Complex organochlorine pesticide mixtures as determinant factor for breast cancer risk: a population-based case-control study in the Canary Islands (Spain)

[EN] Background: All the relevant risk factors contributing to breast cancer etiology are not fully known. Exposure to organochlorine pesticides has been linked to an increased incidence of the disease, although not all data have been consistent. Most published studies evaluated the exposure to organochlorines individually, ignoring the potential effects exerted by the mixtures of chemicals. Methods: This population-based study was designed to evaluate the profile of mixtures of organochlorines detected in 103 healthy women and 121 women diagnosed with breast cancer from Gran Canaria Island, and the relation between the exposure to these compounds and breast cancer risk.Results: The most prevalent mixture of organochlorines among healthy women was the combination of lindane and endrin, and this mixture was not detected in any affected women. Breast cancer patients presented more frequently a combination of aldrin, dichlorodiphenyldichloroethylene (DDE) and dichlorodiphenyldichloroethane (DDD), and this mixture was not found in any healthy woman. After adjusting for covariables, the risk of breast cancer was moderately associated with DDD (OR = 1.008, confidence interval 95% 1.001-1.015, p = 0.024).Conclusions: This study indicates that healthy women show a very different profile of organochlorine pesticide mixtures than breast cancer patients, suggesting that organochlorine pesticide mixtures could play a relevant role in breast cancer risk.

Multi-Sensor people counting

[EN]An accurate estimation of the number of people entering / leaving a controlled area is an interesting capability for automatic surveil- lance systems. Potential applications where this technology can be ap- plied include those related to security, safety, energy saving or fraud control. In this paper we present a novel con guration of a multi-sensor system combining both visual and range data specially suited for trou- blesome scenarios such as public transportation. The approach applies probabilistic estimation lters on raw sensor data to create intermediate level hypothesis that are later fused using a certainty-based integration stage. Promising results have been obtained in several tests performed on a realistic test bed scenario under variable lightning conditions.

A Low Consumption Real Time Environmental Monitoring System for Smart Cities based on ZigBee Wireless Sensor Network

Nowadays, there is an increasing interest in wireless sensor networks (WSN) for environmental monitoring systems because it can be used to improve the quality of life and living conditions are becoming a major concern to people. This paper describes the design and development of a real time monitoring system based on ZigBee WSN characterized by a lower energy consumption, low cost, reduced dimensions and fast adaptation to the network tree topology. The developed system encompasses an optimized sensing process about environmental parameters, low rate transmission from sensor nodes to the gateway, packet parsing and data storing in a remote database and real time visualization through a web server.