Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models

Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model selection to construct a prior for the covariance matrix that is a mixture over all decomposable graphs, where a graph means the configuration of nonzero offdiagonal elements in the inverse of the covariance matrix. Our prior for the covariance matrix is such that the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We give empirical results that show the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix. The advantage is greatest when the number of observations is small relative to the dimension of the covariance matrix. The article also shows empirically that there is minimal change in statistical efficiency in using the mixture over decomposable graphs prior for estimating a general covariance compared to the Bayesian estimator by Wong et al. (2003), even when the graph of the covariance matrix is nondecomposable. However, our approach has some important advantages over that of Wong et al. (2003). Our method requires the number of decomposable graphs for each graph size. We show how to estimate these numbers using simulation and that the simulation results agree with analytic results when such results are known. We also show how to estimate the posterior distribution of the covariance matrix using Markov chain Monte Carlo with the elements of the covariance matrix integrated out and give empirical results that show the sampler is computationally efficient and converges rapidly. Finally, we note that both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.Covariance selection; Graphical models; Reduced conditional sampling; Variable selection

Effect of diet-induced obesity on protein expression in insulin signalling pathways of skeletal muscle in male Wistar rats

BACKGROUND: The prevalence of diet-induced obesity is increasing globally, and posing significant health problems for millions of people worldwide. Diet-induced obesity is a major contributor to the global pandemic of type 2 diabetes mellitus. The reduced ability of muscle tissue to regulate glucose homeostasis plays a major role in the development and prognosis of type 2 diabetes. In this study, an animal model of diet-induced obesity was used to elucidate changes in skeletal muscle insulin signaling in obesity-induced diabetes. METHODS: Adult male Wistar rats were randomized and assigned to either a control group or to a test group. Controls were fed a standard laboratory pellet diet (chow-fed), while the test group had free access to a highly palatable diet (diet-fed). After 8 weeks, the diet-fed animals were subdivided into three subgroups and their diets were altered as follows: diet-to-chow, diet-fed with addition of fenofibrate given by oral gavage for a further 7 weeks, or diet-fed with vehicle given by oral gavage for a further 7 weeks, respectively. RESULTS: Untreated diet-fed animals had a significantly higher body weight and metabolic profile than the control chow-fed animals. Intramuscular triacylglyceride levels in the untreated obese animals were significantly higher than those in the control chow-fed group. Expression of protein kinase C beta, phosphatidylinositol 3, Shc, insulin receptor substrate 1, ERK1/2, and endothelial nitric oxide synthase was significantly increased by dietary obesity, while that of insulin receptor beta, insulin receptor substrate 1, and protein kinase B (Akt) were not affected by obesity. CONCLUSION: These data suggest that diet-induced obesity affects insulin signaling mechanisms, leading to insulin resistance in muscle

The variability of currents and sea level in the upper Delaware estuary

The variability of currents and sea levels in the upper Delaware estuary are examined based on measurements from bottom mounted acoustic Doppler current profilers (ADCP) deployed at two sites (New Castle and Tinicum) from 18 March to 10 June 2003. New Castle is located 104 km from the mouth, and Tinicum is located another 32 km up-estuary. Supplemental data, including sea level at the mouth of the estuary, river discharge, and wind speed and direction, were also obtained from various federal agencies. The instantaneous current represents a superposition of variability driven by the tide, wind, and river discharge. Over the short (\u3c36 hr) time scale, the tide is the dominant forcing mechanism, with M2 being the principal tidal constituent. The amplitude of the M2 tide increases from the mouth to the upper estuary and gives rise to a vigorous M2 current of the order 80 cm s–1. On time scales of 36 to 120 hr, the effect of wind drives a weak subtidal current with a standard deviation of 2 cm s–1 in the upper estuary. At time scales longer than 120 hr, the subtidal current variability, with a standard deviation of 6 cm s–1, is dominated by the barotropic response of the upper estuary to variations in the river discharge. The upper estuary exhibits a strong down-estuary mean current of the order—15 cm s–1. At Tinicum, river discharge accounts for more than half of the mean current, which is characterized by down-estuary flow throughout the water column. The magnitude of the river discharge-induced mean current is reduced at New Castle, in direct response to the down-estuary increase in the cross-sectional area. Tidally rectified current accounts for the remainder of the overall mean flow at Tinicum, and the effect of tidal rectification may be more important than river discharge in producing the mean flow at New Castle. There is no evidence of a baroclinic gravitational circulation, as the salt intrusion generally does not extend into the upper estuary

Information-based methods for predicting gene function from systematic gene knock-downs

<p>Abstract</p> <p>Background</p> <p>The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode <it>Caenorhabditis elegans</it>. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role.</p> <p>Results</p> <p>We compared several methods for detecting gene-gene functional similarity from phenotypic knock-down profiles. We found that information-based measures, which explicitly incorporate a phenotype's genomic frequency when calculating gene-gene similarity, outperform non-information-based methods. We report the presence of newly predicted modules identified from an integrated functional network containing phenotypic congruency links derived from an information-based measure. One such module is a set of genes predicted to play a role in regulating body morphology based on their multiply-supported interactions with members of the TGF-<it>β </it>signaling pathway.</p> <p>Conclusion</p> <p>Information-based metrics significantly improve the comparison of phenotypic knock-down profiles, based upon their ability to enhance gene function prediction and identify novel functional modules.</p

New interleukin-15 superagonist (IL-15SA) significantly enhances graft-versus-tumor activity.

Interleukin-15 (IL-15) is a potent cytokine that increases CD8+ T and NK cell numbers and function in experimental models. However, obstacles remain in using IL-15 therapeutically, specifically its low potency and short in vivo half-life. To help overcome this, a new IL-15 superagonist complex comprised of an IL-15N72D mutation and IL-15RαSu/Fc fusion (IL-15SA, also known as ALT-803) was developed. IL-15SA exhibits a significantly longer serum half-life and increased in vivo activity against various tumors. Herein, we evaluated the effects of IL-15SA in recipients of allogeneic hematopoietic stem cell transplantation. Weekly administration of IL-15SA to transplant recipients significantly increased the number of CD8+ T cells (specifically CD44+ memory/activated phenotype) and NK cells. Intracellular IFN-γ and TNF-α secretion by CD8+ T cells increased in the IL-15SA-treated group. IL-15SA also upregulated NKG2D expression on CD8+ T cells. Moreover, IL-15SA enhanced proliferation and cytokine secretion of adoptively transferred CFSE-labeled T cells in syngeneic and allogeneic models by specifically stimulating the slowly proliferative and nonproliferative cells into actively proliferating cells.We then evaluated IL-15SA\u27s effects on anti-tumor activity against murine mastocytoma (P815) and murine B cell lymphoma (A20). IL-15SA enhanced graft-versus-tumor (GVT) activity in these tumors following T cell infusion. Interestingly, IL-15 SA administration provided GVT activity against A20 lymphoma cells in the murine donor leukocyte infusion (DLI) model without increasing graft versus host disease. In conclusion, IL-15SA could be a highly potent T- cell lymphoid growth factor and novel immunotherapeutic agent to complement stem cell transplantation and adoptive immunotherapy

Constant Size Molecular Descriptors For Use With Machine Learning

A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules. These features are evaluated by monitoring performance of kernel ridge regression models on well-studied data sets of small organic molecules. The features include connectivity counts, which require only the bonding pattern of the molecule, and encoded distances, which summarize distances between both bonded and non-bonded atoms and so require the full molecular geometry. In addition to having constant size, these features summarize information regarding the local environment of atoms and bonds, such that models can take advantage of similarities resulting from the presence of similar chemical fragments across molecules. Combining these two types of features leads to models whose performance is comparable to or better than the current state of the art. The features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules.Comment: 18 pages, 5 figure

The effects of diet-induced obesity on hepatocyte insulin signaling pathways and induction of non-alcoholic liver damage

The prevalence of diet-induced obesity is increasing amongst adults and children worldwide, predisposing millions of people to an array of health problems that include metabolic syndrome, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. In this study we used experimental animals to investigate the effects of dietary obesity on markers of hepatic insulin signaling as well as structural changes in hepatocytes. Adult male Wistar rats were randomized and assigned to either a control group or a test group. Controls were fed standard laboratory pelleted diet (chow-fed), while the test group had free access to a highly-palatable diet (HPD). After eight weeks, the HPD-fed animals were subdivided into three subgroups and their diets altered as follows: HPD-to-chow, HPD with the addition of fenofibrate given by oral gavage for a further seven weeks, or HPD with vehicle (1% carboxymethylcellulose at 1 mL/kg body weight) given by oral gavage for a further seven weeks, respectively. Untreated diet-fed animals had significantly higher body weight, liver weight, and all measured metabolic profiles compared with chow-fed and treated diet-fed groups. Expression of kinases IRβ, IRS-1, AKt, eNOS, Shc and ERK1/2 were unaffected by obesity, while IRS-2 and P I3 kinase levels were significantly reduced in untreated HPD animals. Compared with chow-fed animals, steatosis and steatohepatitis were almost doubled in animals from untreated HPD, while removal of HPD and fenofibrate-treatment reduced steatosis by 40% and 80% respectively. These data suggest that diet-induced obesity affects intracellular insulin signaling mechanisms, namely IRS-2 and PI 3-kinase, leading to hepatic insulin resistance. Moreover, diet-induced obesity induces fatty liver, an effect which can be reversed by either removal of the source of obesity or treatment with fenofibrate, a peroxisome proliferator-activated receptor alpha agonist

Inhibition of microbial sulfate reduction in a flow-through column system by (per)chlorate treatment.

Microbial sulfate reduction is a primary cause of oil reservoir souring. Here we show that amendment with chlorate or perchlorate [collectively (per)chlorate] potentially resolves this issue. Triplicate packed columns inoculated with marine sediment were flushed with coastal water amended with yeast extract and one of nitrate, chlorate, or perchlorate. Results showed that although sulfide production was dramatically reduced by all treatments, effluent sulfide was observed in the nitrate (10 mM) treatment after an initial inhibition period. In contrast, no effluent sulfide was observed with (per)chlorate (10 mM). Microbial community analyses indicated temporal community shifts and phylogenetic clustering by treatment. Nitrate addition stimulated Xanthomonadaceae and Rhizobiaceae growth, supporting their role in nitrate metabolism. (Per)chlorate showed distinct effects on microbial community structure compared with nitrate and resulted in a general suppression of the community relative to the untreated control combined with a significant decrease in sulfate reducing species abundance indicating specific toxicity. Furthermore, chlorate stimulated Pseudomonadaceae and Pseudoalteromonadaceae, members of which are known chlorate respirers, suggesting that chlorate may also control sulfidogenesis by biocompetitive exclusion of sulfate-reduction. Perchlorate addition stimulated Desulfobulbaceae and Desulfomonadaceae, which contain sulfide oxidizing and elemental sulfur-reducing species respectively, suggesting that effluent sulfide concentrations may be controlled through sulfur redox cycling in addition to toxicity and biocompetitive exclusion. Sulfur isotope analyses further support sulfur cycling in the columns, even when sulfide is not detected. This study indicates that (per)chlorate show great promise as inhibitors of sulfidogenesis in natural communities and provides insight into which organisms and respiratory processes are involved