Symposium review: Decomposing efficiency of milk production and maximizing profit

The dairy industry has focused on maximizing milk yield, as it is believed that this maximizes profit mainly through dilution of maintenance costs. Efficiency of milk production has received, until recently, considerably less attention. The most common method to determine biological efficiency of milk production is feed efficiency (FE), which is defined as the amount of milk produced relative to the amount of nutrients consumed. Economic efficiency is best measured as income over feed cost or gross margin obtained from feed investments. Feed efficiency is affected by a myriad of factors, but overall they could be clustered as follows: (1) physiological status of the cow (e.g., age, state of lactation, health, level of production, environmental conditions), (2) digestive function (e.g., feeding behavior, passage rate, rumen fermentation, rumen and hindgut microbiome), (3) metabolic partitioning (e.g., homeorhesis, insulin sensitivity, hormonal profile), (4) genetics (ultimately dictating the 2 previous aspects), and (5) nutrition (e.g., ration formulation, nutrient balance). Over the years, energy requirements for maintenance seem to have progressively increased, but efficiency of overall nutrient use for milk production has also increased due to dilution of nutrient requirements for maintenance. However, empirical evidence from the literature suggests that marginal increases in milk require progressively greater marginal increases in nutrient supply. Thus, the dilution of maintenance requirements associated with increases in production is partially overcome by a progressive diminishing marginal biological response to incremental energy and protein supplies. Because FE follows the law of diminishing returns, and because marginal feed costs increase progressively with milk production, profits associated with improving milk yield might, in some cases, be considerably lower than expected.info:eu-repo/semantics/publishedVersio

Response: Commentary: Past, present and future of epigenetics applied to livestock breeding

Following our recent Review article (González-Recio et al., 2015), we received correspondence by Steele (2016). We thank Dr. Steele for his comments, which provide a thorough review of his work on human immunology, which has persuaded him that “hard types of soma-to-germline transfer are ongoing at very high frequency in human immune system germlines.” His and other researchers' studies on reverse transcriptase (RT) based feedback mechanisms showed that RNA could be retrotranscripted to DNA, and it can be inserted into the mammalian germline, and therefore be transferred to the progeny

TriS: A Statistical Sentence Simplifier with Log-linear Models and Margin-based Discriminative Training

We propose a statistical sentence simplification system with log-linear models. In contrast to state-of-the-art methods that drive sentence simplification process by hand-written linguistic rules, our method used a margin-based discriminative learning algorithm operates on a feature set. The feature set is defined on statistics of surface form as well as syntactic and dependency structures of the sentences. A stack decoding algorithm is used which allows us to efficiently generate and search simplification hypotheses. Experimental results show that the simplified text produced by the proposed system reduces 1.7 Flesch-Kincaid grade level when compared with the original text. We will show that a comparison of a state-of-the-art rule-based system (Heilman and Smith, 2010) to the proposed system demonstrates an improvement of 0.2, 0.6, and 4.5 points in ROUGE-2, ROUGE-4, and AveF10, respectively