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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.

Therapeutic blockade of the programmed cell death protein-1 (PD-1) immune checkpoint pathways has resulted in significant reactivation of T cell-mediated antitumor immunity and is a promising clinical anticancer treatment modality in several tumor types, but the durable response rate remains relatively low (15%-20%) in most patients with HCC for unknown reasons. Evidence reveals that the interferon signaling pathway plays a critical role in modulating the efficacy and sensitivity of anti-PD-1 therapy against multiple tumor types, but the mechanisms are unclear.Using Kaplan-Meier survival analysis based on HCC databases, we found that deceased expression of interferon regulatory factor (IRF) 8 in HCC, among all the nine IRF members that regulate interferon signals, was associated with poor prognosis of patients with HCC. Moreover, gene set enrichment analysis identified the interferon-gamma and PD-1 signaling signatures as the top suppressed pathways in patients with IRF8-low HCC. Contrarily, overexpression of IRF8 in HCC cells significantly enhanced antitumor effects in immune-competent mice, modulating infiltration of tumor-associated macrophages (TAMs) and T cell exhaustion in tumor microenvironment. We further demonstrated that IRF8 regulated recruitment of TAMs by inhibiting the expression of chemokine (C-C motif) ligand 20 (CCL20). Mechanically, IRF8-mediated repression of c-fos transcription resulted in decreased expression of CCL20, rather than directly bound to CCL20 promoter region. Importantly, adeno-associated virus 8-mediated hepatic IRF8 rescue significantly suppressed HCC progression and enhanced the response to anti-PD-1 therapy.This work identified IRF8 as an important prognostic biomarker in patients with HCC that predicted the response and sensitivity to anti-PD-1 therapy and uncovered it as a therapeutic target for enhancing the efficacy of immune therapy.

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.

Diffusion-order NMR spectroscopy (DOSY) presents a powerful tool for studying solution mixtures by recording diffusion coefficients of individual components and separating their signals into respective 1D NMR spectra. Existing DOSY experiments, however, are generally unsuitable for measurements under adverse magnetic field conditions, because calculations for diffusion coefficients strictly rely on resolved resonances in the 1D NMR spectral domain. Herein, we propose a general DOSY method by introducing intermolecular zero-quantum coherence mechanism into molecular diffusion evolution to overcome the challenge of magnetic field inhomogeneity and to record high-resolution DOSY spectra free of magnetic field inhomogeneity. Our experimental results and theoretical interpretation suggest that the proposed method allows to diffusion analysis and component discrimination on solution mixtures under externally inhomogeneous magnetic field conditions and on intact biological tissues with field inhomogeneity internally caused by magnetic susceptibility variations. This study provides a previously unreported NMR protocol for high-resolution DOSY measurements in inhomogeneous magnetic fields, thus broadening the scope of DOSY applications and showing the promising prospect for studies on chemical and biological mixture samples.

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.

Hepatic ischemia/reperfusion (I/R) injury is an inevitable complication of hepatic surgery occasioned by liver transplantation and resection. The progression from liver ischemia to reperfusion injury is accompanied by abnormal metabolism, Kupffer cell activation, neutrophil recruitment and the release of cytokines. Activation of several interferon regulatory factors (IRFs) has been reported to either enhance or restrict I/R progression, but the role of IRF8 in the regulation of I/R injury progression is still unknown. In this study, we explore the IRF8 function in the I/R-mediated liver injury using overexpressed hepatic IRF8 and knockout mice. According to our results, IRF8 knockout mice had significantly lower inflammatory cells infiltration, inflammatory cytokines release and serum aspartate aminotransferase/alanine aminotransferase levels that improved the necrotic injury after I/R, unlike the control mice. Conversely, the overexpression of IRF8 in WT mice markedly aggravated the liver structure damage and its abnormal function. We further showed that IRF8-mediated inflammatory cells infiltration were partly dependent on early autophagy and NF-κΒ signal pathway during I/R. AAV8-IRF8-I/R mice pretreated with autophagy inhibitor hydroxychloroquine and NF-κΒ signal pathway inhibitor secukinumab could drastically reverse the IRF8-mediated increase of neutrophil infiltration and chemokine release at different degrees. This work uncovered a critical role of IRF8 in the modulation of the hepatic microenvironment and as a potential target in the initial treatment of I/R injury.

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 this paper, we develop a resilience modelling and analysis framework for multistate systems. The developed framework is based on Markov reward processes, in which the performance degradation and recovery processes of the multistate system are modeled by a continuous time discrete state Markov chain, and the losses caused by disruptive events are modeled by reward rates. A numerical metric is defined to quantify resilience based on the losses the system suffered from the disruptive events. The developed methods are applied to analyze the resilience of a nuclear power plant under the threat of earthquakes.

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.

A Markov repairable system with history-dependent up and down states is presented. The state space of the repairable system is divided into three sets, which are up states, down states and changeable states. Using aggregated stochastic processes, we can get the multi-interval availability and multi-point availability for the Markov repairable system. Finally, numerical examples are given to illustrate the obtained results.

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.

Phased-mission systems (PMS) can be widely found in a lot of practical application areas. Reliability evaluations and analysis for this kind of systems become important issues. The reliability of PMS is typically defined as the probability that the system successfully accomplishes the missions of all phases. However, the k-out-of-n system success criterion for PMS has not been investigated. In this paper, according to this criterion, we develop two new models, which are static and dynamic, respectively. The assumptions for these two models are described in detail as well. The system reliabilities for both models are presented for the first time by employing finite Markov chain imbedding approach (FMCIA). In terms of FMCIA, we define different state spaces for the two models, and transition probability matrices are obtained. Then some numerical examples are given to illustrate the application of FMCIA. Finally, some discussions are made and conclusions are summarized.

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.

The performance of maintenance systems can be described by many indexes such as availability, mean up-time and mean down-time and so forth. The availability is the most important measure among them...

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.

This article analyzes a single-unit repairable system consisting of an operating subsystem and a maintenance subsystem that is solely used to repair the operating subsystem in the event of it breaking down. Formulae for reliability indexes such as availability and distributions concerned with visits to certain subsets of states are presented in terms of state aggregations in which two partitions are used. The situation with exponentially distributed operational times is discussed not only as a special case of the approach used in this article but also using a Markov model. A numerical example is given to illustrate the results obtained in this article.