Sofosbuvir Selects for Drug-Resistant Amino Acid Variants in the Zika Virus RNA-Dependent RNA-Polymerase Complex In Vitro

The nucleotide analog sofosbuvir, licensed for the treatment of hepatitis C, recently revealed activity against the Zika virus (ZIKV) in vitro and in animal models. However, the ZIKV genetic barrier to sofosbuvir has not yet been characterized. In this study, in vitro selection experiments were performed in infected human hepatoma cell lines. Increasing drug pressure significantly delayed viral breakthrough (p = 0.029). A double mutant in the NS5 gene (V360L/V607I) emerged in 3 independent experiments at 40–80 µM sofosbuvir resulting in a 3.9 ± 0.9-fold half- maximal inhibitory concentration (IC₅₀) shift with respect to the wild type (WT) virus. A triple mutant (C269Y/V360L/V607I), detected in one experiment at 80 µM, conferred a 6.8-fold IC₅₀ shift with respect to the WT. Molecular dynamics simulations confirmed that the double mutant V360L/V607I impacts the binding mode of sofosbuvir, supporting its role in sofosbuvir resistance. Due to the distance from the catalytic site and to the lack of reliable structural data, the contribution of C269Y was not investigated in silico. By a combination of sequence analysis, phenotypic susceptibility testing, and molecular modeling, we characterized a double ZIKV NS5 mutant with decreased sofosbuvir susceptibility. These data add important information to the profile of sofosbuvir as a possible lead for anti-ZIKV drug development.     

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.     

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

Availability growth models and verification of power equipment

The general availability growth models for large scale complicated repairable system such as electric generating units, power station auxiliaries, and transmission and distribution installations are presented. The calculation formulas for the maintenance coefficient, mathematical expressions for general availability growth models, ways for estimating, and fitting on checking the parameters of the model are introduced. Availability growth models for electric generating units, power station auxiliaries, and transmission and distribution installations are given together with verification examples for availability growth models of 320–1000 MW nuclear power units and 1000 MW thermal power units, 200–1000 MW power station auxiliaries, and 220–500 kV transmission and distribution installations. The verification results for operation availability data show that the maintenance coefficients for electric generating units, power station auxiliaries, transmission and distribution installations conform to the power function, and general availability growth models conform to rules of availability growth tendency of power equipment.     

Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams

This work aims to connect two rarely combined research directions, i.e., non-stationary data stream classification and data analysis with skewed class distributions. We propose a novel framework employing stratified bagging for training base classifiers to integrate data preprocessing and dynamic ensemble selection methods for imbalanced data stream classification. The proposed approach has been evaluated based on computer experiments carried out on 135 artificially generated data streams with various imbalance ratios, label noise levels, and types of concept drift as well as on two selected real streams. Four preprocessing techniques and two dynamic selection methods, used on both bagging classifiers and base estimators levels, were considered. Experimentation results showed that, for highly imbalanced data streams, dynamic ensemble selection coupled with data preprocessing could outperform online and chunk-based state-of-art methods.     

High-speed,two-dimensional digital image correlation algorithm using heterogeneous (CPU-GPU) framework

Two-dimensional digital image correlation (2D-DIC) is an experimental technique used to measure in-plane displacement of a test specimen. Real-time measurement of full-field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU-based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU-GPU) framework in combination with a pyramid-based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU-based implementation for a pair of gray-scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.     

芳烃连续重整装置先进控制系统的开发和应用

针对某石化公司芳烃连续重整装置的生产现状、存在问题及需求,为实现节能降耗而进行控制优化,在常规DCS控制的基础上提出了芳烃连续重整装置先进控制系统的控制目标、控制策略、系统结构及控制方案。利用机理建模,单值预估控制来进行先进控制器搭建,应用结果证实了所开发系统实现了质量指标的卡边控制,降低了能耗,提升了装置的平稳性。     

喷丸奥氏体耐热钢抗蒸汽氧化性的研究与使用现状

奥氏体钢内壁喷丸处理作为一种新的提高材料抗蒸汽氧化性的技术，在超超临界火电机组受热面管的使用越来越广泛和成熟。概述了传统奥氏体钢在蒸汽氧化过程中氧化皮的形成结构，材料临界Cr含量 对富Cr层形成的影响，以及氧化皮结构与临界Cr含量 的关系。奥氏体钢内壁喷丸处理提高抗蒸汽氧化性的机理为：喷丸在管子内表面形成一层微米级厚度的喷丸层，喷丸层中晶粒碎化、晶格畸变、位错增多，在蒸汽氧化过程中，为基体内的Cr原子向表面扩散提供短路通道，使喷丸层表面Cr浓度提高，以形成富Cr层，减小氧化速率，提高抗蒸汽氧化性。归纳得出国内外关于喷丸奥氏体钢与传统钢管在600~750 ℃温度范围内，抗蒸汽氧化性的顺序依次为：600~700 ℃温度下，喷丸SUPER304(S30432)>喷丸TP347H≈喷丸TP321H≈TP310HCbN(HR3C)>SUPER304≈TP347HFG>TP304≈TP347H>TP321H；700~750 ℃下，TP310HCbN(HR3C)>喷丸TP347H>喷丸TP321H。同时归纳了喷丸工艺中，各因素对喷丸效果的影响，喷丸层质量的金相和硬度评价方法，以及目前喷丸奥氏体钢的实际应用现状。最后分析了喷丸奥氏体钢关于加工、焊接以及使用寿命等仍需研究解决的问题，展望了喷丸工艺向马氏体钢的推广使用。     

Field Performance of High Oleic Soybeans with Mutant FAD2-1A and FAD2-1B Genes in Tennessee

Soybean [Glycine max (L.) Merr.] oil with high oleic acid (>75%) has increased oxidative stability and health benefits that are valuable for food, fuel, and industrial products. It has been determined that two naturally occurring mutations in genes FAD2-1A and FAD2-1B can combine to produce high oleic soybeans. The objective of this study was to test the effect of these mutant alleles on seed yield and oil and protein concentration. Molecular markers assisted in the creation of a population of 48 BC₃F_2:4 lines (93.75% expected genome commonality). Each line was classified into one of four genotypic groups where both FAD2-1A and FAD2-1B genes were either homozygous wild type or mutant, respectively. Twelve lines for each genotypic group were evaluated in three replications at six locations across Tennessee. There was no seed yield difference between the high oleic genotypic group and the other groups (P < 0.05). On the other hand, there were differences in fatty acid profiles and oil and protein concentrations. In combination, the mutant FAD2-1A and FAD2-1B alleles produced a mean of 803.1 g kg⁻¹ oleic acid. This is, on average, approximately 500 g kg⁻¹ more oleic acid compared to soybean lines with only one mutant FAD2-1 allele. The high oleic double mutant group had more total oil (228.0 g kg⁻¹) and protein (401.0 g kg⁻¹) compared to all other genotypic groups (P < 0.05). Overall, this specific combination of mutant FAD2-1A and FAD2-1B alleles appears to generate conventional high oleic soybeans without a yield drag.