A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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.Aleppo University, Syri

Enhancement of DNA microarray images using mathematical morphological image processing

DNA microarray images contain spots that represent the gene expression of normal and cancer samples. As there are numerous spots on DNA microarray images, image processing can help in enhancing an image and assisting analysis. The mathematical morphology is proposed to enhance the microarray image and analyse noise removal on the image. This follows an experiment in which the erosion, dilation, opening, closing, white top-hat (WTH) and black top-hat (BTH) operations were applied on a DNA microarray image and its results analysed. Noise was completely removed by the erosion operation and the images were enhanced

Phosphodiesterase Inhibition and Adenosine A2B Receptor Signaling in Mitochondrial Function: Pharmacology and Toxicology of 3-isobutyl 1-methylxanthine (IBMX) in the 661w Retina-Derived Cell Line

3-isobutyl 1-methylxanthine (IBMX) is a commonly used inhibitor of cyclic nucleotide phosphodiesterases (PDE) and G protein coupled adenosine receptors. Administration of IBMX to retina photoreceptor-derived 661w cell line results in decreased mitochondrial respiration followed by cell death, mimicking phenomena observed in inherited retina neurodegeneration in humans and animals and thus suggesting their use as a cell-based model of retina degeneration. In this dissertation, the temporal relationship between pathways altered by IBMX and how these pathways affect mitochondrial physiology are evaluated. These studies utilize a novel high content microscopic method for simultaneously measuring mitochondrial morphology and membrane potential in living cultured cells to identify an increase in networked morphology (pro-fusion) and mitochondrial membrane potential within 1-4 h of IBMX administration that is sustained and accompanied by decreased uncoupled respiration at 24 h. cGMP PDE inhibition was observed to recapitulate both the immediate and sustained physiological alterations as selective inhibitors of PDE3 and PDE5 increased mitochondrial membrane potential and interconnected morphology at 1 h which was followed by a decrease in mitochondrial respiration at 24 h similar to that elicited by IBMX. Administration of selective inverse agonist of adenosine A2B receptor recapitulated the latent (24 h), but not early (1 h) IBMX-induced alterations in mitochondrial physiology. Administration of a selective agonist and antagonist prior to IBMX abrogated latent but not early IBMX-induced alterations. A similar pattern of reductions in latent but not early alterations were achieved by activating adenylate cyclase or protein kinase C. Inhibition of protein kinase A resulted in increased mitochondrial membrane potential at 1 h and 24 h. The concomitant increase of mitochondrial membrane potential with decreased respiration suggested decreased permeability of the mitochondrial inner membrane to inward proton flux, perhaps due to lack of ADP availability or decreased mitochondrial Complex V activity. Inhibiting mitochondrial permeability transition pore (mPTP) regulator GSK-3β prior to IBMX administration prevented increases in both membrane potential and networked morphology, suggesting GSK-3β activation follows IBMX. Taken together, this work demonstrates that IBMX first increases cGMP via inhibition of PDEs at 1 h to modulate a rise in mitochondrial membrane potential and networked morphology, followed by inverse agonism at the adenosine A2B receptor at 24 h. Downstream pathways invoked at 24 h include decreased adenylate cyclase and PKA signaling, as well as PKC. GSK-3β activation following loss in PKA and PKC activity may integrate these PDE- and adenosine A2B receptor-dependent signals

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception

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

Clinical trials of MRS methods

In order to determine the applicability of noninvasive magnetic resonance spectroscopy (MRS) to the study of a diseased tissue or organ in the human body, it is necessary to determine if MRS is safe and effective. This is the primary purpose of a clinical trial. A clinical trial for MRS may also reveal which technical approach works best for the specific application and characteristics of the population being studied. In this chapter, we discuss the legal, ethical, and scientific requirements to be considered prior to the start of a clinical trial of an MRS protocol, as well as constraints that may arise during its execution. MRS-specific issues arising from a couple of successful clinical MRS trials for classifying brain tumors with 1H MRS (INTERPRET and eTUMOUR) and body tumors with 31P MRS (the Cooperative Group on MRS Application in Cancer, CoGMAC), serve as illustrative examples.JRG thanks The University of Cambridge, CRUK [grant number C14303/A17197] and Hutchison Whampoa Limited. FAM thanks the National Cancer Institute (NIH) from the United States for their support through grants R01-CA118559 and R21-CA152858. FAM wish to thank Dr. Radka Stoyanova from the University of Miami for helpful contributions to the principal component analysis discussion. MJ is funded by SAF2014-52332-R from MINECO (ES) and CIBER-BBN (Centro de Investigación Biomédica en Red – Bioingeniería, Biomateriales y Nanomedicina [http://www.ciber-bbn.es/en]), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU FEDER funds.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/9780470034590.emrstm147

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field

Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on