24 resultados para T helper subsets
em CentAUR: Central Archive University of Reading - UK
Resumo:
Toll-like receptors (TLR) recognize microbial and viral patterns and activate dendritic cells (DC). TLR distribution among human DC subsets is heterogeneous: plasmacytoid DC (PDC) express TLR1, 7 and 9, while other DC types do not express TLR9 but express other TLR. Here, we report that mRNA for most TLR is expressed at similar levels by murine splenic DC sub-types, including PDC, but that TLR3 is preferentially expressed by CD8α+ DC while TLR5 and TLR7 are selectively absent from the same subset. Consistent with the latter, TLR7 ligand activates CD8α– DC and PDC, but not CD8α+ DC as measured by survival ex vivo, up-regulation of surface markers and production of IL-12p40. These data suggest that the dichotomy in TLR expression between plasmacytoid and non-plasmacytoid DC is not conserved between species. However, lack of TLR7 expression could restrict the involvement of CD8α+ DC in recognition of certain mouse pathogens.
Resumo:
There is considerable interest in the strain specificity of immune modulation by probiotics. The present study compared the immunomodulatory properties of six probiotic strains of different species and two genera in a human peripheral blood mononuclear cell (PBMC) model in vitro. Live cells of lactobacilli (Lactobacillus casei Shirota, L. rhamnosus GG, L. plantarum NCIMB 8826 and L. reuteri NCIMB 11951) and bifidobacteria (Bifidobacterium longum SP 07/3 and B. bifidum MF 20/5) were individually incubated with PBMC from seven healthy subjects for 24 h. Probiotic strains increased the proportion of CD69+ on lymphocytes, T cells, T cell subsets and natural killer (NK) cells, and increased the proportion of CD25+, mainly on lymphocytes and NK cells. The effects on activation marker expression did not appear to be strain specific. NK cell activity was significantly increased by all six strains, without any significant difference between strains. Probiotic strains increased production of IL-1β, IL-6, IL-10, TNF-α, granulocyte-macrophage colony-stimulating factor and macrophage inflammatory protein 1α to different extents, but had no effect on the production of IL-2, IL-4, IL-5 or TNF-β. The cytokines that showed strain-specific modulation included IL-10, interferon-γ, TNF-α, IL-12p70, IL-6 and monocyte chemotactic protein-1. The Lactobacillus strains tended to promote T helper 1 cytokines, whereas bifidobacterial strains tended to produce a more anti-inflammatory profile. The results suggest that there was limited evidence of strain-specific effects of probiotics with respect to T cell and NK cell activation or NK cell activity, whereas production of some cytokines was differentially influenced by probiotic strains.
Resumo:
We have performed a screen combining subtractive hybridization with PCR to isolate genes that are regulated when neuroepithelial (NE) cells differentiate into neurons. From this screen, we have isolated a number of known genes that have not previously been associated with neurogenesis, together with several novel genes. Here we report that one of these genes, encoding a guanine nucleotide exchange factor (GEF), is regulated during the differentiation of distinct neuronal populations. We have cloned both rat and mouse GEF genes and shown that they are orthologs of the human gene, MR-GEF, which encodes a GEF that specifically activates the small GTPase, Rap1. We have therefore named the rat gene rat mr-gef (rmr-gef) and the mouse gene mouse mr-gef (mmr-gef). Here, we will collectively refer to these two rodent genes as mr-gef. Expression studies show that mr-gef is expressed by young neurons of the developing rodent CNS but not by progenitor cells in the ventricular zone (VZ). The expression pattern of mr-gef during early telencephalic neurogenesis is strikingly similar to that of GABA and the LIM homeobox gene Lhx6, a transcription factor expressed by GABAergic interneurons generated in the ventral telencephalon, some of which migrate into the cortex during development. These observations suggest that mr-gef encodes a protein that is part of a signaling pathway involved in telencephalic neurogenesis; particularly in the development of GABAergic interneurons.
Resumo:
Dendritic cells (DC) can produce Th-polarizing cytokines and direct the class of the adaptive immune response. Microbial stimuli, cytokines, chemokines, and T cell-derived signals all have been shown to trigger cytokine synthesis by DC, but it remains unclear whether these signals are functionally equivalent and whether they determine the nature of the cytokine produced or simply initiate a preprogrammed pattern of cytokine production, which may be DC subtype specific. Here, we demonstrate that microbial and T cell-derived stimuli can synergize to induce production of high levels of IL-12 p70 or IL-10 by individual murine DC subsets but that the choice of cytokine is dictated by the microbial pattern recognition receptor engaged. We show that bacterial components such as CpG-containing DNA or extracts from Mycobacterium tuberculosis predispose CD8alpha(+) and CD8alpha(-)CD4(-) DC to make IL-12 p70. In contrast, exposure of CD8alpha(+), CD4(+) and CD8alpha(-)CD4(-) DC to heat-killed yeasts leads to production of IL-10. In both cases, secretion of high levels of cytokine requires a second signal from T cells, which can be replaced by CD40 ligand. Consistent with their differential effects on cytokine production, extracts from M. tuberculosis promote IL-12 production primarily via Toll-like receptor 2 and an MyD88-dependent pathway, whereas heat-killed yeasts activate DC via a Toll-like receptor 2-, MyD88-, and Toll/IL-1R domain containing protein-independent pathway. These results show that T cell feedback amplifies innate signals for cytokine production by DC and suggest that pattern recognition rather than ontogeny determines the production of cytokines by individual DC subsets.
Resumo:
The functional relationships and properties of different subtypes of dendritic cells (DC) remain largely undefined. To better characterize these cells, we used global gene analysis to determine gene expression patterns among murine CD11c(high) DC subsets. CD4(+), CD8alpha(+), and CD8alpha(-) CD4(-) (double negative (DN)) DC were purified from spleens of normal C57/BL6 mice and analyzed using Affymetrix microarrays. The CD4(+) and CD8alpha(+) DC subsets showed distinct basal expression profiles differing by >200 individual genes. These included known DC subset markers as well as previously unrecognized, differentially expressed CD Ags such as CD1d, CD5, CD22, and CD72. Flow cytometric analysis confirmed differential expression in nine of nine cases, thereby validating the microarray analysis. Interestingly, the microarray expression profiles for DN cells strongly resembled those of CD4(+) DC, differing from them by <25 genes. This suggests that CD4(+) and DN DC are closely related phylogenetically, whereas CD8alpha(+) DC represent a more distant lineage, supporting the historical distinction between CD8alpha(+) and CD8alpha(-) DC. However, staining patterns revealed that in contrast to CD4(+) DC, the DN subset is heterogeneous and comprises at least two subpopulations. Gene Ontology and literature mining analyses of genes expressed differentially among DC subsets indicated strong associations with immune response parameters as well as cell differentiation and signaling. Such associations offer clues to possible unique functions of the CD11c(high) DC subsets that to date have been difficult to define as rigid distinctions.
Resumo:
In this paper we investigate the use of the perfectly matched layer (PML) to truncate a time harmonic rough surface scattering problem in the direction away from the scatterer. We prove existence and uniqueness of the solution of the truncated problem as well as an error estimate depending on the thickness and composition of the layer. This global error estimate predicts a linear rate of convergence (under some conditions on the relative size of the real and imaginary parts of the PML function) rather than the usual exponential rate. We then consider scattering by a half-space and show that the solution of the PML truncated problem converges globally at most quadratically (up to logarithmic factors), providing support for our general theory. However we also prove exponential convergence on compact subsets. We continue by proposing an iterative correction method for the PML truncated problem and, using our estimate for the PML approximation, prove convergence of this method. Finally we provide some numerical results in 2D.(C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.
Resumo:
Modulation of host immunity is an important potential mechanism by which probiotics confer health benefits. This study was designed to investigate the effects of a probiotic strain, Lactobacillus casei Shirota (LcS), on immune function, using human peripheral blood mononuclear cells (PBMC) in vitro. In addition, the role of monocytes in LcS-induced immunity was also explored. LcS promoted natural killer (NK) cell activity and preferentially induced expression of CD69 and CD25 on CD8+ and CD56+ subsets in the absence of any other stimulus. LcS also induced production of IL-1β, IL-6, TNF-α, IL-12 and IL-10 in the absence of lipopolysaccharide (LPS). In the presence of LPS, LcS enhanced IL-1β production, but inhibited LPS-induced IL-10 and IL-6 production, and had no further effect on TNF-α and IL-12 production. Monocyte-depletion significantly reduced the impact of LcS on lymphocyte activation, cytokine production and NK cell activity. In conclusion, LcS preferentially activated cytotoxic lymphocytes in both the innate and specific immune system, which suggests that LcS could potentiate the destruction of infected cells in the body. LcS also induced both pro-inflammatory and anti-inflammatory cytokine production in the absence of LPS, but inhibited LPS-induced cytokine production in some cases. Monocytes play an important role in LcS-induced immunological responses.
Resumo:
Individuals are typically co-infected by a diverse community of microparasites (e.g. viruses or protozoa) and macroparasites (e.g. helminths). Vertebrates respond to these parasites differently, typically mounting T helper type 1 (Th1) responses against microparasites and Th2 responses against macroparasites. These two responses may be antagonistic such that hosts face a 'decision' of how to allocate potentially limiting resources. Such decisions at the individual host level will influence parasite abundance at the population level which, in turn, will feed back upon the individual level. We take a first step towards a complete theoretical framework by placing an analysis of optimal immune responses under microparasite-macroparasite co-infection within an epidemiological framework. We show that the optimal immune allocation is quantitatively sensitive to the shape of the trade-off curve and qualitatively sensitive to life-history traits of the host, microparasite and macroparasite. This model represents an important first step in placing optimality models of the immune response to co-infection into an epidemiological framework. Ultimately, however, a more complete framework is needed to bring together the optimal strategy at the individual level and the population-level consequences of those responses, before we can truly understand the evolution of host immune responses under parasite co-infection.
Resumo:
Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.
Resumo:
Three large deformation rheological tests, the Kieffer dough extensibility system, the D/R dough inflation system and the 2 g mixograph test, were carried out on doughs made from a large number of winter wheat lines and cultivars grown in Poland. These lines and cultivars represented a broad spread in baking performance in order to assess their suitability as predictors of baking volume. The parameters most closely associated with baking volume were strain hardening index, bubble failure strain, and mixograph bandwidth at 10min. Simple correlations with baking volume indicate that bubble failure strain and strain hardening index give the highest correlations, whilst the use of best subsets regression, which selects the best combination of parameters, gave increased correlations with R-2 = 0.865 for dough inflation parameters, R-2 = 0. 842 for Kieffer parameters and R-2 = 0.760 for mixograph parameters. (c) 2007 Elsevier Ltd. All rights reserved.
Resumo:
Simple Adaptive Momentum [1] was introduced as a simple means of speeding the training of multi-layer perceptrons (MLPs) by changing the momentum term depending on the angle between the current and previous changes in the weights of the MLP. In the original paper. the weight changes of the whole network are used in determining this angle. This paper considers adapting the momentum term using certain subsets of these weights. This idea was inspired by the author's object oriented approach to programming MLPs. successfully used in teaching students: this approach is also described. It is concluded that the angle is best determined using the weight changes in each layer separately.
Resumo:
Commensal bacteria, including some species of lactobacilli commonly present in human breast milk, appear to colonize the neonatal gut and contribute to protection against infant infections, suggesting that lactobacilli could potentially modulate immunity. In this study, we evaluated the potential of two Lactobacillus strains isolated from human milk to modulate the activation and cytokine profile of peripheral blood mononuclear cell (PBMC) subsets in vitro. Moreover, these effects were compared to the same probiotic species of non-milk origin. Lactobacillus salivarius CECT5713 and Lactobacillus fermentum CECT5716 at 105, 106 and 107 bacteria/mL were co-cultured with PBMC (106/mL) from 8 healthy donors for 24 h. Activation status (CD69 and CD25 expressions) of natural killer (NK) cells (CD56+), total T cells (CD3+), cytotoxic T cells (CD8+) and CD4+ T cells was determined by flow cytometry. Regulatory T cells (Treg) were also quantified by intracellular Foxp3 evaluation. Regarding innate immunity, NK cells were activated by addition of both Lactobacillus strains, and in particular, the CD8+ NK subset was preferentially induced to highly express CD69 (90%, p<0.05). With respect to acquired immunity, approximately 9% of CD8+ T cells became activated after co-cultivation with L. fermentum or L salivarius. Although CD4+ T cells demonstrated a weaker response, there was a preferential activation of Treg cells (CD4+CD25+Foxp3+) after exposure to both milk probiotic bacteria (p<0.05). Both strains significantly induced the production of a number of cytokines and chemokines, including TNFα, IL-1β, IL-8, MIP-1α, MIP-1β, and GM-CSF, but some strain-specific effects were apparent. This work demonstrates that L salivarius CECT5713 and L. fermentum CECT5716 enhanced both natural and acquired immune responses, as evidenced by the activation of NK and T cell subsets and the expansion of Treg cells, as well as the induction of a broad array of cytokines.
Resumo:
Accurate single trial P300 classification lends itself to fast and accurate control of Brain Computer Interfaces (BCIs). Highly accurate classification of single trial P300 ERPs is achieved by characterizing the EEG via corresponding stationary and time-varying Wackermann parameters. Subsets of maximally discriminating parameters are then selected using the Network Clustering feature selection algorithm and classified with Naive-Bayes and Linear Discriminant Analysis classifiers. Hence the method is assessed on two different data-sets from BCI competitions and is shown to produce accuracies of between approximately 70% and 85%. This is promising for the use of Wackermann parameters as features in the classification of single-trial ERP responses.
Resumo:
Lightning data, collected using a Boltek Storm Tracker system installed at Chilton, UK, were used to investigate the mean response of the ionospheric sporadic-E layer to lightning strokes in a superposed epoch study. The lightning detector can discriminate between positive and negative lightning strokes and between cloud-to-ground ( CG) and inter-cloud ( IC) lightning. Superposed epoch studies carried out separately using these subsets of lightning strokes as trigger events have revealed that the dominant cause of the observed ionospheric enhancement in the Es layer is negative cloud-to-ground lightning.
Resumo:
Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.