Can you Manage Risk ?

Risk management is a increasing important management focus on dairy farms. Why this is true and the basics of risk management using the futures markets explained

Fiber is Complicated

This information was presented at the 2017 Cornell Nutrition Conference for Feed Manufacturers, organized by the Department of Animal Science In the College of Agriculture and Life Sciences at Cornell University. Softcover copies of the entire conference proceedings may be purchased at http://ansci.cals.cornell.edu/extension-outreach/adult-extension/dairy-management/order-proceedings-resources.Fiber is complicated! We will take a look at aNDF, aNDFom, NDFD, uNDFom and other nutrient parameters as it relates to updated biology in rumen models and its practical applications in the field

Risk Management Part II

What are some of the different methods to utilized future markets to minimize risk and set milk prices

Evaluation of models for assessing Medicago sativa L. hay quality

Abstract A study was conducted to evaluate current proposed models for assessing Medicago sativa L. hay quality, using near infrared reflectance spectroscopy (NIRS) analyses and Cornell Nett Carbohydrate and Protein System (CNCPS) milk production prediction as a criterion of accuracy. Application of the theoretically-based summative total digestible nutrients (TDNlig) model o

Characterization of Proteins in Feeds

Several methods have been evaluated to partition feed crude protein (CP) into rumen-degradable protein (RDP) and rumen-undegradable protein (RUP) and to estimate the intestinal digestibility of RUP. These methods include in vivo, in situ, and a variety of in vitro methods. In situ-derived protein fractions were adopted for use to estimate RDP, RUP, and RUP digestibility in the most recent Dairy National Research Council model. In vitro, chemically determined protein fractions are used in the Cornell Net Carbohydrate and Protein System (CNCPS) that was subsequently adopted for use in level 2 of the Beef National Research Council model and in the Cornell-Penn-Miner (CPM) model (version 1). A comparison of the two methods for predicting RDP/RUP and RUP digestibility indicated remarkable similarity. Using data from 78 studies that reported measured flows of nonammonia nonmicrobial N (NANMN) to the small intestine of growing cattle and dairy cows fed 278 different diets, it was observed that the mean bias of prediction for NANMN was +1 g/d for NRC and −24 g/d for CNCPS. Whereas some disparity exists in predicted estimates of RUP for a few feeds, in most cases values are similar. To determine the impact of input uncertainty in the in situ protein system of NRC and the chemical partitioning method of the CNCPS on predicted metabolizable protein (MP) allowable milk and model-predicted RDP, a sensitivity analysis of the two models was conducted. For NRC, the highest variance in MP allowable milk was caused by variance in digestion rates of the B protein fraction, followed by the variance in the proportional sizes of the three CP fractions (i.e., A, B, and C), feed composition (e.g., CP), and RUP digestibility. For CNCPS, the highest variance in MP allowable milk was caused by the variance in feed composition, followed by the variance in the chemical components of feeds that affects the size of the five CP fractions in CNCPS (i.e., CP, soluble CP, neutral detergent insoluble CP [NDICP], acid detergent insoluble CP [ADICP], and nonprotein nitrogen [NPN]), RUP digestibility, and the digestion rates of the CP fractions. This analysis indicates that both models are sensitive to their respective inputs and that the size of the protein pools and the digestion rates of the potentially degradable protein fractions are strongly correlated to feed composition. Whenever possible, actual vs. model-default values for feed composition and pool sizes should be used

Real Patient and its Virtual Twin: Application of Quantitative Systems Toxicology Modelling in the Cardiac Safety Assessment of Citalopram

Abstract. A quantitative systems toxicology (QST) model for citalopram was established to simulate, in silico, a ‘virtual twin’ of a real patient to predict the occurrence of cardiotoxic events previously reported in patients under various clinical conditions. The QST model considers the effects of citalopram and its most notable electrophysiologically active primary (desmethylcitalopram) and secondary (didesmethylcitalopram) metabolites, on cardiac electrophysiology. The in vitro cardiac ion channel current inhibition data was coupled with the biophysically detailed model of human cardiac electrophysiology to investigate the impact of (i) the inhibition of multiple ion currents (IKr, IKs, ICaL); (ii) the inclusion of metabolites in the QST model; and (iii) unbound or total plasma as the operating drug concentration, in predicting clinically observed QT prolongation. The inclusion of multiple ion channel current inhibition and metabolites in the simulation with unbound plasma citalopram concentration provided the lowest prediction error. The predictive performance of the model was verified with three additional therapeutic and supra-therapeutic drug exposure clinical cases. The results indicate that considering only the hERG ion channel inhibition of only the parent drug is potentially misleading, and the inclusion of active metabolite data and the influence of other ion channel currents should be considered to improve the prediction of potential cardiac toxicity. Mechanistic modelling can help bridge the gaps existing in the quantitative translation from preclinical cardiac safety assessment to clinical toxicology. Moreover, this study shows that the QST models, in combination with appropriate drug and systems parameters, can pave the way towards personalised safety assessment