935 resultados para UBP domain


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Cells respond to DNA damage in a complex way and the fate of damaged cells depends on the balance between pro- and antiapoptotic signals. This is of crucial importance in cancer as genotoxic stress is implied both in oncogenesis and in classical tumor therapies. p53-induced protein with a death domain (PIDD), initially described as a p53-inducible gene, is one of the molecular switches able to activate a survival or apoptotic program. Two isoforms of PIDD, PIDD (isoform 1) and LRDD (isoform 2), have already been reported and we describe here a third isoform. These three isoforms are differentially expressed in tissues and cell lines. Genotoxic stress only affects PIDD isoform 3 mRNA levels, whereas isoforms 1 and 2 mRNA levels remain unchanged. All isoforms are capable of activating nuclear factor-kappaB in response to genotoxic stress, but only isoform 1 interacts with RIP-associated ICH-1/CED-3 homologous protein with a death domain and activates caspase-2. Isoform 2 counteracts the pro-apoptotic function of isoform 1, whereas isoform 3 enhances it. Thus, the differential splicing of PIDD mRNA leads to the formation of at least three proteins with antagonizing/agonizing functions, thereby regulating cell fate in response to DNA damage

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The outcome of infection depends on multiple layers of immune regulation, with innate immunity playing a decisive role in shaping protection or pathogenic sequelae of acquired immunity. The contribution of pattern recognition receptors and adaptor molecules in immunity to malaria remains poorly understood. Here, we interrogate the role of the caspase recruitment domain-containing protein 9 (CARD9) signaling pathway in the development of experimental cerebral malaria (ECM) using the murine Plasmodium berghei ANKA infection model. CARD9 expression was upregulated in the brains of infected wild-type (WT) mice, suggesting a potential role for this pathway in ECM pathogenesis. However, P. berghei ANKA-infected Card9(-/-) mice succumbed to neurological signs and presented with disrupted blood-brain barriers similar to WT mice. Furthermore, consistent with the immunological features associated with ECM in WT mice, Card9(-/-) mice revealed (i) elevated levels of proinflammatory responses, (ii) high frequencies of activated T cells, and (iii) CD8(+) T cell arrest in the cerebral microvasculature. We conclude that ECM develops independently of the CARD9 signaling pathway.

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RelA (NF-kappaB) is a transcription factor inducible by distinct stimuli in many different cell types. To find new cell type specific cofactors of NF-kappaB dependent transcription, we isolated RelA transcription activation domain binding proteins from the nuclear extracts of three different cell types. Analysis by electrophoresis and liquid chromatography tandem mass spectrometry identified several novel putative molecular partners. Some were strongly enriched in the complex formed from the nuclear extracts of specific cell types.

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Tumors are often compared to wounds that do not heal, where the crosstalk between tumor cells and their surrounding stroma is crucial at all stages of development, from the initial primary growth to metastasis. Similar to wound healing, fibroblasts in the tumor stroma differentiate into myofibroblasts, also referred to as "cancer-associated fibroblasts" (CAFs), primarily, but not exclusively, in response to transforming growth factor-ß (TGF-ß). Myofibroblasts in turn enhance tumor progression by remodeling the stroma. Among molecules implicated in stroma remodeling, matrix metalloproteinases (MMPs), and MMP-g in particular, play a prominent role. However, the mechanisms that regulate MMP-g activation and function remain poorly understood. Recent evidence indicates that tumor cell surface association of MMP-g is an important event in its activation, and more generally in tumor growth and invasion. In the present work we address the potential association of MMP-g activity with cell-surface recruitment to human fibroblasts. We show for the first time that recruitment of MMP-g to the MRC-5 fibroblast cell surface occurs through the fibronectin-like (FN) domain, shared only by MMP-g and MMP-2 among all the MMPs. Functional assays suggest that both the pro- and active form of MMP-g trigger a-smooth muscle actin (aSMA) expression in resting fibroblasts that reflects myofibroblast differentiation, possibly through TGF-ß activation. Moreover, the FN domain of MMP-g inhibits both MMP-g-induced TGF-ß activation and aSMA expression by sequestering MMP-g. Xenograft experiments in NOD/SCID mice using HT1080 fibrosarcoma or MDA-MD231 breast adenocarcinoma cells stably expressing the FN domain of MMP-g revealed no changes in primary tumor growth. However, in the context of metastasis, expression of the FN domain by these same tumor cells dramatically increased their metastatic proclivity whereas expression of wt MMP-g either promoted no change or actually reduced the number of metastases. We observed a decrease of an active form of MMP-g in MDA-MB231 cells overexpressing the FN domain suggesting that the FN domain may inhibit MMP-g activity in Tumors are often compared to wounds that do not heal, where the crosstalk between tumor cells and their surrounding stroma is crucial at all stages of development, from the initial primary growth to metastasis. Similar to wound healing, fibroblasts in the tumor stroma differentiate into myofibroblasts, also referred to as "cancer-associated fibroblasts" (CAFs), primarily, but not exclusively, in response to transforming growth factor-ß (TGF-ß). Myofibroblasts in turn enhance tumor progression by remodeling the stroma. Among molecules implicated in stroma remodeling, matrix metalloproteinases (MMPs), and MMP-g in particular, play a prominent role. However, the mechanisms that regulate MMP-g activation and function remain poorly understood. Recent evidence indicates that tumor cell surface association of MMP-g is an important event in its activation, and more generally in tumor growth and invasion. In the present work we address the potential association of MMP-g activity with cell-surface recruitment to human fibroblasts. We show for the first time that recruitment of MMP-g to the MRC-5 fibroblast cell surface occurs through the fibronectin-like (FN) domain, shared only by MMP-g and MMP-2 among all the MMPs. Functional assays suggest that both the pro- and active form of MMP-g trigger a-smooth muscle actin (aSMA) expression in resting fibroblasts that reflects myofibroblast differentiation, possibly through TGF-ß activation. Moreover, the FN domain of MMP-g inhibits both MMP-g-induced TGF-ß activation and aSMA expression by sequestering MMP-g. Xenograft experiments in NOD/SCID mice using HT1080 fibrosarcoma or MDA-MD231 breast adenocarcinoma cells stably expressing the FN domain of MMP-9 revealed no changes in primary tumor growth. However, in the context of metastasis, expression of the FN domain by these same tumor cells dramatically increased their metastatic proclivity whereas expression of wt MMP-g either promoted no change or actually reduced the number of metastases. We observed a decrease of an active form of MMP-9 in MDA-MB231 cells overexpressing the FN domain suggesting that the FN domain may inhibit MMP-9 activity in those cells and therefore prevent MMP-9-induced activation of TGF-b, which results in increased invasion. Curiously, xenografts of SW480 colorectal adenocarcinoma cells stably expressing the FN domain of MMP-9 displayed reduced growth at both the primary (subcutaneous) injection site and the lungs of NOD/SCID mice, in experimental metastasis assays, whilst the same cells overexpressing wt MMP-9 showed enhanced growth and dissemination. Gelatin zymography of conditioned medium revealed that these effects may be due to the FN domain, which displaces MMP-9 from SW480 cell surface. These observations suggest a dual role of MMP-9 and its FN domain in primary tumor growth and metastasis, underscoring the notion that the effect of MMP-9 on tumor cells may depend on the cell type and highlighting possible protective effects of MMPs in tumor progression.

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In this paper we propose a novel unsupervised approach to learning domain-specific ontologies from large open-domain text collections. The method is based on the joint exploitation of Semantic Domains and Super Sense Tagging for Information Retrieval tasks. Our approach is able to retrieve domain specific terms and concepts while associating them with a set of high level ontological types, named supersenses, providing flat ontologies characterized by very high accuracy and pertinence to the domain.

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Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

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The study of the thermal behavior of complex packages as multichip modules (MCM¿s) is usually carried out by measuring the so-called thermal impedance response, that is: the transient temperature after a power step. From the analysis of this signal, the thermal frequency response can be estimated, and consequently, compact thermal models may be extracted. We present a method to obtain an estimate of the time constant distribution underlying the observed transient. The method is based on an iterative deconvolution that produces an approximation to the time constant spectrum while preserving a convenient convolution form. This method is applied to the obtained thermal response of a microstructure as analyzed by finite element method as well as to the measured thermal response of a transistor array integrated circuit (IC) in a SMD package.

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The c-Jun N-terminal kinase (JNK) is a mitogen-activated protein kinase (MAPK) activated by stress-signals and involved in many different diseases. Previous results proved the powerful effect of the cell permeable peptide inhibitor d-JNKI1 (d-retro-inverso form of c-Jun N-terminal kinase-inhibitor) against neuronal death in CNS diseases, but the precise features of this neuroprotection remain unclear. We here performed cell-free and in vitro experiments for a deeper characterization of d-JNKI1 features in physiological conditions. This peptide works by preventing JNK interaction with its c-Jun N-terminal kinase-binding domain (JBD) dependent targets. We here focused on the two JNK upstream MAPKKs, mitogen-activated protein kinase kinase 4 (MKK4) and mitogen-activated protein kinase kinase 7 (MKK7), because they contain a JBD homology domain. We proved that d-JNKI1 prevents MKK4 and MKK7 activity in cell-free and in vitro experiments: these MAPKK could be considered not only activators but also substrates of JNK. This means that d-JNKI1 can interrupt downstream but also upstream events along the JNK cascade, highlighting a new remarkable feature of this peptide. We also showed the lack of any direct effect of the peptide on p38, MEK1, and extracellular signal-regulated kinase (ERK) in cell free, while in rat primary cortical neurons JNK inhibition activates the MEK1-ERK-Ets1/c-Fos cascade. JNK inhibition induces a compensatory effect and leads to ERK activation via MEK1, resulting in an activation of the survival pathway-(MEK1/ERK) as a consequence of the death pathway-(JNK) inhibition. This study should hold as an important step to clarify the strong neuroprotective effect of d-JNKI1.

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It is very well known that the first succesful valuation of a stock option was done by solving a deterministic partial differential equation (PDE) of the parabolic type with some complementary conditions specific for the option. In this approach, the randomness in the option value process is eliminated through a no-arbitrage argument. An alternative approach is to construct a replicating portfolio for the option. From this viewpoint the payoff function for the option is a random process which, under a new probabilistic measure, turns out to be of a special type, a martingale. Accordingly, the value of the replicating portfolio (equivalently, of the option) is calculated as an expectation, with respect to this new measure, of the discounted value of the payoff function. Since the expectation is, by definition, an integral, its calculation can be made simpler by resorting to powerful methods already available in the theory of analytic functions. In this paper we use precisely two of those techniques to find the well-known value of a European call

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We have studied domain growth during spinodal decomposition at low temperatures. We have performed a numerical integration of the deterministic time-dependent Ginzburg-Landau equation with a variable, concentration-dependent diffusion coefficient. The form of the pair-correlation function and the structure function are independent of temperature but the dynamics is slower at low temperature. A crossover between interfacial diffusion and bulk diffusion mechanisms is observed in the behavior of the characteristic domain size. This effect is explained theoretically in terms of an equation of motion for the interface.

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We present numerical results of the deterministic Ginzburg-Landau equation with a concentration-dependent diffusion coefficient, for different values of the volume fraction phi of the minority component. The morphology of the domains affects the dynamics of phase separation. The effective growth exponents, but not the scaled functions, are found to be temperature dependent.

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Ginzburg-Landau equations with multiplicative noise are considered, to study the effects of fluctuations in domain growth. The equations are derived from a coarse-grained methodology and expressions for the resulting concentration-dependent diffusion coefficients are proposed. The multiplicative noise gives contributions to the Cahn-Hilliard linear-stability analysis. In particular, it introduces a delay in the domain-growth dynamics.