A review of the pharmacological action and mechanism of natural plant polysaccharides in depression.

Depression is a prevalent mental disorder. However, clinical treatment options primarily based on chemical drugs have demonstrated varying degrees of adverse reactions and drug resistance, including somnolence, nausea, and cognitive impairment. Therefore, the development of novel antidepressant medications that effectively reduce suffering and side effects has become a prominent area of research.

The role of ncRNA regulatory mechanisms in diseases-case on gestational diabetes.

Non-coding RNAs (ncRNAs) are a class of RNA molecules that do not have the potential to encode proteins. Meanwhile, they can occupy a significant portion of the human genome and participate in gene expression regulation through various mechanisms. Gestational diabetes mellitus (GDM) is a pathologic condition of carbohydrate intolerance that begins or is first detected during pregnancy, making it one of the most common pregnancy complications.

i2OM: Toward a better prediction of 2'-O-methylation in human RNA.

2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA.

iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism.

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods.

Protein-ligand interactions are necessary for majority protein functions. Adenosine- 5'-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design.

PPD: A Manually Curated Database for Experimentally Verified Prokaryotic Promoters.

As a key region, promoter plays a key role in transcription regulation. A eukaryotic promoter database called EPD has been constructed to store eukaryotic POL II promoters. Although there are some promoter databases for specific prokaryotic species or specific promoter type, such as RegulonDB for Escherichia coli K-12, DBTBS for Bacillus subtilis and Pro54DB for sigma 54 promoter, because of the diversity of prokaryotes and the development of sequencing technology, huge amounts of prokaryotic promoters are scattered in numerous published articles, which is inconvenient for researchers to explore the process of gene regulation in prokaryotes.

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information.

Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes.

iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features.

Motivation: Protein carbonylation is one of the most important oxidative stress-induced post-translational modifications, which is generally characterized as stability, irreversibility and relative early formation. It plays a significant role in orchestrating various biological processes and has been already demonstrated to be related to many diseases. However, the experimental technologies for carbonylation sites identification are not only costly and time consuming, but also unable of processing a large number of proteins at a time.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features.

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals.

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques.

Design powerful predictor for mRNA subcellular location prediction in Homo sapiens.

Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive.

An optimised grey buffer operator for forecasting the production and sales of new energy vehicles in China.

In recent years, under a series of policy shocks, the production and sales of new energy vehicles in China show the characteristics of trend mutation and non-smoothness. In order to forecast the production and sales of new energy vehicles in China, an optimised grey buffer operator is proposed by introducing accumulation and translation transformations. Meanwhile, a genetic algorithm is employed to ascertain its optimum parameters.

Abscisic acid-generating bacteria can reduce Cd concentration in pakchoi grown in Cd-contaminated soil.

Contamination of vegetable plants with cadmium (Cd) has become a serious issue in recent years. In the present study, pakchoi (Brassica chinensis L.) grown in Cd-contaminated soil inoculated with abscisic acid (ABA)-generating bacteria, Azospirillum brasilense and Bacillus subtilis, showed 28%-281% and 26%-255% greater biomass, and 40%-79% and 43%-77% lower Cd concentrations, respectively, than those of the control plants.