Search Results (68)

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

The accurate prediction of motor progression in Parkinson’s disease (PD) remains a major clinical challenge that limits personalized treatment planning and efficient clinical trial design. In this study, we developed and validated a machine-learning framework integrating a targeted panel of plasma proteins measured by Olink proximity extension assays with clinical variables to stratify patients according to their progression risk. We analyzed baseline plasma samples from 211 early-stage PD patients enrolled in the Parkinson’s Progression Markers Initiative (PPMI) cohort using four targeted Olink panels, from which 28 circulating proteins were retained after quality-control filtering. Patients were classified as rapid or slow progressors based on their annualized change in MDS-UPDRS Part III scores. Among the algorithms tested, Random Forest achieved the highest discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.751 (95% CI: 0.684–0.811), which exceeded that of clinical predictors alone (AUC 0.666). The integration of targeted proteomic and clinical features further improved model performance (AUC 0.773; p = 0.009). Nested cross-validation confirmed minimal optimistic bias (AUC 0.743). To enhance clinical interpretability, we applied SHapley Additive exPlanations (SHAP) analysis, which identified interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor A (VEGF-A) as the most influential predictors. SHAP feature rankings were highly stable across cross-validation folds (mean Spearman ρ = 0.91). The robustness of these findings was confirmed through sensitivity analyses using extreme quartile comparisons (AUC 0.823), treatment-naïve subgroup analysis (AUC 0.738), and a clinically anchored outcome definition based on the minimal clinically important difference (AUC 0.739). A decision curve analysis demonstrated a net clinical benefit across threshold probabilities of 0.25–0.70. Our results establish targeted plasma protein profiling combined with interpretable machine learning as a promising tool for PD motor progression risk stratification, with potential applications in individualized patient counseling regarding motor prognosis and the selection of candidates for disease-modifying trials. Full article

The grain aphid (Sitobion avenae) is a major pest of winter wheat, causing significant yield losses through direct feeding and as a vector of barley yellow dwarf virus (BYDV). Populations can increase rapidly under moderate temperatures and low rainfall, potentially leading to severe infestations if not effectively monitored and managed. This study develops and validates a UAV-based RGB imaging methodology, which relies on deep learning for accurate detection and assessment of Sitobion avenae in wheat crops. The RGB images are preliminarily filtered using “histogram equalization”, which allows for highlighting the infested areas. An experimental study was conducted under the specific climatic conditions of Southern Dobruja, Bulgaria, to quantify Sitobion avenae infestations. Three neural network architectures were used (DeepLabv3, U-Net, and PSPNet) in combination with three backbone models: ResNet34, ResNet50, and ResNet101. The optimal combination was determined to be the U-Net + ResNet101 model, which achieved an average F1 score of 0.982 and a Cohen’s Kappa coefficient of 0.966. The results demonstrate that UAV-based detection allows precise mapping of infested areas, enabling targeted insecticide applications and effective pest management while substantially reducing chemical inputs. These findings indicate that the proposed framework provides a reliable and scalable tool for precision pest monitoring and control in winter wheat. Full article

Landfills represent areas of pronounced anthropogenic disturbance, with substantial impacts on local vegetation. The composition and structure of plant communities serve as indicators of eco-system alteration and may function as reservoirs of species with potential utility in ecological restoration. This study provides the first detailed assessment of vegetation structure on a closed MSW landfill in Kokshetau (Akmola Region, northern Kazakhstan; semi-arid steppe/forest-steppe setting) and demonstrates an integrative, restoration-oriented monitoring and target-setting workflow, including a localized phytoremediation screening framework integrating field performance, ecological indicator values, and literature-based functional traits, with a risk/governance filter. A total of 76 vascular plant species were recorded during the field survey, predominantly comprising annual herbaceous taxa adapted to highly disturbed environments. The families Asteraceae and Poaceae were the most species-rich, while Chenopodiaceae and Brassicaceae were also notably represented. Meadow-steppe species constituted the majority (45.5%) of the phytosociological spectrum. Multivariate ecological and statistical analyses revealed that community composition was primarily influenced by the degree of disturbance (p = 0.016), rather than soil pH, with Cannabis sativa and Bassia scoparia emerging as key indicators of less disturbed sectors, contrasting with actively disturbed dumping areas. Consequently, restoration efforts should prioritize mesophytic species adapted to open, sunlit habitats and capable of establishing on slightly alkaline soils, while accounting for site-specific constraints to support long-term vegetation recovery. Notably, Artemisia absinthium and Bassia scoparia were identified as candidate taxa for phytoremediation-oriented restoration, based on their in situ ecological performance and literature-reported traits, albeit with limitations due to allergenic pollen and invasive tendencies, respectively. These findings support phytoremediation strategy design on disturbed landscapes by emphasizing regionally adapted species selection that balances ecological suitability with potential ecological risks. Full article

Background: Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Mitochondria-associated endoplasmic reticulum membranes (MAMs) have recently emerged as critical mediators in cardiovascular pathophysiology; however, their specific contributions to CHD pathogenesis remain largely unexplored. Objective: This study aimed to identify and validate MAM-related biomarkers in CHD through integrated analysis of transcriptomic sequencing data and Mendelian randomization, and to elucidate their underlying mechanisms. Methods: We analyzed two gene expression microarray datasets (GSE113079 and GSE42148) and one genome-wide association study (GWAS) dataset (ukb-d-I9_CHD) to identify differentially expressed genes (DEGs) associated with CHD. MAM-related DEGs were filtered using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis, Mendelian randomization, and machine learning algorithms were employed to identify biomarkers with direct causal relationships to CHD. A diagnostic model was constructed to evaluate the clinical utility of the identified biomarkers. Additionally, we validated the two hub genes in peripheral blood samples from CHD patients and normal controls, as well as in aortic tissue samples from a low-density lipoprotein receptor-deficient (LDLR−/−) atherosclerosis mouse model. Results: We identified 4174 DEGs, from which 3326 MAM-related DEGs (DE-MRGs) were further filtered. Mendelian randomization analysis coupled with machine learning identified two biomarkers, DHX36 and GPR68, demonstrating direct causal relationships with CHD. These biomarkers exhibited excellent diagnostic performance with areas under the receiver operating characteristic (ROC) curve exceeding 0.9. A molecular interaction network was constructed to reveal the biological pathways and molecular mechanisms involving these biomarkers. Furthermore, validation using peripheral blood from CHD patients and aortic tissues from the Ldlr−/− atherosclerosis mouse model corroborated these findings. Conclusions: This study provides evidence supporting a mechanistic link between MAM dysfunction and CHD pathogenesis, identifying candidate biomarkers that have the potential to serve as diagnostic tools and therapeutic targets for CHD. While the validated biomarkers offer valuable insights into the molecular pathways underlying disease development, additional studies are needed to confirm their clinical relevance and therapeutic potential in larger, independent cohorts. Full article

Distributed acoustic sensing (DAS) systems enable real-time monitoring of physical events across extended areas using optical fiber that detects vibrations through changes in backscattered light patterns. In perimeter security applications, these systems must accurately distinguish between legitimate activities and potential security threats by analyzing complex spatio-temporal data patterns. However, the high dimensionality and noise content of raw DAS data presents significant challenges for effective feature extraction and event classification, particularly when computational efficiency is required for real-time deployment. Traditional approaches or current machine learning methods often struggle with the balance between information preservation and computational complexity. This study addresses the critical need for efficient and accurate feature extraction methods that can identify informative signal components while maintaining real-time processing capabilities in DAS-based security systems. Here we show that wavelet packet decomposition (WPD) combined with a cascaded machine learning approach achieves 98% classification accuracy while reducing computational load through intelligent channel selection and preliminary filtering. Our modified peak signal-to-noise ratio metric successfully identifies the most informative frequency bands, which we validate through comprehensive neural network experiments across all possible WPD channels. The integration of principal component analysis with logistic regression as a preprocessing filter eliminates a substantial portion of non-target events while maintaining high recall level, significantly improving upon methods that processed all available data. These findings establish WPD as a powerful preprocessing technique for distributed sensing applications, with immediate applications in critical infrastructure protection. The demonstrated gains in computational efficiency and accuracy improvements suggest broad applicability to other pattern recognition challenges in large-scale sensor networks, seismic monitoring, and structural health monitoring systems, where real-time processing of high-dimensional acoustic data is essential. Full article

Background/Objectives: The advent of biologics targeting key inflammatory pathways has significantly advanced psoriasis treatment. Among them, the Interleukin-23 inhibitors Guselkumab and Risankizumab have demonstrated high efficacy and rapid clinical response in both clinical trials and real-world studies. However, up to 30% of patients fail to respond. This study aimed to identify pharmacogenetic markers associated with treatment response using a genome-wide association study (GWAS) and protein network-based approach. Methods: Fifty-three patients of Greek origin with moderate-to-severe plaque psoriasis were treated with Guselkumab or Risankizumab. Based on Psoriasis Area and Severity Index (PASI) improvement at 3 and 6 months, patients were categorized as responders or non-responders. Approximately 730,000 single-nucleotide polymorphisms (SNPs) were genotyped. After filtering, a GWAS was performed to identify variants associated with treatment response. Additionally, protein–protein interaction (PPI) network analysis was applied to the two Interleukin-23 subunits and SNPs within or near genes encoding Interleukin-23-interacting proteins to test for their association. Results: The GWAS identified two novel variants, rs73641950 and rs6627462, significantly associated with treatment response, with both surpassing the genome-wide significance threshold after Bonferroni correction. The PPI-based approach revealed rs13086445, located downstream of the Interleukin-12 subunit alpha (IL12A) gene, as another associated variant. All three SNPs lie in genomic regions with potential regulatory roles. Conclusions: This study identifies three novel genetic variants associated with response to Interleukin-23 inhibitors in psoriasis. These findings provide promising pharmacogenetic markers which, upon validation in larger, independent cohorts, will enable the translation of a patient’s genotype into a response phenotype, thereby guiding clinical decisions and improving drug effectiveness. Full article

The long-term safety and operational reliability of zoned earth dams depend on the structural integrity of their internal components, including core, filters, and shell zones. This is particularly relevant for old dams which have been operational for a long period of time. Such existing infrastructure systems are exposed to various loading types over time, including environmental, seepage-related, extreme event, and climate change effects. As a result, even when they look intact externally, changes might affect their internal structure, composition, and possibly functionality. Thus, it is important to delineate a comprehensive and cost-effective strategy to identify potential issues and derive the health status of existing earth dams. This paper outlines a systematic approach for conducting a comprehensive health check of these structures through the implementation of a multi-epoch geotechnical approach based on a variety of standard measured and monitored quantities. The goal is to compare current properties with baseline data obtained during pre-, during-, and post-construction site investigation and laboratory tests. Guidance is provided on how to judge such multi-epoch comparisons, identifying potential outcomes and scenarios. The proposed approach is tested on a well-documented case study in Southern Italy, an area prone to climate change and subjected to very high seismic hazard. The case study demonstrates how the integration of historical and contemporary geotechnical data allows for the identification of critical zones requiring attention, the validation of numerical models, and the proactive formulation of targeted maintenance and rehabilitation strategies. This comprehensive, multi-epoch-based approach provides a robust and reliable assessment of dams’ health, enabling better-informed decision-making workflows and processes for asset management and risk mitigation strategies. Full article

Background: Histone deacetylase 6 (HDAC6) is a unique class IIb HDAC isozyme characterized by two catalytic domains and a zinc finger ubiquitin-binding domain. It plays critical roles in various cellular processes, including protein degradation, autophagy, immune regulation, and cytoskeletal dynamics. Due to its multifunctional nature and overexpression in several cancer types, HDAC6 has emerged as a promising therapeutic target. Methods: In this study, we employed a ligand-based pharmacophore modeling approach using a structurally diverse set of known HDAC6 inhibitors. This was followed by the virtual screening of over 140,000 commercially available compounds from both the MolPort and Asinex databases. The screening workflow incorporated pharmacophore filtering, molecular docking, and molecular dynamic (MD) simulations. Binding free energies were estimated using Molecular Mechanics Generalized Born Surface Area (MM-GBSA) analysis to prioritize top candidates. A fluorometric enzymatic assay was used to measure HDAC6 activity, while cell viability assay by Cell Titer Glo was used to assess the anti-tumor activity against drug-sensitive and -resistant multiple myeloma (MM) cells. Western blotting was used to evaluate the acetylation of tubulin or histone H4 after treatment with selected compounds. Results: Three promising compounds were identified based on stable binding conformations and favorable interactions within the HDAC6 catalytic pocket. Among them, Molecular Mechanics Generalized Born Surface Area (MM-GBSA) analysis identified Compound 10 (AKOS030273637) as the top theoretical binder, with a ΔG_bind value of −45.41 kcal/mol. In vitro enzymatic assays confirmed its binding to the HDAC6 catalytic domain and inhibitory activity. Functional studies on MM cell lines, including drug-resistant variants, showed that Compound 10 reduced cell viability. Increased acetylation of α-tubulin, a substrate of HDAC6, likely suggested on-target mechanism of action. Conclusions: Compound 10, featuring a benzyl 4-[4-(hydroxyamino)-4-oxobutylidene] piperidine-1-carboxylate scaffold, demonstrates potential drug-like properties and a predicted bidentate zinc ion coordination, supporting its potential as an HDAC6 inhibitor for further development in hematologic malignancies. Full article

Numerous studies on specific cannabis compounds (cannabinoids and phenolic acids) have demonstrated their therapeutic potential, with their administration methods remaining a key research focus. Transdermal drug delivery (TDD) systems are gaining attention due to their advantages, such as painless administration, controlled release, direct absorption into the bloodstream, and its ability to bypass hepatic metabolism. The thin films obtained via pulsed laser deposition consist of micro- and nanoparticles capable of migrating through skin pores upon contact. This study investigates the interaction of phenolic compounds in hemp seeds with pulsed laser beams. The main goal is to achieve the ablation and deposition of these compounds as thin films suitable for TDD applications. The other key objective is optimizing laser energy to enhance the industrial feasibility of this method. Thin layers were deposited on glass and hemp fabric using dual pulsed laser (DPL) ablation on a compressed hemp seed target held in a stainless steel ring. The target was irradiated for 30 min with two synchronized pulsed laser beams, each with parameters of 30 mJ, 532 nm, pulse width of 10 ns, and a repetition rate of 10 Hz. Each beam had an angle of incidence with the target surface of 45°, and the angle between the two beams was also 45°. To improve laser absorption, two approaches were used: (1) HS-DPL/glass and HS-DPL/hemp fabric, in which a portion of the stainless steel ring was included in the irradiated area, and (2) HST-DPL/glass and HST-DPL/hemp fabric—hemp seeds were mixed with turmeric powder, which is known to improve laser interaction and biocompatibility. The FTIR and Micro-FTIR spectroscopy (ATR) performed on thin films compared to the target material confirmed the presence of hemp-derived phenolic compounds, including tetrahydrocannabinol (THC), cannabidiol (CBD), ferulic acid, and coumaric acid, along with other functional groups such as amides. The ATR spectra have been validated against Gaussian 6 numerical simulations. Scanning electron microscopy (SEM) and substance transfer tests revealed the microgranular structure of thin films. Through the analyzes carried out, the following were highlighted: spherical structures (0.3–2 μm) for HS-DPL/glass, HS-DPL/hemp fabric, HST-DPL/glass, and HST-DPL/hemp fabric; larger spherical structures (8–13 μm) for HS-DPL/glass and HST-DPL/glass; angular, amorphous-like structures (~3.5 μm) for HS-DPL/glass; and crystalline-like structures (0.6–1.3 μm) for HST-DPL/glass. Microparticle transfer from thin films on the hemp fabric to the filter paper at a human body temperature (37 °C) confirmed their suitability for TDD applications, aligning with the “whole plant medicine” or “entourage effect” concept. Granular, composite, thin films were successfully developed, capable of releasing microparticles upon contact with a surface whose temperature is 37 °C, specific to the human body. Each of the microparticles in the thin films obtained with the DPL technique contains phenolic compounds (cannabinoids and phenolic acids) comparable to those in hemp seeds, effectively acting as “microseeds.” The obtained films are viable for TDD applications, while the DPL technique ensures industrial scalability due to its low laser energy requirements. Full article

The growth of urban areas and climate change affect the performance of water management, increasing the rate of flooding and decreasing the quality of available water. To address this issue, the sustainable urban drainage systems (SUDs) and conventional urban drainage systems (UDIs) must be promoted. In both systems, grey infrastructure plays an important role, in the form of reinforced concrete tanks, filters, and water treatment plants. Nowadays, the use of reinforced concrete is a major contributor of the environmental impact of human activities environmental impacts. This study aims to assess the potential of nanoparticle-based concrete to mitigate the environmental impacts of water management facilities. To achieve this target, a comparative Life Cycle Assessment (LCA) analysis was performed on a multi walled carbon nanotubes (MWCNTs) based concrete, and a conventional one. To evaluate the corresponding benefits, a Functional Unit has been defined representing a frequently used element in water management facilities. The conducted review found no similar research. It is noted that the functional units used in published studies on nanoproducts are usually defined for the production of mass units. This study, found that using MWCNT-based concrete reduced the weight of the steel reinforcement by 47%. This reduction in steel outweighs the environmental impacts corresponding to used MWCNTs. The impact scores obtained are significantly lower for the MWCNT-based concrete. Therefore, the use of this material is recommended in Water management facilities, only on an environmental basis. Further investigation is recommended into the economic viability of this use. Full article

Due to the sparse distribution of ship targets in wide-area offshore scenarios, the typical cascade mode of imaging and detection for space-borne Synthetic Aperture Radar (SAR) echo data would consume substantial computational time and resources, severely affecting the timeliness of ship target information acquisition tasks. Therefore, we propose a ship target integrated imaging and detection framework (ST-IIDF) for SAR oceanic region data. A two-step filtering structure is added in the SAR imaging process to extract the potential areas of ship targets, which can accelerate the whole process. First, an improved peak-valley detection method based on one-dimensional scattering characteristics is used to locate the range gate units for ship targets. Second, a dynamic quantization method is applied to the imaged range gate units to further determine the azimuth region. Finally, a lightweight YOLO neural network is used to eliminate false alarm areas and obtain accurate positions of the ship targets. Through experiments on Hisea-1 and Pujiang-2 data, within sparse target scenes, the framework maintains over 90% accuracy in ship target detection, with an average processing speed increase of 35.95 times. The framework can be applied to ship target detection tasks with high timeliness requirements and provides an effective solution for real-time onboard processing. Full article

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article

Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. Full article