Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved

Local three-nucleon interaction from chiral effective field theory

The three-nucleon (NNN) interaction derived within the chiral effective field theory at the next-to-next-to-leading order (N2LO) is regulated with a function depending on the magnitude of the momentum transfer. The regulated NNN interaction is then local in the coordinate space, which is advantages for some many-body techniques. Matrix elements of the local chiral NNN interaction are evaluated in a three-nucleon basis. Using the ab initio no-core shell model (NCSM) the NNN matrix elements are employed in 3H and 4He bound-state calculations.Comment: 17 pages, 9 figure

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Ethanol Oxidation and Toxicity: Role of Alcohol P-450 Oxygenase

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66172/1/j.1530-0277.1986.tb05179.x.pd

Should patients with abnormal liver function tests in primary care be tested for chronic viral hepatitis: cost minimisation analysis based on a comprehensively tested cohort

Background Liver function tests (LFTs) are ordered in large numbers in primary care, and the Birmingham and Lambeth Liver Evaluation Testing Strategies (BALLETS) study was set up to assess their usefulness in patients with no pre-existing or self-evident liver disease. All patients were tested for chronic viral hepatitis thereby providing an opportunity to compare various strategies for detection of this serious treatable disease. Methods This study uses data from the BALLETS cohort to compare various testing strategies for viral hepatitis in patients who had received an abnormal LFT result. The aim was to inform a strategy for identification of patients with chronic viral hepatitis. We used a cost-minimisation analysis to define a base case and then calculated the incremental cost per case detected to inform a strategy that could guide testing for chronic viral hepatitis. Results Of the 1,236 study patients with an abnormal LFT, 13 had chronic viral hepatitis (nine hepatitis B and four hepatitis C). The strategy advocated by the current guidelines (repeating the LFT with a view to testing for specific disease if it remained abnormal) was less efficient (more expensive per case detected) than a simple policy of testing all patients for viral hepatitis without repeating LFTs. A more selective strategy of viral testing all patients for viral hepatitis if they were born in countries where viral hepatitis was prevalent provided high efficiency with little loss of sensitivity. A notably high alanine aminotransferase (ALT) level (greater than twice the upper limit of normal) on the initial ALT test had high predictive value, but was insensitive, missing half the cases of viral infection. Conclusions Based on this analysis and on widely accepted clinical principles, a "fast and frugal" heuristic was produced to guide general practitioners with respect to diagnosing cases of viral hepatitis in asymptomatic patients with abnormal LFTs. It recommends testing all patients where a clear clinical indication of infection is present (e.g. evidence of intravenous drug use), followed by testing all patients who originated from countries where viral hepatitis is prevalent, and finally testing those who have a notably raised ALT level (more than twice the upper limit of normal). Patients not picked up by this efficient algorithm had a risk of chronic viral hepatitis that is lower than the general population

The vitamin D receptor polymorphism in the translation initiation codon is a risk factor for insulin resistance in glucose tolerant Caucasians

BACKGROUND: Although vitamin D receptor (VDR) polymorphisms have been shown to be associated with abnormal glucose metabolism, the reported polymorphisms are unlikely to have any biological consequences. The VDR gene has two potential translation initiation sites. A T-to-C polymorphism has been noted in the first ATG (f allele), abolishing the first translation initiation site and resulting in a peptide lacking the first three amino acids (F allele). We examined the role of this polymorphism in insulin sensitivity and beta cell function. This study included 49 healthy Caucasian subjects (28 females, age 28 ± 1 years old, body mass index 24.57 ± 0.57 kg/m(2), waist-hip ratio 0.81 ± 0.01 cm/cm). They were all normotensive (less than 140/90 mmHg) and glucose tolerant, which was determined by a standard 75-gm oral glucose tolerance test. Their beta cell function (%B) and insulin sensitivity (%S) were calculated based on the Homeostasis Model Assessment (HOMA). Their genotypes were determined by a polymerase chain reaction-restriction fragment length polymorphism analysis. Phenotypes were compared between genotypic groups. RESULTS: There were 18 FF, 21 Ff, and 10 ff subjects. Since only 10 ff subjects were identified, they were pooled with the Ff subjects during analyses. The FF and Ff/ff groups had similar glucose levels at each time point before and after a glucose challenge. The Ff/ff group had higher insulin levels than the FF group at fasting (P=0.006), 30 minutes (P=0.009), 60 minutes (P=0.049), and 90 minutes (P=0.042). Furthermore, the Ff/ff group also had a larger insulin area under the curve than the FF group (P=0.009). While no difference was noted in %B, the Ff/ff group had a lower %S than the FF group (0.53 vs. 0.78, P=0.006). A stepwise regression analysis confirmed that the Fok I polymorphism was an independent determinant for %S, accounting for 29.3% of variation in %S when combined with waist-hip ratio. CONCLUSIONS: We report that the Fok I polymorphism at the VDR gene locus is associated with insulin sensitivity, but has no influence on beta cell function in healthy Caucasians. Although this polymorphism has been shown to affect the activation of vitamin D-dependent transcription, the molecular basis of the association between this polymorphism and insulin resistance remains to be determined

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models

<p>Abstract</p> <p>Background</p> <p>Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.</p> <p>Method</p> <p>To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.</p> <p>Result</p> <p>We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.</p> <p>Conclusions</p> <p>We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.</p

Longitudinal associations between television in the bedroom and body fatness in a UK cohort study.

OBJECTIVE: To assess longitudinal associations between screen-based media use (television (TV) and computer hours, having a TV in the bedroom) and body fatness among UK children. METHODS: Participants were 12 556 children from the UK Millennium Cohort Study who were followed from age 7 to age 11 years. Associations were assessed between screen-based media use and the following outcomes: body mass index (BMI), fat mass index (FMI), and overweight. RESULTS: In fully adjusted models, having a bedroom TV at age 7 years was associated with significantly higher BMI and FMI (excess BMI for boys=0.29, 95% confidence interval (CI) 0.06-0.52; excess BMI for girls=0.57, 95% CI 0.31-0.84; excess FMI for boys=0.20, 95% CI 0.04-0.37; excess FMI for girls=0.39, 95% CI 0.21-0.57) and increased risk of being overweight (relative risk (RR) for boys=1.21, 95% CI 1.07-1.36; RR for girls=1.31, 95% CI 1.15-1.48) at age 11 years, compared with having no bedroom TV. Hours spent watching TV or digital versatile disks were associated with increased risk of overweight among girls only. Computer use at age 7 years was not related to later body fatness for either gender. CONCLUSION: Having a TV in the child's bedroom was an independent risk factor for overweight and increased body fatness in this nationally representative sample of UK children. Childhood obesity prevention strategies should consider TVs in children's bedrooms as a risk factor for obesity.International Journal of Obesity advance online publication, 27 June 2017; doi:10.1038/ijo.2017.129