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Abstract Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

The 5G mobile Internet facilitates contents generation for online communities and platforms through human-to-human collaboration. Wikipedia, a well-known online community, uses wiki technology to build an encyclopedia through collective intelligence and collaboration. Mainstream wiki systems adopt a centralized implementation, and while existing studies have optimized the efficiency of the wiki systems' centralized implementation, these systems still suffer from a lot of problems, for example, opacity and distrust. Over the years, blockchain has brought a flurry of fervour and decentralization to the system architecture, meanwhile giving users a sense of trust and participation. Thus, an innovative blockchain-enabled wiki framework called DecWiki is proposed to build one transparent, truthful, collaborative and autonomous encyclopedia. After several participatory design iterations, we present DecWiki's detailed architecture and its implementation in the form of a smart contract and use the interplanetary file system to complement the big data storage. Meanwhile, we use the trusted execution environment to secure sensitive information in the wireless scenario. Finally, the system overhead and the acceptance of the prototype are evaluated. Extensive experiments present its significant performance.

The most challenging task of Community Question Answering (CQA) is to provide high-quality answers to users' questions. Currently, a variety of expert recommendation methods have been proposed and greatly improved the effective matching between questions and potential good answerers. However, the performance of existing methods can be adversely affected by many common factors such as data sparsity and noise problem, which cause less precise user modeling. Moreover, existing methods often model user-question interactions through simple ways, failing to capture the multiple scale interactions of question and answerers, which make it difficult to find answerers who are able to provide the best answers. In this paper, we propose an attention-based variant of Factorization Machines (FM) called Hierarchical Attentional Factorization Machines (HaFMRank) for answerer recommendation in CQA, which not only models the interactions between pairs of individual features but emphasizes the roles of crucial features and pairwise interactions. Specifically, we introduce the within-field attention layer to capture the inner structure of features belonging to the same field, while a feature-interaction attention layer is adopted to examine the importance of each pairwise interaction. A pre-training procedure is designed to generate latent FM feature embedding that encode question context and user history into the training process of HaFMRank. The performance of the proposed HaFMRank is evaluated by using real-world datasets of Stack Exchange and experimental results demonstrate that it outperforms several state-of-the-art methods in best answerer recommendation.

The rapid development of self-driving technology has made self-driving cars come into reality, and some people have already adopted different levels of self-driving technology in real driving practices. With the help of self-driving technology, nowadays people can share and schedule their routes together while driving. However, currently, there are poor supports for such activities, as it demands highly responsiveness, strong consistency guarantees, and low transmission costs. As to support the novel scenario, we developed an edge computing oriented and highly efficient AST-Based lock-free synchronizing and sharing system called Themis on a fundamental itinerary planning model. We optimized the system by adopting partial replication, snapshots of history operations, and compression on consecutive operations strategies, which leave certain calculations at the cloud side, and reduce the amount and size of transmission data in the network. Besides, we have analyzed and proved the correctness of our scheme in detail; examining the significant improvements in performances through experiments.

The main functional components of green tea, such as epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG) and epicatechin (EC), are found to have a broad antineoplastic activity. The discovery of their targets plays an important role in revealing the antineoplastic mechanism. Therefore, to identify potential target proteins for tea polyphenols, we have taken a comparative virtual screening approach using two reverse docking systems, one based on Autodock software and the other on Tarfisdock. Two separate in silico workflows were implemented to derive a set of target proteins related to human diseases and ranked by the binding energy score. Several conventional clinically important proteins with anti-tumor effects are screened out from the PDTD protein database as the potential receptors by both procedures. To further analyze the validity of docking results, we study the binding mode of EGCG and the potential target protein Leukotriene A4 hydrolase in detail. We indicate that interactions mediated by electrostatic and hydrogen bond play a key role in ligand binding. EGCG binds to the enzyme with certain orientation and conformation that is suitable for nucleophilic attacks by several electrical residues inside the enzyme’s activity cavity. This study provides useful information for studying the antitumor mechanism of tea’s functional components. The comparative reverse docking strategy presented generates a tractable set of antineoplastic proteins for future experimental validation as drug targets against tumors.

A new application of antibodies is to use them as macromolecular chaperones. Protein antigens usually have multiple epitopes, thus, there may be a plurality of antibodies binding to one antigen. However, not all antibodies that bind to one antigen could act as a chaperone. Experiments show that some screened anti-human creatine kinase single chain antibodies (scFV) could assist in the folding and stabilizing of the enzyme, while others could not. We built the model of the single chain antibody (scFv-A4) that increased the stability of human creatine kinase (HCK) by the homology modeling method. Epitopes of human creatine kinase were predicted by computer and then the binding of scFv-A4 and HCK was modeled with computer. The calculation results were further combined with the peptide array membrane experiment results to obtain reliable models for the scFv-A4-HCK complex. Based on the above study we gave an explanation about how scFv-A4 could act as a macromolecular chaperone assisting the folding of HCK. This study provides an approach for predicting antigen-antibody binding mode and also a useful theoretical guidance for the study of antibodies' chaperone-like function.