Genome evolution in yeasts

Identifying the mechanisms of eukaryotic genome evolution by comparative genomics is often complicated by the multiplicity of events that have taken place throughout the history of individual lineages, leaving only distorted and superimposed traces in the genome of each living organism. The hemiascomycete yeasts, with their compact genomes, similar lifestyle and distinct sexual and physiological properties, provide a unique opportunity to explore such mechanisms. We present here the complete, assembled genome sequences of four yeast species, selected to represent a broad evolutionary range within a single eukaryotic phylum, that after analysis proved to be molecularly as diverse as the entire phylum of chordates. A total of approximately 24,200 novel genes were identified, the translation products of which were classified together with Saccharomyces cerevisiae proteins into about 4,700 families, forming the basis for interspecific comparisons. Analysis of chromosome maps and genome redundancies reveal that the different yeast lineages have evolved through a marked interplay between several distinct molecular mechanisms, including tandem gene repeat formation, segmental duplication, a massive genome duplication and extensive gene loss.     

Using the life cycle to anticipate satisfaction at work

It was hypothesized that there are important non-linear life-cycle influences upon job and organizational satisfaction. Five common life-cycle stages were identified from the literature: the phases of ‘reality shock’, ‘socialization and growth’, ‘mid-career crisis’, ‘acceptance’, and ‘pre-retirement’. The first, third and last stages were expected to show declines in job and organizational satisfaction because of the personal and job-related disappointments and crises that typically occur during these periods of life. The second and fourth stages were expected to show increases in satisfaction because of the pleasant life experiences that often occur then. All but the first stage supported these hypotheses when we controlled for the influence of sex, education, job experience, level in the hierarchy and occupational upward mobility.     

一种基于半马尔可夫网络流量模型的延迟估计算法

提出一种基于半马尔可夫网络流量模型的延迟估计算法,用于估计传榆链路的延迟超过某个阈值的概率,该值可做为控制器设计及控制策略选择的依据.利用半马尔可夫理论对网络流量进行建模,建立了延迟与网络传输中的队列队长、及队长与网络流速率的函数关系,之后经过推导和有限分割,得出网络延迟概率计算公式,并分析了该算法的复杂度.在计算布朗运动积分时采用Itǒ积分实现.仿真证明该算法是有效、可行的.