A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information

<p>Abstract</p> <p>Background</p> <p>Prediction of protein-ligand binding sites is an important issue for protein function annotation and structure-based drug design. Nowadays, although many computational methods for ligand-binding prediction have been developed, there is still a demanding to improve the prediction accuracy and efficiency. In addition, most of these methods are purely geometry-based, if the prediction methods improvement could be succeeded by integrating physicochemical or sequence properties of protein-ligand binding, it may also be more helpful to address the biological question in such studies.</p> <p>Results</p> <p>In our study, in order to investigate the contribution of sequence conservation in binding sites prediction and to make up the insufficiencies in purely geometry based methods, a simple yet efficient protein-binding sites prediction algorithm is presented, based on the geometry-based cavity identification integrated with sequence conservation information. Our method was compared with the other three classical tools: PocketPicker, SURFNET, and PASS, and evaluated on an existing comprehensive dataset of 210 non-redundant protein-ligand complexes. The results demonstrate that our approach correctly predicted the binding sites in 59% and 75% of cases among the TOP1 candidates and TOP3 candidates in the ranking list, respectively, which performs better than those of SURFNET and PASS, and achieves generally a slight better performance with PocketPicker.</p> <p>Conclusions</p> <p>Our work has successfully indicated the importance of the sequence conservation information in binding sites prediction as well as provided a more accurate way for binding sites identification.</p

Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths.We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.Comment: 6 page

On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles

Car-hailing services have become a prominent data source for urban traffic studies. Extracting useful information from car-hailing trace data is essential for effective traffic management, while discrepancies between car-hailing vehicles and urban traffic should be considered. This paper proposes a generic framework for estimating and analyzing urban traffic patterns using car-hailing trace data. The framework consists of three layers: the data layer, the interactive software layer, and the processing method layer. By pre-processing car-hailing GPS trace data with operations such as data cutting, map matching, and trace correction, the framework generates tensor matrices that estimate traffic patterns for car-hailing vehicle flow and average road speed. An analysis block based on these matrices examines the relationships and differences between car-hailing vehicles and urban traffic patterns, which have been overlooked in previous research. Experimental results demonstrate the effectiveness of the proposed framework in examining temporal-spatial patterns of car-hailing vehicles and urban traffic. For temporal analysis, urban road traffic displays a bimodal characteristic while car-hailing flow exhibits a 'multi-peak' pattern, fluctuating significantly during holidays and thus generating a hierarchical structure. For spatial analysis, the heat maps generated from the matrices exhibit certain discrepancies, but the spatial distribution of hotspots and vehicle aggregation areas remains similar

Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines

High-Dielectric PVP@PANI/PDMS Composites Fabricated via an Electric Field-Assisted Approach

Polymer-based composite films with multiple properties, such as low dielectric loss tangent, high dielectric constant, and low cost are promising materials in the area of electronics and electric industries. In this study, flexible dielectric films were fabricated via an electric field-assisted method. Polyaniline (PANI) was modified by polyvinylpyrrolidone (PVP) to form a core–shell structure to serve as functional particles and silicone rubber polydimethylsiloxane (PDMS) served as the matrix. The dielectric constant of the composites prepared under electric fields was improved by the micro-structures formed by external electric fields. With the addition of 2.5 wt% PVP@PANI, the dielectric constant could be significantly enhanced, up to 23; the dielectric loss tangent is only 1, which is lower than that of the aligned PANI samples. This new processing technology provides important insights for aligning fillers in polymer matrix to form composites with enhanced dielectric properties

Snowflake-Shaped ZnO Nanostructures-Based Gas Sensor for Sensitive Detection of Volatile Organic Compounds

Volatile organic compounds (VOCs) have been considered severe risks to human health. Gas sensors for the sensitive detection of VOCs are highly required. However, the preparation of gas-sensing materials with a high gas diffusion performance remains a great challenge. Here, through a simple hydrothermal method accompanied with a subsequent thermal treatment, a special porous snowflake-shaped ZnO nanostructure was presented for sensitive detection of VOCs including diethyl ether, methylbenzene, and ethanol. The fabricated gas sensors exhibit a good sensing performance including high responses to VOCs and a short response/recovery time. The responses of the ZnO-based gas sensor to 100 ppm ethanol, methylbenzene, and diethyl ether are about 27, 21, and 11, respectively, while the response times to diethyl ether and methylbenzene are less than 10 seconds. The gas adsorption-desorption kinetics is also investigated, which shows that the gas-sensing behaviors to different target gases are remarkably different, making it possible for target recognition in practical applications

Genetic testing and microstructure characterization of biological crusts on the rammed soil surface at the Shanhaiguan great wall in China

Protection of cultural relics and sites is of great significance. In this study, the new gray-green thin-layer biological crust on the rammed soil surface at the Shanhaiguan Great Wall in China was found. The emergence of this material has substantially improved the resistance of the rammed Earth Great Wall to rain erosion. 16S rRNA gene sequencing on the surface crusts of rammed Earth was performed. Results show the biological crusts were mainly algae-based composite crusts containing fungi. At the genus level, microalgae and Sphingomycetes were predominant. Under scanning electron microscopy (SEM), algae filaments dominated by filamentous algae overlapped and intertwined with each other. Furthermore, polysaccharide organic matter secreted by algae formed a covering film. The two formed a complex spatial network structure to envelop soil particles, which enhances erosion resistance. The conformable biological crusting is expected to be used as a new civil engineering material for the protection of rammed Earth sites in the future