Zinc, Copper, and Manganese Homeostasis and Potential Trace Metal Accumulation in Dairy Cows: Longitudinal Study from Late Lactation to Subsequent Mid-Lactation

BackgroundTrace metals are supplemented to cattle to prevent nutrient deficiencies. Levels supplemented to mitigate worst-case basal supply and availability scenarios can, however, result in trace metal intakes far above nutritional requirements of dairy cows with high feed intakes.ObjectivesWe evaluated Zn, Mn and Cu balance in dairy cows from late lactation through the subsequent mid-lactation, a period of 24 wk characterized by large changes in dry matter intake.MethodsTwelve Holstein dairy cows were housed in a tie-stall from 10 wk before to 16 wk after parturition, and fed one unique lactation diet when lactating, and a dry cow diet otherwise. After two weeks of adaptation to the facility and diet, Zn, Mn and Cu balances were determined at weekly intervals, by calculating the difference between total intakes and complete fecal, urinary and milk outputs, with the latter three fluxes quantified over a 48-h period. Repeated measures mixed models were used to evaluate the effects on trace mineral balances over time.ResultsThe Mn and Cu balances of cows were not significantly different from 0 mg·d-1 between 8 wk prepartum and calving (P ≥ 0.54), when dietary intake was the lowest of the period evaluated. However, when dietary intake was highest, between wk 6 and 16 postpartum, positive Mn and Cu balances were observed (80 and 20 mg·d-1, respectively, P ≤ 0.05). Cows were in positive Zn balance throughout the study except during the first 3 wk after calving during which Zn balance was negative.ConclusionsLarge adaptations occur in trace metal homeostasis in transition cows in response to changes in dietary intake. High dry-matter intakes, associated with high milk production of dairy cows, combined with current Zn, Mn and Cu supplementation practices may exceed regulatory homeostatic mechanisms resulting in potential body accumulation of Zn, Mn and Cu.Keywords: health; environment; accumulation; regulation; dietary supplementatio

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed

Zinc, copper, and manganese homeostasis and potential trace metal accumulation in dairy cows: Longitudinal study from late lactation to subsequent mid-lactation

Background: Trace metals are supplemented to cattle to prevent nutrient deficiencies. Levels supplemented to mitigate worst-case basal supply and availability scenarios can, however, result in trace metal intakes far above nutritional requirements of dairy cows with high feed intakes. Objectives: We evaluated Zn, Mn and Cu balance in dairy cows from late lactation through the subsequent mid-lactation, a period of 24 wk characterized by large changes in dry matter intake. Methods: Twelve Holstein dairy cows were housed in a tie-stall from 10 wk before to 16 wk after parturition, and fed one unique lactation diet when lactating, and a dry cow diet otherwise. After two weeks of adaptation to the facility and diet, Zn, Mn and Cu balances were determined at weekly intervals, by calculating the difference between total intakes and complete fecal, urinary and milk outputs, with the latter three fluxes quantified over a 48-h period. Repeated measures mixed models were used to evaluate the effects on trace mineral balances over time. Results: The Mn and Cu balances of cows were not significantly different from 0 mg·d-1 between 8 wk prepartum and calving (P ≥ 0.54), when dietary intake was the lowest of the period evaluated. However, when dietary intake was highest, between wk 6 and 16 postpartum, positive Mn and Cu balances were observed (80 and 20 mg·d-1, respectively, P ≤ 0.05). Cows were in positive Zn balance throughout the study except during the first 3 wk after calving during which Zn balance was negative. Conclusions: Large adaptations occur in trace metal homeostasis in transition cows in response to changes in dietary intake. High dry-matter intakes, associated with high milk production of dairy cows, combined with current Zn, Mn and Cu supplementation practices may exceed regulatory homeostatic mechanisms resulting in potential body accumulation of Zn, Mn and Cu

Effects of a single glucocorticoid injection on propylene glycol-treated cows with clinical ketosis

This study investigated the metabolic effects of glucocorticoids when administered to propylene glycol-treated cows with clinical ketosis. Clinical ketosis was defined by depressed feed intake and milk production, and a maximal score for acetoacetate in urine. All cows received 250 mL oral propylene glycol twice daily for 3 days and were randomly assigned to a single intramuscular injection with sterile isotonic saline solution (n = 14) or dexamethasone-21-isonicotinate (n = 17). Metabolic blood variables were monitored for 6 days and adipose tissue variables for 3 days. β-Hydroxybutyrate (BHBA) concentrations in blood decreased in all cows during treatment, but were lower in glucocorticoid-treated cows. Cows treated with glucocorticoids had higher plasma glucose and insulin concentrations, whereas concentrations of non-esterified fatty acids, 3-methylhistidine and growth hormone were unaffected. mRNA expression of hormone-sensitive lipase, BHBA receptor and peroxisome proliferator-activated receptor type γ in adipose tissue was not affected. This shows that lipolytic effects do not appear to be important in ketotic cows when glucocorticoids are combined with PG. Plasma 3-methyl histidine concentrations were similar in both groups, suggesting that glucocorticoids did not increase muscle breakdown and that the greater rise in plasma glucose in glucocorticoid-treated cows may not be due to increased supply of glucogenic amino acids from muscle

Effects of a single glucocorticoid injection on propylene glycol-treated cows with clinical ketosis

This study investigated the metabolic effects of glucocorticoids when administered to propylene glycol-treated cows with clinical ketosis. Clinical ketosis was defined by depressed feed intake and milk production, and a maximal score for acetoacetate in urine. All cows received 250 mL oral propylene glycol twice daily for 3 days and were randomly assigned to a single intramuscular injection with sterile isotonic saline solution (n = 14) or dexamethasone-21-isonicotinate (n = 17). Metabolic blood variables were monitored for 6 days and adipose tissue variables for 3 days. β-Hydroxybutyrate (BHBA) concentrations in blood decreased in all cows during treatment, but were lower in glucocorticoid-treated cows. Cows treated with glucocorticoids had higher plasma glucose and insulin concentrations, whereas concentrations of non-esterified fatty acids, 3-methylhistidine and growth hormone were unaffected. mRNA expression of hormone-sensitive lipase, BHBA receptor and peroxisome proliferator-activated receptor type γ in adipose tissue was not affected. This shows that lipolytic effects do not appear to be important in ketotic cows when glucocorticoids are combined with PG. Plasma 3-methyl histidine concentrations were similar in both groups, suggesting that glucocorticoids did not increase muscle breakdown and that the greater rise in plasma glucose in glucocorticoid-treated cows may not be due to increased supply of glucogenic amino acids from muscle

Expert opinion as priors for random effects in Bayesian prediction models: Subclinical ketosis in dairy cows as an example

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed

Constructing mixed density functionals for describing dissociative chemisorption on metal surfaces : basic principles

Abstract: The production of a majority of chemicals involves heterogeneous catalysis at some stage, and the rates of many heterogeneously catalyzed processes are governed by transition states for dissociative chemisorption on metals. Accurate values of barrier heights for dissociative chemisorption on metals are therefore important to benchmarking electronic structure theory in general and density functionals in particular. Such accurate barriers can be obtained using the semiempirical specific reaction parameter (SRP) approach to density functional theory. However, this approach has thus far been rather ad hoc in its choice of the generic expression of the SRP functional to be used, and there is a need for better heuristic approaches to determining the mixing parameters contained in such expressions. Here we address these two issues. We investigate the ability of several mixed, parametrized density functional expressions combining exchange at the generalized gradient approximation (GGA) level with either GGA or nonlocal correlation to reproduce barrier heights for dissociative chemisorption on metal surfaces. For this, seven expressions of such mixed density functionals are tested on a database consisting of results for 16 systems taken from a recently published slightly larger database called SBH17. Three expressions are derived that exhibit high tunability and use correlation functionals that are either of the PBE GGA form or of one of two limiting nonlocal forms also describing the attractive van der Waals interaction in an approximate way. We also find that, for mixed density functionals incorporating GGA correlation, the optimum fraction of repulsive RPBE GGA exchange obtained with a specific GGA density functional is correlated with the charge-transfer parameter, which is equal to the difference in the work function of the metal surface and the electron affinity of the molecule. However, the correlation is generally not large and not large enough to obtain accurate guesses of the mixing parameter for the systems considered, suggesting that it does not give rise to a very effective search strategy