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Record W4389704989 · doi:10.3168/jds.2023-23826

Synthesis of milk components involves different mammary metabolism adaptations in response to net energy and protein supplies in dairy cows

2023· article· en· W4389704989 on OpenAlexaff
Jean-Charles Anger, Christelle Loncke, C. Omphalius, Marion Boutinaud, Jocelyne Guinard-Flament, H. Lapierre, Sophie Lemosquet

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsAgriculture and Agri-Food Canada
FundersInstitut National de la Recherche AgronomiqueCargill
KeywordsLactoseLatin squareDry matterLactationMammary glandTriglycerideChemistryAnimal scienceInternal medicineDairy cattleEndocrinologyBiologyFood scienceCholesterolBiochemistryRumenMedicine

Abstract

fetched live from OpenAlex

Net energy for lactation (NE L ) and metabolizable protein (MP) are the 2 main nutritional forces that drive synthesis of milk components. This study investigated mammary gland metabolism in dairy cows in response to variations in the supply of NE L and MP. Four Holstein dairy cows were randomly assigned to a 4 × 4 Latin square design, in which each experimental period consisted of 14 d of dietary treatment. The diets provided 2 levels of NE L (low energy: 25.0 Mcal/d vs. high energy: 32.5 Mcal/d) and 2 levels of MP (low protein: 1266 g/d vs. high protein: 2254 g/d of protein digestible in the intestine) in a 2 × 2 factorial arrangement. Performance and dry matter intake (DMI) were measured during the last 5 d of each period, and the mammary net balance was measured on d 13 by collecting 6 sets of blood samples from left carotid artery and left mammary vein. Mammary plasma flow was measured according to the Fick principle for Phe and Tyr. The mammary net balance of carbon equaled the uptake of nutrients expressed as carbon minus the output of lactose, fatty acids (FA) synthesized in the mammary gland, AA of milk protein, and glycerol-3P from triglyceride on d 13. Milk, lactose, fat, and protein yields increased when NE L and MP supplies increased. However, increasing the NE L supply increased FA synthesis more than increasing the protein supply did. In addition, FA secretion increased more than lactose secretion did when the NE L supply increased. Increasing the NE L supply increased the left half-udder uptake of all major energy-yielding nutrients by increasing mammary plasma flow. However, nutrient uptake increased more than milk output did, which increased carbon dioxide output. This increase in nutrient oxidation by the mammary gland decreased the mammary efficiency of nutrients utilization when the NE L supply increased. Increasing MP supply tended to increase glucose uptake through mammary clearance and increased mammary AA uptake with no change in mammary plasma flow. In addition, the protein supply did not change the mammary uptake of acetate or β-hydroxybutyrate. The increase in milk component secretions in response to either NE L or MP supplies occurred through different metabolic adaptations (increase in mammary plasma flow vs. clearances, respectively). These results suggest that the nutrient utilization by the mammary gland is highly flexible, which helps maintaining milk and milk component yields even with limiting nutrient supplies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.229
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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