Shear banding in a yield stress bearing Langmuir monolayer

We examine the shear-thinning behaviour of a two dimensional yield stress bearing monolayer of sorbitan tristearate at air/water interface. The flow curve consists of a linear region at low shear stresses/shear rates, followed by a stress plateau at higher values. The velocity profile obtained from particle imaging velocimetry indicates that shear banding occurs, showing coexistence of the fluidized region near the rotor and solid region with vanishing shear-rate away from the rotor. In the fluidized region, the velocity profile, which is linear at low shear rates, becomes exponential at the onset of shear-thinning, followed by a time varying velocity profile in the plateau region. At low values of constant applied shear rates, the viscosity of the film increases with time, thus showing aging behaviour like in soft glassy three-dimensional (3D) systems. Further, at the low values of the applied stress in the yield stress regime, the shear-rate fluctuations in time show both positive and negative values, similar to that observed in sheared 3D jammed systems. By carrying out a statistical analysis of these shear-rate fluctuations, we estimate the effective temperature of the soft glassy monolayer using the Galavatti-Cohen steady state fluctuation relation.

Yield stress, thixotropy and shear banding in a dilute aqueous suspension of few layer graphene oxide platelets

We demonstrate a rigidity percolation transition and the onset of yield stress in a dilute aqueous dispersion of graphene oxide platelets (aspect ratio similar to 5000) above a critical volume fraction of 3.75 x 10(-4) with a percolation exponent of 2.4 +/- 0.1. The viscoelastic moduli of the gel at rest measured as a function of time indicate the absence of structural evolution of the 3D percolated network of disks. However a shear-induced aging giving rise to a compact jammed state and shear rejuvenation indicating a homogenous flow is observed when a steady shear stress (sigma) is imposed in creep experiments. We construct a shear diagram (sigma vs. volume fraction phi) and the critical stress above which shear rejuvenation occurs is identified as the yield stress sigma(y) of the gel. The minimum steady state shear rate (gamma) over dot(m) obtained from creep experiments agrees well with the end of the plateau region in a controlled shear rate flow curve, indicating a shear localization below (gamma) over dot(m). A steady state shear banding in the plateau region of the flow curve observed in particle velocimetry measurements in a Couette geometry confirms that the dilute suspensions of GO platelets form a thixotropic yield stress fluid.

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

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Buffalos milk yield analysis using random regression models

Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed), daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genetic de Bubalinos (PROMEBUL) and from records of EMBRAPA Amazonia Oriental - EAO herd, located in Belem, Para, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre's polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve) and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre's polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

Nitrogen and potassium fertilization on the yield and intensity of the maize white spot

A plant’s nutritional balance can influence its resistance to diseases. In order to evaluate the effect of increasing doses of N and K on the yield and severity of the maize white spot, two experiments were installed in the field, one in the city of Ijaci, Minas Gerais, and the other in the city of Sete Lagoas, Minas Gerais. The experimental delimitation was in randomized blocks with 5 x 5 factorial analysis of variance, and four repetitions. The treatments consisted of five doses of N (20; 40; 80; 150; 190 Kg ha-1 of N in the experiments 1 and 2) and five doses of K (15; 30; 60; 120; 180 Kg ha-1 of K in experiment 1 and 8.75; 17.5; 35; 50; 100 Kg ha-1 of K in experiment 2). The susceptible cultivar 30P70 was planted in both experiments. The plot consisted of four rows 5 meters long, with a useful area consisting of two central rows 3 meters each. Evaluations began 43 days after emergence (DAE) in the first experiment and 56 DAE in the second one. There was no significant interaction between doses of N and K and the disease progress. The effect was only observed for N. The K did not influence the yield and the severity of the disease in these experiments. Bigger areas below the severity progress curve of the white spot and better yield were observed with increasing doses of N. Thus, with increasing doses of N, the white spot increased and also did the yield.

Predicting breeding values for milk yield of Guzera (Bos indicus) cows using random regression models

Given the importance of Guzera breeding programs for milk production in the tropics, the objective of this study was to compare alternative random regression models for estimation of genetic parameters and prediction of breeding values. Test-day milk yields records (TDR) were collected monthly, in a maximum of 10 measurements. The database included 20,524 records of first lactation from 2816 Guzera cows. TDR data were analyzed by random regression models (RRM) considering additive genetic, permanent environmental and residual effects as random and the effects of contemporary group (CG), calving age as a covariate (linear and quadratic effects) and mean lactation curve as fixed. The genetic additive and permanent environmental effects were modeled by RRM using Wilmink, All and Schaeffer and cubic B-spline functions as well as Legendre polynomials. Residual variances were considered as heterogeneous classes, grouped differently according to the model used. Multi-trait analysis using finite-dimensional models (FDM) for testday milk records (TDR) and a single-trait model for 305-days milk yields (default) using the restricted maximum likelihood method were also carried out as further comparisons. Through the statistical criteria adopted, the best RRM was the one that used the cubic B-spline function with five random regression coefficients for the genetic additive and permanent environmental effects. However, the models using the Ali and Schaeffer function or Legendre polynomials with second and fifth order for, respectively, the additive genetic and permanent environmental effects can be adopted, as little variation was observed in the genetic parameter estimates compared to those estimated by models using the B-spline function. Therefore, due to the lower complexity in the (co)variance estimations, the model using Legendre polynomials represented the best option for the genetic evaluation of the Guzera lactation records. An increase of 3.6% in the accuracy of the estimated breeding values was verified when using RRM. The ranks of animals were very close whatever the RRM for the data set used to predict breeding values. Considering P305, results indicated only small to medium difference in the animals' ranking based on breeding values predicted by the conventional model or by RRM. Therefore, the sum of all the RRM-predicted breeding values along the lactation period (RRM305) can be used as a selection criterion for 305-day milk production. (c) 2014 Elsevier B.V. All rights reserved.

Relationship between sweet orange yield and intensity of Citrus Variegated Chlorosis

Citrus Variegated Chlorosis (CVC) is currently present in approximately 40% of citrus plants in Brazil and causes an annual loss of around 120 million US dollars to the Brazilian citrus industry. Despite the fact that CVC has been present in Brazil for over 20 years, a relationship between disease intensity and yield loss has not been established. In order to achieve this, an experiment was carried out in a randomized block design in a 3 x 2 factorial scheme with 10-year-old Natal sweet orange. The following treatments were applied: irrigation with 0, 50 or 100% of the evapotranspiration of the crop, combined with natural infection or artificial inoculation with Xylella fastidiosa, the causal agent of CVC. The experiment was evaluated during three seasons. A negative exponential model was fitted to the relationships between yield versus CVC severity and yield versus Area Under Disease Progress Curve (AUDPC). In addition, the relationship between yield versus CVC severity and canopy volume was fitted by a multivariate exponential model. The use of the AUDPC variable showed practical limitations when compared with the variable CVC severity. The parameter values in the relationship of yieldCVC severity were similar for all treatments unlike in the multivariate model. Consequently, the yieldCVC intensity relationship (with 432 data points) could be described by one single model: y = 114.07 exp(-0.017 x), where y is yield (symptomless fruit weight in kg) and x is disease severity (R2 = 0.45; P < 0.01).

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

The objective of this paper is to model variations in test-day milk yields of first lactations of Holstein cows by RR using B-spline functions and Bayesian inference in order to fit adequate and parsimonious models for the estimation of genetic parameters. They used 152,145 test day milk yield records from 7317 first lactations of Holstein cows. The model established in this study was additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. Authors modeled the average lactation curve of the population with a fourth-order orthogonal Legendre polynomial. They concluded that a cubic B-spline with seven random regression coefficients for both the additive genetic and permanent environment effects was to be the best according to residual mean square and residual variance estimates. Moreover they urged a lower order model (quadratic B-spline with seven random regression coefficients for both random effects) could be adopted because it yielded practically the same genetic parameter estimates with parsimony. (C) 2012 Elsevier B.V. All rights reserved.

Increased CO2 modifies the carbon balance and the photosynthetic yield of two common Arctic brown seaweeds: Desmarestia aculeata and Alaria esculenta

Ocean acidification affects with special intensity Arctic ecosystems, being marine photosynthetic organisms a primary target, although the consequences of this process in the carbon fluxes of Arctic algae are still unknown. The alteration of the cellular carbon balance due to physiological acclimation to an increased CO2 concentration (1300 ppm) in the common Arctic brown seaweeds Desmarestia aculeata and Alaria esculenta from Kongsfjorden (Svalbard) was analysed. Growth rate of D. aculeata was negatively affected by CO2 enrichment, while A. esculenta was positively affected, as a result of a different reorganization of the cellular carbon budget in both species. Desmarestia aculeata showed increased respiration, enhanced accumulation of storage biomolecules and elevated release of dissolved organic carbon, whereas A. esculenta showed decreased respiration and lower accumulation of storage biomolecules. Gross photosynthesis (measured both as O2 evolution and 14C fixation) was not affected in any of them, suggesting that photosynthesis was already saturated at normal CO2 conditions and did not participate in the acclimation response. However, electron transport rate changed in both species in opposite directions, indicating different energy requirements between treatments and species specificity. High CO2 levels also affected the N-metabolism, and 13C isotopic discrimination values from algal tissue pointed to a deactivation of carbon concentrating mechanisms. Since increased CO2 has the potential to modify physiological mechanisms in different ways in the species studied, it is expected that this may lead to changes in the Arctic seaweed community, which may propagate to the rest of the food web.