Insulin receptor substrate 1 (IRS1), plays a critical role in insulin signaling and its control has an important place in the development of insulin resistance. The tyrosine phosphorylation of IRS1 serves as docking molecules for downstream effectors such as Phosphatidylinositol 3-kinase and phosphotyrosine phosphatase-2. We focused on the Gly972Arg and Ala513Pro variants of the IRS1 gene, since these specific allelic variants are located near the Tyr-Met-X-Met (YMXM) motifs around Tyr987 and Tyr612. Thus, we aimed to investigate the effects of Gly972Arg/Ala513Pro polymorphisms in IRS1 gene on development of insulin resistance and the risk of type 2 diabetes in a non-obese Turkish population. This work included 306 individuals comprising 178 subjects with type 2 diabetes and 128 healthy subjects matched for body mass index. Gly972Arg/Ala513Pro polymorphisms had no effect on type 2 diabetes risk and its phenotypes (P > 0.05). Although IRS1 gene and its variants are associated with type 2 diabetes and insulin resistance in several studies worldwide, our data showed that there is no association between Gly972Arg and Ala513Pro variants in IRS1 and disease in Turkish population.
Dynamic reliability analysis of a multi-state manufacturing system analiza Dynamicznej niezawoDności wielostanowego systemu proDukcyjnego Dynamic reliability analysis of binary systems has been widely studied in case of homogeneous continuous time Markov process assumption in the literature. In this study, we evaluate dynamic performance of a multi-state rotor line of electric motors manufacturing system under non-homogeneous continuous time Markov process (NHCTMP) degradation by using lifetime distributions of seven workstations within the system. By means of this degradation process assumption we capture the effect of age on the state change of components in the analysis by means of time dependent transition rates between states of the workstations. Essentially this is typical of many systems and more practical to use in real life applications. The working principle is based on a three state structure. If all the machines within each workstation work, the workstation is defined as working with the full performance. Whenever at least one machine fails within each workstation, then the workstation is defined as working with partial performance. If all the machines in the workstation fail then the workstation is defined as failed. The lifetime properties of the workstations under NHCTMP assumption have been studied for this three-state structure of the workstations. The workstations are all working independently and nonidentically from each other and they are connected in series within the system.We especially performed an extensive application study based on the lifetime data regarding the seven workstations within a manufacturing system. Dynamic reliability results are also discussed for the system structure. Some performance characteristics are developed for both workstations and the system as well. Numerical results for the performance characteristics of those workstations and the system are provided and supported with some graphical illustrations.
Dynamic reliability measures are important characteristics for understanding the lifetime behaviour of multi-state systems. This study aims to analyze the mean residual lifetime (MRL) function for a one-unit three-state system.The system considered has just three states such as “0, 1, 2”. State “2” and “1” signify the perfect functioning and partially working states, respectively while state “0” is the failure state. We let [Formula: see text] and [Formula: see text] be the lifetimes of the system spent at state “1” and “2,” respectively. We assess a particular performance characteristic, MRL, of this three-state system via conditional survival functions, when [Formula: see text] and [Formula: see text] are both independent and dependent. In case of independency, MRL functions are obtained and the results are discussed with reference to a case study. The effect of different distributions on the MRLs are also investigated in this case study. In case of dependency, on the other hand, the effect of dependency on MRL functions is especially underlined given that the system is at state “ j” ([Formula: see text]) for [Formula: see text]. To understand the time dependent behaviour of the related MRL functions, certain graphical illustrations are also presented.
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