Multiple linear regression equations predicting total skeletal muscle (TM) and total skeletal fat-free muscle (TFFM) of chuck, rib, loin, and round were developed from data of 33 beef cows. Primal cuts were obtained in accordance with NAMP specifications. A four-terminal impedance meter/plethysmograph measured resistance and reactance on each cut using 20- and 13-gauge needles as electrodes. Weight, internal temperature, and distance between detector electrodes were recorded. Each cut was physically separated into muscle, fat, and bone. Chemical composition (moisture, protein, and fat) was obtained on the muscle portion of each cut so that TFFM could be obtained by multiplying TM x 1 minus the percentage of fat from the proximate analysis. The predictor variables for all combinations of cuts and electrode sizes were weight, distance between detector electrodes, temperature, and resistance, except for the round for which reactance was a fifth predictor. The P value for the resistance coefficient was < .001 for all 16 prediction equations. Adjusted R2 values of the prediction equations ranged from an average of .91 for the rib to an average of .96 for the round. Mallows Cp values were close to the ideal value of the number of independent variables in the prediction equations plus one (1). Prediction equations for the different size electrodes were similar.(ABSTRACT TRUNCATED AT 250 WORDS)
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2527/1994.72123124x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
An alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using Genetic Programming.
Environ Technol
January 2025
Centre for Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, India.
Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates.
View Article and Find Full Text PDFResponse Theory via Generative Score Modeling.
Phys Rev Lett
December 2024
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the generalized fluctuation-dissipation theorem. The methodology enables accurate estimation of system responses, including those with non-Gaussian statistics. We numerically validate our approach using time-series data from three different stochastic partial differential equations of increasing complexity: an Ornstein-Uhlenbeck process with spatially correlated noise, a modified stochastic Allen-Cahn equation, and the 2D Navier-Stokes equations.
View Article and Find Full Text PDFExtended Time-Dependent Density Functional Theory for Multibody Densities.
Phys Rev Lett
December 2024
Key Laboratory for Laser Plasmas and School of Physics and Astronomy, and Collaborative Innovation Center of IFSA, Shanghai Jiao Tong University, Shanghai 200240, China.
Time-dependent density functional theory (TDDFT) is widely used for understanding and predicting properties and behaviors of matter. As one of the fundamental theorems in TDDFT, Van Leeuwen theorem [Phys. Rev.
View Article and Find Full Text PDFNeuro-computational simulation of blood flow loaded with gold and maghemite nanoparticles inside an electromagnetic microchannel under rapid and unexpected change in pressure gradient.
Electromagn Biol Med
January 2025
Department of Mathematics, University of Gour Banga, Malda, India.
In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method.
View Article and Find Full Text PDFRadiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!