External evaluation of the prediction equation for milk fat yield by the 2021 NASEM dairy model using data from eastern Canadian dairy herds.

In 2021, the National Academies of Sciences, Engineering, and Medicine (NASEM) issued an equation to predict milk fat yield using dairy cow characteristics and diet composition as input variables. This model was evaluated externally using a data set composed of 541 feed and production records obtained from 23 eastern Canadian dairy herds. The use of the developed equation requires the prediction of dry matter intake. Cow intake used in the model assessment has been obtained by NASEM equations based on (1) animal factors, or (2) a combination of feed composition and animal factors. The prediction of milk fat yield was shown to be accurate. The best prediction was obtained using intake estimated based solely on animal factors (concordance correlation coefficient = 0.68).

Comparison of feed evaluation models on predictions of milk protein yield on Québec commercial dairy farms.

Feed evaluation models (FEM) are a core part in dairy cow feeding. As these models are developed using different biological and mathematical approaches mainly tested in a research context, their abilities to predict production in commercial farms need to be validated, even more so when they are used outside the context of their development. Four FEM-National Research Council, 2001 (NRC_2001); Cornell Net Carbohydrate and Protein System, 2015 (CNCPS); NorFor, 2011; and INRA, 2018 (INRA_2018)-were evaluated on their abilities to predict daily milk protein yield (MPY) of 541 cows from 23 dairy herds in the province of Québec, Canada. The effects of cow and diet characteristics were tested on the residuals of MPY. Sensitivity and uncertainty analyses were then performed to evaluate the influence of the uncertainty of the main characteristics of cows and feed ingredients measured on the farm and used in the 4 FEM on the predictions of metabolizable protein (MP) supply and MPY. The 4 models had acceptable predictions of MPY, with concordance correlation coefficients (CCC) ranging from 0.75 to 0.82 and total bias ranging from 12.8% to 19.3% of the observed mean. The Scandinavian model NorFor had the best predictions with a CCC of 0.82, whereas the 3 other models had similar CCC at 0.75 to 0.76. The INRA_2018 and NRC_2001 models presented strong central tendency biases. Removing herd effect put the 4 FEM at the same level of performance, with 11.9 to 12.4% error. Analyzing model behavior within a herd seems to partly negate the effect of using predicted dry matter intake (DMI) in the comparison of models. Diet energy density, days in milk, and MPY estimated breeding value were related to the residual in the 4 models, and Lys and Met (as percent of MP) only in NRC_2001 and NorFor. This suggests that inclusion of these factors in these models would improve MPY predictions. From the sensitivity analysis, for the 4 FEM, DMI and factors affecting its prediction had the greatest influence on the predictions of MP supply and MPY. Of the feed ingredients, forage composition had the greatest effect on these predictions, including a strong effect of legume proportion with NorFor. Diet acid detergent fiber concentration had a very strong effect on MP supply and MPY predictions only in INRA_2018, because of its effect on organic matter digestibility estimation. The range of predictions of MP supply and MPY when combining all these potential uncertainties varied depending on the models. The INRA_2018 model presented the lowest standard deviation (SD) and NorFor the highest SD for the predictions of both MP supply and MPY. Overall, despite the fact that FEM were developed in a research context, their use in a commercial context yields acceptable predictions, with NorFor yielding the best predictions overall, although within-herd responses varied similarly for the 4 tested models.

Economic and environmental effects of revised metabolizable protein and amino acid recommendations on Canadian dairy farms.

The objective of this research was to evaluate the potential economic and environmental effects of the formulation model used to balance dairy rations for metabolizable protein (MP) or 3 essential AA (EAA: His, Lys, and Met) in 3 regions of Canada with different farming systems. The Maritimes, Central Canada, and the Prairies reference dairy farms averaged 63, 71, 144 mature cows per herd and 135, 95, 255 ha of land, respectively. Using N-CyCLES, a whole-farm linear program model, dairy rations were balanced for (1) MP, based on National Research Council (NRC) requirements (MP_2001); (2) MP plus Lys and Met, based on NRC (AA_2001); (3) MP (MP_Rev); or (4) for His, Lys, and Met (AA_Rev), both based on a revised factorial approach revisiting both supply and requirements of MP and EAA. Energy was balanced to meet requirements based on NRC (2001). Assuming the requirements were met within each approach, it was considered that milk yield and composition were not affected by the type of formulation. Given the assumptions of the study, when compared with MP_2001 formulation, balancing dairy rations using the AA_Rev approach reduced calculated farm N balance by 3.8%, on average from 12.71 to 12.24 g/kg of fat- and protein-corrected milk; it also enhanced farm net income by 4.5%, from 19.00 to 19.70 $CAN/100 kg of fat- and protein-corrected milk, by reducing inclusion of protein concentrate in dairy rations. Calculated animal N efficiency was on average 4.3% higher with AA_Rev than with MP_2001 for mid-lactation cows. This gain in N efficiency would result in a reduction in N2O emission by manure, contributing to a partial decrease of total greenhouse gas emission by 1.7%, through a reduction of N excreted in manure. With the AA_2001 formulation, farm N balance was 1% higher than with MP_2001 formulation while reducing farm net income by 6.4%, due to the need to purchase rumen-protected AA, with no effect on total greenhouse gas emission. Both MP formulations lead to fairly similar outputs. The AA_Rev formulation also indicated that His might be a co-limiting AA with Met in dairy rations balanced with ingredients usually included in Canadian dairy rations. Given the assumptions of the study, balancing dairy rations for 3 EAA (His, Lys, and Met) rather than MP, has some potential positive effects on Canadian dairy farms by increasing net incomes through a reduction of crude protein supply, leading to a decreased environmental effect.

Estimation of correction factors to determine the true amino acid concentration of protein after a 24-hour hydrolysis.

Although it has been acknowledged for a long time that a single period of hydrolysis, normally 21 to 24 h, is not the optimal time for most of the AA, a single period is routinely used due to time and cost constraints. As models to balance dairy rations for proteins are evolving toward balancing for AA, it becomes critical to improve the predictions of AA supply from digested proteins. Our objective was to develop correction factors that could systematically be applied to AA concentrations obtained after a 24-h hydrolysis of proteins to account for incomplete recovery and therefore determine their true AA composition. Thirteen substrates were selected to represent different types of proteins commonly used to estimate the supply of AA in ration formulation models: feed ingredients (grass silage, corn silage, soybean meal, canola meal, high-protein corn dried distillers grains, and wheat dried distillers grains plus solubles), 16-h rumen residues (soybean meal and canola meal), digesta (duodenal digesta and feces), and rumen microorganisms (fluid-associated bacteria, particle-associated bacteria and protozoa). Each protein was hydrolyzed in 6 N HCl for multiple hydrolysis times: 13 (2, 4, 8, 12, 18, 21, 24, 30, 48, 72, 96, 120, and 168 h) for feed ingredients, rumen residues, and digesta, and 9 (2, 4, 8, 18, 24, 30, 48, 96, and 168 h) for rumen microorganisms; all analyses were conducted in triplicate. Using nonlinear regression, the AA composition in the protein before the hydrolysis (A0) was derived for each AA in each protein. Two ratios were calculated as potential correction factors: A0/24-h concentration (A0/24h) and the maximal concentration/24-h concentration (max/24h). Both ratios were tested to determine if the type of proteins was affecting them. The ratios A0/24h were not affected by the type of proteins, whereas the ratios max/24h were also not affected by the type of proteins except for 3 nonessential AA (Ala, Glu, and Gly). In an attempt to propose correction factors, our results were combined with results from the literature reporting ratios A0/24h, ratios max/24h, or the ratio of the AA composition calculated from gene structure/24 h. The correction factors proposed for individual AA varied from 1.02 (Asp) to 1.12 (Thr). For the essential AA, the highest ratios were obtained, as expected, for the branched-chain AA and Thr. Formulation programs balancing dairy rations for essential AA would need to acknowledge the incomplete recovery of AA when obtained from 24-h hydrolysis and include correction factors, specific for each AA, but the same across different types of proteins, to correctly estimate the true AA supply to dairy cows.

Polyomavirus BK-specific cellular immune response to VP1 and large T-antigen in kidney transplant recipients.

Polyomavirus BK (BKV) is the primary cause of polyomavirus-associated nephropathy (PVAN) in kidney transplant (KT) recipients. Using ELISpot assays, we compared the frequency of interferon-gamma (IFN-gamma) secreting peripheral blood mononuclear cells (PBMC) after stimulation with overlapping peptide pools covering BKV large T-antigen (LT) and VP1 capsid proteins (VP1). In 10 healthy donors, LT and VP1 responses were low with median 24 (range 15-95) and 25 (7-113) spot-forming units/10(6) PBMC (SFU), respectively. In 42 KT patients with current or recent plasma BKV loads, median LT and VP1 responses of 29 (0-524) and 114 (0-1432) SFU were detected, respectively. In KT patients with decreasing or past plasma BKV loads, significantly higher median BKV-specific IFN-gamma responses were detected compared to KT patients with increasing or persisting BKV loads [LT: 78 (8-524) vs. 22 (0-120) SFU, p=0.003; VP1: 285 (45-1432) vs. 53 (0-423) SFU, p=0.001, respectively]. VP1-specific IFN-gamma responses were higher and more likely to involve CD4(+) T cells, while CD8(+) T cells were more frequently directed against LT. Stimulation with JCV-specific VP1 and LT peptides indicated only low-level cross-recognition. The data suggest that control of BKV replication is correlated with differentiated expansion of BKV-specific cellular immune responses.

Polyomavirus-associated nephropathy: update on BK virus-specific immunity.

The human polyomavirus type 1, also called BK virus (BKV), causes polyomavirus-associated nephropathy (PVAN) in 1-10% of renal transplant recipients, with graft loss in over 50% of cases. The risk factors for PVAN are not conclusively defined and likely involve complementing determinants of recipient, graft, and virus. A central element seems to be the failing balance between BKV replication and BKV-specific immune control, which can result from intense triple immunosuppression, HLA-mismatches, prior rejection and anti-rejection treatment, or BKV-seropositive donor/seronegative recipient pairs. Consistent with this general hypothesis, the timely reduction of immunosuppression in kidney transplant recipients reduced graft loss to less than 10% of cases. However, the BKV-specific humoral and cellular immune response is not well characterized. Recent work from several groups suggest that changes in antibody titers and BKV-specific CD4+ and CD8+ T cells may help to better define the risk and the course of PVAN in renal transplant patients.