基于CiteSpace的资源型产业研究知识图谱分析

资源型产业发展为推进国家经济增长和工业化进程提供了重要保障。为深入了解资源型产业的研究情况，以CNKI数据库为数据源，搜集2000—2020年关于资源型产业的核心及以上期刊论文，利用CiteSpace软件从发文作者与研究机构分布、关键词共现网络和时区图谱等方面，绘制知识图谱，进行可视化分析。研究发现：资源型产业领域的研究成果愈加丰富，但研究群体间联系合作较少，且现有的合作研究主要集中在所处地域资源富集和具有学科优势的研究机构及学者；资源型产业领域的研究热点可概括为产业发展、资源型城市、产业集群、产业结构、产业链和产业集聚等方面；针对资源型产业领域未来可从资源型产业相关理论研究、创新发展模式和可持续发展等方面深入展开。     

Identification of Novel Fragments Binding to the PDZ1-2 Domain of PSD-95

Inhibition of PSD-95 has emerged as a promising strategy for the treatment of ischemic stroke, as shown with peptide-based compounds that target the PDZ domains of PSD-95. In contrast, developing potent and drug-like small molecules against the PSD-95 PDZ domains has so far been unsuccessful. Here, we explore the druggability of the PSD-95 PDZ1-2 domain and use fragment screening to investigate if this protein is prone to binding small molecules. We screened 2500 fragments by fluorescence polarization (FP) and validated the hits by surface plasmon resonance (SPR), including an inhibition counter-test, and found four promising fragments. Three ligand efficient fragments were shown by ¹H,¹⁵N HSQC NMR to bind in the small hydrophobic P⁰ pockets of PDZ1-2, and one of them underwent structure-activity relationship (SAR) studies. Overall, we demonstrate that fragment screening can successfully be applied to PDZ1-2 of PSD-95 and disclose novel fragments that can serve as starting points for optimization towards small-molecule PDZ domain inhibitors.     

金属增材制造技术研究热点聚类分析

为深入分析金属增材制造技术分类和发展状况,采集中国期刊全文数据库(CNKI)收录的核心期刊上的768篇科技文献,借助文献分析可视化软件CiteSpace,对关键词聚类进行了全景式描绘,构建金属增材制造技术知识图谱,揭示该技术研究分类以及演化趋势。     

结合预训练模型和语言知识库的文本匹配方法

针对文本匹配任务,该文提出一种大规模预训练模型融合外部语言知识库的方法。该方法在大规模预训练模型的基础上,通过生成基于WordNet的同义—反义词汇知识学习任务和词组—搭配知识学习任务引入外部语言学知识。进而,与MT-DNN多任务学习模型进行联合训练,以进一步提高模型性能。最后利用文本匹配标注数据进行微调。在MRPC和QQP两个公开数据集的实验结果显示,该方法可以在大规模预训练模型和微调的框架基础上,通过引入外部语言知识进行联合训练有效提升文本匹配性能。     

Activity of Riminophenazines against Mycobacterium tuberculosis: A Review of Studies that Might be Contenders for Use as Antituberculosis Agents

Tuberculosis is one of the leading cause of death in the world, mainly due to the increasing number of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) strains. Factors such as the HIV pandemic contribute further. Also, the ineffectiveness of the chemotherapy in current use increases the mortality rate. Therefore, new and repurposed antituberculosis drugs are urgently needed for the treatment of MDR-TB, and riminophenazines are among those drugs that are being reinvestigated for their potential in the treatment of TB. This review delivers a brief historical account of riminophenazines, their general synthesis, mechanisms of action, and their physicochemical properties. The discussion is limited to those studies that investigated the activity of these compounds as antituberculosis agents. Given their unique properties, this review will be of great significance in giving direction towards the design and development of new riminophenazine analogues.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

Design,Synthesis, Radiosynthesis and Biological Evaluation of Fenretinide Analogues as Anticancer and Metabolic Syndrome-Preventive Agents

Fenretinide (4-HPR) is a synthetic derivative of all-trans-retinoic acid (ATRA) characterised by improved therapeutic properties and toxicological profile relative to ATRA. 4-HPR has been mostly investigated as an anti-cancer agent, but recent studies showed its promising therapeutic potential for preventing metabolic syndrome. Several biological targets are involved in 4-HPR's activity, leading to the potential use of this molecule for treating different pathologies. However, although 4-HPR displays quite well-understood multitarget promiscuity with regards to pharmacology, interpreting its precise physiological role remains challenging. In addition, despite promising results in vitro, the clinical efficacy of 4-HPR as a chemotherapeutic agent has not been satisfactory so far. Herein, we describe the preparation of a library of 4-HPR analogues, followed by the biological evaluation of their anti-cancer and anti-obesity/diabetic properties. The click-type analogue 3 b showed good capacity to reduce the amount of lipid accumulation in 3T3-L1 adipocytes during differentiation. Furthermore, it showed an IC₅₀ of 0.53±0.8 μM in cell viability tests on breast cancer cell line MCF-7, together with a good selectivity (SI=121) over noncancerous HEK293 cells. Thus, 3 b was selected as a potential PET tracer to study retinoids in vivo, and the radiosynthesis of [¹⁸F] 3b was successfully developed. Unfortunately, the stability of [¹⁸F] 3b turned out to be insufficient to pursue imaging studies.     

A Strategy for Discovering Heterochiral Bioactive Peptides by Using the OB2nP Library and SPOTs Method

d -Amino acid containing peptides are promising as drug lead compounds because of their expected higher stability in vivo. A heterochiral random peptide library called the one-bead–2ⁿ-peptide (OB2ⁿP) library, which can display 2ⁿ peptide diastereomers per bead, has been developed. Through screening of the OB2ⁿP library and subsequent binding assay among the peptide diastereomers synthesized in parallel by means of the SPOTs method, new heterochiral mimotopes for the anti-β-endorphin monoclonal antibody have been obtained. One mimotope was a new ligand for the μ-opioid receptor. The screening strategy enabled d -amino acid containing drug leads to be obtained efficiently by expanding searchable chemical space without increasing the experimental scale.     

网络匿名扫描系统设计及优化

针对网络扫描工具在进行扫描时面临的溯源问题，提出了一种匿名网络扫描系统。首先将匿名系统与网络扫描工具结合以实现匿名扫描；然后在现有匿名系统的基础上实现了该系统的本地私有化；接着通过流量分析发现，Nmap的多进程扫描因为代理链的原因会变成单进程扫描而导致其扫描扫描性能较低；最后提出了一种基于多Namp进程并发的性能优化方案，将总体扫描任务分割为多个扫描任务，并分配给多个单独的Nmap进程并行运行。实验结果表明，该性能优化方案的扫描时延接近正常扫描情况下的时延，达到了提高匿名扫描系统性能的目的。因此，该优化后的网络匿名扫描系统在阻碍溯源的同时提升了扫描效率。     

Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes

Numerical simulation techniques such as Finite Element Analyses are essential in today's engineering design practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength and further product properties. Due to limited capacities, design-accompanying simulations are performed too rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations lead to wrong design decisions, if they are applied by less experienced users. This results in belated redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development.Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a novel ontology-based approach is presented. The knowledge management tools developed on the basis of this approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This knowledge is acquired from existing simulation models and text-based documentations from previous product developments by Text and Data Mining. By offering support to less experienced simulation users, the presented approach may finally lead to a more efficient and extensive application of reliable FEA in product development.