A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Análise proposicional quantitativa aplicada à pesquisa em administração

The analysis of interviews with open-ended questions is a common practice amongst researchers in the field of Management. The difficulty therein is to convert the linguistic data into categories or quantitative values for subsequent statistical treatment. Proposals made to this end generally entail counting lexical occurrences which, since they are founded on previously established meanings, fail to include semantic associations made by interviews. This article aims to present an analysis tool comprising a set of techniques apt to generate linguistic units that can be statistically described, compared, modeled and inferred: the Quantitative Propositional Analysis (QPA), Its main difference from other such methods lies in the choice of a proposition - and not the lexical unit - as analysis unit. We present the application of this method through a study about the international expansion of retail firms.

Proteomic analysis of total cellular proteins of human neutrophils

Background: Neutrophils are the most abundant leukocytes in peripheral blood and represent one of the most important elements of innate immunity. Recent subcellular proteomic studies have focused on the identification of human neutrophil proteins in various subcellular membrane and granular fractions. Although there are relatively few studies dealing with the analysis of the total extract of human neutrophils, many biological problems such as the role of chemokines, adhesion molecules, and other activating inputs involved in neutrophil responses and signaling can be approached on the basis of the identification of the total cellular proteins. Results: Using gel-LC-MS/MS, 251 total cellular proteins were identified from resting human neutrophils. This is more than ten times the number of proteins identified by an initial proteome analysis of human neutrophils and almost five times the number of proteins identified by the first 2-DE map of extracts of rat polymorphonuclear leukocytes. Most of the proteins identified in the present study are well-known, but some of them, such as neutrophil-secreted proteins and centaurin beta-1, a cytoplasmic protein involved in the regulation of NF-kappa B activity, are described here for the first-time. Conclusion: The present report provides new information about the protein content of human neutrophils. Importantly, our study resulted in the discovery of a series of proteins not previously reported to be associated with human neutrophils. These data are relevant to the investigation of comparative pathological states and models for novel classes of pharmaceutical drugs that could be useful in the treatment of inflammatory disorders in which neutrophils participate.

New methodology to assess activity status of occlusal caries in primary teeth using laser fluorescence device

An in vivo study was conducted to verify the ability of laser fluorescence (LF) to assess the activity status of occlusal caries in primary teeth, using different air-drying times. Occlusal sites (707) were examined using LF (DIAGNOdent) after air-drying for 3 s and 15 s, and the difference between readings (DIF15 s-3 s) was calculated. For concurrent validation of LF, visual criteria-Nyvad (NY) and Lesion Activity Assessment associated with the International Caries Detection and Assessment System (LAA-ICDAS)-were the reference standards for lesion activity. Histological exam using a pH-indicator dye (0.1% methyl red) was performed in 46 exfoliated/extracted teeth for criterion validation. LF readings and DIF15 s-3 s were compared using Kruskall-Wallis and Mann-Whitney tests. Receiver operating characteristic analyses were performed and validity parameters calculated, considering the caries activity assessment. Using NY, active lesions (3 s: 30.0 +/- 29.3; 15 s: 34.2 +/- 30.6) presented higher LF readings than inactive lesions (3 s: 17.0 +/- 16.3; 15 s: 19.2 +/- 17.3; p <0.05), different from LAA-ICDAS. Active cavitated caries resulted in higher LF readings (3 s: 50.3 +/- 3.5; 15 s: 54.7 +/- 30.2) than inactive cavitated caries (3 s: 19.9 +/- 16.3; 15 s: 22.8 +/- 16.8). Therefore, LF can distinguish cavitated active and inactive lesions classified by NY, but not by LAA-ICDAS; however, this difference might be related to the visual system rather than to LF. The air-drying time could be an alternative to improve the caries activity assessment; however, longer air-drying time is suggested to be tested subsequently. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3463007]

Phase-disorder-induced double resonance of neuronal activity

It is well known that resonance can be induced by external noise or diversity. Here we show that resonance can be induced even by a phase disorder in coupled excitable neurons with subthreshold activity. In contrast to the case of identical phase, we find that phase disorder plays an active role in enhancing neuronal activity. We also uncover that the presence of phase disorder can induce a double resonance phenomenon: phase disorder and coupling strength both can enhance neuronal firing activity. A physical theory is formulated to help understand the mechanism behind this double resonance phenomenon.

Gene Expression Noise in Spatial Patterning: hunchback Promoter Structure Affects Noise Amplitude and Distribution in Drosophila Segmentation

Positional information in developing embryos is specified by spatial gradients of transcriptional regulators. One of the classic systems for studying this is the activation of the hunchback (hb) gene in early fruit fly (Drosophila) segmentation by the maternally-derived gradient of the Bicoid (Bcd) protein. Gene regulation is subject to intrinsic noise which can produce variable expression. This variability must be constrained in the highly reproducible and coordinated events of development. We identify means by which noise is controlled during gene expression by characterizing the dependence of hb mRNA and protein output noise on hb promoter structure and transcriptional dynamics. We use a stochastic model of the hb promoter in which the number and strength of Bcd and Hb (self-regulatory) binding sites can be varied. Model parameters are fit to data from WT embryos, the self-regulation mutant hb(14F), and lacZ reporter constructs using different portions of the hb promoter. We have corroborated model noise predictions experimentally. The results indicate that WT (self-regulatory) Hb output noise is predominantly dependent on the transcription and translation dynamics of its own expression, rather than on Bcd fluctuations. The constructs and mutant, which lack self-regulation, indicate that the multiple Bcd binding sites in the hb promoter (and their strengths) also play a role in buffering noise. The model is robust to the variation in Bcd binding site number across a number of fly species. This study identifies particular ways in which promoter structure and regulatory dynamics reduce hb output noise. Insofar as many of these are common features of genes (e. g. multiple regulatory sites, cooperativity, self-feedback), the current results contribute to the general understanding of the reproducibility and determinacy of spatial patterning in early development.

Comparison of multispectral wide-field optical imaging modalities to maximize image contrast for objective discrimination of oral neoplasia

Multispectral widefield optical imaging has the potential to improve early detection of oral cancer. The appropriate selection of illumination and collection conditions is required to maximize diagnostic ability. The goals of this study were to (i) evaluate image contrast between oral cancer/precancer and non-neoplastic mucosa for a variety of imaging modalities and illumination/collection conditions, and (ii) use classification algorithms to evaluate and compare the diagnostic utility of these modalities to discriminate cancers and precancers from normal tissue. Narrowband reflectance, autofluorescence, and polarized reflectance images were obtained from 61 patients and 11 normal volunteers. Image contrast was compared to identify modalities and conditions yielding greatest contrast. Image features were extracted and used to train and evaluate classification algorithms to discriminate tissue as non-neoplastic, dysplastic, or cancer; results were compared to histologic diagnosis. Autofluorescence imaging at 405-nm excitation provided the greatest image contrast, and the ratio of red-to-green fluorescence intensity computed from these images provided the best classification of dysplasia/cancer versus non-neoplastic tissue. A sensitivity of 100% and a specificity of 85% were achieved in the validation set. Multispectral widefield images can accurately distinguish neoplastic and non-neoplastic tissue; however, the ability to separate precancerous lesions from cancers with this technique was limited. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3516593]

Crystallization and preliminary X-ray diffraction analysis of a glutathione S-transferase from Xylella fastidiosa

Glutathione S-transferases (GSTs) form a group of multifunctional isoenzymes that catalyze the glutathione-dependent conjugation and reduction reactions involved in the cellular detoxification of xenobiotic and endobiotic compounds. GST from Xylella fastidiosa (xfGST) was overexpressed in Escherichia coli and purified by conventional affinity chromatography. In this study, the crystallization and preliminary X-ray analysis of xfGST is described. The purified protein was crystallized by the vapour-diffusion method, producing crystals that belonged to the triclinic space group P1. The unit-cell parameters were a = 47.73, b = 87.73, c = 90.74 angstrom, alpha = 63.45, beta = 80.66, gamma = 94.55 degrees. xfGST crystals diffracted to 2.23 angstrom resolution on a rotating-anode X-ray source.

Crystallization and preliminary X-ray diffraction analysis of human IL-22 bound to its soluble decoy receptor IL-22BP

Interleukin-22 (IL-22) is a pleiotropic cytokine that is involved in inflammatory responses. Human IL-22 was incubated with its soluble decoy receptor IL-22BP (IL-22 binding protein) and the IL-22 -IL-22BP complex was crystallized in hanging drops using the vapour-diffusion method. Suitable crystals were obtained from polyethylene glycol solutions and diffraction data were collected to 2.75 angstrom resolution. The crystal belonged to the tetragonal space group P41, with unit-cell parameters a = b = 67.9, c = 172.5 angstrom, and contained two IL-22-IL- 22BP complexes per asymmetric unit.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Programmable diagnostic devices made from paper and tape

This paper describes three-dimensional microfluidic paper-based analytical devices (3-D mu PADs) that can be programmed (postfabrication) by the user to generate multiple patterns of flow through them. These devices are programmed by pressing single-use 'on' buttons, using a stylus or a ballpoint pen. Pressing a button closes a small space (gap) between two vertically aligned microfluidic channels, and allows fluids to wick from one channel to the other. These devices are simple to fabricate, and are made entirely out of paper and double-sided adhesive tape. Programmable devices expand the capabilities of mu PADs and provide a simple method for controlling the movement of fluids in paper-based channels. They are the conceptual equivalent of field-programmable gate arrays (FPGAs) widely used in electronics.

Disparity between Multilocus Enzyme Electrophoresis, Microsatellite Markers and Pulsed-Field Gel Electrophoresis in epidemiological tracking of Candida albicans

Various molecular systems are available for epidemiological, genetic, evolutionary, taxonomic and systematic studies of innumerable fungal infections, especially those caused by the opportunistic pathogen C. albicans. A total of 75 independent oral isolates were selected in order to compare Multilocus Enzyme Electrophoresis (MLEE), Electrophoretic Karyotyping (EK) and Microsatellite Markers (Simple Sequence Repeats - SSRs), in their abilities to differentiate and group C. albicans isolates (discriminatory power), and also, to evaluate the concordance and similarity of the groups of strains determined by cluster analysis for each fingerprinting method. Isoenzyme typing was performed using eleven enzyme systems: Adh, Sdh, M1p, Mdh, Idh, Gdh, G6pdh, Asd, Cat, Po, and Lap (data previously published). The EK method consisted of chromosomal DNA separation by pulsed-field gel electrophoresis using a CHEF system. The microsatellite markers were investigated by PCR using three polymorphic loci: EF3, CDC3, and HIS3. Dendrograms were generated by the SAHN method and UPGMA algorithm based on similarity matrices (S(SM)). The discriminatory power of the three methods was over 95%, however a paired analysis among them showed a parity of 19.7-22.4% in the identification of strains. Weak correlation was also observed among the genetic similarity matrices (S(SM)(MLEE) x S(SM)(EK) x S(SM)(SSRs)). Clustering analyses showed a mean of 9 +/- 12.4 isolates per cluster (3.8 +/- 8 isolates/taxon) for MLEE, 6.2 +/- 4.9 isolates per cluster (4 +/- 4.5 isolates/taxon) for SSRs, and 4.1 +/- 2.3 isolates per cluster (2.6 +/- 2.3 isolates/taxon) for EK. A total of 45 (13%), 39(11.2%), 5 (1.4%) and 3 (0.9%) clusters pairs from 347 showed similarity (Si) of 0.1-10%, 10.1-20%, 20.1-30% and 30.1-40%, respectively. Clinical and molecular epidemiological correlation involving the opportunistic pathogen C. albicans may be attributed dependently of each method of genotyping (i.e., MLEE, EK, and SSRs) supplemented with similarity and grouping analysis. Therefore, the use of genotyping systems that give results which offer minimum disparity, or the combination of the results of these systems, can provide greater security and consistency in the determination of strains and their genetic relationships. (C) 2010 Elsevier B.V. All rights reserved.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

a-SiC : H anti-resonant layer ARROW waveguides

Over the last decades, anti-resonant reflecting optical waveguides (ARROW) have been used in different integrated optics applications. In this type of waveguide, light confinement is partially achieved through an anti-resonant reflection. In this work, the simulation, fabrication and characterization of ARROW waveguides using dielectric films deposited by a plasma-enhanced chemical vapor deposition (PECVD) technique, at low temperatures(similar to 300 degrees C), are presented. Silicon oxynitride (SiO(x)N(y)) films were used as core and second cladding layers and amorphous hydrogenated silicon carbide(a-SiC:H) films as first cladding layer. Furthermore, numerical simulations were performed using homemade routines based on two computational methods: the transfer matrix method (TMM) for the determination of the optimum thickness of the Fabry-Perot layers; and the non-uniform finite difference method (NU-FDM) for 2D design and determination of the maximum width that yields single-mode operation. The utilization of a silicon carbide anti-resonant layer resulted in low optical attenuations, which is due to the high refractive index difference between the core and this layer. Finally, for comparison purposes, optical waveguides using titanium oxide (TiO(2)) as the first ARROW layer were also fabricated and characterized.