Anthropogenic carbon distribution in the eastern South Pacific Ocean

We present results of the CO(2)/carbonate system from the BIOSOPE cruise in the Eastern South Pacific Ocean, in an area not sampled previously. In particular, we present estimates of the anthropogenic carbon (C(ant)(TrOCA)) distribution in the upper 1000m of this region using the TrOCA method. The highest concentrations of C(ant)(TrOCA) found around 13 degrees S, 132 degrees W and 32 degrees S, 91 degrees W, are higher than 80 mu mol.kg(-)1 and 70 mu mol.kg(-1), respectively. The lowest concentrations are observed below 800m depth (<= 2 mu mol.kg(-1)) and within the Oxygen Minimum Zone (OMZ), mainly around 140 degrees W (< 11 mu mol.kg(-1)). As a result of the anthropogenic carbon penetration there has been decrease in pH by over 0.1 on an average in the upper 200 m. This work further improves our understanding on the penetration of anthropogenic carbon in the Eastern Pacific Ocean.

Distribution and behavior of manganese in the Alto do Paranapanema Basin

This paper reports manganese (Mn) fractionation in samples collected from the water column and sediments in an environmental protection area in the Alto do Paranapanema Basin (Sao Paulo State, Brazil). The three locations studied showed equivalent Mn levels, with moderate seasonal differences (p < 0.05). The sediment samples contained five Mn species (p < 0.05): iron and manganese (hydr)oxides > Mn bound to carbonates approximate to exchangeable Mn approximate to Mn bound to silicates > Mn bound to organic matter (p < 0.05). The water samples contained three species (p < 0.05): particulate Mn > labile Mn approximate to non-labile Mn. The data suggest that Mn has a natural origin (Enrichment Factor EF < 2; Geoaccumulation Index I(geo) < 0) and moderate environmental risk (Risk Assessment Code RAC similar to 30%). At the same time, under certain conditions some manganese species could be present in a state of equilibrium between the water column and sediment. These results could provide a basis for Mn management in the Alto do Paranapanema Basin.

The role of exon shuffling in shaping protein-protein interaction networks

Background: Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks. Results: We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling. Conclusions: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion.

Self-organized distribution of periodicity and chaos in an electrochemical oscillator

We report a detailed numerical investigation of a prototype electrochemical oscillator, in terms of high-resolution phase diagrams for an experimentally relevant section of the control (parameter) space. The prototype model consists of a set of three autonomous ordinary differential equations which captures the general features of electrochemical oscillators characterized by a partially hidden negative differential resistance in an N-shaped current-voltage stationary curve. By computing Lyapunov exponents, we provide a detailed discrimination between chaotic and periodic phases of the electrochemical oscillator. Such phases reveal the existence of an intricate structure of domains of periodicity self-organized into a chaotic background. Shrimp-like periodic regions previously observed in other discrete and continuous systems were also observed here, which corroborate the universal nature of the occurrence of such structures. In addition, we have also found a structured period distribution within the order region. Finally we discuss the possible experimental realization of comparable phase diagrams.

DISTRIBUTION OF CAPYBARAS IN AN AGROECOSYSTEM, SOUTHEASTERN BRAZIL, BASED ON ECOLOGICAL NICHE MODELING

Southeastern Brazil has seen dramatic landscape modifications in recent decades, due to expansion of agriculture and urban areas; these changes have influenced the distribution and abundance of vertebrates. We developed predictive models of ecological and spatial distributions of capybaras (Hydrochoerus hydrochaeris) using ecological niche modeling. Most Occurrences of capybaras were in flat areas with water bodies Surrounded by sugarcane and pasture. More than 75% of the Piracicaba River basin was estimated as potentially habitable by capybara. The models had low omission error (2.3-3.4%), but higher commission error (91.0-98.5%); these ""model failures"" seem to be more related to local habitat characteristics than to spatial ones. The potential distribution of capybaras in the basin is associated with anthropogenic habitats, particularly with intensive land use for agriculture.

BORON UPTAKE AND DISTRIBUTION IN FIELD GROWN CITRUS TREES

In low fertility tropical soils, boron (B) deficiency impairs fruit production. However, little information is available on the efficiency of nutrient application and use by trees. Therefore, this work verified the effects of soil and foliar applications of boron in a commercial citrus orchard. An experiment was conducted with fertigated 4-year-old `Valencia` sweet orange trees on `Swingle` citrumelo rootstock. Boron (isotopically-enriched 10B) was supplied to trees once or twice in the growing season, either dripped in the soil or sprayed on the leaves. Trees were sampled at different periods and separated into different parts for total B contents and 10B/11B isotope ratios analyses. Soil B applied via fertigation was more efficient than foliar application for the organs grown after the B fertilization. Recovery of labeled B by fruits was 21% for fertigation and 7% for foliar application. Residual effects of nutrient application in the grove were observed in the year after labeled fertilizer application, which greater proportions derived from the soil supply.

Variation in the Distribution of Trace Elements in Renal Cell Carcinoma

The development of cancer is a complex, multistage process during which a normal cell undergoes genetic changes that result in phenotypic alterations and in the acquisition of the ability to invade other sites. Inductively coupled plasma optical emission spectroscopy was used to estimate the contents of Al, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, P, Pb, and Zn in healthy kidney and renal cell carcinoma (RCC), and significant differences were found for all elements. Along with the progression of the malignant disease, a progressive decrease of Cd and K was observed. In fact, for Cd, the concentration in stage T4 was 263.9 times lower than in stage T1, and for K, the concentration in stage T4 was 1.73 times lower than in stage T1. Progressive accumulation was detected for P, Pb, and Zn in stage T4. For P, the concentration in stage T4 was 11.1 times higher than in stage T1; for Pb, the concentration in stage T4 was 232.7 times higher than in T1; and for Zn, the concentration in T4 was 8.452 times higher than in T1. This study highlights the marked differences in the concentrations of selected trace metals in different malignant tumor stages. These findings indicate that some trace metals may play important roles in the pathogenesis of RCC.

A new species and new distribution records of Pickeliana (Opiliones: Laniatores: Stygnidae)

A new species of Pickeliana Mello-Leitao,1932, P. albimaculata sp. nov, is described from Jussari, Bahia, Brazil. It can be easily distinguished from the already described species by the presence of white spots on mesotergal area I and anal opercle. It is similar to P. pickeli Mello-Leitao,1932 by the presence of a large, ventro-apical pointed tubercle on femora III-IV A cladistic analysis was performed adding a new character to the available character matrix, the presence of a large and ventro-apical pointed tubercle on male femur IV According to this analysis, P. albimaculata sp. nov is sister species of P.pickeli.Additionally,we present an identification key and an update on the geographical distribution of species of this genus in northeastern Brazil.

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.

The process of drug dispensing and distribution at four Brazilian hospitals: A multicenter descriptive study

A multicenter descriptive study was carried out in two steps: an interview with providers involved in the medication processes, and then non-participating observation of their environment and practices. Only one hospital was found to have a bar-coding, dispensing system connected to a computerized prescription system. fit all participating hospitals at least 90% of the drugs were dispensed and distributed as unit doses, but in none of them did pharmacists assess prescriptions. The study findings showed that the processes of drug dispensing and distribution in Brazilian hospitals encounter several problems, mostly associated to work environment conditions and inadequacy in drug ordering and requests.

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.

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.

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.