Restoration of oligodendrocyte pools in a mouse model of chronic cerebral hypoperfusion

Chronic cerebral hypoperfusion, a sustained modest reduction in cerebral blood flow, is associated with damage to myelinated axons and cognitive decline with ageing. Oligodendrocytes (the myelin producing cells) and their precursor cells (OPCs) may be vulnerable to the effects of hypoperfusion and in some forms of injury OPCs have the potential to respond and repair damage by increased proliferation and differentiation. Using a mouse model of cerebral hypoperfusion we have characterised the acute and long term responses of oligodendrocytes and OPCs to hypoperfusion in the corpus callosum. Following 3 days of hypoperfusion, numbers of OPCs and mature oligodendrocytes were significantly decreased compared to controls. However following 1 month of hypoperfusion, the OPC pool was restored and increased numbers of oligodendrocytes were observed. Assessment of proliferation using PCNA showed no significant differences between groups at either time point but showed reduced numbers of proliferating oligodendroglia at 3 days consistent with the loss of OPCs. Cumulative BrdU labelling experiments revealed higher numbers of proliferating cells in hypoperfused animals compared to controls and showed a proportion of these newly generated cells had differentiated into oligodendrocytes in a subset of animals. Expression of GPR17, a receptor important for the regulation of OPC differentiation following injury, was decreased following short term hypoperfusion. Despite changes to oligodendrocyte numbers there were no changes to the myelin sheath as revealed by ultrastructural assessment and fluoromyelin however axon-glial integrity was disrupted after both 3 days and 1 month hypoperfusion. Taken together, our results demonstrate the initial vulnerability of oligodendroglial pools to modest reductions in blood flow and highlight the regenerative capacity of these cells

Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

A simple two-module problem to exemplify building-block assembly under crossover

Theoretically and empirically it is clear that a genetic algorithm with crossover will outperform a genetic algorithm without crossover in some fitness landscapes, and vice versa in other landscapes. Despite an extensive literature on the subject, and recent proofs of a principled distinction in the abilities of crossover and non-crossover algorithms for a particular theoretical landscape, building general intuitions about when and why crossover performs well when it does is a different matter. In particular, the proposal that crossover might enable the assembly of good building-blocks has been difficult to verify despite many attempts at idealized building-block landscapes. Here we show the first example of a two-module problem that shows a principled advantage for cross-over. This allows us to understand building-block assembly under crossover quite straightforwardly and build intuition about more general landscape classes favoring crossover or disfavoring it

A New Metaheuristic Bat-Inspired Algorithm

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.Comment: 10 pages, 2 figure

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

Two-dimensional projections of an hypercube

We present a method to project a hypercube of arbitrary dimension on the plane, in such a way as to preserve, as well as possible, the distribution of distances between vertices. The method relies on a Montecarlo optimization procedure that minimizes the squared difference between distances in the plane and in the hypercube, appropriately weighted. The plane projections provide a convenient way of visualization for dynamical processes taking place on the hypercube.Comment: 4 pages, 3 figures, Revtex

Development of EM-CCD-based X-ray detector for synchrotron applications

A high speed, low noise camera system for crystallography and X-ray imaging applications is developed and successfully demonstrated. By coupling an electron-multiplying (EM)-CCD to a 3:1 fibre-optic taper and a CsI(Tl) scintillator, it was possible to detect hard X-rays. This novel approach to hard X-ray imaging takes advantage of sub-electron equivalent readout noise performance at high pixel readout frequencies of EM-CCD detectors with the increase in the imaging area that is offered through the use of a fibre-optic taper. Compared with the industry state of the art, based on CCD camera systems, a high frame rate for a full-frame readout (50 ms) and a lower readout noise (<1 electron root mean square) across a range of X-ray energies (6–18 keV) were achieved

Implementation of Standard Genetic Algorithm on MIMD machines

Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases the GA has been adapted to the hardware structure of the system. This paper describes the implementation of a standard genetic algorithm on several MIMD multiprocessor systems. It discusses the data dependencies of the different parts of the algorithm and the changes necessary to adapt the serial version to the parallel versions. Timing measurements and speedups are given for a common problem implemented on all machines

Measurement of Fluorescence Phenomena from Yttrium and Gadolinium Oxysulfide Phosphors using a 45-MeV Proton Beam

This research was sponsored by the National Science Foundation Grant NSF PHY-931478