Biomarkers of pesticide exposure and diabetes in the 1999–2004 National Health and Nutrition Examination Survey

The associations of 8 pesticides and pesticide metabolites with total diabetes (diagnosed and undiagnosed) and pre-diabetes (glycohemoglobin 5.7–6.4%) were evaluated using the National Health and Nutrition Examination Survey (NHANES), 1999–2004. Six of the pesticides were found to be associated with total diabetes in separate adjusted logistic regressions. These pesticides and pesticide metabolites were beta-hexachlorocyclohexane, p,p′-DDE, p,p′-DDT, oxychlordane, trans-nonachlor, and heptachlor epoxide. When the number of compounds elevated was tested, 4 or more, of the 6, elevated had an odds ratio of 4.99 (95% CI 1.97–12.61) compared to none elevated. When the 6 compounds were tested together in a single combined adjusted logistic regression only oxychlordane, a metabolite of chlordane, and heptachlor epoxide, a metabolite of heptachlor, were significantly associated with total diabetes. In the combined adjusted logistic regression, oxychlordane ≥ 14.5 ng/g lipid adjusted had an odds ratios of 1.90 (95% CI 1.09–3.32) compared to oxychlordane < 14.5 ng/g lipid adjusted, and heptachlor epoxide ≥ 14.6 ng/g lipid adjusted had an odds ratio of 1.70 (95% CI 1.16–2.49) compared to heptachlor epoxide < 14.6 ng/g lipid adjusted. Heptachlor epoxide and p,p′-DDT were significantly associated with pre-diabetes in separate adjusted logistic regressions. When these 2 compounds were tested together only heptachlor epoxide remained significantly associated with pre-diabetes. The evidence supporting the relationship between pesticides and pesticide metabolites, with diabetes, was strongest for heptachlor epoxide and oxychlordane, intermediate for p,p′-DDT, and least for beta-hexachlorocyclohexane, p,p′-DDE, and trans-nonachlor. Mirex and dieldrin were not associated with total diabetes or pre-diabetes.     

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.     

Are ecological differences of irruptive bird species from typical migrants real? (The example of the long-tailed tit <Emphasis Type="Italic">Aegithalos c. caudatus</Emphasis>)

Using the long-tailed tit (Aegithalos c. caudatus) as an example of the so-called irruptive species, the ecophysiological characteristics (body mass, fat reserves) and migration speed during autumn period were analyzed. The mean body mass and the median fat score did not depend on the number of migrating individuals. There was no evidence of the so-called stress in birds, participating in migration: migrants had even higher fat reserves than residents, while no significant difference in the body mass was recorded. The increase in fat reserves in the long-tailed tit during migration period was similar to the seasonal changes in energy reserves known for typical migrants. This makes it possible for late-migrating individuals to maintain higher total speed of migration compared to early-migrating ones. Basing on the obtained data, we conclude that the ecophysiological characteristics of the long-tailed tits migrating in autumn are similar to those of regular migrants.     

Effects of agricultural pesticides on the health of Rana pipiens frogs sampled from the field

There is evidence that over the last 30 years, there have been mass declines in diverse geographic locations among amphibian populations due to disease outbreaks. Multiple causes have been suggested to explain this increase in disease incidence. Among these, climate changes, environmental pollution and reduced water quality are gaining attention. Indeed, some chemicals of environmental concerns are known to alter the immune system. It is possible that exposure to these pollutants could alter the immune system of anurans and render them more susceptible to different pathogens. In this study, we sampled Rana pipiens in five different sites near St. Lawrence River (Quebec, Canada) during the months of July and September in 2001. Two of these sites were protected areas, in which low levels of pesticides were detected, while the remaining three sites were located in areas with intensive corn and soybeans cultivations. Our results demonstrated that frogs living in agricultural regions are smaller in size and weight than frogs living in areas with lower levels of pesticides at both sampling times. Moreover, we have observed a significant decrease in the number of splenocytes (cellularity) and the phagocytic activity in frogs sampled in impacted sites. Taken together, these results suggest that frogs living in agricultural regions might be more vulnerable to infections and diseases through their smaller size and alteration of their immune system. Our results also contribute to the overall discussion on factors involved in amphibian declines.     

PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM₁₀ and PM_2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO₂, PM₁₀, NO₂, CO, and O₃; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ng?m^?3, surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10^?6) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98?×?10^?7 in PM₁₀ and 1.06?×?10^?6 in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO₂, NO₂, PM₁₀, CO, and solar radiation had positive correlation with PAHs concentrations, while O₃, temperature, relative humidity, and wind speed were negatively correlated.