arly lactation. For the first 20 DIM, correlations ranged from -0.31 to 0.00 in GH and from -0.42 to -0.01 in FV. The results illustrate that future breeding for dairy cow efficiency should focus on DMI and EB in early lactation to avoid health problems.The objective of this study was to clarify how bias in genomic predictions is created by investigating a relationship among selection intensity, a change in heritability (Δh2), and assortative mating (ASM). A change in heritability, resulting from selection, reflects the impact that the Bulmer effect has on the reduction in between-family variation, whereas assortative mating impacts the within-family variance or Mendelian sampling variation. A partial data set up to 2014, including 841K genotyped animals, was used to calculate genomic predictions with a single-step genomic model for 18 linear type traits in US Holsteins. A full data set up to 2018, including 2.3 million genotyped animals, was used to calculate benchmark genomic predictions. Inbreeding and unknown parent groups for missing parents of animals were included in the model. Genomic evaluation was performed using 2 different genetic parameters those estimated 14 yr ago, which have been used in the national genetic evaluation for linear type traits ranged from -0.09 to 0.04. Traits with a greater decline in heritability tended to have more deflated genomic predictions. Biases (inflation or deflation) in genomic predictions were not improved by using the latest genetic parameters, implying that bias in genomic predictions due to preselection was not substantial for a large-scale genomic evaluation. Moreover, the strong selection intensity was not fully responsible for bias in genomic predictions. The directional selection can decrease heritability; however, positive assortative mating, which was strongly associated with large genetic gains, could minimize the decline in heritability for a trait under strong selection and could affect bias in genomic predictions.Streptococcus lutetiensis, previously termed Streptococcus bovis type II/1, has rarely been associated with bovine mastitis. The objectives of this work were to characterize the molecular diversity, antimicrobial resistance profiles, virulence genes of Strep. lutetiensis (n = 37) isolated from bovine clinical mastitis, as well as its pathogenic effects in a murine mastitis model. Genetic relationships of isolates were determined by random amplified polymorphic DNA (RAPD)-PCR, virulence genes were detected by PCR. Antimicrobial susceptibility testing was carried out by broth microdilution technique. The pathogenic effects of Strep. SC75741 lutetiensis were studied with 2 infection models bovine mammary epithelial cells cultured in vitro and murine mammary infection in vivo. Streptococcus lutetiensis isolates were clustered into 5 RAPD-types (A-E), with a dominant type A representing 84% of isolates. Eighteen (49%), 16 (43%), and 9 (24%) isolates were resistant to ceftiofur, tetracycline, and erythromycin, respectively. Prevalence of multidrug resistance (resistant to ≥3 classes of antimicrobials) was 24% (9/37). The most prevalent virulence genes were bca (100%), speG (100%), hly (97%), scpB (95%), and ssa (95%). There was no difference between isolates from mild and moderate cases of bovine mastitis in prevalence of virulence genes. Streptococcus lutetiensis rapidly adhered to and subsequently invaded (1 and 3 h after infection, respectively) bovine mammary epithelial cells, resulting in elevated lactate dehydrogenase release (4 h after infection). Edema and hyperemia were observed in challenged mammary glands and bacteria were consistently isolated at 12, 24, and 48 h after infection. In addition, numerous neutrophils migrated into gland alveoli and interstitium of infected mammary tissue. We concluded that Strep. lutetiensis had potential to spread within a dairy herd and good adaptive ability in bovine mammary cells or tissue, which are generally characteristics of a contagious mastitis pathogen.The molecular basis of the anti-diabetic properties of camel milk reported in many studies and the exact active agent are still elusive. Recent studies have reported effects of camel whey proteins (CWP) and their hydrolysates (CWPH) on the activities of dipeptidyl peptidase IV (DPP-IV) and the human insulin receptor (hIR). In this study, CWPH were generated, screened for DPP-IV binding in silico and inhibitory activity in vitro, and processed for peptide identification. Furthermore, pharmacological action of intact CWP and their selected hydrolysates on hIR activity and signaling and on glucose uptake were investigated in cell lines. Results showed inhibition of DPP-IV by CWP and CWPH and their positive action on hIR activation and glucose uptake. Interestingly, the combination of CWP or CWPH with insulin revealed a positive allosteric modulation of hIR that was drastically reduced by the competitive hIR antagonist. Our data reveal for the first time the profiling and pharmacological actions of CWP and their derived peptides fractions on hIR and their pathways involved in glucose homeostasis. This sheds more light on the anti-diabetic properties of camel milk by providing the molecular basis for the potential use of camel milk in the management of diabetes.Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows.