TGF-ß 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 ß-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. Knockout of SMAD4 in the model significantly suppressed tumor growth. Proteomic analysis revealed that SMAD4 knockout reduced the level of Cysteine-and-Glycine-Rich Protein 2 (CSRP2) in DTs, and treatment of DT-derived cells with a TGF-ß receptor inhibitor reduced CSRP2 RNA levels. Knockdown of CSRP2 in DT cells significantly suppressed their proliferation. These results indicate that the TGF-ß/CSRP2 axis is a potential therapeutic target for DTs downstream of TGF-ß signaling.

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. It introduces an IFA melting furnace, as an onsite modular integration, which serves as a bridge between the MSW incinerator and the commercial rock wool production line. The incinerator burns MSW for heating and electricity supply. The melting furnace further burns out the highly toxic dioxins adsorbed on IFA, as well as solidifying the potentially toxic elements into the molten slag, which substitutes for basalt as raw materials used for high value-added rock wool production. That achieves collaborative reduction, stabilization, harmlessness and resource reutilization of MSW as an energy source, and its IFA as energy-saving materials, as well as a net carbon emission reduction and high economicbenefits. Even more exciting, as opposed to the serious losses of the other existing scenarios, it is profitable even without the feed-in tariff and fiscal subsidy, both that are the dominating income source of other scenarios including conventional MSWI & IFA landfill and demonstration MSWI with IFA melting & landfill. Discounted Cash Flow technique shows that the profit is âˆ¼ 9.2 RMB per ton of MSW, and it increases with insulation price, feed-in tariff, and fiscal subsidy. With the feed-in tariff and fiscal subsidy, the existing two scenarios and the proposed harmless integrated scenario can produce revenue of 103.8, 98.1-110.5, and 145.0 RMB per ton of MSW, respectively. Nonetheless, several challenges are posed for future industrial applications, such as liquid slag discharge, unstable combustion and possible environmental issues.

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. Then, an attention neural network is employed to obtain attentive representations of drugs and miRNAs, and miRNA-drug resistance associations are predicted by the inner product of learned attentive representations. The computational experiments show that AMMGC outperforms other state-of-the-art methods and baseline methods, achieving the AUPR score of 0.2399 and the AUC score of 0.9467. The analysis demonstrates that leveraging multiple features of drugs and miRNAs can make a contribution to the miRNA-drug resistance association prediction. The usefulness of AMMGC is further validated by case studies.

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. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network. RESULTS: In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations. CONCLUSION: The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction.

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. RESULTS: In this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs. CONCLUSION: The study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction.

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. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE). CONCLUSION: We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of 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. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. CONCLUSION: This study provides the guide to the development of computational methods for the 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. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. RESULTS: Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

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. METHODS: In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set. RESULTS: Two datasets named 'IMMA2' and 'PAAQD' are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant. CONCLUSIONS: The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in S1 File.

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. In the past few years, a number of computational methods (especially the machine learning-based methods) have been developed to predict the conformational B-cell epitopes. In this chapter, we introduce important data resources and computational methods, which are publicly available. Moreover, we introduce our ensemble learning-based method that can predict the conformational epitopes from sequences. These promising methods may assist immunologists in identifying potential vaccine candidates.

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. Except of the evaporation of KCl and K2SO4 aerosol which cause the release of K, Cl and S, K may be also reduced by the organic decomposition and the releases of metal K and KOH. The ash fusion characteristics of biomass are mainly dependent on the high-temperature molten material built up by quartz, potassium iron oxide and silicates.

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. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences. METHODS: In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences. RESULTS: Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction. AVAILABILITY: The web server and datasets are freely available at http://bcell.whu.edu.cn.

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. Therefore, the IDT may be as an evaluation index of biomass AFC rather than the ST used as an evaluation index of coal AFC. XRD shows that no matter what the ashing temperature is, the biomass ashes contain same high-temperature molten material. Therefore, evaluation of the biomass AFC should not be simply on the proportion of elements except IDT, but the high-temperature molten material in biomass ash.

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. METHODOLOGY: Motivated by the collective search strategy of the particle swarm optimization algorithm (PSO), a method was developed to make the direct prediction of peptide binding affinity. In our paper, PSO was utilized to search for the optimal position-specific scoring matrices (PSSM) from the experimentally derived allele-related peptides, and then the prediction models were constructed based on the matrices. Moreover, we evaluated several factors influencing the binding affinity, including peptide length and flanking residue length, and incorporated them into our models. RESULTS: The performance of our models was evaluated on three MHC-II alleles from AntiJen database and 14 MHC-II alleles from IEDB database. When compared to the existing popular quantitative methods such as MHCPred, SVRMHC, ARB and SMM-align, our method can give out better performance in terms of correlation coefficient (r) and area under ROC curve (AUC). In addition, the results demonstrated that the performance of models was further improved by incorporating the global length information, achieving average AUC value of 0.7534 and average r value of 0.4707. CONCLUSIONS: Quantitative prediction of MHC-II binding affinity can be modeled as an optimization problem. Our PSO based method can find the optimal PSSM, which will then be used for identifying the binding cores and scoring the binding affinities of the peptides. The experiment results show that our method is promising for the prediction of MHC-II binding affinity.