Global Monte Carlo Simulation with High Order Polynomial Expansions

The functional expansion technique (FET) was recently developed for Monte Carlo simulation. The basic idea of the FET is to expand a Monte Carlo tally in terms of a high order expansion, the coefficients of which can be estimated via the usual random walk process in a conventional Monte Carlo code. If the expansion basis is chosen carefully, the lowest order coefficient is simply the conventional histogram tally, corresponding to a flat mode. This research project studied the applicability of using the FET to estimate the fission source, from which fission sites can be sampled for the next generation. The idea is that individual fission sites contribute to expansion modes that may span the geometry being considered, possibly increasing the communication across a loosely coupled system and thereby improving convergence over the conventional fission bank approach used in most production Monte Carlo codes. The project examined a number of basis functions, including global Legendre polynomials as well as “local” piecewise polynomials such as finite element hat functions and higher order versions. The global FET showed an improvement in convergence over the conventional fission bank approach. The local FET methods showed some advantages versus global polynomials in handling geometries with discontinuous material properties. The conventional finite element hat functions had the disadvantage that the expansion coefficients could not be estimated directly but had to be obtained by solving a linear system whose matrix elements were estimated. An alternative fission matrix-based response matrix algorithm was formulated. Studies were made of two alternative applications of the FET, one based on the kernel density estimator and one based on Arnoldi’s method of minimized iterations. Preliminary results for both methods indicate improvements in fission source convergence. These developments indicate that the FET has promise for speeding up Monte Carlo fission source convergence

A Genome-Wide Study of Cytogenetic Changes in Colorectal Cancer Using SNP Microarrays: Opportunities for Future Personalized Treatment

In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh frozen tissue from 86 CRC patients using Illumina's Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF) - a representation of allelic composition. These data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g. amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From Therapeutic Target Database, we identified relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions, harboring genes, which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high density SNP arrays for cytogenetic study in CRC and its potential utility for personalized treatment

Disruption of Yarrowia lipolytica TPS1 Gene Encoding Trehalose-6-P Synthase Does Not Affect Growth in Glucose but Impairs Growth at High Temperature

We have cloned the Yarrowia lipolytica TPS1 gene encoding trehalose-6-P synthase by complementation of the lack of growth in glucose of a Saccharomyces cerevisiae tps1 mutant. Disruption of YlTPS1 could only be achieved with a cassette placed in the 3′half of its coding region due to the overlap of its sequence with the promoter of the essential gene YlTFC1. The Yltps1 mutant grew in glucose although the Y. lipolytica hexokinase is extremely sensitive to inhibition by trehalose-6-P. The presence of a glucokinase, insensitive to trehalose-6-P, that constitutes about 80% of the glucose phosphorylating capacity during growth in glucose may account for the growth phenotype. Trehalose content was below 1 nmol/mg dry weight in Y. lipolytica, but it increased in strains expressing YlTPS1 under the control of the YlTEF1promoter or with a disruption of YALI0D15598 encoding a putative trehalase. mRNA levels of YlTPS1 were low and did not respond to thermal stresses, but that of YlTPS2 (YALI0D14476) and YlTPS3 (YALI0E31086) increased 4 and 6 times, repectively, by heat treatment. Disruption of YlTPS1 drastically slowed growth at 35°C. Homozygous Yltps1 diploids showed a decreased sporulation frequency that was ascribed to the low level of YALI0D20966 mRNA an homolog of the S. cerevisiae MCK1 which encodes a protein kinase that activates early meiotic gene expression

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201

Hemoglobin A1c as a Predictor of Incident Diabetes

Objective: Several studies have suggested that $HbA_{1c}$ levels may predict incident diabetes. With new recommendations for use of $HbA_{1c}$ in diagnosing diabetes, many patients with $HbA_{1c}$ results below the diagnostic threshold will be identified. Clinicians will need to categorize risk for a subsequent diabetic diagnosis in such patients. The objective of this study was to determine the ability of $HbA_{1c}$ to predict the incidence of a diabetic diagnosis. Research Design and Methods: We performed a historical cohort study using electronic medical record data from two Department of Veterans Affairs Medical Centers. Patients (n = 12,589) were identified with a baseline $HbA_{1c}$ <6.5% between January 2000 and December 2001 and without a diagnosis of diabetes. Patients (12,375) had at least one subsequent follow-up visit. These patients were tracked for 8 years for a subsequent diagnosis of diabetes. Results: During an average follow-up of 4.4 years, 3,329 (26.9%) developed diabetes. $HbA_{1c}$ $\geq$5.0% carried a significant risk for developing diabetes during follow-up. When compared with the reference group ($HbA_{1c}$ <4.5%), $HbA_{1c}$ increments of 0.5% between 5.0 and 6.4% had adjusted odds ratios of 1.70 (5.0–5.4%), 4.87 (5.5–5.9%), and 16.06 (6.0–6.4%) (P < 0.0001). Estimates of hazard ratios similarly showed significant increases for $HbA_{1c}$ $\geq$5.0%. A risk model for incident diabetes within 5 years was developed and validated using $HbA_{1c}$, age, BMI, and systolic blood pressure. Conclusions: The incidence of diabetes progressively and significantly increased among patients with an $HbA_{1c}$ $\geq$5.0%, with substantially expanded risk for those with $HbA_{1c}$ 6.0–6.4%

Association between hemoglobin A1c variability and hypoglycemia-related hospitalizations in veterans with diabetes mellitus

Introduction To study the impact of hemoglobin A1c (A1c) variability on the risk of hypoglycemia-related hospitalization (HRH) in veterans with diabetes mellitus.Research design and methods 342 059 veterans with diabetes aged 65 years or older were identified for a retrospective cohort study. All participants had a 3-year baseline period from January 1, 2005 to December 31, 2016, during which they had at least four A1c tests. A1c variability measures included coefficient of variation (A1c CV), A1c SD, and adjusted A1c SD. HRH was identified during a 2-year follow-up period from Medicare and the Veterans Health Administration through validated algorithms of International Classification of Diseases (ICD)-9 and ICD-10 codes. Logistic regression modeling was used to evaluate the relationship between A1c variability and HRH risk while controlling for relevant clinical covariates.Results 2871 patients had one or more HRH in the 2-year follow-up period. HRH risk increased with greater A1c variability, and this was consistent across A1c CV, A1c SD, and adjusted A1c SD. Average A1c levels were also independently associated with HRH, with levels &lt;7.0% (53 mmol/mol) having lower risk and &gt;9% (75 mmol/mol) with greater risk. The relationships between A1c variability remained significant after controlling for average A1c levels and prior HRH during the baseline period.Conclusion Increasing A1c variability and elevated A1c levels are associated with a greater risk of HRH in older adults with diabetes. Clinicians should consider A1c variability when assessing patients for risk of severe hypoglycemia