地质微生物学:一门新兴的交叉学科

正地质微生物学(Geomicrobiology)涉及地质学和微生物学,是一门研究地质环境与微生物相互作用的交叉学科。没有地质微生物就没有我们现在所看到的地球,更没有地球上的动植物和人类。地质微生物在地球上无处不在、种类繁多、功能丰富,且在漫长的地质演化历史中发挥着举足轻重的作用。地质微生物决定了地球生态系统的演化方向。微生物大约占据生命进化树的2/3,具有30多亿年的地球演化历史。地球早期产氧光合作用     

Enzymes as a subject of invention

A patent-legal situation is analyzed which has arisen in the world in connection with the problem on protection of native enzymes as objects of invention. The objects under study are shown to correspond to the demands of novelty, essential distinctions and positive practical effect which are made by the legislation of the USSR to inventions. Possibility of legal protection of enzymes as substances with unstated structure obtained microbiologically is determined in terms of acting standard statements. A set of characters necessary and sufficient for recognition of enzymes and substantiation of their novelty is suggested for characteristics of enzymes in original papers and patent documents. General approaches to optimization of native enzyme examination for the novelty and essential characteristics are developed.     

Rare variant of apolipoprotein E (Arg136-->Cys) in a subject with normal lipid values

During the screening of apolipoprotein (apo) E gene polymorphism with PCR and subsequent restriction analysis, we have identified a female carrier with a mutant allele Arg136-->Cys. This proband had normal lipid parameters and no history of coronary artery disease (CAD). We did not confirm the previously described connection between apo E Arg136-->Cys mutation and elevated lipid levels. In the case of this mutation, other factors (environmental and/or genetic) are important for the development of lipid metabolism disorders.     

Binary regression with misclassified response and covariate subject to measurement error: a bayesian approach

We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. Unlike existing methods, no model parameters need be assumed known. Markov chain Monte Carlo methods are utilized to perform the necessary computations. The methods developed are illustrated using atomic bomb survival data. A simulation experiment explores advantages of the approach.