The 1979 Southeastern Virginia Urban Plume Study (SEV-UPS): Surface and airborne studies

The operation of two surface monitoring stations (one in downtown Norfolk, Virginia, one south of the city near the Great Dismal Swamp) and the collection of 40 hours of airborne measurements is described. Surface site measurements of ozone, oxides of nitrogen, sulfur dioxide, temperature, dew point, b sub seat, and condensation nuclei were made. Instrument calibrations, quality assurance audits, and preliminary data analysis in support of the Urban Plume Study were also made. The air pollution problems that were addressed are discussed. Data handling procedures followed for the surface stations are presented. The operation of the aircraft sampling platform is described. Aircraft sampling procedures are discussed. A preliminary descriptive analysis of the aircraft data is given along with data or plots for surface sites, airborne studies, hydrocarbon species, and instrument performance audits. Several of the aircraft flights clearly show the presence of an urban ozone plume downwind of Norfolk in the direction of the mean wind flow

In situ sensors for measurements in the global trosposphere

Current techniques available for the in situ measurement of ambient trace gas species, particulate composition, and particulate size distribution are reviewed. The operational specifications of the various techniques are described. Most of the techniques described are those that have been used in airborne applications or show promise of being adaptable to airborne applications. Some of the instruments described are specialty items that are not commercially-available. In situ measurement techniques for several meteorological parameters important in the study of the distribution and transport of ambient air pollutants are discussed. Some remote measurement techniques for meteorological parameters are also discussed. State-of-the-art measurement capabilities are compared with a list of capabilities and specifications desired by NASA for ambient measurements in the global troposphere

Spatial adaptivity of the SAAF and Weighted Least Squares (WLS) forms of the neutron transport equation using constraint based, locally refined, isogeometric analysis (IGA) with dual weighted residual (DWR) error measures

This paper describes a methodology that enables NURBS (Non-Uniform Rational B-spline) based Isogeometric Analysis (IGA) to be locally refined. The methodology is applied to continuous Bubnov-Galerkin IGA spatial discretisations of second-order forms of the neutron transport equation. In particular this paper focuses on the self-adjoint angular flux (SAAF) and weighted least squares (WLS) equations. Local refinement is achieved by constraining degrees of freedom on interfaces between NURBS patches that have different levels of spatial refinement. In order to effectively utilise constraint based local refinement, adaptive mesh refinement (AMR) algorithms driven by a heuristic error measure or forward error indicator (FEI) and a dual weighted residual (DWR) or goal-based error measure (WEI) are derived. These utilise projection operators between different NURBS meshes to reduce the amount of computational effort required to calculate the error indicators. In order to apply the WEI to the SAAF and WLS second-order forms of the neutron transport equation the adjoint of these equations are required. The physical adjoint formulations are derived and the process of selecting source terms for the adjoint neutron transport equation in order to calculate the error in a given quantity of interest (QoI) is discussed. Several numerical verification benchmark test cases are utilised to investigate how the constraint based local refinement affects the numerical accuracy and the rate of convergence of the NURBS based IGA spatial discretisation. The nuclear reactor physics verification benchmark test cases show that both AMR algorithms are superior to uniform refinement with respect to accuracy per degree of freedom. Furthermore, it is demonstrated that for global QoI the FEI driven AMR and WEI driven AMR produce similar results. However, if local QoI are desired then WEI driven AMR algorithm is more computationally efficient and accurate per degree of freedom

Biomarkers of gut injury in neonates – where are we in predicting necrotising enterocolitis?

Despite advances in neonatal care Necrotising Enterocolitis (NEC) continues to have a significant mortality and morbidity rate, and with increasing survival of those more immature infants the population at risk of NEC is increasing. Ischaemia, reperfusion, and inflammation underpin diseases affecting intestinal blood flow causing gut injury including Necrotising Enterocolitis. There is increasing interest in tissue biomarkers of gut injury in neonates, particularly those representing changes in intestinal wall barrier and permeability, to determine whether these could be useful biomarkers of gut injury. This article reviews current and newly proposed markers of gut injury, the available literature evidence, recent advances and considers how effective they are in clinical practice. We discuss each biomarker in terms of its effectiveness in predicting NEC onset and diagnosis or predicting NEC severity and then those that will aid in surveillance and identifying those infants are greatest risk of developing NEC

Modelling the spread of American foulbrood in honeybees

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae, that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction

Determinants of anemia and hemoglobin concentration in haitian school-aged children

Anemia diminishes oxygen transport in the body, resulting in potentially irreversible growth and developmental consequences for children. Limited evidence for determinants of anemia exists for school-aged children. We conducted a cluster randomized controlled trial in Haiti from 2012 to 2013 to test the efficacy of a fortified school snack. Children (N = 1,047) aged 3–13 years were followed longitudinally at three time points for hemoglobin (Hb) concentrations, anthropometry, and bioelectrical impedance measures. Dietary intakes, infectious disease morbidities, and socioeconomic and demographic factors were collected at baseline and endline. Longitudinal regression modeling with generalized least squares and logit models with random effects identified anemia risk factors beyond the intervention effect. At baseline, 70.6% of children were anemic and 2.6% were severely anemic. Stunting increased the odds of developing anemia (adjusted odds ratio [OR]: 1.48, 95% confidence interval [CI]: 1.05–2.08) and severe anemia (adjusted OR: 2.47, 95% CI: 1.30–4.71). Parent-reported vitamin A supplementation and deworming were positively associated with Hb concentrations, whereas fever and poultry ownership showed a negative relationship with Hb concentration and increased odds of severe anemia, respectively. Further research should explore the full spectrum of anemia etiologies in school children, including genetic causes

Reliable estimation of prediction uncertainty for physico-chemical property models

The predictions of parameteric property models and their uncertainties are sensitive to systematic errors such as inconsistent reference data, parametric model assumptions, or inadequate computational methods. Here, we discuss the calibration of property models in the light of bootstrapping, a sampling method akin to Bayesian inference that can be employed for identifying systematic errors and for reliable estimation of the prediction uncertainty. We apply bootstrapping to assess a linear property model linking the 57Fe Moessbauer isomer shift to the contact electron density at the iron nucleus for a diverse set of 44 molecular iron compounds. The contact electron density is calculated with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91, PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error diagnostics and reliable, locally resolved uncertainties for isomer-shift predictions. Pure and hybrid density functionals yield average prediction uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter being close to the average experimental uncertainty of 0.02 mm/s. Furthermore, we show that both model parameters and prediction uncertainty depend significantly on the composition and number of reference data points. Accordingly, we suggest that rankings of density functionals based on performance measures (e.g., the coefficient of correlation, r2, or the root-mean-square error, RMSE) should not be inferred from a single data set. This study presents the first statistically rigorous calibration analysis for theoretical Moessbauer spectroscopy, which is of general applicability for physico-chemical property models and not restricted to isomer-shift predictions. We provide the statistically meaningful reference data set MIS39 and a new calibration of the isomer shift based on the PBE0 functional.Comment: 49 pages, 9 figures, 7 table

Core components of mental health stigma reduction interventions in low- and middle-income countries: a systematic review

Aims To identify and categorise core components of effective stigma reduction interventions in the field of mental health in low- and middle-income countries (LMICs) and compare these components across cultural contexts and between intervention characteristics. Methods Seven databases were searched with a strategy including four categories of terms ('stigma’, ‘mental health’, ‘intervention’ and ‘low- and middle-income countries’). Additional methods included citation chaining of all papers identified for inclusion, consultation with experts and hand searching reference lists from other related reviews. Studies on interventions in LMICs aiming to reduce stigma related to mental health with a stigma-related outcome measure were included. All relevant intervention characteristics and components were extracted and a quality assessment was undertaken. A ‘best fit’ framework synthesis was used to organise data, followed by a narrative synthesis. Results Fifty-six studies were included in this review, of which four were ineffective and analysed separately. A framework was developed which presents a new categorisation of stigma intervention components based on the included studies. Most interventions utilised multiple methods and of the 52 effective studies educational methods were used most frequently (n = 83), and both social contact (n = 8) and therapeutic methods (n = 3) were used infrequently. Most interventions (n = 42) based their intervention on medical knowledge, but a variety of other themes were addressed. All regions with LMICs were represented, but every region was dominated by studies from one country. Components varied between regions for most categories indicating variation between cultures, but only a minority of studies were developed in the local setting or culturally adapted. Conclusions Our study suggests effective mental health stigma reduction interventions in LMICs have increased in quantity and quality over the past five years, and a wide variety of components have been utilised successfully – from creative methods to emphasis on recovery and strength of people with mental illness. Yet there is minimal mention of social contact, despite existing strong evidence for it. There is also a lack of robust research designs, a high number of short-term interventions and follow-up, nominal use of local expertise and the research is limited to a small number of LMICs. More research is needed to address these issues. Some congruity exists in components between cultures, but generally they vary widely. The review gives an in-depth overview of mental health stigma reduction core components, providing researchers in varied resource-poor settings additional knowledge to help with planning mental health stigma reduction interventions

Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning

Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.Comment: Early-accept at MICCAI 2018, 8 pages, 4 figure