Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX.

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

Correlation Analysis of Variables From the Atherosclerosis Risk in Communities Study.

The need to test chemicals in a timely and cost-effective manner has driven the development of new alternative methods (NAMs) that utilize in silico and in vitro approaches for toxicity prediction. There is a wealth of existing data from human studies that can aid in understanding the ability of NAMs to support chemical safety assessment. This study aims to streamline the integration of data from existing human cohorts by programmatically identifying related variables within each study. Study variables from the Atherosclerosis Risk in Communities (ARIC) study were clustered based on their correlation within the study. The quality of the clusters was evaluated via a combination of manual review and natural language processing (NLP). We identified 391 clusters including 3,285 variables. Manual review of the clusters containing more than one variable determined that human reviewers considered 95% of the clusters related to some degree. To evaluate potential bias in the human reviewers, clusters were also scored via NLP, which showed a high concordance with the human classification. Clusters were further consolidated into cluster groups using the Louvain community finding algorithm. Manual review of the cluster groups confirmed that clusters within a group were more related than clusters from different groups. Our data-driven approach can facilitate data harmonization and curation efforts by providing human annotators with groups of related variables reflecting the themes present in the data. Reviewing groups of related variables should increase efficiency of the human review, and the number of variables reviewed can be reduced by focusing curator attention on variable groups whose theme is relevant for the topic being studied.

Defining the relationship between vaginal and urinary microbiomes.

BACKGROUND: Although the vaginal and urinary microbiomes have been increasingly well-characterized in health and disease, few have described the relationship between these neighboring environments. Elucidating this relationship has implications for understanding how manipulation of the vaginal microbiome may affect the urinary microbiome and treatment of common urinary conditions. OBJECTIVE: To describe the relationship between urinary and vaginal microbiomes using 16S rRNA gene sequencing. We hypothesized that the composition of the urinary and vaginal microbiomes would be significantly associated, with similarities in predominant taxa. STUDY DESIGN: This multicenter study collected vaginal swabs and catheterized urine samples from 186 women with mixed urinary incontinence enrolled in a parent study and 84 similarly aged controls. Investigators decided a priori that if vaginal and/or urinary microbiomes differed between continent and incontinent women, the groups would be analyzed separately; if similar, samples from continent and incontinent women would be pooled and analyzed together. A central laboratory sequenced variable regions 1-3 (v1-3) and characterized bacteria to the genus level. Operational taxonomic unit abundance was described for paired vaginal and urine samples. Pearson's correlation characterized the relationship between individual operational taxonomic units of paired samples. Canonical correlation analysis evaluated the association between clinical variables (including mixed urinary incontinence and control status) and vaginal and urinary operational taxonomic units, using the Canonical correlation analysis function in the Vegan package (R version 3.5). Linear discriminant analysis effect size was used to find taxa that discriminated between vaginal and urinary samples. RESULTS: Urinary and vaginal samples were collected from 212 women (mean age 53±11 years) and results from 197 paired samples were available for analysis. As operational taxonomic units in mixed urinary incontinence and control samples were related in canonical correlation analysis and since taxa did not discriminate between mixed urinary incontinence or controls in either vagina or urine, mixed urinary incontinence and control samples were pooled for further analysis. Canonical correlation analysis of vaginal and urinary samples indicated that that 60 of the 100 most abundant operational taxonomic units in the samples largely overlapped. Lactobacillus was the most abundant genus in both urine and vagina (contributing on average 53% to an individual's urine sample and 64% to an individual's vaginal sample) (Pearson correlation r=0.53). Although less abundant than Lactobacillus, other bacteria with high Pearson correlation coefficients also commonly found in vagina and urine included: Gardnerella (r=0.70), Prevotella (r=0.64), and Ureaplasma (r=0.50). Linear discriminant analysis effect size analysis identified Tepidimonas and Flavobacterium as bacteria that distinguished the urinary environment for both mixed urinary incontinence and controls as these bacteria were absent in the vagina (Tepidimonas effect size 2.38, P<.001, Flavobacterium effect size 2.15, P<.001). Although Lactobacillus was the most abundant bacteria in both urine and vagina, it was more abundant in the vagina (linear discriminant analysis effect size effect size 2.72, P<.001). CONCLUSION: Significant associations between vaginal and urinary microbiomes were demonstrated, with Lactobacillus being predominant in both urine and vagina. Abundance of other bacteria also correlated highly between the vagina and urine. This inter-relatedness has implications for studying manipulation of the urogenital microbiome in treating conditions such as urgency urinary incontinence and urinary tract infections.

Design and development of a phantom for tomosynthesis with potential for automated analysis via the cloud.

This paper describes Development of a Phantom for Tomosynthesis with Potential for Automated Analysis via the Cloud. Several studies are underway to investigate the effectiveness of Tomosynthesis Mammographic Image Screening, including the large TMIST project as funded by the National Cancer Institute https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/tmist. The development of the phantom described in this paper follows initiatives from the FDA, the AAPM TG245 task group, and European Reference Organization (EUREF) for Quality Assured Breast Screening and Diagnostic Services Committee report noting, that no formal endorsement nor recommendation for use has been sought, or granted by any of these groups. This paper reports on the possibility of using this newly developed Tomosynthesis Phantom for Quality Assurance, field testing of image performance, including remote monitoring of DBT system performance, e.g., via transmission over the cloud. The phantom includes tests for: phantom positioning and alignment (important for remote analysis), scan geometry (x and y), chest wall offset, scan slice width and Slice Sensitivity Profile (SSP(z)) slice geometry (slice width), scan slice incrementation (z), z axis geometry bead, low contrast detectability using low contrast spheres, spatial resolution via Point Spread Function (PSF), Image uniformity, Signal to Noise Ratio (SNR), and Contrast to Noise Ratio (CNR) via readings over an Aluminum square. The phantom is designed for use with automated analysis via transmission of images over the cloud and the analysis package includes test of positioning accuracy (roll, pitch, and yaw). Data are shown from several commercial Tomosynthesis Scanners including Fuji, GE, Hologic, IMS-Giotti, and Siemens; however, the focus of this paper is on phantom design, and not in general aimed at direct commercial comparisons, and wherever possible the identity of the data is anonymized. Results of automated analysis of the phantom are shown, and it is demonstrated that reliable analysis of such a phantom can be achieved remotely, including transmission of data through the cloud.

Draft Genome Sequence of Lactobacillus crispatus C25 Isolated from Chicken Cecum.

Lactic acid bacteria are important members of the gut microbiota of humans and animals. Here, we present the genome sequence of Lactobacillus crispatus strain C25, originally isolated from the cecum of 4-week-old chicken fed a standard diet. This isolate represents a potential probiotic strain for poultry.

Draft Genome Sequences of Lactobacillus animalis Strain P38 and Lactobacillus reuteri Strain P43 Isolated from Chicken Cecum.

Here, we present the genome sequence of Lactobacillus animalis strain P38 and Lactobacillus reuteri strain P43, both isolated from the cecum content of a 4-week old chicken fed a diet supplemented with the prebiotic ß(1-4)galacto-oligosaccharide (GOS). These indigenous Lactobacillus isolates are potential probiotic organisms for poultry.

Method and phantom to study combined effects of in-plane (x,y) and z-axis resolution for 3D CT imaging.

Increasingly, the advent of multislice CT scanners, volume CT scanners, and total body spiral acquisition modes has led to the use of Multi Planar Reconstruction and 3D datasets. In considering 3D resolution properties of a CT system it is important to note that both the in-plane (x,y) and z-axis (slice thickness) influence the visual-ization and detection of objects within the scanned volume. This study investigates ways to consider both the in-plane resolution and the z-axis resolution in a single phantom wherein analytic or visualized analysis can yield information on these combined effects. A new phantom called the "Wave Phantom" is developed that can be used to sample the 3D resolution properties of a CT image, including in-plane (x,y) and z-axis information. The key development in this Wave Phantom is the incorporation of a z-axis aspect of a more traditional step (bar) resolution gauge phantom. The phantom can be examined visually wherein a cutoff level may be seen; and/or the analytic analysis of the various characteristics of the waveform profile by including amplitude, frequency, and slope (rate of climb) of the peaks, can be extracted from the Wave Pattern using mathematical analysis such as the Fourier transform. The combined effect of changes in in-plane resolution and z-axis (thickness), are shown, as well as the effect of changes in either in-plane resolu-tion, or z-axis thickness. Examples of visual images of the Wave pattern as well as the analytic characteristics of the various harmonics of a periodic Wave pattern resulting from changes in resolution filter and/or slice thickness, and position in the field of view are shown. The Wave Phantom offers a promising way to investigate 3D resolution results from combined effect of in-plane (x-y) and z-axis resolution as contrasted to the use of simple 2D resolution gauges that need to be used with separate measures of z-axis dependency, such as angled ramps. It offers both a visual pattern as well as a pattern amenable to analytic analysis using Fourier Transform methods, and is believed to offer an image quality test closer to the diagnostic task where the 2D image has the hidden third (z) axis effects.

Genome Sequence of Stachybotrys chartarum Strain 51-11.

The Stachybotrys chartarum strain 51-11 genome was sequenced by shotgun sequencing utilizing Illumina HiSeq 2000 and PacBio technologies. Since S. chartarum has been implicated as having health impacts within water-damaged buildings, any information extracted from the genomic sequence data relating to toxins or the metabolism of the fungus might be useful.

Roscovitine in cancer and other diseases.

Roscovitine [CY-202, (R)-Roscovitine, Seliciclib] is a small molecule that inhibits cyclin-dependent kinases (CDKs) through direct competition at the ATP-binding site. It is a broad-range purine inhibitor, which inhibits CDK1, CDK2, CDK5 and CDK7, but is a poor inhibitor for CDK4 and CDK6. Roscovitine is widely used as a biological tool in cell cycle, cancer, apoptosis and neurobiology studies. Moreover, it is currently evaluated as a potential drug to treat cancers, neurodegenerative diseases, inflammation, viral infections, polycystic kidney disease and glomerulonephritis. This review focuses on the use of roscovitine in the disease model as well as clinical model research.

Image estimation from marker locations for dose calculation in prostate radiation therapy.

Tracking implanted markers in the prostate during each radiation treatment delivery provides an accurate approximation of prostate location, which enables the use of higher daily doses with tighter margins of the treatment beams and thus improves the efficiency of the radiotherapy. However, the lack of 3D image data with such a technique prevents calculation of delivered dose as required for adaptive planning. We propose to use a reference statistical shape model generated from the planning image and a deformed version of the reference model fitted to the implanted marker locations during treatment to estimate a regionally dense deformation from the planning space to the treatment space. Our method provides a means of estimating the treatment image by mapping planning image data to treatment space via the deformation field and therefore enables the calculation of dose distributions with marker tracking techniques during each treatment delivery.

Geometrically proper models in statistical training.

In deformable model segmentation, the geometric training process plays a crucial role in providing shape statistical priors and appearance statistics that are used as likelihoods. Also, the geometric training process plays a crucial role in providing shape probability distributions in methods finding significant differences between classes. The quality of the training seriously affects the final results of segmentation or of significant difference finding between classes. However, the lack of shape priors in the training stage itself makes it difficult to enforce shape legality, i.e., making the model free of local self-intersection or creases. Shape legality not only yields proper shape statistics but also increases the consistency of parameterization of the object volume and thus proper appearance statistics. In this paper we propose a method incorporating explicit legality constraints in training process. The method is mathematically sound and has proved in practice to lead to shape probability distributions over only proper objects and most importantly to better segmentation results.

A computational model to define the molecular causes of type 2 diabetes mellitus.

BACKGROUND: Metabolic abnormalities associated with type 2 diabetes mellitus (DM2) are caused in part by inadequate insulin action and resulting changes in gene expression in the skeletal muscle. Two recent, independent studies of human skeletal muscle biopsies from ethnically diverse DM2 patients have identified coordinated reductions in the expression of the oxidative phosphorylation (OXPHOS) genes. Whether these reductions are a consequence or a cause of impaired insulin sensitivity remains an open question. METHODS: To address this question and to define the underlying molecular causes consistent with the expression changes reported in the muscle studies, we created a large-scale computable model to analyze the molecular actions and effects of insulin on muscle gene expression. The model enables computer-aided reasoning using over 210,000 molecular relationships assembled from the DM2 literature. RESULTS: We integrated the data from these muscle biopsy studies into the model and used computer-aided causal reasoning to discover mechanisms that can link alterations in OXPHOS genes to decreases in glucose transport, insulin signaling, and risk factors associated to post-transplant diabetes mellitus. CONCLUSIONS: The emerging hypotheses describe biologic effects in DM2 and offer important cues for molecular targeted therapy.