Identification, biological characterization of Cladobotryum mycophilum and its control during the fruiting process of Lyophyllum decastes.

Lyophyllum decastes is a type of edible and medicinal mushroom with high nutritional value. However, it can be infected by fungi during the fruiting process, which impairs the development of the industry. In this study, one pathogenic fungus was isolated from the diseased fruiting bodies of L.

Characteristics and multi-omics analysis of spontaneous spondyloarthritis in non-human primates: Case report.

Spondyloarthritis is a prevalent and persistent condition that significantly impacts the quality of life. Its intricate pathological mechanisms have led to a scarcity of animal models capable of replicating the disease progression in humans, making it a prominent area of research interest in the field. To delve into the pathological and physiological traits of spontaneous non-human primate spondyloarthritis, this study meticulously examined the disease features of this natural disease model through an array of techniques including X-ray imaging, MRI imaging, blood biochemistry, markers of bone metabolism, transcriptomics, proteomics, and metabolomics.

Tailoring second harmonic generation in a multilayer WS flake via a silicon circular Bragg grating.

Nonlinear emission phenomena observed in transition metal dichalcogenides (TMDCs) have significantly advanced the development of robust nonlinear optical sources within two-dimensional materials. However, the intrinsic emission characteristics of TMDCs are inherently dependent on the specific material, which constrains their tunability for practical applications. In this study, we propose a strategy for the selective enhancement and modification of second-harmonic generation (SHG) emission in a multilayer WS flake through the implementation of a silicon (Si)-based circular Bragg grating (CBG) structure positioned on an Au/SiO substrate.

MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.

Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.

A clinical study exploring the prediction of microvascular invasion in hepatocellular carcinoma through the use of combined enhanced CT and MRI radiomics.

Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI).

Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue.