49 resultados para Immune Tolerance


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Background: Tuberculosis (TB) is an enduring health problem worldwide and the emerging threat of multidrug resistant (MDR) TB and extensively drug resistant (XDR) TB is of particular concern. A better understanding of biomarkers associated with TB will aid to guide the development of better targets for TB diagnosis and for the development of improved TB vaccines. Methods: Recombinant proteins (n = 7) and peptide pools (n = 14) from M. tuberculosis (M.tb) antigens associated with M.tb pathogenicity, modification of cell lipids or cellular metabolism, were used to compare T cell immune responses defined by IFN-gamma production using a whole blood assay (WBA) from i) patients with TB, ii) individuals recovered from TB and iii) individuals exposed to TB without evidence of clinical TB infection from Minsk, Belarus. Results: We identified differences in M.tb target peptide recognition between the test groups, i.e. a frequent recognition of antigens associated with lipid metabolism, e.g. cyclopropane fatty acyl phospholipid synthase. The pattern of peptide recognition was broader in blood from healthy individuals and those recovered from TB as compared to individuals suffering from pulmonary TB. Detection of biologically relevant M.tb targets was confirmed by staining for intracellular cytokines (IL-2, TNF-alpha and IFN-gamma) in T cells from non-human primates (NHPs) after BCG vaccination. Conclusions: PBMCs from healthy individuals and those recovered from TB recognized a broader spectrum of M.tb antigens as compared to patients with TB. The nature of the pattern recognition of a broad panel of M.tb antigens will devise better strategies to identify improved diagnostics gauging previous exposure to M.tb; it may also guide the development of improved TB-vaccines.

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The progesterone-regulated glycoprotein glycodelin-A (GdA), secreted by the decidualized endometrium at high concentrations in primates, inhibits the maternal immune response against fetal antigens and thereby contributes to the tolerance of the semi-allogenic fetus during a normal pregnancy. Our earlier studies demonstrated the ability of GdA to induce an intrinsic apoptotic cascade in CD4 T-lymphocytes and suppress the cytolytic effector function of CD8 T-lymphocytes. In this report, we investigated further into the mechanism of action of GdA controlling perforin and granzyme B expression in CD8 T-lymphocytes and the mechanism of action of GdA leading to lymphocyte death. Flow cytometry analysis was performed to check for the surface expression of interleukin-2 receptor (IL-2R) and intracellular eomesodermin (Eomes) in activated T-lymphocytes, whereas quantitative RTPCR analysis was used to find out their mRNA profile upon GdA treatment. Western analysis was carried out to confirm the protein level of Bax and Bcl-2. GdA reduces the surface expression of the high-affinity IL-2R complex by down-regulating the synthesis of IL-2R (CD25). This disturbs the optimal IL-2 signalling and decreases the Eomes expression, which along with IL-2 directly regulates perforin and granzymes expression. Consequently, the CD8 T-lymphocytes undergo growth arrest and are unable to mature into competent cytotoxic T-lymphocytes. In the CD4 T-lymphocytes, growth factor IL-2 deprivation leads to proliferation inhibition, decreased Bcl-2/enhanced Bax expression, culminating in mitochondrial stress and cell death. GdA spurs cell cycle arrest, loss of effector functions and apoptosis in different T-cell subsets by making T-lymphocytes unable to respond to IL-2.

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Exascale systems of the future are predicted to have mean time between failures (MTBF) of less than one hour. Malleable applications, where the number of processors on which the applications execute can be changed during executions, can make use of their malleability to better tolerate high failure rates. We present AdFT, an adaptive fault tolerance framework for long running malleable applications to maximize application performance in the presence of failures. AdFT framework includes cost models for evaluating the benefits of various fault tolerance actions including checkpointing, live-migration and rescheduling, and runtime decisions for dynamically selecting the fault tolerance actions at different points of application execution to maximize performance. Simulations with real and synthetic failure traces show that our approach outperforms existing fault tolerance mechanisms for malleable applications yielding up to 23% improvement in application performance, and is effective even for petascale systems and beyond.

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In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed PLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The PLC's were also tested in the presence of faulty rules. Finally, Kruskal Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS.

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This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

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Background: A better understanding of the quality of cellular immune responses directed against molecularly defined targets will guide the development of TB diagnostics and identification of molecularly defined, clinically relevant M.tb vaccine candidates. Methods: Recombinant proteins (n = 8) and peptide pools (n = 14) from M. tuberculosis (M.tb) targets were used to compare cellular immune responses defined by IFN-gamma and IL-17 production using a Whole Blood Assay (WBA) in a cohort of 148 individuals, i.e. patients with TB + (n = 38), TB- individuals with other pulmonary diseases (n = 81) and individuals exposed to TB without evidence of clinical TB (health care workers, n = 29). Results: M.tb antigens Rv2958c (glycosyltransferase), Rv2962c (mycolyltransferase), Rv1886c (Ag85B), Rv3804c (Ag85A), and the PPE family member Rv3347c were frequently recognized, defined by IFN-gamma production, in blood from healthy individuals exposed to M.tb (health care workers). A different recognition pattern was found for IL-17 production in blood from M.tb exposed individuals responding to TB10.4 (Rv0288), Ag85B (Rv1886c) and the PPE family members Rv0978c and Rv1917c. Conclusions: The pattern of immune target recognition is different in regard to IFN-gamma and IL-17 production to defined molecular M.tb targets in PBMCs from individuals frequently exposed to M.tb. The data represent the first mapping of cellular immune responses against M.tb targets in TB patients from Honduras.

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In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

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Regular vaccinations with potent vaccine, in endemic countries and vaccination to live in non-endemic countries are the methods available to control foot-and-mouth disease. Selection of candidate vaccine strain is not only cumbersome but the candidate should grow well for high potency vaccine preparation. Alternative strategy is to generate an infectious cDNA of a cell culture-adapted virus and use the replicon for development of tailor-made vaccines. We produced a chimeric `O' virus in the backbone of Asia 1 and studied its characteristics. The chimeric virus showed high infectivity titre (>10(10)) in BHK 21 cell lines, revealed small plague morphology and there was no cross reactivity with antiserum against Asia I. The virus multiplies rapidly and reaches peak at 12 h post infection. The vaccine prepared with this virus elicited high antibody titres.

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Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p < 0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

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A new hybrid multilevel power converter topology is presented in this paper. The proposed power converter topology uses only one DC source and floating capacitors charged to asymmetrical voltage levels, are used for generating different voltage levels. The SVPWM based control strategy used in this converter maintains the capacitor voltages at the required levels in the entire modulation range including the over-modulation region. For the voltage levels: nine and above, the number of components required in the proposed topology is significantly lower, compared to the conventional multilevel inverter topologies. The number of capacitors required in this topology reduces drastically compared to the conventional flying capacitor topology, when the number of levels in the inverter output increases. This topology has better fault tolerance, as it is capable of operating with reduced number of levels, in the entire modulation range, in the event of any failure in the H-bridges. The transient as well as the steady state performance of the nine-level version of the proposed topology is experimentally verified in the entire modulation range including the over-modulation region.

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Delamination is one of the most commonly occurring defects in laminated composite structures. Under operating fatigue loads on the laminate this delamination could grow and totally delaminate certain number of layers from the base laminate. This will result in loss of both compressive residual strength and buckling margins available. In this paper, geometrically non-linear analysis and evaluation of Strain Energy Release Rates using MVCCI technique is presented. The problems of multiple delamination, effect of temperature exposure and delamination from pin loaded holes are addressed. Numerical results are presented to draw certain inferences of importance to design of high technology composite structures such as aircraft wing.

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The superior catalytic activity along with improved CO tolerance for formic acid electro-oxidation has been demonstrated on a NiO-decorated reduced graphene oxide (rGO) catalyst. The cyclic voltammetry response of rGO-NiO/Pt catalyst elucidates improved CO tolerance and follows direct oxidation pathway. It is probably due to the beneficial effect of residual oxygen groups on rGO support which is supported by FT-IR spectrum. A strong interaction of rGO support with NiO nanoparticles facilitates the removal of CO from the catalyst surface. The chronoamperometric response indicates a higher catalytic activity and stability of rGO-NiO/Pt catalyst than the NiO/Pt and unmodified Pt electrode catalyst for a prolonged time of continuous oxidation of formic acid. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

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In many organisms ``Universal Stress Proteins'' CUSPS) are induced in response to a variety of environmental stresses. Here we report the structures of two USPs, YnaF and YdaA from Salmonella typhimurium determined at 1.8 angstrom and 2.4 angstrom resolutions, respectively. YnaF consists of a single USP domain and forms a tetrameric organization stabilized by interactions mediated through chloride ions. YdaA is a larger protein consisting of two tandem USP domains. Two protomers of YdaA associate to form a structure similar to the YnaF tetramer. YdaA showed ATPase activity and an ATP binding motif G-2X-G-9X-G(S/T/N) was found in its C-terminal domain. The residues corresponding to this motif were not conserved in YnaF although YnaF could bind ATP. However, unlike YdaA, YnaF did not hydrolyse ATP in vitro. Disruption of interactions mediated through chloride ions by selected mutations converted YnaF into an ATPase. Residues that might be important for ATP hydrolysis could be identified by comparing the active sites of native and mutant structures. Only the C-terminal domain of YdaA appears to be involved in ATP hydrolysis. The structurally similar N-terminal domain was found to bind a zinc ion near the segment equivalent to the phosphate binding loop of the C-terminal domain. Mass spectrometric analysis showed that YdaA might bind a ligand of approximate molecular weight 800 daltons. Structural comparisons suggest that the ligand, probably related to an intermediate in lipid A biosynthesis, might bind at a site close to the zinc ion. Therefore, the N-terminal domain of YdaA binds zinc and might play a role in lipid metabolism. Thus, USPs appear to perform several distinct functions such as ATP hydrolysis, altering membrane properties and chloride sensing. (C) 2015 Elsevier Inc. All rights reserved.

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Exascale systems of the future are predicted to have mean time between failures (MTBF) of less than one hour. At such low MTBFs, employing periodic checkpointing alone will result in low efficiency because of the high number of application failures resulting in large amount of lost work due to rollbacks. In such scenarios, it is highly necessary to have proactive fault tolerance mechanisms that can help avoid significant number of failures. In this work, we have developed a mechanism for proactive fault tolerance using partial replication of a set of application processes. Our fault tolerance framework adaptively changes the set of replicated processes periodically based on failure predictions to avoid failures. We have developed an MPI prototype implementation, PAREP-MPI that allows changing the replica set. We have shown that our strategy involving adaptive process replication significantly outperforms existing mechanisms providing up to 20 percent improvement in application efficiency even for exascale systems.