Discharge transient coupling in large space power systems

Experiments have shown that plasma environments can induce discharges in solar arrays. These plasmas simulate the environments found in low earth orbits where current plans call for operation of very large power systems. The discharges could be large enough to couple into the power system and possibly disrupt operations. Here, the general concepts of the discharge mechanism and the techniques of coupling are discussed. Data from both ground and flight experiments are reviewed to obtain an expected basis for the interactions. These concepts were applied to the Space Station solar array and distribution system as an example of the large space power system. The effect of discharges was found to be a function of the discharge site. For most sites in the array discharges would not seriously impact performance. One location at the negative end of the array was identified as a position where discharges could couple to charge stored in system capacitors. This latter case could impact performance

SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.

Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons

A BAYESIAN AND COVARIATE APPROACH TO COMBINE RESULTS FROM MULTIPLE MICROARRAY STUDIES

The growing popularity of microarray technology for testing changes in gene expression has resulted in multiple laboratories independently seeking to identify genes related to the same disease in the same organism. Despite the uniform nature of the technology, chance variation and fundamental differences between laboratories can result in considerable disagreement between the lists of significant candidate genes from each laboratory. By adjusting for known differences between laboratories through the use of covariates and employing a Bayesian framework to effectively account for between-laboratory variability, the results of multiple similar studies can be systematically combined via a meta-analysis. Meta-analyses yield additional information not available from any single study and provide a clearer understanding of each gene’s true relationship to the disease of interest. A simulation model based on the Barley Affymetrix GeneChip microarray demonstrates the utility of this approach. Further illustration is provided from a mouse model for multiple sclerosis

Combining Affymetrix microarray results

BACKGROUND: As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis. RESULTS: We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis. CONCLUSION: The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest

Handling Non-detects with Imputation in a Nested Design: A Simulation Study

In this paper, a simulation study was conducted to assess whether it is ideal to address the issue of non-detects in data using a traditional substitution approach for non-detects, imputation, or a non-imputation based approach. Simulated data used were simple nested designs motivated by a real-life data in a study of bumble bee activity in a commercial cherry orchard by Kuivila et al. (2021). The simulated data were generated at different thresholds or censoring levels and at different effect sizes. For each simulated data, seven popular existing techniques to handle non-detects were applied: (i) Zero substitution, (ii) Substitution with half Limit of Detection (LOD/2), (iii) Substitution with LOD/√ 2, (iv) Multiple Imputation (MI), (v) Regression on Order Statistics (ROS) (Imputation approach), and (vi) Maximum Likelihood Estimation (MLE) (likelihood estimation approach) and (vii) Kaplan-Meier (KM). Multiple Imputation (MI) was not applicable as the design of the simulated data violated the assumption of having a multivariate distribution. By comparative analysis of the simulated data, substituting with LOD/2 seemed appropriate for the design simulated, as it outperformed the other techniques (i.e ROS, MLE, KM, LOD/√ 2, and zero substitution) by yielding a lower Type I error, lower bias, and a better power across increasing effect sizes

GENE SET TESTING TO CHARACTERIZE MULTIVARIATELY DIFFERENTIALLY EXPRESSED GENES

In a gene expression experiment (using oligo array, RNA-Seq, or other platform), researchers typically seek to characterize di erentially expressed genes based on common gene function or pathway involve-ment. The eld of gene set testing provides numerous characterization methods, some of which have proven to be more valid and powerful than others. These existing gene set testing methods focus on experimental designs where there is a single null hypothesis (usually involving association with a continuous or categorical phenotype) for each gene. Increasingly common experimental designs lead to multiple null hypotheses for each gene, and the characterization of these multivariately di erentially expressed genes is of great interest. We explore extensions of existing gene set testing methods to achieve this characterization, with application to a RNA-Seq study in livestock cloning

DOSE-RESPONSE MODELING WITH MARGINAL INFORMATION ON A MISSING CATEGORICAL COVARIATE

When the relationship between a dosage-type variable and a binary outcome depends on a categorical variable, a common analysis would employ a dose-response model with the categorical variable as a covariate. When the level of the categorical variable is not known for all subjects, however, the standard dose-response model alone cannot provide useful inference. We present an EM-based approach to account for the missing covariate in a dose-response model setting when additional knowledge about the marginal distribution of the covariate is available. This approach is motivated by a study of the beetle Rhyzopertha dominica, a pest of stored grain in Australia. Certain genotypes of this beetle have developed inheritable resistance to a widely-used insecticidal fumigant. In this study, the effects of various dosage levels of the fumigant were considered, and it was feasible to genotype only the surviving beetles

Ray-optical negative refraction and pseudoscopic imaging with Dove-prism arrays

A sheet consisting of an array of small, aligned Dove prisms can locally (on the scale of the width of the prisms) invert one component of the ray direction. A sandwich of two such Dove-prism sheets that inverts both transverse components of the ray direction is a ray-optical approximation to the interface between two media with refractive indices +n and –n. We demonstrate the simulated imaging properties of such a Dove-prism-sheet sandwich, including a demonstration of pseudoscopic imaging

The effects of minimal tillage, contour cultivation and in-field vegetative barriers on soil erosion and phosphorus loss.

Runoff, sediment, total phosphorus and total dissolved phosphorus losses in overland flow were measured for two years on unbounded plots cropped with wheat and oats. Half of the field was cultivated with minimum tillage (shallow tillage with a tine cultivator) and half was conventionally ploughed. Within each cultivation treatment there were different treatment areas (TA). In the first year of the experiment, one TA was cultivated up and down the slope, one TA was cultivated on the contour, with a beetle bank acting as a vegetative barrier partway up the slope, and one had a mixed direction cultivation treatment, with cultivation and drilling conducted up and down the slope and all subsequent operations conducted on the contour. In the second year, this mixed treatment was replaced with contour cultivation. Results showed no significant reduction in runoff, sediment losses or total phosphorus losses from minimum tillage when compared to the conventional plough treatment, but there were increased losses of total dissolved phosphorus with minimum tillage. The mixed direction cultivation treatment increased surface runoff and losses of sediment and phosphorus. Increasing surface roughness with contour cultivation reduced surface runoff compared to up and down slope cultivation in both the plough and minimum tillage treatment areas, but this trend was not significant. Sediment and phosphorus losses in the contour cultivation treatment followed a very similar pattern to runoff. Combining contour cultivation with a vegetative barrier in the form of a beetle bank to reduce slope length resulted in a non-significant reduction in surface runoff, sediment and total phosphorus when compared to up and down-slope cultivation, but there was a clear trend towards reduced losses. However, the addition of a beetle bank did not provide a significant reduction in runoff, sediment losses or total phosphorus losses when compared to contour cultivation, suggesting only a marginal additional benefit. The economic implications for farmers of the different treatment options are investigated in order to assess their suitability for implementation at a field scale