Photon-Atom Coupling with Parabolic Mirrors

Efficient coupling of light to single atomic systems has gained considerable attention over the past decades. This development is driven by the continuous growth of quantum technologies. The efficient coupling of light and matter is an enabling technology for quantum information processing and quantum communication. And indeed, in recent years much progress has been made in this direction. But applications aside, the interaction of photons and atoms is a fundamental physics problem. There are various possibilities for making this interaction more efficient, among them the apparently 'natural' attempt of mode-matching the light field to the free-space emission pattern of the atomic system of interest. Here we will describe the necessary steps of implementing this mode-matching with the ultimate aim of reaching unit coupling efficiency. We describe the use of deep parabolic mirrors as the central optical element of a free-space coupling scheme, covering the preparation of suitable modes of the field incident onto these mirrors as well as the location of an atom at the mirror's focus. Furthermore, we establish a robust method for determining the efficiency of the photon-atom coupling.Comment: Book chapter in compilation "Engineering the Atom-Photon Interaction" published by Springer in 2015, edited by A. Predojevic and M. W. Mitchell, ISBN 9783319192307, http://www.springer.com/gp/book/9783319192307. Only change to version1: now with hyperlinks to arXiv eprints of other book chapters mentioned in this on

Varicella Viruses Inhibit Interferon-Stimulated JAK-STAT Signaling through Multiple Mechanisms

Varicella zoster virus (VZV) causes chickenpox in humans and, subsequently, establishes latency in the sensory ganglia from where it reactivates to cause herpes zoster. Infection of rhesus macaques with simian varicella virus (SVV) recapitulates VZV pathogenesis in humans thus representing a suitable animal model for VZV infection. While the type I interferon (IFN) response has been shown to affect VZV replication, the virus employs counter mechanisms to prevent the induction of anti-viral IFN stimulated genes (ISG). Here, we demonstrate that SVV inhibits type I IFN-activated signal transduction via the JAK-STAT pathway. SVV-infected rhesus fibroblasts were refractory to IFN stimulation displaying reduced protein levels of IRF9 and lacking STAT2 phosphorylation. Since previous work implicated involvement of the VZV immediate early gene product ORF63 in preventing ISG-induction we studied the role of SVV ORF63 in generating resistance to IFN treatment. Interestingly, SVV ORF63 did not affect STAT2 phosphorylation but caused IRF9 degradation in a proteasome-dependent manner, suggesting that SVV employs multiple mechanisms to counteract the effect of IFN. Control of SVV ORF63 protein levels via fusion to a dihydrofolate reductase (DHFR)-degradation domain additionally confirmed its requirement for viral replication. Our results also show a prominent reduction of IRF9 and inhibition of STAT2 phosphorylation in VZV-infected cells. In addition, cells expressing VZV ORF63 blocked IFN-stimulation and displayed reduced levels of the IRF9 protein. Taken together, our data suggest that varicella ORF63 prevents ISG-induction both directly via IRF9 degradation and indirectly via transcriptional control of viral proteins that interfere with STAT2 phosphorylation. SVV and VZV thus encode multiple viral gene products that tightly control IFN-induced anti-viral responses

Modeling of biochemical networks via classification and regression tree methods

In the description of biological networks, a number of modeling approaches has been suggested based on different assumptions. The major problems in these models and their associated inference approaches are the complexity of biological systems, resulting in high number of model parameters, few observations from each variable in the system, their sparse structures, and high correlation between model parameters. From recent studies, it has been seen that the nonparametric methods can ameliorate these challenges and be one of the strong alternative approaches. Furthermore, it has been observed that not only the regression type of nonparametric models but also nonparametric clustering methods whose calculations are adapted to the biochemical systems can be another promising choice. Hereby, in this study, we propose the classification and regression tree (CART) method as a new approach in the construction of the complex systems when the system’s activity is described under its steady-state condition. Basically, CART is a classification technique for highly correlated data and can be represented as the nonparametric version of the generalized additive model. In this work, we use CART in the construction of biological modules and then networks. We analyze the performance of CART comprehensively under various Monte Carlo scenarios such as different data distributions and dimensions. We compare our results with the outputs of the Gaussian graphical model (GGM) which is the most well-known model under the given condition of the system. In our study, we also evaluate the performance of CART with the GGM findings by using real systems. For this purpose, we choose the pathways which have a crucial role on the cervical cancer. In the analyses, we consider this particular illness since it is the second most common cancer type in women both in Turkey and in the world after the breast cancer, and there is only a limited information for the description of this complex system disease