A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane.

Correlation of photodynamic activity and singlet oxygen quantum yields in two series of hydrophobic monocationic porphyrins

The photodynamic properties of eight hydrophobic monocationic methyl and ruthenium polypyridine complex derivatives of free-base and zinc(II) meso-triphenyl-monopyridylporphyrin series were evaluated and compared using HeLa cells as model. The cream-like polymeric nanocapsule formulations of marine atelocollagen/xanthan gum, prepared by the coacervation method, exhibited high phototoxicity but negligible cytotoxicity in the dark. Interestingly, the formulations of a given series presented similar photodynamic activities but the methylated free-base derivatives were significantly more phototoxic than the respective ruthenated photosensitizers, reflecting the higher photoinduced singlet oxygen quantum yields of those monocationic porphyrin dyes.

Correlation Between Molecular Conformation, Packing, and Dynamics in Oligofluorenes: A Theoretical/Experimental Study

Fluorene-based systems have shown great potential as components in organic electronics and optoelectronics (organic photovoltaics, OPVs, organic light emitting diodes, OLEDs, and organic transistors, OTFTs). These systems have drawn attention primarily because they exhibit strong blue emission associated with relatively good thermal stability. It is well-known that the electronic properties of polymers are directly related to the molecular conformations and chain packing of polymers. Here, we used three oligofluorenes (trimer, pentamer, and heptamer) as model systems to theoretically investigate the conformational properties of fluorene molecules, starting with the identification of preferred conformations. The hybrid exchange correlation functional, OPBE, and ZINDO/S-CI showed that each oligomer exhibits a tendency to adopt a specific chain arrangement, which could be distinguished by comparing their UV/vis electronic absorption and C-13 NMR spectra. This feature was used to identify the preferred conformation of the oligomer chains in chloroform-cast films by comparing experimental and theoretical UV/vis and C-13 NMR spectra. Moreover, the oligomer chain packing and dynamics in the films were studied by DSC and several solid state NMR techniques, which indicated that the phase behavior of the films may be influenced by the tendency that each oligomeric chain has to adopt a given conformation.

Application of an active attachment model as a high-throughput demineralization biofilm model

Objectives: To investigate the potential of an active attachment biofilm model as a highthroughput demineralization biofilm model for the evaluation of caries-preventive agents. Methods: Streptococcus mutans UA159 biofilms were grown on bovine dentine discs in a highthroughput active attachment model. Biofilms were first formed in a medium with high buffer capacity for 24 h and then subjected to various photodynamic therapies (PACT) using the combination of Light Emitting Diodes (LEDs, Biotable (R)) and Photogem (R). Viability of the biofilms was evaluated by plate counts. To investigate treatment effects on dentine lesion formation, the treated biofilms were grown in a medium with low buffer capacity for an additional 24 h. Integrated mineral loss (IML) and lesion depth (LD) were assessed by transversal microradiography. Calcium release in the biofilm medium was measured by atomic absorption spectroscopy. Results: Compared to the water treated control group, significant reduction in viability of S. mutans biofilms was observed when the combination of LEDs and Photogem (R) was applied. LEDs or Photogem (R) only did not result in biofilm viability changes. Similar outcomes were also found for dentine lesion formation. Significant lower IML and LD values were only found in the group subjected to the combined treatment of LEDs and Photogem (R). There was a good correlation between the calcium release data and the IML or LD values. Conclusions: The high-throughput active attachment biofilm model is applicable for evaluating novel caries-preventive agents on both biofilm and demineralization inhibition. PACT had a killing effect on 24 h S. mutans biofilms and could inhibit the demineralization process. (C) 2011 Elsevier Ltd. All rights reserved.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Study of using marker assisted selection on a beef cattle breeding program by model comparison

A data set of a commercial Nellore beef cattle selection program was used to compare breeding models that assumed or not markers effects to estimate the breeding values, when a reduced number of animals have phenotypic, genotypic and pedigree information available. This herd complete data set was composed of 83,404 animals measured for weaning weight (WW), post-weaning gain (PWG), scrotal circumference (SC) and muscle score (MS), corresponding to 116,652 animals in the relationship matrix. Single trait analyses were performed by MTDFREML software to estimate fixed and random effects solutions using this complete data. The additive effects estimated were assumed as the reference breeding values for those animals. The individual observed phenotype of each trait was adjusted for fixed and random effects solutions, except for direct additive effects. The adjusted phenotype composed of the additive and residual parts of observed phenotype was used as dependent variable for models' comparison. Among all measured animals of this herd, only 3160 animals were genotyped for 106 SNP markers. Three models were compared in terms of changes on animals' rank, global fit and predictive ability. Model 1 included only polygenic effects, model 2 included only markers effects and model 3 included both polygenic and markers effects. Bayesian inference via Markov chain Monte Carlo methods performed by TM software was used to analyze the data for model comparison. Two different priors were adopted for markers effects in models 2 and 3, the first prior assumed was a uniform distribution (U) and, as a second prior, was assumed that markers effects were distributed as normal (N). Higher rank correlation coefficients were observed for models 3_U and 3_N, indicating a greater similarity of these models animals' rank and the rank based on the reference breeding values. Model 3_N presented a better global fit, as demonstrated by its low DIC. The best models in terms of predictive ability were models 1 and 3_N. Differences due prior assumed to markers effects in models 2 and 3 could be attributed to the better ability of normal prior in handle with collinear effects. The models 2_U and 2_N presented the worst performance, indicating that this small set of markers should not be used to genetically evaluate animals with no data, since its predictive ability is restricted. In conclusion, model 3_N presented a slight superiority when a reduce number of animals have phenotypic, genotypic and pedigree information. It could be attributed to the variation retained by markers and polygenic effects assumed together and the normal prior assumed to markers effects, that deals better with the collinearity between markers. (C) 2012 Elsevier B.V. All rights reserved.

Quantum critical scaling at a Bose-glass/superfluid transition: Theory and experiment for a model quantum magnet

In this paper we investigate the quantum phase transition from magnetic Bose Glass to magnetic Bose-Einstein condensation induced by amagnetic field in NiCl2 center dot 4SC(NH2)(2) (dichloro-tetrakis-thiourea-nickel, or DTN), doped with Br (Br-DTN) or site diluted. Quantum Monte Carlo simulations for the quantum phase transition of the model Hamiltonian for Br-DTN, as well as for site-diluted DTN, are consistent with conventional scaling at the quantum critical point and with a critical exponent z verifying the prediction z = d; moreover the correlation length exponent is found to be nu = 0.75(10), and the order parameter exponent to be beta = 0.95(10). We investigate the low-temperature thermodynamics at the quantum critical field of Br-DTN both numerically and experimentally, and extract the power-law behavior of the magnetization and of the specific heat. Our results for the exponents of the power laws, as well as previous results for the scaling of the critical temperature to magnetic ordering with the applied field, are incompatible with the conventional crossover-scaling Ansatz proposed by Fisher et al. [Phys. Rev. B 40, 546 (1989)]. However they can all be reconciled within a phenomenological Ansatz in the presence of a dangerously irrelevant operator.

Generalized correlation functions for conductance fluctuations and the mesoscopic spin Hall effect

We study the spin Hall conductance fluctuations in ballistic mesoscopic systems. We obtain universal expressions for the spin and charge current fluctuations, cast in terms of current-current autocorrelation functions. We show that the latter are conveniently parametrized as deformed Lorentzian shape lines, functions of an external applied magnetic field and the Fermi energy. We find that the charge current fluctuations show quite unique statistical features at the symplectic-unitary crossover regime. Our findings are based on an evaluation of the generalized transmission coefficients correlation functions within the stub model and are amenable to experimental test. DOI: 10.1103/PhysRevB.86.235112

Finite element analysis of bonded model Class I 'restorations' after shrinkage

Objectives. The C-Factor has been used widely to rationalize the changes in shrinkage stress occurring at the tooth/resin-composite interfaces. Experimentally, such stresses have been measured in a uniaxial direction between opposed parallel walls. The situation of adjoining cavity walls has been neglected. The aim was to investigate the hypothesis that: within stylized model rectangular cavities of constant volume and wall thickness, the interfacial shrinkage-stress at the adjoining cavity walls increases steadily as the C-Factor increases. Methods. Eight 3D-FEM restored Class I 'rectangular cavity' models were created by MSC.PATRAN/MSC.Marc, r2-2005 and subjected to 1% of shrinkage, while maintaining constant both the volume (20 mm(3)) and the wall thickness (2 mm), but varying the C-Factor (1.9-13.5). An adhesive contact between the composite and the teeth was incorporated. Polymerization shrinkage was simulated by analogy with thermal contraction. Principal stresses and strains were calculated. Peak values of maximum principal (MP) and maximum shear (MS) stresses from the different walls were displayed graphically as a function of C-Factor. The stress-peak association with C-Factor was evaluated by the Pearson correlation between the stress peak and the C-Factor. Results. The hypothesis was rejected: there was no clear increase of stress-peaks with C-Factor. The stress-peaks particularly expressed as MP and MS varied only slightly with increasing C-Factor. Lower stress-peaks were present at the pulpal floor in comparison to the stress at the axial walls. In general, MP and MS were similar when the axial wall dimensions were similar. The Pearson coefficient only expressed associations for the maximum principal stress at the ZX wall and the Z axis. Significance. Increase of the C-Factor did not lead to increase of the calculated stress-peaks in model rectangular Class I cavity walls. (C) 2011 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Early detection of doxorubicin myocardial injury by ultrasonic tissue characterization in an experimental animal model

In the clinical setting, the early detection of myocardial injury induced by doxorubicin (DXR) is still considered a challenge. To assess whether ultrasonic tissue characterization (UTC) can identify early DXR-related myocardial lesions and their correlation with collagen myocardial percentages, we studied 60 rats at basal status and prospectively after 2mg/Kg/week DXR endovenous infusion. Echocardiographic examinations were conducted at baseline and at 8,10,12,14 and 16 mg/Kg DXR cumulative dose. The left ventricle ejection fraction (LVEF), shortening fraction (SF), and the UTC indices: corrected coefficient of integrated backscatter (IBS) (tissue IBS intensity/phantom IBS intensity) (CC-IBS) and the cyclic variation magnitude of this intensity curve (MCV) were measured. The variation of each parameter of study through DXR dose was expressed by the average and standard error at specific DXR dosages and those at baseline. The collagen percent (%) was calculated in six control group animals and 24 DXR group animals. CC-IBS increased (1.29 +/- 0.27 x 1.1 +/- 0.26-basal; p=0.005) and MCV decreased (9.1 +/- 2.8 x 11.02 +/- 2.6-basal; p=0.006) from 8 mg/Kg to 16mg/Kg DXR. LVEF presented only a slight but significant decrease (80.4 +/- 6.9% x 85.3 +/- 6.9%-basal, p=0.005) from 8 mg/Kg to 16 mg/Kg DXR. CC-IBS was 72.2% sensitive and 83.3% specific to detect collagen deposition of 4.24%(AUC=0.76). LVEF was not accurate to detect initial collagen deposition (AUC=0.54). In conclusion: UTC was able to early identify the DXR myocardial lesion when compared to LVEF, showing good accuracy to detect the initial collagen deposition in this experimental animal model.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Exact correlation functions in particle-reaction models with immobile particles

Exact results on particle densities as well as correlators in two models of immobile particles, containing either a single species or else two distinct species, are derived. The models evolve following a descent dynamics through pair annihilation where each particle interacts once at most throughout its entire history. The resulting large number of stationary states leads to a non-vanishing configurational entropy. Our results are established for arbitrary initial conditions and are derived via a generating function method. The single-species model is the dual of the 1D zero-temperature kinetic Ising model with Kimball-Deker-Haake dynamics. In this way, both in finite and semi-infinite chains and also the Bethe lattice can be analysed. The relationship with the random sequential adsorption of dimers and weakly tapped granular materials is discussed.

Ground-level ozone prediction using a neural network model based on meteorological variables and applied to the metropolitan area of Sao Paulo

A neural network model to predict ozone concentration in the Sao Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00-12:00 hr) and afternoon (13:00-17: 00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00-17:00 hr). The correlation coefficient between computed and measured values was 0.82 and 0.88 for the maximum and average ozone concentration, respectively. The model presented good performance as a prediction tool for the maximum ozone concentration. For prediction periods from 1 to 5 days 0 to 23% failures (95% confidence) were obtained.

A Poisson mixed model with nonnormal random effect distribution

In this paper, we propose a random intercept Poisson model in which the random effect is assumed to follow a generalized log-gamma (GLG) distribution. This random effect accommodates (or captures) the overdispersion in the counts and induces within-cluster correlation. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are, in general, required for deriving the marginal models, we obtain the multivariate negative binomial model from a particular parameter setting of the hierarchical model. An iterative process is derived for obtaining the maximum likelihood estimates for the parameters in the multivariate negative binomial model. Residual analysis is proposed and two applications with real data are given for illustration. (C) 2011 Elsevier B.V. All rights reserved.