A Saddlepoint Approximation to Left-Tailed Hypothesis Tests of Variance for Non-normal Populations

When the variance of a single population needs to be assessed, the well-known chi-squared test of variance is often used but relies heavily on its normality assumption. For non-normal populations, few alternative tests have been developed to conduct left tailed hypothesis tests of variance. This thesis outlines a method for generating new test statistics using a saddlepoint approximation. Several novel test statistics are proposed. The type-I error rates and power of each test are evaluated using a Monte Carlo simulation study. One of the proposed test statistics, R_gamma2, controls type-I error rates better than existing tests, while having comparable power. The only observed limitation is for populations that are highly skewed with heavy-tails, for which all tests under consideration performed poorly

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available from the Comprehensive R Archive Network at https://CRAN.R-project.org/package= SeqNet and on GitHub at https://github.com/tgrimes/SeqNet

A Pseudo-Value Regression Approach for Differential Network Analysis of Co-Expression Data

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a Pseudo-value Regression Approach for Network Analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information (MI) criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus (GEO) database to identify DC genes that are associated with chronic obstructive pulmonary disease (COPD) to demonstrate its utility. To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.Comment: 5 figures, 6 tables, Presented at the ISMB 2022 (NetBio COSI

Maternity care provider knowledge, attitudes, and practices regarding provision of postpartum intrauterine contraceptive devices at a tertiary center in Ghana

ObjectiveTo assess knowledge, attitudes, and practices of maternity care providers regarding the provision of postpartum intrauterine contraceptive devices (IUDs) in Komfo Anokye Teaching Hospital (KATH), Kumasi, Ghana.MethodsA descriptive, cross‐sectional study was conducted between June 28 and July 15, 2011. Specialists, residents, house officers, and nurse midwives who had been working in the Department of Obstetrics and Gynecology for at least 3 months were included. Self‐administered questionnaires assessed formal training, current proficiency in IUD insertion, and attitudes toward postpartum IUD provision.ResultsOf 91 providers surveyed, 70 (77%) reported previous training in contraceptive counseling. Fewer than one in three respondents had ever inserted an IUD: 17 (44%) of 39 physicians and 9 (17%) of 52 midwives reported ever having inserted an IUD. A total of 33 (36%) respondents reported that they would recommend an IUD in the immediate postpartum period.ConclusionAlthough most maternity care providers at KATH had received training in contraceptive counseling, few felt confident in their ability to insert an IUD. Further training in postpartum contraceptive management is needed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135552/1/ijgo137.pd

Multi-modal biomarkers of low back pain: A machine learning approach

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66-0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Integrated multiomics network analysis reveals signaling profiles in lung cancer.</jats:p

X-ray emission from the extended disks of spiral galaxies

We present a study of the X-ray properties of a sample of six nearby late-type spiral galaxies based on XMM-Newton observations. Since our primary focus is on the linkage between X-ray emission and star formation in extended, extranuclear galactic disks, we have selected galaxies with near face-on aspect and sufficient angular extent so as to be readily amenable to investigation with the moderate spatial resolution afforded by XMM-Newton. After excluding regions in each galaxy dominated by bright point sources, we study both the morphology and spectral properties of the residual X-ray emission, comprised of both diffuse emission and the integrated signal of the fainter discrete source populations. The soft X-ray morphology generally traces the inner spiral arms and shows a strong correlation with the distribution of UV light, indicative of a close connection between the X-ray emission and recent star formation. The soft (0.3-2 keV) X-ray luminosity to star formation rate (SFR) ratio varies from 1-5 x 10^39 erg/s(/Msun/yr), with an indication that the lower range of this ratio relates to regions of lower SFR density. The X-ray spectra are well matched by a two-temperature thermal model with derived temperatures of typically ~0.2 keV and ~0.65 keV, in line with published results for other normal and star-forming galaxies. The hot component contributes a higher fraction of the soft luminosity in the galaxies with highest X-ray/SFR ratio, suggesting a link between plasma temperature and X-ray production efficiency. The physical properties of the gas present in the galactic disks are consistent with a clumpy thin-disk distribution, presumably composed of diffuse structures such as superbubbles together with the integrated emission of unresolved discrete sources including young supernova remnants.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 17 pages, 6 figures, 7 table

Expressions 1990

https://openspace.dmacc.edu/expressions/1012/thumbnail.jp