scholarly journals Standard Errors of Nitrogen-Corrected True Metabolizable Energy Estimates: Effects of Pooling Excreta Samples and Ignoring Among-Control Bird Variation

1989 ◽  
Vol 68 (10) ◽  
pp. 1361-1367
Author(s):  
M.S. WOLYNETZ ◽  
I.R. SIBBALD
Keyword(s):  
Metabolizable Energy ◽  
Energy Estimates ◽  
Standard Errors ◽  
Control Bird

The Variability of Metabolizable Energy Estimates

1987 ◽  
Vol 117 (4) ◽  
pp. 779-780 ◽  
Author(s):  
Mark Wolynetz
Keyword(s):  
Metabolizable Energy ◽  
Energy Estimates

True Metabolizable Energy Estimates of Canada Goose Foods

1998 ◽  
Vol 62 (3) ◽  
pp. 1147 ◽  
Author(s):  
Mark J. Petrie ◽  
Ronald D. Drobney ◽  
David A. Graber
Keyword(s):  
Metabolizable Energy ◽  
Energy Estimates ◽  
Canada Goose

True Metabolizable Energy Estimates for Wild and Game-Farm Mallards

1992 ◽  
Vol 56 (2) ◽  
pp. 321 ◽  
Author(s):  
Richard M. Kaminski ◽  
H. Werner Essig
Keyword(s):  
Metabolizable Energy ◽  
Energy Estimates

Implications of uncertainty in true metabolizable energy estimates for estimating wintering waterfowl carrying capacities

2015 ◽  
Vol 39 (4) ◽  
pp. 827-833 ◽  
Author(s):  
Mark C. Livolsi ◽  
Kevin M. Ringelman ◽  
John M. Coluccy ◽  
Matthew T. Dibona ◽  
Christopher K. Williams
Keyword(s):  
Metabolizable Energy ◽  
Energy Estimates ◽  
Wintering Waterfowl

scholarly journals The Importance of Reporting Energy Values of Human Milk as Metabolizable Energy

2021 ◽  
Vol 8 ◽  
Author(s):  
Tanis R. Fenton ◽  
Seham Elmrayed
Keyword(s):  
Human Milk ◽  
Metabolizable Energy ◽  
Energy Estimates ◽  
High Fat ◽  
Food Energy ◽  
Milk Samples ◽  
Nutrition Science ◽  
Gross Energy

Nutrition science has a convention to report metabolizable energy instead of gross energy. Metabolizable energy at 4 kilocalories per gram for protein and carbohydrate, 9 kcal per gram for fat (kilojoules: 17 and 37, respectively) represents the food energy available for metabolism. However, this convention to use metabolizable energy has not been uniformly applied to human milk. Human milk is often reported as gross energy, which is about 5–10% higher than metabolizable energy. To obtain accurate human milk energy estimates, milk samples need to contain the same proportion of high fat hind milk that an infant obtains.

Variation between and within birds in the estimation of the metabolizable energy content of diets for laying hens

1968 ◽  
Vol 71 (2) ◽  
pp. 153-159 ◽  
Author(s):  
W. H. Foster
Keyword(s):  
Variance Components ◽  
Laying Hens ◽  
Energy Content ◽  
Metabolizable Energy ◽  
Standard Errors ◽  
Breed Differences ◽  
The Mean

SUMMARYThree experiments were performed in which variation in metabolizable energy (M.E.) determinations was studied using a total of 137 laying hens. Consistent breed differences in classical M.E. estimates were found, and there were statistically significant differences between pullets within strains. Estimates were obtained of the within-bird variation in classical M.E. determinations and, from the estimated variance components, standard errors of the mean classical M.E. value were predicted for determinations using various numbers of pullets of one breed and various numbers of measurements per pullet.

Multi-Center Calibration of the Second Reference Material for Thromboplastin, Rabbit, Plain, Coded CRM 149R

1991 ◽  
Vol 65 (03) ◽  
pp. 263-267 ◽  
Author(s):  
A M H P van den Besselaar ◽  
R M Bertina
Keyword(s):  
Reference Material ◽  
Standard Error ◽  
Standard Errors ◽  
Sensitivity Index ◽  
Long Term Stability ◽  
Significant Bias ◽  
The Mean ◽  
The Relationship ◽  
International Sensitivity Index

SummaryIn a collaborative trial of eleven laboratories which was performed mainly within the framework of the European Community Bureau of Reference (BCR), a second reference material for thromboplastin, rabbit, plain, was calibrated against its predecessor RBT/79. This second reference material (coded CRM 149R) has a mean International Sensitivity Index (ISI) of 1.343 with a standard error of the mean of 0.035. The standard error of the ISI was determined by combination of the standard errors of the ISI of RBT/79 and the slope of the calibration line in this trial.The BCR reference material for thromboplastin, human, plain (coded BCT/099) was also included in this trial for assessment of the long-term stability of the relationship with RBT/79. The results indicated that this relationship has not changed over a period of 8 years. The interlaboratory variation of the slope of the relationship between CRM 149R and RBT/79 was significantly lower than the variation of the slope of the relationship between BCT/099 and RBT/79. In addition to the manual technique, a semi-automatic coagulometer according to Schnitger & Gross was used to determine prothrombin times with CRM 149R. The mean ISI of CRM 149R was not affected by replacement of the manual technique by this particular coagulometer.Two lyophilized plasmas were included in this trial. The mean slope of relationship between RBT/79 and CRM 149R based on the two lyophilized plasmas was the same as the corresponding slope based on fresh plasmas. Tlowever, the mean slope of relationship between RBT/79 and BCT/099 based on the two lyophilized plasmas was 4.9% higher than the mean slope based on fresh plasmas. Thus, the use of these lyophilized plasmas induced a small but significant bias in the slope of relationship between these thromboplastins of different species.

SAMPL7 TrimerTrip Host-Guest Binding Poses and Binding Affinities from Spherical-Coordinates-Biased Simulations

2020 ◽  
Author(s):  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Computational Modelling ◽  
Free Energy Calculations ◽  
Energy Estimates ◽  
Binding Affinities ◽  
Spherical Coordinates ◽  
Collective Variables ◽  
Guest Binding ◽  
Starting Point ◽  
Sampl Challenge

Host-guest binding remains a major challenge in modern computational modelling. The newest 7<sup>th</sup> statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and predict the binding affinities in all three host-guest binding cases of the 6<sup>th</sup> SAMPL challenge. In this work, we employed the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The predicted binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations.

D3R Grand Challenge 4: Ligand Similarity and MM-GBSA-Based Pose Prediction and Affinity Ranking for BACE-1 Inhibitors

2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Léa El Khoury ◽  
David Mobley
Keyword(s):  
Binding Mode ◽  
Energy Estimates ◽  
Poor Correlation ◽  
Grand Challenge ◽  
Pose Prediction ◽  
Design Data ◽  
Data Resource ◽  
Affinity Ranking ◽  
Ligand Similarity ◽  
Bace 1

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.

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