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Motivation Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welch’s approximate test is most reliable one. The Welch’s test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on approximate degrees of freedom. This study attempts to improve upon Welch’s approximate test by avoiding one layer of approximation. Results We introduce a new distribution that generalizes the t-distribution and propose a Monte Carlo based test that uses only one layer of approximation for statistical inferences. Experimental results based on extensive simulation studies show that the Monte Carol based tests enhance the statistical power and performs better than Welch’s t-approximation, especially when the equal variance assumption is not met and the sample size of the sample with a larger variance is smaller. We analyzed two gene-expression datasets, namely the childhood acute lymphoblastic leukemia gene-expression dataset with 22 283 genes and Golden Spike dataset produced by a controlled experiment with 13 966 genes. The new test identified additional genes of interest in both datasets. Some of these genes have been proven to play important roles in medical literature. Availability and implementation R scripts and the R package mcBFtest is available in CRAN and to reproduce all reported results are available at the GitHub repository, https://github.com/iullah1980/MCTcodes. Supplementary information Supplementary data is available at Bioinformatics online.

When earthquakes occur in the mountains, numerous rescuing resources need to be distributed among affected areas. The distribution mission is a long-term response process, which requires consideration of factors such as weather conditions, aftershock damage, and complex mountainous topography. These factors lead to uncertainties, such as transportation time and supply and demand information as well as several disaster sites. This study aims to develop a new scheduling method based on uncertainties of mountain earthquakes and several disaster sites in a long-term response process. The proposed methodology focuses on two problems, namely, (1) how to coordinate supplies between different phases of the response process and (2) how to deal with uncertainties. A follow-up sharing mechanism is put forward to ensure that resources from previous phases are partly shared with the ensuing phases as needed. A scheduling methodology is then established with the uncertainties in demand and traffic conditions. Results from numerous studies show that a schedule with this mechanism is effective, and the resource distribution principle significantly affects the resource allocation plan.INDEX TERMS Decision making, earthquakes response, humanitarian logistics, optimal scheduling, resource management.

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