Analysis Of The Watershed Algorithms Based On The Breadth-first And Depth-first Exploring Methods

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)In this paper, fifteen watershed algorithms are reviewed. For clarity, first we expose two graph exploring methods modified to be guidelines for understanding the approaches taken by these algorithms: the breadth-first watershed and the depth-first watershed. Both paradigms rely on the visiting order applied by the algorithms. The breadth-first is more recognisable as a seed region growing or marker expansion process, grouping both methods based on flooding and hierarchical queue. The depth-first groups the algorithms based on the drop of water simulation, forming a simple path until a regional minimum is found. We analyse and classify fifteen algorithms, and two of them were better characterised. Along with this, some useful information (i.e. use of markers and line over pixel) is organised, in order to facilitate the choice of an algorithm. © 2009 IEEE.133140Petrobras,CNPq,CAPES,INCTMat,FAPERJConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Digabel, H., Lantuéjoul, C., Iterative algorithms (1978) Proc. Second European Symp. Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, pp. 85-99. , J.-L. Chermant, Ed. Stuttgart, Germany: Riederer VerlagBeucher, S., Lantuéjoul, C., Use of watersheds in contour detection (1979) International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation, , Rennes, France, SeptemberMeyer, F., Beucher, S., Morphological segmentation (1990) Journal of Visual Communication and Image Representation, 1 (1), pp. 21-46. , SeptemberR. Audigier and R. A. Lotufo, Watershed by image foresting transform, tie-zone, and theoretical relationships with other watershed definitions, in ISMM'2007 Proceedings, 1, Universidade de São Paulo (USP). São José dos Campos: Instituto Nacional de Pesquisas Espaciais (INPE), October 2007Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C., (2001) Introduction to Algorithms, , 2nd ed. Cambridge, Massachusetts: The MIT PressVincent, L., Soille, P., Watersheds in digital spaces: An efficient algorithm based on immersion simulations (1991) IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (6), pp. 583-598S. Beucher and F. Meyer, Mathematical morphology in image processing, ser. Optical Engineering. New York: M. Dekker, 1993, ch. 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Adessowiki On-line Collaborative Scientific Programming Platform

Adessowiki (http://www.adessowiki.org) is a collaborative environment for development, documentation, teaching and knowledge repository of scientific computing algorithms. The system is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the page is rendered, incorporating the results as figures, texts and tables on the document. The execution of code at the server allows hardware and software centralization and access through a web browser. This combination of a collaborative wiki environment, central server and execution of code at rendering time enables a host of possible applications like, for example: a teaching environment, where students submit their reports and exercises on Adessowiki without needing to install special software; authoring of texts, papers and scientific computing books, where figures are generated in a reproducible way by programs written by the authors; comparison of solutions and benchmarking of algorithms given that all the programs are executed under the same configuration; creation of an encyclopedia of algorithms and executable source code. Adessowiki is an environment that carries simultaneously documentation, programming code and results of its execution without any software configuration such as compilers, libraries and special tools at the client side. Copyright © 2009 ACM.John Ernest FoundationKnuth, D.E., Literate Programming (1984) The Computer Journal, 27 (2), pp. 97-111Walsh, N., Literate Programming in XML, , http://nwalsh.com/docs/articles/xml2002, Oct. 2002J. B. Buckheit, D. L. Donoho. WaveLab and Reproducible Research. Dept. of Statistics, Stanford University, Tech. Rep. 474, 1995. http://wwwstat. stanford.edu/ donoho/Reports/1995/wavelab.pdfO'Reilly, T., What is Web 2.0 O'Reilly Media, , http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html, Sep. 2005Xiao, W., Chi, C.Y., Yan, M., On-line Collaborative Software Development via Wiki (2007) Proceedings of the 2007 International Symposium on Wikis, pp. 177-183. , ACM, San Diego, California, USAAguiar, A., David, G., WikiWiki Weaving Heterogeneous Software Artifacts (2005) Proceedings of the 2005 International Symposium on Wikis, pp. 67-74. , ACM, San Diego, California, USAMader, S., (2007) Wikipatterns, , Wiley, December 10Adrian Holovaty, Jacob Kaplan-Moss. The Definitive Guide to Django: Web Development Done Right. Apress. December 6, 2007Parker, K.R., Chao, J.T., Wiki as a Teaching Tool (2007) Interdisciplinary Journal of Knowledge and Learning Objects, 3, pp. 58-72Lotufo, R.A., Interactive DSP Course Development/Teaching Environment (1997) 1997/IEEE Intern. Conf. on Acoustics, Speech and Signal Processing, 1997, Munich. Proc. of 1997/IEEE Intern. Conf. on Acoustics, Speech and Signal Processing, 3, pp. 2249-2252R. A. Lotufo, Ramiro Jordan, John Rasure. Teaching Image Processing with Khoros. In: First IEEE International Conference on Image Processing, 1994, Austin, Texas. Proc. of First IEEE International Conference on Image Processing, 1994. v. S/N. p. 506-510Lotufo, R.A., Audigier, R., Saúde, A.V., Machado, R.C., Morphological Image Processing (2008) Microscope Image Processing, pp. 113-158. , Qiang WuFatima A. MerchantKenneth R. Castleman, Editors, 1 ed, Elsevier Academic PressR. C. Machado. \Adesso: Ambiente para Desenvolvimento de Software Científico. Dissertação de Mestrado, Faculdade de Engenharia Elétrica e Computação, UNICAMP, Brasil, Junho de 2002Machado, R.C., Lotufo, R.A., Silva, A.G., Saúde, A.V., Adesso: Scientific Software Development Environment (2003) Journal of Computer Science and Technology, , AprilA. G. Silva, R. A. Lotufo, R. C. Machado, A. V. Saúde. Toolbox of Image Processing Using the Python Language. In: ICIP 2003 - IEEE International Conference on Image Processing, 2003, Barcelona. IEEE Proceeding of ICIP 2003, p. 1049-1052Edward R. Dougherty, Roberto A. Lotufo. Hands-on Morphological Image Processing. SPIE Publications. July 24, 200

Identifying MicroRNAs and transcript targets in Jartropha seeds

MicroRNAs, or miRNAs, are endogenously encoded small RNAs that play a key role in diverse plant biological processes. Jatropha curcas L. has received significant attention as a potential oilseed crop for the production of renewable oil. Here, a sRNA library of mature seeds and three mRNA libraries from three different seed development stages were generated by deep sequencing to identify and characterize the miRNAs and pre-miRNAs of J. curcas. Computational analysis was used for the identification of 180 conserved miRNAs and 41 precursors (pre-miRNAs) as well as 16 novel pre-miRNAs. The predicted miRNA target genes are involved in a broad range of physiological functions, including cellular structure, nuclear function, translation, transport, hormone synthesis, defense, and lipid metabolism. Some pre-miRNA and miRNA targets vary in abundance between the three stages of seed development. A search for sequences that produce siRNA was performed, and the results indicated that J. curcas siRNAs play a role in nuclear functions, transport, catalytic processes and disease resistance. This study presents the first large scale identification of J. curcas miRNAs and their targets in mature seeds based on deep sequencing, and it contributes to a functional understanding of these miRNAs

Identifying MicroRNAs and transcript targets in Jartropha seeds

MicroRNAs, or miRNAs, are endogenously encoded small RNAs that play a key role in diverse plant biological processes. Jatropha curcas L. has received significant attention as a potential oilseed crop for the production of renewable oil. Here, a sRNA library of mature seeds and three mRNA libraries from three different seed development stages were generated by deep sequencing to identify and characterize the miRNAs and pre-miRNAs of J. curcas. Computational analysis was used for the identification of 180 conserved miRNAs and 41 precursors (pre-miRNAs) as well as 16 novel pre-miRNAs. The predicted miRNA target genes are involved in a broad range of physiological functions, including cellular structure, nuclear function, translation, transport, hormone synthesis, defense, and lipid metabolism. Some pre-miRNA and miRNA targets vary in abundance between the three stages of seed development. A search for sequences that produce siRNA was performed, and the results indicated that J. curcas siRNAs play a role in nuclear functions, transport, catalytic processes and disease resistance. This study presents the first large scale identification of J. curcas miRNAs and their targets in mature seeds based on deep sequencing, and it contributes to a functional understanding of these miRNAs