A ballistic graphene superconducting microwave circuit

Josephson junctions (JJ) are a fundamental component of microwave quantum circuits, such as tunable cavities, qubits and parametric amplifiers. Recently developed encapsulated graphene JJs, with supercurrents extending over micron distance scales, have exciting potential applications as a new building block for quantum circuits. Despite this, the microwave performance of this technology has not been explored. Here, we demonstrate a microwave circuit based on a ballistic graphene JJ embedded in a superconducting cavity. We directly observe a gate-tunable Josephson inductance through the resonance frequency of the device and, using a detailed RF model, we extract this inductance quantitatively. We also observe the microwave losses of the device, and translate this into sub-gap resistances of the junction at {\mu}eV energy scales, not accessible in DC measurements. The microwave performance we observe here suggests that graphene Josephson junctions are a feasible platform for implementing coherent quantum circuits.Comment: 43 pages, 20 figure

A measure of statistical complexity based on predictive information with application to finite spin systems

NOTICE: this is the author’s version of a work that was accepted for publication in 'Physical Letters A'. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PHYSICAL LETTERS A, 376 (4): 275-281, JAN 2012. DOI:10.1016/j.physleta.2011.10.066

Genome-Wide Association Study Identifies Loci for Liver Enzyme Concentrations in Mexican Americans: The GUARDIAN Consortium.

ObjectivePopulations of Mexican American ancestry are at an increased risk for nonalcoholic fatty liver disease. The objective of this study was to determine whether loci in known and novel genes were associated with variation in aspartate aminotransferase (AST) (n = 3,644), alanine aminotransferase (ALT) (n = 3,595), and gamma-glutamyl transferase (GGT) (n = 1,577) levels by conducting the first genome-wide association study (GWAS) of liver enzymes, which commonly measure liver function, in individuals of Mexican American ancestry.MethodsLevels of AST, ALT, and GGT were determined by enzymatic colorimetric assays. A multi-cohort GWAS of individuals of Mexican American ancestry was performed. Single-nucleotide polymorphisms (SNP) were tested for association with liver outcomes by multivariable linear regression using an additive genetic model. Association analyses were conducted separately in each cohort, followed by a nonparametric meta-analysis.ResultsIn the PNPLA3 gene, rs4823173 (P = 3.44 × 10-10 ), rs2896019 (P = 7.29 × 10-9 ), and rs2281135 (P = 8.73 × 10-9 ) were significantly associated with AST levels. Although not genome-wide significant, these same SNPs were the top hits for ALT (P = 7.12 × 10-8 , P = 1.98 × 10-7 , and P = 1.81 × 10-7 , respectively). The strong correlation (r2  = 1.0) for these SNPs indicated a single hit in the PNPLA3 gene. No genome-wide significant associations were found for GGT.ConclusionsPNPLA3, a locus previously identified with ALT, AST, and nonalcoholic fatty liver disease in European and Japanese GWAS, is also associated with liver enzymes in populations of Mexican American ancestry

Reliability and importance of structural diversity of climate model ensembles

PublishedJournal ArticleWe investigate the performance of the newest generation multi-model ensemble (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the ensemble to the previous generation models (CMIP3) as well as several single model ensembles (SMEs), which are constructed by varying components of single models. These SMEs range from ensembles where parameter uncertainties are sampled (perturbed physics ensembles) through to an ensemble where a number of the physical schemes are switched (multi-physics ensemble). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and ensemble members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being "over-dispersed" rather than "under-dispersed". In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching ensembles. For distance measures, the observations and models ensemble members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the ensemble. We suggest that multi-model ensembles should represent an important component of uncertainty analysis. © 2013 The Author(s).We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. For CMIP the US Department of Energy’s Pro- gram for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. M.C. was partially supported by funding from NERC grants NE/I006524/1 and NE/I022841/1. MW is supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). T.Y., J.D.A, H.S., S.E., M.Y., J.C.H. were supported by the Global Environment Research Fund of the Ministry of the Environment of Japan (S-10, Integrated Climate Assessment – Risks,Uncertainties and Society, ICA-RUS)

Gastric cancer and Helicobacter pylori: a combined analysis of 12 case control studies nested within prospective cohorts

BACKGROUND: The magnitude of the association between Helicobacter pylori and incidence of gastric cancer is unclear. H pylori infection and the circulating antibody response can be lost with development of cancer; thus retrospective studies are subject to bias resulting from classifi- cation of cases as H pylori negative when they were infected in the past. AIMS: To combine data from all case control studies nested within prospective cohorts to assess more reliably the relative risk of gastric cancer associated with H pylori infection.To investigate variation in relative risk by age, sex, cancer type and subsite, and interval between blood sampling and cancer diagnosis. METHODS: Studies were eligible if blood samples for H pylori serology were collected before diagnosis of gastric cancer in cases. Identified published studies and two unpublished studies were included. Individual subject data were obtained for each. Matched odds ratios (ORs) and 95% confidence intervals (95% CI) were calculated for the association between H pylori and gastric cancer. RESULTS: Twelve studies with 1228 gastric cancer cases were considered. The association with H pylori was restricted to noncardia cancers (OR 3.0; 95% CI 2.3–3.8) and was stronger when blood samples for H pylori serology were collected 10+ years before cancer diagnosis (5.9; 3.4–10.3). H pylori infection was not associated with an altered overall risk of cardia cancer (1.0; 0.7–1.4). CONCLUSIONS: These results suggest that 5.9 is the best estimate of the relative risk of non-cardia cancer associated with H pylori infection and that H pylori does not increase the risk of cardia cancer. They also support the idea that when H pylori status is assessed close to cancer diagnosis, the magnitude of the non-cardia association may be underestimated

Applying laboratory thermal desorption data in an interstellar context: sublimation of methanol thin films

Methods by which experimental measurements of thermal desorption can be applied in astrophysical environments have been developed, using the sublimation of solid methanol as an example. The temperature programmed desorption of methanol from graphitic, amorphous silica and polycrystalline gold substrates was compared, with the kinetic parameters of desorption extracted by either a leading edge analysis or by fitting using a stochastic integration method. At low coverages, the desorption shows a substrate-dependent fractional order. However, at higher coverages methanol desorption is zeroth order with kinetic parameters independent of substrate. Using a kinetic model based on the stochastic integration analyses, desorption under astrophysically relevant conditions can be simulated. We find that the chemical and morphological nature of the substrate has relatively little impact on the desorption temperature of solid methanol, and that the substrate independent zeroth-order kinetics can provide a satisfactory model for desorption in astrophysical environments. Uncertainties in the heating rate and the distribution of grain sizes will have the largest influence on the range of desorption temperature. These conclusions are likely to be generally applicable to all species in dust grain ice mantles

An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data

Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory

Defining a spinal microcircuit that gates myelinated afferent input: implications for tactile allodynia

Chronic pain presents a major unmet clinical problem. The development of more effective treatments is hindered by our limited understanding of the neuronal circuits underlying sensory perception. Here, we show that parvalbumin (PV)-expressing dorsal horn interneurons modulate the passage of sensory information conveyed by low-threshold mechanoreceptors (LTMRs) directly via presynaptic inhibition and also gate the polysynaptic relay of LTMR input to pain circuits by inhibiting lamina II excitatory interneurons whose axons project into lamina I. We show changes in the functional properties of these PV interneurons following peripheral nerve injury and that silencing these cells unmasks a circuit that allows innocuous touch inputs to activate pain circuits by increasing network activity in laminae I–IV. Such changes are likely to result in the development of tactile allodynia and could be targeted for more effective treatment of mechanical pain