Abstract. Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident. This paper aims to discover the principles to design effective ConvNet architectures for action recognition in videos and learn these models given limited training samples. Our first contribution is temporal segment network (TSN), a novel framework for video-based action recognition. which is based on the idea of long-range temporal structure modeling. It combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video. The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network. Our approach obtains the state-the-of-art performance on the datasets of HMDB51 (69.4%) and UCF101 (94.2%). We also visualize the learned ConvNet models, which qualitatively demonstrates the effectiveness of temporal segment network and the proposed good practices.

The JUNO experiment locates in Jinji town, Kaiping city, Jiangmen city, Guangdong province. The geographic location is east longitude 112 • 31'05' and North latitude 22 • 07'05'. The experimental site is 43 km to the southwest of the Kaiping city, a county-level city in the prefecture-level city Jiangmen in Guangdong province. There are five big cities, Guangzhou, Hong Kong, Macau, Shenzhen, and Zhuhai, all in ∼200 km drive distance, as shown in figure 3.

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/) is an original database initially online in 2003. The APD2 (2009 version) has been regularly updated and further expanded into the APD3. This database currently focuses on natural antimicrobial peptides (AMPs) with defined sequence and activity. It includes a total of 2619 AMPs with 261 bacteriocins from bacteria, 4 AMPs from archaea, 7 from protists, 13 from fungi, 321 from plants and 1972 animal host defense peptides. The APD3 contains 2169 antibacterial, 172 antiviral, 105 anti-HIV, 959 antifungal, 80 antiparasitic and 185 anticancer peptides. Newly annotated are AMPs with antibiofilm, antimalarial, anti-protist, insecticidal, spermicidal, chemotactic, wound healing, antioxidant and protease inhibiting properties. We also describe other searchable annotations, including target pathogens, molecule-binding partners, post-translational modifications and animal models. Amino acid profiles or signatures of natural AMPs are important for peptide classification, prediction and design. Finally, we summarize various database applications in research and education.

Interest in two-dimensional (2D) van der Waals materials has grown rapidly across multiple scientific and engineering disciplines in recent years. However, ferroelectricity, the presence of a spontaneous electric polarization, which is important in many practical applications, has rarely been reported in such materials so far. Here we employ first-principles calculations to discover a branch of the 2D materials family, based on In2Se3 and other III2-VI3 van der Waals materials, that exhibits room-temperature ferroelectricity with reversible spontaneous electric polarization in both out-of-plane and in-plane orientations. The device potential of these 2D ferroelectric materials is further demonstrated using the examples of van der Waals heterostructures of In2Se3/graphene, exhibiting a tunable Schottky barrier, and In2Se3/WSe2, showing a significant band gap reduction in the combined system. These findings promise to substantially broaden the tunability of van der Waals heterostructures for a wide range of applications.

3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields. In this paper, we propose the Point-Voxel Region based Convolution Neural Networks (PV-RCNNs) for accurate 3D detection from point clouds. First, we propose a novel 3D object detector, PV-RCNN-v1, which employs the voxel-to-keypoint scene encoding and keypoint-to-grid RoI feature abstraction two novel steps. These two steps deeply incorporate both 3D voxel CNN and PointNet-based set abstraction for learning discriminative point-cloud features. Second, we propose a more advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection. It consists of two major improvements, where the first one is the sectorized proposal-centric strategy for efficiently producing more representative and uniformly distributed keypoints, and the second one is the VectorPool aggregation to replace set abstraction for better aggregating local point-cloud features with much less resource consumption. With these two major modifications, our PV-RCNN-v2 runs more than twice as fast as the v1 version while still achieving better performance on the large-scale Waymo Open Dataset with 150m × 150m detection range. Extensive experiments demonstrate that our proposed PV-RCNNs significantly outperform previous state-of-the-art 3D detection methods on both the Waymo Open Dataset and the highly-competitive KITTI benchmark.

Molecular mechanics Poisson−Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein−ligand binding, protein−protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields. CONTENTS 1. Introduction 9478 2. Methodology 9480 3. Assessing the Performance of MM/PBSA and MM/GBSA 9484 4. The Polar Solvation Energy and Entropy Terms in MM/PB(GB)SA Calculations 9485 4.1. The Polar Solvation Energy Term in MM/ PBSA 9485 4.2. The Polar Energy Solvation Term in MM/ GBSA 9486 4.3. Theory, Implementation, and Limitations of the Variable Dielectric Model in MM/GBSA 9487 4.4. Comparison between PB and GB 9488 4.5. Efficient Entropy Calculation Methods To Estimate the Entropy Change upon Ligand Binding 9488 5.

The antimicrobial peptide database (APD, http://aps.unmc.edu/AP/main.php) has been updated and expanded. It now hosts 1228 entries with 65 anticancer, 76 antiviral (53 anti-HIV), 327 antifungal and 944 antibacterial peptides. The second version of our database (APD2) allows users to search peptide families (e.g. bacteriocins, cyclotides, or defensins), peptide sources (e.g. fish, frogs or chicken), post-translationally modified peptides (e.g. amidation, oxidation, lipidation, glycosylation or d-amino acids), and peptide binding targets (e.g. membranes, proteins, DNA/RNA, LPS or sugars). Statistical analyses reveal that the frequently used amino acid residues (>10%) are Ala and Gly in bacterial peptides, Cys and Gly in plant peptides, Ala, Gly and Lys in insect peptides, and Leu, Ala, Gly and Lys in amphibian peptides. Using frequently occurring residues, we demonstrate database-aided peptide design in different ways. Among the three peptides designed, GLK-19 showed a higher activity against Escherichia coli than human LL-37.

As one of the most popular computational approaches in modern structure-based drug design, molecular docking can be used not only to identify the correct conformation of a ligand within the target binding pocket but also to estimate the strength of the interaction between a target and a ligand. Nowadays, as a variety of docking programs are available for the scientific community, a comprehensive understanding of the advantages and limitations of each docking program is fundamentally important to conduct more reasonable docking studies and docking-based virtual screening. In the present study, based on an extensive dataset of 2002 protein-ligand complexes from the PDBbind database (version 2014), the performance of ten docking programs, including five commercial programs (LigandFit, Glide, GOLD, MOE Dock, and Surflex-Dock) and five academic programs (AutoDock, AutoDock Vina, LeDock, rDock, and UCSF DOCK), was systematically evaluated by examining the accuracies of binding pose prediction (sampling power) and binding affinity estimation (scoring power). Our results showed that GOLD and LeDock had the best sampling power (GOLD: 59.8% accuracy for the top scored poses; LeDock: 80.8% accuracy for the best poses) and AutoDock Vina had the best scoring power (rp/rs of 0.564/0.580 and 0.569/0.584 for the top scored poses and best poses), suggesting that the commercial programs did not show the expected better performance than the academic ones. Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs. However, for some types of protein families, relatively high linear correlations between docking scores and experimental binding affinities could be achieved. To our knowledge, this study has been the most extensive evaluation of popular molecular docking programs in the last five years. It is expected that our work can offer useful information for the successful application of these docking tools to different requirements and targets.