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

Identificação de genes em Chromobacterium violaceum relacionados à resposta ao estresse

The sequencing of the genome of Chromobacterium violaceum identified one single circular chromosome of 4.8 Mb, in which approximately 40% of the founded ORFs are classified as hypothetical conserved or hypothetical. Some genic regions of biotechnological and biological interest had been characterized, e. g., environmental detoxification and DNA repair genes, respectively. Given this fact, the aim of this work was to identify genes of C. violaceum related to stress response, as the ones involved with mechanisms of DNA repair and/or genomic integrity maintenance. For this, a genomic library of C. violaceum was built in Escherichia coli strain DH10B (RecA-), in which clones were tested to UVC resistance, resulting in five candidates clones. In the PLH6A clone were identified four ORFs (CV_3721 to 3724). Two ORFs, CV_3722 and CV_3724, were subcloned and a synergic complementation activity was observed. The occurrence of an operon was confirmed using cDNA from C. violaceum in a RT-PCR assay. Further, it was observed the induction of the operon after the treatment with UVC. Thus, this operon was related to the stress response in C. violaceum. The mutagenesis assay with rifampicin after the treatment with UVC light showed high frequency of mutagenicity for the ORF CV_3722 (Pol III δ subunit). In this way, we propose that the C. violaceum δ subunit can act in DH10B in the translesion synthesis using Pol IV in a RecA independent-manner pathway. In growth curve assays other four clones (PLE1G, PLE7B, PLE10B and PLE12H) were able to complement the function at the dose 5 J/m2 and in mutagenicity assays PLE7B, PLE10B and PLE12H showed frequencies of mutation with significant differences upon the control (DH10B), demonstrating that in some way they are involved with the stress response in C. violaceum. These clones appear to be interrelated, probably regulated by a messenger molecule (eg., nucleotide c-di-GMP) and/or global regulatory molecule (eg., σS subunit of RNA polymerase).The results obtained contribute for a better genetic knowledge of this specie and its response mechanisms to environmental stress.

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Postharvest harpin or Bacillus thuringiensis treatments suppress citrus black spot in 'Valencia' oranges

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Previsão do módulo de elasticidade transversal de compósitos unidirecionais através de redes neurais mistas

The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from different networks were developed, one with only two inputs, one with three inputs and the third with mixed architecture combining an ANN with a model developed by Halpin-Tsai. After algorithm training, the results demonstrate that the use of ANNs is quite promising, given that when they were compared with those of the Halpín-Tsai mathematical model, higher correlation coefficient values and lower root mean square values were observed

Recuperação e purificação de quitosanases usando adsorção em leito expandido com streamline DEAE com modelagem e simulação usando redes neurais

Expanded Bed Adsorption (EBA) is an integrative process that combines concepts of chromatography and fluidization of solids. The many parameters involved and their synergistic effects complicate the optimization of the process. Fortunately, some mathematical tools have been developed in order to guide the investigation of the EBA system. In this work the application of experimental design, phenomenological modeling and artificial neural networks (ANN) in understanding chitosanases adsorption on ion exchange resin Streamline® DEAE have been investigated. The strain Paenibacillus ehimensis NRRL B-23118 was used for chitosanase production. EBA experiments were carried out using a column of 2.6 cm inner diameter with 30.0 cm in height that was coupled to a peristaltic pump. At the bottom of the column there was a distributor of glass beads having a height of 3.0 cm. Assays for residence time distribution (RTD) revelead a high degree of mixing, however, the Richardson-Zaki coefficients showed that the column was on the threshold of stability. Isotherm models fitted the adsorption equilibrium data in the presence of lyotropic salts. The results of experiment design indicated that the ionic strength and superficial velocity are important to the recovery and purity of chitosanases. The molecular mass of the two chitosanases were approximately 23 kDa and 52 kDa as estimated by SDS-PAGE. The phenomenological modeling was aimed to describe the operations in batch and column chromatography. The simulations were performed in Microsoft Visual Studio. The kinetic rate constant model set to kinetic curves efficiently under conditions of initial enzyme activity 0.232, 0.142 e 0.079 UA/mL. The simulated breakthrough curves showed some differences with experimental data, especially regarding the slope. Sensitivity tests of the model on the surface velocity, axial dispersion and initial concentration showed agreement with the literature. The neural network was constructed in MATLAB and Neural Network Toolbox. The cross-validation was used to improve the ability of generalization. The parameters of ANN were improved to obtain the settings 6-6 (enzyme activity) and 9-6 (total protein), as well as tansig transfer function and Levenberg-Marquardt training algorithm. The neural Carlos Eduardo de Araújo Padilha dezembro/2013 9 networks simulations, including all the steps of cycle, showed good agreement with experimental data, with a correlation coefficient of approximately 0.974. The effects of input variables on profiles of the stages of loading, washing and elution were consistent with the literature

Nitric oxide release using natural rubber latex as matrix

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Large-scale transcriptome analyses reveal new genetic marker candidates of head, neck, and thyroid cancer

A detailed genome mapping analysis of 213,636 expressed sequence tags (EST) derived from nontumor and tumor tissues of the oral cavity, larynx, pharynx, and thyroid was done. Transcripts matching known human genes were identified; potential new splice variants were flagged and subjected to manual curation, pointing to 788 putatively new alternative splicing isoforms, the majority (75%) being insertion events. A subset of 34 new splicing isoforms (5% of 788 events) was selected and 23 (68%) were confirmed by reverse transcription-PCR and DNA sequencing. Putative new genes were revealed, including six transcripts mapped to well-studied chromosomes such as 22, as well as transcripts that mapped to 253 intergenic regions. In addition, 2,251 noncoding intronic RNAs, eventually involved in transcriptional regulation, were found. A set of 250 candidate markers for loss of heterozygosis or gene amplification was selected by identifying transcripts that mapped to genomic regions previously known to be frequently amplified or deleted in head, neck, and thyroid tumors. Three of these markers were evaluated by quantitative reverse transcription-PCR in an independent set of individual samples. Along with detailed clinical data about tumor origin, the information reported here is now publicly available on a dedicated Web site as a resource for further biological investigation. This first in silico reconstruction of the head, neck, and thyroid transcriptomes points to a wealth of new candidate markers that can be used for future studies on the molecular basis of these tumors. Similar analysis is warranted for a number of other tumors for which large EST data sets are available.

Dor: aspectos atuais da sensibilização periférica e central

JUSTIFICATIVA E OBJETIVOS: As pesquisas recentes têm focalizado a plasticidade bioquímica e estrutural do sistema nervoso decorrente da lesão tissular. Os mecanismos envolvidos na transição da dor aguda para crônica são complexos e envolvem a interação de sistemas receptores e o fluxo de íons intracelulares, sistemas de segundo mensageiro e novas conexões sinápticas. O objetivo deste artigo foi discutir os novos mecanismos que envolvem a sensibilização periférica e central. CONTEÚDO: A lesão tissular provoca aumento na resposta dos nociceptores, chamada de sensibilização ou facilitação. Esses fenômenos iniciam-se após a liberação local de mediadores inflamatórios e a ativação de células do sistema imune ou de receptores específicos no sistema nervoso periférico e central. CONCLUSÕES: As lesões do tecido e dos neurônios resultam em sensibilização de nociceptores e facilitação da condução nervosa central e periférica.

Triiodothyronine (T3) does not induce RANKL expression in rat ROS 17/2.8 cells

Osteoclastogenesis may be regulated via activation of the RANK/RANKL (receptor activator of nuclear factor-kappa B/ receptor activator of nuclear factor-kappa B ligand) system, which is mediated by osteoblasts. However, the bone loss mechanism induced by T3 (triiodothyronine) is still controversial. In this study, osteoblastic lineage rat cells (ROS 17/2.8) were treated with T3 (10(-8) M 10(-9) 10 M, and 10(-10) M), and RANKL mRNA (messenger RNA) expression was measured by semiquantitative RT-PCR. Our results show that T3 concentrations used did not significantly enhance RANKL expression compared to controls without hormone treatment. This data suggests that other mechanisms, unrelated to the RANK/RANKL system, might be to activate osteoclast differentiation in these cells.

FGF10 inhibits dominant follicle growth and estradiol secretion in vivo in cattle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Strand Analysis, a free online program for the computational identification of the best RNA interference (RNAi) targets based on Gibbs free energy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Schistosoma mansoni: Evaluation of an RNAi-based treatment targeting HGPRTase gene

Hypoxanthine-guanine phosphoribosyltransferase (HGPRTase) is an essential gene of the parasite Schistosoma mansoni and it is well conserved in its hosts (mouse and human) at the protein but not at the RNA level. This feature prompted us to assess RNA interference (RNAi) to combat schistosomiasis. Small interfering RNAs (siRNAs) were Produced against HGPRTase, injected in infected mice and the number of worms was counted six days after injection. The total number of parasites was reduced by approximately 27% after treatment. RT-PCR analyzes showed a significant reduction in parasite target mRNA but not in host's homologue. The use of low doses of molecules did not oversaturate si- or miRNA pathways as mice survival rates were not affected by siRNAs. This is the first successful in vivo demonstration of a RNAi-based treatment against schistosomiasis. We believe that improvements in molecule delivery and an increase on siRNA dose could rapidly eliminate parasite. (c) 2007 Elsevier B.V. All rights reserved.

The 20S proteasome alpha(5) subunit of Arabidopsis thaliana carries an RNase activity and interacts in planta with the Lettuce mosaic potyvirus HcPro protein

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)