RSM型耐硫CO变换催化剂性能评价

针对现行的变换催化剂稳定性较差、易失活的缺陷,研究以拟薄水铝石及氢氧化镁为原料制备Mg-Al尖晶石载体,开发了一种Co-Mo系新型耐硫变换催化剂——RSM型催化剂。对其孔结构、物相及强度进行了测定;通过水热实验、常压本征活性实验、加压活性试验评价了催化剂的使用性能。结果表明,RSM型催化剂在工况条件下具有优良的活性、稳定性和较长的使用寿命。     

关于用户兴趣度的判定树算法的优化

判定树学习有很多算法,着重研究对引入用户兴趣度参数的ID3算法在面对多值属性时的快速分类的优化,提高计算速度,节约计算时间。     

用NetSolve构建数值计算网格

介绍了网格中间件NetSolve的体系结构及其工作过程,讨论了使用NetSolve软件搭建数值计算网格的基本过程,以简单实例说明如何调用计算网格的计算服务。     

Performance evaluation principles for vector- and multiprocessor systems

Skilful computer system measurement, modelling and performance evaluation techniques are needed for supercomputer architectures. They allow to accurately determine characteristics performance values, to find potential hardware- and software-bottleneck; they also help to efficiently distribute and schedule user tasks. This paper is an extended version of a tutorial contribution at the IEEE CompEuro 87 and surveys fundamental performance issues and their solution for supercomputer architectures.     

一个基于博弈学习的多主体竞价模型

根据多主体撮合交易模型,把整个撮合交易看成各交易主体的动态交互过程,设计了基于Multi-Agent的电子商务交易市场中交易主体动态竞价策略,提出了博弈学习的概念,并建立了基于博弈学习的动态竞价模型,根据撮合密度的定义,分析了所建立模型的性能和效率,试验表明,基于博弈学习的多主体动态竞价模型使多主体撮合交易系统具有一定的自均衡和自学习能力和良好的交易性能。     

智能化机动车综合性能检测系统的设计与开发

针对以单片机为核心开发的原机动车综合性能检测仪所存在的数据处理能力弱、功能不易扩充等缺点,基于上位机与下位机结构提出了一套智能化机动车综合性能检测系统设计方案.给出了系统结构及工作原理,介绍了系统组成中的关键硬件技术,并对软件系统编程进行了详细的论述,提出了机动车综合性能检测系统的全面解决方案.该方案可以解决原测试仪系统中存在的测试精度低、编程复杂、用户界面不够友好等一系列问题.     

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence,Severity, and Treatment Response

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging.