Endothelial Cells Potentiate Interferon-γ Production in a Novel Tripartite Culture Model of Human Cerebral Malaria

We have established a novel in vitro co-culture system of human brain endothelial cells (HBEC), Plasmodium falciparum parasitised red blood cells (iRBC) and peripheral blood mononuclear cells (PBMC), in order to simulate the chief pathophysiological lesion in cerebral malaria (CM). This approach has revealed a previously unsuspected pro-inflammatory role of the endothelial cell through potentiating the production of interferon (IFN)-γ by PBMC and concurrent reduction of interleukin (IL)-10. The IFN-γ increased the expression of CXCL10 and intercellular adhesion molecule (ICAM)-1, both of which have been shown to be crucial in the pathogenesis of CM. There was a shift in the ratio of IL-10:IFN-γ protein from >1 to <1 in the presence of HBEC, associated with the pro-inflammatory process in this model. For this to occur, a direct contact between PBMC and HBEC, but not PBMC and iRBC, was necessary. These results support HBEC playing an active role in the pathogenesis of CM. Thus, if these findings reflect the pathogenesis of CM, inhibition of HBEC and PBMC interactions might reduce the occurrence, or improve the prognosis, of the condition. © 2013 Khaw et al

Branching ratios of B+→D(∗)+K(∗)0B^+ \to D^{(*)+}K^{(*)0} decays in perturbative QCD approach

We study the rare decays $B^+ \to D^{(*)+}K^{(*)0}$, which can occur only via annihilation type diagrams in the standard model. We calculate all of the four modes, $B \to PP, VP, PV, VV$, in the framework of perturbative QCD approach and give the branching ratios of the order about $10^{-6}$.Comment: 18 pages, 1 figure, Revte

Invariant Synthesis for Incomplete Verification Engines

We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counter-example guided inductive synthesis principle (CEGIS) and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs

Phylogenetic Relationships of Tribes Within Harpalinae (Coleoptera: Carabidae) as Inferred from 28S Ribosomal DNA and the Wingless Gene

Harpalinae is a large, monophyletic subfamily of carabid ground beetles containing more than 19,000 species in approximately 40 tribes. The higher level phylogenetic relationships within harpalines were investigated based on nucleotide data from two nuclear genes, wingless and 28S rDNA. Phylogenetic analyses of combined data indicate that many harpaline tribes are monophyletic, however the reconstructed trees showed little support for deeper nodes. In addition, our results suggest that the Lebiomorph Assemblage (tribes Lebiini, Cyclosomini, Graphipterini, Perigonini, Odacanthini, Lachnophorini, Pentagonicini, Catapiesini and Calophaenini), which is united by a morphological synapomorphy, is not monophyletic, and the tribe Lebiini is paraphyletic with respect to members of Cyclosomini. Two unexpected clades of tribes were supported: the Zuphiitae, comprised of Anthiini, Zuphiini, Helluonini, Dryptini, Galeritini, and Physocrotaphini; and a clade comprised of Orthogoniini, Pseudomorphini, and Graphipterini. The data presented in this study represent a dense sample of taxa to examine the molecular phylogeny of Harpalinae and provide a useful framework to examine the origin and evolution of morphological and ecological diversity in this group

Underground Neutrino Detectors for Particle and Astroparticle Science: the Giant Liquid Argon Charge Imaging ExpeRiment (GLACIER)

The current focus of the CERN program is the Large Hadron Collider (LHC), however, CERN is engaged in long baseline neutrino physics with the CNGS project and supports T2K as recognized CERN RE13, and for good reasons: a number of observed phenomena in high-energy physics and cosmology lack their resolution within the Standard Model of particle physics; these puzzles include the origin of neutrino masses, CP-violation in the leptonic sector, and baryon asymmetry of the Universe. They will only partially be addressed at LHC. A positive measurement of $\sin^22\theta_{13}>0.01$ would certainly give a tremendous boost to neutrino physics by opening the possibility to study CP violation in the lepton sector and the determination of the neutrino mass hierarchy with upgraded conventional super-beams. These experiments (so called ``Phase II'') require, in addition to an upgraded beam power, next generation very massive neutrino detectors with excellent energy resolution and high detection efficiency in a wide neutrino energy range, to cover 1st and 2nd oscillation maxima, and excellent particle identification and $\pi^0$ background suppression. Two generations of large water Cherenkov detectors at Kamioka (Kamiokande and Super-Kamiokande) have been extremely successful. And there are good reasons to consider a third generation water Cherenkov detector with an order of magnitude larger mass than Super-Kamiokande for both non-accelerator (proton decay, supernovae, ...) and accelerator-based physics. On the other hand, a very massive underground liquid Argon detector of about 100 kton could represent a credible alternative for the precision measurements of ``Phase II'' and aim at significantly new results in neutrino astroparticle and non-accelerator-based particle physics (e.g. proton decay).Comment: 31 pages, 14 figure

Real earnings management activities prior to bondissuance

We examine real activities manipulation by firms prior to their debt issuances andhow such manipulation activities affect bond yield spreads. We find that bond-issuing firmsincrease their real activities manipulation in the five quarters leading to a bond issuance. Wedocument an inverse association between yield spread and pre-issue real activities manipula-tion, i.e., firms engaged in abnormally high levels of real activities manipulation are associatedwith subsequent lower cost of debt

The key importance of soy isoflavone bioavailability to understanding health benefits

Research over the past two decades has provided significant epidemiological and other evidence for the health benefits of the consumption of soy-based foods. A large number of dietary intervention studies have examined the effects of soy isoflavones on risk factors for cardiovascular disease and hormone-dependent cancers. However, these report large variability in outcome measures, very limited reproducibility between studies and in some cases, controversy between results of clinical trials using dietary soy or soy protein and isoflavone supplementation. This highlights a major gap in our understanding of soy isoflavone uptake, metabolism, distribution, and overall bioavailability. There are many potential factors that may influence bioavailability and a better knowledge is necessary to rationalize the inconsistencies in the intervention and clinical studies. This review focuses attention on our current state of knowledge in this area and highlights the importance of metabolism of the parent soy isoflavones and the critical role of gut microbiota on the bioavailability of these compounds and their metabolites

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm