AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms.

Identifying essential proteins is of utmost importance in the field of biomedical research due to their essential functions in cellular activities and their involvement in mechanisms related to diseases. In this research, a novel approach called AttentionEP for predicting essential proteins (EP) is introduced by attention mechanisms. This method leverages both cross-attention and self-attention frameworks, focusing on enhancing prediction accuracy through the integration of features across diverse scales.

Degradation of phenol by metal-free electro-fenton using a carbonyl-modified activated carbon cathode: Promoting simultaneous HO generation and activation.

The low yield of hydrogen peroxide, narrow pH application range, and secondary pollution due to iron sludge precipitation are the major drawbacks of the electro-Fenton (EF) process. Metal-free electro-Fenton technology based on carbonaceous materials is a promising green pollutant degradation technology. Activated carbon cathodes enriched with carbonyl functional groups were prepared using a two-step annealing method for the degradation of phenol pollutants.

Strategies for promoting the degradation of phenol by electro-Fenton: Simultaneously promoting the generation and utilization of HO.

The use of the electro-Fenton process to continuously generate HO and efficiently degrade organic pollutants is considered a promising technology. The ratio of generation of HO is usually regarded as the critical step; however, how the HO is utilized is also of particular importance. Herein, activated carbon was activated at different temperatures and used to explore the effect of nitrogen doping on the production and utilization of HO in the electro-Fenton-based degradation of organic pollutants.

Epigenome-Wide Histone Acetylation Changes in Peripheral Blood Mononuclear Cells in Patients with Type 2 Diabetes and Atherosclerotic Disease.

There is emerging evidence of an association between epigenetic modifications, glycemic control and atherosclerosis risk. In this study, we mapped genome-wide epigenetic changes in patients with type 2 diabetes (T2D) and advanced atherosclerotic disease. We performed chromatin immunoprecipitation sequencing (ChIP-seq) using a histone 3 lysine 9 acetylation (H3K9ac) mark in peripheral blood mononuclear cells from patients with atherosclerosis with T2D ( = 8) or without T2D (ND, = 10).

Elevated circulating follistatin associates with an increased risk of type 2 diabetes.

The hepatokine follistatin is elevated in patients with type 2 diabetes (T2D) and promotes hyperglycemia in mice. Here we explore the relationship of plasma follistatin levels with incident T2D and mechanisms involved. Adjusted hazard ratio (HR) per standard deviation (SD) increase in follistatin levels for T2D is 1.

FS-GBDT: identification multicancer-risk module via a feature selection algorithm by integrating Fisher score and GBDT.

Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers.

PTPD: predicting therapeutic peptides by deep learning and word2vec.

*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information.

PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO.

Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1).

A novel model for protein sequence similarity analysis based on spectral radius.

Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences.

Speech endpoint detection based on speech time-frequency enhancement and spectral entropy.

In the process of speech recognition, it is especially crucial to precisely locate endpoints of the input utterance to be free of non-speech regions. This paper proposes a novel approach that finds robust features for endpoint detection in a noisy environment. In this proposed method, we integrate both time-frequency enhancement and the spectral entropy feature.