Search Results (3,310)

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases. Full article

Impurities in polypropylene random copolymer (PPR) raw materials can seriously affect the performance of the final product, and efficient and accurate impurity detection is crucial to ensure high production quality. In order to solve the problems of high small-target miss rates, weak anti-interference ability, and difficulty in balancing accuracy and speed in existing detection methods used in complex industrial scenarios, this paper proposes an enhanced machine vision detection algorithm based on YOLOv11. Firstly, the FasterLDConv module dynamically adjusts the position of sampling points through linear deformable convolution (LDConv), which improves the feature extraction ability of small-scale targets on complex backgrounds while maintaining lightweight features. The IR-EMA attention mechanism is a novel approach that combines an efficient reverse residual architecture with multi-scale attention. This combination enables the model to jointly capture feature channel dependencies and spatial relationships, thereby enhancing its sensitivity to weak impurity features. Again, a DC-DyHead deformable dynamic detection head is constructed, and deformable convolutions are embedded into the spatial perceptual attention of DyHead to enhance its feature modelling ability for anomalies and occluded impurities. We introduce an enhanced InnerMPDIoU loss function to optimise the bounding box regression strategy. This new method addresses issues related to traditional CIoU losses, including excessive penalties imposed on small targets and a lack of sufficient gradient guidance in situations where there is almost no overlap. The results indicate that the average precision (mAP@0.5) of the improved algorithm on the self-made PPR impurity dataset reached 88.6%, which is 2.3% higher than that of the original YOLOv11n, while precision (P) and recall (R) increased by 2.4% and 2.8%, respectively. This study provides a reliable technical solution for the quality inspection of PPR raw materials and serves as a reference for algorithm optimisation in the field of industrial small-target detection. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

Chimeric antigen receptor T (CAR-T)-cell therapy has revolutionized the treatment of hematological malignancies, yet long-term efficacy remains constrained by antigen escape and T-cell dysfunction. Recent advances have rapidly elucidated the molecular underpinnings of antigen escape mechanisms and intrinsic T-cell dysfunction, revealing novel vulnerabilities in current CAR-T paradigms. In this review, we discuss the limitations of CAR-T-cell therapy in hematological malignancies, particularly regarding antigen escape mechanisms and T-cell dysfunction. It is noteworthy that in recent years, multi-targeted CAR-T and engineered CAR-T cells have demonstrated promising clinical efficacy in overcoming drug resistance and relapse in hematological malignancies. Here, we also discuss emerging approaches to enhance the efficacy of CAR-T-cell therapy, including advanced CAR-T-cell engineering techniques, the identification of novel therapeutic targets, and the development of multi-targeted CAR-T-cell strategies. Full article

Coumarin derivatives, whether natural or synthetic, have attracted considerable interest from medicinal chemists due to their versatile biological properties. Their appealing pharmacological activities—such as anticancer, anti-inflammatory, neuroprotective, anticoagulant, and antioxidant effects—combined with the ease of their synthesis and the ability to introduce chemical modifications at multiple positions have made them a widely explored class of compounds. In the scientific literature, there are many examples. On the other hand, dithiocarbamates, originally employed as pesticides and fungicides in agriculture, have recently emerged as potential therapeutic agents for the treatment of serious diseases such as cancer and microbial infections. Moreover, dithiocarbamates bearing diverse organic functionalities have demonstrated significant antifungal properties against resistant phytopathogenic fungi, presenting a promising approach to combat the growing global issue of fungal resistance. Dithiocarbamates linked to coumarin derivatives have been shown to exhibit cytotoxic activity against various human cancer cell lines, including MGC-803 (gastric), MCF-7 (breast), PC-3 (prostate), EC-109 (esophageal), H460 (non-small cell lung), HCCLM-7 (hepatocellular carcinoma), HeLa (cervical carcinoma), MDA-MB-435S (mammary adenocarcinoma), SW480 (colon carcinoma), and Hep-2 (laryngeal carcinoma). Numerous studies have revealed that the inclusion of a dithiocarbamate moiety can provide central nervous system (CNS) activity, particularly through inhibitory potency and selectivity toward acetylcholinesterase (AChE) and monoamine oxidases (MAO-A and MAO-B). Recently, it has been reported that coumarin–dithiocarbamate derivatives exhibit α-glucosidase inhibitory effects and also possess promising antimicrobial activity. This study presents an overview of recent progress in the chemistry of coumarin–dithiocarbamate derivatives, with a focus on their biological activity. Previous review papers focused on coumarin derivatives as multitarget compounds for neurodegenerative diseases and described various types of compounds, with dithiocarbamate derivatives representing only a small part of them. Our work deals exclusively with coumarin dithiocarbamates and their biological activity. Full article

Understanding household vehicle ownership dynamics in the post-COVID-19 era is critical for designing equitable, resilient, and sustainable transportation policies. This study employs an interpretable machine learning framework to model household vehicle ownership using data from the 2022 National Household Travel Survey (NHTS)—the first nationally representative U.S. dataset collected after the onset of the pandemic. A binary classification task distinguishes between single- and multi-vehicle households, applying an ensemble of algorithms, including Random Forest, XGBoost, Support Vector Machines (SVM), and Naïve Bayes. The Random Forest model achieved the highest predictive accuracy (86.9%). To address the interpretability limitations of conventional machine learning approaches, SHapley Additive exPlanations (SHAP) were applied to extract global feature importance and directionality. Results indicate that the number of drivers, household income, and vehicle age are the most influential predictors of multi-vehicle ownership, while contextual factors such as housing tenure, urbanicity, and household lifecycle stage also exert substantial influence highlighting the spatial and demographic heterogeneity in ownership behavior. Policy implications include the design of equity-sensitive strategies such as targeted mobility subsidies, vehicle scrappage incentives, and rural transit innovations. By integrating explainable artificial intelligence into national-scale transportation modeling, this research bridges the gap between predictive accuracy and interpretability, contributing to adaptive mobility strategies aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics data have shown promise in predicting pharmacological response, selecting the features with the highest predictive power critically affects model performance and biological interpretability. This study aims to compare computational and biologically informed gene selection strategies for predicting drug response in cancer cell lines and to propose a feature selection strategy that optimizes performance. Methods: Using gene expression and drug response data, we trained models on both data-driven and biologically informed gene sets based on the drug target pathways to predict IC₅₀ values for seven anticancer drugs. Several feature selection methods were tested on gene expression profiles of cancer cell lines, including Recursive Feature Elimination (RFE) with Support Vector Regression (SVR) against gene sets derived from drug-specific pathways in KEGG and CTD databases. The predictability was comparatively analyzed using both AUC and IC₅₀ values and further assessed on proteomics data. Results: RFE with SVR outperformed other computational methods, while pathway-based gene sets showed lower performance compared to data-driven methods. The integration of computational and biologically informed gene sets consistently improved prediction accuracy across several anticancer drugs, while the predictive value of the corresponding proteomic features was significantly lower compared with the mRNA profiles. Conclusions: Integrating biological knowledge into feature selection enhances both the accuracy and interpretability of drug response prediction models. Integrative approaches offer a more robust and generalizable framework with potential applications in biomarker discovery, drug repurposing, and personalized treatment strategies. Full article

Recent progress in tumor immunotherapy highlights the important role of the immune system in combating various cancers. Traditionally designed to protect against infectious diseases, vaccines are now being adapted to stimulate immune responses against tumor-specific neoantigens. Both preclinical studies and clinical trials have explored innovative approaches for identifying neoantigens and optimizing vaccine design, advancing the field of personalized oncology. Among these, DNA-based vaccines have become a particularly attractive approach for cancer immunotherapy. This evolution has been driven by improvements in molecular biology techniques, including more precise methods for detecting tumor-specific mutations, computational tools for predicting immunogenic antigens, and novel platforms for delivering nucleic acid vaccines. Personalized DNA vaccines are typically developed through a complex, multi-step process that involves sequencing a patient’s tumor, computational analysis to identify potential targets, and custom vaccine production. In this review, we examine the use of both shared tumor antigens and individualized neoantigens in cancer vaccine development. We outline strategies for neoantigen identification that provide insights into tumor-specific alterations. Furthermore, we highlight recent advances in DNA vaccine technologies, address the current limitations facing cancer vaccines, propose strategies to overcome these challenges, and consider key clinical and technical factors for successful implementation. Full article

Neurodevelopmental disorders (NDDs), including autism spectrum disorder, intellectual disability, and attention-deficit/hyperactivity disorder, are genetically and phenotypically heterogeneous conditions affecting millions worldwide. High-throughput omics technologies—transcriptomics, proteomics, metabolomics, and epigenomics—offer a unique opportunity to link genetic variation to molecular and cellular mechanisms underlying these disorders. However, the high dimensionality, sparsity, batch effects, and complex covariance structures of omics data present significant statistical challenges, requiring robust normalization, batch correction, imputation, dimensionality reduction, and multivariate modeling approaches. This review provides a comprehensive overview of statistical frameworks for analyzing high-dimensional omics datasets in NDDs, including univariate and multivariate models, penalized regression, sparse canonical correlation analysis, partial least squares, and integrative multi-omics methods such as DIABLO, similarity network fusion, and MOFA. We illustrate how these approaches have revealed convergent molecular signatures—synaptic, mitochondrial, and immune dysregulation—across transcriptomic, proteomic, and metabolomic layers in human cohorts and experimental models. Finally, we discuss emerging strategies, including single-cell and spatially resolved omics, machine learning-driven integration, and longitudinal multi-modal analyses, highlighting their potential to translate complex molecular patterns into mechanistic insights, biomarkers, and therapeutic targets. Integrative multi-omics analyses, grounded in rigorous statistical methodology, are poised to advance mechanistic understanding and precision medicine in NDDs. Full article

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article

In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article

Next-generation cancer immunotherapy increasingly combines tumor-targeting antibodies or antibody–drug conjugates (ADCs) with immune effector cells to enhance therapeutic precision. However, many existing approaches rely on genetic modification or complex manufacturing, limiting their clinical scalability and rapid deployment. To address this issue, we developed an antibody capture protein (ACP)-based surface engineering platform that enables the rapid, reversible, and non-genetic functionalization of NK cells with therapeutic antibodies or ADCs. This approach uses a DMPE-PEG-lipid conjugate to anchor thiolated protein A (ACP) to the NK cell membrane via hydrophobic insertion, thereby stably and selectively binding to the Fc region of IgG molecules. Using this strategy, we developed ACP-modified NK cells (AC-NKs) that can selectively capture therapeutic antibodies (trastuzumab (TZ), trastuzumab-emtansine (T-DM1), and sacituzumab (SZ)) pre-bound to each target antigen on tumor cells and induce antigen-specific cytotoxic responses. The resulting AC-NKs exhibited enhanced tumor recognition and cytotoxicity against HER2-positive and Trop-2-positive cancer cells in vitro. Compared with conventional combination therapies, AC-NKs enhanced immune activation, as demonstrated by effective delivery of cytotoxic agents, enhanced cancer cell engagement, and upregulation of CD107a expression. Notably, the system supports multiple antigen targeting and tunable antibody loading, enabling adaptation to tumor heterogeneity and resistant phenotypes. This platform might also provide a simple, scalable, and safe method for rapidly developing programmable immune cell therapies without genetic modification. Its versatility supports multi-antigen targeting and broad applicability across NK and T cell therapies, offering a promising path toward personalized, off-the-shelf chemoimmunotherapy. Full article

Smartphone addiction has emerged as a significant public health concern, particularly among adolescents and young adults. This narrative review, conducted in line with the ANDJ checklist, synthesizes evidence from 25 systematic reviews and meta-analyses, complemented by randomized controlled trials and clinical studies, to provide a structured overview of the field. The study selection flow and publication trends reveal a rapidly expanding research landscape, with most evidence produced in the last decade, reflecting both the ubiquity of smartphones and increasing awareness of their health impacts. The synthesis highlights converging findings across reviews: excessive smartphone use is consistently associated with psychosocial, behavioral, and academic challenges, alongside sleep disturbances and mental health symptoms. Common messages include the recognition of smartphone addiction as a multidimensional phenomenon, while emerging themes point to heterogeneity in definitions, tools, and methodological approaches. Comparative analysis of reviews underscores both shared risk factors—such as emotional dysregulation and social isolation—and differences in study designs and target populations. Importantly, this review identifies critical gaps, including the lack of standardized definitions, limited longitudinal evidence, and scarce cross-cultural validation. At the same time, promising opportunities are noted, from lifestyle-based interventions (e.g., physical activity) to educational and policy-level strategies fostering digital literacy and self-regulation. The post-pandemic context further emphasizes the need for sustained monitoring and adaptive responses. Overall, this review calls for youth-centered, multi-sector interventions aligned with WHO recommendations, supporting coordinated, evidence-based action across health, education, and policy domains. Full article

This paper investigates the leader–follower consensus problem for T-S fuzzy multi-agent systems with positive constraints by designing observer-based control protocols, where the T-S fuzzy model is mainly used to characterize the nonlinearity in the system. First, a stable system is chosen as the leader. Then, a fuzzy observer that satisfies the positivity condition is constructed for follower agents. Meanwhile, an observer-based fuzzy controller design is proposed using a matrix decomposition approach. On this basis, the positivity and asymptotic consensus of the system are achieved by a set of sufficient conditions in the form of linear programming. Subsequently, an unstable system is chosen as the leader. A virtual target is introduced. By means of the co-positive Lyapunov function and linear programming approach, an observer and controller are designed to ensure both positivity and practical consensus of systems. Compared to existing literature, the consideration of positivity constraints and the linear programming-based observation-control scheme expand the application scope of multi-agent systems while reducing the computational burden. Finally, two illustrative examples are provided to verify the effectiveness of the obtained results. Full article