Development and study of the functional properties of marshmallow enriched with bee (Apis mellifera) honey and encapsulated probiotics (Lactobacillus rhamnosus).

Consumer demand for healthier confectionery products has prompted the confectionery industry to create products that are reduced in sugar content and supplemented with vitamins, antioxidants or biological elements beneficial to health. The aim of this study was to develop marshmallows enriched with Apis mellifera honey and Lactobacillus rhamnosus and to evaluate the effect of honey concentration and gelatin bloom degrees on marshmallow properties. A completely randomized design with a factorial structure was applied with different honey concentrations (0, 50 and 75%) and at different gelatin bloom degrees (265, 300 and 315 bloom degrees); moreover, the physicochemical properties, total phenol content and antioxidant activity of the marshmallow were studied, as well as the viability of the probiotic. The physicochemical properties of the marshmallows were found to be adequate and showed good stability over time. The concentration of honey and gelatin bloom degrees did not significantly affect probiotic viability. The density of the marshmallows decreased as the percentage of honey increased. Additionally, the pH was lower at higher honey concentrations. The marshmallow with 75% honey and 265 bloom degrees had a higher °Brix value. The honey treatments exhibited higher levels of total antioxidant activity and total phenolic compounds than the sugar-only marshmallows. However, the bloom degrees did not have a significant impact on the antioxidant activity and total phenolic compound content. Although the probiotics did not reach the minimum viability needed, their use as paraprobiotics can be considered.

Levan Production by Suhomyces kilbournensis Using Sugarcane Molasses as a Carbon Source in Submerged Fermentation.

The valorization of byproducts from the sugarcane industry represents a potential alternative method with a low energy cost for the production of metabolites that are of commercial and industrial interest. The production of exopolysaccharides (EPSs) was carried out using the yeast Suhomyces kilbournensis isolated from agro-industrial sugarcane, and the products and byproducts of this agro-industrial sugarcane were used as carbon sources for their recovery. The effect of pH, temperature, and carbon and nitrogen sources and their concentration in EPS production by submerged fermentation (SmF) was studied in 170 mL glass containers of uniform geometry at 30 °C with an initial pH of 6.5. The resulting EPSs were characterized with Fourier-transform infrared spectroscopy (FT-IR). The results showed that the highest EPS production yields were 4.26 and 44.33 g/L after 6 h of fermentation using sucrose and molasses as carbon sources, respectively. Finally, an FT-IR analysis of the EPSs produced by S. kilbournensis corresponded to levan, corroborating its origin. It is important to mention that this is the first work that reports the production of levan using this yeast. This is relevant because, currently, most studies are focused on the use of recombinant and genetically modified microorganisms; in this scenario, Suhomyces kilbournensis is a native yeast isolated from the sugar production process, giving it a great advantage in the incorporation of carbon sources into their metabolic processes in order to produce levan sucrose, which uses fructose to polymerize levan.

A marker weighting approach for enhancing within-family accuracy in genomic prediction.

Genomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain reasonable accuracies, which is of paramount importance to improve the selection process. For this reason, in this research, we propose the Adversaria-Boruta (AB) method, which combines the virtues of the adversarial validation (AV) method and the Boruta feature selection method. The AB method operates primarily by minimizing the disparity between training and testing distributions. This is accomplished by reducing the weight assigned to markers that display the most significant differences between the training and testing sets. Therefore, the AB method built a weighted genomic relationship matrix that is implemented with the genomic best linear unbiased predictor (GBLUP) model. The proposed AB method is compared using 12 real data sets with the GBLUP model that uses a nonweighted genomic relationship matrix. Our results show that the proposed AB method outperforms the GBLUP by 8.6, 19.7, and 9.8% in terms of Pearson's correlation, mean square error, and normalized root mean square error, respectively. Our results support that the proposed AB method is a useful tool to improve the prediction accuracy of a complete family, however, we encourage other investigators to evaluate the AB method to increase the empirical evidence of its potential.

Plant Extracts Control In Vitro Growth of Disease-Causing Fungi in Chayote.

The use of agrochemicals has caused environmental problems and toxicity to humans, so natural alternatives for disease control during harvest and postharvest have been evaluated. The aim of this study was to evaluate cinnamon essential oil, neem oil, and black sapote fruit extract for in vitro inhibition of fungi isolated from chayote fruit. The extracts were applied at 300, 350, and 400 ppm in Petri dishes and the mycelial growth of Fusarium oxysporum, Fusarium solani, Goetrichum sp., and Phytophthora capsici was evaluated for 7 days, and the percentage of mycelial growth inhibition per day was calculated. Cinnamon oil showed a fungicidal effect at all concentrations. Neem oil at 400 ppm showed a 42.3% reduction in the growth of F. solani and 27.8% reduction in the growth of F. oxysporum, while at 350 ppm it inhibited the mycelial growth of Phytophthora capsici by 53.3% and of Goetrichum sp. by 20.9%; finally, the black sapote extract at 400 ppm inhibited 21.9-28.6% of the growth of all fungi. The growth of postharvest fungi on chayote fruit could be prevented or reduced by applying the plant extracts evaluated at adequate concentrations.

Integrating Parental Phenotypic Data Enhances Prediction Accuracy of Hybrids in Wheat Traits.

Genomic selection (GS) is a methodology that is revolutionizing plant breeding because it can select candidate genotypes without phenotypic evaluation in the field. However, its practical implementation in hybrid prediction remains challenging since many factors affect its accuracy. The main objective of this study was to research the genomic prediction accuracy of wheat hybrids by adding covariates with the hybrid parental phenotypic information to the model. Four types of different models (MA, MB, MC, and MD) with one covariate (same trait to be predicted) (MA_C, MB_C, MC_C, and MD_C) or several covariates (of the same trait and other correlated traits) (MA_AC, MB_AC, MC_AC, and MD_AC) were studied. We found that the four models with parental information outperformed models without parental information in terms of mean square error by at least 14.1% (MA vs. MA_C), 5.5% (MB vs. MB_C), 51.4% (MC vs. MC_C), and 6.4% (MD vs. MD_C) when parental information of the same trait was used and by at least 13.7% (MA vs. MA_AC), 5.3% (MB vs. MB_AC), 55.1% (MC vs. MC_AC), and 6.0% (MD vs. MD_AC) when parental information of the same trait and other correlated traits were used. Our results also show a large gain in prediction accuracy when covariates were considered using the parental phenotypic information, as opposed to marker information. Finally, our results empirically demonstrate that a significant improvement in prediction accuracy was gained by adding parental phenotypic information as covariates; however, this is expensive since, in many breeding programs, the parental phenotypic information is unavailable.

Genetic Diversity of Creole Sheep Managed by Indigenous Communities of the Central Region of Veracruz, Mexico.

In the indigenous communities of central Veracruz, herds of creole sheep have been established and managed through traditional practices of crossing, but their genetic characteristics have never been examined in order to evaluate their state of endogamy, and to help the management programs to protect this genetic resource. The objective of the present study was to characterize the genetic diversity of three populations of creole sheep managed by indigenous communities in the central region of Veracruz, Mexico. Indigenous family producers of creole sheep were located and blood samples taken from 90 individual sheep from the municipalities of Tehuipango, Astacinga and Tlaquilpa, Veracruz. In the laboratory, the genomic DNA was extracted and genetic diversity characterized using four microsatellites (ILSTS11, ILSTS5, SRCRSP9 and OarFCB128) amplified by PCR and visualized on polyacrylamide gels. The four microsatellites were highly informative (PIC = 85%) and presented values of 0.6 to 0.81 of heterozygosity, with an average number of 16 alleles. According to the Hardy-Weinberg equilibrium model, three of the loci were not significant (p < 0.05), presumably this means that they do not deviate significantly from H-W predictions and there was slight genetic differentiation (FST = 0.025), along with a slight decrease in homozygotes (FIS = -0.021). According to the analysis of variance, 99% of the total variation was hosted at the individual level. It is concluded that the three creole sheep populations still present genetic diversity at the four loci and non-random pairings have occurred.

Silver Nanoparticles Increase Nitrogen, Phosphorus, and Potassium Concentrations in Leaves and Stimulate Root Length and Number of Roots in Tomato Seedlings in a Hormetic Manner.

BACKGROUND: Silver nanoparticles (AgNPs) display unique biological activities and may serve as novel biostimulators. Nonetheless, their biostimulant effects on germination, early growth, and major nutrient concentrations (N, P, and K) in tomato (Solanum lycopersicum) have been little explored. METHODS: Tomato seeds of the Vengador and Rio Grande cultivars were germinated on filter paper inside plastic containers in the presence of 0, 5, 10, and 20 mg/L AgNPs. Germination parameters were recorded daily, while early growth traits of seedlings were determined 20 days after applying the treatments (dat). To determine nutrient concentrations in leaves, a hydroponic experiment was established, adding AgNPs to the nutrient solution. Thirty-day-old plants were established in the hydroponic system and kept there for 7 days, and subsequently, leaves were harvested and nutrient concentrations were determined. RESULTS: The AgNPs applied did not affect germination parameters, whereas their application stimulated length and number of roots in a hormetic manner. In 37-day-old plants, low AgNP applications increased the concentrations of N, P, and K in leaves. CONCLUSION: As novel biostimulants, AgNPs promoted root development, especially when applied at 5 mg/L. Furthermore, they increased N, P, and K concentration in leaves, which is advantageous for seedling performance during the early developmental stages.

Productive performance of Mexican Creole chickens from hatching to 12 weeks of age fed diets with different concentrations of metabolizable energy and crude protein.

OBJECTIVE: The study aimed to evaluate the productive performance, carcass yield, size of digestive organs and nutrient utilization in Mexican Creole chickens, using four diets with different concentrations of metabolizable energy (ME, kcal/kg) and crude protein (CP, %). METHODS: Two hundred thirty-six chickens, coming from eight incubation batches, were randomly distributed to four experimental diets with the following ME/CP ratios: 3,000/20, 2,850/19, 2,700/18 and 2,550/17. Each diet was evaluated with 59 birds from hatching to 12 weeks of age. The variables feed intake (FI), body weight gain (BWG), feed conversion (FC), mortality, carcass yield, size of digestive organs, retention of nutrients, retention efficiency of gross energy (GE) and CP, and excretion of N were recorded. Data were analyzed as a randomized block design with repeated measures using the GLIMMIX procedure of SAS, with covariance AR (1) and adjustment of degrees of freedom (KendwardRoger), the adjusted means were compared with the least significant difference method at a significance level of 5%. RESULTS: The productive performance variables BWG, mortality, carcass yield, fat and GE retention and excretion of N were not different (p>0.05) due to the diet effect. In the 3,000/20 diet, the chickens had lower values of FI, FC, crop weight, gizzard weight, retention, and retention efficiency of CP (p<0.05) than the chickens of the 2,550/17 diet. CONCLUSION: The Mexican Creole chickens from hatching to 12 weeks of age can be feed with a diet with 2,550 kcal ME and 17% CP, without compromising productive parameters (BWG, mortality, carcass yield) but improving retention and retention efficiency of CP.

Evaluation of in vitro shoot multiplication and ISSR marker based assessment of somaclonal variants at different subcultures of vanilla (Vanilla planifolia Jacks).

The effect of subculture cycles on somaclonal variation of V. planifolia using intersimple sequence repeat (ISSR) markers was analyzed. Nodal segments of 2 cm in length were established in vitro and multiplied by 10 subculture cycles in Murashige and Skoog (MS) medium supplemented with 8.86 µM BAP (benzylaminopurine). After 45 days in each culture, the length and number of shoots per explant were evaluated. For ISSR markers, ten shoots per each subculture and the mother plant were used. Ten ISSR primers were used and a total of 118 bands were obtained. The polymorphism (%) was calculated and a dendrogram based on Jaccard's genetic distance between the subcultures and the donor plant was obtained. These results show that the multiplication rate tends to increase until subculture five, whereas shoot length decreases as the number of subcultures increases. The ISSR markers revealed an increase in the polymorphism percentage after the fifth culture cycle. The dendrogram showed the formation of two groups. The first group, with less genetic variability, is the donor plant and subcultures 1-5; the second group has greater genetic distance and is formed by subcultures 6-10. The results revealed that the number of subcultures with 8.86 µM BAP is a factor that affects the somaclonal variation during in vitro regeneration of V. planifolia. In conclusion, the subculture number affects somaclonal variation and in vitro development of V. planifolia.

Hormetic Response by Silver Nanoparticles on In Vitro Multiplication of Sugarcane (Saccharum spp. Cv. Mex 69-290) Using a Temporary Immersion System.

BACKGROUND: Hormesis is considered a dose-response phenomenon characterized by growth stimulation at low doses and inhibition at high doses. The hormetic response by silver nanoparticles (AgNPs) on in vitro multiplication of sugarcane was evaluated using a temporary immersion system. METHODS: Sugarcane shoots were used as explants cultured in Murashige and Skoog medium with AgNPs at concentrations of 0, 25, 50, 100, and 200 mg/L. Shoot multiplication rate and length were used to determine hormetic response. Total content of phenolic compounds of sugarcane, mineral nutrition, and reactive oxygen species (ROS) was determined. RESULTS: Results were presented as a dose-response curve. Stimulation phase growth was observed at 50 mg/L AgNPs, whereas inhibition phase was detected at 200 mg/L AgNPs. Mineral nutrient analysis showed changes in macronutrient and micronutrient contents due to the effect of AgNPs. Moreover, AgNPs induced ROS production and increased total phenolic content, with a dose-dependent effect. CONCLUSION: Results suggested that the production of ROS and mineral nutrition are key mechanisms of AgNP-induced hormesis and that phenolic accumulation was obtained as a response of the plant to stress produced by high doses of AgNPs. Therefore, small doses of AgNPs in the culture medium could be an efficient strategy for commercial micropropagation.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods. For this reason, in this paper, we propose a new genomic variational Bayes version of the Bayesian genomic model with G×E using half-t priors on each standard deviation (SD) term to guarantee highly noninformative and posterior inferences that are not sensitive to the choice of hyper-parameters. We show the complete theoretical derivation of the full conditional and the variational posterior distributions, and their implementations. We used eight experimental genomic maize and wheat data sets to illustrate the new proposed variational Bayes approximation, and compared its predictions and implementation time with a standard Bayesian genomic model with G×E. Results indicated that prediction accuracies are slightly higher in the standard Bayesian model with G×E than in its variational counterpart, but, in terms of computation time, the variational Bayes genomic model with G×E is, in general, 10 times faster than the conventional Bayesian genomic model with G×E. For this reason, the proposed model may be a useful tool for researchers who need to predict and select genotypes in several environments.

A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction.

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.