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  • 1
    Publication Date: 2010-03-17
    Description: The freshwater cnidarian Hydra was first described in 1702 and has been the object of study for 300 years. Experimental studies of Hydra between 1736 and 1744 culminated in the discovery of asexual reproduction of an animal by budding, the first description of regeneration in an animal, and successful transplantation of tissue between animals. Today, Hydra is an important model for studies of axial patterning, stem cell biology and regeneration. Here we report the genome of Hydra magnipapillata and compare it to the genomes of the anthozoan Nematostella vectensis and other animals. The Hydra genome has been shaped by bursts of transposable element expansion, horizontal gene transfer, trans-splicing, and simplification of gene structure and gene content that parallel simplification of the Hydra life cycle. We also report the sequence of the genome of a novel bacterium stably associated with H. magnipapillata. Comparisons of the Hydra genome to the genomes of other animals shed light on the evolution of epithelia, contractile tissues, developmentally regulated transcription factors, the Spemann-Mangold organizer, pluripotency genes and the neuromuscular junction.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479502/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479502/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Chapman, Jarrod A -- Kirkness, Ewen F -- Simakov, Oleg -- Hampson, Steven E -- Mitros, Therese -- Weinmaier, Thomas -- Rattei, Thomas -- Balasubramanian, Prakash G -- Borman, Jon -- Busam, Dana -- Disbennett, Kathryn -- Pfannkoch, Cynthia -- Sumin, Nadezhda -- Sutton, Granger G -- Viswanathan, Lakshmi Devi -- Walenz, Brian -- Goodstein, David M -- Hellsten, Uffe -- Kawashima, Takeshi -- Prochnik, Simon E -- Putnam, Nicholas H -- Shu, Shengquiang -- Blumberg, Bruce -- Dana, Catherine E -- Gee, Lydia -- Kibler, Dennis F -- Law, Lee -- Lindgens, Dirk -- Martinez, Daniel E -- Peng, Jisong -- Wigge, Philip A -- Bertulat, Bianca -- Guder, Corina -- Nakamura, Yukio -- Ozbek, Suat -- Watanabe, Hiroshi -- Khalturin, Konstantin -- Hemmrich, Georg -- Franke, Andre -- Augustin, Rene -- Fraune, Sebastian -- Hayakawa, Eisuke -- Hayakawa, Shiho -- Hirose, Mamiko -- Hwang, Jung Shan -- Ikeo, Kazuho -- Nishimiya-Fujisawa, Chiemi -- Ogura, Atshushi -- Takahashi, Toshio -- Steinmetz, Patrick R H -- Zhang, Xiaoming -- Aufschnaiter, Roland -- Eder, Marie-Kristin -- Gorny, Anne-Kathrin -- Salvenmoser, Willi -- Heimberg, Alysha M -- Wheeler, Benjamin M -- Peterson, Kevin J -- Bottger, Angelika -- Tischler, Patrick -- Wolf, Alexander -- Gojobori, Takashi -- Remington, Karin A -- Strausberg, Robert L -- Venter, J Craig -- Technau, Ulrich -- Hobmayer, Bert -- Bosch, Thomas C G -- Holstein, Thomas W -- Fujisawa, Toshitaka -- Bode, Hans R -- David, Charles N -- Rokhsar, Daniel S -- Steele, Robert E -- P 21108/Austrian Science Fund FWF/Austria -- R24 RR015088/RR/NCRR NIH HHS/ -- England -- Nature. 2010 Mar 25;464(7288):592-6. doi: 10.1038/nature08830. Epub 2010 Mar 14.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉US Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20228792" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Anthozoa/genetics ; Comamonadaceae/genetics ; DNA Transposable Elements/genetics ; Gene Transfer, Horizontal/genetics ; Genome/*genetics ; Genome, Bacterial/genetics ; Hydra/*genetics/microbiology/ultrastructure ; Molecular Sequence Data ; Neuromuscular Junction/ultrastructure
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 2
    Publication Date: 1987-08-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 3
    Publication Date: 1987-05-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 4
    Publication Date: 1986-09-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 5
    Publication Date: 1986-02-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 6
    Publication Date: 2004-12-07
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 7
    Publication Date: 1964-06-01
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Springer Nature
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 57 (1987), S. 57-71 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract A biologically plausible method for rapidly learning specific instances is described. It is contrasted with a formal model of classical conditioning (Rescorla-Wagner learning/perception training), which is shown to be relatively good for learning generalizations, but correspondingly poor for learning specific instances. A number of behaviorally relevant applications of specific instance learning are considered. For category learning, various combinations of specific instance learning and generalization are described and analyzed. Two general approaches are considered: the simple inclusion of Specific Instance Detectors (SIDs) as additional features during perception training, and specialized treatment in which SID-based categorization takes precedence over generalization-based categorization. Using the first approach, analysis and empirical results demonstrate a potential problem in representing feature presence and absence in a symmetric fashion when the frequencies of feature presence and absence are very different. However, it is shown that by using the proper representation, the addition of SIDs can only improve the convergence rate of perceptron training, the greatest improvement being achieved when SIDs are preferentially allocated for peripheral positive and negative instances. Some further improvement is possible if SIDs are treated in a specialized manner.
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 53 (1986), S. 203-217 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Three different representations for a thresholded linear equation are developed. For binary input they are shown to be representationally equivalent though their training characteristics differ. A training algorithm for linear equations is discussed. The similarities between its simplest mathematical representation (perceptron training), a formal model of animal learning (Rescorla-Wagner learning), and one mechanism of neural learning (Aplysia gill withdrawal) are pointed out. For d input features, perceptron training is shown to have a lower bound of 2 d and an upper bound of d d adjusts. It is possible that the true upper bound is 4 d , though this has not been proved. Average performance is shown to have a lower bound of 1.4 d . Learning time is shown to increase linearly with the number of irrelevant or replicated features. The (X of N) function (a subset of linearly separable functions containing OR and AND) is shown to be learnable in d 3 time. A method of utilizing conditional probability to accelerate learning is proposed. This reduces the observed growth rate from 4 d to the theoretical minimum (for the unmodified version) of 2 d . A different version reduces the growth rate to about 1.7 d . The linear effect of irrelevant features can also be eliminated. Whether such an approach can be made provably convergent is not known.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Biological cybernetics 54 (1986), S. 393-406 
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Several distinct connectionistic/neural representations capable of computing arbitrary Boolean functions are described and discussed in terms of possible tradeoffs between time, space, and expressive clarity. It is suggested that the ability of a threshold logic unit (TLU) to represent prototypical groupings has significant advantages for representing real world categories. Upper and lower bounds on the number of nodes needed for Boolean completeness are demonstrated. The necessary number of nodes is shown to increase exponentially with the number of input features, the exact rate of increase depending on the representation scheme. In addition, in non-recurrent networks, connection weights are shown to increase exponentially with a linear reduction in the number of nodes below approximately 2d. This result suggests that optimum memory efficiency may require unacceptable learning time. Finally, two possible extensions to deal with non-Boolean values are considered.
    Type of Medium: Electronic Resource
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