Search for cytotoxic agents in multiple Laurencia complex seaweed species (Ceramiales, Rhodophyta) harvested from the Atlantic Ocean with emphasis on the Brazilian State of Espírito Santo

The development of new anti-cancer drugs of algal origin represents one of the least explored frontiers in medicinal chemistry. In this regard, the diversity of micro- and macroalgae found in Brazilian coastal waters can be viewed as a largely untapped natural resource. In this report, we describe a comparative study on the cytotoxic properties of extracts obtained from the Laurencia complex: Laurencia aldingensis, L. catarinensis, L. dendroidea, L. intricata, L. translucida, L. sp, and Palisada flagellifera. All of these species were collected in the coastal waters of the State of Espírito Santo, Brazil. Four out of the twelve samples initially investigated were found to show significant levels of toxicity towards a model tumor cell line (human uterine sarcoma, MES-SA). The highest levels of cytotoxicity were typically associated with non-polar (hexane) algal extracts, while the lowest levels of cytotoxicity were found with the corresponding polar (methanol) extracts. In this report, we also describe a biological model currently in development that will not only facilitate the search for new anti-cancer drug candidates of algal origin, but also permit the identification of compounds capable of inducing the destruction of multi-drug resistant tumors with greater efficiency than the pharmaceuticals currently in clinical use.

Effect of an energy-deficient diet on populations of ciliate protozoans in bovine rumen

Ten young rumen-cannulated crossbred steers were randomly divided into two groups: a control group (C; n=4), which was fed a balanced diet for daily weight gain of 900g; and a pronounced energy-deprived group (PED; n=6), receiving 30% less of the required energy for maintenance. After 140 days of these alimentary regimes, rumen fluid and urine samples were collected for biochemical and functional tests, before feeding and at 1, 3, 6, and 9 hours after feeding. The energy-deprivation diet caused a significant reduction in the number of Entodinium, Eodinium, Isotricha, Dasytricha, Eremoplastron, Eudiplodinium, Metadinium, Charonina, Ostracodinium, and Epidinium protozoa. There was no effect of the time of sampling in both groups on the total number of ciliates in rumen fluid. A higher number of protozoan forms in binary division were recorded in the control group, at the 6th and 9th hours after feeding (P<0.019). There was a high positive correlation between the total count of protozoans in rumen fluid and glucose fermentation, ammonia, and urinary allantoin excretion index; and a negative correlation between the total count of protozoa and metilene blue reduction, and a medium correlation between the total count of protozoa and total volatile fatty acids concentration. The determination of the protozoa populations does not imply in the use of complex and hard-to-execute techniques, although it is time consuming and needs practice. This exam particularly helps in clinical expected diagnosis.

High-throughput SNP genotyping in the highly heterozygous genome of Eucalyptus: assay success, polymorphism and transferability across species

Background: High-throughput SNP genotyping has become an essential requirement for molecular breeding and population genomics studies in plant species. Large scale SNP developments have been reported for several mainstream crops. A growing interest now exists to expand the speed and resolution of genetic analysis to outbred species with highly heterozygous genomes. When nucleotide diversity is high, a refined diagnosis of the target SNP sequence context is needed to convert queried SNPs into high-quality genotypes using the Golden Gate Genotyping Technology (GGGT). This issue becomes exacerbated when attempting to transfer SNPs across species, a scarcely explored topic in plants, and likely to become significant for population genomics and inter specific breeding applications in less domesticated and less funded plant genera. Results: We have successfully developed the first set of 768 SNPs assayed by the GGGT for the highly heterozygous genome of Eucalyptus from a mixed Sanger/454 database with 1,164,695 ESTs and the preliminary 4.5X draft genome sequence for E. grandis. A systematic assessment of in silico SNP filtering requirements showed that stringent constraints on the SNP surrounding sequences have a significant impact on SNP genotyping performance and polymorphism. SNP assay success was high for the 288 SNPs selected with more rigorous in silico constraints; 93% of them provided high quality genotype calls and 71% of them were polymorphic in a diverse panel of 96 individuals of five different species. SNP reliability was high across nine Eucalyptus species belonging to three sections within subgenus Symphomyrtus and still satisfactory across species of two additional subgenera, although polymorphism declined as phylogenetic distance increased. Conclusions: This study indicates that the GGGT performs well both within and across species of Eucalyptus notwithstanding its nucleotide diversity >= 2%. The development of a much larger array of informative SNPs across multiple Eucalyptus species is feasible, although strongly dependent on having a representative and sufficiently deep collection of sequences from many individuals of each target species. A higher density SNP platform will be instrumental to undertake genome-wide phylogenetic and population genomics studies and to implement molecular breeding by Genomic Selection in Eucalyptus.

Generalized Sznajd model for opinion propagation

In the last decade the Sznajd model has been successfully employed in modeling some properties and scale features of both proportional and majority elections. We propose a version of the Sznajd model with a generalized bounded confidence rule-a rule that limits the convincing capability of agents and that is essential to allow coexistence of opinions in the stationary state. With an appropriate choice of parameters it can be reduced to previous models. We solved this model both in a mean-field approach (for an arbitrary number of opinions) and numerically in a Barabaacutesi-Albert network (for three and four opinions), studying the transient and the possible stationary states. We built the phase portrait for the special cases of three and four opinions, defining the attractors and their basins of attraction. Through this analysis, we were able to understand and explain discrepancies between mean-field and simulation results obtained in previous works for the usual Sznajd model with bounded confidence and three opinions. Both the dynamical system approach and our generalized bounded confidence rule are quite general and we think it can be useful to the understanding of other similar models.

Scaling properties at freeze-out in relativistic heavy-ion collisions

Identified charged pion, kaon, and proton spectra are used to explore the system size dependence of bulk freeze-out properties in Cu + Cu collisions at root s(NN) = 200 and 62.4 GeV. The data are studied with hydrodynamically motivated blast-wave and statistical model frameworks in order to characterize the freeze-out properties of the system. The dependence of freeze-out parameters on beam energy and collision centrality is discussed. Using the existing results from Au + Au and pp collisions, the dependence of freeze-out parameters on the system size is also explored. This multidimensional systematic study furthers our understanding of the QCD phase diagram revealing the importance of the initial geometrical overlap of the colliding ions. The analysis of Cu + Cu collisions expands the system size dependence studies from Au + Au data with detailed measurements in the smaller system. The systematic trends of the bulk freeze-out properties of charged particles is studied with respect to the total charged particle multiplicity at midrapidity, exploring the influence of initial state effects.

gamma cross section in p plus p collisions at root s=200 GeV

We report on a measurement of the gamma(1S + 2S + 3S) -> e(+)e(-) cross section at midrapidity in p + p collisions at root s = 200 GeV. We find the cross section to be 114 +/- 38(stat + fit)(-24)(+23)(syst) pb. Perturbative QCD calculations at next-to-leading order in the color evaporation model are in agreement with our measurement, while calculations in the color singlet model underestimate it by 2 sigma. Our result is consistent with the trend seen in world data as a function of the center-of-mass energy of the collision and extends the availability of gamma data to RHIC energies. The dielectron continuum in the invariant-mass range near the gamma is also studied to obtain a combined yield of e(+)e(-) pairs from the sum of the Drell-Yan process and b-(b) over bar production.

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.

Understanding solvation

The effects of solvents on different chemical phenomena, including reactivity, spectroscopic data, and swelling of biopolymers can be rationalized by use of solvatochromic probes, substances whose UV-vis spectra, absorption, or emission are sensitive to the properties of the medium. Thermo-solvatochromism refers to the effect of temperature on solvatochromism. The study of both phenomena sheds light on the relative importance of the factors that contribute to solvation, namely, properties of the probe, those of the solvent (acidity, basicity, dipolarity/polarizability, and lipophilicity), and the temperature. Solvation in binary solvent mixtures is complex because of ""preferential solvation"" of the probe by some component of the mixture. A recently introduced solvent exchange model is based on the presence in the binary solvent mixture of the organic component (molecular solvent or ionic liquid), S, water, W, and a 1:1 hydrogen-bonded species (S-W). Solvation by the latter is more efficient than by its precursor solvents, due to probe-solvent hydrogen-bonding and hydrophobic interactions; dimethyl sulfoxide (DMSO)-W is an exception. Solvatochromic data are employed in order to explain apparently disconnected phenomena, namely, medium effect on the pH-independent hydrolysis of esters, (1)H NMR data of water-ionic liquid (IL) mixtures, and the swelling of cellulose.

The effect of the generation and handling in the acquired electrostatic charge in airborne particles

The measurement of the charge distribution in laboratory generated aerosols particles was carried out. Four cases of electrostatic charge acquisition by aerosol particles were evaluated. In two of these cases. the charges acquired by the particles were naturally derived from the aerosol generation procedure itself, without using any additional charging method. Ill the other two cases, a corona charger and an impact charger were utilized as Supplementary methods for charge generation. Two types of aerosol generators were used in the dispersion of particles in the gas Stream: the vibrating orifice generator TSI model 3450 and the rotating plate generator TSI model 3433. In the vibrating orifice generator. a Solution of methylene blue Was used and the generated particles were mono-dispersed. Different mono-aerosols were generated with particle diameters varying from 6.0 x 10(-6) m to 1.4 x 10(-5) m. In the rotating plate generator, a poly-dispersed phosphate rock concentrate with Stokes mean diameter of 1.30 x 10(-6) m and size range between 1.5 x 10(-7) m and 8.0 x 10(-6) m Was utilized as powder material in all tests. In the tests performed with the mono-dispersed particles. the median charges of the particles varied between -3.0 x 10-(16) C and -5.0 x 10(-18) degrees C and a weak dependence between particle size and charge was observed. The particles were predominantly negatively charged. In the tests with the poly-dispersed particles the median charges varied fairly linearly with the particle diameter and were negative. The order of magnitude of the results obtained is in accordance with data reported in the literature. The charge distribution, in this case, was wider, so that an appreciable amount of particles were positively charged. The relative spread of the distribution varied with the charging method. It was also noticed that the corona charger acted very effectively in charging the particles. (C) 2008 Elsevier BY. All rights reserved.

Robust integration of real time optimization with linear model predictive control

Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.

Improved generation lifetime model for the electrical characterization of single- and double-gate SOI nMOSFETs

This work proposes a refined technique for the extraction of the generation lifetime in single- and double-gate partially depleted SOI nMOSFETs. The model presented in this paper, based on the drain current switch-off transients, takes into account the influence of the laterally non-uniform channel doping, caused by the presence of the halo implanted region, and the amount of charge controlled by the drain and source junctions on the floating body effect when the channel length is reduced. The obtained results for single- gate (SG) devices are compared with two-dimensional numerical simulations and experimental data, extracted for devices fabricated in a 0.1 mu m SOI CMOS technology, showing excellent agreement. The improved model to determine the generation lifetime in double-gate (DG) devices beyond the considerations previously presented also consider the influence of the silicon layer thickness on the drain current transient. The extracted data through the improved model for DG devices were compared with measurements and two-dimensional numerical simulations of the SG devices also presenting a good adjustment with the channel length reduction and the same tendency with the silicon layer thickness variation.

Evaluation of Characterization and Performance Modeling of Cementitiously Stabilized Layers in the Mechanistic-Empirical Pavement Design Guide

Cementitious stabilization of aggregates and soils is an effective technique to increase the stiffness of base and subbase layers. Furthermore, cementitious bases can improve the fatigue behavior of asphalt surface layers and subgrade rutting over the short and long term. However, it can lead to additional distresses such as shrinkage and fatigue in the stabilized layers. Extensive research has tested these materials experimentally and characterized them; however, very little of this research attempts to correlate the mechanical properties of the stabilized layers with their performance. The Mechanistic Empirical Pavement Design Guide (MEPDG) provides a promising theoretical framework for the modeling of pavements containing cementitiously stabilized materials (CSMs). However, significant improvements are needed to bring the modeling of semirigid pavements in MEPDG to the same level as that of flexible and rigid pavements. Furthermore, the MEPDG does not model CSMs in a manner similar to those for hot-mix asphalt or portland cement concrete materials. As a result, performance gains from stabilized layers are difficult to assess using the MEPDG. The current characterization of CSMs was evaluated and issues with CSM modeling and characterization in the MEPDG were discussed. Addressing these issues will help designers quantify the benefits of stabilization for pavement service life.

A modified epidemiological model for computer viruses

Since the computer viruses pose a serious problem to individual and corporative computer systems, a lot of effort has been dedicated to study how to avoid their deleterious actions, trying to create anti-virus programs acting as vaccines in personal computers or in strategic network nodes. Another way to combat viruses propagation is to establish preventive policies based on the whole operation of a system that can be modeled with population models, similar to those that are used in epidemiological studies. Here, a modified version of the SIR (Susceptible-Infected-Removed) model is presented and how its parameters are related to network characteristics is explained. Then, disease-free and endemic equilibrium points are calculated, stability and bifurcation conditions are derived and some numerical simulations are shown. The relations among the model parameters in the several bifurcation conditions allow a network design minimizing viruses risks. (C) 2009 Elsevier Inc. All rights reserved.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Genetic potential of a maize population from Southern Brazil for the modified convergent-divergent selection scheme

Maize (Zea mays L.) is a very important cereal to world-wide economy which is also true for Brazil, particularly in the South region. Grain yield and plant height have been chosen as important criteria by breeders and farmers from Santa Catarina State (SC), Brazil. The objective of this work was to estimate genetic-statistic parameters associated with genetic gain for grain yield and plant height, in the first cycle of convergent-divergent half-sib selection in a maize population (MPA1) cultivated by farmers within the municipality of Anchieta (SC). Three experiments were carried out in different small farms at Anchieta using low external agronomic inputs; each experiment represented independent samples of half-sib families, which were evaluated in randomized complete blocks with three replications per location. Significant differences among half-sib families were observed for both variables in all experiments. The expected responses to truncated selection of the 25% better families in each experiment were 5.1, 5.8 and 5.2% for reducing plant height and 3.9, 5.7 and 5.0% for increasing grain yield, respectively. The magnitudes of genetic-statistic parameters estimated evidenced that the composite population MPA1 exhibits enough genetic variability to be used in cyclical process of recurrent selection. There were evidences that the genetic structure of the base population MPA1, as indicated by its genetic variability, may lead to expressive changes in the traits under selection, even under low selection pressure.