Transcriptional and phytohormonal responses to simulated animal grazing in Leymus chinensis.

Simulated animal feeding alters endogenous phytohormone levels and gene expression within related metabolic pathways, thereby regulating the growth, development, and defense mechanisms of Leymus chinensis. Leymus chinensis (Trin.) Tzvel.

Fine-Grained Entity Recognition via Large Language Models.

Fine-grained entity recognition (FGER) attracts increasing attention in information extraction and many other natural language understanding applications. However, it is a quite challenging problem for a specific domain due to the lack of specific-domain labeled data. To address this challenge, recent advancements in language modeling such as generative pretrained transformer (GPT) offer promising alternatives.

Genome-Wide Identification and Characterization of the BZR Transcription Factor Gene Family in .

Background/objectives: The BZR gene family, a critical transcription factor in the brassinosteroid (BR) signaling pathway, regulates plant growth and development. Despite its significance, the BZR gene family in , a valuable forage grass renowned for its stress tolerance and nutritional quality, remains uncharacterized, and its functional roles are largely unexplored.

Methods: Employing advanced bioinformatics tools, we conducted a genome-wide survey to identify members of the BZR gene family in .

Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data.

A responsive organic probe based photoelectrochemical sensor for hydrazine detection.

This study developed a new photoelectrochemical (PEC) sensor for the detection of the hydrazine (NH, HZ) based on a donor-π-bridge-acceptor (D-π-A) configuration organic photoactive dye (Dye-HZ). The dye was covalently immobilized on an FTO/TiO (FTO: fluorine-doped tin oxide) substrate, resulting in a photoanode FTO/TiO/Dye-HZ that exhibits a specific PEC response to NH. Hydrazine reacts with the acetyl group in the Dye-HZ molecule, leading to its removal and the formation of a hydroxy group.

A near-infrared ratiometric fluorescent probe for hydrazine and its application for gaseous sensing and cell imaging.

Hydrazine (NH) is a widely used raw material in the chemical industry, but at the same time hydrazine has extremely high toxicity. Therefore, the development of efficient detection methods is crucial for monitoring hydrazine in the environment and evaluating the biological toxicity of hydrazine. This study reports a near-infrared ratiometric fluorescent probe (DCPBCl-Hz) for the detection of hydrazine by coupling a chlorine-substituted D-π-A fluorophore (DCPBCl) to the recognition group acetyl.

Alzheimer's Disease Prediction Algorithm Based on Group Convolution and a Joint Loss Function.

Alzheimer's disease (AD) can effectively predict by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) of the brain, but current PET images still suffer from indistinct lesion features, low signal-to-noise ratios, and severe artefacts, resulting in poor prediction accuracy for patients with mild cognitive impairment (MCI) and unclear lesion features. In this paper, an AD prediction algorithm based on group convolution and a joint loss function is proposed. First, a group convolutional backbone network based on ResNet18 is designed to extract lesion features from multiple channels, which makes the expression ability of the network improved to a great extent.

[Clinical value of DC and DRs in warning sudden cardiac death of patients with coronary artery disease].

Objective: To make risk stratification of aged patients with coronary artery disease by deceleration capacity of rate (DC) and heart rate deceleration runs (DRs) and to investigate the value of the two detection technologies in warning sudden cardiac death.

Methods: Two hundrend and eighteen patients diagnosed with coronary artery disease (CAD) by coronary angiography (CAG) were selected as observa-tion group:including 55 patients with latent coronary artery disease (LCHD), 56 patients with acute myocardial infarction (AMI), 53 patients with angina pectoris (AP), 54 patients with ischemic heart failure. Fifty-five healthy controls in our hospital were selected at the same time (control group).