Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level.

Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Therefore, predicting drug resistance before treatment is crucial for individual treatments. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods. To transform target sequences into numeric vectors, seven physicochemical properties were used, which can well represent the interacting characteristics of target proteins. Then, principal component analysis (PCA) method was adopted to reduce the feature dimensionality. Random forest (RF) and support vector machine (SVM) based on three different kernel functions, including linear, polynomial and radial basis function (RBF), were all employed. By comparisons, we found that RBF-based SVM method gives a comparative performance with RF model. Further, we added the weight information to RBF-based SVM method by four different weight evaluation methods of RF, eXtreme Gradient Boosting (XGB), CfsSubsetEval and ReliefFAttributeEval, respectively. Results show that the RF-weighted RBF-based SVM yield the superior performance and 13 out of 21 drug models provide the correlation coefficients (R2) over 0.8 and 3 of them are higher than 0.9. Finally, position-specific importance analysis indicates that most of the mutation residues with high RF weight scores are proved to be closely related with drug resistance, which has been revealed in previous reports. Overall, we can expect that this method can be a supplementary tool for predicting HIV drug resistance for newly discovered mutations. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods by fusing the weight information of different mutation positions.

A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background.

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom-up and top-down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application.

Probing the Effects of the Number and Positions of -OCH3 and -CN Substituents on Color Tuning of Ir(III) Complex Derivatives through a Joint Computational and Experimental Study.

We performed a joint theoretical and experimental study on sixteen Ir(III) complexes bearing a similar molecular platform of bis(2-phenylbenzothiozolato-N,C2' ) iridium(III) (acetylacetonate) by grafting -OCH3 group and/or -CN group on different positions of the C-related arene moiety of the C ^ N ligand (C-ring). Our results reveal that the introduction of -CN renders an overall drop in the FMO energy levels while a reverse increase is observed for -OCH3 . The ortho- and para-sites of the C-ring are more effective substitution positions to modulate the HOMO energy level due to the fact that the electronic density of HOMO mainly locates at them while the meta-site would induce a stronger impact on LUMO since the electronic density of LUMO mainly distributes over the position. Utilizing the synergistic effects of the substituents and the substituted positions, a wide color-tuning range from 479 nm to 637 nm was achieved, which covers nearly the whole window of visible spectrum. In particular, the tri-substituted Ir35mo4cn complex (λem max =637 nm) may be a potential candidate for high efficiency red OLEDs materials due to its greatly enhanced absorption processes, relatively higher 3 MLCT (%), lower ΔES1-T1 , enlarged separation between 3 MLCT/π-π* and 3 MC d-d states, and good hole and particle-transporting performances. Finally, six representative complexes were synthesized and their spectra were determined, which confirm the reliability of our computational strategy.