Hardware Parallel Architecture of a 3D Surface Reconstruction: Marching Cubes Algorithm

International audienceIn this paper we present a study of the algorithmic and architectural exploration methodology for a parallelism of the 3D reconstructing algorithm (Marching Cubes) and its optimized implementation on FPGA.We aim at defining a parallel multiprocessor architecture implementing this algorithm in an optimal way and Elementary Processor (EP) architecture dedicated to this algorithm. We use the SynDEx tool which adapts the AAA (Algorithm Architecture Adequacy) methodology, to find a good compromise between the computing power, the functionality of each PE, the optimization constraint (time, area), and the parallelization efficiency. Then, we describe a first implementation of PE on FPGA

Nile Tilapia “Oreochromis niloticus” Farming in Fresh and Geothermal Waters in Tunisia: A Comparative Study

This work aims to compare the farming of Nile Tilapia Oreochromis niloticus in fresh and geothermal waters through monitoring the specie’s zootechnical parameters: growth, mortality and feed conversion rate. For geothermal water rearing, fish was placed in cages in Bechima Station, in southern Tunisia, while Smati Reservoir, in the center of the country was used for fresh water. The spawners were first adapted to geothermal waters in Bechima experimental station. Then, the broodstock phase lasted 60 days and allowed the obtainment of 1–2 g larvae. Fertility was important and varied between 451 and 1589 larvae/female, which is associated with the females’ total weight (F = 1.6 W2.1). In the pre-growing phase, the comparison of fry growth rates (weight 1.3 g) in the geothermal and freshwaters showed a small variation with recorded rates slightly in favor of fish bred in fresh water. During 50 days within the breeding phase, fish weight achieved in freshwater was more important reaching 12.7 g (TCJ = 0.228 g /day compared to 10.51 g (TCJ = 0.184 g/day) recorded in geothermal waters. Similarly, during the fattening phase, the weights gained after 30days demonstrated better growth rates for tilapia cultured in freshwater (up to 60 g) in contrast to that bred in geothermal water (35–40 g)

Breast Cancer Epigenetics: From DNA Methylation to microRNAs

Both appropriate DNA methylation and histone modifications play a crucial role in the maintenance of normal cell function and cellular identity. In cancerous cells these “epigenetic belts” become massively perturbed, leading to significant changes in expression profiles which confer advantage to the development of a malignant phenotype. DNA (cytosine-5)-methyltransferase 1 (Dnmt1), Dnmt3a and Dnmt3b are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells. Intriguingly, DNMTs were found to be overexpressed in cancerous cells, which is believed to partly explain the hypermethylation phenomenon commonly observed in tumors. However, several lines of evidence indicate that further layers of gene regulation are critical coordinators of DNMT expression, catalytic activity and target specificity. Splice variants of DNMT transcripts have been detected which seem to modulate methyltransferase activity. Also, the DNMT mRNA 3′UTR as well as the coding sequence harbors multiple binding sites for trans-acting factors guiding post-transcriptional regulation and transcript stabilization. Moreover, microRNAs targeting DNMT transcripts have recently been discovered in normal cells, yet expression of these microRNAs was found to be diminished in breast cancer tissues. In this review we summarize the current knowledge on mechanisms which potentially lead to the establishment of a DNA hypermethylome in cancer cells

A hybrid Evolutionary Functional Link Artificial Neural Network for Data mining and Classification

Abstract — This paper presents a specific structure of neural network as the functional link artificial neural network (FLANN). This technique has been employed for classification tasks of data mining. In fact, there are a few studies that used this tool for solving classification problems. In this present research, we propose a hybrid FLANN (HFLANN) model, where the optimization process is performed using 3 known population based techniques such as genetic algorithms, particle swarm and differential evolution. This model will be empirically compared to FLANN based back-propagation algorithm and to others classifiers as decision tree, multilayer perceptron based backpropagation algorithm, radical basic function, support vector machine, and K-nearest Neighbor. Our results proved that the proposed model outperforms the other single model. Keywords- component Data mining; Classification; Functional link artificial neural network; genetic algorithms; Particle swarm; Differential evolution. I

Etude algorithmique et conception d'architectures dédiées à la reconstruction 3D d'images médicales

electronic version (8 pp.