Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured “mock” datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states.Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction.Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Background We examined sex differences in nonstenotic carotid plaque composition in patients with embolic stroke of undetermined source (ESUS). Methods and Results Patients with anterior circulation ischemic stroke imaged with neck computed tomographic angiography who met criteria for ESUS or had atrial fibrillation were identified. Patients with atrial fibrillation were included as a negative control. Semiautomated plaque quantification software analyzed carotid artery bifurcations. Plaque subcomponent (calcium, intraplaque hemorrhage [IPH], and lipid rich necrotic core) volumes were compared by sex and in paired analyses of plaque ipsilateral versus contralateral to stroke. Multivariate linear regressions tested for associations. Ninety‐four patients with ESUS (55% women) and 95 patients with atrial fibrillation (47% women) were identified. Men with ESUS showed significantly higher volumes of calcified plaque (63.9 versus 19.6 mm 3 , P <0.001), IPH (9.4 versus 3.3 mm 3 , P =0.008) and a IPH/lipid rich necrotic core ratio (0.17 versus 0.07, P =0.03) in carotid plaque ipsilateral to stroke side than women. The atrial fibrillation cohort showed no significant sex differences in plaque volumes ipsilateral to stroke. Multivariate analyses of the ESUS cohort showed male sex was associated with IPH ipsi (β=0.49; 95% CI, 0.11–0.87) and calcium ipsi (β=0.78; 95% CI, 0.33–1.23). Paired plaque analyses in men with ESUS showed significantly higher calcified plaque (63.9 versus 34.1 mm 3 , P =0.03) and a trend of higher IPH ipsi (9.4 versus 7.5 mm 3 , P =0.73) and lipid rich necrotic core ipsi (59.0 versus 48.4 mm 3 , P =0.94) volumes. Conclusions Sex differences in carotid plaque composition in ESUS suggest the possibility of a differential contribution of nonstenosing carotid plaque as a stroke mechanism in men versus women.

Purpose: Poly(ADP-ribose) polymerase enzyme inhibitors (PARPi) have become the standard-of-care treatment for homologous recombination deficient (HRD) high-grade serous ovarian cancer (HGSOC). However, not all HRD tumors respond to PARPi. Biomarkers to predict response are needed. [18F]FluorThanatrace ([18F]FTT) is a PARPi-analog PET radiotracer that non-invasively measures PARP-1 expression. Herein, we evaluate [18F]FTT as a biomarker to predict response to PARPi in patient-derived xenograft (PDX) models and subjects with HRD HGSOC. Methods: In PDX models, [18F]FTT-PET was performed before and after PARPi (olaparib), ataxia-telangiectasia inhibitor (ATRi), or both (PARPi-ATRi). Changes in [18F]FTT were correlated with tumor volume changes. Subjects were imaged with [18F]FTT-PET at baseline and after ~1 week of PARPi. Changes in [18F]FTT-PET uptake were compared to changes in tumor size (RECIST1.1), CA-125, and progression-free survival (PFS). Results: A decrease in [18F]FTT tumor uptake after PARPi correlated with response to PARPi, or PARPi-ATRi treatment in PARPi-resistant PDX models (r=0.77-0.81). In subjects (n=11), percent difference in [18F]FTT-PET after ~7 days of PARPi compared to baseline correlated with best RECIST response (P=0.01), best CA-125 response (P=0.033), and PFS (P=0.027). All subjects with &gt;50% reduction in [18F]FTT uptake had &gt;6-month PFS and &gt;50% reduction in CA-125. Utilizing only baseline [18F]FTT uptake did not predict such responses. Conclusions: The decline in [18F]FTT uptake shortly after PARPi initiation provides a measure of drug-target engagement and shows promise as a biomarker to guide PARPi therapies in this pilot study. These results support additional pre-clinical mechanistic and clinical studies in subjects receiving PARPi +/- combination therapy.

Ultra-high field MR imaging lacks B 1 + inhomogeneity due to shorter RF wavelengths used at higher field strengths compared to human anatomy.CEST techniques tend to be highly susceptible to B 1 + inhomogeneities due to a high and uniform B 1 + field being necessary to create the endogenous contrast.High-permittivity dielectric pads have seen increasing usage in MR imaging due to their ability to tailor the spatial distribution of the B 1 + field produced. The purpose of this work is to demonstrate that dielectric materials can be used to improve glutamate weighted CEST (gluCEST) at 7T. Theory and Methods:GluCEST images were acquired on a 7T system on six healthy volunteers. Aqueous calcium titanate pads, with a permittivity of approximately 110, were placed on either side in the subject ′ s head near the temporal lobes. A post-processing correction algorithm was implemented in combination with dielectric padding to compare contrast improvement. Tissue segmentation was performed to assess the effect of dielectric pads on gray and white matter separately. Results: GluCEST images demonstrated contrast enhancement in the lateral temporal lobe regions with dielectric pad placement. Tissue segmentation analysis showed an increase in correction effectiveness within the gray matter tissue compared to white matter tissue. Statistical testing suggested a significant difference in gluCEST contrast when pads were used and showed a difference in the gray matter tissue segment. Conclusion:The use of dielectric pads improved the B 1 + field homogeneity and enhanced gluCEST contrast for all subjects when compared to data that did not incorporate padding.