The spatial variations of lightning during small Florida thunderstorms

Networks of field mills (FM's) and lightning direction finders (LDF's) were used to locate lightning over the NASA KSC on three storm days. Over 90 percent of all cloud-to-ground (CG) flashes that were detected by the LDF's in the study area were also detected by the LDF's. About 17 percent of the FM CG events could be fitted to either a monopole or a dipole charge model. These projected FM charge locations are compared to LDF locations, i.e., the ground strike points. It was found that 95 percent of the LDF points are within 12 km of the FM charge, 75 percent are within 8 km, and 50 percent are within 4 km. For a storm on 22 Jul. 1988, there was a systematic 5.6 km shift between the FM charge centers and the LDF strike points that might have been caused by the meteorological structure of the storm

An Operational Configuration of the ARPS Data Analysis System to Initialize WRF in the NM'S Environmental Modeling System

The Weather Research and Forecasting (WRF) model is the next generation community mesoscale model designed to enhance collaboration between the research and operational sectors. The NM'S as a whole has begun a transition toward WRF as the mesoscale model of choice to use as a tool in making local forecasts. Currently, both the National Weather Service in Melbourne, FL (NWS MLB) and the Spaceflight Meteorology Group (SMG) are running the Advanced Regional Prediction System (AIRPS) Data Analysis System (ADAS) every 15 minutes over the Florida peninsula to produce high-resolution diagnostics supporting their daily operations. In addition, the NWS MLB and SMG have used ADAS to provide initial conditions for short-range forecasts from the ARPS numerical weather prediction (NWP) model. Both NM'S MLB and SMG have derived great benefit from the maturity of ADAS, and would like to use ADAS for providing initial conditions to WRF. In order to assist in this WRF transition effort, the Applied Meteorology Unit (AMU) was tasked to configure and implement an operational version of WRF that uses output from ADAS for the model initial conditions. Both agencies asked the AMU to develop a framework that allows the ADAS initial conditions to be incorporated into the WRF Environmental Modeling System (EMS) software. Developed by the NM'S Science Operations Officer (S00) Science and Training Resource Center (STRC), the EMS is a complete, full physics, NWP package that incorporates dynamical cores from both the National Center for Atmospheric Research's Advanced Research WRF (ARW) and the National Centers for Environmental Prediction's Non-Hydrostatic Mesoscale Model (NMM) into a single end-to-end forecasting system. The EMS performs nearly all pre- and postprocessing and can be run automatically to obtain external grid data for WRF boundary conditions, run the model, and convert the data into a format that can be readily viewed within the Advanced Weather Interactive Processing System. The EMS has also incorporated the WRF Standard Initialization (SI) graphical user interface (GUT), which allows the user to set up the domain, dynamical core, resolution, etc., with ease. In addition to the SI GUT, the EMS contains a number of configuration files with extensive documentation to help the user select the appropriate input parameters for model physics schemes, integration timesteps, etc. Therefore, because of its streamlined capability, it is quite advantageous to configure ADAS to provide initial condition data to the EMS software. One of the biggest potential benefits of configuring ADAS for ingest into the EMS is that the analyses could be used to initialize either the ARW or NMM. Currently, the ARPS/ADAS software has a conversion routine only for the ARW dynamical core. However, since the NIvIM runs about 2.5 times faster than the ARW, it is quite advantageous to be able to run an ADAS/NMM configuration operationally due to the increased efficiency

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordObjective: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50. Design: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, BMI) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. Setting: United Kingdom cohorts recruited from primary and secondary care. Participants: 1,352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. Main outcome measures: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. 4 Results: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 [95%CI 0.88, 0.93] (clinical features only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90, 0.96]). Conclusions: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.National Institute for Health Research (NIHR)European Community FP7Oxford Hospitals Charitable FundWellcome TrustMedical Research Council (MRC

Remote Infrared Imaging of the Space Shuttle During Hypersonic Flight: HYTHIRM Mission Operations and Coordination

The Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) project has been responsible for obtaining spatially resolved, scientifically calibrated in-flight thermal imagery of the Space Shuttle Orbiter during reentry. Starting with STS-119 in March of 2009 and continuing through to the majority of final flights of the Space Shuttle, the HYTHIRM team has to date deployed during seven Shuttle missions with a mix of airborne and ground based imaging platforms. Each deployment of the HYTHIRM team has resulted in obtaining imagery suitable for processing and comparison with computational models and wind tunnel data at Mach numbers ranging from over 18 to under Mach 5. This paper will discuss the detailed mission planning and coordination with the NASA Johnson Space Center Mission Control Center that the HYTHIRM team undergoes to prepare for and execute each mission

Updated radiative forcing estimates of 65 halocarbons and nonmethane hydrocarbons

The direct radiative forcing of 65 chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, hydrofluoroethers, halons, iodoalkanes, chloroalkanes, bromoalkanes, perfluorocarbons and nonmethane hydrocarbons has been evaluated using a consistent set of infrared absorption cross sections. For the radiative transfer models, both line-by-line and random band model approaches were employed for each gas. The line-by-line model was first validated against measurements taken by the Airborne Research Interferometer Evaluation System (ARIES) of the U.K. Meteorological Office; the computed spectrally integrated radiance of agreed to within 2% with experimental measurements. Three model atmospheres, derived from a three-dimensional climatology, were used in the radiative forcing calculations to more accurately represent hemispheric differences in water vapor, ozone concentrations, and cloud cover. Instantaneous, clear-sky radiative forcing values calculated by the line-by-line and band models were in close agreement. The band model values were subsequently modified to ensure exact agreement with the line-by-line model values. Calibrated band model radiative forcing values, for atmospheric profiles with clouds and using stratospheric adjustment, are reported and compared with previous literature values. Fourteen of the 65 molecules have forcings that differ by more than 15% from those in the World Meteorological Organization [1999] compilation. Eleven of the molecules have not been reported previously. The 65-molecule data set reported here is the most comprehensive and consistent database yet available to evaluate the relative impact of halocarbons and hydrocarbons on climate change

The HNF4A R76W mutation causes atypical dominant Fanconi syndrome in addition to a β cell phenotype

types: JOURNAL ARTICLEMutation specific effects in monogenic disorders are rare. We describe atypical Fanconi syndrome caused by a specific heterozygous mutation in HNF4A. Heterozygous HNF4A mutations cause a beta cell phenotype of neonatal hyperinsulinism with macrosomia and young onset diabetes. Autosomal dominant idiopathic Fanconi syndrome (a renal proximal tubulopathy) is described but no genetic cause has been defined.This article presents independent research supported by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The research is funded by a Wellcome Trust Senior Investigator Award, (grant number 098395/Z/12/Z).Wellcome Trus

Beta cell function and ongoing autoimmunity in long-standing, childhood onset type 1 diabetes.

This study aimed to determine the frequency of residual beta cell function in individuals with long-standing type 1 diabetes who were recruited at diagnosis, and relate this to baseline and current islet autoantibody profile.This article is freely available via Open Access. Click on the Additional Link above to access the full-text via the publisher's site

Persistent C-peptide secretion in Type 1 diabetes and its relationship to the genetic architecture of diabetes

Background: The objective of this cross-sectional study was to explore the relationship of detectable C-peptide secretion in type 1 diabetes to clinical features and to the genetic architecture of diabetes. Methods: C-peptide was measured in an untimed serum sample in the SDRNT1BIO cohort of 6076 Scottish people with clinically diagnosed type 1 diabetes or latent autoimmune diabetes of adulthood. Risk scores at loci previously associated with type 1 and type 2 diabetes were calculated from publicly available summary statistics. Results: Prevalence of detectable C-peptide varied from 19% in those with onset before age 15 and duration greater than 15 years to 92% in those with onset after age 35 and duration less than 5 years. Twenty-nine percent of variance in C-peptide levels was accounted for by associations with male gender, late age at onset and short duration. The SNP heritability of residual C-peptide secretion adjusted for gender, age at onset and duration was estimated as 26%. Genotypic risk score for type 1 diabetes was inversely associated with detectable C-peptide secretion: the most strongly associated loci were the HLA and INS gene regions. A risk score for type 1 diabetes based on the HLA DR3 and DQ8-DR4 serotypes was strongly associated with early age at onset and inversely associated with C-peptide persistence. For C-peptide but not age at onset, there were strong associations with risk scores for type 1 and type 2 diabetes that were based on SNPs in the HLA region but not accounted for by HLA serotype. Conclusions: Persistence of C-peptide secretion varies widely in people clinically diagnosed as type 1 diabetes. C-peptide persistence is influenced by variants in the HLA region that are different from those determining risk of early-onset type 1 diabetes. Known risk loci for diabetes account for only a small proportion of the genetic effects on C-peptide persistence

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

A Type 1 Diabetes Genetic Risk Score Can Identify Patients With GAD65 Autoantibody-Positive Type 2 Diabetes Who Rapidly Progress to Insulin Therapy

This is the author accepted manuscript. The final version is available from American Diabetes Association via the DOI in this record.Objective Progression to insulin therapy in clinically diagnosed type 2 diabetes is highly variable. GAD65 autoantibodies (GADA) are associated with faster progression, but their predictive value is limited. We aimed to determine if a Type 1 Diabetes Genetic Risk Score (T1DGRS) could predict rapid progression to insulin treatment over and above GADA testing. Research Design and Methods We examined the relationship between T1DGRS, GADA (negative or positive) and rapid insulin requirement (within 5 years) using Kaplan-Meier survival analysis and Cox regression in 8,608 participants with clinical type 2 diabetes (onset >35 years, treated without insulin for ≥6 months). T1DGRS was analyzed both continuously (as standardized scores) and categorized based on previously reported centiles of a type 1 diabetes population (50th (high)). Results In GADA positive participants (3.3%), those with higher T1DGRS progressed to insulin more quickly: Probability of insulin requirement at five years [95% CI]: 47.9%[35.0%,62.78%] (high T1DGRS) vs 27.6%[20.5%,36.5%] (medium T1DGRS) vs 17.6%[11.2%,27.2%] (low T1DGRS), p=0.001. In contrast T1DGRS did not predict rapid insulin requirement in GADA negative participants (p=0.4). In Cox regression analysis with adjustment for age of diagnosis, BMI and cohort, T1DGRS was independently associated with time to insulin only in the presence of GADA: hazard ratio per SD increase 1.48 (1.15,1.90), p=0.002. Conclusions A Type 1 Diabetes Genetic Risk Score alters the clinical implications of a positive GADA test in patients with clinical type 2 diabetes, and is independent of and additive to clinical features.The Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (GoDARTS) was funded by The Wellcome Trust (084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT program. GADA assessment in GoDARTS and DCS was funded by EU Innovative Medicines Initiative 115317 (DIRECT), resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies in kind contribution. The DCS cohort was partially funded by the Netherlands Organization for Health Research and Development (Priority Medicines Elderly Programme 113102006). The Diabetes Alliance for Research in England (DARE) study was funded by the Wellcome Trust and supported by the Exeter NIHR Clinical Research Facility. The MASTERMIND study was funded by the UK Medical Research Council (MR/N00633X/) and supported by the NIHR Exeter Clinical Research Facility. The PRIBA study was funded by the National Institute for Health Research (U.K.) (DRF-2010-03-72) and supported by the NIHR Exeter Clinical Research Facility. B.M.S and A.T.H. are supported by the NIHR Exeter Clinical Research Facility. T.J.M. is a National Institute for Health Research Senior Clinical Senior Lecturer. E.R.P. is a Wellcome Trust New Investigator (102820/Z/13/Z). A.T.H. is a Wellcome Trust Senior Investigator and NIHR Senior Investigator. R.A.O is supported by a Diabetes UK Harry Keen Fellowship (16/0005529). A.G.J. is supported by an NIHR Clinician Scientist award (CS-2015-15-018)