Enhanced probiotic potential of Lactobacillus kefiranofaciens OSU-BDGOA1 through co-culture with Kluyveromyces marxianus bdgo-ym6.

Introduction: Due to the increasing consumer demand for the development and improvement of functional foods containing probiotics, new probiotic candidates need to be explored as well as novel means to enhance their beneficial effects. Lactobacillus kefiranofaciens OSU-BDGOA1 is a strain isolated from kefir grains that has demonstrated probiotic traits. This species is the main inhabitant of kefir grains and is responsible for the production of an exopolysaccharide (EPS) whit vast technological applications and potential bioactivities. Research has shown that interkingdom interactions of yeast and lactic acid bacteria can enhance metabolic activities and promote resistance to environmental stressors. Methods: Comparative genomic analyses were performed to distinguish OSU-BDGOA1 from other strains of the same species, and the genome was mined to provide molecular evidence for relevant probiotic properties. We further assessed the cumulative effect on the probiotic properties of OSU-BDGOA1 and Kluyveromyces marxianus bdgo-ym6 yeast co-culture compared to monocultures. Results: Survival during simulated digestion assessed by the INFOGEST digestion model showed higher survival of OSU-BDGOA1 and bdgo-ym6 in co-culture. The adhesion to intestinal cells assessed with the Caco-2 intestinal cell model revealed enhanced adhesion of OSU-BDGOA1 in co-culture. The observed increase in survival during digestion could be associated with the increased production of EPS during the late exponential and early stationary phases of co-culture that, by enhancing co-aggregation between the yeast and the bacterium, protects the microorganisms from severe gastrointestinal conditions as observed by SEM images. Immune modulation and barrier function for recovery and prevention of flagellin-mediated inflammation by Salmonella Typhimurium heat-killed cells (HKSC) in Caco-2 cells were also measured. OSU-BDGOA1 in mono- and co-culture regulated inflammation through downregulation of pro-inflammatory cytokine expression and increased membrane barrier integrity assessed by TEER, FD4 permeability, and expression of tight junctions. Discussion: The results of the study warrant further research into the application of co-cultures of yeast and LAB in functional probiotic products and the potential to increase EPS production by co-culture strategies.

Draft genome sequence of Lactobacillus helveticus OSU-BDGOAK2 and Lactobacillus kefiranofaciens OSU-BDGOA1, kefir grain isolates with potential antibacterial activity.

We present the draft genome sequence and assembly of Lactobacillus helveticus OSU-BDGOAK2 and Lactobacillus kefiranofaciens OSU-BDGOA1 isolated from kefir grains that exhibited in vitro antibacterial activity against Escherichia coli ATCC 25922, Listeria innocua ATCC 51742, and Staphylococcus epidermidis ATCC 1222. Genome analysis of both strains revealed gene clusters encoding bacteriocins.

Mitigation of undesirable volatile aroma compounds in kefir by freeze drying and vacuum evaporation.

Commercial kefir was recently found to be effective in curing recurrent Clostridioides difficile infection when consumed alongside antibiotic treatment. However, kefir products have limited acceptance among Western consumers due to their characteristic flavor and texture. Plain, unsweetened commercial kefir with 1% milkfat was subjected to vacuum evaporation and freeze-drying processes to assess the effect on volatile organic compound concentration, sensory quality, and microbial viability. Vacuum evaporation and freeze-drying both significantly decreased the concentration of 26 out of 27 volatiles in the kefir (p < 0.05), by an average of 61%. Vacuum evaporation exhibited an apparent greater reduction than freeze-drying in the concentrations of the ketones and aldehydes, while freeze-drying showed a more noticeable reduction in the concentrations of the acids, alcohols, and esters. While lowering the concentration of volatiles did not significantly improve the average consumer acceptance of commercial kefir, both treatments showed differences in the range of acceptability responses. Both treatments also produced a small but significant reduction in the concentrations of Lactobacillus and Lactococcus species present.

Metagenomic analysis and antibacterial activity of kefir microorganisms.

The microbiota composition of kefir grain and milk kefir was assessed via a metagenomic approach. Significant microorganisms were isolated and identified using molecular methods. A safety assessment was conducted based on antibiotic susceptibility and blood hemolysis. Probiotic traits such as resistance to gastric tract conditions, surface characteristics, adhesion to intestinal cells, and antibacterial activity were also assessed. Metagenomic analysis revealed that kefir grains are a more stable community with clear dominant species as compared to milk kefir. Lactobacillus kefiranofaciens BDGO-A1, Lactobacillus helveticus BDGO-AK2, and Lactobacillu kefiri strains showed tolerance to acidic pH and the presence of bile salts, adhesion capability to Caco-2 cells, in vitro antibacterial activity, and the production of antibacterial proteins. In the metagenomic analysis, contigs associated with these species showed the presence of genes involved in exporting polyketide antibiotics and bacteriocin production. To fully exploit the potential probiotic properties of these microorganisms to help human health, further investigation is necessary to elucidate the mechanisms behind the biological activity and the genotypic characteristics of the isolated strains.

Invited review: Milk kefir microbiota-Direct and indirect antimicrobial effects.

Kefir is a fermented dairy product with well recognized probiotic properties. Recently, consumer interest in fermented products with probiotic microorganisms has increased due to the accumulating evidence of the effects of kefir microorganisms on the modulation of gut microbiota and their antimicrobial activity. Although the health properties of kefir have been reviewed in other works, the present review addresses the antimicrobial effects of kefir microbiota and associated compounds. The antimicrobial activity of kefir microorganisms could derive from different mechanisms. The microorganisms' capacity to adhere to the intestinal epithelium, preventing the adhesion of pathogens, and their immunomodulation properties are among the mechanisms suggested. Bacteria and yeast isolated from kefir have been shown to have in vivo and in vitro antimicrobial activity against enteropathogenic bacteria and spoilage fungi. However, most reports have focused their approach on single-strain antimicrobial properties; evaluation of antimicrobial activity of cocultures of kefir microbiota and their potential mechanisms of action has been neglected. Kefir microbiota and associated compounds have shown promising antimicrobial effects; however, more research needs to be done to discern the mechanisms of action.

Functional Properties of a High Protein Beverage Stabilized with Oat-ß-Glucan.

This study evaluated the effect of oat flour and milk protein on the functional properties and sensory acceptability of shelf stable high protein dairy beverages containing at least 0.75 g of oat-ß-glucan per serving size. Formulations adjusted to levels of 1.50% to 2.30% oat flour and 2.50% to 4.00% milk protein isolate (MPI) were thermal processed in a rotary retort. The finished product exhibited good suspension stability (>80%). The increase of oat and MPI contents lead to nectar-like beverages (51 to 100 mPas). However, oat flour was the component showing the highest effect on the viscosity coefficient values of the beverages. Sensory evaluation indicated that formulations with less than 1.9% oat flour and 2.5% MPI (thin liquid, <50 mPas) were the most accepted. Mouthfeel (perceived thickness), sweetness and aftertaste had the most influence on overall liking of the beverages. PRACTICAL APPLICATION: Overall, this study comprises the development of a functional food product. Supplementation of beverages with fiber from oats is an innovative approach to stabilize high protein beverages. Ready to drink protein beverage formulations use gums to stabilize the product and provide a desirable mouthfeel. The levels of oat-ß-glucan used in the beverage increased the thickness and meet the requirement of the FDA approved health claim for reduction of the cardiovascular disease risk (21 CFR 101.81).

Evaluation of longevity modeling censored records in Nellore.

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ 2 average and the highest number of sets in which a model had the smallest value of χ 2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM-CLM and LM-WM models, considering all sires, and 75% between LM-CLM and LM-WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

Ventricular electrical storm after acute myocardial infarction successfully treated with temporary atrial overdrive pacing / Tormenta eléctrica ventricular tras infarto agudo de miocardio tratado con éxito mediante sobreestimulación auricular temporal

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Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle.

Meat quality is one of the most important traits determining carcass price in the Japanese beef market. Optimized breeding goals and management practices for the improvement of meat quality traits requires knowledge regarding any potential functional relationships between them. In this context, the objective of this research was to infer phenotypic causal networks involving beef marbling score (BMS), beef color score (BCL), firmness of beef (FIR), texture of beef (TEX), beef fat color score (BFS), and the ratio of MUFA to SFA (MUS) from 11,855 Japanese Black cattle. The inductive causation (IC) algorithm was implemented to search for causal links among these traits and was conditionally applied to their joint distribution on genetic effects. This information was obtained from the posterior distribution of the residual (co)variance matrix of a standard Bayesian multiple trait model (MTM). Apart from BFS, the IC algorithm implemented with 95% highest posterior density (HPD) intervals detected only undirected links among the traits. However, as a result of the application of 80% HPD intervals, more links were recovered and the undirected links were changed into directed ones, except between FIR and TEX. Therefore, 2 competing causal networks resulting from the IC algorithm, with either the arrow FIR → TEX or the arrow FIR ← TEX, were fitted using a structural equation model () to infer causal structure coefficients between the selected traits. Results indicated similar genetic and residual variances as well as genetic correlation estimates from both structural equation models. The genetic variances in BMS, FIR, and TEX from the structural equation models were smaller than those obtained from the MTM. In contrast, the variances in BCL, BFS, and MUS, which were not conditioned on any of the other traits in the causal structures, had no significant differences between the structural equation model and MTM. The structural coefficient for the path from MUS (BCL) to BMS showed that a 1-unit improvement in MUS (BCL) resulted in an increase of 0.85 or 1.45 (an decrease of 0.52 or 0.54) in BMS in the causal structures. The analysis revealed some interesting functional relationships, direct genetic effects, and the magnitude of the causal effects between these traits, for example, indicating that BMS would be affected by interventions on MUS and BCL. In addition, if interventions existed in this scenario, a breeding strategy based only on the MTM would lead to a mistaken selection for BMS.

Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle.

Zebu () cattle, mostly of the Nellore breed, comprise more than 80% of the beef cattle in Brazil, given their tolerance of the tropical climate and high resistance to ectoparasites. Despite their advantages for production in tropical environments, zebu cattle tend to produce tougher meat than Bos taurus breeds. Traditional genetic selection to improve meat tenderness is constrained by the difficulty and cost of phenotypic evaluation for meat quality. Therefore, genomic selection may be the best strategy to improve meat quality traits. This study was performed to compare the accuracies of different Bayesian regression models in predicting molecular breeding values for meat tenderness in Polled Nellore cattle. The data set was composed of Warner-Bratzler shear force (WBSF) of longissimus muscle from 205, 141, and 81 animals slaughtered in 2005, 2010, and 2012, respectively, which were selected and mated so as to create extreme segregation for WBSF. The animals were genotyped with either the Illumina BovineHD (HD; 777,000 from 90 samples) chip or the GeneSeek Genomic Profiler (GGP Indicus HD; 77,000 from 337 samples). The quality controls of SNP were Hard-Weinberg Proportion -value ≥ 0.1%, minor allele frequency > 1%, and call rate > 90%. The FImpute program was used for imputation from the GGP Indicus HD chip to the HD chip. The effect of each SNP was estimated using ridge regression, least absolute shrinkage and selection operator (LASSO), Bayes A, Bayes B, and Bayes Cπ methods. Different numbers of SNP were used, with 1, 2, 3, 4, 5, 7, 10, 20, 40, 60, 80, or 100% of the markers preselected based on their significance test (-value from genomewide association studies [GWAS]) or randomly sampled. The prediction accuracy was assessed by the correlation between genomic breeding value and the observed WBSF phenotype, using a leave-one-out cross-validation methodology. The prediction accuracies using all markers were all very similar for all models, ranging from 0.22 (Bayes Cπ) to 0.25 (Bayes B). When preselecting SNP based on GWAS results, the highest correlation (0.27) between WBSF and the genomic breeding value was achieved using the Bayesian LASSO model with 15,030 (3%) markers. Although this study used relatively few animals, the design of the segregating population ensured wide genetic variability for meat tenderness, which was important to achieve acceptable accuracy of genomic prediction. Although all models showed similar levels of prediction accuracy, some small advantages were observed with the Bayes B approach when higher numbers of markers were preselected based on their -values resulting from a GWAS analysis.

Comparison of models for the genetic evaluation of reproductive traits with censored data in Nellore cattle.

In typical genetic evaluation, often some females have missing records due to reproductive failure and due to voluntary and involuntary culling before the breeding season. These partially or unobserved phenotypes are known as censored records and their inclusion into genetic evaluations might lead to better inferences and breeding value predictions. Then, the objective was to compare prediction ability of models in which the phenotypic expression of age at the first calving (AFC) and days to calving (DC) were considered to be censored and uncensored in a Nellore cattle population. Age at first calving and days to calving were analyzed as following: uncensored animals (LM); penalization of 21 d (PLM); censored records simulated from truncated normal distributions (CLM); threshold-linear model in which censored records were handled as missing (TLM) or coded as the upper AFC/DC value within contemporary group (PTLM); and Weibull frailty hazard model (WM). Pearson correlations (PC), the percentage of the 10% best bulls in common (pTOP10%), accuracy of estimated breeding values (), and a cross-validation scheme were performed. Heritability estimates for AFC were 0.18, 0.12, 0.12, 0.17, 0.14, and 0.07 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. PC and pTOP10% were higher among linear models and smaller between these models and WM. The models provided similar r of sire breeding values. Heritability estimates for DC were 0.03, 0.08, 0.06, 0.02, 0.07, and 0.10 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. Strongly associated predictions were observed in CLM, PLM, PTLM, and WM. The highest coincidence levels of sires in the TOP10% were between CLM, PLM, and PTLM. The r of sire breeding values obtained applying CLM, PLM, PTLM, and WM were similar and higher than those obtained with LM and TLM. In terms of prediction ability, WM, PLM, TLM, and PTLM showed similar prediction performance for AFC. On the other hand, CLM, PLM, PTLM, and WM showed the similar prediction ability for DC Therefore, these models would be recommended to perform genetic evaluation of age at first calving and days to calving in this Nellore population.

Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs.

Structural equation models (SEQM) can be used to model causal relationships between multiple variables in multivariate systems. Among the strengths of SEQM is its ability to consider causal links between latent variables. The use of latent variables allows modeling complex phenomena while reducing at the same time the dimensionality of the data. One relevant aspect in the quantitative genetics context is the possibility of correlated genetic effects influencing sets of variables under study. Under this scenario, if one aims at inferring causality among latent variables, genetic covariances act as confounders if ignored. Here we describe a methodology for assessing causal networks involving latent variables underlying complex phenotypic traits. The first step of the method consists of the construction of latent variables defined on the basis of prior knowledge and biological interest. These latent variables are jointly evaluated using confirmatory factor analysis. The estimated factor scores are then used as phenotypes for fitting a multivariate mixed model to obtain the covariance matrix of latent variables conditional on the genetic effects. Finally, causal relationships between the adjusted latent variables are evaluated using different SEQM with alternative causal specifications. We have applied this method to a data set with pigs for which several phenotypes were recorded over time. Five different latent variables were evaluated to explore causal links between growth, carcass, and meat quality traits. The measurement model, which included 5 latent variables capturing the information conveyed by 19 different phenotypic traits, showed an acceptable fit to data (e.g., χ/df = 1.3, root-mean-square error of approximation = 0.028, standardized root-mean-square residual = 0.041). Causal links between latent variables were explored after removing genetic confounders. Interestingly, we found that both growth (-0.160) and carcass traits (-0.500) have a significant negative causal effect on quality traits (-value ≤ 0.001). This result may have important implications for strategies for pig production improvement. More generally, the proposed method allows further learning regarding phenotypic causal structures underlying complex traits in farm species.

An assessment of the effectiveness of four in-house treatments to reduce the bacterial levels in poultry litter.

Although the use of quicklime (CaO) and tarping are common handling practices aimed at the reuse of litter in the Brazilian poultry industry, few scientific studies have proven the effectiveness of these methods in reducing the pathogenic microbial load during fallowing. The objective of this study was to evaluate the following litter treatments: T1 no treatment (control), T2 quicklime (300 g m(-2)), T3 tarping, T4 tarping+quicklime (300 g m(-2)). The litter samples were collected on day zero and on the sixth and twelfth days after the start of fallowing. The use of quicklime alone or quicklime+tarping was more effective (P<0.05) in reducing bacteria when compared to litter tarping. Except for the control group, all treatments resulted in a more than 84% reduction in the count of colony-forming units (CFUs) at the end of fallowing. It is concluded that the use of quicklime alone in practical terms is the most indicated treatment for the reduction of the bacterial load of poultry litter.

Parâmetros genéticos para características de prolificidade nas raças Landrace e Large White / Genetic parameters for prolificacy in Landrace and Large White breeds

Dados de suínos das raças Landrace (LD) e Large White (LW) foram utilizados para estimar componentes de variância para número total de leitões nascidos (NTN), nascidos vivos (NV) e de leitões vivos ao quinto dia (LV5). Usou-se o método da máxima verossimilhança restrita (REML) para estimar componentes de variância. O modelo misto incluiu os efeitos fixos de mês e ano de nascimento e da inseminação da porca e ordem de parto. Análises unicaracterísticas incluíram os efeitos genético direto, genético materno e de ambiente permanente. Análises multicaracterísticas foram feitas para estimar correlações genéticas. Os modelos unicaracterísticas foram comparados e o que continha apenas o efeito genético direto foi considerado o mais adequado. As estimativas de herdabilidade para NTN foram de 0,15 para LW e de 0,08 a 0,12 para LD, dependendo do modelo, para NV foram de 0,14 para LW e de 0,05 a 0,12 para LD, e para LV5, variaram de 0,11 a 0,12 para LW e de 0,03 a 0,08 para LD. As correlações fenotípicas e genéticas entre as três características foram altas e favoráveis. Conclui-se que a seleção para aumento do LV5 pode ser uma via interessante para o aumento do tamanho da leitegada, da sobrevivência de leitões e da habilidade materna em suínos.

Data from Landrace (LD) and Large White (LW) sows were analyzed to estimate variance components for total number of piglets born (NTN), number of piglets born alive (NV) and number alive on day 5 after birth (LV5). REML mixed model equations included the fixed effects of sow´s month and year of birth and insemination and farrowing order. Univariate analyses included, alternatively, direct genetic, maternal genetic and permanent environmental effects. Multiple trait analysis was performed to estimate genetic correlations among the traits. Comparisons between univariate models indicated that the model containing only direct genetic effect was the most appropriate for parameter estimation. Estimates of heritability for NTN were 0.15 in LW and ranged from 0.08 to .12 in LD, 0.14 in LW, from 0.05 to 0.12 in LD for NV, from 0.11 to 0.12 in LW, and from 0.03 to 0.08 in LD for LV5. Phenotypic and genetic correlations among traits were high and favorable. Results suggest that selection for LV5 is an interesting alternative way to increase litter size, piglet survival and maternal ability in swine.

Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits.

Bootstrap aggregation (bagging) is a resampling method known to produce more accurate predictions when predictors are unstable or when the number of markers is much larger than sample size, because of variance reduction capabilities. The purpose of this study was to compare genomic best linear unbiased prediction (GBLUP) with bootstrap aggregated sampling GBLUP (Bagged GBLUP, or BGBLUP) in terms of prediction accuracy. We used a 600 K Affymetrix platform with 1351 birds genotyped and phenotyped for three traits in broiler chickens; body weight, ultrasound measurement of breast muscle and hen house egg production. The predictive performance of GBLUP versus BGBLUP was evaluated in different scenarios consisting of including or excluding the TOP 20 markers from a standard genome-wide association study (GWAS) as fixed effects in the GBLUP model, and varying training sample sizes and allelic frequency bins. Predictive performance was assessed via five replications of a threefold cross-validation using the correlation between observed and predicted values, and prediction mean-squared error. GBLUP overfitted the training set data, and BGBLUP delivered a better predictive ability in testing sets. Treating the TOP 20 markers from the GWAS into the model as fixed effects improved prediction accuracy and added advantages to BGBLUP over GBLUP. The performance of GBLUP and BGBLUP at different allele frequency bins and training sample sizes was similar. In general, results of this study confirm that BGBLUP can be valuable for enhancing genome-enabled prediction of complex traits.

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10-0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R(2) = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R(2) = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.

Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.

The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.

Estimates of genetic parameters and eigenvector indices for milk production of Holstein cows.

The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated.

Quicklime treatment and stirring of different poultry litter substrates for reducing pathogenic bacteria counts.

Testing different management practices can help to identify conditions that decrease or even eliminate pathogenic bacteria in poultry litter. A trial was conducted to evaluate the effects of daily manual stirring (rotation of the litter with a pitchfork) for the first 14 d of a bird's life (WDR), in 3 types of poultry litter substrates and quicklime treatment (CaO) during layout time between flocks on pathogenic bacteria occurrence (cfu). A total of 216 male Cobb broilers were randomly allotted to 18 pens with new litter (experimental unit). A split-plot design, with 6 treatments allotted to the main plots, was used: 1) wood shavings (WS) + WDR, 2) WS without stirring up to 14 d (WODR), 3) rice hulls (RIH) + WDR, 4) RIH + WODR, 5) mixture of 50% RIH and WS + WDR, and 6) mixture of 50% RIH and WS + WODR. Two treatments were allotted to the subplots: 0 and 300 g of CaO•m(-2) litter. After depopulation, litter samples were collected, and CaO was incorporated into the litter in the designated half of each pen. The cfu from litter samples after 7 d of the quicklime treatment were counted on Chapman agar, brain heart infusion media, and MacConkey agar. The data were analyzed using ANOVA, and the means were compared by least squares means (P < 0.05). Neither the type of substrate nor the act of stirring affected the cfu. The incorporation of 300 g of CaO•m(-2) litter efficiently reduced the cfu observed on brain heart infusion, Chapman agar, and MacConkey agar media by 57.2, 66.9, and 92.1%, respectively, compared with control (6.4, 17.9, and 46.1%; P < 0.001). In conclusion, the incorporation of 300 g/m(-2) of quicklime in poultry litter reduces the cfu, regardless of the substrate and stirring performed.