Conversion of brain cytosol profile from fetal to adult type during the perinatal period: Taurine-NAA exchange

Mammals face drastic environmental changes at birth. Appropriate adjustments of various systems must take place rapidly to accommodate this once in a life time event. The brain undergoes significant adjustments as well, the most obvious of which is in its need to meet the drastic increase in energy consumption at the neuronal cell membrane due to the explosive increase in neural activities after birth. Actual changes were found to be taken place in two systems, namely, acid base balance control and cytosolic energy transport. The adjustments are accomplished by converting cytosol microenvironment from a taurine rich fetal type environment to an N-acetyl-aspartate (NAA) rich adult type environment during the post-natal period. High concentrations of taurine are necessary to provide effective buffering in the fetal brain, because the fetus cannot utilize the adult type of pCO2 dependent acid–base balance control system, namely respiration driven pCO2 changes. To accommodate the significantly higher demand of energy consumption at the membrane due to the increased neuronal activities, taurine has to be replaced by NAA, since the latter facilitates HEP transport from mitochondria to the membrane by passive diffusion

A Metaheuristic Framework for Bi-level Programming Problems with Multi-disciplinary Applications

Bi-level programming problems arise in situations when the decision maker has to take into account the responses of the users to his decisions. Several problems arising in engineering and economics can be cast within the bi-level programming framework. The bi-level programming model is also known as a Stackleberg or leader-follower game in which the leader chooses his variables so as to optimise his objective function, taking into account the response of the follower(s) who separately optimise their own objectives, treating the leader’s decisions as exogenous. In this chapter, we present a unified framework fully consistent with the Stackleberg paradigm of bi-level programming that allows for the integration of meta-heuristic algorithms with traditional gradient based optimisation algorithms for the solution of bi-level programming problems. In particular we employ Differential Evolution as the main meta-heuristic in our proposal.We subsequently apply the proposed method (DEBLP) to a range of problems from many fields such as transportation systems management, parameter estimation and game theory. It is demonstrated that DEBLP is a robust and powerful search heuristic for this class of problems characterised by non smoothness and non convexity

A genetic symbiotic algorithm applied to the one-dimensional cutting stock problem

This work presents a genetic symbiotic algorithm to minimize the number of objects and the setup in a one-dimensional cutting stock problem. The algorithm implemented can generate combinations of ordered lengths of stock (the cutting pattern) and, at the same time, the frequency of the cutting patterns, through a symbiotic process between two distinct populations, solutions and cutting patterns. Working with two objectives in the fitness function and with a symbiotic relationship between the two populations, we obtained positive results when compared with other methods described in the literature.<br>Neste trabalho desenvolvemos um algoritmo genético simbiótico com objetivo de minimizar o número de objetos processados e o setup num problema de corte unidimensional. Nosso algoritmo genético gera seus próprios padrões em conjunto com soluções para o problema, através de um processo simbiótico entre duas populações distintas, a de soluções e a de padrões. Trabalhando com os dois objetivos na função de aptidão e com a relação simbiótica entre as duas populações, obtivemos resultados competitivos em relação aos métodos descritos na literatura