First-trimester prenatal diagnosis of cystic fibrosis using the polymerase chain reaction: Report of eight cases

Eight pregnancies at risk for cystic fibrosis have been monitored by first-trimester prenatal diagnosis with DNA amplification analysis. The polymerase chain reaction (PCR) was used in all cases to amplify the region detected by KM 19. In two cases, the region detected by CS·7, another DNA probe tightly linked to the CF locus, was also examined. The results of the PCR determinations were confirmed using the Southern blotting procedure, by segregation analysis of restriction fragment length polymorphisms (RFLPs) relative to XV-2c, J3·11, metH, metD, and KM19 probes.     

Urinary methyl tert-butyl ether and benzene as biomarkers of exposure to urban traffic

Methyl tert-butyl ether (MTBE) and benzene are added to gasoline to improve the combustion process and are found in the urban environment as a consequence of vehicular traffic. Herein we evaluate urinary MTBE (MTBE-U) and benzene (BEN-U) as biomarkers of exposure to urban traffic. Milan urban policemen (130 total) were investigated in May, July, October, and December for a total of 171 work shifts. Personal exposure to airborne benzene and carbon monoxide (CO), and atmospheric data, were measured during the work shift, while personal characteristics were collected by a questionnaire. A time/activity diary was completed by each subject during the work shift. Spot urine samples were obtained for the determination of MTBE-U and BEN-U. Median personal exposure to CO and airborne benzene were 3.3 mg/m(3) and 9.6 μg/m(3), respectively; median urinary levels in end-of-shift (ES) samples were 147 ng/L (MTBE-U) and 207 ng/L (BEN-U). The time spent on traffic duty at crossing was about 40% of work time. Multiple linear regression models, taking into account within-subject correlations, were applied to investigate the role of urban pollution, atmospheric conditions, job variables and personal characteristics on the level of biomarkers. MTBE-U was influenced by the month of sampling and positively correlated to the time spent in traffic guarding, CO exposure and atmospheric pressure, while negatively correlated to wind speed (R(2) for total model 0.63, P<0.001). BEN-U was influenced by the month and smoking habit, and positively correlated to urinary creatinine; moreover, an interaction between CO and smoking was found (R(2)=0.62, P<0.001). These results suggest that MTBE-U is a reliable marker for assessing urban traffic exposure, while BEN-U is determined mainly by personal characteristics.     

Integrated Ambient and Microenvironment Model for Estimation of PM10 Exposures of Children in Annual and Episode Settings

Particulate matter air pollution is estimated to cause in the order of 350,000 excess deaths in the European Union calling for policy development and evaluation tools. In the current work, a model for PM₁₀ exposures of children is developed using microenvironment time activities and infiltration of ambient pollution indoors, both evaluated against observations earlier and integrated with city-wide air quality models in the current work. The model is demonstrated using data from two cities in Italy. High-end short-term exposures are characterized by an episode-day situation in Turin, and annual mean exposures in downtown Bologna. The air quality model was unable to capture the highest levels during the episode, and therefore, the exposure model was adjusted using observed–modeled ratio from a monitoring station. Air quality model performance for the annual levels was significantly better. Annual exposure variability within the target population was 1.5-fold in the downtown area Bologna and tenfold during the episode day in Turin.     

Exploring management strategies for community-based forests using multi-agent systems: A case study in Palawan, Philippines

This paper describes the experiences and lessons learned in applying a multi-agent systems (MAS) model to study the dynamics and complex interactions among stakeholders in the management of community-based forests. The MAS model is developed using the companion modelling (ComMod) approach, which allows for a collaborative development of the model between the stakeholders and researchers. This approach involves the development and application of role-playing games (RPGs) and computer simulation as learning tools and to validate the model. Inferences are drawn from the learning and negotiation processes that the stakeholders and researchers underwent in the collaborative development of the MAS model. These processes ultimately led to the development of a collaborative resource management plan. The approach and the MAS model were applied to a case study involving a community-based forest managed by three villages in the island of Palawan, Philippines.     

Predicting dioxin-like PCBs soil contamination levels using milk of grazing animal as indicator

Dioxin-like PCBs (dl-PCBs) are ubiquitous persistent organic pollutants of recognized negative effects on human health. Assessing highly polluted areas should be an important public health issue. This study proposes to use the milk of grazing animals as a bioindicator of dl-PCB contamination in the environment. The hypothesis is that milk concentration of dl-PCBs are related to soil concentrations of these compounds, and that soils are generally reflective of a larger environmental issue of dl-PCB contamination. In this study, we evaluate the possibility of predicting soil concentrations using milk of sheep, cows and buffalos in a spatial model. For this purpose, samples of soil and milk collected in Campania (Italy) were introduced in a GIS platform to perform geostatistical analysis for building a simple predictive model. The ordinary least squares regressions (OLS) showed a statistically significant correlation (p < 0.05) between soil and milk contamination. However, this relationship was spatially variable. Thus, a geographically weighted regression (GWR) was performed, obtaining R² values of 0.91, 0.77 and 0.66 for sheep’s, buffalo’s and cow’s milk respectively. Assessed the mathematical relationships between the variables, new data was introduced to evaluate the performance of the model. Predictions of soil contamination with dl-PCBs using sheep’s, cow’s and buffalo’s milk showed a mean error of 23%, 25% and 36% respectively. According to these results the sheep’s milk can be considered the best bioindicator of dl-PCBs contamination among the three species. The results of this project evidence the potentialities of the proposed approach to assess bioindicator performance in a spatial predictive model.     

Sun light degradation of 4-chloroaniline in waters and its effect on toxicity. A high performance liquid chromatography - Diode array - Tandem mass spectrometry study

This paper studies the degradation reactions that 4-chloroaniline can naturally undergo in waters for the action of sun light. 10.00 mg L⁻¹ 4-chloroaniline aqueous solution, without any addition of organic solvent, are undergone to photoirradiation under conditions that simulate sun light. The degradation pathway, followed by HPLC-DAD-MS/MS methods, is complex since the pollutant gives rise to many photoproducts: the predominant species are characterized by m/z values of 217 (P5) and 218 (P6) and are compatible with dimeric structures of 4-chloroaniline. Vibrio fischeri tests indicate that the photoproducts of 4-chloroaniline are characterized by a toxicity level significantly greater than the precursor.     

Aerospace wetland monitoring by hyperspectral imaging sensors: a case study in the coastal zone of San Rossore Natural Park

The San Rossore Natural Park, located on the Tuscany (Italy) coast, has been utilized over the last 10 years for many remote sensing campaigns devoted to coastal zone monitoring. A wet area is located in the south-west part of the Natural Park and it is characterized by a system of ponds and dunes formed by sediment deposition occurring at the Arno River estuary. The considerable amount of collected data has permitted us to investigate the evolution of wetland spreading and land coverage as well as to retrieve relevant biogeochemical parameters, e.g. green biomass, from remote sensing images and products. This analysis has proved that the monitoring of coastal wetlands, characterized by shallow waters, moor and dunes, demands dedicated aerospace sensors with high spatial and spectral resolution. The outcomes of the processing of images gathered during several remote sensing campaigns by airborne and spaceborne hyperspectral sensors are presented and discussed. A particular effort has been devoted to sensor response calibration and data validation due to the complex heterogeneity of the observed natural surfaces.