镁质波形瓦机械化设备的研制

讲述了镁质波形瓦机械化设备的构造,并对生产过程中碰到的难题作了剖析,也指出了其中的不足之处。     

循环水厂余压势能回收利用

循环水厂以水轮机代替电机带动风扇转动,充分利用富余的压力势能冷却循环水,达到节能的目的.     

3-UPU并联机构的运动学仿真与分析

为了提高并联机构的设计效率和准确性,以3-UPU型三平动并联机构为研究对象,对其进行运动学位置正逆解仿真与分析。在ADAMS环境下,得出该机构位置正逆解的仿真曲线。运用MATLAB作出其理论位置逆解曲线并与仿真位置逆解曲线相比较,验证了仿真位置逆解的正确性。将仿真逆解曲线以样条驱动函数形式输入给该机构,得出样条驱动函数下的位置正解曲线并与仿真位置正解曲线相比较,验证了仿真位置正解的正确性。说明了该并联机构的理论数学模型与仿真实体模型的正确性与合理性,为后续的控制提供理论依据。     

基于虚拟仪器动态信号测试系统的设计

以PC机、数据采集板为主要硬件,以图形化编程语言DASYLab为软件开发平台．设计了基于虚拟仪器的动态信号测试系统。系统软件主要由数据采集模块、数据处理分析模块和数据库管理模块等构成,文中对各模块的功能进行了详细的介绍。     

NMC 800切断机调整方法的应用

通过对天津石化公司20万t聚酯短丝装置NEUMAGNMC800卧式切断机的组成与结构特点的分析,介绍了切断机球体与铜压环的调整方法,并在生产过程的实际应用中验证了此调整方法的可行性。     

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.     

Chemistry towards Biology—Instruct: Snapshot

The knowledge of interactions between different molecules is undoubtedly the driving force of all contemporary biomedical and biological sciences. Chemical biology/biological chemistry has become an important multidisciplinary bridge connecting the perspectives of chemistry and biology to the study of small molecules/peptidomimetics and their interactions in biological systems. Advances in structural biology research, in particular linking atomic structure to molecular properties and cellular context, are essential for the sophisticated design of new medicines that exhibit a high degree of druggability and very importantly, druglikeness. The authors of this contribution are outstanding scientists in the field who provided a brief overview of their work, which is arranged from in silico investigation through the characterization of interactions of compounds with biomolecules to bioactive materials.     

Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data

Bayesian inference is ubiquitous in science and widely used in biomedical research such as cell sorting or “omics” approaches, as well as in machine learning (ML), artificial neural networks, and “big data” applications. However, the calculation is not robust in regions of low evidence. In cases where one group has a lower mean but a higher variance than another group, new cases with larger values are implausibly assigned to the group with typically smaller values. An approach for a robust extension of Bayesian inference is proposed that proceeds in two main steps starting from the Bayesian posterior probabilities. First, cases with low evidence are labeled as “uncertain” class membership. The boundary for low probabilities of class assignment (threshold ε) is calculated using a computed ABC analysis as a data-based technique for item categorization. This leaves a number of cases with uncertain classification (p < ε). Second, cases with uncertain class membership are relabeled based on the distance to neighboring classified cases based on Voronoi cells. The approach is demonstrated on biomedical data typically analyzed with Bayesian statistics, such as flow cytometric data sets or biomarkers used in medical diagnostics, where it increased the class assignment accuracy by 1–10% depending on the data set. The proposed extension of the Bayesian inference of class membership can be used to obtain robust and plausible class assignments even for data at the extremes of the distribution and/or for which evidence is weak.     

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as “Acute Effects”, “NIOSH Toxicity Data”, “Interactions”, “Hepatotoxicity”, “Carcinogenicity”, “Symptoms”, and “Human Toxicity Values” sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.