CASE-CONTROL STUDY OF AN OUTBREAK OF ACUTE TOXOPLASMOSIS IN AN INDUSTRIAL PLANT IN THE STATE OF SÃO PAULO, BRAZIL

Foodborne diseases represent operational risks in industrial restaurants. We described an outbreak of nine clustered cases of acute illness resembling acute toxoplasmosis in an industrial plant with 2300 employees. These patients and another 36 similar asymptomatic employees were diagnosed with anti-T. gondii IgG titer and avidity by ELISA. We excluded 14 patients based on high IgG avidity and chronic toxoplasmosis: 13 from controls and one from acute disease other than T. gondii infection. We also identified another three asymptomatic employees with T.gondii acute infection and also anti-T. gondii IgM positive as remaining acute cases. Case control study was conducted by interview in 11 acute infections and 20 negative controls. The ingestion of green vegetables, but not meat or water, was observed to be associated with the incidence of acute disease. These data reinforce the importance of sanitation control in industrial restaurants and also demonstrate the need for improvement in quality control regarding vegetables at risk for T. gondii oocyst contamination. We emphasized the accurate diagnosis of indexed cases and the detection of asymptomatic infections to determine the extent of the toxoplasmosis outbreak.

Case-control study of an outbreak of acute toxoplasmosis in an industrial plant in the state of São Paulo, Brazil

Foodborne diseases represent operational risks in industrial restaurants. We described an outbreak of nine clustered cases of acute illness resembling acute toxoplasmosis in an industrial plant with 2300 employees. These patients and another 36 similar asymptomatic employees were diagnosed with anti-T. gondii IgG titer and avidity by ELISA. We excluded 14 patients based on high IgG avidity and chronic toxoplasmosis: 13 from controls and one from acute disease other than T. gondii infection. We also identified another three asymptomatic employees with T.gondii acute infection and also anti-T. gondii IgM positive as remaining acute cases. Case control study was conducted by interview in 11 acute infections and 20 negative controls. The ingestion of green vegetables, but not meat or water, was observed to be associated with the incidence of acute disease. These data reinforce the importance of sanitation control in industrial restaurants and also demonstrate the need for improvement in quality control regarding vegetables at risk for T. gondii oocyst contamination. We emphasized the accurate diagnosis of indexed cases and the detection of asymptomatic infections to determine the extent of the toxoplasmosis outbreak.

Polycyclic aromatic hydrocarbons (PAHs) in a large South American industrial coastal area (Santos Estuary, Southeastern Brazil): Sources and depositional history

Located in southeastern Brazil, the Santos Estuary has the most important industrial and urban population area of South America. Since the 1950`s, increased urbanization and industrialization near the estuary margins has caused the degradation of mangroves and has increased the discharge of sewage and industrial effluents. The main objectives of this work were to determine the concentrations and sources of polycyclic aromatic hydrocarbons (PAHs) in sediment cores in order to investigate the input of these substances in the last 50 years. The PAHs analyses indicated multiple sources of these compounds (oil and pyrolitic origin), basically anthropogenic contributions from biomass, coal and fossil fuels combustion. The distribution of PAHs in the cores was associated with the formation and development of Cubatao industrial complex and the Santos harbour, waste disposal, world oil crisis and the pollution control program, which results in the decrease of organic pollutants input in this area. (C) 2011 Elsevier Ltd. All rights reserved.

Changes in the intermediate water mass formation rates in the global ocean for the Last Glacial Maximum, mid-Holocene and pre-industrial climates

The paleoclimate version of the National Center for Atmospheric Research Community Climate System Model version 3 (NCAR-CCSM3) is used to analyze changes in the water formation rates in the Atlantic, Pacific, and Indian Oceans for the Last Glacial Maximum (LGM), mid-Holocene (MH) and pre-industrial (PI) control climate. During the MH, CCSM3 exhibits a north-south asymmetric response of intermediate water subduction changes in the Atlantic Ocean, with a reduction of 2 Sv in the North Atlantic and an increase of 2 Sv in the South Atlantic relative to PI. During the LGM, there is increased formation of intermediate water and a more stagnant deep ocean in the North Pacific. The production of North Atlantic Deep Water (NADW) is significantly weakened. The NADW is replaced in large extent by enhanced Antarctic Intermediate Water (AAIW), Glacial North Atlantic Intermediate Water (GNAIW), and also by an intensified of Antarctic Bottom Water (AABW), with the latter being a response to the enhanced salinity and ice formation around Antarctica. Most of the LGM intermediate/mode water is formed at 27.4 < sigma(theta) < 29.0 kg/m(3), while for the MH and PI most of the subduction transport occurs at 26.5 < sigma(theta) < 27.4 kg/m(3). The simulated LGM Southern Hemisphere winds are more intense by 0.2-0.4 dyne/cm(2). Consequently, increased Ekman transport drives the production of intermediate water (low salinity) at a larger rate and at higher densities when compared to the other climatic periods.

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.