HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases.

Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion.

Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence drug efficacy. Therefore, research into the relationships between circRNAs and drugs can be of great significance in increasing the comprehension of circRNAs function, as well as contributing to the discovery of new drugs and the repurposing of existing drugs. However, it is time-consuming and costly to validate the function of circRNA with traditional medical research methods.

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network.

The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases.

Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer.

Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer.

Self-supervised learning-based Multi-Scale feature Fusion Network for survival analysis from whole slide images.

Understanding prognosis and mortality is critical for evaluating the treatment plan of patients. Advances in digital pathology and deep learning techniques have made it practical to perform survival analysis in whole slide images (WSIs). Current methods are usually based on a multi-stage framework which includes patch sampling, feature extraction and prediction.

Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.

Increasing evidence shows that the occurrence of human complex diseases is closely related to the mutation and abnormal expression of microRNAs(miRNAs). MiRNAs have complex and fine regulatory mechanisms, which makes it a promising target for drug discovery and disease diagnosis. Therefore, predicting the potential miRNA-disease associations has practical significance.

Schumannella soli sp. nov., a novel actinomycete isolated from mangrove soil by in situ cultivation.

A novel actinobacterial strain, designated 10F1D-1, was isolated from soil sample collected from Futian mangrove nature reserve, China using of the in situ cultivation technique. Preliminary analysis based on the 16S rRNA gene sequence revealed that strain 10F1D-1 was the member of genus Schumannella with sharing highest sequence similarity (99.7%) to Schumannella luteola DSM 23141.

Metagenomic Analysis Reveals Microbial Community Structure and Metabolic Potential for Nitrogen Acquisition in the Oligotrophic Surface Water of the Indian Ocean.

Despite being the world's third largest ocean, the Indian Ocean is one of the least studied and understood with respect to microbial diversity as well as biogeochemical and ecological functions. In this study, we investigated the microbial community and its metabolic potential for nitrogen (N) acquisition in the oligotrophic surface waters of the Indian Ocean using a metagenomic approach. Proteobacteria and Cyanobacteria dominated the microbial community with an average 37.

Deciphering the Microbial Taxonomy and Functionality of Two Diverse Mangrove Ecosystems and Their Potential Abilities To Produce Bioactive Compounds.

Mangroves, as important and special ecosystems, create unique ecological environments for examining the microbial gene capacity and potential for producing bioactive compounds. However, little is known about the biogeochemical implications of microbiomes in mangrove ecosystems, especially the variations between pristine and anthropogenic mangroves. To elucidate this, we investigated the microbial taxonomic and functional shifts of the mangrove microbiomes and their potential for bioactive compounds in two different coastal mangrove ecosystems in southern China.

Labedella phragmitis sp. nov. and Labedella populi sp. nov., two endophytic actinobacteria isolated from plants in the Taklamakan Desert and emended description of the genus Labedella.

Two novel strains, designated 11W25H-1 and 8H24J-4-2, were isolated from surface-sterilized plant tissues collected from the Taklamakan Desert in the Xinjiang Uygur Autonomous Region, China. The strains were characterized by a polyphasic approach in order to clarify their taxonomic positions. They were Gram-stain positive, aerobic, non-motile, non-spore-forming and rod-shaped.

Deciphering the Composition and Functional Profile of the Microbial Communities in Chinese Moutai Liquor Starters.

Moutai is a world-famous traditional Chinese liquor with complex taste and aroma, which are considered to be strongly influenced by the quality of fermentation starters (Daqu). However, the role of microbial communities in the starters has not been fully understood. In this study, we revealed the microbial composition of 185 Moutai starter samples, covering three different types of starters across immature and mature phases, and functional gene composition of mature starter microbiome.

Aeromicrobium endophyticum sp. nov., an endophytic actinobacterium isolated from reed (Phragmites australis).

A Gram-staining-positive, motile and short-rod-shaped actinobacterium designated 9W16Y-2 was isolated from surface-sterilized leaves of reed (Phragmites australis) collected from Taklamakan Desert in Xinjiang Uygur Autonomous Region, China. Colonies were pale greenish yellow, circular, smooth, and convex. The 16S rRNA gene sequence of strain 9W16Y-2 exhibited highest sequence similarities with Aeromicrobium camelliae CGMCC 1.

Amnibacterium endophyticum sp. nov., an endophytic actinobacterium isolated from Aegiceras corniculatum.

A Gram-stain-positive, aerobic, non-motile, non-spore-forming and short-rod-shaped actinobacterium, designated strain 1T4Z-3, was isolated from a piece of surface-sterilized branch of Aegiceras corniculatum collected from the Cotai Ecological Zones in Macao, China. Comparative 16S rRNA gene sequence analysis showed that strain 1T4Z-3 was clearly affiliated to the genus Amnibacterium and exhibited 97.9 % gene sequence similarity to Amnibacterium kyonggiense JCM 16463, 97.