RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical challenge for rice research and the development of resistant varieties. However, the limited availability of high-quality rice genotype data has hindered this research.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides.

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models.

Haematological and serum biochemical reference values in Chinese water deer (Hydropotes inermis): a preliminary study.

Background: A selection of haematological and serum biochemical profile was first presented from the 81 samples of Chinese water deer (Hydropotes inermis). The deer health assessment database was initially established, especially in relation to determining potential effects associated with diseases diagnosis.

Results: Blood samples were analyzed for different haematological parameters viz.