1,502,866 research outputs found

    Genetic Polymorphisms of the Coding Region (Exon 6) of Calpastatin in Indonesian Sheep

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    Calpastatin (CAST) is an indigenous inhibitor of calpain that involved in regulation of protein turn over and growth. The objective of this research was to identify genetic polymorphisms in the entire exon 6 of calpastatin gene in Indonesian local sheep. A PCR-SSCP method was carried out to identify genetic variation of CAST gene. In total 258 heads of local sheep from 8 populations were investigated, three groups of samples were Thin Tail Sheep (TTS) from Sukabumi, Jonggol, and Kissar. The rest samples were Priangan sheep (PS) from Margawati (Garut meat type) and Wanaraja (Garut fighting type) and Fat Tail Sheep (FTS) from Donggala, Sumbawa, and Rote islands. SSCP analysis revealed that three different SSCP patterns corresponded to three different alleles in the CAST locus (CAST-1, 2, and 3 allele) with five different genotypes. Genetic variation between local sheep populations were calculated based on genotypic and allelic frequencies. Most populations studied were polymorphic, with genotype frequencies of CAST-11, CAST-12, CAST-22, CAST-32, and CAST-33 were 0.286, 0.395, 0.263, 0.046, and 0.007 respectively. CAST-1 and 2 alleles were most commonly found in all populations with total frequency was 0.970, while CAST-3 was a rare allele 0.030 and only found in TTS population. Variation in the CAST gene could be used for the next research as genetic diversity study or to find any association between CAST polymorphism with birth weight, growth trait and carcass quality in Indonesian local sheep

    Coding-theorem Like Behaviour and Emergence of the Universal Distribution from Resource-bounded Algorithmic Probability

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    Previously referred to as `miraculous' in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60\% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution.Comment: 27 pages main text, 39 pages including supplement. Online complexity calculator: http://complexitycalculator.com

    From Instantly Decodable to Random Linear Network Coding

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    Our primary goal in this paper is to traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC) in terms of throughput and decoding delay. We first redefine the concept of packet generation and use it to partition a block of partially-received data packets in a novel way, based on the coding sets in an IDNC solution. By varying the generation size, we obtain a general coding framework which consists of a series of coding schemes, with RLNC and IDNC identified as two extreme cases. We then prove that the throughput and decoding delay performance of all coding schemes in this coding framework are bounded between the performance of RLNC and IDNC and hence throughput-delay tradeoff becomes possible. We also propose implementations of this coding framework to further improve its throughput and decoding delay performance, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Extensive simulations are then provided to verify the performance of the proposed coding schemes and their implementations.Comment: 30 pages with double space, 14 color figure

    Coding for Errors and Erasures in Random Network Coding

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    The problem of error-control in random linear network coding is considered. A ``noncoherent'' or ``channel oblivious'' model is assumed where neither transmitter nor receiver is assumed to have knowledge of the channel transfer characteristic. Motivated by the property that linear network coding is vector-space preserving, information transmission is modelled as the injection into the network of a basis for a vector space VV and the collection by the receiver of a basis for a vector space UU. A metric on the projective geometry associated with the packet space is introduced, and it is shown that a minimum distance decoder for this metric achieves correct decoding if the dimension of the space V∩UV \cap U is sufficiently large. If the dimension of each codeword is restricted to a fixed integer, the code forms a subset of a finite-field Grassmannian, or, equivalently, a subset of the vertices of the corresponding Grassmann graph. Sphere-packing and sphere-covering bounds as well as a generalization of the Singleton bound are provided for such codes. Finally, a Reed-Solomon-like code construction, related to Gabidulin's construction of maximum rank-distance codes, is described and a Sudan-style ``list-1'' minimum distance decoding algorithm is provided.Comment: This revised paper contains some minor changes and clarification
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