Significance tests for analyzing gene expression data with small sample sizes.

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.

Influencing factors on ecological efficiency: Based on 11 cities in Zhejiang Province, China.

Ecological efficiency (eco-efficiency) is the effectiveness of ecological resources in meeting human needs and is a good representation of the quality of a region's development. The traditional concept of improving eco-efficiency refers to maximizing economic benefits by minimizing resource costs and environmental loads. This article argues that the goal of eco-efficiency evaluation is not only to maximize economic benefits but also to achieve high-quality and coordinated development in many aspects so that more people can enjoy the fruits of development. Therefore, in the evaluation system of eco-efficiency, the input indexes take into account the consumption of human, energy resources, and the environmental load caused by them in a region. The output indexes take into account the four dimensions of "economy, innovation, social harmony, and openness." This study first establishes the nonexpected output superefficiency slacks-based measure model under the assumption of variable returns to scale to measure eco-efficiency in 11 cities of Zhejiang Province, China. Second, the spatial and temporal trends of eco-efficiency are studied with the help of the Malmquist index model. Moreover, regression analysis was conducted using the panel Tobit method to discuss the influencing factors of eco-efficiency. Several key results were obtained in this study: (1) The overall eco-efficiency in Zhejiang Province is rising steadily, but there are serious regional imbalances. (2) The improvement of eco-efficiency mainly relied on the scale efficiency from 2008 to 2013, but on pure technical efficiency from 2013 to 2018. (3) The share of tertiary industry, the number of scientific researchers, and the foreign trade dependence positively affect the improvement of eco-efficiency, but highway transportation mileage has a negative impact on the improvement of eco-efficiency. Integr Environ Assess Manag 2023;19:139-151. © 2022 SETAC.

The enrichment of cancer stem cells using composite alginate/polycaprolactone nanofibers.

Cancer stem cells (CSCs) are potential platforms to high-throughput screen anti-cancer drugs. However, they are difficult to isolate from cancer cells. Therefore, we proposed to fabricate 3-D scaffolds for CSC enrichment. Alginate is a biocompatible polysaccharide with poor cell adhesion, whereas polycaprolactone (PCL) is relative cell adhesive. These two materials were coelectrospun as composite scaffolds. Cells collected from alginate and composite fibers demonstrated high stemness, epithelial-mesenchymal transition, invasion, drug resistance, and angiogenesis. Interestingly, cells collected from composite fibers with low ratio of PCL were significantly improved their CSC properties compared to those from pure alginate fibers because few PCL fibers spatially separated cell populations to concentrate CSCs. These results suggested that alginate fibers effectively enriched CSCs and composite fibers created an uneven microenvironment to regulate cell morphology and distribution, by which cell-cell interaction was thus manipulated. These tunable scaffolds are potential to isolate CSCs from different tissues to facilitate the cancer research.