Exergy-based method for analyzing the composition of the electricity cost generated in gas-fired combined cycle plants

The proposed method to analyze the composition of the cost of electricity is based on the energy conversion processes and the destruction of the exergy through the several thermodynamic processes that comprise a combined cycle power plant. The method uses thermoeconomics to evaluate and allocate the cost of exergy throughout the processes, considering costs related to inputs and investment in equipment. Although the concept may be applied to any combined cycle or cogeneration plant, this work develops only the mathematical modeling for three-pressure heat recovery steam generator (HRSG) configurations and total condensation of the produced steam. It is possible to study any n x 1 plant configuration (n sets of gas turbine and HRSGs associated to one steam turbine generator and condenser) with the developed model, assuming that every train operates identically and in steady state. The presented model was conceived from a complex configuration of a real power plant, over which variations may be applied in order to adapt it to a defined configuration under study [Borelli SJS. Method for the analysis of the composition of electricity costs in combined cycle thermoelectric power plants. Master in Energy Dissertation, Interdisciplinary Program of Energy, Institute of Eletro-technical and Energy, University of Sao Paulo, Sao Paulo, Brazil, 2005 (in Portuguese)]. The variations and adaptations include, for instance, use of reheat, supplementary firing and partial load operation. It is also possible to undertake sensitivity analysis on geometrical equipment parameters. (C) 2007 Elsevier Ltd. All rights reserved.

Genetic Diversity and a PCR-Based Method for Xanthomonas axonopodis Detection in Passion Fruit

Xanthomonas axonopodis pv. passiflorae causes bacterial spot in passion fruit. It attacks the purple and yellow passion fruit as well as the sweet passion fruit. The diversity of 87 isolates of pv. passiflorae collected from across 22 fruit orchards in Brazil was evaluated using molecular profiles and statistical procedures, including an unweighted pair-group method with arithmetical averages-based dendrogram, analysis of molecular variance (AMOVA), and an assigning test that provides information on genetic structure at the population level. Isolates from another eight pathovars were included in the molecular analyses and all were shown to have a distinct repetitive sequence-based polymerase chain reaction profile. Amplified fragment length polymorphism technique revealed considerable diversity among isolates of pv. passiflorae, and AMOVA showed that most of the variance (49.4%) was due to differences between localities. Cluster analysis revealed that most genotypic clusters were homogeneous and that variance was associated primarily with geographic origin. The disease adversely affects fruit production and may kill infected plants. A method for rapid diagnosis of the pathogen, even before the disease symptoms become evident, has value for producers. Here, a set of primers (Xapas) was designed by exploiting a single-nucleotide polymorphism between the sequences of the intergenic 16S-23S rRNA spacer region of the pathovars. Xapas was shown to effectively detect all pv. passiflorae isolates and is recommended for disease diagnosis in passion fruit orchards.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton`s method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

Fragment-Based QSAR and Molecular Modeling Studies on a Series of Discodermolide Analogs as Microtubule-Stabilizing Anticancer Agents

Inhibition of microtubule function is an attractive rational approach to anticancer therapy. Although taxanes are the most prominent among the microtubule-stabilizers, their clinical toxicity, poor pharmacokinetic properties, and resistance have stimulated the search for new antitumor agents having the same mechanism of action. Discodermolide is an example of nontaxane natural product that has the same mechanism of action, demonstrating superior antitumor efficacy and therapeutic index. The extraordinary chemical and biological properties have qualified discodermolide as a lead structure for the design of novel anticancer agents with optimized therapeutic properties. In the present work, we have employed a specialized fragment-based method to develop robust quantitative structure - activity relationship models for a series of synthetic discodermolide analogs. The generated molecular recognition patterns were combined with three-dimensional molecular modeling studies as a fundamental step on the path to understanding the molecular basis of drug-receptor interactions within this important series of potent antitumoral agents.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.

Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography

To present a novel algorithm for estimating recruitable alveolar collapse and hyperdistension based on electrical impedance tomography (EIT) during a decremental positive end-expiratory pressure (PEEP) titration. Technical note with illustrative case reports. Respiratory intensive care unit. Patients with acute respiratory distress syndrome. Lung recruitment and PEEP titration maneuver. Simultaneous acquisition of EIT and X-ray computerized tomography (CT) data. We found good agreement (in terms of amount and spatial location) between the collapse estimated by EIT and CT for all levels of PEEP. The optimal PEEP values detected by EIT for patients 1 and 2 (keeping lung collapse < 10%) were 19 and 17 cmH(2)O, respectively. Although pointing to the same non-dependent lung regions, EIT estimates of hyperdistension represent the functional deterioration of lung units, instead of their anatomical changes, and could not be compared directly with static CT estimates for hyperinflation. We described an EIT-based method for estimating recruitable alveolar collapse at the bedside, pointing out its regional distribution. Additionally, we proposed a measure of lung hyperdistension based on regional lung mechanics.

Association of mannose-binding lectin gene polymorphism but not of mannose-binding serine protease 2 with chronic severe aortic regurgitation of rheumatic etiology

N-Acetylglucosamine (GlcNAc) is the major immunoepitope of group A streptococcal cell wall carbohydrates. Antistreptococcal antibodies cross-reactive with anti-GlcNAc and laminin are present in sera of patients with rheumatic fever. The cross-reactivity of these antibodies with human heart valvular endothelium and the underlying basement membrane has been suggested to be a possible cause of immune-mediated valve lesion. Mannose-binding lectin (MBL) encoded by the MBL2 gene, a soluble pathogen recognition receptor, has high affinity for GlcNAc. We postulated that mutations in exon 1 of the MBL2 gene associated with a deficient serum level of MBL may contribute to chronic severe aortic regurgitation (AR) of rheumatic etiology. We studied 90 patients with severe chronic AR of rheumatic etiology and 281 healthy controls (HC) for the variants of the MBL2 gene at codons 52, 54, and 57 by using a PCR-restriction fragment length polymorphism-based method. We observed a significant difference in the prevalence of defective MBL2 alleles between patients with chronic severe AR and HC. Sixteen percent of patients with chronic severe AR were homozygotes or compound heterozygotes for defective MBL alleles in contrast to 5% for HC (P = 0.0022; odds ratio, 3.5 [ 95% confidence interval, 1.6 to 7.7]). No association was detected with the variant of the MASP2 gene. Our study suggests that MBL deficiency may contribute to the development of chronic severe AR of rheumatic etiology.

Spectrum and Prevalence of FP/TMEM127 Gene Mutations in Pheochromocytomas and Paragangliomas

Context Pheochromocytomas and paragangliomas are genetically heterogeneous neural crest-derived neoplasms. We recently identified germline mutations of the novel transmembrane-encoding gene FP/TMEM127 in familial and sporadic pheochromocytomas consistent with a tumor suppressor effect. Objectives To examine the prevalence and spectrum of FP/TMEM127 mutations in pheochromocytomas and paragangliomas and to test the effect of mutations in vitro. Design, Setting, and Participants We sequenced the FP/TMEM127 gene in 990 individuals with pheochromocytomas and/or paragangliomas, including 898 previously unreported cases without mutations in other susceptibility genes from 8 independent worldwide referral centers between January 2009 and June 2010. A multiplex polymerase chain reaction-based method was developed to screen for large gene deletions in 545 of these samples. Confocal microscopy of 5 transfected mutant proteins was used to determine their subcellular localization. Main Outcome Measures The frequency and type of FP/TMEM127 mutation or deletion was assessed and correlated with clinical variables; the subcellular localization of 5 overexpressed mutants was compared with wild-type FP/TMEM127 protein. Results We identified 19 potentially pathogenic FP/TMEM127 germline mutations in 20 independent families, but no large deletions were detected. All mutation carriers had adrenal tumors, including 7 bilateral (P=2.7 x 10(-4)) and/or with familial disease (5 of 20 samples; P=.005). The median age at disease onset in the FP/TMEM127 mutation group was similar to that of patients without a mutation (41.5 vs 45 years, respectively; P=.54). The most common presentation was that of a single benign adrenal tumor in patients older than 40 years. Malignancy was seen in 1 mutation carrier (5%). Expression of 5 novel FP/TMEM127 mutations in cell lines revealed diffuse localization of the mutant proteins in contrast with the discrete multiorganelle distribution of wild-type TMEM127. Conclusions Germline mutations of FP/TMEM127 were associated with pheochromocytoma but not paraganglioma and occured in an age group frequently excluded from genetic screening algorithms. Disease-associated mutations disrupt intracellular distribution of the FP/TMEM127 protein. JAMA. 2010;304(23):2611-2619 www.jama.com

Molecular diversity of Brazilian strains of porcine circovirus type 2 (PCV-2)

Porcine circovirus 2 (PCV-2) is associated with a broad range of syndromes. In this study, eight pig tissue samples from two Brazilian states were analyzed using six PCR primer pairs amplifying a 1705-bp fragment of the PCV-2 genome. The NJ distance-based method was used for the phylogenetic analysis with the eight field strains herein, 15 GenBank sequences and using PCV-1 as an out-group. This yielded two major clusters (A and B) for this viral species, with the Brazilian strains segregating with European and Asian sequences. Nucleotide identity was 99.7 to 100% among the sequences. This information can be used in further studies of pathogenesis related to PCV-2 in Brazil. (C) 2007 Elsevier Ltd. All rights reserved.

Development of molecular assays for the identification of the 11 Eimeria species of the domestic rabbit (Oryctolagus cuniculus)

Coccidiosis are the major parasitic diseases in poultry and other domestic animals including the domestic rabbit (Oryctolagus cuniculus). Eleven distinct Eimeria species have been identified in this host, but no PCR-based method has been developed so far for unequivocal species differentiation. In this work, we describe the development of molecular diagnostic assays that allow for the detection and discrimination of the 11 Eimeria species that infect rabbits. We determined the nucleotide sequences of the ITS1 ribosomal DNAs and designed species-specific primers for each species. We performed specificity tests of the assays using heterologous sets of primers and DNA samples, and no cross-specific bands were observed. We obtained a detection limit varying from 500 fg to 1 pg, which corresponds approximately to 0.8-1.7 sporulated oocysts, respectively. The test reported here showed good reproducibility and presented a consistent sensitivity with three different brands of amplification enzymes. These novel diagnostic assays will permit population surveys to be performed with high sensitivity and specificity, thus contributing to a better understanding of the epidemiology of this important group of coccidian parasites. (C) 2010 Elsevier B.V. All rights reserved.

A geometric mass-preserving redistancing scheme for the level set function

In this paper we describe and evaluate a geometric mass-preserving redistancing procedure for the level set function on general structured grids. The proposed algorithm is adapted from a recent finite element-based method and preserves the mass by means of a localized mass correction. A salient feature of the scheme is the absence of adjustable parameters. The algorithm is tested in two and three spatial dimensions and compared with the widely used partial differential equation (PDE)-based redistancing method using structured Cartesian grids. Through the use of quantitative error measures of interest in level set methods, we show that the overall performance of the proposed geometric procedure is better than PDE-based reinitialization schemes, since it is more robust with comparable accuracy. We also show that the algorithm is well-suited for the highly stretched curvilinear grids used in CFD simulations. Copyright (C) 2010 John Wiley & Sons, Ltd.

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem`s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution-VSS-and the expected value of perfect information-EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.