Prenatal diagnosis of congenital toxoplasmosis

Ninety-three pregnant women with Toxoplasma gondii seroconversion during pregnancy underwent prenatal diagnosis of fetal toxoplasmosis. The following tests were used: (1) amniocentesis for mouse inoculation (93 subjects), (2) amplification of T. gondii DNA by polymerase chain reaction (PCR) (79 subjects), and (3) cordocentesis for the detection of T. gondii-specific IgM antibodies (13 subjects). All patients had serial ultrasonographic scans to detect those fetuses with abnormalities that could be associated with congenital toxoplasmosis. Eighteen pregnancies (19.4%) had evidence of vertical transmission. A total of 11/18 (61.1%) had positive amniotic mouse inoculation test, while 10/12 (83.3%) had positive PCR results. The combination of both tests allowed the prenatal diagnosis in 17/18 infected fetuses (94.4%). All patients who underwent cordocentesis for the detection of T. gondii-specific IgM antibodies had negative results. However, in two of the above cases fetal toxoplasmosis was detected by amniotic fluid studies. In five of the infected fetuses there were abnormal ultrasonographic findings. All pregnancies with evidence of vertical transmission were terminated, whereas the remaining pregnancies proceeded normally to term. The present data showed that amniotic fluid studies, preferably PCR amplification of T. gondii DNA, are the best diagnostic tools for the detection of vertical transmission in pregnancies with seroconversion during pregnancy. Copyright © 2002 John Wiley & Sons, Ltd.     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

MODELING OF NONPOINT SOURCE POLLUTION OF NITROGEN AT THE WATERSHED SCALE1

ABSTRACT: The Watershed Nutrient Transport and Transformation (NTT-Watershed) model is a physically based, energy-driven, multiple land use, distributed model that is capable of simulating water and nutrient transport in a watershed. The topographic features and subsurface properties of the watershed are refined into uniform, homogeneous square grids. The vertical discretization includes vegetation, overland flow, soil water redistribution and groundwater zones. The chemical submodel simulates the nitrogen dynamics in terrestrial and aquatic systems. Three chemical state variables are considered (NO₃^-_-, NH₄⁺, and Org-N). The NTT-Watershed model was used to simulate the fate and transport of nitrogen in the Muddy Brook watershed in Connecticut. The model was shown to be capable of capturing the hydrologic and portions of the nitrogen dynamics in the watershed. Watershed planners could use this model in developing strategies of best management practices that could result in maximizing the reductions of nitrogen export from a watershed.     

Optimizing safety-constrained solvent selection for process systems with economic uncertainties

Solvents are very commonly used in industrial facilities for a multitude of reasons. Traditionally, solvent selection has been based on minimizing the process operating cost while satisfying a set of operational requirements. Regrettably, safety considerations have typically been overlooked during the design phase. In this paper, a systematic approach is introduced to integrate safety issues into solvent selection and provides a computationally effective method for establishing tradeoffs between the economic and safety objectives. In order to quantify the risk associated with the solvent, we focus on the potential spillage of the solvent and introduce a risk index that is a function of the amount of solvent used and stored, as well as the Permissible Exposure Limit (PEL) dictated by regulatory directives. An optimization formulation is developed and the associated mathematical program solved to select optimal solvents and blends while incorporating economic, technical, and safety considerations. Tradeoff (Pareto) curves are developed to represent the multi-objective optimization results and tradeoffs. Furthermore, economic-data uncertainty and variability over expected ranges are included in the optimization formulation to conduct an insightful sensitivity analysis. Finally, an illustrative case study is considered via increasing levels of complexity in order to evaluate the proposed optimization method which considers both operating cost and safety risk implications in the presence of economic uncertainties.     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Development of a database for accidents and incidents in the Greek petrochemical industry

The present paper describes the development of a database that comprises all incidents from the Greek petrochemical industry for the period 1997–2003. This database includes industrial incidents, accidents, operational accidents and near misses from all petrochemical sites in Greece and Cyprus. The design of the database has been conceived in a user-friendly way with additional possibilities for its further use, such as: statistical analysis of the data, calculation of safety indicators, accident reports and human factors analysis. The database allows the various participating industries to compare the analysis of indicators in their own installations with the national average, as the database comprises data from the entire Greek petrochemical industry. Special care has been given to include data from near misses too.     

Prenatal diagnosis of hemoglobinopathies in twin pregnancies by double simultaneous fetoscopy

A new technique for sampling fetal blood in twin pregnancies using two fetoscopes simultaneously is described. Two fetoscopes were inserted, one after the other, into both amniotic cavities and fetal blood samples were obtained from either the chorionic plate vessels or the umbilical cord insertion area. The observation of the bright tip of the second fetoscope behind the septum using the first fetoscope assured the successful entry of the two fetoscopes into the two different amniotic sacs. This technique was performed on 15 out of 17 patients. In all patients the fetuses were at risk of β-thalassemia major. Sampling was successful in all cases. Double simultaneous fetoscopy seems to be a safe and accurate technique without technical problems or complications. The simultaneous use of two fetoscopes opens new possibilities in intrauterine fetal surgery and research.     

Risk informed optimization of a hazardous material multi-periodic transportation model

This paper presents a systematic framework toward the development of a Transportation Model for Hazardous Materials (HazMat). In practice, the proposed modeling framework is realized through an appropriate generalization of the traditional transportation network problem in the presence of safety constraints that need to be satisfied. The objective is to minimize transportation cost while reducing risks at the desired levels.In particular, the present research study identifies and evaluates different risk factors that influence the HazMat transportation network. Next, the transportation model is depicted graphically using nodes and arcs and optimal conditions are identified by solving the associated minimum cost flow network problem. The results show safety levels that help making informed decisions on choosing the optimal transportation configuration for hazardous material shipments.Within the proposed methodological context, appropriately parameterized simulation studies elucidate the effects of occurrence probabilities of the different risk events on transportation cost. Furthermore, as the appropriate management decisions must consider the effect of actions in one time period on future periods, the proposed model is structured as a multi-periodic model.Finally, the proposed methodological approach is employed to demonstrate the utility of proper analytical tools in decision making and particularly in ensuring that scientifically informed safety procedures are in place while transporting goods that can be potentially proven dangerous to the public and the surroundings.