Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

Mathematical analysis and simulations involving chemotherapy and surgery on large human tumours under a suitable cell-kill functional response

Dosage and frequency of treatment schedules are important for successful chemotherapy. However, in this work we argue that cell-kill response and tumoral growth should not be seen as separate and therefore are essential in a mathematical cancer model. This paper presents a mathematical model for sequencing of cancer chemotherapy and surgery. Our purpose is to investigate treatments for large human tumours considering a suitable cell-kill dynamics. We use some biological and pharmacological data in a numerical approach, where drug administration occurs in cycles (periodic infusion) and surgery is performed instantaneously. Moreover, we also present an analysis of stability for a chemotherapeutic model with continuous drug administration. According to Norton & Simon [22], our results indicate that chemotherapy is less eficient in treating tumours that have reached a plateau level of growing and that a combination with surgical treatment can provide better outcomes.

Thermodynamic analysis and comparison of downdraft gasifiers integrated with gas turbine, spark and compression ignition engines for distributed power generation

The objective of the present article is to assess and compare the performance of electricity generation systems integrated with downdraft biomass gasifiers for distributed power generation. A model for estimating the electric power generation of internal combustion engines and gas turbines powered by syngas was developed. First, the model determines the syngas composition and the lower heating value; and second, these data are used to evaluate power generation in Otto, Diesel, and Brayton cycles. Four synthesis gas compositions were tested for gasification with: air; pure oxygen; 60% oxygen with 40% steam; and 60% air with 40% steam. The results show a maximum power ratio of 0.567 kWh/Nm(3) for the gas turbine system, 0.647 kWh/Nm(3) for the compression ignition engine, and 0.775 kWh/Nm(3) for the spark-ignition engine while running on synthesis gas which was produced using pure oxygen as gasification agent. When these three systems run on synthesis gas produced using atmospheric air as gasification agent, the maximum power ratios were 0.274 kWh/Nm(3) for the gas turbine system, 0.302 kWh/Nm(3) for CIE, and 0.282 kWh/Nm(3) for SIE. The relationship between power output and synthesis gas flow variations is presented as is the dependence of efficiency on compression ratios. Since the maximum attainable power ratio of CIE is higher than that of SIE for gasification with air, more research should be performed on utilization of synthesis gas in CIE. (C) 2014 Elsevier Ltd. All rights reserved.

Greenhouse gas balance in the conversion from extensive pasture to other agricultural systems in Andean region of Colombia

Pós-graduação em Agronomia (Ciência do Solo) - FCAV