25 research outputs found

    A Novel Gaussian Extrapolation Approach for 2D Gel Electrophoresis Saturated Protein Spots

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    Analysis of images obtained from two-dimensional gel electrophoresis (2D-GE) is a topic of utmost importance in bioinformatics research, since commercial and academic software available currently has proven to be neither completely effective nor fully automatic, often requiring manual revision and refinement of computer generated matches. In this work, we present an effective technique for the detection and the reconstruction of over-saturated protein spots. Firstly, the algorithm reveals overexposed areas, where spots may be truncated, and plateau regions caused by smeared and overlapping spots. Next, it reconstructs the correct distribution of pixel values in these overexposed areas and plateau regions, using a two-dimensional least-squares fitting based on a generalized Gaussian distribution. Pixel correction in saturated and smeared spots allows more accurate quantification, providing more reliable image analysis results. The method is validated for processing highly exposed 2D-GE images, comparing reconstructed spots with the corresponding non-saturated image, demonstrating that the algorithm enables correct spot quantificatio

    Determination of porcine oocyte and follicular fluid proteins from small, medium, and large follicles for cell biotechnology research

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    Porcine oocytes from small, medium, and large follicles can be classified into five types. Type I (intact-cumulus oocytes) and type II oocytes (multi-cumulus cell layers surrounding oocytes) have high potential to be developed into in vitromatured oocytes. Determination of porcine follicular fluid (pFF) proteins from 3 follicle sizes by SDS-PAGE and LC/MS/MS, 6 protein bands sizes 52, 65, 79, 90, 160, and >220 kDa were identified as immunoglobulin gamma chain, keratin, porcine inhibitor of carbonic anhydrase, heat shock protein or plasminogen precursor or both, transthyretin, and protease. All proteins were reported for their important roles in promotion and regulation on growth and development of reproductive cells except for the protein band at 79 and 160 kDa which had diminished function in the first metaphase of oocyte. This study provides knowledge on the oocyte and pFF proteins as biological models and in vitro cell maturation supplements in biotechnology research

    A novel neural network approach to cDNA microarray image segmentation

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    This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(ÂŪ) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

    November 10, 2004 17:50 WSPC/Instructions for Typesetting Manuscripts bioj A MICROARRAY IMAGE ANALYSIS SYSTEM BASED ON MULTIPLE-SNAKE

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    Microarray technology is a powerful tool that allows scientists to study expression levels for thousands of genes simultaneously. The technology has been useful in many applications, e.g., disease diagnosis, drug discovery, and gene functional study. In this paper, we present a microarray image analysis system which works specifically on nylon membrane microarray. These membrane microarray images have problems that are different from glass slide microarray images. Some of the problems are that spot sizes are very small due to the low image resolution, spots could be merged into one another, images could be noisy, and that spots could occur in various sizes. The system has been developed to handle (i) automatic image alignment and gridding, (ii) spot contour detection, and (iii) intensity measurement. The alignment and gridding system is automated with possible gridding provided for microarray images. In spot contour detection, we apply the multiple-snake method, which is the high-level segmentation method, to automatically extract the contours of multiple spots. In intensity measurement, different ways to estimate the intensity are used and compared. In the experiments, various designs of microarray images have been tested. The reliability of the system is determined by comparing the results of duplicated pairs of spots. We tested robustness of the system with a set of noisy microarray images at different percentages of Gaussian noise. We also tested the system with glass slide microarray images, and the results are very encouraging

    A MICROARRAY IMAGE ANALYSIS SYSTEM BASED ON MULTIPLE-SNAKEÂĢ

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    We developed a microarray image analysis system which works specifically on nylon membrane microarray. Our system can handle (i) automatic image alignment and gridding, (ii) spot contour detection, and (iii) intensity measurement. The alignment and gridding system is automated with possible gridding provided for microarray images. In spot contour detection, we apply the multiple-snake method, which is the high-level segmentation method, to automatically extract the contours of multiple spots. We tested the system on various designs of microarray images, and we show how the spot intensity is computed. The reliability of the system is determined by comparing the results of duplicated pairs of spots. We also tested the system with glass slide microarray images, and the results are very encouraging. 1

    Willingness to Pay for Sponge City Project Initiatives in Bangkok

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    Urban flooding crisis in Bangkok plays an important role in the lives of people. Sponge City is a concept to alleviate the problem. The paper aims to estimate the willingness to pay of people in Bangkok to initiate a Sponge City project. According to a study of 300 respondents by the Contingent Valuation Method (CVM). The results show that the average of willingness to pay for Sponge city per person is 145 – 158 baht. Furthermore, the factors that contributed to the willingness to pay for the project were the initial bid and the opinion on the future flooding problem. The results indicate that the socio-economic characteristics including anxiety is important in carrying out activities for people to participate. Therefore, to enhance people participation in the Sponge city, the project should start during the period of economic expansion or launch in the areas with high demand or good economic zones. As well, policy makers should promote the project through various channels and should promote to make people aware of the problem.---āļ›āļąāļāļŦāļēāļ™āđ‰āļēāļ—āđˆāļ§āļĄāđƒāļ™āļāļĢāļļāļ‡āđ€āļ—āļžāļĄāļŦāļēāļ™āļ„āļĢāļĄāļĩāļšāļ—āļšāļēāļ—āļŠāļēāļ„āļąāļāđƒāļ™āļāļēāļĢāļ”āļēāđ€āļ™āļīāļ™āļŠāļĩāļ§āļīāļ•āļ‚āļ­āļ‡āļ„āļ™āđƒāļ™āļŠāļąāļ‡āļ„āļĄ āļāļēāļĢāļāđˆāļ­āļŠāļĢāđ‰āļēāļ‡āđ€āļĄāļ·āļ­āļ‡āļŸāļ­āļ‡āļ™āđ‰āļēāļˆāļķāļ‡āđ€āļ›āđ‡āļ™āđāļ™āļ§āļ„āļīāļ”āđ€āļžāļ·āđˆāļ­āļšāļĢāļĢāđ€āļ—āļēāļœāļĨāļāļĢāļ°āļ—āļšāļ—āļĩāđˆāļ­āļēāļˆāļˆāļ°āđ€āļāļīāļ”āđ„āļ”āđ‰ āļ‹āļķāđˆāļ‡āļ›āļĢāļ°āļŠāļēāļŠāļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļˆāļ°āļĄāļĩāļŠāđˆāļ§āļ™āđƒāļ™āļāļēāļĢāđ„āļ”āđ‰āļĢāļąāļšāļ›āļĢāļ°āđ‚āļĒāļŠāļ™āđŒāđ‚āļ”āļĒāļ•āļĢāļ‡ āļšāļ—āļ„āļ§āļēāļĄāļ™āļĩāđ‰āļĄāļĩāļ§āļąāļ•āļ–āļļāļ›āļĢāļ°āļŠāļ‡āļ„āđŒāđ€āļžāļ·āđˆāļ­āļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒāļāļēāļĢāļĢāļąāļšāļĢāļđāđ‰āđāļĨāļ°āļ„āļ§āļēāļĄāđ€āļ•āđ‡āļĄāđƒāļˆāļˆāđˆāļēāļĒāļ‚āļ­āļ‡āļ›āļĢāļ°āļŠāļēāļŠāļ™āđƒāļ™āļāļĢāļļāļ‡āđ€āļ—āļžāļĄāļŦāļēāļ™āļ„āļĢāļ•āđˆāļ­āļāļēāļĢāļĢāļīāđ€āļĢāļīāđˆāļĄāđ‚āļ„āļĢāļ‡āļāļēāļĢāđ€āļĄāļ·āļ­āļ‡āļŸāļ­āļ‡āļ™āđ‰āļē āđ‚āļ”āļĒāļœāļĨāļāļēāļĢāļĻāļķāļāļĐāļēāļˆāļēāļāļāļĨāļļāđˆāļĄāļ•āļąāļ§āļ­āļĒāđˆāļēāļ‡ 300 āļĢāļēāļĒ āļ”āđ‰āļ§āļĒāļ§āļīāļ˜āļĩāļŠāļĄāļĄāļ•āļīāđ€āļŦāļ•āļļāļāļēāļĢāļ“āđŒāđƒāļŦāđ‰āļ›āļĢāļ°āđ€āļĄāļīāļ™āļ„āđˆāļē (Contingent Valuation Method: CVM) āļžāļšāļ§āđˆāļēāļ„āđˆāļēāđ€āļ‰āļĨāļĩāđˆāļĒāļ‚āļ­āļ‡āļ„āļ§āļēāļĄāđ€āļ•āđ‡āļĄāđƒāļˆāļ—āļĩāđˆāļˆāļ°āļˆāđˆāļēāļĒāđ€āļ‡āļīāļ™āļšāļĢāļīāļˆāļēāļ„āđƒāļ™āļāļēāļĢāļ”āļēāđ€āļ™āļīāļ™āđ‚āļ„āļĢāļ‡āļāļēāļĢāđ€āļĄāļ·āļ­āļ‡āļŸāļ­āļ‡āļ™āđ‰āļēāļ•āđˆāļ­āļĢāļēāļĒāļ­āļĒāļđāđˆāļ—āļĩāđˆ 145 – 158 āļšāļēāļ— āđ‚āļ”āļĒāļ›āļąāļˆāļˆāļąāļĒāļ—āļĩāđˆāļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāđ€āļ•āđ‡āļĄāđƒāļˆāļˆāđˆāļēāļĒāđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āđ€āļāļīāļ”āđ‚āļ„āļĢāļ‡āļāļēāļĢ āļ„āļ·āļ­ āļĢāļēāļ„āļēāļ—āļĩāđˆāļ™āļēāđ€āļŠāļ™āļ­āđ€āļĢāļīāđˆāļĄāļ•āđ‰āļ™ āđāļĨāļ°āļ„āļ§āļēāļĄāļāļąāļ‡āļ§āļĨāļ•āđˆāļ­āđ€āļŦāļ•āļļāļāļēāļĢāļ“āđŒāļ™āđ‰āļēāļ—āđˆāļ§āļĄāđƒāļ™āļ­āļ™āļēāļ„āļ• āļœāļĨāļāļēāļĢāļ§āļīāļˆāļąāļĒāļŠāļĩāđ‰āđƒāļŦāđ‰āđ€āļŦāđ‡āļ™āļ§āđˆāļēāļĨāļąāļāļĐāļ“āļ°āļ—āļēāļ‡āđ€āļĻāļĢāļĐāļāļāļīāļˆāđāļĨāļ°āļŠāļąāļ‡āļ„āļĄ āļĢāļ§āļĄāļ–āļķāļ‡āļ„āļ§āļēāļĄāļ§āļīāļ•āļāļāļąāļ‡āļ§āļĨāļĄāļĩāļ„āļ§āļēāļĄāļŠāļēāļ„āļąāļāđƒāļ™āļāļēāļĢāļ”āļēāđ€āļ™āļīāļ™āļāļīāļˆāļāļĢāļĢāļĄ āđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āļ›āļĢāļ°āļŠāļēāļŠāļ™āļĄāļĩāļŠāđˆāļ§āļ™āļĢāđˆāļ§āļĄ āļ āļēāļ„āļĢāļąāļāļŦāļĢāļ·āļ­āļŦāļ™āđˆāļ§āļĒāļ‡āļēāļ™āļ—āļĩāđˆāđ€āļāļĩāđˆāļĒāļ§āļ‚āđ‰āļ­āļ‡āļ„āļ§āļĢāđ€āļĢāļīāđˆāļĄāļˆāļēāļāļāļēāļĢāļ§āļēāļ‡āđāļ™āļ§āļ™āđ‚āļĒāļšāļēāļĒāđƒāļ™āļāļēāļĢāļ—āļēāđ‚āļ„āļĢāļ‡āļāļēāļĢāđƒāļ™āļŠāđˆāļ§āļ‡āļ—āļĩāđˆāļĄāļĩāļāļēāļĢāļ‚āļĒāļēāļĒāļ•āļąāļ§āļ—āļēāļ‡āļ”āđ‰āļēāļ™āđ€āļĻāļĢāļĐāļāļāļīāļˆ āļŦāļĢāļ·āļ­āđ€āļĢāļīāđˆāļĄāļ•āđ‰āļ™āđƒāļ™āļžāļ·āđ‰āļ™āļ—āļĩāđˆāļ—āļĩāđˆāļĄāļĩāļĨāļąāļāļĐāļ“āļ°āļ‚āļ­āļ‡āđ€āļĻāļĢāļĐāļāļāļīāļˆāļ—āļĩāđˆāļ”āļĩ āļ•āļĨāļ­āļ”āļˆāļ™āļāļēāļĢāļ›āļĢāļ°āļŠāļēāļŠāļąāļĄāļžāļąāļ™āļ˜āđŒāđ‚āļ„āļĢāļ‡āļāļēāļĢāļœāđˆāļēāļ™āļŠāđˆāļ­āļ‡āļ—āļēāļ‡āļ•āđˆāļēāļ‡āđ† āļ„āļ§āļĢāđƒāļŦāđ‰āļĄāļĩāļāļēāļĢāđāļ—āļĢāļāđ€āļ™āļ·āđ‰āļ­āļŦāļēāđƒāļ™āđ€āļŠāļīāļ‡āļœāļĨāļāļĢāļ°āļ—āļšāļ—āļĩāđˆāļŠāļēāļ„āļąāļ āđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āļ›āļĢāļ°āļŠāļēāļŠāļ™āđ€āļāļīāļ”āļ„āļ§āļēāļĄāļ•āļĢāļ°āļŦāļ™āļąāļāļ•āđˆāļ­āļ›āļąāļāļŦāļē āļ­āļąāļ™āļˆāļ°āļŠāđˆāļ‡āļœāļĨāļ•āđˆāļ­āļ„āļ§āļēāļĄāđ€āļŠāļ·āđˆāļ­āļĄāļąāđˆāļ™āđƒāļ™āđ‚āļ„āļĢāļ‡āļāļēāļĢāļ—āļĩāđˆāļˆāļ°āđ€āļāļīāļ”āļ‚āļķāđ‰āļ™āđƒāļ™āļ­āļ™āļēāļ„
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