Microarray image processing : a novel neural network framework

Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.EThOS - Electronic Theses Online ServiceAleppo University, SyriaGBUnited Kingdo

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

L'intertextualité dans les publications scientifiques

La base de données bibliographiques de l'IEEE contient un certain nombre de duplications avérées avec indication des originaux copiés. Ce corpus est utilisé pour tester une méthode d'attribution d'auteur. La combinaison de la distance intertextuelle avec la fenêtre glissante et diverses techniques de classification permet d'identifier ces duplications avec un risque d'erreur très faible. Cette expérience montre également que plusieurs facteurs brouillent l'identité de l'auteur scientifique, notamment des collectifs de chercheurs à géométrie variable et une forte dose d'intertextualité acceptée voire recherchée