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Figure S4. IRF8 reduces AR protein levels and activity.

IRF8 was not frequently mutated and methylated in clinical PCa tissues and cells.

In incurable castration-resistant prostate cancer (CRPC), resistance to the novel androgen receptor (AR) antagonist enzalutamide is driven mainly by AR overexpression. Here we report that the expression of interferon regulatory factor 8 (IRF8) is increased in primary prostate cancer but decreased in CRPC compared with normal prostate tissue. Decreased expression of IRF8 positively associated with CRPC progression and enzalutamide resistance. IRF8 interacted with AR and promoted its degradation via activation of the ubiquitin/proteasome systems. Epigenetic knockdown of IRF8 promoted AR-mediated prostate cancer progression and enzalutamide resistance in vitro and in vivo. Furthermore, IFNα increased expression of IRF8 and improved the efficacy of enzalutamide in CRPC by targeting the IRF8–AR axis. We also provide preliminary evidence for the efficacy of IFNα with hormonotherapy in a clinical study. Collectively, this study identifies IRF8 both as a tumor suppressor in prostate cancer pathogenesis and a potential alternative therapeutic option to overcome enzalutamide resistance.Significance:These findings identify IRF8-mediated AR degradation as a mechanism of resistance to AR-targeted therapy, highlighting the therapeutic potential of IFNα in targeting IRF8–AR axis in CRPC.

More evidence shows that the brain energy demands are enormous. This study aimed to examine the metabolism changes in the brain of hypoxic ischemic encephalopathy (HIE) mice model and to evaluate how treadmill exercise enhances brain metabolism. We used unilateral carotid common artery ligation mice model to simulate the clinical HIE patients. Several behavior tests were used to evaluate the motor and cognitive function impairment, western blot and immunofluorescence were used to assess the metabolism related protein changes in the different groups. Meanwhile, the HIE mice models were randomly divided into exercise training group (HIE-T) which were subjected to four weeks of treadmill exercise training and non-exercise training group (HIE-NT). The results revealed that there was decreased expression of glucose transporters GLUTs in HIE and an enhanced expression of monocarboxylate transporters MCTs, which is an important part of energy metabolism adaptation. After the period of treadmill exercise training, the expression of GLUT1 as well as MCT1 increased in the motor cortex of HIE mice model. Moreover, the mitochondrial cristae and edges were clear and intact under Transmission electron microscope compared with HIE-NT group. This suggests that increased brain energy attributed to treadmill exercise training shows promising therapeutic potential for HIE.

Most of existing resilience models assume that system performances are continuous. In this paper, we consider resilience modeling and analysis for multi-state systems, whose performances are characterized by discrete, rather than continuous variables. A non-homogeneous Semi-Markov reward process model is developed for resilience analysis of multi-state systems. In the developed model, system performance changes, caused by either disruptive events or system recoveries, are modeled as state transitions, and rewards are used to model financial losses incurred during and after the disruptions. Four resilience metrics are defined to quantify different aspects of resilience. As the developed model is non-homogeneous, it can capture time-dependent system behaviors and their impact on system resilience. An efficient resilience analysis algorithm is also designed based on linear interpolation and implemented using vectorization. The computational benefits of the developed algorithm are demonstrated through two numerical experiments. We apply the developed method on two practical case studies, an oil tank farm and a re-configurable computing system. The results show that the developed methods can quantify resilience of multi-state systems accurately and efficiently.

Interval availability, defined as the fraction of time that a system is operational during a period of time $[ {0,T} ]$ , is an important indicator of system performance, especially for industries with a Service Level Agreement, e.g., telecommunication industry, computer industry, etc. Most existing methods to compute interval availability are based on numerical simulations. In this paper, we present a new analytical solution for interval availability of Markov repairable systems. Three interval availability indexes, i.e., interval availability, interval availability in a general interval, and interval availability in multiple intervals, are considered. The interval availability indexes are derived based on aggregated stochastic processes and the results are obtained in closed form using Laplace transforms. A numerical example is presented and the results are compared with those of Monte Carlo simulation. The developed methods are applied to calculate the interval availability of a fault-tolerant database system from the literature.

International audience; Markov history-dependent repairable systems refer to the Markov repairable systems in which some states are changeable and dependent on recent evolutional history of the system. In practice, many Markov history-dependent repairable systems are subjected to neglected failures, i.e., some failures do not affect system performances if they can be repaired promptly. In this paper, we develop a model based on the theory of aggregated stochastic processes to describe the history-dependent behavior and the effect of neglected failures on the Markov history-dependent repairable systems. Based on the developed model, instantaneous and steady-state availabilities are derived to characterize the reliability of the system. Four reliability-related time distributions, i.e., distribution for the k th working period, distribution for the k th failure period, distribution for the real working time in an effective working period, distribution for the neglected failure time in an effective working period, are also derived to provide a more comprehensive description of the system's reliability. Thanks to the power of the theory of aggregated stochastic processes, closed-form expressions are obtained for all the reliability indexes and time distributions. Finally, the developed indexes and analysis methods are demonstrated by a numerical example.

In incurable castration-resistant prostate cancer (CRPC), resistance to the novel androgen receptor (AR) antagonist enzalutamide is driven mainly by AR overexpression. Here we report that the expression of interferon regulatory factor 8 (IRF8) is increased in primary prostate cancer but decreased in CRPC compared with normal prostate tissue. Decreased expression of IRF8 positively associated with CRPC progression and enzalutamide resistance. IRF8 interacted with AR and promoted its degradation via activation of the ubiquitin/proteasome systems. Epigenetic knockdown of IRF8 promoted AR-mediated prostate cancer progression and enzalutamide resistance in vitro and in vivo. Furthermore, IFNα increased expression of IRF8 and improved the efficacy of enzalutamide in CRPC by targeting the IRF8–AR axis. We also provide preliminary evidence for the efficacy of IFNα with hormonotherapy in a clinical study. Collectively, this study identifies IRF8 both as a tumor suppressor in prostate cancer pathogenesis and a potential alternative therapeutic option to overcome enzalutamide resistance. Significance: These findings identify IRF8-mediated AR degradation as a mechanism of resistance to AR-targeted therapy, highlighting the therapeutic potential of IFNα in targeting IRF8–AR axis in CRPC.

Here we present the structure functions for the linear and circular $m$ -consecutive- $k$ , $l$ -out-of- $n$ :F (G) systems, which consists of $n$ linearly or circularly ordered components such that the system fails (works) iff there are at least $m$ times $l$ -overlapping runs of consecutive $k$ failed (working) components. The duality of these F and G systems is obtained, and the proofs are given. The generalized reliability formulae for the systems are obtained by using the finite Markov chain imbedding approach for statistically independent and Markov-dependent cases. Some miscellaneous results on the reliability of these systems and numerical examples are provided to illustrate the results obtained in the paper.

Since Birnbaum importance was introduced in 1969, there have been more than twenty kinds of importance measures so far. Among the various measures, Birnbaum importance plays an extremely important role because many importance measures have been defined under its illumination and have relationships with it. A lot of work has been done for Birnbaum importance in consecutive- $k$ systems since the systems were introduced. Because the problems in practice are increasingly complicated, in 2007, Zhao proposed consecutive- $k$ systems with sparse $d$ , which is an extension of the current consecutive- $k$ systems. In this paper, we study Birnbaum importance for linear consecutive- $k$ -out-of- $n$ systems with sparse $d$ . Some equations on Birnbaum importance are proposed. With these equations, the ranking of components in the system on the basis of Birnbaum importance is given; and then some patterns of ranking are presented. Finally, two numerical examples are given to illustrate the results obtained in this paper.

Availability is an important measure of performance for repairable systems because it can describe the probability of a repairable system being in working states. Single point and interval availability measurements (availabilities) have been widely used, but they cannot meet the requirements of all applications. In this paper, multi-point, multi-interval, and mixed multi-point-interval availabilities are introduced, which are extensions of the single point and interval availabilities. The multi-interval availability formula was given by Csenki in 2007 by using the induction method in which the multi-interval availability was called the joint interval reliability, but a new simple technique is provided to present the formula of multi-interval availability in this paper. We also consider the steady-state situations for all new availabilities. For Markov repairable systems, the calculation formulae are presented for the new availabilities, and some properties of new availabilities are discussed too. Finally, some special cases and numerical examples are given to illustrate the results obtained in the paper.

Three models for the aggregated stochastic processes based on an underlying continuous-time Markov repairable system are developed in which two-part partition of states is used. Several availability measures such as interval availability, instantaneous availability and steady-state availability are presented. Some of these availabilities are derived by using Laplace transforms, which are more compact and concise. Other reliability-distributions for these three models are given as well.