065 583 MAPPING INDOOR RADON-222 IN DENMARK: DESIGN AND TEST OF THE STATISTICAL MODEL USED IN THE SECOND NATION-WIDE SURVEY

In Denmark, a new survey of indoor radon-222 has been carried out. One-year alpha track measurements (CR-39) have been done in 3019 single-family houses. There is from 3 to 23 house measurements in each of the 275 municipalities. Within each municipality, houses have been selected randomly. One important outcome of the survey is the prediction of the fraction of houses in each municipality with an annual average radon concentration above . To obtain the most accurate estimate and to assess the associated uncertainties, a statistical model has been developed. The purpose of this paper is to describe the design of this model, and to report results of model tests. The model is based on a transformation of the data to normality and on analytical (conditionally) unbiased estimators of the quantities of interest. Bayesian statistics is used to minimize the effect of small sample size. In each municipality, the correction is dependent on the fraction of area where sand and gravel is a dominating surface geology. The uncertainty analysis is done with a Monte Carlo technique. It is demonstrated that the weighted sum of all municipality model estimates of fractions above 200 Bqm -3 (3.9 % with 95 %-confidence interval = [3.4,4.5]) is consistent with the weighted sum of the observations for Denmark taken as a whole (4.6 % with 95 %-confidence interval = [3. 8,5.6]). The total number of single-family houses within each municipality is used as weight. Model estimates are also found to be consistent with observations at the level of individual counties. These typically include a few hundred house measurements. These tests indicate that the model is well suited for its purpose. Keywords: Houses; Radon-222; Survey; Statistical model INTRODUCTION Radon is believed to cause an increased risk of lung cancer and it is therefore of interest to identify houses with high levels of indoor radon. It is important to know how many houses that have "high" levels (e.g. annual levels above 200 or 400 Bqm -3 ) and it is important to know where these houses are located. Likewise, it is also of interest to know about the low-radon houses where there is no cause for alarm. This paper reports on a new Danish survey of indoor radon designed to tackle these problems. The survey is much larger than the first one from 1985/86 SURVEY DESIGN Denmark is divided into 15 counties. Each county consists of a number of smaller municipalities. In total there are 275 municipalities. One-year alpha track measurements (CR-39) were done in 3019 single-family houses from December 1995 to December 1996. Detectors were placed in living 065 Radon in the Living Environment, 19-23 April 1999, Athens, Greece 584 rooms. Within each municipality, houses were selected randomly by the Building and Dwelling Register (BBR). The median number of house measurements per municipality is 11. Nine municipalities have only 6 or less measurements, and nine municipalities have 18 or more measurements. The only geological information used directly in the model is the fraction of area (later referred to as k g ) in each municipality which is dominated by sand and gravel. These values are found by visual inspection of a map of the surface geology of Denmark MODEL Transformations We define the 'house concentration' c of a given house to be the average radon concentration of the living room and the bedroom: , is closer to normality. All of the statistical analyses are therefore conducted for transformed radon concentrations x . Distribution parameters It is assumed that within each municipality k , the transformed radon concentration x is normally distributed with a (true) mean k µ and a (true) standard deviation σ . We allow that k µ can be different from one municipality to another, but require that all municipalities have the same σ . The latter requirement is supported by an analysis of the homogeneity of variances with a modified version of the Levene test based on absolute deviations from the municipality medians of transformed radon concentrations The estimator σˆof σ is found as follows: First, we calculate the simple mean k x and standard deviation k s of the k N measurements in each municipality k : and . Finally, we pool the 275 k σˆ-values into a single weighted mean value: σˆ. The number of house measurements ( k N ) is used as weight. The value amounts to: 0.59418 = σ . The estimators k μ of k µ are found as follows: A simple estimate would be to let . However, as demonstrated by where k g is an estimate of the fraction of the total area of municipality k that has a surface geology dominated by sand and gravel. Based on all 275 municipalities, the regression coefficients amount to 4.54 0 = β (standard error 0.0296) and -0.69 1 = β (standard error 0.06). The R-squared value is 36 %. The variance 2 ε σ of the residuals k ε is 0.082. For each municipality, we calculate: and use the following weighted average as the model estimate of k µ : where the weights are: . Essentially, we estimate k µ to be equal to the observed value k x with some weigthed correction towards what on-the-averaged is found for municipalities with that type of surface geology. If there are few (or no) measurements in a municipality, then . If there are many measurements, then . Essentially, the influence of k θ equivalents about 4 extra measurements in each municipality. The main source of uncertainty in the survey is the small sample size. We apply equation (6) as a way to gently "stabilize" modelling results in all municipalities except those on the island Bornholm. and the bias term: and insert into: the Living Environment, 19-23 April 1999, Athens, Greece which is different from the observed value given by equation (7 f . RESULTS In the survey, house radon levels ( c ) in the range from 2 to 590 Bqm . The middle plot of DISCUSSION Improved estimates by modelling? The primary purpose of the statistical model is to provide estimates of the fraction of houses above 200 Bqm -3 at the level of individual municipalities. The idea is to make estimates that are better (i.e. more accurate and less variable) than estimates deduced from simple observations: in municipality k , and k N is the number of measurements. The main problem with such simple observations is that for the typical case of about 10 house measurements per municipality, the outcome will be in steps of 10 % (i.e. 0 %, 10 %, 20 % etc.). This can be illustrated with synthetic data. We draw 3019 synthetic (transformed radon concentrations) x from a normal distribution with mean 4.33 and standard deviation 0.5941. Subsequently we transform the data to ordinary radon concentrations ( c -values) using the inverse of ) log( b c x + = . The true value of 200 f in this case is 4.60 % (about the same as the national average). The data are grouped in municipalities and counties exactly as in the survey (this is important as the number of measurements determines the variability of parameter estimates). Also, we preserve the fraction of sand and gravel ( k g ) which is needed in equation (6). In this case, however, the regression (see equation 5) will only be by chance. The model is applied exactly as with the real data set. To evaluate the importance of the Bayesian correction, we will also consider simplified-model estimates where 0 ω in equation 6 is set to 0 (such that mean and standard deviation of the results are 4.9 % and 7.2 %, respectively. In one case, 200 f is found to be as high as 40 %. It is particularly problematic that about 60 % of the municipalities are without measured houses with concentrations above . It is little help that many of the remaining municipalities, have observed fractions above 10 %, such that on-the-average the correct result of about 4.6 % is observed. The curved labelled simplified model are the results of model estimates without the Bayesian correction ( 0 0 = ω ). Compared with the first curve, these estimates are much better in the sense that the results are less variable (mean 4.9 % and standard deviation 3.7 %). The final curve labelled full model present by far the best estimates (mean 4.3 % and standard deviation 1.7 %). However, because the data in each municipality come from the same distribution, the variance of the regression residuals ( ε in equation 5) is lower than in the real survey. This means that in this (synthetic) example, the Bayesian correction will correspond to about 9 extra measurements in each municipality (compared to 4 in the real situation). The confidence intervals of the simulations are not shown in the Model versus measurements The (weighted) national average of model predictions amounts to . The latter agreement (that concerns the tail of the distribution) suggests that the assumption of normality is not greatly violated. As shown in the top plot of To illustrate how the model treats counties with different types of geology it is of interest to study Model elements The model includes some special elements: Measurement uncertainty Considerable measurement uncertainty is associated with the c -estimates (typically about 20 %). Part of this comes from the conversion from living room to house concentrations. Such uncertainties tend to have little impact on averages of quantities that relates linearly to the measurements (e.g. arithmetic means) as such random errors on the average will tend to cancel each other. Unfortunately, estimation of the fraction of houses above 200 Bqm -3 is an non-linear function of the individual radon concentration results, and random errors therefore will bias the estimation. This has previously been demonstrated by CONCLUSION A statistical model has been developed. It predicts the fraction of single-family houses (in each municipality) with an annual radon level above 200 Bqm . The investigation suggests that these estimates are better (more accurate and less variable) than simple observations based on direct observation of houses with levels above 200 Bqm . Also, the model provides estimates of uncertainties associated with these predictions. The main source of uncertainty relates to the small sample size (typically only about 11 measurements in each municipality). Comparison between model predictions and measurements indicated that the model is well suited for mapping of indoor radon in Denmark. ACKNOWLEDGEMEN

Adiabatic description of nonspherical quantum dot models

Within the effective mass approximation an adiabatic description of spheroidal and dumbbell quantum dot models in the regime of strong dimensional quantization is presented using the expansion of the wave function in appropriate sets of single-parameter basis functions. The comparison is given and the peculiarities are considered for spectral and optical characteristics of the models with axially symmetric confining potentials depending on their geometric size making use of the total sets of exact and adiabatic quantum numbers in appropriate analytic approximations

Predictors of 25(OH)D half-life and plasma 25(OH)D concentration in The Gambia and the UK

Summary: Predictors of 25(OH)D3 half-life were factors associated with vitamin D metabolism, but were different between people in The Gambia and the UK. Country was the strongest predictor of plasma 25(OH)D concentration, probably as a marker of UVB exposure. 25(OH)D3 half-life may be applied as a tool to investigate vitamin D expenditure.  Introduction: The aim of this study was to investigate predictors of 25(OH)D3 half-life and plasma 25(OH)D concentration.  Methods: Plasma half-life of an oral tracer dose of deuterated-25(OH)D3 was measured in healthy men aged 24–39 years, resident in The Gambia, West Africa (n = 18) and in the UK during the winter (n = 18), countries that differ in calcium intake and vitamin D status. Plasma and urinary markers of vitamin D, calcium, phosphate and bone metabolism, nutrient intakes and anthropometry were measured.  Results: Normally distributed data are presented as mean (SD) and non-normal data as geometric mean (95 % CI). Gambian compared to UK men had higher plasma concentrations of 25(OH)D (69 (13) vs. 29 (11) nmol/L; P < 0.0001); 1,25(OH)2D (181 (165, 197) vs. 120 (109, 132) pmol/L; P < 0.01); and parathyroid hormone (PTH) (50 (42, 60) vs. 33 (27, 39); P < 0.0001). There was no difference in 25(OH)D3 half-life (14.7 (3.5) days vs. 15.6 (2.5) days) between countries (P = 0.2). In multivariate analyses, 25(OH)D, 1,25(OH)2D, vitamin D binding protein and albumin-adjusted calcium (Caalb) explained 79 % of variance in 25(OH)D3 half-life in Gambians, but no significant predictors were found in UK participants. For the countries combined, Caalb, PTH and plasma phosphate explained 39 % of half-life variability. 1,25(OH)2D, weight, PTH and country explained 81 % of variability in 25(OH)D concentration; however, country alone explained 74 %.  Conclusion: Factors known to affect 25(OH)D metabolism predict 25(OH)D3 half-life, but these differed between countries. Country predicted 25(OH)D, probably as a proxy measure for UVB exposure and vitamin D supply. This study supports the use of 25(OH)D half-life to investigate vitamin D metabolism

Fleming's penicillin producing streain is not Penicillium chrysogenum but P. rubens

Penicillium chrysogenum is a commonly occurring mould in indoor environments and foods, and has gained much attention for its use in the production of the antibiotic penicillin. Phylogenetic analysis of the most important penicillin producing P. chrysogenum isolates revealed the presence of two highly supported clades, and we show here that these two clades represent two species, P. chrysogenum and P. rubens. These species are phenotypically similar, but extrolite analysis shows that P. chrysogenum produces secalonic acid D and F and/or a metabolite related to lumpidin, while P. rubens does not produce these metabolites. Fleming’s original penicillin producing strain and the full genome sequenced strain of P. chrysogenum are re-identified as P. rubens. Furthermore, the well-known claim that Alexander Fleming misidentified the original penicillin producing strain as P. rubrum is discussed

Candida soluble cell wall β-glucan facilitates ovalbumin-induced allergic airway inflammation in mice: Possible role of antigen-presenting cells

<p>Abstract</p> <p>Background</p> <p>Although fungi have been implicated as initiating/deteriorating factors for allergic asthma, their contributing components have not been fully elucidated. We previously isolated soluble β-glucan from <it>Candida albicans </it>(CSBG) (Ohno et al., 2007). In the present study, the effects of CSBG exposure on airway immunopathology in the presence or absence of other immunogenic allergen was investigated <it>in vivo</it>, and their cellular mechanisms were analyzed both <it>in vivo </it>and <it>in vitro</it>.</p> <p>Methods</p> <p><it>In vivo</it>, ICR mice were divided into 4 experimental groups: vehicle, CSBG (25 μg/animal), ovalbumin (OVA: 2 μg/animal), and CSBG + OVA were repeatedly administered intratracheally. The bronchoalveolar lavage cellular profile, lung histology, levels of cytokines and chemokines in the lung homogenates, the expression pattern of antigen-presenting cell (APC)-related molecules in the lung digests, and serum immunoglobulin values were studied. <it>In vitro</it>, the impacts of CSBG (0–12.5 μg/ml) on the phenotype and function of immune cells such as splenocytes and bone marrow-derived dendritic cells (BMDCs) were evaluated in terms of cell proliferation, the surface expression of APC-related molecules, and OVA-mediated T-cell proliferating activity.</p> <p>Results</p> <p><it>In vivo</it>, repeated pulmonary exposure to CSBG induced neutrophilic airway inflammation in the absence of OVA, and markedly exacerbated OVA-related eosinophilic airway inflammation with mucus metaplasia in mice, which was concomitant with the amplified lung expression of Th2 cytokines and IL-17A and chemokines related to allergic response. Exposure to CSBG plus OVA increased the number of cells bearing MHC class II with or without CD80 in the lung compared to that of others. <it>In vitro</it>, CSBG significantly augmented splenocyte proliferation in the presence or absence of OVA. Further, CSBG increased the expression of APC-related molecules such as CD80, CD86, and DEC205 on BMDCs and amplified OVA-mediated T-cell proliferation through BMDCs.</p> <p>Conclusion</p> <p>CSBG potentiates allergic airway inflammation with maladaptive Th immunity, and this potentiation was associated with the enhanced activation of APCs including DC.</p

Deformations Preserving Gauß Curvature

(Proceedings of LHMTS 2013)International audienceIn industrial surface generation, it is important to consider surfaces with minimal areas for two main reasons: these surfaces require less material than non-minimal surfaces, and they are cheaper to manufacture. Based on a prototype, a so-called masterpiece, the final product is created using small deformations to adapt a surface to the desired shape. We present a linear deformation technique preserving the total curvature of the masterpiece. In particular, we derive sufficient conditions for these linear deformations to be total curvature preserving when applied to the masterpiece. It is useful to preserve total curvature of a surface in order to minimise the amount of material needed, and to minimise bending energy

Residential Radon and Brain Tumour Incidence in a Danish Cohort

BACKGROUND: Increased brain tumour incidence over recent decades may reflect improved diagnostic methods and clinical practice, but remain unexplained. Although estimated doses are low a relationship between radon and brain tumours may exist. OBJECTIVE: To investigate the long-term effect of exposure to residential radon on the risk of primary brain tumour in a prospective Danish cohort. METHODS: During 1993-1997 we recruited 57,053 persons. We followed each cohort member for cancer occurrence from enrolment until 31 December 2009, identifying 121 primary brain tumour cases. We traced residential addresses from 1 January 1971 until 31 December 2009 and calculated radon concentrations at each address using information from central databases regarding geology and house construction. Cox proportional hazards models were used to estimate incidence rate-ratios (IRR) and 95% confidence intervals (CI) for the risk of primary brain tumours associated with residential radon exposure with adjustment for age, sex, occupation, fruit and vegetable consumption and traffic-related air pollution. Effect modification by air pollution was assessed. RESULTS: Median estimated radon was 40.5 Bq/m(3). The adjusted IRR for primary brain tumour associated with each 100 Bq/m(3) increment in average residential radon levels was 1.96 (95% CI: 1.07; 3.58) and this was exposure-dependently higher over the four radon exposure quartiles. This association was not modified by air pollution. CONCLUSIONS: We found significant associations and exposure-response patterns between long-term residential radon exposure radon in a general population and risk of primary brain tumours, adding new knowledge to this field. This finding could be chance and needs to be challenged in future studies

Adaptative Potential of the Lactococcus Lactis IL594 Strain Encoded in Its 7 Plasmids

The extrachromosomal gene pool plays a significant role both in evolution and in the environmental adaptation of bacteria. The L. lactis subsp. lactis IL594 strain contains seven plasmids, named pIL1 to pIL7, and is the parental strain of the plasmid-free L. lactis IL1403, which is one of the best characterized lactococcal strains of LAB. Complete nucleotide sequences of pIL1 (6,382 bp), pIL2 (8,277 bp), pIL3 (19,244 bp), pIL4 (48,979), pIL5 (23,395), pIL6 (28,435 bp) and pIL7 (28,546) were established and deposited in the generally accessible database (GeneBank). Nine highly homologous repB-containing replicons, belonging to the lactococcal theta-type replicons, have been identified on the seven plasmids. Moreover, a putative region involved in conjugative plasmid mobilization was found on four plasmids, through identification of the presence of mob genes and/or oriT sequences. Detailed bioinformatic analysis of the plasmid nucleotide sequences provided new insight into the repertoire of plasmid-encoded functions in L. lactis, and indicated that plasmid genes from IL594 strain can be important for L. lactis adaptation to specific environmental conditions (e.g. genes coding for proteins involved in DNA repair or cold shock response) as well as for technological processes (e.g. genes encoding citrate and lactose utilization, oligopeptide transport, restriction-modification system). Moreover, global gene analysis indicated cooperation between plasmid- and chromosome-encoded metabolic pathways