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.

An oracle approach for interaction neighborhood estimation in random fields

We consider the problem of interaction neighborhood estimation from the partial observation of a finite number of realizations of a random field. We introduce a model selection rule to choose estimators of conditional probabilities among natural candidates. Our main result is an oracle inequality satisfied by the resulting estimator. We use then this selection rule in a two-step procedure to evaluate the interacting neighborhoods. The selection rule selects a small prior set of possible interacting points and a cutting step remove from this prior set the irrelevant points. We also prove that the Ising models satisfy the assumptions of the main theorems, without restrictions on the temperature, on the structure of the interacting graph or on the range of the interactions. It provides therefore a large class of applications for our results. We give a computationally efficient procedure in these models. We finally show the practical efficiency of our approach in a simulation study.

Structure-Function Analysis of the HrpB2-HrcU Interaction in the Xanthomonas citri Type III Secretion System

Bacterial type III secretion systems deliver protein virulence factors to host cells. Here we characterize the interaction between HrpB2, a small protein secreted by the Xanthomonas citri subsp. citri type III secretion system, and the cytosolic domain of the inner membrane protein HrcU, a paralog of the flagellar protein FlhB. We show that a recombinant fragment corresponding to the C-terminal cytosolic domain of HrcU produced in E. coli suffers cleavage within a conserved Asn264-Pro265-Thr266-His267 (NPTH) sequence. A recombinant HrcU cytosolic domain with N264A, P265A, T266A mutations at the cleavage site (HrcU(AAAH)) was not cleaved and interacted with HrpB2. Furthermore, a polypeptide corresponding to the sequence following the NPTH cleavage site also interacted with HrpB2 indicating that the site for interaction is located after the NPTH site. Non-polar deletion mutants of the hrcU and hrpB2 genes resulted in a total loss of pathogenicity in susceptible citrus plants and disease symptoms could be recovered by expression of HrpB2 and HrcU from extrachromossomal plasmids. Complementation of the Delta hrcU mutant with HrcU(AAAH) produced canker lesions similar to those observed when complemented with wild-type HrcU. HrpB2 secretion however, was significantly reduced in the Delta hrcU mutant complemented with HrcU(AAAH), suggesting that an intact and cleavable NPTH site in HrcU is necessary for total functionally of T3SS in X. citri subsp. citri. Complementation of the Delta hrpB2 X. citri subsp. citri strain with a series of hrpB2 gene mutants revealed that the highly conserved HrpB2 C-terminus is essential for T3SS-dependent development of citrus canker symptoms in planta.

Interaction between polymorphisms of the Human Leukocyte Antigen and HPV-16 Variants on the risk of invasive cervical cancer

Background: Persistent infection with oncogenic types of human papillomavirus (HPV) is the major risk factor for invasive cervical cancer (ICC), and non-European variants of HPV-16 are associated with an increased risk of persistence and ICC. HLA class II polymorphisms are also associated with genetic susceptibility to ICC. Our aim is to verify if these associations are influenced by HPV-16 variability. Methods: We characterized HPV-16 variants by PCR in 107 ICC cases, which were typed for HLA-DQA1, DRB1 and DQB1 genes and compared to 257 controls. We measured the magnitude of associations by logistic regression analysis. Results: European ( E), Asian-American ( AA) and African (Af) variants were identified. Here we show that inverse association between DQB1*05 ( adjusted odds ratio [ OR] = 0.66; 95% confidence interval [CI]: 0.39-1.12]) and HPV-16 positive ICC in our previous report was mostly attributable to AA variant carriers ( OR = 0.27; 95% CI: 0.10-0.75). We observed similar proportions of HLA DRB1*1302 carriers in E-P positive cases and controls, but interestingly, this allele was not found in AA cases ( p = 0.03, Fisher exact test). A positive association with DRB1*15 was observed in both groups of women harboring either E ( OR = 2.99; 95% CI: 1.13-7.86) or AA variants ( OR = 2.34; 95% CI: 1.00-5.46). There was an inverse association between DRB1*04 and ICC among women with HPV-16 carrying the 350T [83L] single nucleotide polymorphism in the E6 gene ( OR = 0.27; 95% CI: 0.08-0.96). An inverse association between DQB1*05 and cases carrying 350G (83V) variants was also found ( OR = 0.37; 95% CI: 0.15-0.89). Conclusion: Our results suggest that the association between HLA polymorphism and risk of ICC might be influenced by the distribution of HPV-16 variants.

Autocatalysis in the open circuit interaction of alcohol molecules with oxidized Pt surfaces

We studied the open circuit interaction of methanol and ethanol with oxidized platinum electrodes using in situ infrared spectroscopy. For methanol, it was found that formic acid is the main species formed in the initial region of the transient and that the steep decrease of the open circuit potential coincides with an explosive increase in the CO(2) production, which is followed by an increase in the coverage of adsorbed CO. For ethanol, acetaldehyde was the main product detected and only traces of dissolved CO(2) and adsorbed CO were found after the steep potential decay. In both cases, the transients were interpreted in terms of (a) the emergence of sub-surface oxygen in the beginning of the transient, where the oxide content is high, and (b) the autocatalytic production of free platinum sites for lower oxide content during the steep decay of the open circuit potential.

Heparin affects the interaction of kininogen on endothelial cells

In the plasma kallikrein-kinin system, it has been shown that when plasma prekallikrein (PM) and high molecular weight kininogen (HK) assemble on endothelial cells, plasma kallikrein (huPK) becomes available to cleave HK, releasing bradykinin, a potent mediator of the inflammatory response. Because the formation of soluble glycosaminoglycans occurs concomitantly during the inflammatory processes, the effect of these polysaccharides on the interaction of HK on the cell surface or extracellular matrix (ECM) of two endothelial cell lines (ECV304 and RAEC) was investigated. In the presence of Zn(+2), HK binding to the surface or ECM of RAEC was abolished by heparin; reduced by heparan sulfate, keratan sulfate, chondroitin 4-sulfate or dermatan sulfate; and not affected by chondroitin 6-sulfate. By contrast, only heparin reduced HK binding to the ECV304 cell surface or ECM. Using heparin-correlated molecules such as low molecular weight dextran sulfate, low molecular weight heparin and N-desulfated heparin, we suggest that these effects were mainly dependent on the charge density and on the N-sulfated glucosamine present in heparin. Surprisingly, PM binding to cell- or ECM-bound-HK and PM activation was not modified by heparin. However, the hydrolysis of HK by huPK, releasing BK in the fluid phase, was augmented by this glycosaminoglycan in the presence of Zn(2+). Thus, a functional dichotomy exists in which soluble glycosaminoglycans may possibly either increase or decrease the formation of BK. In conclusion, glycosaminoglycans that accumulated in inflammatory fluids or used as a therapeutic drug (e.g., heparin) could act as pro- or anti-inflammatory mediators depending on different factors within the cell environment. (C) 2011 Elsevier Masson SAS. All rights reserved.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Evaluation of three brands of drug interaction software for use in intensive care units

Objective To evaluate drug interaction software programs and determine their accuracy in identifying drug-drug interactions that may occur in intensive care units. Setting The study was developed in Brazil. Method Drug interaction software programs were identified through a bibliographic search in PUBMED and in LILACS (database related to the health sciences published in Latin American and Caribbean countries). The programs` sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting drug-drug interactions. The accuracy of the software programs identified was determined using 100 clinically important interactions and 100 clinically unimportant ones. Stockley`s Drug Interactions 8th edition was employed as the gold standard in the identification of drug-drug interaction. Main outcome Sensitivity, specificity, positive and negative predictive values. Results The programs studied were: Drug Interaction Checker (DIC), Drug-Reax (DR), and Lexi-Interact (LI). DR displayed the highest sensitivity (0.88) and DIC showed the lowest (0.69). A close similarity was observed among the programs regarding specificity (0.88-0.92) and positive predictive values (0.88-0.89). The DIC had the lowest negative predictive value (0.75) and DR the highest (0.91). Conclusion The DR and LI programs displayed appropriate sensitivity and specificity for identifying drug-drug interactions of interest in intensive care units. Drug interaction software programs help pharmacists and health care teams in the prevention and recognition of drug-drug interactions and optimize safety and quality of care delivered in intensive care units.

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.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. 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.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.