The Difference of the Pulmonary-Artery Pressure of the Migrants from Difference Elevation at the 4636 - 4907 m Altitude

Study Objective： To compare and analyze the changes of the pulmonary-artery pressure of the migrants coming from different elevation in the hypoxic environment of 4636 - 4907 m extreme altitude. To explore the susceptibility to hypoxic pulmonary-artery hypertension （PH） in the subjects from different altitude and profession. Methods： By using Color Doppler Ultmsonography （CDU）, measuring the pulmonary-artery pressure of 207 healthy men, who had continuously being lived and worked at the extreme altitude for more than six months, and then were divided into three groups according to their profession and the altitude of original living place. Results： There was a significant difference in the outcomes of pulmonary-artery pressure from the 3 groups. Conclusions： Altitude of original living place, labor intensity are some of factors that impact the pulmonary-artery pressure of the people who exposure to a hypoxic environment. The pulmonary- artery pressure of person without strenuously physical work experience was more sensitive to hypoxic surroundings than that of labor workers. It was not always the fact at an extreme altitude that the moderate altitude mountaineers were superior to other migrants from a lower altitude or plain. The higher PH was found in the groups of the moderate altitude mountaineers and labor workers. It is unlikely certain that one with PH would surfer from HAPE.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

一种新的基于Kohonen网络的图像矢量量化

矢量量化是一种重要的数据压缩方法.本文利用Kohonen自组织映射神经网络进行矢量量化,首次较为详细地讨论了具体实现的步骤与细节,并在此基础上为改善边缘质量,提出一种基于Laplas算子检测边缘弓引导神经网络训练的方法,并通过实验证明其效果是明显的.     

复合氧化物晶体晶格能的近似计算

本文以 Born-Harb 循环为依据,推导了复合晶体晶格能和与其相应的各二元化合物晶体晶格能之间的关系,并由此给出了复合氧化物晶体晶格能近似计算的新公式。对20种复合氧化物晶体晶格能计算结果的分析表明:用本文所给公式对复合氧化物晶体晶格能进行近似计算是可行的。     

边界拟合坐标系中的流动方程

本文提出了流体力学解析边界拟合坐标系方法，及建立在这种坐标系基础上对流动方程进行摄动处理的方法，并列举几种流动建立了解析边界拟合坐标系中的方程。