Agreement # 2009-616A DOTC-14-01-INIT302 N/A N/A NSWC Crane WXRL N/A Unlimited SAIC supported the development and evaluation of new compositions and...support of the Windstream Task 366. 06/28/2016 Purchased Epon Resin from Hexion in support of the Special Projects, Task MJU. 06/29/2016...Windstream Task 366. 06/29/2016 100% 100% 102 Purchased Epon Resin from Hexion in support of the Special Projects, Task MJU. 06/28/2016
The data link for the Ames baseline probe as applied to the MJU spacecraft specifically with an entry at Uranus is analyzed. A frequency analysis, a trajectory analysis, and a discussion of the effects on the spacecraft design by the data link are presented. The possibilities of a two-way link are considered.
Yang, Tae Young; Jeong, Seongmun
In recent years, RNA-seq has become a very competitive alternative to microarrays. In RNA-seq experiments, the expected read count for a gene is proportional to its expression level multiplied by its transcript length. Even when two genes are expressed at the same level, differences in length will yield differing numbers of total reads. The characteristics of these RNA-seq experiments create a gene-level bias such that the proportion of significantly differentially expressed genes increases with the transcript length, whereas such bias is not present in microarray data. Gene-set analysis seeks to identify the gene sets that are enriched in the list of the identified significant genes. In the gene-set analysis of RNA-seq, the gene-level bias subsequently yields the gene-set-level bias that a gene set with genes of long length will be more likely to show up as enriched than will a gene set with genes of shorter length. Because gene expression is not related to its transcript length, any gene set containing long genes is not of biologically greater interest than gene sets with shorter genes. Accordingly the gene-set-level bias should be removed to accurately calculate the statistical significance of each gene-set enrichment in the RNA-seq. We present a new gene set analysis method of RNA-seq, called FDRseq, which can accurately calculate the statistical significance of a gene-set enrichment score by the grouped false-discovery rate. Numerical examples indicated that FDRseq is appropriate for controlling the transcript length bias in the gene-set analysis of RNA-seq data. To implement FDRseq, we developed the R program, which can be downloaded at no cost from http://home.mju.ac.kr/home/index.action?siteId=tyang.