VAR for VaR: measuring systemic risk using multivariate regression quantiles.

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market returns data to analyse spillovers in the values at risk (VaR) of different financial institutions. We construct impulse-response functions for the quantile processes of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system.Quantile impulse-responses; spillover; codependence; CAViaR

Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR

Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis defined in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns. JEL Classification: C13, C32Asset returns, CAViaR, conditional quantiles, Dynamic quantiles, Kurtosis, Skewness

VAR for VaR: measuring tail dependence using multivariate regression quantiles

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market equity returns data to analyse spillovers in the values at risk (VaR) between a market index and financial institutions. We construct impulse-response functions for the quantiles of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system. We show how the long-run risk of the largest and most leveraged financial institutions is very sensitive to market wide shocks in situations of financial distress, suggesting that our methodology can prove a valuable addition to the traditional toolkit of policy makers and supervisors

VAR for VaR: measuring systemic risk using multivariate regression quantiles.

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market returns data to analyse spillovers in the values at risk (VaR) of different financial institutions. We construct impulse-response functions for the quantile processes of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system

VAR for VaR: measuring systemic risk using multivariate regression quantiles.

This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market returns data to analyse spillovers in the values at risk (VaR) of different financial institutions. We construct impulse-response functions for the quantile processes of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system

The actinobacterial transcription factor RbpA binds to the principal sigma subunit of RNA polymerase

RbpA is a small non-DNA-binding transcription factor that associates with RNA polymerase holoenzyme and stimulates transcription in actinobacteria, including Streptomyces coelicolor and Mycobacterium tuberculosis. RbpA seems to show specificity for the vegetative form of RNA polymerase as opposed to alternative forms of the enzyme. Here, we explain the basis of this specificity by showing that RbpA binds directly to the principal σ subunit in these organisms, but not to more diverged alternative σ factors. Nuclear magnetic resonance spectroscopy revealed that, although differing in their requirement for structural zinc, the RbpA orthologues from S. coelicolor and M. tuberculosis share a common structural core domain, with extensive, apparently disordered, N- and C-terminal regions. The RbpA-σ interaction is mediated by the C-terminal region of RbpA and σ domain 2, and S. coelicolor RbpA mutants that are defective in binding σ are unable to stimulate transcription in vitro and are inactive in vivo. Given that RbpA is essential in M. tuberculosis and critical for growth in S. coelicolor, these data support a model in which RbpA plays a key role in the σ cycle in actinobacteria

Mycobacterium tuberculosis ClpP Proteases Are Co-transcribed but Exhibit Different Substrate Specificities

PMCID: PMC3613350This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

From Corynebacterium glutamicum to Mycobacterium tuberculosis—towards transfers of gene regulatory networks and integrated data analyses with MycoRegNet

Year by year, approximately two million people die from tuberculosis, a disease caused by the bacterium Mycobacterium tuberculosis. There is a tremendous need for new anti-tuberculosis therapies (antituberculotica) and drugs to cope with the spread of tuberculosis. Despite many efforts to obtain a better understanding of M. tuberculosis' pathogenicity and its survival strategy in humans, many questions are still unresolved. Among other cellular processes in bacteria, pathogenicity is controlled by transcriptional regulation. Thus, various studies on M. tuberculosis concentrate on the analysis of transcriptional regulation in order to gain new insights on pathogenicity and other essential processes ensuring mycobacterial survival. We designed a bioinformatics pipeline for the reliable transfer of gene regulations between taxonomically closely related organisms that incorporates (i) a prediction of orthologous genes and (ii) the prediction of transcription factor binding sites. In total, 460 regulatory interactions were identified for M. tuberculosis using our comparative approach. Based on that, we designed a publicly available platform that aims to data integration, analysis, visualization and finally the reconstruction of mycobacterial transcriptional gene regulatory networks: MycoRegNet. It is a comprehensive database system and analysis platform that offers several methods for data exploration and the generation of novel hypotheses. MycoRegNet is publicly available at http://mycoregnet.cebitec.uni-bielefeld.de

Roles for Treg expansion and HMGB1 signaling through the TLR1-2-6 axis in determining the magnitude of the antigen-specific immune response to MVA85A

© 2013 Matsumiya et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedA better understanding of the relationships between vaccine, immunogenicity and protection from disease would greatly facilitate vaccine development. Modified vaccinia virus Ankara expressing antigen 85A (MVA85A) is a novel tuberculosis vaccine candidate designed to enhance responses induced by BCG. Antigen-specific interferon-γ (IFN-γ) production is greatly enhanced by MVA85A, however the variability between healthy individuals is extensive. In this study we have sought to characterize the early changes in gene expression in humans following vaccination with MVA85A and relate these to long-term immunogenicity. Two days post-vaccination, MVA85A induces a strong interferon and inflammatory response. Separating volunteers into high and low responders on the basis of T cell responses to 85A peptides measured during the trial, an expansion of circulating CD4+ CD25+ Foxp3+ cells is seen in low but not high responders. Additionally, high levels of Toll-like Receptor (TLR) 1 on day of vaccination are associated with an increased response to antigen 85A. In a classification model, combined expression levels of TLR1, TICAM2 and CD14 on day of vaccination and CTLA4 and IL2Rα two days post-vaccination can classify high and low responders with over 80% accuracy. Furthermore, administering MVA85A in mice with anti-TLR2 antibodies may abrogate high responses, and neutralising antibodies to TLRs 1, 2 or 6 or HMGB1 decrease CXCL2 production during in vitro stimulation with MVA85A. HMGB1 is released into the supernatant following atimulation with MVA85A and we propose this signal may be the trigger activating the TLR pathway. This study suggests an important role for an endogenous ligand in innate sensing of MVA and demonstrates the importance of pattern recognition receptors and regulatory T cell responses in determining the magnitude of the antigen specific immune response to vaccination with MVA85A in humans.This work was funded by the Wellcome Trust. MM has a Wellcome Trust PhD studentship and HM is a Wellcome Trust Senior Fello

Proteomic analysis of cold adaptation in a Siberian permafrost bacterium – Exiguobacterium sibiricum 255–15 by two-dimensional liquid separation coupled with mass spectrometry

Bacterial cold adaptation in Exiguobacterium sibiricum 255–15 was studied on a proteomic scale using a 2-D liquid phase separation coupled with MS technology. Whole-cell lysates of E. sibiricum 255–15 grown at 4°C and 25°C were first fractionated according to p I by chromatofocusing (CF), and further separated based on hydrophobicity by nonporous silica RP HPLC (NPS-RP-HPLC) which was on-line coupled with an ESI-TOF MS for intact protein M r measurement and quantitative interlysate comparison. Mass maps were created to visualize the differences in protein expression between different growth temperatures. The differentially expressed proteins were then identified by PMF using a MALDI-TOF MS and peptide sequencing by MS/MS with a MALDI quadrupole IT TOF mass spectrometer (MALDI-QIT-TOF MS). A total of over 500 proteins were detected in this study, of which 256 were identified. Among these proteins 39 were cold acclimation proteins (Caps) that were preferentially or uniquely expressed at 4°C and three were homologous cold shock proteins (Csps). The homologous Csps were found to be similarly expressed at 4°C and 25°C, where these three homologous Csps represent about 10% of the total soluble proteins at both 4°C and 25°C.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55850/1/5221_ftp.pd