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.

Identification of the biodiesel source using an electronic nose

Biodiesel is an important new alternative fuel. The feedstock used and the process employed determines whether it fulfills the required specifications. In this work, an identification method is proposed using an electronic nose (e-nose). Four samples of biodiesel from different sources and one of petrodiesel were analyzed and well-recognized by the e-nose. Both pure biodiesel and B20 blends were studied. Furthermore, an innovative semiquantitative method is proposed on the basis of the smellprints correlated by a feed-forward artificial neural network. The results have demonstrated that the e-nose can be used to identify the biodiesel source and as a preliminary quantitative assay in place of expensive equipment.

Richards growth model and viability indicators for populations subject to interventions

In this work we study the problem of modeling identification of a population employing a discrete dynamic model based on the Richards growth model. The population is subjected to interventions due to consumption, such as hunting or farming animals. The model identification allows us to estimate the probability or the average time for a population number to reach a certain level. The parameter inference for these models are obtained with the use of the likelihood profile technique as developed in this paper. The identification method here developed can be applied to evaluate the productivity of animal husbandry or to evaluate the risk of extinction of autochthon populations. It is applied to data of the Brazilian beef cattle herd population, and the the population number to reach a certain goal level is investigated.

UNIQUENESS AND NONUNIQUENESS RESULTS FOR A CERTAIN CLASS OF ALMOST PERIODIC DISTRIBUTIONS

We consider distributions u is an element of S'(R) of the form u(t) = Sigma(n is an element of N) a(n)e(i lambda nt), where (a(n))(n is an element of N) subset of C and Lambda = (lambda n)(n is an element of N) subset of R have the following properties: (a(n))(n is an element of N) is an element of s', that is, there is a q is an element of N such that (n(-q) a(n))(n is an element of N) is an element of l(1); for the real sequence., there are n(0) is an element of N, C > 0, and alpha > 0 such that n >= n(0) double right arrow vertical bar lambda(n)vertical bar >= Cn(alpha). Let I(epsilon) subset of R be an interval of length epsilon. We prove that for given Lambda, (1) if Lambda = O(n(alpha)) with alpha < 1, then there exists epsilon > 0 such that u vertical bar I(epsilon) = 0 double right arrow u 0; (2) if Lambda = O(n) is uniformly discrete, then there exists epsilon > 0 such that u vertical bar I(epsilon) = 0 double right arrow u 0; (3) if alpha > 1 and. is uniformly discrete, then for all epsilon > 0, u vertical bar I(epsilon) = 0 double right arrow u = 0. Since distributions of the above mentioned form are very common in engineering, as in the case of the modeling of ocean waves, signal processing, and vibrations of beams, plates, and shells, those uniqueness and nonuniqueness results have important consequences for identification problems in the applied sciences. We show an identification method and close this article with a simple example to show that the recovery of geometrical imperfections in a cylindrical shell is possible from a measurement of its dynamics.

On the analysis of notched concrete beams: From measurement with digital image correlation to identification with boundary element method of a cohesive model

A way of coupling digital image correlation (to measure displacement fields) and boundary element method (to compute displacements and tractions along a crack surface) is presented herein. It allows for the identification of Young`s modulus and fracture parameters associated with a cohesive model. This procedure is illustrated to analyze the latter for an ordinary concrete in a three-point bend test on a notched beam. In view of measurement uncertainties, the results are deemed trustworthy thanks to the fact that numerous measurement points are accessible and used as entries to the identification procedure. (C) 2010 Elsevier Ltd. All rights reserved.

Online identification of chlorogenic acids, sesquiterpene lactones, and flavonoids in the Brazilian arnica Lychnophora ericoides Mart. (Asteraceae) leaves by HPLC-DAD-MS and HPLC-DAD-MS/MS and a validated HPLC-DAD method for their simultaneous analysis

Lychnophora ericoides Mart. (Asteraceae, Vernonieae) is a plant, endemic to Brazil, with occurrence restricted to the ""cerrado"" biome. Traditional medicine employs alcoholic and aqueous-alcoholic preparations of leaves from this species for the treatment of wounds, inflammation, and pain. Furthermore, leaves of L. ericoides are also widely used as flavorings for the Brazilian traditional spirit ""cachaca"". A method has been developed for the extraction and HPLC-DAD analysis of the secondary metabolites of L. ericoides leaves. This analytical method was validated with 11 secondary metabolites chosen to represent the different classes and polarities of secondary metabolites occurring in L. ericoides leaves, and good responses were obtained for each validation parameter analyzed. The same HPLC analytical method was also employed for online secondary metabolite identification by HPLC-DAD-MS and HPLC-DAD-MS/MS, leading to the identification of di-C-glucosylflavones, coumaroylglucosylflavonols, flavone, flavanones, flavonols, chalcones, goyazensolide, and eremantholide-type sesquiterpene lactones and positional isomeric series of chlorogenic acids possessing caffeic and/or ferulic moieties. Among the 52 chromatographic peaks observed, 36 were fully identified and 8 were attributed to compounds belonging to series of caffeoylferuloylquinic and diferuloylquinic acids that could not be individualized from each other.

A method for simultaneous detection and identification of Brazilian dog- and vampire bat-related rabies virus by reverse transcription loop-mediated isothermal amplification assay

At present, the sporadic occurrence of human rabies in Brazil can be attributed primarily to dog- and vampire bat-related rabies viruses. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) was employed as a simultaneous detection method for both rabies field variants within 60 min. Vampire bat-related rabies viruses could be distinguished from dog variants by digesting amplicons of the RT-LAMP reaction using the restriction enzyme Alwl. Amplification and digestion could both be completed within 120 min after RNA extraction. In addition, the RI-LAMP assay also detected rabies virus in isolates from Brazilian frugivorous bats and Ugandan dog, bovine and goat samples. In contrast, there were false negative results from several Brazilian insectivorous bats and all of Chinese dog, pig, and bovine samples using the RI-LAMP assay. This study showed that the RT-LAMP assay is effective for the rapid detection of rabies virus isolates from the primary reservoir in Brazil. Further improvements are necessary so that the RT-LAMP assay can be employed for the universal detection of genetic variants of rabies virus in the field. (C) 2010 Elsevier B.V. All rights reserved.

Mineralogical characterization of a highly-weathered soil by the Rietveld Method

The mineralogical characterization through mineral quantification of Brazilian soils by X-ray diffraction data using the Rietveld Method is not common. A mineralogical quantification of an Acric Ferralsol from the Ponta Grossa region, state of Paraná, Brazil, was carried out using this Method with X-Ray Diffraction data to verify if this method was suitable for mineral quantification of a highly-weathered soil. The A, AB and B3 horizons were fractioned to separate the different particle sizes: clay, silt, fine sand (by Stokes Law) and coarse sand fractions (by sieving), with the procedure free of chemical treatments. X-ray Fluorescence, Inductively Coupled Plasma Atomic Emission Spectrometry, Infrared Spectroscopy and Mössbauer Spectroscopy were used in order to assist the mineral identification and quantification. The Rietveld Method enabled the quantification of the present minerals. In a general way, the quantitative mineralogical characterization by the Rietveld Method revealed that quartz, gibbsite, rutile, hematite, goethite, kaolinite and halloysite were present in the clay and silt fractions of all horizons. The silt fractions of the deeper horizons were different from the more superficial ones due to the presence of large amounts of quartz. The fine and the coarse sand fractions are constituted mainly by quartz. Therefore, a mineralogical quantification of the finer fraction (clay and silt) by the Rietveld Method was successful.

Identification of Neoplasias of Breast Tissues Using a Powder Diffractometer

An investigation was carried out to study the potential use of the angular distribution of scattered photons by human breast samples for a rapid identification of neoplasias of breast tissues. This technique has possible applications as diagnostic aid for breast cancer. In this work, a commercial powder diffractometer was used to obtain the scattering profiles from breast tissues histopathologically classified as normal breast tissues, fibroadenomas (benign breast diseases) and carcinomas (malignant breast diseases), in the interval 0.02 angstrom(-1) < x < 0.62 angstrom(-1). The experimental methods and data corrections are discussed in detail, and they included background subtraction, polarization, self-attenuation and geometric effects. The validation of the experimental procedure was achieved through an analysis of water sample. The results showed that the scattering profile is a unique impression of each type of tissue, being correlated with their microscopic morphological features. Multivariate analysis was applied to these profiles in order to verify if the information carried by these scattering profiles allow the differentiation between normal, benign and malignant breast tissues. The statistical analysis results showed that a correct identification of 75% of the analyzed samples is accomplished. The values of sensibility and specificity of this method in correctly differentiating between normal and neoplastic samples were 95.6% and 82.3%, respectively, while the values for differentiation between benign and malignant neoplasias were 78.6% and 62.5%. These initial results indicate the feasible use of commercial powder diffractometer to provide a rapid diagnostic with a high sensitivity.

A Novel Hepatitis C Virus Genotyping Method Based on Liquid Microarray

The strategy used to treat HCV infection depends on the genotype involved. An accurate and reliable genotyping method is therefore of paramount importance. We describe here, for the first time, the use of a liquid microarray for HCV genotyping. This liquid microarray is based on the 5'UTR - the most highly conserved region of HCV - and the variable region NS5B sequence. The simultaneous genotyping of two regions can be used to confirm findings and should detect inter-genotypic recombination. Plasma samples from 78 patients infected with viruses with genotypes and subtypes determined in the Versant (TM) HCV Genotype Assay LiPA (version I; Siemens Medical Solutions, Diagnostics Division, Fernwald, Germany) were tested with our new liquid microarray method. This method successfully determined the genotypes of 74 of the 78 samples previously genotyped in the Versant (TM) HCV Genotype Assay LiPA (74/78, 95%). The concordance between the two methods was 100% for genotype determination (74/74). At the subtype level, all 3a and 2b samples gave identical results with both methods (17/17 and 7/7, respectively). Two 2c samples were correctly identified by microarray, but could only be determined to the genotype level with the Versant (TM) HCV assay. Genotype ""1'' subtypes (1a and 1b) were correctly identified by the Versant (TM) HCV assay and the microarray in 68% and 40% of cases, respectively. No genotype discordance was found for any sample. HCV was successfully genotyped with both methods, and this is of prime importance for treatment planning. Liquid microarray assays may therefore be added to the list of methods suitable for HCV genotyping. It provides comparable results and may readily be adapted for the detection of other viruses frequently co-infecting HCV patients. Liquid array technology is thus a reliable and promising platform for HCV genotyping.

A Lagrangian Identification of the Main Sources of Moisture Affecting Northeastern Brazil during Its Pre-Rainy and Rainy Seasons

This work examines the sources of moisture affecting the semi-arid Brazilian Northeast (NEB) during its pre-rainy and rainy season (JFMAM) through a Lagrangian diagnosis method. The FLEXPART model identifies the humidity contributions to the moisture budget over a region through the continuous computation of changes in the specific humidity along back or forward trajectories up to 10 days period. The numerical experiments were done for the period that spans between 2000 and 2004 and results were aggregated on a monthly basis. Results show that besides a minor local recycling component, the vast majority of moisture reaching NEB area is originated in the south Atlantic basin and that the nearby wet Amazon basin bears almost no impact. Moreover, although the maximum precipitation in the ""Poligono das Secas'' region (PS) occurs in March and the maximum precipitation associated with air parcels emanating from the South Atlantic towards PS is observed along January to March, the highest moisture contribution from this oceanic region occurs slightly later (April). A dynamical analysis suggests that the maximum precipitation observed in the PS sector does not coincide with the maximum moisture supply probably due to the combined effect of the Walker and Hadley cells in inhibiting the rising motions over the region in the months following April.

Identification of nurses` competencies in primary health care through a Delphi study in southern Brazil

Objective: This investigation aimed to identify and analyze the general and specific competencies of nurses in the primary health care practice of Brazil. Design: The Delphi Technique was used as the method of study. Sample: 2 groups of participants were selected: One contained primary health care nurses (n=52) and the other specialists (n=57), including public health nurses and public or community health faculty. Measurements: 3 questionnaires were developed for the study. The first asked participants to indicate general and specific competencies, which were compiled into a list for each group. A Likert scale of 1-5 was added to these 2 lists in the second and third questionnaires. A consensus criterion of 75% for score 4 or 5 was adopted. Results: In the nurses` group, 17 general and 8 specific competencies reached the consensus criterion; 19 general and 9 specific competencies reached the criterion in the specialists` group. These competencies were classified into 10 domains: professional values, communication, teamwork, management, community-oriented, health promotion, problem solving, health care, and education and basic public health sciences. Conclusions: These competencies reflect Brazilian health policy and constitute a reference for health professional practice and education.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Inverse analysis FOR two-dimensional structures using the boundary element method

Inverse analysis is currently an important subject of study in several fields of science and engineering. The identification of physical and geometric parameters using experimental measurements is required in many applications. In this work a boundary element formulation to identify boundary and interface values as well as material properties is proposed. In particular the proposed formulation is dedicated to identifying material parameters when a cohesive crack model is assumed for 2D problems. A computer code is developed and implemented using the BEM multi-region technique and regularisation methods to perform the inverse analysis. Several examples are shown to demonstrate the efficiency of the proposed model. (C) 2010 Elsevier Ltd. All rights reserved,