DBGRU-SE: predicting drug-drug interactions based on double BiGRU and squeeze-and-excitation attention mechanism.

The prediction of drug-drug interactions (DDIs) is essential for the development and repositioning of new drugs. Meanwhile, they play a vital role in the fields of biopharmaceuticals, disease diagnosis and pharmacological treatment. This article proposes a new method called DBGRU-SE for predicting DDIs. Firstly, FP3 fingerprints, MACCS fingerprints, Pubchem fingerprints and 1D and 2D molecular descriptors are used to extract the feature information of the drugs. Secondly, Group Lasso is used to remove redundant features. Then, SMOTE-ENN is applied to balance the data to obtain the best feature vectors. Finally, the best feature vectors are fed into the classifier combining BiGRU and squeeze-and-excitation (SE) attention mechanisms to predict DDIs. After applying five-fold cross-validation, The ACC values of DBGRU-SE model on the two datasets are 97.51 and 94.98%, and the AUC are 99.60 and 98.85%, respectively. The results showed that DBGRU-SE had good predictive performance for drug-drug interactions.

scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder.

The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.

Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier.

Multi-label proteins can participate in carrier transportation, enzyme catalysis, hormone regulation and other life activities. Meanwhile, they play a key role in the fields of biopharmaceuticals, gene and cell therapy. This article proposes a prediction method called Mps-mvRBRL to predict the subcellular localization (SCL) of multi-label protein. Firstly, pseudo position-specific scoring matrix, dipeptide composition, position specific scoring matrix-transition probability composition, gene ontology and pseudo amino acid composition algorithms are used to obtain numerical information from different views. Based on the contribution of five individual feature extraction methods, differential evolution is used for the first time to learn the weight of single feature, and then these original features use a weighted combination method to fuse multi-view information. Secondly, the fused high-dimensional features use a weighted linear discriminant analysis framework based on binary weight form to eliminate irrelevant information. Finally, the best feature vector is input into the joint ranking support vector machine and binary relevance with robust low-rank learning classifier to predict the SCL. After applying leave-one-out cross-validation, the overall actual accuracy (OAA) and overall location accuracy (OLA) of Mps-mvRBRL on the training set of Gram-positive bacteria are both 99.81%. The OAA on the test sets of plant, virus and Gram-negative bacteria datasets are 97.24%, 98.55% and 98.20%, respectively, and the OLA are 97.16%, 97.62% and 98.28%, respectively. The results show that the model achieves good prediction performance for predicting the SCL of multi-label protein.

MTDCNet: A 3D multi-threading dilated convolutional network for brain tumor automatic segmentation.

Glioma is one of the most threatening tumors and the survival rate of the infected patient is low. The automatic segmentation of the tumors by reliable algorithms can reduce diagnosis time. In this paper, a novel 3D multi-threading dilated convolutional network (MTDC-Net) is proposed for the automatic brain tumor segmentation. First of all, a multi-threading dilated convolution (MTDC) strategy is introduced in the encoder part, so that the low dimensional structural features can be extracted and integrated better. At the same time, the pyramid matrix fusion (PMF) algorithm is used to integrate the characteristic structural information better. Secondly, in order to make the better use of context semantical information, this paper proposed a spatial pyramid convolution (SPC) operation. By using convolution with different kernel sizes, the model can aggregate more semantic information. Finally, the multi-threading adaptive pooling up-sampling (MTAU) strategy is used to increase the weight of semantic information, and improve the recognition ability of the model. And a pixel-based post-processing method is used to prevent the effects of error prediction. On the brain tumors segmentation challenge 2018 (BraTS2018) public validation dataset, the dice scores of MTDC-Net are 0.832, 0.892 and 0.809 for core, whole and enhanced of the tumor, respectively. On the BraTS2020 public validation dataset, the dice scores of MTDC-Net are 0.833, 0.896 and 0.797 for the core tumor, whole tumor and enhancing tumor, respectively. Mass numerical experiments show that MTDC-Net is a state-of-the-art network for automatic brain tumor segmentation.

Quantum Chemical and Kinetic Studies of N2O Formation during Interaction between Char(N) and NO: Influence of Oxygen, Active Sites, and Nitrogen Status.

In order to obtain a deep insight into the N2O formation mechanism in a fluidized bed, density functional theory was used to investigate the interaction between char(N) and NO at a molecular level. Three key influencing factors for the formation of N2O, namely, active sites, nitrogen status, and oxygen molecules, were taken into study. The geometric structures, electron distribution characteristics, and reaction paths were optimized and calculated. The outer orbital electron properties of char(N) and NO indicate that NO acts as an oxidizer, which tends to abstract electrons from char(N) during the char(N)-NO interaction. A stable N2O molecule has a singlet state and presents as a linear molecular structure. The chemisorption on the char surface will weaken the bond energy of NO from 620 to 94.1 kJ/mole, which promotes the catalytic reduction of NO. Active sites on the char surface benefit the reduction of NO to N2, rather than N2O, which indicates that excessive high temperatures will inhibit the production of N2O. The combination of pyridine nitrogen and NO to form N2O needs to overcome a much higher energy barrier of 357.4 kJ/mole. The initial chemisorption of oxygen molecules on the char surface will promote the formation of N2O by lowering the dissociation energy of N2O from the char surface as well as exposing nitrogen to the char surface.

Characterization of ash melting behaviour at high temperatures under conditions simulating combustible solid waste gasification.

To achieve high-temperature gasification-melting of combustible solid waste, ash melting behaviour under conditions simulating high-temperature gasification were studied. Raw ash (RA) and gasified ash (GA) were prepared respectively by waste ashing and fluidized bed gasification. Results of microstructure and composition of the two-ash indicated that GA showed a more porous structure and higher content of alkali and alkali earth metals among metallic elements. Higher temperature promoted GA melting and could reach a complete flowing state at about 1250°C. The order of melting rate of GA under different atmospheres was reducing condition > inert condition > oxidizing condition, which might be related to different existing forms of iron during melting and different flux content with atmosphere. Compared to RA, GA showed lower melting activity at the same condition due to the existence of an unconverted carbon and hollow structure. The melting temperature for sufficient melting and separation of GA should be at least 1250°C in this work.

DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.

Accurate prediction of drug-target interactions (DTIs), which is often used in the fields of drug discovery and drug repositioning, is regarded a key challenge in the study of drug science. In this paper, a new method called DeepStack-DTIs is proposed to predict DTIs. First, for the target protein, pseudo-position specific score matrix, pseudo amino acid composition and SPIDER3 are used to extract the different feature information of the target protein. Meanwhile, the path-based fingerprint features of each drug are extracted. Then, the synthetic minority oversampling technique (SMOTE) and light gradient boosting machine (LightGBM) are used for data balancing and feature selection, respectively. Finally, the processed features are input to the deep-stacked ensemble classifier composed of gated recurrent unit (GRU), deep neural network (DNN), support vector machine (SVM), eXtreme gradient boosting (XGBoost) and logistic regression (LR) to predict DTIs. Under the five-fold cross-validation and compared with existing methods, the proposed method achieves higher prediction accuracy on the gold standard dataset. To evaluate the predictive power of DeepStack-DTIs, we validate the method on another dataset and predict the drug-target interaction network. The results indicate that DeepStack-DTIs has excellent predictive ability than the other methods, and provides novel insights for the prediction of DTIs. A novel method DeepStack-DTIs for drug-target interactions prediction. PsePSSM, PseAAC, SPIDER3 and FP2 are fused to convert protein sequence and drug molecule information into digital information, respectively. The SMOTE algorithm is used to balance the dataset and LightGBM feature selection algorithm is employed to remove redundant and irrelevant features to select the optimal feature subset. This optimal feature subset is inputted into the deep-stacked ensemble classifier to predict drug-target interactions. The experimental results show DeepStack-DTIs method can significantly improve the prediction accuracy of drug-target interactions.

Ferroelectric polymer nanocomposites for room-temperature electrocaloric refrigeration.

Solution-processable ferroelectric polymer nanocomposites are developed as a new form of electrocaloric materials that can be effectively operated under both modest and high electric fields at ambient temperature. By integrating the complementary properties of the constituents, the nanocomposites exhibit state-of-the-art cooling energy densities. Greatly improved thermal conductivity also yields superior cooling power densities validated by finite volume simulations.