Biology subjects

Powers, W.

Publications and source records attributed to Powers, W..

3 recordsLinked to original sources

bioRxiv2022

Prospective Motion Correction and Automatic Segmentation of Penetrating Arteries in Phase Contrast MRI at 7 T

PurposeTo develop a prospective motion correction (MC) method for phase contrast (PC) MRI of penetrating arteries (PA) in centrum semiovale at 7 T and evaluate its performance using automatic PA segmentation. MethodsHead motion was monitored and corrected during the scan based on fat navigator images. Two convolutional neural networks (CNN) were developed to automatically segment PAs and exclude surface vessels. Real-life scans with MC and without MC (NoMC) were performed to evaluate the MC performance. Motion score was calculated from the range of translational and rotational motion parameters. MC vs NoMC pairs were divided according to their score differences into groups with similar, less, or more motions during MC. Data reacquisition was also performed to evaluate whether it can further improve PA visualization. ResultsIn the group with similar motion, more PA counts (NPA) were obtained with MC in 9 (60%) cases, significantly more than the number of cases (1) with less PAs (p = 0.011; binomial test). In the group with less motion during MC, MC images had more or similar NPA in all cases, while in the group with more motion during MC, the numbers of cases with less and more NPA during MC were not significantly different (3 vs 0). Data reacquisition did not further increase NPA. CNNs had higher sensitivity (0.85) and accuracy (Dice coefficient 0.85) of detecting PAs than a threshold based method. ConclusionsProspective MC and CNN based segmentation improved the visualization and delineation of PAs in PC MRI at 7 T.

bioengineering↗

bioRxiv2019

Clinical and molecular characterization of virus-positive and virus-negative Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine carcinoma of the skin mediated by the integration of Merkel cell polyomavirus (MCPyV) and expression of viral T antigens or by ultraviolet induced damage to the tumor genome from excessive sunlight exposure. An increasing number of deep sequencing studies of MCC have identified significant differences between the number and types of point mutations, copy number alterations, and structural variants between virus-positive and virus-negative tumors. In this study, we assembled a cohort of 71 MCC patients and performed deep sequencing with OncoPanel, a next-generation sequencing assay targeting over 400 cancer-associated genes. To improve the accuracy and sensitivity for virus detection compared to traditional PCR and IHC methods, we developed a hybrid capture baitset against the entire MCPyV genome. The viral baitset identified integration junctions in the tumor genome and generated assemblies that strongly support a model of a hybrid, virus-host, circular DNA intermediate during integration that promotes focal amplification of host DNA. Using the clear delineation between virus-positive and virus-negative tumors from this method, we identified recurrent somatic alterations common across MCC and alterations specific to each class of tumor, associated with differences in overall survival. Comparing the molecular and clinical data from these patients revealed a surprising association of immunosuppression with virus-negative MCC and significantly shortened overall survival. These results demonstrate the value of high-confidence virus detection for identifying clinically important features in MCC that impact patient outcome.

cancer biology↗

bioRxiv2017

Statistical Approaches to Decreasing the Discrepancy of Non-detects in qPCR Data

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects - those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis.

bioinformatics↗