Levels of dioxin-like PCBs in low-volume serum samples of male patients attending fertility clinics

An accurate and easy method for the extraction, cleanup, and HRGC-HRMS analysis of dioxin-like PCBs (DL-PCBs) in low-volume serum samples (1 mL) was developed. Serum samples were extracted several times using n-hexane and purified by acid washing. Recovery rates of labeled congeners ranged from 70 to 110 % and the limits of detection were below 1 pg/g on lipid basis. Although human studies are limited and contradictory, several studies have shown that DL-PCBs can have adverse effects on the male reproductive system. In this way, the present method was applied to 21 serum samples of male patients attending fertility clinics. The total levels obtained for the patients ranged from 6.90 to 84.1 pg WHO-TEQ/g lipid, with a mean value of 20.3 pg WHO-TEQ/g lipid. The predominant PCBs (the sum of PCB 118, 156, and 105) contributed 67 % to the mean concentration of total DL-PCBs in the samples analyzed.

Multi-agent system for control of robots inspired on the distributed activity and hormonal regulation of humans

Robotics is an emerging field with great activity. Robotics is a field that presents several problems because it depends on a large number of disciplines, technologies, devices and tasks. Its expansion from perfectly controlled industrial environments toward open and dynamic environment presents a many new challenges. New uses are, for example, household robots or professional robots. To facilitate the low cost, rapid development of robotic systems, reusability of code, its medium and long term maintainability and robustness are required novel approaches to provide generic models and software systems who develop paradigms capable of solving these problems. For this purpose, in this paper we propose a model based on multi-agent systems inspired by the human nervous system able to transfer the control characteristics of the biological system and able to take advantage of the best properties of distributed software systems. Specifically, we model the decentralized activity and hormonal variation.