Intracellular protein degradation in mammalian cells: recent developments

In higher organisms, dietary proteins are broken down into amino acids within the digestive tract but outside the cells, which incorporate the resulting amino acids into their metabolism. However, under certain conditions, an organism loses more nitrogen than is assimilated in the diet. This additional loss was found in the past century to come from intracellular proteins and started an intensive research that produced an enormous expansion of the field and a dispersed literature. Therefore, our purpose is to provide an updated summary of the current knowledge on the proteolytic machinery involved in intracellular protein degradation and its physiological and pathological relevance, especially addressed to newcomers in the field who may find further details in more specialized reviews. However, even providing a general overview, this is an extremely wide field and, therefore, we mainly focus on mammalian cells, while other cells will be mentioned only for comparison purposes.     

杨树木材细胞腔径分布的分形表征

应用杨树木材横切面的细胞直径分布描述木材横切面空隙分布的分形特征。通过对不同高度处木材细胞腔直径分布进行分析,分别计算出它们的分形维数(D=124～131)。结果表明:腔径分布分形维数的大小可以作为表征木材多孔性的一个参数。     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.     

科研管理辅助决策模型研究：以ARP为例

随着经济社会的发展和科学技术的不断进步，科研活动也呈现出许多新的特点，对科研管理工作提出了一系列新要求，实现科研管理整体化、透明化、标准化、规范化和精细化。本文以科研管理过程中辅助决策模型为研究对象，中国科学院资源规划系统（Academia Resource Planning，ARP）为依托，在总结基于ARP 开展科研管理辅助决策模型研究工作的基础上，结合IBM 公司推出的数据集成产业链，从功能需求框架、辅助决策模型库、样本库三个方面提出完整的科研管理辅助决策模型建设方案。     

科研项目经费监测预警模型的探讨

本文结合企业财务预警的方法以及科学研究事业单位的特性，围绕科研项目活动的正常开展为目的，对科研项目经费监测预警模型初步探讨。运用定性财务预警的方法逐步深入分析，为进一步进行定量分析提供素材。     

十年辛勤耕耘 服务一流管理——纪念ARP 项目实施10 周年

中国科学院资源规划项目(Academia Resource Planning， 简称ARP 项目)，是实现中国科学院科学资源规划的信息系统工程，是带有科研活动管理特殊性质的电子政务系统。本文主要从ARP 系统实施十年的过程、形成的成果及系统特点进行了论述，就其在促进中国科学院管理创新、服务一流管理等方面取得的主要成效进行了分析，并结合中国科学院“创新2020”的战略需要，对其未来发展进行了展望。     

地方高校科技成果转化若干问题及对策

本文从地方高校在推进国家创新体系建设中的地位和作用出发，分析了地方高校和省部级高校科技工作尤其是科技成果转化工作的差距。根据目前地方高校科技工作存在的不足，指出了导致地方高校在科技成果转化工作中存在的若干问题，并提出解决这些问题的对策。