Overweight and Obesity Among People Living With HIV on Dolutegravir- and Efavirenz-Based Therapies: A Comparative Cross-Sectional Study.

Overweight and obesity have arisen as major public health challenges, affecting not just the general population but also people living with human immunodeficiency virus (HIV) (PLWH). Obesity and being overweight are both risk factors for heart disease and other related complications. However, little is known in our setting.

Unpacking conservation easements' assessed land use designations and their implications for realizing biodiversity protection.

Native ecosystem and biodiversity loss from land use conversion into human-modified landscapes are evident in the United States and globally. In addition to public land conservation, there is an increase in private land conservation through conservation easements (CEs) across exurban landscapes. Not every CE was established strictly for biodiversity protection and permitted land uses can increase human modification.

Targeting AURKA with multifunctional nanoparticles in CRPC therapy.

Castration-resistant prostate cancer (CRPC) presents significant therapeutic challenges due to its aggressive nature and poor prognosis. Targeting Aurora-A kinase (AURKA) has shown promise in cancer treatment. This study investigates the efficacy of ART-T cell membrane-encapsulated AMS@AD (CM-AMS@AD) nanoparticles (NPs) in a photothermal-chemotherapy-immunotherapy combination for CRPC.

DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction.

Predicting protein-ligand binding affinity is essential for understanding protein-ligand interactions and advancing drug discovery. Recent research has demonstrated the advantages of sequence-based models and graph-based models. In this study, we present a novel hybrid multimodal approach, DeepTGIN, which integrates transformers and graph isomorphism networks to predict protein-ligand binding affinity.

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach.

The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a heuristic trade-off between the two.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

An African vulture optimization algorithm based energy efficient clustering scheme in wireless sensor networks.

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance.

The effect of biosynthesized zinc oxide nanoparticles on gene expression and apoptosis in triple-negative breast cancer cells.

Objective(s): Some forms of breast cancer such as triple-negative phenotype, are serious challenge because of high metastatic cases, high mortality and resistance to conventional therapy motivated the search for alternative treatment approaches. Nanomaterials are promising candidates and suitable alternatives for improving tumor and cancer cell treatments.

Materials And Methods: Biosynthesis of ZnO NPs by help of Berberis integerrima fruit extract, has been done.

Onset and predictors of first-line antiretroviral therapy treatment failure among children in Ethiopia: a systematic review and meta-analysis.

Introduction: The emergence of First-line Antiretroviral Therapy (ART) regimens fails; it necessitates the use of more costly and less tolerable second-line medications. Therefore, it is crucial to identify and address factors that increase the likelihood of first-line ART regimen failure in children. Although numerous primary studies have examined the incidence of first-line ART failure among HIV-infected children in Ethiopia, national-level data on the onset and predictors remain inconsistent.

Annotating protein functions via fusing multiple biological modalities.

Understanding the function of proteins is of great significance for revealing disease pathogenesis and discovering new targets. Benefiting from the explosive growth of the protein universal, deep learning has been applied to accelerate the protein annotation cycle from different biological modalities. However, most existing deep learning-based methods not only fail to effectively fuse different biological modalities, resulting in low-quality protein representations, but also suffer from the convergence of suboptimal solution caused by sparse label representations.

Cultural heritage site selection characteristics and the impact of the natural environment in Jinan city, China.

This study aims to explore the spatial distribution and site selection characteristics of cultural heritage sites, as well as the impact of the natural environment on the site's location. A total of 448 cultural heritage sites in Jinan City (Shandong province), which have been listed as key cultural relic protection units from before the Qin Dynasty to after the Qing Dynasty (ca. 7500 BCE-present), were analyzed using spatial analysis tools in ArcGIS 10.

Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors.

Human activity recognition (HAR) is one of the most important segments of technology advancement in applications of smart devices, healthcare systems & fitness. HAR uses details from wearable sensors that capture the way human beings move or engage with their surrounding. Several researchers have thus presented different ways of modeling human motion, and some have been as follows: Many researchers have presented different methods of modeling human movements.

Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique.

In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG's and improve the robustness of the system under varying load and generation conditions.

LED light improves shoot multiplication, steviol glycosides and phenolic compounds biosynthesis in Stevia rebaudiana Bertoni in vitro culture.

Light-emitting diode (LED) lamps are efficient elicitors of secondary metabolites. To investigate the influence of LED light on steviol glycosides (SGs) and phenolic compounds biosynthesis, stevia shoots were cultured under the following LED lights: white-WL, blue-B, red-R, 70% red and 30% blue-RB, 50% UV, 35% red and 15% blue-RBUV, 50% green, 35% red and 15% blue-RBG, 50% yellow, 35% red and 15% blue-RBY, 50% far-red, 35% red and 15% blue-RBFR and white fluorescent light (WFl, control). RBG light stimulated shoots' biomass production.

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

Fiscal decentralization, local government behavior and environmental pollution: Evidence from "Province-Managing-County" reform in China.

The impact of fiscal decentralization on environmental pollution is a widely debated topic that remains inconclusive. Central to this discussion is whether local governments pursue a "race to the top" or "race to the bottom" competitive strategy. The environmental federalism theory provides insight into this dynamic within federal system but falls short in explaining similar phenomenon in non-federal systems.

UltraNet: Unleashing the Power of Simplicity for Accurate Medical Image Segmentation.

The imperative development of point-of-care diagnosis for accurate and rapid medical image segmentation, has become increasingly urgent in recent years. Although some pioneering work has applied complex modules to improve segmentation performance, resulting models are often heavy, which is not practical for the modern clinical setting of point-of-care diagnosis. To address these challenges, we propose UltraNet, a state-of-the-art lightweight model that achieves competitive performance in segmenting multiple parts of medical images with the lowest parameters and computational complexity.

Multi-Level Feature Fusion in CNN-Based Human Action Recognition: A Case Study on EfficientNet-B7.

Accurate human action recognition is becoming increasingly important across various fields, including healthcare and self-driving cars. A simple approach to enhance model performance is incorporating additional data modalities, such as depth frames, point clouds, and skeleton information, while previous studies have predominantly used late fusion techniques to combine these modalities, our research introduces a multi-level fusion approach that combines information at early, intermediate, and late stages together. Furthermore, recognizing the challenges of collecting multiple data types in real-world applications, our approach seeks to exploit multimodal techniques while relying solely on RGB frames as the single data source.

IngredSAM: Open-World Food Ingredient Segmentation via a Single Image Prompt.

Food semantic segmentation is of great significance in the field of computer vision and artificial intelligence, especially in the application of food image analysis. Due to the complexity and variety of food, it is difficult to effectively handle this task using supervised methods. Thus, we introduce IngredSAM, a novel approach for open-world food ingredient semantic segmentation, extending the capabilities of the Segment Anything Model (SAM).

Tetrodotoxin: The State-of-the-Art Progress in Characterization, Detection, Biosynthesis, and Transport Enrichment.

Tetrodotoxin (TTX) is a neurotoxin that binds to sodium channels and blocks sodium conduction. Importantly, TTX has been increasingly detected in edible aquatic organisms. Because of this and the lack of specific antidotes, TTX poisoning is now a major threat to public health.

AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems.

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO).

Upgrading the Bioinspired Iron-Polyporphyrin Structures by Abiological Metals Toward New-Generation Reactive Oxygen Biocatalysts.

Developing artificial enzymes based on organic molecules or polymers for reactive oxygen biocatalysis has broad applicability. Here, inspired by heme-based enzyme systems, we construct the abiological iron group metal-based polyporphyrin (Ru/Os-coordinated porphyrin-based biocatalyst, Ru/Os-PorBC) to serve as a new generation of efficient and versatile reactive oxygen species (ROS)-related biocatalyst. Due to the structural benefits, including excellent electron configuration, appropriate bandgap, and optimized adsorption and activation of reaction intermediates, Ru/Os-PorBC shows unparalleled ROS-production activities regarding maximum reaction rate and turnover numbers, which also demonstrates superior pH and temperature adaptability compared to natural enzymes.

[Ecological environment quality evaluation based on water benefit-based ecological index in Guangdong Province, China].

As one of the provinces with the largest number of national forest cities, all prefecture-level cities in Guangdong Province have joined the campaigns of building forest cities. Mastering the spatial and temporal variations of ecological environment quality (EEQ) in Guangdong Province is conducive to the benign interaction and coordinated development of urban construction and ecosystem. We used the water benefit-based ecological index (WBEI) to achieve rapid monitoring of EEQ in Guangdong Province, utilized the standard deviation ellipse and gravity center migration, Theil-Sen Median trend method and Mann-Kendall test to explore the spatial distribution disparities and trends, and analyzed the coupling coordination between EEQ and urbanization.

Multi-Task Deep Neural Networks for Irregularly Sampled Multivariate Clinical Time Series.

Multivariate clinical time series data, such as those contained in Electronic Health Records (EHR), often exhibit high levels of irregularity, notably, many missing values and varying time intervals. Existing methods usually construct deep neural network architectures that combine recurrent neural networks and time decay mechanisms to model variable correlations, impute missing values, and capture the impact of varying time intervals. The complete data matrices thus obtained from the imputation task are used for downstream risk prediction tasks.