Genotyping-by-sequencing for Populus population genomics: an assessment of genome sampling patterns and filtering approaches.

Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-scale data acquisition and processing. Here we assess genomic sampling biases and the effects of various population-level data filtering strategies in a genotyping-by-sequencing (GBS) protocol. We focus on data from two species of Populus, because this genus has a relatively small genome and is emerging as a target for population genomic studies. We estimate the proportions and patterns of genomic sampling by examining the Populus trichocarpa genome (Nisqually-1), and demonstrate a pronounced bias towards coding regions when using the methylation-sensitive ApeKI restriction enzyme in this species. Using population-level data from a closely related species (P. tremuloides), we also investigate various approaches for filtering GBS data to retain high-depth, informative SNPs that can be used for population genetic analyses. We find a data filter that includes the designation of ambiguous alleles resulted in metrics of population structure and Hardy-Weinberg equilibrium that were most consistent with previous studies of the same populations based on other genetic markers. Analyses of the filtered data (27,910 SNPs) also resulted in patterns of heterozygosity and population structure similar to a previous study using microsatellites. Our application demonstrates that technically and analytically simple approaches can readily be developed for population genomics of natural populations.

Few-view single photon emission computed tomography (SPECT) reconstruction based on a blurred piecewise constant object model.

A sparsity-exploiting algorithm intended for few-view single photon emission computed tomography (SPECT) reconstruction is proposed and characterized. The algorithm models the object as piecewise constant subject to a blurring operation. To validate that the algorithm closely approximates the true object in the noiseless case, projection data were generated from an object assuming this model and using the system matrix. Monte Carlo simulations were performed to provide more realistic data of a phantom with varying smoothness across the field of view and a cardiac phantom. Reconstructions were performed across a sweep of two primary design parameters. The results demonstrate that the algorithm recovers the object in a noiseless simulation case. While the algorithm assumes a specific blurring model, the results suggest that the algorithm may provide high reconstruction accuracy even when the object does not match the assumed blurring model. Generally, increased values of the blurring parameter and total variation weighting parameters reduced streaking artifacts, while decreasing spatial resolution. The proposed algorithm demonstrated higher correlation with respect to the true phantom compared to maximum-likelihood expectation maximization (MLEM) reconstructions. Images reconstructed with the proposed algorithm demonstrated reduced streaking artifacts when reconstructing from few views compared to MLEM. The proposed algorithm introduced patchy artifacts in some reconstructed images, depending on the noise level and the selected algorithm parameters. Overall, the results demonstrate preliminary feasibility of a sparsity-exploiting reconstruction algorithm which may be beneficial for few-view SPECT.

Characterization of the risk of deer-cattle interactions in Minnesota by use of an on-farm environmental assessment tool.

OBJECTIVE: To characterize the risk of interactions that may lead to the transmission of Mycobacterium bovis between cattle and white-tailed deer (Odocoileus virginianus) on farms in northern Minnesota. SAMPLE: 53 cattle farms in northwestern Minnesota adjacent to an area where bovine tuberculosis-infected cattle and deer were detected. PROCEDURES: A semiquantitative deer-cattle interaction assessment tool was used for the 53 cattle herds. Farm risk scores were analyzed on the basis of deer damage to stored feed. RESULTS: 27 (51%) farms reported deer damage to stored cattle feeds within the year previous to the farm visit. A strong association was found between increases in the percentage of land that could serve as deer cover and deer damage to stored feeds on a farm. The total risk score was significantly associated with the probability of a farm having deer damage. By use of a logistic regression model, the total risk score and proportion of nonagricultural land around a farm could be used to predict the likelihood of deer damage to stored feeds. CONCLUSIONS AND CLINICAL RELEVANCE: Management practices on many farms in northwestern Minnesota allowed potential deer-cattle interactions. The on-farm risk assessment tool served as a valuable tool for prioritizing the biosecurity risks for farms. Continued development of biosecurity is needed to prevent potential transmission of bovine tuberculosis between deer and cattle, especially on farms that have a higher risk of deer damage.

Quantitative assessment of intestinal injury using a novel in vivo, near-infrared imaging technique.

Intestinal injury owing to inflammation, severe trauma, and burn is a leading cause of morbidity and mortality. Currently, animal models employed to study the intestinal response to injury and inflammation depend on outdated methods of analysis. Given that these classic intestinal assays are lethal to the experimental animal, there is no ability to study the gut response to injury in the same animal over time. We postulated that by developing an in vivo assay to image intestinal injury using fluorescent dye, it could complement other expensive, time-consuming, and semiquantitative classic means of detecting intestinal injury. We describe a novel in vivo, noninvasive method to image intestinal injury using a charge-coupled device (CCD) camera that allows for serial visual and quantitative analysis of intestinal injury. Our results correlate with traditional, time-consuming, semiquantitative assays of intestinal injury, now allowing the noninvasive, nonlethal assessment of injury over time.