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Impact of health education on knowledge and perception of cervical cancer and screening among rural women in Bangladesh
Crocetin curbs radiation induced intestinal injury by blocking KAT2A/NLRP3 succinylation
Assessment of Chinese local government disaster resilience based on the D-FAHP method
Modeling freshwater yield: deep learning applications in seawater greenhouses in Iran
The relationship between subthreshold anxiety disorder and symptoms and signs of dry eye disease
Machine learning improves detection of alpha thalassemia carriers compared to clinical features
Abstract Alpha-thalassemia is a widespread genetic disorder, and accurately distinguishing between alpha-plus (α⁺) and alpha-zero (α⁰) types is critical for effective screening and management. This study developed and evaluated machine learning models to classify α⁺ and α⁰ carriers based on hematological parameters. A dataset of 956 cases was analyzed, including variables such as red blood cell (RBC) count, hemoglobin (Hb) level, and RBC indices. Feature selection identified the most predictive markers, and five machine learning models were trained and compared. The stacking ensemble model demonstrated the best performance, achieving 94% accuracy and a high F1-score. Key predictors included RBC count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and mean corpuscular hemoglobin concentration (MCHC). Correlation analysis revealed strong interrelationships among RBC indices, while platelet (PLT) and white blood cell (WBC) parameters had moderate associations. These findings suggest that machine learning, particularly ensemble methods, can enhance the detection of alpha-thalassemia carriers. The development of models based on both data-driven and clinical features provides a flexible framework for screening and could support more personalized approaches in future research.
An outbreak of food poisoning likely caused by Bacillus cereus at a secondary school in Mukono District, Uganda, July 2023
A novel method for selective adsorption of toxic H₂S gas using core–shell hybrid Nano adsorbent MIL-101(Cr)@MIPs@H₂S
One-pot synthesized g-C3N4/Fe3O4/CuO magnetic photocatalyst for one-pot synthesis of 2-amino-4H-benzochromenes and methylene blue photodegradation
Cross domain fault diagnosis in internal combustion engines using multisensor data with transfer federated and transformer based federated transfer learning
Osthol ameliorates obesity-associated lipid metabolic disorders by inhibiting ADRA1D-dependent Th17 cell differentiation
LLMs augmented hierarchical reinforcement learning with action primitives for long-horizon manipulation tasks
Abstract Deep reinforcement learning methods have shown promising results in learning specific tasks, but struggle to cope with the challenges of long horizon manipulation tasks. As task complexity increases, the large state space and sparse reward make it difficult to collect effective samples through random exploration. Hierarchical reinforcement learning decomposes complex tasks into subtasks, which can reduce the difficulty of skill learning, but still suffers from limitations such as inefficient training and poor transferability. Recently, large language models (LLMs) have demonstrated the ability to encode vast amounts of knowledge about the world and to excel in context-based learning and reasoning tasks. However, applying LLMs to real-world tasks remains challenging due to their lack of grounding in specific task contexts. In this paper, we leverage the planning capabilities of LLMs alongside reinforcement learning (RL) to facilitate learning from the environment. The proposed approach yields a hierarchical agent that combines LLMs with parameterized action primitives (LARAP) to address long-horizon manipulation tasks. Rather than relying solely on LLMs, the agent uses them to guide a high-level policy, improving sample efficiency during training. Experimental results show that LARAP significantly outperforms baseline methods across various simulated manipulation tasks. The source code is available at: https://github.com/ningzhang-buaa/LARAP-code.
Enhancing disease clustering through symptom-based analysis and large language model interpretations
Engineering NiO/g-C₃N₄ and NiO/rGO composites for dual applications in electrochemical water splitting and energy storage
Abstract The development of multifunctional electrode materials that can simultaneously serve as efficient electrocatalysts and high-performance energy storage devices is highly desirable for next‐generation energy conversion technologies. In this work, we report a facile hydrothermal strategy followed by annealing at 400 °C, which enables the in-situ reduction of GO to rGO without external reducing agents, leading to robust NiO/rGO and NiO/g-C₃N₄ nanocomposites. This approach ensures intimate interfacial contact between NiO and the conductive carbon matrix, thereby enhancing charge transfer and catalytic activity. The composites were systematically characterized using XRD, BET, Raman, FESEM–EDX, XPS, and TEM analyses to confirm structural and surface features. Electrochemical evaluation revealed that NiO/rGO and NiO/g-C₃N₄ electrodes delivered low overpotentials of 126 and 73 mV at 10 mA cm⁻² for HER in 1 M KOH, with NiO/g-C₃N₄ exhibiting long-term stability over 23 h. As supercapacitor electrodes, NiO/rGO and NiO/g-C₃N₄ achieved remarkable specific capacitances of 597 and 366 F g⁻¹ at 1 A g⁻¹, respectively, surpassing many previously reported NiO–carbon systems. These results demonstrate that the unique synthesis route and synergistic coupling of NiO with conductive carbon frameworks enable a substantial advancement in multifunctional electrode design, offering a promising pathway for integrated energy conversion and storage systems.
Enhanced isolation unique fractal diminutive MIMO antenna with identical elliptical notch and cross stub for wireless uses
Phage host interactions reveal LPS and OmpA as receptors for two Erwinia amylovora phages
Abstract Erwinia amylovora is the causative agent of fire blight. Resistance to streptomycin, the main antibiotic in fire blight management, has led to an urgent requirement to develop alternative biological control agents, such as the phage-carrier system (PCS). Previous studies have focused on the dynamic interactions between the carrier ( Pantoea agglomerans ), lytic phages, and the pathogen. However, crucial information about phage receptors on these hosts is still lacking. Here, a biochemical approach was used and the phage receptors of two E. amylovora phages (ϕEa21-4 and ϕEa46-1-A1) on both hosts, have been identified as LPS and OmpA on E. amylovora and OmpA only on P. agglomerans. Interestingly, this work uncovered for the first time that amylovoran is tightly attached to the LPS of E. amylovora . Confirmation of this interaction and an infection model are presented that have far reaching implications for additional PCS improvement and pathogen-host interaction details.
Phytochemical screening and evaluating in vitro antibacterial and antifungal activity of 80% methanolic Impatiens rothii root extract
Nanoindentation under retrocorneal pressure to determine the biomechanical response of the human cornea to accelerated UVA crosslinking and riboflavin osmolarity ex vivo
Abstract Riboflavin-mediated UVA corneal crosslinking (CXL) is an established treatment to halt the progression of keratoconus. However, the biomechanical effects of accelerated protocols and varying riboflavin osmolarities under physiological conditions remain poorly understood. Traditional biomechanical assessments using nanoindentation typically analyze tissue flatmounts, disregarding the natural tissue strain exerted by intraocular pressure. We developed a novel experimental setup combining nanoindentation with adjustable retrocorneal fluid pressure (RCP) to simulate physiological conditions during measurements. Using this approach, we first examined five corneas under incrementally increasing RCP levels to quantify the effect of tissue pre-tension on biomechanical properties. In a subsequent series, we investigated 70 human corneas to evaluate seven different protocols, including accelerated treatment protocols and different riboflavin osmolarities, by analyzing the Hertz-elastic modulus (E HZ ) and creep behavior (C IT ). Our results demonstrate that all CXL protocols significantly enhanced corneal stiffness and reduced tissue creep. Compared to the standard Dresden protocol, the biomechanical effect was enhanced with hyperosmolar riboflavin but diminished when high radiation intensity was used for shorter duration. Furthermore, increasing RCP led to non-linear changes in corneal biomechanics, manifesting as increased E HZ and decreased C IT . These findings emphasize the importance of considering physiological tissue pre-tension in biomechanical assessments and provide new insights for optimizing CXL treatment protocols.