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work analysis and the hub gene identification. We validated the findings in an independent data set. Overall, our study suggested that differences in feed efficiency in dairy cows may be linked to differences in cellular energy demand. This study broadens our knowledge of the biology of feed efficiency in dairy cattle.Most dairy cows experience a period of energy deficit in early lactation, resulting in increased plasma concentrations of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHB). Our objectives were to determine (1) the diurnal variation in plasma BHB and NEFA, (2) the correlation between plasma NEFA and BHB when accounting for diurnal changes, and (3) the effect of hyperketonemia (HYK) on the diurnal pattern of blood metabolites. Jugular catheters were placed in 28 multiparous Holstein cows between 3 and 9 days in milk, and blood samples were collected every 2 h for 96 h. Cows were retrospectively classified as HYK positive (HYK; n = 13) if they had plasma BHB concentrations ≥1.2 mmol/L for ≥3 study days, or HYK negative (non-HYK; n = 15) if they had plasma BHB concentrations ≥1.2 mmol/L for ≤2 study days. Generalized linear mixed models were used to analyze concentrations of analytes over time and differences in metabolites between HYK groups. The correlation between total plasma NEFA and BHB was analyzed by calculating the area under the curve for plasma NEFA and BHB for all cows. Plasma NEFA reached a peak approximately 2 h before morning feed delivery, falling to a nadir in the late evening. Plasma BHB was at a nadir at the time of morning feed delivery, peaking 4 h later. We observed a strong positive correlation between daily plasma NEFA and BHB. Additionally, HYK cows had greater concentrations of plasma NEFA and BHB than non-HYK cows. The HYK cows also experienced a greater magnitude of change in BHB throughout the day than the non-HYK cows. Our results suggest that the time relative to feeding should be considered when analyzing plasma metabolites, as classification of energy status may change throughout a day.The objective of this study was to determine the role of GCN2 in the response to AA deprivation of primary bovine mammary epithelial cells (BMEC). Cells were isolated from the mammary tissue of 2 lactating Holstein cows by enzymatic digestion, expanded, and induced to differentiate for 5 to 7 d. Relative mRNA expression was measured by real-time quantitative PCR. Protein abundance and site-specific phosphorylation were measured by immunoblotting. CucurbitacinI Knockout of GCN2 in BMEC was accomplished by lentiviral delivery of a targeted single guide RNA and endonuclease Cas9. To investigate the role of GCN2, we treated lactogenic differentiated BMEC with either culture medium lacking Arg, Leu, and Lys combined or lacking only one of the 3 AA of interest, in comparison to a control with a full complement of AA. Activation of GCN2 was inferred by the phosphorylation status of its downstream target eIF2α Ser51. We found that GCN2 was activated by both the deprivation of Arg, Leu, and Lys combined and of Arg alone, as shown bprivation, we ablated GCN2 in BMEC using clustered regularly interspaced short palindromic repeats-Cas9. We showed that BMEC transduced with single guide RNA targeting EIF2AK4 were not as responsive to combined AA deprivation, compared with BMEC transduced with nontargeting single guide RNA. Taken together, our results demonstrate a critical role for GCN2 in sensing AA deprivation in BMEC.Casein in fluid milk determines cheese yield and affects cheese quality. Traditional methods of measuring casein in milk involve lengthy sample preparations with labor-intensive nitrogen-based protein quantifications. The objective of this study was to quantify casein in fluid milk with different casein-to-crude-protein ratios using front-face fluorescence spectroscopy (FFFS) and chemometrics. We constructed calibration samples by mixing microfiltration and ultrafiltration retentate and permeate in different ratios to obtain different casein concentrations and casein-to-crude-protein ratios. We developed partial least squares regression and elastic net regression models for casein prediction in fluid milk using FFFS tryptophan emission spectra and reference casein contents. We used a set of 20 validation samples (including raw, skim, and ultrafiltered milk) to optimize and validate model performance. We externally tested another independent set of 20 test samples (including raw, skim, and ultrafiltered milk) by root mean square error of prediction (RMSEP), residual prediction deviation (RPD), and relative prediction error (RPE). The RMSEP for casein content quantification in raw, skim, and ultrafiltered milk ranged from 0.12 to 0.13%, and the RPD ranged from 3.2 to 3.4. The externally validated error of prediction was comparable to the existing rapid method and showed practical model performance for quality-control purposes. This FFFS-based method can be implemented as a routine quality-control tool in the dairy industry, providing rapid quantification of casein content in fluid milk intended for cheese manufacturing.Early-career academic faculty from underrepresented minority groups are under-represented among medical school faculty, less likely to receive research grants, less likely to be promoted, and report lower career satisfaction. The Training in Research for Academic Neurologists to Sustain Careers and Enhance the Numbers of Diverse Scholars (TRANSCENDS) program was established as a research training and mentoring program to foster careers of diverse early-career individuals in neurology. Early career individuals from underrepresented groups in the biomedical-research workforce were selected from applicants during the initial cycle (2016-2020). An innovative component of TRANSCENDS is the incorporation of multiple training activities including an online graduate research degree program; monthly webinar conferences; specific interaction sessions at the annual American Academy of Neurology meeting and year-round communications between matched mentors and mentees. The program complements these attributes with the Master of Science in Clinical Research (MSCR) degree that includes the competencies for the clinical and translational research workforce.
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