血沉、白细胞计数和C反应蛋白在糖尿病足评估中的作用

目的本文旨在评估炎性因子而对不同程度的糖尿病足病患者作出早期判断的策略。方法对68例不同程度的糖尿病足病患者入院分别进行血沉、白细胞计数和C反应蛋白的测定并作比较。结果 C反应蛋白在2级~4级的糖尿病足病患者中是最有价值的标记物,而血沉、白细胞计数无法预示其程度。结论在不同程度的糖尿病足病患者中C反应蛋白是一个有价值的预测因子。     

高敏C反应蛋白与冠心病相关性的研究

目的评价血清高敏C反应蛋白（hs—CLIP）在不同类型冠心病（CHD）患者中的血清水平,总结临床经验。方法随机选取60例冠心病患者,包括稳定型心绞痛（SAP）20例,不稳定型心绞痛（UAP）20例,急性心肌梗死（AMI）20例,并以30例健康人作为对照组。采用透射免疫比浊法,分别测定各组血清中hs—CRP的浓度,并分析治疗前后hs—CRP水平的差异。结果不同临床类型冠心病hs—CRP浓度与正常对照组比较有显著性差异（P〈0．01）。不同临床类型冠心病hs—CRP浓度也有显著性差异（P〈0．05）。冠心病各组的hs—CRP水平在治疗前后比较有统计学意义（P〈0．01）。结论hs—CRP不仅参与冠心病的发生与发展,且其血清浓度越高,临床情况越重,预后越差,心脏事件发生率越高。检测hs—CRP对冠心痛的辅助诊断、疗效观察及预后判断具有重要价值。     

室内杀虫剂煤油溶剂的脱色脱硫醇研究

用改性蒙脱石预先对煤油脱色，再以活性炭为载体，氯化稀土混合物为催化剂，加入适当的助催化剂、活化剂，在弱碱性介质中，以空气氧为氧化剂，对工业煤油进行脱硫醇．硫醇的脱除率为１００％；色度小于１号；抗腐蚀实验为１级，无水溶性酸碱，酸值（ＫＯＨ）为０．０２ｍｇ／１００ｍＬ．处理后的煤油可作室内杀虫剂的超级溶剂．     

铝、镉、铜互作对不同耐铝大麦基因型糖及蛋白含量的影响

所有胁迫处理均导致耐性品种根系和叶片的蛋白质含量增加,三重复合胁迫时最高.低pH和Cu,Cd,Al复合胁迫使敏感基因型根系蛋白质含量增加而叶片蛋白质含量减少,其它胁迫结果恰好相反.Cu与Cd以及Cu,Cd,Al复合胁迫使耐性品种根系和叶片可溶性糖含量增加,单独Al胁迫使耐性品种根系和叶片可溶性糖含量减少,但对敏感基因型的影响因器官而异.Cu与Cd复合胁迫及单独Al胁迫均使耐性品种根系还原糖含量减少而使叶片还原糖含量增加,但对敏感基因型的影响存在差异;Cu,Cd,Al复合胁迫使敏感基因型根系、叶片和耐性品种根系还原糖含量增加,却使耐性品种叶片还原糖含量减少.各种胁迫处理均使耐性品种根系和叶片总糖含量下降,但敏感品种器官和处理间结果存在显著差异.     

前沿科学方法论与创新思维

"超—自"原则和"不变—可变"原则是前沿科学方法论的一般方法论原则,这一科学方法论与创新思维具有紧密相关性。前沿科学方法论的科学哲学基础是宇宙物质自身内在必然运动与内在自随机运动的相互整合,是"对称—对称破缺"方法论、"超耦合—自随机"方法论以及这两种科学方法论的辩证整合。创新思维的科学实质是在人们的知识信息、工具手段、研究经验、科技水平等相互超耦合达到足够充分或呈现特定可能时,将导致具有灵活性、新异性等特征的自随机思维激发及其与客观事物的不断关联和相互作用,从而实现以"超耦合—自随机"探索、应变、选择、创造和缔新为其基本表征的创新思维与创新行为。基于这一观点,前沿科学方法论与创新思维方法论二者将整合统一。     

21世纪的主要技术──生物技术─—生物技术产品的回顾和展望

本文介绍了生物技术的定义及其技术体系，并从生物技术产品的角度综述了生物技术的过去、现在和未来．     

An Approximation Algorithm for Alignment of Multiple Sequences using Motif Discovery

Given a set of N sequence, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. The quality of the alignment is usually measured by penalizing the mis-matches and gaps, and rewarding the matches with appropriate weight functions. However for larger values of N, additional constraints are required to give meaningful alignments. We identify a user-controlled parameter, an alignment number K (2 K N): this additional requirement constrains the alignment to have at least K sequences agree on a character, whenever possible, in the alignment. We identify a natural optimization problem for this approach called the K-MSA problem. We show that the problem is MAX SNP hard. We give a natural extension of this problem that incorporates biological relevance by using motifs (common patterns in the sequences) and give an approximation algorithm for this problem in terms of the motifs in the data. MUSCA is an implementation of this approach and our experimental results indicate that this approach is efficient, particularly on large numbers of long sequences, and gives good alignments when tested on biological data such as DNA and protein sequences.     

Computational Comparison Studies of Quadratic Assignment Like Formulations for the In Silico Sequence Selection Problem in De Novo Protein Design

In this paper an O(n²) mathematical formulation for in silico sequence selection in de novo protein design proposed by Klepeis et al. (2003, 2004), in which the number of additional variables and linear constraints scales with the square of the number of binary variables, is compared to three O(n) formulations. It is found that the O(n²) formulation is superior to the O(n) formulations on most sequence search spaces. The superiority of the O(n²) formulation is due to the reformulation linearization techniques (RLTs), since the O(n²) formulation without RLTs is found to be computationally less efficient than the O(n) formulations. In addition, new algorithmic enhancing components of RLTs with inequality constraints, triangle inequalities, and Dead-End Elimination (DEE) type preprocessing are added to the O(n²) formulation. The current best O(n²) formulation, which is the original formulation from Klepeis et al. (2003, 2004) plus DEE type preprocessing, is proposed for in silico sequence search. For a test problem with a search space of 3.4×10⁴⁵ sequences, this new improved model is able to reduce the required CPU time by 67%.     

超效率DEA模型的区间扩展

将一种改进的DEA模型-超效率DEA(SE-DEA)模型[1]拓展到区间投入产出情形,得到区间SE-DEA模型。定义了一种反映决策者满意度的区间数序关系。当决策者给定一满意度水平,将区间SE-DEA中的区间不等式约束转化为确定型约束。研究了该满意度水平的另一层含义,即决策者对除被评价决策单元外的其它决策单元的偏好程度,据此将区间SE-DEA中的区间等式约束和区间目标函数转化为确定型。最终将区间SE-DEA转化为某一满意度水平下的确定型SE-DEA,并进行求解。最后将文中方法应用于天津市某4家科研所的效率预测问题之中。     

基于修正Russell方法的模糊决策单元的排序

文章首先建立基于修正Russell方法的超效率DEA模型,然后基于模糊数的比较,建立并求解模糊环境下的基于修正Russell方法的超效率DEA模型,从而解决了模糊决策单元的全排序问题。文末的算例将基于修正Russell方法的模糊超效率DEA模型,与基于CCR模型的模糊超效率DEA模型的结果进行了比较分析。