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Quesillo is an artisanal Honduran cheese made from raw milk. During fabrication, curd melting is considered a killing step for pathogenic bacteria. This work was aimed at determining the survival of Salmonella enterica and Listeria monocytogenes on inoculated curd packaged in plastic bags and immersed in a water bath at 48, 54, 60, 65, and 70°C for predetermined times. Survival counts of each pathogen were used to estimate D values by linear regression, and z values were estimated by the linear regression of the D values. S. entericaD values ranged from 4.5 min at 60°C to 0.80 min at 70°C (z = 10.7°C). For L. monocytogenes, D values ranged from 6.08 min at 60°C to 0.90 min at 70°C (z = 11.3°C). Validation of 7-log reduction was performed on inoculated curd heated at 65°C for 34.7 min, and recovering enrichment procedures were used for each pathogen. Neither S. enterica nor L. monocytogenes cells were recovered after the enrichment of samples. The results obtained in this study could be applied by Honduran quesillo processors to improve the safety of their products.
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RESUMEN La enfermedad de hígado graso no alcohólico es considerada hoy en día un problema de salud pública. Se caracteriza por la acumulación de ácidos grasos y de triglicéridos en el citoplasma de los hepatocitos, con aumento del peso total del hígado, que va desde una esteatosis hepática no alcohólica (ausencia de inflamación) hasta una esteatohepatitis no alcohólica (presencia de inflamación), fibrosis e incluso hepatocarcinoma. Actualmente, no cuenta con un tratamiento aprobado por agencias regulatorias de la enfermedad y se ha convertido en la enfermedad hepática con mayor prevalencia mundial. Los β-glucanos son homopolisacáridos de glucosa a los que se les atribuyen propiedades para la reducción de grasas debido a que son considerados fibra dietética. En esta revisión de literatura, se resaltó la importancia y los efectos de los β-glucanos en la dieta para su tratamiento como prevención de la enfermedad del hígado graso no alcohólico.
ABSTRACT Non-alcoholic fatty liver disease is considered a public health problem, characterized by the accumulation of fatty acids and triglycerides in the cytoplasm of hepatocytes, resulting in an increase in the total weight of the liver, ranging from a non-alcoholic hepatic steatosis (absence of inflammation) to non-alcoholic steatohepatitis (presence of inflammation), fibrosis and even hepatocarcinoma. Currently, there are no treatments approved by regulatory agencies for the disease, which has led to a high prevalence of liver disease worldwide. The β-glucans are homopolysaccharides of glucose to which fat-reducing properties are attributed since they are considered dietary fiber. The importance and effects of β-glucans in the diet for the prevention of nonalcoholic fatty liver disease are highlighted in this literature review.
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Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.
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The University of Florida strawberry (Fragaria × ananassa) breeding program has implemented genomic prediction (GP) as a tool for choosing outstanding parents for crosses over the last five seasons. This has allowed the use of some parents 1 year earlier than with traditional methods, thus reducing the duration of the breeding cycle. However, as the number of breeding cycles increases over time, greater knowledge is needed on how multiple cycles can be used in the practical implementation of GP in strawberry breeding. Advanced selections and cultivars totaling 1,558 unique individuals were tested in field trials for yield and fruit quality traits over five consecutive years and genotyped for 9,908 SNP markers. Prediction of breeding values was carried out using Bayes B models. Independent validation was carried out using separate trials/years as training (TRN) and testing (TST) populations. Single-trial predictive abilities for five polygenic traits averaged 0.35, which was reduced to 0.24 when individuals common across trials were excluded, emphasizing the importance of relatedness among training and testing populations. Training populations including up to four previous breeding cycles increased predictive abilities, likely due to increases in both training population size and relatedness. Predictive ability was also strongly influenced by heritability, but less so by changes in linkage disequilibrium and effective population size. Genotype by year interactions were minimal. A strategy for practical implementation of GP in strawberry breeding is outlined that uses multiple cycles to predict parental performance and accounts for traits not included in GP models when constructing crosses. Given the importance of relatedness to the success of GP in strawberry, future work could focus on the optimization of relatedness in the design of TRN and TST populations to increase predictive ability in the short-term without compromising long-term genetic gains.
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The crystal structures of (E)-1-methyl-5-nitro-1H-imidazole-2-carbaldehyde O-benzyl-oxime, C12H12N4O3, (I), (E)-1-methyl-5-nitro-1H-imidazole-2-carb-alde-hyde O-(4-fluoro-benz-yl) oxime, C12H11FN4O3, (II), and (E)-1-methyl-5-nitro-1H-imidazole-2-carbaldehyde O-(4-bromo-benz-yl) oxime, C12H11BrN4O3, (III), are described. The dihedral angle between the ring systems in (I) is 49.66â (5)° and the linking Nm-C-C=N (m = methyl-ated) bond shows an anti conformation [torsion angle = 175.00â (15)°]. Compounds (II) and (III) are isostructural [dihedral angle between the aromatic rings = 8.31â (5)° in (II) and 5.34â (15)° in (III)] and differ from (I) in showing a near-syn conformation for the Nm-C-C=N linker [torsion angles for (II) and (III) = 17.64â (18) and 8.7â (5)°, respectively], which allows for the occurrence of a short intra-molecular C-Hâ¯N contact. In the crystal of (I), C-Hâ¯N hydrogen bonds link the mol-ecules into [010] chains, which are cross-linked by very weak C-Hâ¯O bonds into (100) sheets. Weak aromatic π-π stacking inter-actions occur between the sheets. The extended structures of (II) and (III) feature several C-Hâ¯N and C-Hâ¯O hydrogen bonds, which link the mol-ecules into three-dimensional networks, which are consolidated by aromatic π-π stacking inter-actions. Conformational energy calculations and Hirshfeld fingerprint analyses for (I), (II) and (III) are presented and discussed.
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The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013-2014 and 2014-2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values.
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Effects of mineral oil (MO) and 4 emulsions (prepared with different emulsifier types) of MO and chitosan solution (CH) at a fixed ratio of MO:CH = 25:75 as coating materials in preserving the internal quality of eggs were evaluated during 5 wk at 25 °C and 20 wk at 4 °C. Generally, as storage time increased, Haugh unit and yolk index values decreased whereas weight loss increased. However, MO and/or 4 emulsion coatings minimized the weight loss (<1.5%) and preserved the albumen and yolk quality of eggs (with the final B grade) for at least 3 wk longer than those observed for noncoated eggs at 25 °C. At 4 °C, all coated eggs changed from AA to A grade after 5 wk and they maintained this grade for 10 wk (5 wk longer than that of noncoated eggs). Although refrigeration (4 °C) alone could maintain the B grade of noncoated eggs for up to 20 wk, coating treatments were necessary to keep the weight loss below 2%. Compared with 4 °C, the increasing weight loss showed stronger negative correlation (P < 0.01) with the decreasing Haugh unit (-0.46 to -0.89) and yolk index (-0.36 to -0.89) at 25 °C. The emulsifier type used in this study generally did not affect the internal quality of eggs. Salmonella spp. detection was negative for all coated and noncoated eggs. This study demonstrated that MO and MO:CH emulsion coatings preserved the internal quality, prolonged the shelf-life, and minimized weight loss (<2%) of eggs.