958,678 research outputs found

    Mutation testing from probabilistic finite state machines

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    Mutation testing traditionally involves mutating a program in order to produce a set of mutants and using these mutants in order to either estimate the effectiveness of a test suite or to drive test generation. Recently, however, this approach has been applied to specifications such as those written as finite state machines. This paper extends mutation testing to finite state machine models in which transitions have associated probabilities. The paper describes several ways of mutating a probabilistic finite state machine (PFSM) and shows how test sequences that distinguish between a PFSM and its mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected

    Silting mutation in triangulated categories

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    In representation theory of algebras the notion of `mutation' often plays important roles, and two cases are well known, i.e. `cluster tilting mutation' and `exceptional mutation'. In this paper we focus on `tilting mutation', which has a disadvantage that it is often impossible, i.e. some of summands of a tilting object can not be replaced to get a new tilting object. The aim of this paper is to take away this disadvantage by introducing `silting mutation' for silting objects as a generalization of `tilting mutation'. We shall develope a basic theory of silting mutation. In particular, we introduce a partial order on the set of silting objects and establish the relationship with `silting mutation' by generalizing the theory of Riedtmann-Schofield and Happel-Unger. We show that iterated silting mutation act transitively on the set of silting objects for local, hereditary or canonical algebras. Finally we give a bijection between silting subcategories and certain t-structures.Comment: 29 page

    DNA-dependent protein kinase: Epigenetic alterations and the role in genomic stability of cancer

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    DNA-dependent protein kinase (DNA-PK), a member of phosphatidylinositol-kinase family, is a key protein in mammalian DNA double-strand break (DSB) repair that helps to maintain genomic integrity. DNA-PK also plays a central role in immune cell development and protects telomerase during cellular aging. Epigenetic deregulation due to endogenous and exogenous factors may affect the normal function of DNA-PK, which in turn could impair DNA repair and contribute to genomic instability. Recent studies implicate a role for epigenetics in the regulation of DNA-PK expression in normal and cancer cells, which may impact cancer progression and metastasis as well as provide opportunities for treatment and use of DNA-PK as a novel cancer biomarker. In addition, several small molecules and biological agents have been recently identified that can inhibit DNA-PK function or expression, and thus hold promise for cancer treatments. This review discusses the impact of epigenetic alterations and the expression of DNA-PK in relation to the DNA repair mechanisms with a focus on its differential levels in normal and cancer cells

    The genetic equidistance result of molecular evolution is independent of mutation rates

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    The well-established genetic equidistance result shows that sister species are approximately equidistant to a simpler outgroup as measured by DNA or protein dissimilarity. The equidistance result is the most direct evidence, and remains the only evidence, for the constant mutation rate interpretation of this result, known as the molecular clock. However, data independent of the equidistance result have steadily accumulated in recent years that often violate a constant mutation rate. Many have automatically inferred non-equidistance whenever a non-constant mutation rate was observed, based on the unproven assumption that the equidistance result is an outcome of constant mutation rate. Here it is shown that the equidistance result remains valid even when different species can be independently shown to have different mutation rates. A random sampling of 50 proteins shows that nearly all proteins display the equidistance result despite the fact that many proteins have non-constant mutation rates. Therefore, the genetic equidistance result does not necessarily mean a constant mutation rate. Observations of different mutation rates do not invalidate the genetic equidistance result. New ideas are needed to explain the genetic equidistance result that must grant different mutation rates to different species and must be independently testable

    Use of the q-Gaussian mutation in evolutionary algorithms

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    Copyright @ Springer-Verlag 2010.This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.This work was supported in part by FAPESP and CNPq in Brazil and in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant EP/E060722/1 and Grant EP/E060722/2

    Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation

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    This paper presents a generalized hybrid evolutionary optimization structure that not only combines both nondeterministic and deterministic algorithms on their individual merits and distinct advantages, but also offers behaviors of the three originating classes of evolutionary algorithms (EAs). In addition, a robust mutation operator is developed in place of the necessity of mutation adaptation, based on the mutation properties of binary-coded individuals in a genetic algorithm. The behaviour of this mutation operator is examined in full and its performance is compared with adaptive mutations. The results show that the new mutation operator outperforms adaptive mutation operators while reducing complications of extra adaptive parameters in an EA representation
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