouple the multi-scale features of non-stationary groundwater level signals, resulting in limited prediction accuracy and insufficient generalization ability. To address these research gaps, this study takes Zhengzhou, a typical water-deficient city in the Yellow River Basin, as the study area, and proposes a hybrid deep learning framework combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) neural network for predicting shallow and intermediate-deep groundwater level changes. Kolmogorov–Arnold Networks (KANs) and Gated Recurrent Units (GRUs) are selected as benchmark models to verify the superior performance of the proposed framework. In this framework, the non-stationary groundwater level signal is adaptively decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics via VMD. An independent LSTM model is constructed for each IMF to capture its unique temporal variation pattern, and the final groundwater level prediction is obtained by linearly reconstructing the predicted results of all IMFs. The results show that the coefficient of determination (R²) of the VMD-LSTM model exceeds 0.90 for all monitoring datasets, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). It significantly outperforms the benchmark models in handling nonlinear and non-stationary time series features. Using only historical groundwater level data as input, the proposed framework effectively overcomes the limitation of insufficient driving variables in data-scarce regions and fully explores the multi-scale evolution of groundwater dynamics through the synergistic effect of multi-scale decomposition and deep learning. The method presented in this study provides a novel and reliable technical approach for groundwater level prediction in water-deficient and data-limited areas, and also offers scientific support for the rational management and sustainable utilization of regional groundwater resources. Future research will incorporate driving factors such as meteorology and exploitation to further improve the model’s ability to capture abrupt changes in groundwater level dynamics. Full article

The increasing demand for efficient last-mile delivery has spurred interest in optimizing vehicle and drone routing. This review presents a novel classification of synchronization levels: (i) non-synchronized scenarios, where vehicles and drones operate independently; (ii) low synchronization level scenarios, where one party is passive in the delivery process; (iii) high synchronization level scenarios, where both parties cooperate using diverse strategies. The primary objective is to identify and classify functional preferences of vehicles and drones across these synchronization scenarios. We offer a unique perspective by analyzing the functional setups of vehicles and drones along with synchronization aspects like drone flight synchronization and vehicle synchronization. To the best of our knowledge, these detailed setups based on the operational functionalities of vehicles and drones in last-mile delivery has not been previously explored in the literature. Through a systematic review of the literature, we identify key challenges and emerging trends in vehicle and drone route planning within these scenarios which enable researchers to systematically understand and design vehicle–drone delivery systems. This paper integrates existing models and solution methods and provides new insights into the interactions between vehicle and drone functionalities in last-mile delivery. By analyzing solutions across different synchronization scenarios, it guides researchers in choosing appropriate methodologies and identifying future research directions. Our work presents a novel classification framework, enabling a comprehensive understanding of how the functional setups of vehicles and drones under different synchronization levels influence route planning, thus offering both theoretical and practical insights for advancing last-mile delivery optimization. Full article

Background and Objectives: Male infertility has emerged as a growing global health concern, contributing to 20–30% of all infertility cases. It is a multifactorial condition, arising from genetic, endocrine, structural, environmental and lifestyle factors. This narrative review synthesizes current evidence on epidemiology, diagnostic advances and therapeutic strategies while highlighting emerging trends and research priorities. Materials and Methods: This review adheres to SANRA guidelines. Literature was sourced from PubMed, Saudi Digital Library, Google Scholar, and PsycINFO using MeSH terms including “Male Infertility,” “Diagnosis,” “Treatment,” and “Epidemiology.” Results: Diagnostic evaluation of male infertility includes clinical assessment, advanced semen analysis, imaging techniques, hormonal assays and molecular testing. Despite significant advances in the evaluation of male infertility, idiopathic causes (30–40%) remain challenging. Management strategies include lifestyle modifications, medical therapies including hormones and drugs, surgical interventions, and assisted reproductive technologies (ARTs). However, outcomes remain suboptimal in idiopathic and severe cases, particularly regarding sperm DNA fragmentation and environmental exposures. Conclusions: Substantial knowledge gaps exist in male infertility, particularly in idiopathic cases, molecular mechanisms of environmental pollutants, and long-term ART offspring outcomes. Future research priorities include: (1) molecular and epigenetic biomarkers for improved diagnosis and prognosis; (2) environmental exposure assessment and mitigation strategies; (3) metabolomics-guided personalized therapies; (4) regenerative medicine approaches including spermatogonial stem cell therapy; and (5) multidisciplinary integrative care models. Addressing these gaps through coordinated research and clinical innovation is essential for improving male reproductive health globally. Full article