LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

Biomass burning in Brazil`s Amazonian ""arc of deforestation"": Burning efficiency and charcoal formation in a fire after mechanized clearing at Feliz Natal, Mato Grosso

Estimates of greenhouse-gas emissions from deforestation are highly uncertain because of high variability in key parameters and because of the limited number of studies providing field measurements of these parameters. One such parameter is burning efficiency, which determines how much of the original forest`s aboveground carbon stock will be released in the burn, as well as how much will later be released by decay and how much will remain as charcoal. In this paper we examined the fate of biomass from a semideciduous tropical forest in the ""arc of deforestation,"" where clearing activity is concentrated along the southern edge of the Amazon forest. We estimated carbon content, charcoal formation and burning efficiency by direct measurements (cutting and weighing) and by line-intersect sampling (LIS) done along the axis of each plot before and after burning of felled vegetation. The total aboveground dry biomass found here (219.3 Mg ha(-1)) is lower than the values found in studies that have been done in other parts of the Amazon region. Values for burning efficiency (65%) and charcoal formation (6.0%, or 5.98 Mg C ha(-1)) were much higher than those found in past studies in tropical areas. The percentage of trunk biomass lost in burning (49%) was substantially higher than has been found in previous studies. This difference may be explained by the concentration of more stems in the smaller diameter classes and the low humidity of the fuel (the dry season was unusually long in 2007, the year of the burn). This study provides the first measurements of forest burning parameters for a group of forest types that is now undergoing rapid deforestation. The burning parameters estimated here indicate substantially higher burning efficiency than has been found in other Amazonian forest types. Quantification of burning efficiency is critical to estimates of trace-gas emissions from deforestation. (C) 2009 Elsevier B.V. All rights reserved.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

Mechanical properties and cytotoxicity of 3Y-TZP bioceramics reinforced with Al(2)O(3) particles

The influence of Al(2)O(3) addition and sintering parameters on the mechanical properties and cytotoxicity of tetragonal ZrO(2)-3 mol% Y(2)O(3) ceramics was evaluated. Samples containing 0, 10, 20 and 30 wt.% of Al(2)O(3) particles were prepared by cold uniaxial pressing (80 MPa) and sintered in air at 1500, 1550 and 1600 degrees C for 120 min. The effects of the sintering conditions on the microstructure were analyzed by X-ray diffraction analysis and scanning electron microscopy. Hardness and fracture toughness were determined by the Vickers indentation method and the mechanical resistance by four-point bending tests. As a preliminary biological evaluation, ""in vitro"" cytotoxicity tests were realized to determine the cytotoxic level of the ZrO(2)-Al(2)O(3) composites, using the neutral red uptake method with NCTC clones L929 from the American Type Culture Collection (ATCC) bank. Fully dense ceramic materials were obtained with a hardness ranging between 1340 HV and 1585 HV, depending on the amount of Al(2)O(3) in the ZrO(2) matrix. On the other hand, no significant influence of the Al(2)O(3) addition on fracture toughness was observed, exhibiting values near 8 MPa m(1/2) for all compositions and sintering conditions studied. The non-cytotoxic behavior, the elevated fracture toughness, the good bending strength (sigma(f) = 690 MPa) and the elevated Weibull`s modulus (m = 11) exhibited by the material, show that these ceramic composites are highly suitable biomaterials for dental implant applications. (C) 2008 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Adverse drug events in an intensive care unit of a university hospital

Purpose Adverse drug events (ADEs) are harmful and occur with alarming frequency in critically ill patients. Complex pharmacotherapy with multiple medications increases the probability of a drug interaction (DI) and ADEs in patients in intensive care units (ICUs). The objective of the study is to determine the frequency of ADEs among patients in the ICU of a university hospital and the drugs implicated. Also, factors associated with ADEs are investigated. Methods This cross-sectional study investigated 299 medical records of patients hospitalized for 5 or more days in an ICU. ADEs were identified through intensive monitoring adopted in hospital pharmacovigilance and also ADE triggers. Adverse drug reactions (ADR) causality was classified using the Naranjo algorithm. Data were analyzed through descriptive analysis, and through univariate and multiple logistic regression. Results The most frequent ADEs were ADRs type A, of possible causality and moderate severity. The most frequent ADR was drug-induced acute kidney injury. Patients with ADEs related to DIs corresponded to 7% of the sample. The multiple logistic regression showed that length of hospitalization (OR = 1.06) and administration of cardiovascular drugs (OR = 2.2) were associated with the occurrence of ADEs. Conclusion Adverse drug reactions of clinical significance were the most frequent ADEs in the ICU studied, which reduces patient safety. The number of ADEs related to drug interactions was small, suggesting that clinical manifestations of drug interactions that harm patients are not frequent in ICUs.

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.

Connection Admission Control for a WDM Optical Network Based on Traffic Monitoring and Bandwidth Release

We proposed a connection admission control (CAC) to monitor the traffic in a multi-rate WDM optical network. The CAC searches for the shortest path connecting source and destination nodes, assigns wavelengths with enough bandwidth to serve the requests, supervises the traffic in the most required nodes, and if needed activates a reserved wavelength to release bandwidth according to traffic demand. We used a scale-free network topology, which includes highly connected nodes ( hubs), to enhance the monitoring procedure. Numerical results obtained from computational simulations show improved network performance evaluated in terms of blocking probability.

Performance analysis of an optical network employing waveband and traffic grooming

This paper analyses an optical network architecture composed by an arrangement of nodes equipped with multi-granular optical cross-connects (MG-OXCs) in addition to the usual optical cross-connects (OXCs). Then, selected network nodes can perform both waveband as well as traffic grooming operations and our goal is to assess the improvement on network performance brought by these additional capabilities. Specifically, the influence of the MG-OXC multi-granularity on the blocking probability is evaluated for 16 classes of service over a network based on the NSFNet topology. A mechanism of fairness in bandwidth capacity is also added to the connection admission control to manage the blocking probabilities of all kind of bandwidth requirements. Comprehensive computational simulation are carried out to compare eight distinct node architectures, showing that an adequate combination of waveband and single-wavelength ports of the MG-OXCs and OXCs allow a more efficient operation of a WDM optical network carrying multi-rate traffic.

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.

A Fault-Tolerant Manipulator Robot Based on H(2), H(infinity), and Mixed H(2)/H(infinity) Markovian Controls

This paper develops a Markovian jump model to describe the fault occurrence in a manipulator robot of three joints. This model includes the changes of operation points and the probability that a fault occurs in an actuator. After a fault, the robot works as a manipulator with free joints. Based on the developed model, a comparative study among three Markovian controllers, H(2), H(infinity), and mixed H(2)/H(infinity) is presented, applied in an actual manipulator robot subject to one and two consecutive faults.

Bending of stochastic Kirchhoff plates on Winkler foundations via the Galerkin method and the Askey-Wiener scheme

In this paper, the method of Galerkin and the Askey-Wiener scheme are used to obtain approximate solutions to the stochastic displacement response of Kirchhoff plates with uncertain parameters. Theoretical and numerical results are presented. The Lax-Milgram lemma is used to express the conditions for existence and uniqueness of the solution. Uncertainties in plate and foundation stiffness are modeled by respecting these conditions, hence using Legendre polynomials indexed in uniform random variables. The space of approximate solutions is built using results of density between the space of continuous functions and Sobolev spaces. Approximate Galerkin solutions are compared with results of Monte Carlo simulation, in terms of first and second order moments and in terms of histograms of the displacement response. Numerical results for two example problems show very fast convergence to the exact solution, at excellent accuracies. The Askey-Wiener Galerkin scheme developed herein is able to reproduce the histogram of the displacement response. The scheme is shown to be a theoretically sound and efficient method for the solution of stochastic problems in engineering. (C) 2009 Elsevier Ltd. All rights reserved.

Coupled reliability and boundary element model for probabilistic fatigue life assessment in mixed mode crack propagation

Fatigue and crack propagation are phenomena affected by high uncertainties, where deterministic methods fail to predict accurately the structural life. The present work aims at coupling reliability analysis with boundary element method. The latter has been recognized as an accurate and efficient numerical technique to deal with mixed mode propagation, which is very interesting for reliability analysis. The coupled procedure allows us to consider uncertainties during the crack growth process. In addition, it computes the probability of fatigue failure for complex structural geometry and loading. Two coupling procedures are considered: direct coupling of reliability and mechanical solvers and indirect coupling by the response surface method. Numerical applications show the performance of the proposed models in lifetime assessment under uncertainties, where the direct method has shown faster convergence than response surface method. (C) 2010 Elsevier Ltd. All rights reserved.

The random barrier-crossing problem

This paper addresses the time-variant reliability analysis of structures with random resistance or random system parameters. It deals with the problem of a random load process crossing a random barrier level. The implications of approximating the arrival rate of the first overload by an ensemble-crossing rate are studied. The error involved in this so-called ""ensemble-crossing rate"" approximation is described in terms of load process and barrier distribution parameters, and in terms of the number of load cycles. Existing results are reviewed, and significant improvements involving load process bandwidth, mean-crossing frequency and time are presented. The paper shows that the ensemble-crossing rate approximation can be accurate enough for problems where load process variance is large in comparison to barrier variance, but especially when the number of load cycles is small. This includes important practical applications like random vibration due to impact loadings and earthquake loading. Two application examples are presented, one involving earthquake loading and one involving a frame structure subject to wind and snow loadings. (C) 2007 Elsevier Ltd. All rights reserved.

Effects of temperature at different organic loading levels on the performance of a fluidized-bed anaerobic sequencing batch bioreactor

An investigation was performed on the effect of temperature and organic load on the stability and efficiency of a 1.8-L fluidized-bed anaerobic sequencing batch reactor (ASBR), containing granulated biomass. Assays were carried out employing superficial up How velocity of 7 m/h, total cycle length of 6 h and synthetic wastewater volume of 1.3 L per cycle. The fluidized-bed ASH was operated at 15, 20, 25 and 30 degrees C with influent organic matter concentrations of 500 and 1000 mgCOD/L The system showed stability under all conditions and presented filtered samples removal efficiency ranging from 79 to 86%. A first-order kinetic model could be fitted to the experimental values of the organic matter concentration profiles. The specific kinetic parameter values of this model ranged from 0.0435 to 0.2360 L/(gTVS h) at the implemented operation conditions. in addition, from the slope of an Arrhenius plot, the activation energy values were calculated to be 16,729 and 12,673 cal/mol for operation with 500 and 1000 mgCOD/L, respectively. These results show that treatment of synthetic wastewater. with concentration of 500 mgCOD/L, was more sensitive to temperature variations than treatment of the same residue with concentration of 1000 mgCOD/L. Comparing the activation energy value for operation at 500 mgCOD/L with the value obtained by Agibert et al. (S.A. Agibert, M.B. Moreira, S.M. Ratusznei, J.A.D. Rodrigues, M. Zaiat, E. Foresti. Influence of temperature on performance of an ASBBR with circulation applied to treatment of low-strength wastewater. journal of Applied Biochemistry and Biotechnology, 136 (2007) 193-206) in an ASBBR treating the same wastewater at the same concentration, the value obtained in the fluidized-bed ASBR showed to be superior, indicating that treatment of synthetic wastewater in a reactor containing granulated biomass was more sensitive to temperature variations than the treatment using immobilized biomass. (c) 2008 Elsevier B.V. All rights reserved.

Glass formation of alloys selected by lambda and electronegativity criteria in the Ti-Zr-Fe-Co system

This work evaluates the glass formation of selected alloys based on the Ti-Zr-Fe-Co system, assuming the synergy of two distinct criteria: minimum topological instability and average electronegativity plots. Combining the minimum topological instability and the average electronegativity values result in a plot in which the most probable good glass former compositions are identified Ti-Zr rich alloys with Fe and Co additions were produced, compared against the final plot, and the best glass forming alloy composition was found to be very close the theoretically predicted ones on the Ti-Zr rich side, for both Ti-Zr-Fe and Ti-Zr-Co systems. (C) 2009 Elsevier B V All rights reserved