Small rivers in the southwestern Amazon and their role in CO(2) outgassing

A recent estimate of CO(2) outgassing from Amazonian wetlands suggests that an order of magnitude more CO(2) leaves rivers through gas exchange with the atmosphere than is exported to the ocean as organic plus inorganic carbon. However, the contribution of smaller rivers is still poorly understood, mainly because of limitations in mapping their spatial extent. Considering that the largest extension of the Amazon River network is composed of small rivers, the authors` objective was to elucidate their role in air-water CO(2) exchange by developing a geographic information system ( GIS)- based model to calculate the surface area covered by rivers with channels less than 100 m wide, combined with estimated CO(2) outgassing rates at the Ji-Parana River basin, in the western Amazon. Estimated CO(2) outgassing was the main carbon export pathway for this river basin, totaling 289 Gg C yr(-1), about 2.4 times the amount of carbon exported as dissolved inorganic carbon ( 121 Gg C yr(-1)) and 1.6 times the dissolved organic carbon export ( 185 Gg C yr(-1)). The relationships established here between drainage area and channel width provide a new model for determining small river surface area, allowing regional extrapolations of air - water gas exchange. Applying this model to the entire Amazon River network of channels less than 100 m wide ( third to fifth order), the authors calculate that the surface area of small rivers is 0.3 +/- 0.05 million km(2), and it is potentially evading to the atmosphere 170 +/- 42 Tg C yr(-1) as CO(2). Therefore, these ecosystems play an important role in the regional carbon balance.

THREE-DIMENSIONAL PHOTOIONIZATION STRUCTURE AND DISTANCES OF PLANETARY NEBULAE. IV. NGC 40

Continuing our series of papers on the three-dimensional (3D) structure and accurate distances of planetary nebulae (PNe), we present here the results obtained for PN NGC 40. Using data from different sources and wavelengths, we construct 3D photoionization models and derive the physical quantities of the ionizing source and nebular gas. The procedure, discussed in detail in the previous papers, consists of the use of 3D photoionization codes constrained by observational data to derive the 3D nebular structure, physical and chemical characteristics, and ionizing star parameters of the objects by simultaneously fitting the integrated line intensities, the density map, the temperature map, and the observed morphologies in different emission lines. For this particular case we combined hydrodynamical simulations with the photoionization scheme in order to obtain self-consistent distributions of density and velocity of the nebular material. Combining the velocity field with the emission-line cubes we also obtained the synthetic position-velocity plots that are compared to the observations. Finally, using theoretical evolutionary tracks of intermediate-and low-mass stars, we derive the mass and age of the central star of NGC 40 as (0.567 +/- 0.06) M(circle dot) and (5810 +/- 600) yr, respectively. The distance obtained from the fitting procedure was (1150 +/- 120) pc.

Financial distress, financial constraint and investment decision: Evidence from Brazil

This paper analyses the presence of financial constraint in the investment decisions of 367 Brazilian firms from 1997 to 2004, using a Bayesian econometric model with group-varying parameters. The motivation for this paper is the use of clustering techniques to group firms in a totally endogenous form. In order to classify the firms we used a hybrid clustering method, that is, hierarchical and non-hierarchical clustering techniques jointly. To estimate the parameters a Bayesian approach was considered. Prior distributions were assumed for the parameters, classifying the model in random or fixed effects. Ordinate predictive density criterion was used to select the model providing a better prediction. We tested thirty models and the better prediction considers the presence of 2 groups in the sample, assuming the fixed effect model with a Student t distribution with 20 degrees of freedom for the error. The results indicate robustness in the identification of financial constraint when the firms are classified by the clustering techniques. (C) 2010 Elsevier B.V. All rights reserved.

Fast experimental identification of suspension models for control design

This paper presents both the theoretical and the experimental approaches of the development of a mathematical model to be used in multi-variable control system designs of an active suspension for a sport utility vehicle (SUV), in this case a light pickup truck. A complete seven-degree-of-freedom model is successfully quickly identified, with very satisfactory results in simulations and in real experiments conducted with the pickup truth. The novelty of the proposed methodology is the use of commercial software in the early stages of the identification to speed up the process and to minimize the need for a large number of costly experiments. The paper also presents major contributions to the identification of uncertainties in vehicle suspension models and in the development of identification methods using the sequential quadratic programming, where an innovation regarding the calculation of the objective function is proposed and implemented. Results from simulations of and practical experiments with the real SUV are presented, analysed, and compared, showing the potential of the method.

Harmonic Distortion of Unstrained and Strained FinFETs Operating in Saturation

The harmonic distortion (HD) exhibited by un-strained and biaxially strained fin-shaped field-effect transistors operating in saturation as single-transistor amplifiers has been investigated for devices with different channel lengths L and fin widths W(fin). The study has been performed through device characterization, 3-D device simulations, and modeling. Nonlinearity has been evaluated in terms of second- and third-order HDs (HD2 and HD3, respectively), and a discussion on its physical sources has been carried out. Also, the influence of the open-loop voltage gain AV in HD has been observed.

Adaptation for tandem cold mill models

The ideal conditions for the operation of tandem cold mills are connected to a set of references generated by models and used by dynamic regulators. Aiming at the optimization of the friction and yield stress coefficients an adaptation algorithm is proposed in this paper. Experimental results obtained from an industrial cold rolling mill are presented. (C) 2008 Elsevier B.V. All rights reserved.

Cloning, expression, molecular modelling and docking analysis of glutathione transferase from Saccharum officinarum

Sugarcane yield and quality are affected by a number of biotic and abiotic stresses. In response to such stresses, plants may increase the activities of some enzymes such as glutathione transferase (GST), which are involved in the detoxification of xenobiotics. Thus, a sugarcane GST was modelled and molecular docked using the program LIGIN to investigate the contributions of the active site residues towards the binding of reduced glutathione (GSH) and 1-chloro-2,4-dinitrobenzene (CDNB). As a result, W13 and I119 were identified as key residues for the specificity of sugarcane GSTF1 (SoGSTF1) towards CDNB. To obtain a better understanding of the catalytic specificity of sugarcane GST (SoGSTF1), two mutants were designed, W13L and I119F. Tertiary structure models and the same docking procedure were performed to explain the interactions between sugarcane GSTs with GSH and CDNB. An electron-sharing network for GSH interaction was also proposed. The SoGSTF1 and the mutated gene constructions were cloned and expressed in Escherichia coli and the expressed protein purified. Kinetic analyses revealed different Km values not only for CDNB, but also for GSH. The Km values were 0.2, 1.3 and 0.3 mM for GSH, and 0.9, 1.2 and 0.5 mM for CDNB, for the wild type, W13L mutant and I119F mutant, respectively. The V(max) values were 297.6, 224.5 and 171.8 mu mol min(-1) mg(-1) protein for GSH, and 372.3, 170.6 and 160.4 mu mol min(-1) mg(-1) protein for CDNB.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Within-stand and seasonal variations of specific leaf area in a clonal Eucalyptus plantation in the Republic of Congo

Specific leaf area (SLA; m(leaf)(2) kg(leaf)(-1)) is a key ecophysiological parameter influencing leaf physiology, photosynthesis, and whole plant carbon gain. Both individual tree-based models and other forest process-based models are generally highly sensitive to this parameter, but information on its temporal or within-stand variability is still scarce. In a 2-4-year-old Eucalyptus plantation in Congo, prone to seasonal drought, the within-stand and seasonal variability in SLA were investigated by means of destructive sampling carried out at 2-month intervals, over a 2-year period. Within-crown vertical gradients of SLA were small. Highly significant relationships were found between tree-average SLA (SLA(t)) and tree size (tree height, H(t), or diameter at breast height, DBH): SLA(t) ranged from about 9 m(2) kg(-1) for dominant trees to about 14-15 m(2) kg(-1) for the smallest trees. The decrease in SLA(t) with increasing tree size was accurately predicted from DBH using power functions. Stand-average SLA varied by about 20% during the year, with lowest values at the end of the 5-month dry season, and highest values about 2-3 months after the onset of the wet season. Variability in leaf water status according to tree size and season is discussed as a possible determinant of both the within-stand and seasonal variations in SM. (C) 2009 Elsevier B.V. All rights reserved.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

A new graphic format to facilitate the understanding of technological innovation models: the seesaw of competitiveness

Many models exist in the literature to explain the success of technological innovation. However, no studies have been made regarding graphic formats representing the technological innovation models and their impact, or on the understanding of these models by non-specialists in technology management. Thus, the main objective of this paper is to propose a new graphic configuration to represent the technological innovation management. Based on the literature, the innovation model is presented in the traditional format. Next, the same model is designed in the graphic format - named `the see-saw of competitiveness` - showing the interfaces among the identified factors. The two graphic formats were compared by a group of graduate students in terms of the ease in understanding the conceptual model of innovation. The statistical analysis shows that the seesaw of competitiveness is preferred.

Evaluation of the enthalpy of formation, proton affinity, and gas-phase basicity of gamma-butyrolactone and 2-pyrrolidinone by isodesmic reactions

The knowledge of thermochemical parameters such as the enthalpy of formation, gas-phase basicity, and proton affinity may be the key to understanding molecular reactivity. The obtention of these thermochemical parameters by theoretical chemical models may be advantageous when experimental measurements are difficult to accomplish. The development of ab initio composite models represents a major advance in the obtention of these thermochemical parameters,. but these methods do not always lead to accurate values. Aiming at achieving a comparison between the ab initio models and the hybrid models based on the density functional theory (DFT), we have studied gamma-butyrolactone and 2-pyrrolidinone with a goal of obtaining high-quality thermochemical parameters using the composite chemical models G2, G2MP2, MP2, G3, CBS-Q, CBS-4, and CBS-QB3; the DFT methods B3LYP, B3P86, PW91PW91, mPW1PW, and B98; and the basis sets 6-31G(d), 6-31+G(d), 6-31G(d,p), 6-31+G(d,p), 6-31++G(d,p), 6-311G(d), 6-311+G(d), 6-311G(d,p), 6-311+G(d,p), 6-311++G(d,p), aug-cc-pVDZ, and aug-cc-pVTZ. Values obtained for the enthalpies of formation, proton affinity, and gas-phase basicity of the two target molecules were compared to the experimental data reported in the literature. The best results were achieved with the use of DFT models, and the B3LYP method led to the most accurate data.

Arg72Pro TP53 polymorphism and cancer susceptibility: a comprehensive meta-analysis of 302 case-control studies

Arg72Pro is a common polymorphism in TP53, showing differences in its biological functions. Case-control studies have been performed to elucidate the role of Arg72Pro in cancer, although the results are conflicting and heterogeneous. Here, we analyzed pooled data from case-control studies to determine the role of Arg72Pro in different cancer sites. We performed a systematic review and meta-analysis of 302 case-control studies that analyzed Arg72Pro in cancer susceptibility. Odds ratios were estimated for different tumor sites using distinct genetic models, and the heterogeneity between studies was explored using I(2) values and meta-regression. We adopted quality criteria to classify the studies. Subgroup analyses were done for tumor sites according to ethnicity, histological, and anatomical sites. Results indicated that Arg72Pro is associated with higher susceptibility to cancer in some tumor sites, mainly hepatocarcinoma. For some tumor sites, quality of studies was associated with the size of genetic association, mainly in cervical, head and neck, gastric, and lung cancer. However, study quality did not explain the observed heterogeneity substantially. Meta-regression showed that ethnicity, allelic frequency and genotyping method were responsible for a substantial part of the heterogeneity observed. Our results suggest ethnicity and histological and anatomical sites may modulate the penetrance of Arg72Pro in cancer susceptibility. This meta-analysis denotes the importance for more studies with good quality and that the covariates responsible for heterogeneity should be controlled to obtain a more conclusive response about the function of Arg72Pro in cancer.

THE TEMPO AND MODE OF EVOLUTION OF TRANSPOSABLE ELEMENTS AS REVEALED BY MOLECULAR PHYLOGENIES RECONSTRUCTED FROM MOSQUITO GENOMES

Although many mathematical models exist predicting the dynamics of transposable elements (TEs), there is a lack of available empirical data to validate these models and inherent assumptions. Genomes can provide a snapshot of several TE families in a single organism, and these could have their demographics inferred by coalescent analysis, allowing for the testing of theories on TE amplification dynamics. Using the available genomes of the mosquitoes Aedes aegypti and Anopheles gambiae, we indicate that such an approach is feasible. Our analysis follows four steps: (1) mining the two mosquito genomes currently available in search of TE families; (2) fitting, to selected families found in (1), a phylogeny tree under the general time-reversible (GTR) nucleotide substitution model with an uncorrelated lognormal (UCLN) relaxed clock and a nonparametric demographic model; (3) fitting a nonparametric coalescent model to the tree generated in (2); and (4) fitting parametric models motivated by ecological theories to the curve generated in (3).

Family history of cancer: Pooled analysis in the International Head and Neck Cancer Epidemiology consortium

Alcohol and tobacco consumption are well-recognized risk factors for head and neck cancer (HNC). Evidence suggests that genetic predisposition may also play a role. Only a few epidemiologic studies, however, have considered the relation between HNC risk and family history of HNC and other cancers. We pooled individual-level data across 12 case-control studies including 8,967 HNC cases and 13,627 controls. We obtained pooled odds ratios (OR) using fixed and random effect models and adjusting for potential confounding factors. All statistical tests were two-sided. A family history of HNC in first-degree relatives increased the risk of HNC (OR = 1.7, 95% confidence interval, CI, 1.2-2.3). The risk was higher when the affected relative was a sibling (OR = 2.2, 95% CI 1.6-3.1) rather than a parent (OR = 1.5, 95% CI 1.1-1.8) and for more distal HNC anatomic sites (hypopharynx and larynx). The risk was also higher, or limited to, in subjects exposed to tobacco. The OR rose to 7.2 (95% CI 5.5-9.5) among subjects with family history, who were alcohol and tobacco users. A weak but significant association (OR = 1.1, 95% CI 1.0-1.2) emerged for family history of other tobacco-related neoplasms, particularly with laryngeal cancer (OR = 1.3, 95% CI 1.1-1.5). No association was observed for family history of nontobacco-related neoplasms and the risk of HNC (OR = 1.0, 95% CI 0.9-1.1). Familial factors play a role in the etiology of HNC. In both subjects with and without family history of HNC, avoidance of tobacco and alcohol exposure may be the best way to avoid HNC. (C) 2008 Wiley-Liss, Inc,