Riccardo Borgoni

A geostatistical approach to assess the spatial association between indoor radon concentration, geological features and building characteristics: the case of Lombardy, Northern Italy.

Radon is a natural gas known to be the main contributor to natural background radiation exposure and second to smoking, a major leading cause of lung cancer. The main source of radon is the soil, but the gas can enter buildings in many different ways and reach high indoor concentrations. Monitoring surveys have been promoted in many countries in order to assess the exposure of people to radon. In this paper, two complementary aspects are investigated. Firstly, we mapped indoor radon concentration in a large and inhomogeneous region using a geostatistical approach which borrows strength from the geologic nature of the soil. Secondly, knowing that geologic and anthropogenic factors, such as building characteristics, can foster the gas to flow into a building or protect against this, we evaluated these effects through a multiple regression model which takes into account the spatial correlation of the data. This allows us to rank different building typologies, identified by architectonic and geological characteristics, according to their proneness to radon. Our results suggest the opportunity to differentiate construction requirements in a large and inhomogeneous area, as the one considered in this paper, according to different places and provide a method to identify those dwellings which should be monitored more carefully.

Hierarchical modeling of indoor radon concentration: how much do geology and building factors matter?

Radon is a natural gas known to be the main contributor to natural background radiation exposure and only second to smoking as major leading cause of lung cancer. The main concern is in indoor environments where the gas tends to accumulate and can reach high concentrations. The primary contributor of this gas into the building is from the soil although architectonic characteristics, such as building materials, can largely affect concentration values. Understanding the factors affecting the concentration in dwellings and workplaces is important both in prevention, when the construction of a new building is being planned, and in mitigation when the amount of Radon detected inside a building is too high. In this paper we investigate how several factors, such as geologic typologies of the soil and a range of building characteristics, impact on indoor concentration focusing, in particular, on how concentration changes as a function of the floor level. Adopting a mixed effects model to account for the hierarchical nature of the data, we also quantify the extent to which such measurable factors manage to explain the variability of indoor radon concentration.

Modelling the Progression of Bird Migration with Conditional Autoregressive Models Applied to Ringing Data

Migration is a fundamental stage in the life history of several taxa, including birds, and is under strong selective pressure. At present, the only data that may allow for both an assessment of patterns of bird migration and for retrospective analyses of changes in migration timing are the databases of ring recoveries. We used ring recoveries of the Barn Swallow Hirundo rustica collected from 1908–2008 in Europe to model the calendar date at which a given proportion of birds is expected to have reached a given geographical area (‘progression of migration’) and to investigate the change in timing of migration over the same areas between three time periods (1908–1969, 1970–1990, 1991–2008). The analyses were conducted using binomial conditional autoregressive (CAR) mixed models. We first concentrated on data from the British Isles and then expanded the models to western Europe and north Africa. We produced maps of the progression of migration that disclosed local patterns of migration consistent with those obtained from the analyses of the movements of ringed individuals. Timing of migration estimated from our model is consistent with data on migration phenology of the Barn Swallow available in the literature, but in some cases it is later than that estimated by data collected at ringing stations, which, however, may not be representative of migration phenology over large geographical areas. The comparison of median migration date estimated over the same geographical area among time periods showed no significant advancement of spring migration over the whole of Europe, but a significant advancement of autumn migration in southern Europe. Our modelling approach can be generalized to any records of ringing date and locality of individuals including those which have not been recovered subsequently, as well as to geo-referenced databases of sightings of migratory individuals.

Handling dropout and clustering in longitudinal multicentre clinical trials

Many clinical trials enrol patients from different medical centres. Multi-centre studies are particularly helpful in cancer research as they allow researchers to evaluate the efficacy of a therapy in a variety of patients and settings, making it possible to investigate the effect of treatments in those cases when it is difficult, or even impossible, for a single centre to recruit the required number of patients. It is often argued, however, that despite agreement among different centres to follow common standardized protocols, variation may occur in both baseline characteristics of the recruited patients and in treatment effects. This heterogeneity should be detected and, if present, accounted for in the data analysis. Furthermore, the longitudinal nature of these types of experimental studies raises the problem of attrition, that is, patients may dropout of the study for a number of reasons mainly death or disease progression. In this paper, we consider the health related quality of life of advanced melanoma patients in a longitudinal multi-centre randomized clinical trial comparing two different anti-tumoural treatments. We propose a Heckman type model to account for the possibility that patients dropout according to a non-ignorable mechanism. The model is extended to a multilevel setting to account both for the longitudinal nature and the multi-centre structure of the design. We found a strong variation across centres in the quality of life evaluation. The effect of centres on the dropout was not found to be relevant in the considered data although dropout does depend on patient′s characteristics.

Use and performance of the Forest Fire Weather Index to model the risk of wildfire occurrence in the Alpine region

Assessing a territory’s fire proneness is fundamental when planning and undertaking effective forest protection and land management. Accurate methods to estimate the risk of fire ignition in natural environments have been proposed over the last decades and digital mapping has been used to identify critical areas. The Canadian Forest Fire Weather Index is a well-known fire danger rating index created and improved during the last 45 years by the Canadian Forest Service. The goal of this paper is twofold. Firstly, we evaluated whether the Forest Fire Weather Index is an adequate instrument to predict fire ignition in Alpine and sub-Alpine areas using quite a large dataset of meteorological and forest fire data collected in the Lombardy region (Northern Italy) between 2003 and 2011. By means of a spatial binary regression model, we demonstrated that Forest Fire Weather Index has a significant impact on the probability of fire ignition. Since this approach allows us to account for other characteristics of the territory in order to provide a more accurate estimate of the spatial wildfire dynamics at a moderately large scale, the second goal of the paper aims at creating a model to assess fire risk occurrence using the Forest Fire Weather Index and land use information. It has been found that ignition can easily occur in large forested areas whereas denser urban areas are less exposed to fire since they usually have no fuels to ignite. Nevertheless, since human activity has a direct impact on fire ignition human presence, it fosters ignition in forested areas. Finally, the model, including these spatial dimensions, has been employed to derive a probability map of fire occurrences at 1.5 km resolution, which is a fundamental instrument to develop optimal prevention and risk management policy plans for the decision maker.

Therapy modifies HLA‐G secretion differently in Crohn's disease and ulcerative colitis patients

To the Editor: In our previous report, we documented a different expression of soluble HLA (human leukocyte antigen)-G molecules between ulcerative colitis (UC) and Crohn’s disease (CD) patients, confirming the presence of different biological characteristics. We observed a spontaneous secretion in peripheral blood mononuclear cell (PBMC) cultures of CD patients but not in those of UC patients. Moreover, a lack of sHLA-G antigens has been reported in UC patient cultures after lipopolysaccharide (LPS) activation. HLA-G molecules are nonclassical HLA Ib antigens characterized by a lower allelic polymorphism and a restricted tissue distribution in comparison with other HLA-Ia molecules. Furthermore, the mRNA alternative splicing generates different membranebound (HLA-G1, G2, G3, and G4) and soluble isoforms (HLA-G5, G6, and G7). HLA-G molecules, in both membrane-bound (mHLA-G) and soluble (sHLA-G) isoforms, are mediators of immunosuppressive functions against innate and adaptive immune responses inducing apoptosis in CD8þ T cells, inhibiting natural killer cell activity, and allo-specific CD4þ T-cell proliferation, enhancing the production of regulatory CD4þ T cells and suppressing the differentiation of dendritic cells. Detectable levels of sHLA-G molecules have been reported in a percentage of plasma samples from healthy subjects and significant variations in the concentrations have been evidenced in different pathological conditions. In our previous study we analyzed the production of sHLA-G molecules in 20 UC (9 females, 8 males; median age 54 years) and 17 CD (11 females, 9 males; median age 51 years) patients without immunosuppressant therapy. Since different molecules are able to induce HLA-G production as cytokines (interleukin10), hormones (progesterone), and immunosuppressant drugs (methotrexate), we evaluated 27 UC (17 females, 10 males; median age 50 years) and 22 CD (12 females, 10 males; median age 55 years) patients attending the Gastroenterology and Digestive Endoscopy Unit of the Ferrara Sant’Anna Hospital for their production of sHLA-G molecules during treatment with multiple drugs (azathioprine, corticosteroids, mesalazine). We analyzed sHLA-G levels in ‘‘in vitro’’ cultures of PBMCs with or without LPS-activation and compared them with the values obtained in the previous untreated cohort. PBMCs were extracted by Ficoll centrifugation (Cederlane, Hornby, Ontario, Canada) from whole peripheral blood and activated by 10 ng/mL of LPS (Calbiochem, La Jolla, CA) for 48 hours. The culture supernatants and the plasma samples were analyzed in triplicate by enzymelinked immunosorbent assay (ELISA) for sHLA-G quantification following the Essen Workshop protocol. Informed consent for blood drawing was obtained from each subject. We compared the sHLA-G levels in PBMC cultures with or without LPS activation from untreated and treated patients. The levels of HLA-G secretion remained higher in nonactivated PBMC cultures from CD patients versus UC patients, even in the treated cohort (Fig. 1A,B) (P 1⁄4 0.011, Wilcoxon test). UC treated patients presented an increased HLA-G secretion in both nonactivated (P 1⁄4 0.057 Mann–Whitney U-test) and LPS-activated PBMC cultures (P 1⁄4 0.004 Mann–Whitney U-test) in comparison with the previous cohort of untreated UC patients (Fig. 1A,B). On the contrary, LPS-activated PBMC cultures from treated CD and UC patients released similar amounts of HLA-G (P 1⁄4 0.075; Wilcoxon test). These data suggest that therapy is able to control the hyperactivation of CD PBMCs and the unbalanced microenvironment of UC patients in the presence of an inflammatory stimulus such as LPS. Age, gender, activity, localization, or duration of the disease and previous surgery did not influence the sHLA-G levels (P 1⁄4 NS; Wilcoxon test). We also analyzed sHLA-G levels in plasma samples from treated CD and UC patients and compared the results with the HLA-G secretion by the corresponding nonactivated PBMC cultures. There was a strict correlation between in vitro and plasmatic levels of sHLAG in both CD (r 1⁄4 0.693; P 1⁄4 0.003; Spearman Correlation test) (Fig. 1C) and UC (r 1⁄4 0.911; P 1⁄4 0.0002; Spearman Correlation test) (Fig. 1D) patients, suggesting the possible use of plasma samples to screen the follow-up of immunosuppressant therapy. Our results suggest that the therapy is able to normalize the production of HLA-G molecules in both CD and UC patients but in a different way: immunosuppressant therapy decreases the hyperproduction of HLA-G in CD patients, while it starts the release of HLA-G in UC patients. These data confirm the diversity in the behavior of these two pathologies and propose the analysis of sHLA-G levels in CD and UC patients during therapeutic follow-up with the goal of distinguishing between UC and CD patients and to monitor therapy. If these results are confirmed in a prospective study, HLA-G could be considered a plasmatic biomarker to establish an individualized treatment to control inflammation, to reduce symptoms, Supported by a grant from AMICI (Associazione per le Malattie Infiammatorie Croniche dell’Intestino). Copyright VC 2011 Crohn’s & Colitis Foundation of America, Inc. DOI 10.1002/ibd.21756 Published online 20May 2011 inWiley Online Library (wileyonlinelibrary.com).

Selecting subgrids from a spatial monitoring network: Proposal and application in semiconductor manufactoring process

The monitoring of spatial production processes typically involves sampling network to gather information about the status of the process. Sampling costs are often not marginal, and once the process has been accurately calibrated, it might be appropriate to reduce the dimension of the sampling grid. This aim is often achieved through the allocation of a brand new network of less dimension. In some cases that is not possible and it might be necessary the selection of a subgrid extracted from the original network. Motivated by a real semiconductor problem, we propose a method to extract a monitoring subgrid from a given one, based upon grid representativeness, accuracy, and spatial coverage of the subgrid and, if available, by expert knowledge of the weights to be assigned to those areas where production may need greater precision. Discussion is mainly focused on circular spatial domain, since, in microelectronics, the basic production support, called wafer, is a circle. Straightforward generalizations to different spatial domains are possible. Furthermore, conditionally upon the availability of experimental data, we check the loss of accuracy by fitting a dual mean-variance response surface on the reduced grid. Joining the latter information and the criteria used to select the subgrid, we provide additional guidelines on how to fine-tune the subgrid selection. Real case studies are used to show the effectiveness of the proposal.

Identifying radon-prone building typologies by marginal modelling

Radon is a naturally occurring decay product of uranium known to be the main contributor to natural background radiation exposure. It has been established that the health risk related to radon exposure is lung cancer. In fact, radon is considered to be a major leading cause of lung cancer, second only to smoking. In this paper, we identified building typologies that affect the probability of detecting indoor radon concentration above reference values, using the data collected within two monitoring campaigns recently conducted in Northern Italy. This information is fundamental both in prevention, i.e. when the construction of a new building is planned and in mitigation, i.e. when a high concentration detected inside buildings has to be reduced. A spatial regression approach for binary data was adopted for this goal where some relevant covariates on the soil were retrieved by linking external spatial databases.

On the Uniformly Most Powerful Invariant Test for the Shoulder Condition in Line Transect Sampling

In wildlife population studies one of the main goals is estimating the population abundance. Line transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a given distance from the observer). Two common models for this function are the half-normal model and the negative exponential model. The estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is almost certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Therefore, testing whether such a hypothesis is consistent with the data is a primary concern in every study aiming at estimating animal abundance. In this paper we propose a test for this purpose. This is the uniformly most powerful test in the class of the scale invariant tests. The asymptotic distribution of the test statistic is worked out by utilising both the half-normal and negative exponential model while the critical values and the power are tabulated via Monte Carlo simulations for small samples. .

Nonparametric Estimation Methods for Sparse Contingency Tables

The problems related with multinomial sparse data analysis have been widely underlined in statistical literature in recent years. Concerning the estimation of the mass distribution, it has been widely spread the usage of nonparametric methods, particularly in the framework of ordinal variables. The aim of this paper is to evaluate the performance of kernel estimators in the framework of sparse contingency tables with ordinal variables comparing them with alternative methodologies. Moreover, an approach to estimate the mass distribution nominal variables based on a kernel estimator is proposed. At the end a case study in actuarial field is presented.