Jianxin Wang

The emergence of aggregation-induced emission luminogens (AIEgens) has significantly stimulated the development of luminescent supramolecular materials because their strong emissions in the aggregated state have resolved the notorious obstacle of the aggregation-caused quenching (ACQ) effect, thereby enabling AIEgen-based supramolecular materials to have a promising prospect in the fields of luminescent materials, sensors, bioimaging, drug delivery, and theranostics. Moreover, in contrast to conventional fluorescent molecules, the configuration of AIEgens is highly twisted in space. Investigating AIEgens and the corresponding supramolecular materials provides fundamental insights into the self-assembly of nonplanar molecules, drastically expands the building blocks of supramolecular materials, and pushes forward the frontiers of supramolecular chemistry. In this review, we will summarize the basic concepts, seminal studies, recent trends, and perspectives in the construction and applications of AIEgen-based supramolecular materials with the hope to inspire more interest and additional ideas from researchers and further advance the development of supramolecular chemistry.

MOTIVATION: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug-disease association information on the similarity measures. RESULTS: In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug-disease interactions. Based on the drug-disease heterogeneous network, BiRW algorithm is adopted to predict novel potential drug-disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically. AVAILABILITY AND IMPLEMENTATION: http://github.com//bioinfomaticsCSU/MBiRW CONTACT: jxwang@mail.csu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI-based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.