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Background: Women with inflammatory bowel disease (IBD) are at increased risk for adverse birth outcomes such as preterm delivery and small for gestational age (SGA) infants. Most recognized cases of fetal growth restriction in singleton pregnancies have underlying placental causes. However, studies in IBD examining poor birth outcomes have focused on maternal factors. We examined whether women with IBD have a higher rate of placental inflammation than non-IBD controls. Methods: Between 2008 and 2011, the placental tissue of 7 ulcerative colitis, 5 Crohn's disease, and 2 IBD-unclassified subjects enrolled in the Pregnancy in inflammatory Bowel Disease and Neonatal Outcome (PIANO) registry were evaluated for villitis, deciduitis, and chorioamnionitis with/without a fetal inflammatory response. The history and birth outcomes of all IBD subjects were reviewed and matched to 26 non-IBD controls by gestational age at delivery. Results: Of women with IBD, 29% delivered preterm infants and 21% delivered SGA infants. Half of the IBD patients had mild-moderate disease flares during pregnancy. Five (36%) patients required corticosteroids, 2 (14%) were maintained on an immunomodulator, and 3 (21%) others received tumor necrosis factor-alpha inhibitors during their pregnancy. Chorioamnionitis was the only identified placental pathology present in the placentas reviewed, occurring less frequently in cases compared to controls (7% vs. 27%, P=0.32). Conclusions: Placental inflammatory activation does not appear to be responsible for the increase in adverse birth outcome in women with IBD. Further studies are necessary to validate these findings in IBD to explain poor birth outcomes. [ABSTRACT FROM AUTHOR]

This study aims to assess the impact of a class of radar rainfall errors on prediction uncertainty of a conceptual water balance model. Uncertainty assessment is carried out by means of the Generalized Likelihood Uncertainty Estimation procedure (GLUE). The effects of model input and structural error are separated, and the potential for compensating errors between them is investigated. It is shown that the radar rainfall bias term operates in a multiplicative sense on the model structural uncertainty, by either magnifying or reducing it according to the sign of the bias. The results show also that adjustment of radar rainfall, aimed to remove local biases and to reduce random errors, allows ensuring that a larger percentage of the observed flows are enclosed by the uncertainty bounds, with respect to nonadjusted radar input. However, this is obtained at the price of increasing the wideness of the uncertainty bounds. This effect is emphasized with increasing the radar beam elevation. As a second step, the issue of the impact of radar rainfall estimation error on model parameter distribution and parameter transferability across sites under the radar umbrella is examined. Radar data at different radar beam elevations are used to simulate radar estimation errors at different distances from the radar site and to analyze the impact of these errors on prediction uncertainty. The results show that distortion of parameter distribution due to radar error may be considerable and that adjustment of radar rainfall estimates improves the regionalization potential of radar-based precipitation estimates (at least for ranges less than 70 km). [ABSTRACT FROM AUTHOR]

Hepatitis C (HCV) is an indolent and often fatal disease affecting four million Americans commonly associated with low socio-economic status. We assessed its prevalence in a sample of 334 consecutively admitted middle class substance abusers in a private urban hospital, and ascertained risk factors for its transmission. We found that the point prevalence rate for HCV was 27.7% among all substance abusers, and 76.7% among intravenous drug users. Using logistic regression, we found risk factors associated with HCV were intravenous drug use, needle sharing, prior liver disease, opioid dependence, HIV infection, and benzodiazepine dependence. Not found to increase infective risk were lower social class, male gender, African-American race, male homosexuality, unemployment, and the absence of private health insurance. Multiple viral genotype types were identified in this sample, suggesting diverse sources of transmission in the sample. This study documents an epidemic of HCV in an American middle class sample. [ABSTRACT FROM AUTHOR]

Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997–2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities. [ABSTRACT FROM AUTHOR]