Integration of airborne and ground observations of nitryl chloride in the Seoul metropolitan area and the implications on regional oxidation capacity during KORUS-AQ 2016

Nitryl chloride (ClNO2) is a radical reservoir species that releases chlorine radicals upon photolysis. An integrated analysis of the impact of ClNO2 on regional photochemistry in the Seoul metropolitan area (SMA) during the Korea-United States Air Quality Study (KORUS-AQ) 2016 field campaign is presented. Comprehensive multiplatform observations were conducted aboard the NASA DC-8 and at two ground sites (Olympic Park, OP; Taehwa Research Forest, TRF), representing an urbanized area and a forested suburban region, respectively. Positive correlations between daytime Cl2 and ClNO2 were observed at both sites, the slope of which was dependent on O3 levels. The possible mechanisms are explored through box model simulations constrained with observations. The overall diurnal variations in ClNO2 at both sites appeared similar but the nighttime variations were systematically different. For about half of the observation days at the OP site the level of ClNO2 increased at sunset but rapidly decreased at around midnight. On the other hand, high levels were observed throughout the night at the TRF site. Significant levels of ClNO2 were observed at both sites for 4-5 h after sunrise. Airborne observations, box model calculations, and back-trajectory analysis consistently show that these high levels of ClNO2 in the morning are likely from vertical or horizontal transport of air masses from the west. Box model results show that chlorine-radical-initiated chemistry can impact the regional photochemistry by elevating net chemical production rates of ozone by 25% in the morning

Prevalence and Predictors of Vitamin D Insufficiency in Children: A Great Britain Population Based Study

Objectives To evaluate the prevalence and predictors of vitamin D insufficiency (VDI) in children In Great Britain. Design A nationally representative cross-sectional study survey of children (1102) aged 4–18 years (999 white, 570 male) living in private households (January 1997–1998). Interventions provided information about dietary habits, physical activity, socio-demographics, and blood sample. Outcome measures were vitamin D insufficiency (<50 nmol/L). Results Vitamin D levels (mean = 62.1 nmol/L, 95%CI 60.4–63.7) were insufficient in 35%, and decreased with age in both sexes (p<0.001). Young People living between 53–59 degrees latitude had lower levels (compared with 50–53 degrees, p = 0.045). Dietary intake and gender had no effect on vitamin D status. A logistic regression model showed increased risk of VDI in the following: adolescents (14–18 years old), odds ratio (OR) = 3.6 (95%CI 1.8–7.2) compared with younger children (4–8 years); non white children (OR = 37 [95%CI 15–90]); blood levels taken December-May (OR = 6.5 [95%CI 4.3–10.1]); on income support (OR = 2.2 [95%CI 1.3–3.9]); not taking vitamin D supplementation (OR = 3.7 [95%CI 1.4–9.8]); being overweight (OR 1.6 [95%CI 1.0–2.5]); <1/2 hour outdoor exercise/day/week (OR = 1.5 [95%CI 1.0–2.3]); watched >2.5 hours of TV/day/week (OR = 1.6[95%CI 1.0–2.4]). Conclusion We confirm a previously under-recognised risk of VDI in adolescents. The marked higher risk for VDI in non-white children suggests they should be targeted in any preventative strategies. The association of higher risk of VDI among children who exercised less outdoors, watched more TV and were overweight highlights potentially modifiable risk factors. Clearer guidelines and an increased awareness especially in adolescents are needed, as there are no recommendations for vitamin D supplementation in older children

Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling

We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potential to guide future trial design and appropriate selection of therapy. Assuming a log-normal distribution of both resistant disease and tumor doubling times generates disease-free survival (DFS) or invasive DFS curves with specific shapes. Using a multivariate mathematical model, both treatment and tumor characteristics are related to quantified resistant disease and tumor regrowth rates by allowing different mean values for the influence of different treatments or clinical subtypes on these two log-normal distributions. Application of the model to the CALGB 9741 adjuvant breast cancer trial showed that dose-dense therapy was estimated to achieve an extra 3/4 log of cell-kill compared to standard therapy, but only in patients with more rapidly growing ER-negative tumors. Application of the model to the AZURE trial of adjuvant bisphosphonate treatment suggested that the 5-year duration of zoledronic acid was adequate for ER-negative tumors, but may not be so for ER-positive cases, with increased recurrences after ceasing the intervention. Mathematical models can identify different effects of treatment by subgroup and may aid in treatment design, trial analysis, and appropriate selection of therapy. They may provide a more appropriate and insightful tool than the conventional Cox model for the statistical analysis of response durations

Towards the glueball spectrum from unquenched lattice QCD

We use a variational technique to study heavy glueballs on gauge configurations generated with 2+1 flavours of ASQTAD improved staggered fermions. The variational technique includes glueball scattering states. The measurements were made using 2150 configurations at 0.092 fm with a pion mass of 360 MeV. We report masses for 10 glueball states. We discuss the prospects for unquenched lattice QCD calculations of the oddballs.Comment: 19 pages, 4 tables and 8 figures. One figure added. Now matches the published versio

Bayesian Methods for Exoplanet Science

Exoplanet research is carried out at the limits of the capabilities of current telescopes and instruments. The studied signals are weak, and often embedded in complex systematics from instrumental, telluric, and astrophysical sources. Combining repeated observations of periodic events, simultaneous observations with multiple telescopes, different observation techniques, and existing information from theory and prior research can help to disentangle the systematics from the planetary signals, and offers synergistic advantages over analysing observations separately. Bayesian inference provides a self-consistent statistical framework that addresses both the necessity for complex systematics models, and the need to combine prior information and heterogeneous observations. This chapter offers a brief introduction to Bayesian inference in the context of exoplanet research, with focus on time series analysis, and finishes with an overview of a set of freely available programming libraries.Comment: Invited revie