Low level laser therapy increases angiogenesis in a model of ischemic skin flap in rats mediated by VEGF, HIF-1α and MMP-2

It is known that low level laser therapy is able to improve skin flap viability by increasing angiogenesis. However, the mechanism for new blood vessel formation is not completely understood. Here, we investigated the effects of 660 nm and 780 nm lasers at fluences of 30 and 40 J/cm2 on three important mediators activated during angiogenesis. Sixty male Wistar rats were used and randomly divided into five groups with twelve animals each. Groups were distributed as follows: skin flap surgery non-irradiated group as a control; skin flap surgery irradiated with 660 nm laser at a fluence of 30 or 40 J/cm2 and skin flap surgery irradiated with 780 nm laser at a fluence of 30 or 40 J/cm2. The random skin flap was performed measuring 10 × 4 cm, with a plastic sheet interposed between the flap and the donor site. Laser irradiation was performed on 24 points covering the flap and surrounding skin immediately after the surgery and for 7 consecutive days thereafter. Tissues were collected, and the number of vessels, angiogenesis markers (vascular endothelial growth factor, VEGF and hypoxia inducible factor, HIF-1α) and a tissue remodeling marker (matrix metalloproteinase, MMP-2) were analyzed. LLLT increased an angiogenesis, HIF-1α and VEGF expression and decrease MMP-2 activity. These phenomena were dependent on the fluences, and wavelengths used. In this study we showed that LLLT may improve the healing of skin flaps by enhancing the amount of new vessels formed in the tissue. Both 660 nm and 780 nm lasers were able to modulate VEGF secretion, MMP-2 activity and HIF-1α expression in a dose dependent manner. © 2013 Published by Elsevier B.V.

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.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Genetic parameters for body weight in buffaloes (Bubalus bubalis) in Colombia using random regression models

The objective of this research was to estimate (co) variance functions and genetic parameters for body weight in Colombian buffalo populations using random regression models with Legendre polynomials. Data consisted of 34,738 weight records from birth to 900 days of age from 7815 buffaloes. Fixed effects in the model were contemporary group and parity order of the mother. Random effects were direct and maternal additive genetic, as well as animal and maternal permanent environmental effects. A cubic orthogonal Legendre polynomial was used to model the mean curve of the population. Eleven models with first to sixth order polynomials were used to describe additive genetic direct and maternal effects, and animal and maternal permanent environmental effects. The residual was modeled considering five variance classes. The best model included fourth and sixth order polynomials for direct additive genetic and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects. The direct heritability increased from birth until 120 days of age (0.32 +/- 0.05), decreasing thereafter until one year of age (0.18 +/- 0.04) and increased again, reaching 0.39 +/- 0.09, at the end of the evaluated period. The highest maternal heritability estimates (0.11 +/- 0.05), were obtained for weights around weaning age (weaning age range is between 8 and 9.5 months). Maternal genetic and maternal permanent environmental variances increased from birth until about one year of age, decreasing at later ages. Direct genetic correlations ranged from moderate (0.60 +/- 0.060) to high (0.99 +/- 0.001), maternal genetic correlations showed a similar range (0.41 +/- 0.401 and 0.99 +/- 0.003), and all of them decreased as time between weighings increased. Direct genetic correlations suggested that selecting buffalos for heavier weights at any age would increase weights from birth through 900 days of age. However, higher heritabilities for direct genetic weights effects after 600 days of age suggested that selection for these effects would be more effective if done during this age period. A greater response to selection for maternal ability would be expected if selection used maternal genetic predictions for weights near weaning. (C) 2013 Elsevier B.V. All rights reserved.

Polynomials to model the growth of young bulls in performance tests

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

Ineraction Model Between HRSV G-Protein and Flavonoids

Background: Acute respiratory infections (ARI) are the leading cause of infant mortality in the world, and human respiratory syncytial virus (HRSV) is one of the main agents of ARI. One of the key targets of the adaptive host immune response is the RSV G-protein, which is responsible for attachment to the host cell. There is evidence that compounds such as flavonoids can inhibit viral infection in vitro. With this in mind, the main purpose of this study was to determine, using computational tools, the potential sites for interactions between G-protein and flavonoids. Results: Our study allowed the recognition of an hRSV G-protein model, as well as a model of the interaction with flavonoids. These models were composed, mainly, of -helix and random coil proteins. The docking process showed that molecular interactions are likely to occur. The flavonoid kaempferol-3-O-α-L-arabinopyranosil-(2 → 1)-α-L-apiofuranoside-7-O-α-L-rhamnopyranoside was selected as a candidate inhibitor. The main forces of the interaction were hydrophobic, hydrogen and electrostatic. Conclusions: The model of G-protein is consistent with literature expectations, since it was mostly composed of random coils (highly glycosylated sites) and -helices (lipid regions), which are common in transmembrane proteins. The docking analysis showed that flavonoids interact with G-protein in an important ectodomain region, addressing experimental studies to these sites. The determination of the G-protein structure is of great importance to elucidate the mechanism of viral infectivity, and the results obtained in this study will allow us to propose mechanisms of cellular recognition and to coordinate further experimental studies in order to discover effective inhibitors of attachment proteins.

N-Terminal microdomain peptide from human dihydroorotate dehydrogenase: structure and model membrane interactions

The N-terminus of the human dihydroorotate dehydrogenase (HsDHODH) has been described as important for the enzyme attachment in the inner mitochondrial membrane and possibly to regulate enzymatic activity. In this study, we synthesized the peptide acetyl-GDERFYAEHLMPTLQGLLDPESAHRL AVRFTSLGamide, comprising the residues 33-66 of HsDHODH N-terminal conserved microdomain. Langmuir monolayers and circular dichroism (CD) were employed to investigate the interactions between the peptide and membrane model, as micelles and monolayers of the lipids phosphatidylcholine (PC), 3-phosphatidylethanolamine (PE) and cardiolipin (CL). These lipids represent the major constituents of inner mitochondrial membranes. According to CD data, the peptide adopted a random structure in water, whereas it acquired α-helical structures in the presence of micelles. The π–A isotherms and polarization- modulated infrared reflection-absorption spectroscopy on monolayers showed that the peptide interacted with all lipids, but in different ways. In DPPC monolayers, the peptide penetrated into the hydrophobic region. The strongest initial interaction occurred with DPPE, but the peptide was expelled from this monolayer at high surface pressures. In CL, the peptide could induce a partial dissolution of the monolayer, leading to shorter areas at the monolayer collapse. These results corroborate the literature, where the HsDHODH microdomain is anchored into the inner mitochondrial membrane. Moreover, the existence of distinct conformations and interactions with the different membrane lipids indicates that the access to the enzyme active site may be controlled not only by conformational changes occurring at the microdomain of the protein, but also by some lipid-protein synergetic mechanism, where the HsDHODH peptide would be able to recognize lipid domains in the membrane. - See more at: http://www.eurekaselect.com/122062/article#sthash.1ZZbc7E0.dpuf

Random regression models for milk, fat and protein in Colombian Buffaloes

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

Genotype x environment interaction for age at first calving, scrotal circumference, and yearling weight in Nellore cattle using reaction norms in multitrait random regression models

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

Yang-Lee zeros of the two- and three-state Potts model defined on π3 Feynman diagrams

We present both analytical and numerical results on the position of partition function zeros on the complex magnetic field plane of the q=2 state (Ising) and the q=3 state Potts model defined on phi(3) Feynman diagrams (thin random graphs). Our analytic results are based on the ideas of destructive interference of coexisting phases and low temperature expansions. For the case of the Ising model, an argument based on a symmetry of the saddle point equations leads us to a nonperturbative proof that the Yang-Lee zeros are located on the unit circle, although no circle theorem is known in this case of random graphs. For the q=3 state Potts model, our perturbative results indicate that the Yang-Lee zeros lie outside the unit circle. Both analytic results are confirmed by finite lattice numerical calculations.

Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A note on permutation regularity

The existence of a small partition of a combinatorial structure into random-like subparts, a so-called regular partition, has proven to be very useful in the study of extremal problems, and has deep algorithmic consequences. The main result in this direction is the Szemeredi Regularity Lemma in graph theory. In this note, we are concerned with regularity in permutations: we show that every permutation of a sufficiently large set has a regular partition into a small number of intervals. This refines the partition given by Cooper (2006) [10], which required an additional non-interval exceptional class. We also introduce a distance between permutations that plays an important role in the study of convergence of a permutation sequence. (C) 2011 Elsevier B.V. All rights reserved.

Partially Observed Markov Random Fields Are Variable Neighborhood Random Fields

The present paper has two goals. First to present a natural example of a new class of random fields which are the variable neighborhood random fields. The example we consider is a partially observed nearest neighbor binary Markov random field. The second goal is to establish sufficient conditions ensuring that the variable neighborhoods are almost surely finite. We discuss the relationship between the almost sure finiteness of the interaction neighborhoods and the presence/absence of phase transition of the underlying Markov random field. In the case where the underlying random field has no phase transition we show that the finiteness of neighborhoods depends on a specific relation between the noise level and the minimum values of the one-point specification of the Markov random field. The case in which there is phase transition is addressed in the frame of the ferromagnetic Ising model. We prove that the existence of infinite interaction neighborhoods depends on the phase.

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.