Search Results (261)

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is examined regarding the identification of novel disease drivers, prediction of synthetic lethality interactions, and prioritization of therapeutic targets. Finally, addressing persistent challenges related to data sparsity, model interpretability, and target druggability and validation hurdles, emerging opportunities driven by Generative AI, Large Multimodal Models (LMMs), Explainable AI (XAI), and multidimensional feasibility assessment frameworks are discussed in the context of advancing precision medicine. Full article

This study presents the results of continued work on the search for the most suitable low-melting mixtures (LMMs) for the ultrasound-assisted extraction of phenolic compounds from Vaccinium vitis-idaea L. leaves using LMMs of choline chloride, malonic, malic, tartaric, and/or citric acids combined with water. Kinetics of extraction was studied, and Box–Behnken experimental design coupled with Response Surface Methodology was applied to optimize extraction conditions. The most suitable composition of solvent was choline chloride + citric acid + water in a molar ratio of 1:1:16. The optimal extraction conditions were as follows: extraction duration of 30 min, temperature of 30 °C, and volume-to-mass ratio of 20:1. Under these conditions, the yields were 335.1 ± 18.7 mg gallic acid equivalent/g for total phenolic contents and 50.9 ± 3.6 mg rutin equivalent/g for total flavonoids content. The advantage of using LMMs over ethanol has been shown. The effect of the extract on the development of Drosophila melanogaster was also evaluated. The data obtained can be applied to the development of green technologies for the production of extracts from medicinal plant raw materials. Full article

Thermo-hydro-mechanical (THM) densification is an effective method for improving the mechanical performance of low-density, fast-growing hardwoods such as poplar. This study examined the bending performance of THM-densified poplar wood at different compression ratios (CR = 0%, 50%, 60%, and 65%), with emphasis on the effects of ultrasonic pretreatment (US) and thermal modification posttreatment (TM), applied individually and in combination. A paired sampling design was used to reduce material variability, and modulus of rupture (MOR) and modulus of elasticity (MOE) were evaluated using linear mixed-effects models (LMM). Bending tests were performed in accordance with EN 310:1993. Increasing the compression ratio led to substantial increases in MOR and MOE; compared with non-densified specimens, MOR increased by approximately 240% and MOE by about 140% at CR = 65%, confirming densification as the dominant factor controlling bending performance. US did not affect non-densified wood but significantly enhanced MOR and MOE after densification, particularly at CR = 50%. In contrast, TM consistently reduced MOR and, to a lesser extent, MOE across all compression ratios. The results demonstrate that the bending performance of densified poplar wood is governed by both compression ratio and compression-dependent treatment effects. Full article

In applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can flexibly fit population growth trajectories, but higher orders substantially increase computational complexity. Instead of using Legendre polynomials, we first estimated fewer individual-specific parameters using biologically meaningful non-linear models and then associated these phenotypic regressions with genetic markers using a multivariate linear mixed model (mvLMM). After performing a canonical transformation of the regressions based on the pre-estimated covariance matrices under the null genomic mvLMM, we decomposed the mvLMM into mutually independent univariate models and incorporated EMMAX to enable rapid genome-wide mixed-model associations for each transformed phenotype. Simulations for longitudinal association analysis in maize and GWAS for the growth trajectories of body weights in mice demonstrated the advantages of hierarchical non-linear mixed models in computing efficiency and statistical power for detecting quantitative trait loci (QTL), compared with mvLMM for multiple growth points and the hierarchical random regression model using Legendre polynomials as sub-models. Full article

Background: Foam rolling is a popular self-myofascial release (SMR) technique, yet empirical evidence regarding its long-term impact on cycling endurance remains inconclusive. This study investigated the effects of chronic SMR on cardiorespiratory capacity, metabolic kinetics, and mechanical performance in road cyclists. Methods: We conducted a six-month randomized controlled trial (RCT) with 32 male recreational cyclists. Both an intervention group (IG) and a control group (CG) followed a standardized training protocol. The IG additionally applied a Blackroll^® foam roller immediately after cycling training sessions. Outcomes included maximum oxygen uptake (VO₂max), submaximal heart rate, lactate slope, and relative mechanical power (W/kg) at aerobic and anaerobic thresholds. Data were analyzed using linear mixed-effects models (LMM), with age included as a fixed-effect covariate to control for baseline imbalances between groups. Effect sizes were determined via marginal and conditional R². Additionally, model robustness was verified through Shapiro–Wilk tests and Q–Q plots of conditional residuals. Results: No significant effects were observed for VO₂max or submaximal heart rate. In contrast the IG demonstrated significant improvements in metabolic kinetics, evidenced by a reduced lactate slope (p = 0.004). Furthermore, foam rolling yielded a statistically significant positive effect on relative mechanical performance at both the aerobic (p = 0.031) and anaerobic (p = 0.007) lactate thresholds. Sensitivity analyses confirmed that these effects were independent of the age difference between groups. Conclusions: Foam rolling did not enhance all endurance-related variables but showed positive effects on metabolic kinetics and mechanical performance. While it did not shift systemic cardiorespiratory limits, SMR appeared to optimize performance through improved metabolic economy and mechanical efficiency, suggesting it is a valuable supplemental tool for recovery and long-term performance maintenance in cycling. Full article

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively. Full article

Over recent years, the intensity of forest fires has escalated, with wildfire-emitted pollutants exerting substantial impacts on the environment, ecosystems, and human well-being. This study developed a robust predictive framework to quantify wildfire-induced PM_2.5 emission factors (EFs) using seven shrub species—Corylus mandshurica, Eleutherococcus senticosus, Philadelphus schrenkii, Sorbaria sorbifolia, Syringa reticulata, Spiraea salicifolia, and Lonicera maackii. These species represent ecological cornerstones of Northeast Asian forests and hold global relevance as widely introduced or invasive taxa in North America and Europe. The novelty of this research lies in the integration of traditional statistical inference with machine learning to resolve the complex coupling between fuel traits and emissions. We conducted 1134 laboratory-controlled burns in the Liangshui National Nature Reserve, evaluating two continuous and three categorical variables. Initial screening via Analysis of Variance (ANOVA) and stepwise linear regression (Step-AIC) identified the primary drivers of emissions and revealed that interspecific differences among the seven shrubs did not significantly affect the EF (p = 0.0635). To ensure statistical rigor, a log-transformation was applied to the EF data to correct for right-skewness and heteroscedasticity inherent in raw observations. Linear Mixed-effects Models (LMMs) and Gradient Boosting Machines (GBMs) were subsequently employed to quantify factor effects and capture potential nonlinearities. The LMM results consistently identified burning type and plant part as the dominant determinants: smoldering combustion and leaf components exerted strong positive effects on PM_2.5 emissions compared to flaming and branch components. Fuel load was positively correlated with emissions, while moisture content showed a significant negative effect. Notably, the model identified a significant negative quadratic effect for moisture content, indicating a non-linear inhibitory trend as moisture increases. While interspecific differences among the seven shrubs did not significantly affect EFs suggesting that physical fuel traits exert a more consistent influence than species-specific genetic backgrounds, complex interactions were captured. These include a negative synergistic effect between leaves and smoldering, and a positive interaction between moisture content and leaves that significantly amplified emissions. This research bridges the gap between physical fuel traits and chemical smoke production, providing a high-resolution tool for refining global biomass burning emission inventories and assisting international forest management in similar temperate biomes. Full article

Background and Objectives: The optimal pharmacological strategy to mitigate progressive vertebral collapse during the acute phase of osteoporotic vertebral compression fractures (OVCFs) remains a subject of debate. This initial 3-month window is the most critical period for evaluating the structural stability of the fracture, as the majority of progressive height loss occurs before solid bone union is achieved, directly influencing the decision to continue conservative management or transition to surgical intervention. Materials and Methods: In this retrospective study, 123 patients were allocated to control (n = 26), denosumab (n = 35), teriparatide (n = 30), or romosozumab (n = 32) groups. Treatment choice was non-randomized, driven by clinical pragmatism and patient preference. Serial changes in vertebral compression rate (VCR) and pain (VAS) were analyzed over 3 months using linear mixed models (LMMs) specifically adjusted for baseline imbalances in initial VCR. Results: In the unadjusted analysis, DMAB appeared to show a slower progression of compression compared to the control group. However, after adjusting for the initial VCR, no significant structural benefit was observed in any medication group (p > 0.05), with all groups showing small effect sizes (Cohen’s d < 0.4). In contrast, unstable fracture morphology was identified as the most potent driver of vertebral collapse (β = 2.758, 95% CI: 1.51–4.01, p < 0.001). Clinically, the RM group showed significantly lower overall pain levels throughout the follow-up period compared to the control group (p = 0.014). Conclusions: Short-term osteoporosis medication does not significantly mitigate vertebral collapse during the acute phase of OVCFs. Practically, these findings suggest that unstable fracture morphology and the baseline VCR—reflecting a potential ‘floor effect’ where less initially collapsed vertebrae may undergo more significant progression—are more informative predictors of acute collapse than medication choice. Consequently, early imaging-based risk stratification is crucial to identify patients at high risk for progressive deformity, regardless of their pharmacological regimen. Full article

Background/Objectives: AI models such as ChatGPT-4 have shown strong performance in dermatology; however, the diagnostic value of AI-generated clinical image descriptions remains underexplored. This study assesses whether ChatGPT-4’s image descriptions can support accurate dermatologic diagnosis and evaluates their potential integration into the Electronic Medical Record (EMR) system. Materials & Methods: In this Exploratory cross-sectional study, we analyzed images and descriptions from teledermatology consultations conducted between December 2023 and February 2024. ChatGPT-4 generated clinical descriptions for each image, which two senior dermatologists then used to formulate differential diagnoses. Diagnoses based on ChatGPT-4’s output were compared to those derived from the original clinical notes written by teledermatologists. Concordance was categorized as Top1 (exact match), Top3 (correct within top three), Partial, or No match. Results: The study included 154 image descriptions from 67 male and 87 female patients, aged 0 to 93 years. ChatGPT-4 descriptions averaged 74.3 ± 33.1 words, compared to 7.9 ± 3.0 words for teledermatologists. At least one of the two dermatologists achieved a Top 3 concordance rate of 82.5% using ChatGPT-4’s descriptions and 85.3% with teledermatologist descriptions. Conclusions: Preliminary findings highlight the potential integration of ChatGPT-4-generated descriptions into EMRs to enhance documentation. Although AI descriptions were longer, they did not enhance diagnostic accuracy, and expert validation remained essential. Full article

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

Genome-wide association studies (GWAS) are essential for identifying genomic regions associated with agronomic traits, but Linear Mixed Model (LMM)-based GWAS face challenges in capturing complex gene interactions. This study explores the potential of machine learning (ML) methodologies to enhance marker identification and association modeling in plant breeding. Unlike LMM-based GWAS, ML approaches do not require prior assumptions about marker–phenotype relationships, enabling the detection of epistatic effects and non-linear interactions. The research sought to assess and contrast approaches utilizing ML (Decision Tree—DT; Bagging—BA; Random Forest—RF; Boosting—BO; and Multivariate Adaptive Regression Splines—MARS) and LMM-based GWAS. A simulated F₂ population comprising 1000 individuals was analyzed using 4010 SNP markers and ten traits modeled with epistatic interactions. The simulation included quantitative trait loci (QTL) counts varying between 8 and 240, with heritability levels set at 0.5 and 0.8. These characteristics simulate traits of candidate crops that represent a diverse range of agronomic species, including major cereal crops (e.g., maize and wheat) as well as leguminous crops (e.g., soybean), such as yield, with moderate heritability and a high number of QTLs, and plant height, with high heritability and an average number of QTLs, among others. To validate the simulation findings, the methodologies were further applied to a real Coffea arabica population (n = 195) to identify genomic regions associated with yield, a complex polygenic trait. Results demonstrated a fundamental trade-off between sensitivity and precision. Specifically, for the most complex trait evaluated (240 QTLs under epistatic control), Ensemble methods (Bagging and Random Forest) maintained a Detection Power (DP) exceeding 90%, significantly outperforming state-of-the-art GWAS methods (FarmCPU), which dropped to approximately 30%, and traditional Linear Mixed Models, which failed to detect signals (0%). However, this sensitivity resulted in lower precision for ensembles. In contrast, MARS (Degree 1) and BLINK achieved exceptional Specificity (>99%) and Precision (>90%), effectively minimizing false positives. The real data analysis corroborated these trends: while standard GWAS models failed to detect significant associations, the ML framework successfully prioritized consensus genomic regions harboring functional candidates, such as SWEET sugar transporters and NAC transcription factors. In conclusion, ML Ensembles are recommended for broad exploratory screening to recover missing heritability, while MARS and BLINK are the most effective methods for precise candidate gene validation. Full article

Waste textiles may contain heavy metals, which can originate from dyes, mordants, or other chemical treatments used during manufacturing. To explore the impact of heavy metals on the adsorption properties of activated carbon derived from discarded textiles through pyrolysis and to mitigate heavy metal migration, this study investigated the adsorption behavior of copper-impregnated pyrolytic carbon toward typical pollutants—methylene blue and lead—in simulated dyeing wastewater. Aqueous copper nitrate was used to impregnate the waste pure cotton textiles (WPCTs) to introduce copper species as precursors for creating additional active sites. The study systematically examined adsorption mechanisms, single and binary adsorption systems, adsorption kinetics, adsorption isotherms, adsorption thermodynamics, and the influence of pH. Key findings and conclusions are as follows: Under optimal conditions, the copper-containing biochar (Cu-BC) demonstrated maximum adsorption capacities of 36.70 ± 1.54 mg/g for Pb(II) and 104.93 ± 8.71 mg/g for methylene blue. In a binary adsorption system, when the contaminant concentration reached 80 mg/L, the adsorption capacity of Cu-BC for Pb(II) was significantly enhanced, with the adsorption amount increasing by over 26%. However, when the Pb(II) concentration reached 40 mg/L, it inhibited the adsorption of contaminants, reducing the adsorption amount by 20%. SEM, XRD, Cu LMM, FTIR and XPS result analysis proves that the adsorption mechanism of methylene blue involves π–π interactions, hydrogen bonding, electrostatic interactions, and pore filling. For Pb(II) ions, the adsorption likely occurs via electrostatic interactions, complexation with functional groups, and pore filling. This study supplements the research content on the copper adsorption mechanism supported by biochar for heavy metal adsorption research and broadens the application scope of biochar in the field of heavy metal adsorption. Full article

Monitoring the volume and intensity of physical load is essential in elite women’s futsal to optimize performance and prevent injuries. However, external load indicators such as PlayerLoad may underestimate critical moments in competition where the intensity and volume of accelerations and decelerations sharply increase. This study aimed to identify and characterize such critical moments by analyzing the interaction between current score, playing position, match half, and location on acceleration and deceleration volume (distance, km/h) and intensity (peak, m/s²). Thirteen elite female futsal players (age: 29.9 ± 5.1 years; height: 164.96 ± 4.22 cm; body mass: 60.31 ± 4.56 kg) competing in the Spanish First Division were analyzed over a full season. All match accelerations and decelerations recorded with WIMU PRO™ inertial devices were processed using four Linear Mixed Models (LMMs). Significant interactions emerged across all models. Volume increased when winning, particularly among pivots, while intensity rose during adverse conditions, especially when losing at home. Interindividual variability was minimal (ICC < 1%). Physical load in women’s futsal follows two situational patterns: volume increases when leading, and intensity peaks when trailing. Identifying these critical moments provides insight beyond total load metrics, offering guidance for individualized and context-specific injury prevention. Full article

Background: This study evaluates the increase in gingival thickness following the administration of injectable atelocollagen. Materials and Methods: A retrospective analysis was conducted using the medical records of 60 patients with a thin gingival phenotype at baseline, treated between 2017 and 2025. All patients received a standardised protocol for soft tissue thickness modification using atelocollagen injections. Based on the continuation of maintenance therapy, patients were divided into Group A (n = 30), consisting of patients who received booster doses at six-month intervals following completion of the full treatment protocol, and Group B (n = 30), consisting of patients who did not continue maintenance therapy. The observation period for all patients was five years. Gingival thickness was assessed by periodontal probe transparency using a standard WHO probe (WHO 621) and the Hu-Friedy Colorvue Biotype Probe. Longitudinal changes were analysed using linear mixed-effects models (LMMs) for continuous outcomes and generalised linear mixed-effects models (GLMMs) with a binomial distribution and logit link for binary outcomes, accounting for repeated measurements at the patient level. Results: Significant effects of Group and Time, as well as their interaction, were observed for the proportion of sites with a thick gingiva (Group effect: F (1,93.14) = 57.94, p < 0.001; Group × Time interaction: p < 0.001). GLMM analysis confirmed a significant Group × Time interaction (χ² = 23.11, p < 0.001), indicating sustained gingival thickness improvement in Group A and a gradual decrease in effectiveness in Group B. Conclusions: Injectable atelocollagen represents a reliable, effective, and user-friendly method for long-term modification of gingival thickness, particularly when supported by maintenance therapy. Full article