Browse Articles

Enlargement of microvascular dropout toward the disc-fovea axis indicates new central visual field defect in glaucoma

Declining activity of serum response factor in aging aorta in relation to aneurysm progression

Children’s gendered expectations of moral parties

Antimicrobial peptides selectively target malaria parasites by a cholesterol-dependent mechanism

Physiological regulation underlying the alleviation of cadmium stress in maize seedlings by exogenous glycerol

Abstract Cadmium (Cd) contamination in maize poses a significant threat to global food security due to its persistent accumulation in crops. In this study, the effects of foliar application of glycerol on Cd accumulation in maize seedlings were studied. Our results demonstrated that under Cd treatment, biomass, total chlorophyll content, net photosynthetic rate (Pn), Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) activity, Phosphoenolpyruvate carboxylase (PEPC) activity, sucrose levels, and carbohydrate levels in maize seedlings significantly increased after glycerol application. H2O2 and MDA levels in both the aboveground and belowground portions of the maize plants significantly decreased. Moreover, superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities in the aboveground parts significantly increased. Notably, maize plants used glycerol to chelate Cd, which was fixed within the cell wall and soluble fraction of the roots, reducing Cd transport to the shoots and significantly lowering the Cd transport coefficient (TF). Transcriptomic data suggested that glycerol-mediated alleviation of Cd stress in maize seedlings may be associated with phenylpropanoid biosynthesis, plant-pathogen interactions and photosynthesis pathways. These molecular patterns align with the observed physiological improvements. This study provided a novel approach to effectively alleviate excessive Cd in maize and suggested possible applications of glycerol in cultivating plant resistance to heavy metals.

The E3 ubiquitin ligase RNF126 facilitates quality control of unimported mitochondrial membrane proteins

Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures

Methionine cycle inhibition disrupts antioxidant metabolism and reduces glioblastoma cell survival

Quantitative analysis of drug–drug interactions among active components of Xuebijing in inhibiting LPS-induced TLR4 signaling and NO production

A combination of alveolar type 2–specific p38α activation with a high-fat diet increases inflammatory markers in mouse lungs

Influence of decompression surgery on sagittal balance parameters in patients with lumbar spinal stenosis

Cardioprotective effect of genetic ablation of the G-protein-coupled receptor kinase GRK2 in adult pancreatic β-cells during high-fat diet

Orbital Hall conductivity in a Graphene Haldane and Haldane Haldane bilayers

Characterization of a rhodopsin-phosphodiesterase from Choanoeca flexa to be combined with rhodopsin-cyclases for bidirectional optogenetic cGMP control

Essential health risk communication for recovery after lifting evacuation orders following the Fukushima Daiichi nuclear power plant accident

Dioxygenation of tryptophan residues by superoxide and myeloperoxidase

Grouped multi-scale vision transformer for medical image segmentation

Abstract Medical image segmentation plays a pivotal role in clinical diagnosis and pathological research by delineating regions of interest within medical images. While early approaches based on Convolutional Neural Networks (CNNs) have achieved significant success, their limited receptive field constrains their ability to capture long-range dependencies. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable improvements by leveraging self-attention mechanisms. However, existing ViT-based segmentation models often struggle to effectively capture multi-scale variations within a single attention layer, limiting their capacity to model complex anatomical structures. To address this limitation, we propose Grouped Multi-Scale Attention (GMSA), which enhances multi-scale feature representation by grouping channels and performing self-attention at different scales within a single layer. Additionally, we introduce Inter-Scale Attention (ISA) to facilitate cross-scale feature fusion, further improving segmentation performance. Extensive experiments on the Synapse, ACDC, and ISIC2018 datasets demonstrate the effectiveness of our model, achieving state-of-the-art results in medical image segmentation. Our code is available at: https://github.com/Chen2zheng/ScaleFormer.

Immune checkpoint protein PD-L1 promotes transcription of angiogenic and oncogenic proteins IL-8, Bcl3, and STAT1 in ovarian cancer cells

Biomimetic propulsion system efficiency for unmanned underwater vehicle

Abstract This paper covers experimental research provided for Biomimetic Unmanned Underwater Vehicle (BUUV). The tests were conducted in a laboratory water tunnel equipped with a direct force-measured sensor and system for Particle Image Velocimetry (PIV) analysis. Different control parameters were tested, and then the generated thrust was compared with electric energy consumption. The main goal of the research is to develop a low hydroacoustic noise and high-energy efficiency propulsion system based on single, flexible fins. The final result is a set of Pareto optimal solutions, which makes it possible to draw more general conclusions on the design of the undulating propulsion system.

New routes for spermine biosynthesis