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

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

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.

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.

The Effect of the Choice of the Nodal Plane on Tsunami Energy Estimates

2020 ◽  
Vol 75 (5) ◽  
pp. 501-506
Author(s):  
M. A. Nosov ◽  
S. V. Kolesov ◽  
A. V. Bolshakova ◽  
G. N. Nurislamova
Keyword(s):  
Energy Estimates ◽  
Nodal Plane ◽  
Tsunami Energy

Productivity of the forage of perennial grasses and their mixtures on drained peatlands

2016 ◽  
Vol 2 (91) ◽  
pp. 74-79
Author(s):  
V.H. Kurhak ◽  
M.I. Shtakal ◽  
V.M. Shtakal
Keyword(s):  
Dactylis Glomerata ◽  
Dry Weight ◽  
Phleum Pratense ◽  
Bromus Inermis ◽  
Metabolizable Energy ◽  
Perennial Grasses ◽  
Forest Steppe ◽  
Left Bank ◽  
Peat Soils ◽  
High Productivity

There showed the productivity, chemical composition of feed and the timing of mowing of grass and variety mixes of permanent grasses on drained peat soils of Left Bank Forest-Steppe of Ukraine. The presence of early rip­ ened seeded grass provides a uniform supply use mowed mass from middle May until the end of September and the productivity of lands, which ranges from 10 to 14 t/ha of dry weight, metabolizable energy – 100.0 – 130.0 GJ and feed units 7-11 t/ha. Additional manuring of N90 on the background Р45К120 is effective at the start of second year of use. On the organization of hay conveyors of different ripening time herbages is possible to extend the optimal tim­ ing of mowing of green mass to 25-35 days. Best among the early-maturing grass crops are Dactylis glomerata va­ riety Kyivska rannia-1 with Alopecurus pratensis variety Sarnenskiy ranniy or its mixture with Bromus inermis and Festuca pratensis. With medium ripening – pure sowing eastern fescue of variety Lyudmila, Phalaris arundinacea variety Sarnenski-40, Bromus inermis variety Arsen and their compounds. High productivity of late-ripening herb­ age is provided by the inclusion in the composition of grass mixtures of Phleum pratense L. variety Vyshgorodska and Dactylis glomerata of variety Ukrainka, and Agrostis gigantea Roth variety Sarnenska piznia. It is also possible organization hay conveyors of different ripening varieties of Dactylis glomerata varieties Kyivska rannia, Muravka, Ukrainka.

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