GIS-Based Estimation of Exposure to Particulate Matter and NO(2) in an Urban Area: Stochastic versus Dispersion Modeling

Stochastic modeling was used to predict nitrogen dioxide and fine particles [particles collected with an upper 50% cut point of 2.5 μm aerodynamic diameter (PM(2.5))] levels at 1,669 addresses of the participants of two ongoing birth cohort studies conducted in Munich, Germany. Alternatively, the Gaussian multisource dispersion model IMMIS(net/em) was used to estimate the annual mean values for NO(2) and total suspended particles (TSP) for the 40 measurement sites and for all study subjects. The aim of this study was to compare the measured NO(2) and PM(2.5) levels with the levels predicted by the two modeling approaches (for the 40 measurement sites) and to compare the results of the stochastic and dispersion modeling for all study infants (1,669 sites). NO(2) and PM(2.5) concentrations obtained by the stochastic models were in the same range as the measured concentrations, whereas the NO(2) and TSP levels estimated by dispersion modeling were higher than the measured values. However, the correlation between stochastic- and dispersion-modeled concentrations was strong for both pollutants: At the 40 measurement sites, for NO(2), r = 0.83, and for PM, r = 0.79; at the 1,669 cohort sites, for NO(2), r = 0.83 and for PM, r = 0.79. Both models yield similar results regarding exposure estimate of the study cohort to traffic-related air pollution, when classified into tertiles; that is, 70% of the study subjects were classified into the same category. In conclusion, despite different assumptions and procedures used for the stochastic and dispersion modeling, both models yield similar results regarding exposure estimation of the study cohort to traffic-related air pollutants

Long-term Exposure to Traffic-related Air Pollution and Type 2 Diabetes Prevalence in a Cross-sectional Screening-study in the Netherlands

<p>Abstract</p> <p>Background</p> <p>Air pollution may promote type 2 diabetes by increasing adipose inflammation and insulin resistance. This study examined the relation between long-term exposure to traffic-related air pollution and type 2 diabetes prevalence among 50- to 75-year-old subjects living in Westfriesland, the Netherlands.</p> <p>Methods</p> <p>Participants were recruited in a cross-sectional diabetes screening-study conducted between 1998 and 2000. Exposure to traffic-related air pollution was characterized at the participants' home-address. Indicators of exposure were land use regression modeled nitrogen dioxide (NO₂) concentration, distance to the nearest main road, traffic flow at the nearest main road and traffic in a 250 m circular buffer. Crude and age-, gender- and neighborhood income adjusted associations were examined by logistic regression.</p> <p>Results</p> <p>8,018 participants were included, of whom 619 (8%) subjects had type 2 diabetes. Smoothed plots of exposure versus type 2 diabetes supported some association with traffic in a 250 m buffer (the highest three quartiles compared to the lowest also showed increased prevalence, though non-significant and not increasing with increasing quartile), but not with the other exposure metrics. Modeled NO₂-concentration, distance to the nearest main road and traffic flow at the nearest main road were not associated with diabetes. Exposure-response relations seemed somewhat more pronounced for women than for men (non-significant).</p> <p>Conclusions</p> <p>We did not find consistent associations between type 2 diabetes prevalence and exposure to traffic-related air pollution, though there were some indications for a relation with traffic in a 250 m buffer.</p

Surfing the internet for health information: an italian survey on use and population choices

BACKGROUND: Recent international sources have described how the rapid expansion of the Internet has precipitated an increase in its use by the general population to search for medical information. Most studies on e-health use investigated either through the prevalence of such use and the social and income patterns of users in selected populations, or the psychological consequences and satisfaction experienced by patients with particular diseases. Few studies have been carried out in Europe that have tried to identify the behavioral consequences of Internet use for health-related purposes in the general population.The aims of this study are to provide information about the prevalence of Internet use for health-related purposes in Italy according to demographic and socio-cultural features, to investigate the impact of the information found on health-related behaviors and choices and to analyze any differences based on health condition, self-rated health and relationships with health professionals and facilities. METHODS: A multicenter survey was designed within six representative Italian cities. Data were collected through a validated questionnaire administered in hospital laboratories by physicians. Respondents were questioned about their generic condition, their use of the Internet and their health behaviors and choices related to Internet use. Data were analyzed using descriptive statistics and logistic regression to assess any differences by socio-demographic and health-related variables. RESULTS: The sample included 3018 individuals between the ages of 18 and 65 years. Approximately 65% of respondents reported using the Internet, and 57% of them reported using it to search for health-related information. The main reasons for search on the Internet were faster access and a greater amount of information. People using the Internet more for health-related purposes were younger, female and affected by chronic diseases. CONCLUSIONS: A large number of Internet users search for health information and subsequently modify their health behaviors and relationships with their medical providers. This may suggest a strong public health impact with consequences in all European countries, and it would be prudent to plan educational and prevention programs. However, it could be important to investigate the quality of health-related websites to protect and inform user

A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science

Модельные характеристики кардиореспираторной системы высококвалифицированных гребцов на байдарках и каноэ

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the "modeling by satisfaction of spatial restraints" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program's predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER's objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance