Strategies for promoting the degradation of phenol by electro-Fenton: Simultaneously promoting the generation and utilization of H2O2.

The use of the electro-Fenton process to continuously generate H2O2 and efficiently degrade organic pollutants is considered a promising technology. The ratio of generation of H2O2 is usually regarded as the critical step; however, how the H2O2 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 H2O2 in the electro-Fenton-based degradation of organic pollutants. The experimental results indicate that nitrogen-doped activated carbon simultaneously promotes the generation and utilization of H2O2, which is attributed to the regulation of the competition between phenol and O2 adsorption by the doped nitrogen. Nitrogen doping not only improves 2e-ORR selectivity but also aggregates phenol near the cathode to balance the concentrations of phenol and ·OH. Density functional theory (DFT) calculations further confirmed that pyrrole-N as a dopant promoted the adsorption of phenol, while pyridine-N was more favorable for O2 adsorption. The unique balance of nitrogen types possessed by modified activated carbon NAC-750 permits the efficient synergistic generation and utilization of H2O2 in a balanced manner during the degradation of phenol. This work provides a new direction for the rational nitrogen-doping modification of activated carbon for 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 (n = 8) or without T2D (ND, n = 10). We mapped epigenome changes and identified 23,394 and 13,133 peaks in ND and T2D individuals, respectively. Out of all the peaks, 753 domains near the transcription start site (TSS) were unique to T2D. We found that T2D in atherosclerosis leads to an H3K9ac increase in 118, and loss in 63 genomic regions. Furthermore, we discovered an association between the genomic locations of significant H3K9ac changes with genetic variants identified in previous T2D GWAS. The transcription factor 7-like 2 (TCF7L2) rs7903146, together with several human leukocyte antigen (HLA) variants, were among the domains with the most dramatic changes of H3K9ac enrichments. Pathway analysis revealed multiple activated pathways involved in immunity, including type 1 diabetes. Our results present novel evidence on the interaction between genetics and epigenetics, as well as epigenetic changes related to immunity in patients with T2D and advanced atherosclerotic disease.

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 1.24 (CI: 1.04-1.47, p < 0.05) during 19-year follow-up (n = 4060, Sweden); and 1.31 (CI: 1.09-1.58, p < 0.01) during 4-year follow-up (n = 883, Finland). High circulating follistatin associates with adipose tissue insulin resistance and non-alcoholic fatty liver disease (n = 210, Germany). In human adipocytes, follistatin dose-dependently increases free fatty acid release. In genome-wide association study (GWAS), variation in the glucokinase regulatory protein gene (GCKR) associates with plasma follistatin levels (n = 4239, Sweden; n = 885, UK, Italy and Sweden) and GCKR regulates follistatin secretion in hepatocytes in vitro. Our findings suggest that GCKR regulates follistatin secretion and that elevated circulating follistatin associates with an increased risk of T2D by inducing adipose tissue insulin resistance.

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. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS-GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS-GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

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. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.

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). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.

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. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.