Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.

Uttarkashi region of Himalaya was struck by a destructive earthquake (M w ¼ 6.8) on 19 October 1991 with its epicentre located at 30.78 N latitude and 78.77 E longitude.Here, we use Coulomb stress model for the study of this major event. Coulomb 3.1 application is used to generate the earthquake model and develop the stress change maps of the region. The 1991 Uttarkashi earthquake and succeeding earthquakes of M b > 3.5 are used for the study. One of the motives of this work is to correlate the main shock and the successive minor earthquakes that have occurred in the region. Further, we have studied the 1999 Chamoli earthquake using this model and attempted to relate its occurrence with the Uttarkashi earthquake based on propagation of strain energy.The study of Uttarkashi 1991 earthquake by this method supports the main shock and its controlling effect on the later earthquakes' observations in the nearby region with the view that many zones of such shock can occur in the future. This model is also used to predict the direction of propagation of strain energy, thereby locating the region that will be affected by the future shocks.

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