Publications

The combination of the recent advances in computational and distributed sensor network technologies provide a unique opportunity for focused efforts on high confidence modelling and simulation of multiphysics systems. Responding to this opportunity, we present in this paper the architecture of a data-driven environment for multiphysics applications (DDEMA) as a multidisciplinary problem solving environment (MPSE). The goal of this environment is to support the automated identification and efficient prediction of the behavioral response of multiphysics continuous interacting systems. The design takes into consideration heterogeneous and distributed information technologies, coupled multiphysics sciences, and sensor originating data to drive and to steer adaptive modelling and simulation of the underlying systemic behavior. The design objectives and proposed software architecture are described in the context of two multidisciplinary applications related to material structure design of supersonic platforms and fire/material/environment interaction monitoring, assessment and management. These applications of DDEMA will be distributed over a highly heterogeneous networks that extend from light and ubiquitous resources (thin portable devices/clients) to heavy GRID-based computational infrastructure.

No abstract is provided for this article.

Additional file 13: Table S9. Results of the sex interaction analysis. Results for all 148 replication CpG site-metabolic measure associations are shown for models identical to the discovery, but with additional “sex × methylation” interaction term. Presented are the obtained coefficient (interaction_coef) for the term of the interaction between methylation and sex (0: male, 1: female), standard error of the coefficient (interaction_SE), p value of the coefficient (interaction_P), the Bonferroni-corrected p value of the coefficient (interaction_bonf_p), the methylation-metabolite measure coefficient for males (coef_CpG_Met_male), with the calculated females coefficient (coef_CpG_Met_female, i.e., coef_CpG_Met_male + interaction_coef), and number of observations for the model (N). No interaction coefficient results pass a Bonferroni-corrected p value threshold of 0.05/148 ~ 3.4e−4 for statistical significance. All coefficients are change in natural-log transformed metabolite measurement unit (as given in Additional file 1: Table S1) per unit increase in methylation (beta value on 0–1 scale).

Molecular rectification is a particularly attractive phenomenon to examine in studying structure-property relationships in charge transport across molecular junctions, since the tunneling currents across the same molecular junction are measured, with only a change in the sign of the bias, with the same electrodes, molecule(s), and contacts. This type of experiment minimizes the complexities arising from measurements of current densities at one polarity using replicate junctions. This paper describes a new organic molecular rectifier: a junction having the structure Ag(TS)/S(CH2)11-4-methyl-2,2'-bipyridyl//Ga2O3/EGaIn (Ag(TS): template-stripped silver substrate; EGaIn: eutectic gallium-indium alloy) which shows reproducible rectification with a mean r(+) = |J(+1.0 V)|/|J(-1.0 V)| = 85 ± 2. This system is important because rectification occurs at a polarity opposite to that of the analogous but much more extensively studied systems based on ferrocene. It establishes (again) that rectification is due to the SAM, and not to redox reactions involving the Ga2O3 film, and confirms that rectification is not related to the polarity in the junction. Comparisons among SAM-based junctions incorporating the Ga2O3/EGaIn top electrode and a variety of heterocyclic terminal groups indicate that the metal-free bipyridyl group, not other features of the junction, is responsible for the rectification. The paper also describes a structural and mechanistic hypothesis that suggests a partial rationalization of values of rectification available in the literature.

Identification of genetic variants affecting complex traits such as obesity is confounded by many types of bias, especially when effect sizes are small. Given our findings of a positive association between rs7566605 and body mass index in four out of five separate samples, a false positive finding cannot be ruled out with certainty but seems unlikely. Meta-analyses of multiple large studies will help refine the estimate of the effects of rs7566605 on body mass index.