Quantitative Assessment of Affinity Selection Performance by Using DNA-Encoded Chemical Libraries

DNA-encoded chemical libraries are often used for the discovery of ligands against protein targets of interest. These large collections of DNA-barcoded chemical compounds are typically screened by using affinity capture methodologies followed by PCR amplification and DNA sequencing procedures. However, the performance of individual steps in the selection procedures has been scarcely investigated, so far. Herein, the quantitative analysis of selection experiments, by using three ligands with different affinity to carbonic anhydrase IX as model compounds, is described. In the first set of experiments, quantitative PCR (qPCR) procedures are used to evaluate the recovery and selectivity for affinity capture procedures performed on different solid-phase supports, which are commonly used for library screening. In the second step, both qPCR and analysis of DNA sequencing results are used to assess the recovery and selectivity of individual carbonic anhydrase IX ligands in a library, containing 360 000 compounds. Collectively, this study reveals that selection procedures can be efficient for ligands with sub-micromolar dissociation constants to the target protein of interest, but also that selection performance dramatically drops if 10⁴ copies per library member are used as the input.     

Compilation of Modern Technologies To Map Genome-Wide Cytosine Modifications in DNA

Over the past few decades, various DNA modification detection methods have been developed; many of the high-resolution methods are based on bisulfite treatment, which leads to DNA degradation, to a degree. Thus, novel bisulfite-free approaches have been developed in recent years and shown to be useful for epigenome analysis in otherwise difficult-to-handle, but important, DNA samples, such as hmC-seal and hmC-CATCH. Herein, an overview of advances in the development of epigenome sequencing methods for these important DNA modifications is provided.     

基于深度卷积长短时神经网络的视频帧预测

针对视频帧预测中难以准确预测空间结构信息细节的问题，通过对卷积长短时记忆（LSTM）神经网络的改进，提出了一种深度卷积长短时神经网络的方法。首先，将输入序列图像输入到两个不同通道的深度卷积LSTM网络组成的编码网络中，由编码网络学习输入序列图像的位置信息变化特征和空间结构信息变化特征；然后，将学习到的变化特征输入到与编码网络通道数对应的解码网络中，由解码网络输出预测的下一张图；最后，将这张图输入回解码网络中，预测接下来的一张图，循环预先设定的次后输出全部的预测图。与卷积LSTM神经网络相比，在Moving-MNIST数据集上的实验中，相同训练步数下所提方法不仅保留了位置信息预测准确的特点，而且空间结构信息细节表征能力更强。同时，将卷积门控循环单元（GRU）神经网络的卷积层加深后，该方法在空间结构信息细节表征上也取得了提升，检验了该方法思想的通用性。     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Bifunctional DNA Duplexes Permit Efficient Incorporation of pH Probes into Liposomes

Enzyme-mediated proton transport across biological membranes is critical for many vital cellular processes. pH-sensitive fluorescent dyes are an indispensable tool for investigating the molecular mechanism of proton-translocating enzymes. Here, we present a novel strategy to entrap pH-sensitive probes in the lumen of liposomes that has several advantages over the use of soluble or lipid-coupled probes. In our approach, the pH sensor is linked to a DNA oligomer with a sequence complementary to a second oligomer modified with a lipophilic moiety that anchors the DNA conjugate to the inner and outer leaflets of the lipid bilayer. The use of DNA as a scaffold allows subsequent selective enzymatic removal of the probe in the outer bilayer leaflet. The method shows a high yield of insertion and is compatible with reconstitution of membrane proteins by different methods. The usefulness of the conjugate for time-resolved proton pumping measurements was demonstrated by using two large membrane protein complexes.     

Reducing the complexity of distance measurement methods for circular turbo codes that use structured interleavers

The knowledge of turbo code's minimum Hamming distance (d_min) and its corresponding codeword multiplicity (A_min) is of a great importance because the error correction capability of a code is strongly tied to the values of d_min and A_min. Unfortunately, the computational complexity associated with the search for d_min and A_min can be very high, especially for a turbo code that has high d_min value. This paper introduces some useful properties of turbo codes that use structured interleavers together with circular encoding. These properties allow for a significant reduction of search space and thus reduce significantly the computational complexity associated with the determination of d_min and A_min values. © 2014 The Authors. International Journal of Communication Systems published by John Wiley & Sons, Ltd.     

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.