Evolutionary conservation of sequence elements controlling cytoplasmic polyadenylylation.

Cytoplasmic polyadenylylation is an evolutionarily conserved mechanism involved in the translational activation of a set of maternal messenger RNAs (mRNAs) during early development. In this report, we show by interspecies injections that Xenopus and mouse use the same regulatory sequences to control cytoplasmic poly(A) addition during meiotic maturation. Similarly, Xenopus and Drosophila embryos exploit functionally conserved signals to regulate polyadenylylation during early post-fertilization development. These experiments demonstrate that the sequence elements that govern cytoplasmic polyadenylylation, and hence one form of translational activation, function across species. We infer that the requisite regulatory sequence elements, and likely the trans-acting components with which they interact, have been conserved since the divergence of vertebrates and arthropods.

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.

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.

A novel neural network based system for assessing risks associated with information technology security breaches

Security remains a top priority for organizations as their information systems continue to be plagued by security breaches. This dissertation developed a unique approach to assess the security risks associated with information systems based on dynamic neural network architecture. The risks that are considered encompass the production computing environment and the client machine environment. The risks are established as metrics that define how susceptible each of the computing environments is to security breaches. ^ The merit of the approach developed in this dissertation is based on the design and implementation of Artificial Neural Networks to assess the risks in the computing and client machine environments. The datasets that were utilized in the implementation and validation of the model were obtained from business organizations using a web survey tool hosted by Microsoft. This site was designed as a host site for anonymous surveys that were devised specifically as part of this dissertation. Microsoft customers can login to the website and submit their responses to the questionnaire. ^ This work asserted that security in information systems is not dependent exclusively on technology but rather on the triumvirate people, process and technology. The questionnaire and consequently the developed neural network architecture accounted for all three key factors that impact information systems security. ^ As part of the study, a methodology on how to develop, train and validate such a predictive model was devised and successfully deployed. This methodology prescribed how to determine the optimal topology, activation function, and associated parameters for this security based scenario. The assessment of the effects of security breaches to the information systems has traditionally been post-mortem whereas this dissertation provided a predictive solution where organizations can determine how susceptible their environments are to security breaches in a proactive way. ^

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

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

CD40 ligation conditions dendritic cell antigen-presenting function through sustained activation of NF-kappa B

An understanding of the biochemical control of dendritic cell (DC) differentiation/activation is essential for improving T cell immunity by various immunotherapeutic approaches, including DC immunization. Ligation of CD40 enhances DC function, including conditioning for CTL priming. NF-kappaB, and particularly RelB, is an essential control pathway for myeloid DC differentiation. Furthermore, RelB regulates B cell Ag-presenting function. We hypothesized that CD40 ligand (CD40L) and TNF-alpha, which differ in their capacity to condition DC, would also differ in their capacity to activate NF-kappaB. DC differentiated for 2 days from monocytes in the presence of GM-CSF and IL-4 were used as a model, as NF-kappaB activity was constitutively low. The capacity of DC to activate T cells following CD40L treatment was enhanced compared with TNF-alpha treatment, and this was NF-kappaB dependent. Whereas RelB/p50 translocation induced by TNF-alpha was attenuated after 6 h, RelB/p50 nuclear translocation induced by CD40L was sustained for at least 24 h. The mechanism of this difference related to enhanced degradation of IkappaBalpha following CD40L stimulation. However, NF-kappaB activation induced by TNF-alpha could be sustained by blocking autocrine IL-10. These data indicate that NF-kappaB activation is essential for T cell activation by DC, and that this function is enhanced if DC NF-kappaB activation is prolonged. Because IL-10 moderates DC NF-kappaB activation by TNF-alpha, sustained NF-kappaB activation can be achieved by blocking IL-10 in the presence of stimuli that induce TNF-alpha.

The t-SNARE syntaxin 4 is regulated during macrophage activation to function in membrane traffic and cytokine secretion

Activation of macrophages with lipopolysaccharide (LPS) induces the rapid synthesis and secretion of proinflammatory cytokines, such as tumor necrosis factor (TNFalpha), for priming the immune response [1, 2]. TNFalpha plays a key role in inflammatory disease [3]; yet, little is known of the intracellular trafficking events leading to its secretion. In order to identify molecules involved in this secretory pathway, we asked whether any of the known trafficking proteins are regulated by LPS. We found that the levels of SNARE proteins were rapidly and significantly up- or downregulated during macrophage activation. A subset of t-SNAREs (Syntaxin 4/SNAP23/Munc18c) known to control regulated exocytosis in other cell types [4, 5] was substantially increased by LPS in a temporal pattern coinciding with peak TNFalpha secretion. Syntaxin 4 formed a complex with Munc18c at the cell surface of macrophages. Functional studies involving the introduction of Syntaxin 4 cDNA or peptides into macrophages implicate this t-SNARE in a rate-limiting step of TNFalpha secretion and in membrane ruffling during macrophage activation. We conclude that in macrophages, SNAREs are regulated in order to accommodate the rapid onset of cytokine secretion and for membrane traffic associated with the phenotypic changes of immune activation. This represents a novel regulatory role for SNAREs in regulated secretion and in macrophage-mediated host defense.

A new function of the Fas-FasL pathway in macrophage activation.

Upon infection with the protozoan parasite Leishmania major, susceptible BALB/c mice develop unhealing lesions associated with the maturation of CD4(+)Th2 cells secreting IL-4. In contrast, resistant C57BL/6 mice heal their lesions, because of expansion and secretion of IFN-gamma of CD4(+) Th1 cells. The Fas-FasL pathway, although not involved in Th cell differentiation, was reported to be necessary for complete resolution of lesions. We investigate here the role of IFN-gamma and IL-4 on Fas-FasL nonapoptotic signaling events leading to the modulation of macrophage activation. We show that addition of FasL and IFN-gamma to BMMø led to their increased activation, as reflected by enhanced secretion of TNF, IL-6, NO, and the induction of their microbicidal activity, resulting in the killing of intracellular L. major. In contrast, the presence of IL-4 decreased the synergy of IFN-gamma/FasL significantly on macrophage activation and the killing of intracellular L. major. These results show that FasL synergizes with IFN-gamma to activate macrophages and that the tight regulation by IFN-gamma and/or IL-4 of the nonapoptotic signaling events triggered by the Fas-FasL pathway affects significantly the activation of macrophages to a microbicidal state and may thus contribute to the pathogenesis of L. major infection.

An activation-independent role of transcription factors in insulator function.

Chromatin insulators are defined as transcriptionally neutral elements that prevent negative or positive influence from extending across chromatin to a promoter. Here we show that yeast subtelomeric anti-silencing regions behave as boundaries to telomere-driven silencing and also allow discontinuous propagation of silent chromatin. These two facets of insulator activity, boundary and silencing discontinuity, can be recapitulated by tethering various transcription activation domains to tandem sites on DNA. Importantly, we show that these insulator activities do not involve direct transcriptional activation of the reporter promoter. These findings predict that certain promoters behave as insulators and partition genomes in functionally independent domains.

FANCD2 binds MCM proteins and controls replisome function upon activation of s phase checkpoint signaling.

Proteins disabled in Fanconi anemia (FA) are necessary for the maintenance of genome stability during cell proliferation. Upon replication stress signaling by ATR, the FA core complex monoubiquitinates FANCD2 and FANCI in order to activate DNA repair. Here, we identified FANCD2 and FANCI in a proteomic screen of replisome-associated factors bound to nascent DNA in response to replication arrest. We found that FANCD2 can interact directly with minichromosome maintenance (MCM) proteins. ATR signaling promoted the transient association of endogenous FANCD2 with the MCM2-MCM7 replicative helicase independently of FANCD2 monoubiquitination. FANCD2 was necessary for human primary cells to restrain DNA synthesis in the presence of a reduced pool of nucleotides and prevented the accumulation of single-stranded DNA, the induction of p21, and the entry of cells into senescence. These data reveal that FANCD2 is an effector of ATR signaling implicated in a general replisome surveillance mechanism that is necessary for sustaining cell proliferation and attenuating carcinogenesis.

Electron donor sites on Y2O3 catalyst as a function of the activation temperature

Cochin University of Science and Technology