On the influence of external stochastic excitation on linear oscillators with subcritical self-excitation applied to brake squeal

A characteristic of linear systems with self-excitation is the occurrence of non-normal modes. Because of this non-normality, there may be a significant growth in the vibration amplitude at the beginning of the transient process even in the case of solely negative real parts of the eigenvalues, i.e. asymptotic stability of the trivial solution. If such a system is excited additionally with white noise, this process is continually restarted and a stationary vibration with dominating frequencies and comparably large amplitudes can be observed. Similar observations can be made during brake squeal, a high-frequency noise resulting from self-excitation due to the frictional disk-pad contact. Although commonly brake squeal is considered as a stable limit cycle with the necessity of corresponding nonlinearities, comparable noise phenomena can in the described model even observed in a pure linear case when the trivial solution is asymptotically stable

Foreign direct investment and domestic entrepreneurship: blessing or curse?

This paper explores the effects of foreign direct investment, measured by mergers and acquisitions, on domestic entrepreneurial entry. We use a micro‐panel of more than two thousand individuals disaggregated by industry in seventy countries including both developed and developing economies, 2000-2009. The theory yields ambiguous predictions about the relationship between FDI and entrepreneurship; positive spillovers via dissemination of technology or negative because of crowding out. Our empirical analysis is conducted at three levels of aggregation. We find the relationship between FDI and domestic entrepreneurship in aggregate and intra-industry to be negative. Policies need to consider how to counteract this effect

Human Autoimmune Sera as Molecular Probes for the Identification of an Autoantigen Kinase Signaling Pathway

Using human autoimmune sera as molecular probes, we previously described the association of phosphorylated serine/arginine splicing factors (SR splicing factors) with the U1-small nuclear ribonucleoprotein (U1-snRNP) and U3-small nucleolar RNP (snoRNP) in apoptotic cells. SR proteins are highly conserved autoantigens whose activity is tightly regulated by reversible phosphorylation of serine residues by at least eight different SR protein kinase kinases (SRPKs), including SRPK1, SRPK2, and the scleroderma autoantigen topoisomerase I. In this report, we demonstrate that only one of the known SRPKs, SRPK1, is associated with the U1-snRNP autoantigen complex in healthy and apoptotic cells. SRPK1 is activated early during apoptosis, followed by caspase-mediated proteolytic inactivation at later time points. SRPKs are cleaved in vivo after multiple apoptotic stimuli, and cleavage can be inhibited by overexpression of bcl-2 and bcl-xL, and by exposure to soluble peptide caspase inhibitors. Incubation of recombinant caspases with in vitro–translated SRPKs demonstrates that SRPK1 and SRPK2 are in vitro substrates for caspases-8 and -9, respectively. In contrast, topoisomerase I is cleaved by downstream caspases (-3 and -6). Since each of these SRPKs sits at a distinct checkpoint in the caspase cascade, SRPKs may serve an important role in signaling pathways governing apoptosis, alternative mRNA splicing, SR protein trafficking, RNA stability, and possibly the generation of autoantibodies directed against splicing factors

Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease

BackgroundPublicly available data repositories facilitate the sharing of an ever-increasing amount of microarray data. However, these datasets remain highly underutilized. Reutilizing the data could offer insights into questions and diseases entirely distinct from those considered in the original experimental design.MethodsWe first analyzed microarray datasets derived from known perturbations of specific pathways using the samr package in R to identify specific patterns of change in gene expression. We refer to these pattern of gene expression alteration as a "pathway signatures." We then used Spearman's rank correlation coefficient, a non-parametric measure of correlation, to determine similarities between pathway signatures and disease profiles, and permutation analysis to evaluate false discovery rate. This enabled detection of statistically significant similarity between these pathway signatures and corresponding changes observed in human disease. Finally, we evaluated pathway activation, as indicated by correlation with the pathway signature, as a risk factor for poor prognosis using multiple unrelated, publicly available datasets.ResultsWe have developed a novel method, Expression-based Pathway Signature Analysis (EPSA). We demonstrate that ESPA is a rigorous computational approach for statistically evaluating the degree of similarity between highly disparate sources of microarray expression data. We also show how EPSA can be used in a number of cases to stratify patients with differential disease prognosis. EPSA can be applied to many different types of datasets in spite of different platforms, different experimental designs, and different species. Applying this method can yield new insights into human disease progression.ConclusionEPSA enables the use of publicly available data for an entirely new, translational purpose to enable the identification of potential pathways of dysregulation in human disease, as well as potential leads for therapeutic molecular targets

Type I interferon receptor controls B-cell expression of nucleic acid-sensing Toll-like receptors and autoantibody production in a murine model of lupus

INTRODUCTION: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of high-titer IgG autoantibodies directed against nuclear autoantigens. Type I interferon (IFN-I) has been shown to play a pathogenic role in this disease. In the current study, we characterized the role of the IFNAR2 chain of the type I IFN (IFN-I) receptor in the targeting of nucleic acid-associated autoantigens and in B-cell expression of the nucleic acid-sensing Toll-like receptors (TLRs), TLR7 and TLR9, in the pristane model of lupus. METHODS: Wild-type (WT) and IFNAR2-/- mice were treated with pristane and monitored for proteinuria on a monthly basis. Autoantibody production was determined by autoantigen microarrays and confirmed using enzyme-linked immunosorbent assay (ELISA) and immunoprecipitation. Serum immunoglobulin isotype levels, as well as B-cell cytokine production in vitro, were quantified by ELISA. B-cell proliferation was measured by thymidine incorporation assay. RESULTS: Autoantigen microarray profiling revealed that pristane-treated IFNAR2-/- mice lacked autoantibodies directed against components of the RNA-associated autoantigen complexes Smith antigen/ribonucleoprotein (Sm/RNP) and ribosomal phosphoprotein P0 (RiboP). The level of IgG anti-single-stranded DNA and anti-histone autoantibodies in pristane-treated IFNAR2-/- mice was decreased compared to pristane-treated WT mice. TLR7 expression and activation by a TLR7 agonist were dramatically reduced in B cells from IFNAR2-/- mice. IFNAR2-/- B cells failed to upregulate TLR7 as well as TLR9 expression in response to IFN-I, and effector responses to TLR7 and TLR9 agonists were significantly decreased as compared to B cells from WT mice following treatment with IFN-alpha. CONCLUSIONS: Our studies provide a critical link between the IFN-I pathway and the regulation of TLR-specific B-cell responses in a murine model of SLE