TGF-β signaling promotes desmoid tumor formation via CSRP2 upregulation.

Desmoid tumors (DTs), also called desmoid-type fibromatoses, are locally aggressive tumors of mesenchymal origin. In the present study, we developed a novel mouse model of DTs by inducing a local mutation in the Ctnnb1 gene, encoding β-catenin in PDGFRA-positive stromal cells, by subcutaneous injection of 4-hydroxy-tamoxifen. Tumors in this model resembled histologically clinical samples from DT patients and showed strong phosphorylation of nuclear SMAD2.

Co-disposal and reutilization of municipal solid waste and its hazardous incineration fly ash.

Compared to landfill, MSW incineration (MSWI) not only eliminates its innate secondary pollution and land occupation, but also yields a net emission reduction. Regretfully, MSWI produces hazardous incineration fly ash (IFA) enriched with potentially toxic elements and dioxins. Given these, a harmless integrated scenario of co-disposal and resource reutilization of MSW and its hazardous IFA is proposed and subjected to technical and economic analysis.

MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network.

MiRNAs can regulate genes encoding specific proteins which are related to the efficacy of drugs, and predicting miRNA-drug resistance associations is of great importance. In this work, we propose an attentive multimodal graph convolution network method (AMMGC) to predict miRNA-drug resistance associations. AMMGC learns the latent representations of drugs and miRNAs from four graph convolution sub-networks with distinctive combinations of features.

Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction.

Background: Researchers discover LncRNA-miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions.

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.

Background: Researchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods.

A network embedding-based multiple information integration method for the MiRNA-disease association prediction.

Background: MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited.

Results: In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction.

Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction.

Background: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods.

Results: In the paper, we review the recent advances in machine learning-based drug-target interaction prediction.

Quantitative prediction of drug side effects based on drug-related features.

Motivation: Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them.

Methods: In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights.

Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning.

Background: T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.

Prediction of conformational B-cell epitopes.

Conformational B-cell epitopes play an important role in the epitope-based vaccine design. The increase of available data promotes the development of computational methods. Compared with the wet experiments, the computational methods are faster and more economic.

Further study on biomass ash characteristics at elevated ashing temperatures: the evolution of K, Cl, S and the ash fusion characteristics.

Based on the ash-related problems during biomass combustion, the evolution of element S, Cl, K and chemical components and ash fusion characteristics of capsicum stalks, cotton stalks and wheat stalks ashed at 1000, 1200 and 1400 °C are further studied by XRF and XRD. Cl disappears at 815 °C in the form of HCl due to the aluminosilicate of sylvite. Above 1000 °C, inorganic S is released in the form of SO2 by the silicate of K2SO4, which is the main reason that ashing ratio decreases at high temperature.

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.

Motivation: The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures.

Study on fusion characteristics of biomass ash.

The ash fusion characteristics (AFC) of Capsicum stalks ashes, cotton stalks ashes and wheat stalks ashes that all prepared by ashing at 400 degrees C, 600 degrees C and 815 degrees C are consistent after 860 degrees C, 990 degrees C and 840 degrees C, respectively in the ash fusion temperature test and TG. Initial deformation temperature (IDT) increases with decreased K(2)O and went up with increased MgO, CaO, Fe(2)O(3) and Al(2)O(3). Softening temperature (ST), hemispherical temperature (HT) and fluid temperature (FT) do not affected by the concentrations of each element and the ashing temperature obviously.

Quantitative prediction of MHC-II binding affinity using particle swarm optimization.

Objective: Helper T-cell epitopes (Th epitopes) are the basic units which activate helper T-cell's immune response, and they are helpful for understanding the immune mechanism and developing vaccines. Peptide and major histocompatibility complex class II (MHC-II) binding is an important prerequisite event for helper T-cell immune response, and the binding peptides are usually recognized as Th epitopes, therefore we can identify Th epitopes by predicting MHC-II binding peptides. Recently, instead of differentiating the peptides as binder or non-binder, researchers are more interested in predicting binding affinities between MHC-II molecules and peptides.