Toxicity of eosinophil MBP is repressed by intracellular crystallization and promoted by extracellular aggregation.

Eosinophils are white blood cells that function in innate immunity and participate in the pathogenesis of various inflammatory and neoplastic disorders. Their secretory granules contain four cytotoxic proteins, including the eosinophil major basic protein (MBP-1). How MBP-1 toxicity is controlled within the eosinophil itself and activated upon extracellular release is unknown. Here we show how intragranular MBP-1 nanocrystals restrain toxicity, enabling its safe storage, and characterize them with an X-ray-free electron laser. Following eosinophil activation, MBP-1 toxicity is triggered by granule acidification, followed by extracellular aggregation, which mediates the damage to pathogens and host cells. Larger non-toxic amyloid plaques are also present in tissues of eosinophilic patients in a feedback mechanism that likely limits tissue damage under pathological conditions of MBP-1 oversecretion. Our results suggest that MBP-1 aggregation is important for innate immunity and immunopathology mediated by eosinophils and clarify how its polymorphic self-association pathways regulate toxicity intra- and extracellularly.

Transcriptional repression of serum amyloid A1 by AP -2 contributes to its liver -specific expression

Studies on the transcriptional regulation of serum amyloid A1 (SAA1) gene, a liver specific acute-phase gene, identified a regulatory element in its promoter that functioned to repress (SAA1) gene transcription in nonliver cells. This silencer element interacts with a nuclear protein that is detectable in HeLa cells, fibroblasts and placental tissues but not in liver or liver-derived cells. As the expression pattern of this repressor is consistent with its potential regulatory role in repressing SAA1 expression, and that many other liver gene promoters also contain this repressor binding site, we sought to investigate whether this repressor may have a broader functional role in repressing liver genes. ^ We have utilized protein purification, cell culture, transient and stable gene transfection, and molecular biology approaches to identify this protein and investigate its possible function in the regulation of (SAA1) and other liver genes. Analyses of amino acid sequence of the purified nuclear protein, and western blot and gel shift studies identified the repressor as transcription factor AP-2 or AP-2-like protein. Using transient transfection of DNA into cultured cells, we demonstrate that AP-2 can indeed function as a repressor to inhibit transcription of SAA1 gene promoter. This conclusion is supported by the following experimental results: (1) overexpression of AP-2 in hepatoma cells inhibits conditioned medium (CM)-induced expression of SAA1 promoter; (2) binding of AP-2 to the SAA1 promoter is required for AP-2 repression function; (3) one mechanism by which AP-2 inhibits SAA1 may be by antagonizing the activation function of the strong transactivator NFκB; (4) mutation of AP-2 binding sites results in derepression of SAM promoter in HeLa cells; and (5) inhibition of endogenous AP-2 activity by a dominant-negative mutant abolishes AP-2's inhibitory effect on SAM promoter in HeLa cells. In addition to the SAM promoter, AP-2 also can bind to the promoter regions of six other liver genes tested, suggesting that it may have a broad functional role in restricting the expression of many liver genes in nonliver cells. Consistent with this notion, ectopic expression of AP-2 also represses CM-mediated activation of human third component of complement 3 promoter. Finally, in AP-2-expressing stable hepatoma cell lines, AP-2 inhibits not only the expression of endogenous SAA, but also the expression of several other endogenous liver genes including albumin, α-fetoprotein. ^ Our findings that AP-2 has the ability to repress the expression of liver genes in nonliver cells opens a new avenue of investigation of negative regulation of gene transcription, and should improve our understanding of tissue-specific expression of liver genes. In summary, our data provide evidence suggesting a novel role of AP-2 as a repressor, inhibiting the expression of liver genes in nonliver cells. Thus, the tissue-specific expression of AP-2 may constitute an important mechanism contributing to the liver-specific expression of liver genes. ^

Functional Hierarchy of Simultaneously Expressed Adhesion Receptors: Integrin α2β1 but Not CD44 Mediates MV3 Melanoma Cell Migration and Matrix Reorganization within Three-dimensional Hyaluronan-containing Collagen MatricesV⃞

Haptokinetic cell migration across surfaces is mediated by adhesion receptors including β1 integrins and CD44 providing adhesion to extracellular matrix (ECM) ligands such as collagen and hyaluronan (HA), respectively. Little is known, however, about how such different receptor systems synergize for cell migration through three-dimensionally (3-D) interconnected ECM ligands. In highly motile human MV3 melanoma cells, both β1 integrins and CD44 are abundantly expressed, support migration across collagen and HA, respectively, and are deposited upon migration, whereas only β1 integrins but not CD44 redistribute to focal adhesions. In 3-D collagen lattices in the presence or absence of HA and cross-linking chondroitin sulfate, MV3 cell migration and associated functions such as polarization and matrix reorganization were blocked by anti-β1 and anti-α2 integrin mAbs, whereas mAbs blocking CD44, α3, α5, α6, or αv integrins showed no effect. With use of highly sensitive time-lapse videomicroscopy and computer-assisted cell tracking techniques, promigratory functions of CD44 were excluded. 1) Addition of HA did not increase the migratory cell population or its migration velocity, 2) blocking of the HA-binding Hermes-1 epitope did not affect migration, and 3) impaired migration after blocking or activation of β1 integrins was not restored via CD44. Because α2β1-mediated migration was neither synergized nor replaced by CD44–HA interactions, we conclude that the biophysical properties of 3-D multicomponent ECM impose more restricted molecular functions of adhesion receptors, thereby differing from haptokinetic migration across surfaces.

Ligand-dependent, transcriptionally productive association of the amino- and carboxyl-terminal regions of a steroid hormone nuclear receptor.

The estrogen receptor (ER), a 66-kDa protein that mediates the actions of estrogens in estrogen-responsive tissues, is a member of a large superfamily of nuclear hormone receptors that function as ligand-activated transcription factors. ER shares a conserved structural and functional organization with other members of this superfamily, including two transcriptional activation functions (AFs), one located in its amino-terminal region (AF-1) and the second located in its carboxyl-terminal, ligand-binding region (AF-2). In most promoter contexts, synergism between AF-1 and AF-2 is required for full ER activity. In these studies, we demonstrate a functional interaction of the two AF-containing regions of ER, when expressed as separate polypeptides in mammalian cells, in response to 17 beta-estradiol (E2) and antiestrogen binding. The interaction was transcriptionally productive only in response to E2, and was eliminated by point or deletion mutations that destroy AF-1 or AF-2 activity or E2 binding. Our results suggest a definitive mechanistic role for E2 in the activity of ER--namely, to alter receptor conformation to promote an association of the amino- and carboxyl-terminal regions, leading to transcriptional synergism between AF-1 and AF-2. The productive re assembly of two portions of ER expressed in cells as separate polypeptides demonstrates the evolutionarily conserved modular structural and functional organization of the nuclear hormone receptors. The ligand-dependent interaction of the two AF-containing regions of ER allows for the assembly of a complete activation function from two distinct regions within the same protein, providing a mechanism for hormonally regulated transcription.

Transcription factor Tfec contributes to the IL-4-inducible expression of a small group of genes in mouse macrophages including the granulocyte colony-stimulating factor receptor

Expression of the mouse transcription factor EC (Tfec) is restricted to the myeloid compartment, suggesting a function for Tfec in the development or function of these cells. However, mice lacking Tfec develop normally, indicating a redundant role for Tfec in myeloid cell development. We now report that Tfec is specifically induced in bone marrow-derived macrophages upon stimulation with the Th2 cytokines, IL-4 and IL-13, or LPS. LPS induced a rapid and transient up-regulation of Tfec mRNA expression and promoter activity, which was dependent on a functional NF-kappa B site. IL-4, however, induced a rapid, but long-lasting, increase in Tfec mRNA, which, in contrast to LPS stimulation, also resulted in detectable levels of Tfec protein. IL-4-induced transcription of Tfec was absent in macrophages lacking Stat6, and its promoter depended on two functional Stat6-binding sites. A global comparison of IL-4-induced genes in both wild-type and Tfec mutant macrophages revealed a surprisingly mild phenotype with only a few genes affected by Tfec deficiency. These included the G-CSFR (Csf3r) gene that was strongly up-regulated by IL-4 in wild-type macrophages and, to a lesser extent, in Tfec mutant macrophages. Our study also provides a general definition of the transcriptome in alternatively activated mouse macrophages and identifies a large number of novel genes characterizing this cell type.

Promoter-, cell-, and ligand-specific transactivation responses of the VDRB1 isoform

The vitamin D receptor (VDR) mediates the effects of 1,25(OH)(2)D-3, the active form of vitamin D. The human VDRB1 isoform differs from the originally described VDR by an N-terminal extension of 50 amino acids. Here we investigate cell-, promoter-, and ligand-specific transactivation by the VDRB1 isoform. Transactivation by these isoforms of the cytochrome P450 CYP24 promoter was compared in kidney (HEK293 and COS1), tumor-derived colon (Caco-2, LS174T, and HCT15), and mammary (HS578T and MCF7) cell lines. VDRB1 transactivation in response to 1,25(OH)(2)D-3 was greater in Cost and HCT15 cells (145%), lower in HEK293 and Caco-2 cells (70-85%) and similar in other cell lines tested. By contrast, on the cytochrome P450 CYP3A4 promoter, 1,25(OH)(2)D-3-induced VDRB1 transactivation was significantly lower than VDRA in Caco-2 (68%), but comparable to VDRA in HEK293 and COS1 cells. Ligand-dependence of VDRB1 differential transactivation was investigated using the secondary bile acid lithocholic acid (LCA). On the CYP24 promoter LCA-induced transactivation was similar for both isoforms in COS1, whereas in Caco-2 and HEK293 cells VDRB1 was less active. On the CYP3A4 promoter, LCA activation of VDRB1 was comparable to VDRA in all the cell lines tested. Mutational analysis indicated that both the 1,25(OH)(2)D-3 and LCA-regulated activities of both VDR isoforms required a functional ligand-dependent activation function (AF-2) domain. In gel shift assays VDR:DNA complex formation was stronger in the presence of 1,25(OH)(2)D-3 than with LCA. These results indicate that regulation of VDRB1 transactivation activity is dependent on cellular context, promoter, and the nature of the ligand. (c) 2005 Elsevier Inc. All rights reserved.

Disruption prediction at JET [Joint European Torus]

The sudden loss of the plasma magnetic confinement, known as disruption, is one of the major issue in a nuclear fusion machine as JET (Joint European Torus), Disruptions pose very serious problems to the safety of the machine. The energy stored in the plasma is released to the machine structure in few milliseconds resulting in forces that at JET reach several Mega Newtons. The problem is even more severe in the nuclear fusion power station where the forces are in the order of one hundred Mega Newtons. The events that occur during a disruption are still not well understood even if some mechanisms that can lead to a disruption have been identified and can be used to predict them. Unfortunately it is always a combination of these events that generates a disruption and therefore it is not possible to use simple algorithms to predict it. This thesis analyses the possibility of using neural network algorithms to predict plasma disruptions in real time. This involves the determination of plasma parameters every few milliseconds. A plasma boundary reconstruction algorithm, XLOC, has been developed in collaboration with Dr. D. Ollrien and Dr. J. Ellis capable of determining the plasma wall/distance every 2 milliseconds. The XLOC output has been used to develop a multilayer perceptron network to determine plasma parameters as ?i and q? with which a machine operational space has been experimentally defined. If the limits of this operational space are breached the disruption probability increases considerably. Another approach for prediction disruptions is to use neural network classification methods to define the JET operational space. Two methods have been studied. The first method uses a multilayer perceptron network with softmax activation function for the output layer. This method can be used for classifying the input patterns in various classes. In this case the plasma input patterns have been divided between disrupting and safe patterns, giving the possibility of assigning a disruption probability to every plasma input pattern. The second method determines the novelty of an input pattern by calculating the probability density distribution of successful plasma patterns that have been run at JET. The density distribution is represented as a mixture distribution, and its parameters arc determined using the Expectation-Maximisation method. If the dataset, used to determine the distribution parameters, covers sufficiently well the machine operational space. Then, the patterns flagged as novel can be regarded as patterns belonging to a disrupting plasma. Together with these methods, a network has been designed to predict the vertical forces, that a disruption can cause, in order to avoid that too dangerous plasma configurations are run. This network can be run before the pulse using the pre-programmed plasma configuration or on line becoming a tool that allows to stop dangerous plasma configuration. All these methods have been implemented in real time on a dual Pentium Pro based machine. The Disruption Prediction and Prevention System has shown that internal plasma parameters can be determined on-line with a good accuracy. Also the disruption detection algorithms showed promising results considering the fact that JET is an experimental machine where always new plasma configurations are tested trying to improve its performances.

In vivo and in vitro aspects of imidazoline-2(1/2)sites within the central nervous system

The in vivo and in vitro characteristics of the I2 binding site were probed using the technique of drug discrimination and receptor autoradiography. Data presented in this thesis indicates the I2 ligand 2-BFI generates a cue in drug discrimination. Further studies indicated agmatine, a proposed endogenous imidazoline ligand, and a number of imidazoline and imidazole analogues of 2-BFI substitute significantly for 2-BFI. In addition to specific I2 ligands the administration of NRl's (noradrenaline reuptake inhibitors), the sympathomimetic d-amphetamine, the α1-adrenoceptor agonist methoxamine, but not the β1 agonist dobutamine or the β2 agonist salbutamol, gave rise to significant levels of substitution for the 2-BFI cue. The administration of the α1-adrenoceptor antagonist WB4101, prior to 2- BFI itself significantly reduced levels of 2-BFI appropriate responding. Administration of the reversible MAO-A inhibitors moclobemide and Ro41-1049, but not the reversible MAO-B inhibitors lazabemide and Ro16-6491, gave rise to potent dose dependent levels of substitution for the 2-BFI cue. Further studies indicated the administration of a number of β-carbolines and the structurally related indole alkaloid ibogaine also gave rise to dose dependent significant levels of substitution. Due to the relationship of indole alkaloids to serotonin the 5-HT releaser fenfluramine and a number of SSRI's (selective serotonin reuptake inhibitor) were also administered and these compounds gave rise to significant partial (20-80% responses to the 2-BFI lever) levels of substitution. The autoradiographical studies reported here indicate [3H]2-BFI labels I2 sites within the rat arcuate nucleus, area postrema, pineal gland, interpeduncular nucleus and subfornical organ. Subsequent experiments confirmed that the drug discrimination dosing schedule significantly increases levels of [3H]2-BFI 12 binding within two of these nuclei. However, levels of [3H]2-BFI specific binding were significantly reduced within four of these nuclei after chronic treatment with the irreversible MAO inhibitors deprenyl and tranylcypromine but not pargyline, which only reduced levels significantly in two. Further autoradiographical studies indicated that the distribution of [3H]2-BFI within the C57/B mouse compares favourably to that within the rat. Comparison of these levels of binding to those from transgenic mice who over-express MAO-B indicates two possibly distinct populations of [3H]2-BFI 12 sites exist in mouse brain. The data presented here indicates the 2-BFI cue is associated with the selective activation of α1-adrenoceptors and possibly 5-HT receptors. 2-BFI trained rats recognise reversible MAO-A but not MAO-B inhibitors. However, data within this thesis indicates the autoradiographical distribution of I2 sites bears a closer resemblance to that of MAO-B not MAO-A and further studies using transgenic mice that over-express MAO-B suggests a non-MAO-B I2 site exists in mouse brain.

Low temperature 1,1,1-trichloroethane dehydrochlorination over Pt catalysts:from model surfaces to the real world

Fast X-ray photoelectron spectroscopy reveals efficient C–Cl activation of 1,1,1-trichloroethane occurs over platinum surfaces at 150 K, and in the presence of hydrogen, sustained ambient temperature dehydrochlorination to HCl and ethane is possible over supported Pt/Al2O3 catalysts.

Double-Wavelet Neuron Based on Analytical Activation Functions

In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Asymptotic and structural properties of special cases of the Wright function arising in probability theory

This analysis paper presents previously unknown properties of some special cases of the Wright function whose consideration is necessitated by our work on probability theory and the theory of stochastic processes. Specifically, we establish new asymptotic properties of the particular Wright function 1Ψ1(ρ, k; ρ, 0; x) = X∞ n=0 Γ(k + ρn) Γ(ρn) x n n! (|x| < ∞) when the parameter ρ ∈ (−1, 0)∪(0, ∞) and the argument x is real. In the probability theory applications, which are focused on studies of the Poisson-Tweedie mixtures, the parameter k is a non-negative integer. Several representations involving well-known special functions are given for certain particular values of ρ. The asymptotics of 1Ψ1(ρ, k; ρ, 0; x) are obtained under numerous assumptions on the behavior of the arguments k and x when the parameter ρ is both positive and negative. We also provide some integral representations and structural properties involving the ‘reduced’ Wright function 0Ψ1(−−; ρ, 0; x) with ρ ∈ (−1, 0) ∪ (0, ∞), which might be useful for the derivation of new properties of members of the power-variance family of distributions. Some of these imply a reflection principle that connects the functions 0Ψ1(−−;±ρ, 0; ·) and certain Bessel functions. Several asymptotic relationships for both particular cases of this function are also given. A few of these follow under additional constraints from probability theory results which, although previously available, were unknown to analysts.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model