RNase L, a principal mediator of innate immunity to viral infections in higher vertebrates, is required for a complete IFN antiviral response against certain RNA stranded viruses. dsRNA produced during viral infections activates IFN-inducible synthetases that produce 5 -phosphorylated, 2 ,5 -oligoadenylates (2-5A) from ATP. 2-5A activates RNase L in a wide range of different mammalian cell types, thus blocking viral replication. However, 2-5A has unfavorable pharmacologic properties; it is rapidly degraded, does not transit cell membranes, and leads to apoptosis. To obtain activators of RNase L with improved drug-like properties, high-throughput screening was performed on chemical libraries by using fluorescence resonance energy transfer. Seven compounds were obtained that activated RNase L at micromolar concentrations, and structureactivity relationship studies resulted in identification of an additional four active compounds. Two lead compounds were shown to have a similar mechanistic path toward RNase L activation as the natural activator 2-5A. The compounds bound to the 2-5A-binding domain of RNase L (as determined by surface plasmon resonance and confirmed by computational docking), and the compounds induced RNase L dimerization and activation. Interestingly, the low-molecular-weight activators of RNase L had broad-spectrum antiviral activity against diverse types of RNA viruses, including the human pathogen human parainfluenza virus type 3, yet these compounds by themselves were not cytotoxic at the effective concentrations. Therefore, these RNase L activators are prototypes for a previously uncharacterized class of broad-spectrum antiviral agents.high-throughput screening ͉ interferon ͉ 2-5A ͉ virus

Abstract-Obtaining accurate system models for verification is a hard and time consuming process, which is seen by industry as a hindrance to adopt otherwise powerful modeldriven development techniques and tools. In this paper we pursue an alternative approach where an accurate high-level model can be automatically constructed from observations of a given black-box embedded system. We adapt algorithms for learning finite probabilistic automata from observed system behaviors. We prove that in the limit of large sample sizes the learned model will be an accurate representation of the data-generating system. In particular, in the large sample limit, the learned model and the original system will define the same probabilities for linear temporal logic (LTL) properties. Thus, we can perform PLTL model-checking on the learned model to infer properties of the system. We perform experiments learning models from system observations at different levels of abstraction. The experimental results show the learned models provide very good approximations for relevant properties of the original system.

. (2016). Learning deterministic probabilistic automata from a model checking perspective. Machine Learning, 105(2), 255-299. https://doi.org/10.1007/s10994-016-5565-9General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. Abstract Probabilistic automata models play an important role in the formal design and analysis of hard-and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification.

Latently infected cells remain a primary barrier to eradication of HIV-1. Over the past decade, a better understanding of the molecular mechanisms by which latency is established and maintained has led to the discovery of a number of compounds that selectively reactivate latent proviruses without inducing polyclonal T cell activation. Recently, the histone deacetylase (HDAC) inhibitor vorinostat has been demonstrated to induce HIV transcription from latently infected cells when administered to patients. While vorinostat will be given in the context of antiretroviral therapy (ART), infection of new cells by induced virus remains a clinical concern. Here, we demonstrate that vorinostat significantly increases the susceptibility of CD4؉ T cells to infection by HIV in a dose-and time-dependent manner that is independent of receptor and coreceptor usage. Vorinostat does not enhance viral fusion with cells but rather enhances the kinetics and efficiency of postentry viral events, including reverse transcription, nuclear import, and integration, and enhances viral production in a spreading-infection assay. Selective inhibition of the cytoplasmic class IIb HDAC6 with tubacin recapitulated the effect of vorinostat. These findings reveal a previously unknown cytoplasmic effect of HDAC inhibitors promoting productive infection of CD4 ؉ T cells that is distinct from their well-characterized effects on nuclear histone acetylation and long-terminal-repeat (LTR) transcription. Our results indicate that careful monitoring of patients and ART intensification are warranted during vorinostat treatment and indicate that HDAC inhibitors that selectively target nuclear class I HDACs could reactivate latent HIV without increasing the susceptibility of uninfected cells to HIV. IMPORTANCEHDAC inhibitors, particularly vorinostat, are currently being investigated clinically as part of a "shock-and-kill" strategy to purge latent reservoirs of HIV. We demonstrate here that vorinostat increases the susceptibility of uninfected CD4؉ T cells to infection with HIV, raising clinical concerns that vorinostat may reseed the viral reservoirs it is meant to purge, particularly under conditions of suboptimal drug exposure. We demonstrate that vorinostat acts following viral fusion and enhances the kinetics and efficiency of reverse transcription, nuclear import, and integration. The effect of vorinostat was recapitulated using the cytoplasmic histone deacetylase 6 (HDAC6) inhibitor tubacin, revealing a novel and previously unknown cytoplasmic mechanism of HDAC inhibitors on HIV replication that is distinct from their well-characterized effects of long-terminal-repeat (LTR)-driven gene expression. Moreover, our results suggest that treatment of patients with class I-specific HDAC inhibitors could induce latent viruses without increasing the susceptibility of uninfected cells to HIV.