Segurança em redes sem fio: estudo sobre o desenvolvimento de conjuntos de dados para comparação de IDS

Pós-graduação em Engenharia Elétrica - FEIS

RANKL expression is differentially modulated by TLR2 and TLR4 signaling in fibroblasts and osteoblasts

Resident, non-immune cells express various pattern-recognition receptors and produce inflammatory cytokines in response to microbial antigens, during the innate immune response. Alveolar bone resorption is the hallmark of destructive periodontitis and it is caused by the host response to bacteria and their mediators present on the biofilm. The balance between the expression levels of receptor activator of nuclear factorkappa B ligand (RANKL) and osteoprotegerin (OPG) is pivotal for osteoclast differentiation and activity and has been implicated in the progression of bone loss in periodontitis. To assess the contribution of resident cells to the bone resorption mediated by innate immune signaling, we stimulated fibroblasts and osteoblastic cells with LPS from. Escherichia coli (TLR4 agonist), Porphyromonas gingivalis (TLR2 and -4 agonist), and interleukin-1 beta (as a control for cytokine signaling through Toll/IL-1receptor domain) in time-response experiments. Expression of RANKL and OPG mRNA was studied by RT-PCR, whereas the production of RANKL protein and the activation of p38 MAPK and NF-kB signaling pathways were analyzed by western blot. We used biochemical inhibitors to assess the relative contribution of p38 MAPK and NF-kB signaling to the expression of RANKL and OPG induced by TLR2, -4 and IL1β in these cells. Both p38 MAPK and NFkB pathways were activated by these stimuli in fibroblasts and osteoblasts, but the kinetics of this activation varied in each cell type and with the nature of the stimulation. E. coli LPS was a stronger inducer of RANKL mRNA in fibroblasts, whereas LPS from P. gingivalis downregulated RANKL mRNA in periodontal ligament cells but increased its expression in osteoblasts. IL-1β induced RANKL in both cell types and without a marked effect on OPG expression. p38 MAPK was more relevant than NF-kB for the expression of RANKL and OPG in these cell types.

Interleukin-18 increases TLR4 and mannose receptor expression and modulates cytokine production in human monocytes

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

Paracoccidioides brasiliensis interferes on dendritic cells maturation by inhibiting PGE(2) production

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

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Uso de Redes Neurais ARTMAP Nebulosas para a classificação de padrões em sinais ECoG relacionados ao movimento dos dedos

Pós-graduação em Engenharia Elétrica - FEIS

Preliminary diagnosis of ophtalmological diseases through machine learning techniques

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

Papel dos receptores TLR2 e TLR4 na produção de citocinas em pacientes chagásicos crônicos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Quantitative Chemical Profile and Multivariate Statistical Analysis of Alembic Distilled Sugarcane Spirit Fractions

Concentrations of 39 organic compounds were determined in three fractions (head, heart and tail) obtained from the pot still distillation of fermented sugarcane juice. The results were evaluated using analysis of variance (ANOVA), Tukey's test, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). According to PCA and HCA, the experimental data lead to the formation of three clusters. The head fractions give rise to a more defined group. The heart and tail fractions showed some overlap consistent with its acid composition. The predictive ability of calibration and validation of the model generated by LDA for the three fractions classification were 90.5 and 100%, respectively. This model recognized as the heart twelve of the thirteen commercial cachacas (92.3%) with good sensory characteristics, thus showing potential for guiding the process of cuts.

Toll-like receptor 9 activation: a novel mechanism linking placenta-derived mitochondrial DNA and vascular dysfunction in pre-eclampsia

Emerging evidence suggests that in addition to being the 'power houses' of our cells, mitochondria facilitate effector responses of the immune system. Cell death and injury result in the release of mtDNA (mitochondrial DNA) that acts via TLR9 (Toll-like receptor 9), a pattern recognition receptor of the immune system which detects bacterial and viral DNA but not vertebrate DNA. The ability of mtDNA to activate TLR9 in a similar fashion to bacterial DNA stems from evolutionarily conserved similarities between bacteria and mitochondria. mtDNA may be the trigger of systemic inflammation in pathologies associated with abnormal cell death. PE (pre-eclampsia) is a hypertensive disorder of pregnancy with devastating maternal and fetal consequences. The aetiology of PE is unknown and removal of the placenta is the only effective cure. Placentas from women with PE show exaggerated necrosis of trophoblast cells, and circulating levels of mtDNA are higher in pregnancies with PE. Accordingly, we propose the hypothesis that exaggerated necrosis of trophoblast cells results in the release of mtDNA, which stimulates TLR9 to mount an immune response and to produce systemic maternal inflammation and vascular dysfunction that lead to hypertension and IUGR (intra-uterine growth restriction). The proposed hypothesis implicates mtDNA in the development of PE via activation of the immune system and may have important preventative and therapeutic implications, because circulating mtDNA may be potential markers of early detection of PE, and anti-TLR9 treatments may be promising in the management of the disease.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.