The mass, metabolism and length explanation can simultaneously calculate an animal’s mass and metabolic rate from its characteristic length

It is shown that the mass, metabolism and length explanation (MMLE) can simultaneously compute an animal’s body mass and BMR given its characteristic length using data for humans. MMLE was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. It was modernized in 2015 by explicitly treating dynamic similarity of mammals’ skeletal musculature and revising the treatment of BMR. Using two primary equations MMLE deterministically computes the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measureable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidea and a BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora were used to estimate values for the parameters occurring in the equations. With the estimated values MMLE can exactly compute every BMR and mass datum from the BMR and mass data set. Furthermore, MMLE can exactly compute every body mass datum from the mass and length data set. Since there is not a data set that simultaneously reports body mass, BMR and characteristic length for individual animals from the mammal orders that were analyzed it could not be determined whether or not MMLE could simultaneously compute both an animal’s BMR and body mass given its characteristic length. There are large data sets that report body mass, BMR and height for humans. A human’s characteristic length can be estimated from height. In this paper human data categorized by sex, age and body mass index (BMI) are used to show that MMLE can indeed simultaneously compute a human’s body mass and BMR given his or her characteristic length. The MMLE body mass equation is modified to explicitly address body fat because it appears that humans are fatter than other running/walking placental mammals. Differences in body fat seem to account for body mass and BMR sexual dimorphism among humans. The impact on BMR of the large and metabolically expensive human brain is addressed. Also mitochondria capability decline with age is addressed.

An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

10.7287/peerj.preprints.437v2 ◽

2014 ◽

Author(s):

Charles Frasier

Keyword(s):

Regression Analysis ◽

Metabolic Rate ◽

Body Mass ◽

Characteristic Length ◽

Data Set ◽

Homogeneous Groups ◽

Mass Data ◽

Length Data ◽

Unexplained Variance ◽

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

10.7287/peerj.preprints.437 ◽

2014 ◽

Author(s):

Charles Frasier

Keyword(s):

Regression Analysis ◽

Metabolic Rate ◽

Body Mass ◽

Characteristic Length ◽

Data Set ◽

Homogeneous Groups ◽

Mass Data ◽

Length Data ◽

Unexplained Variance ◽

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

10.7287/peerj.preprints.437v3 ◽

2014 ◽

Author(s):

Charles Frasier

Keyword(s):

Regression Analysis ◽

Metabolic Rate ◽

Body Mass ◽

Characteristic Length ◽

Data Set ◽

Homogeneous Groups ◽

Mass Data ◽

Length Data ◽

Unexplained Variance ◽

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE deterministically predicts the absolute value of Basal Metabolic Rate (BMR) and body mass for individual animals. MMLE is thus distinct from other examinations of these topics that use species-averaged data to estimate the parameters in a statistically best fit power law relationship such as BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measurable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set with no error and thus no unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set with no error and thus no unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals. However for many of the addressed phylogenetic homogeneous groups, MMLE can predict the exponent obtained by regression analysis of the BMR and mass data using the exponent obtained by regression analysis of the mass and length data. This argues that MMLE may be able to accurately simultaneously predict BMR and mass for an individual animal.

An explanation of the relationship between mass, metabolic rate and characteristic length for placental mammals

10.7287/peerj.preprints.437v1 ◽

2014 ◽

Author(s):

Charles Frasier

Keyword(s):

Metabolic Rate ◽

Body Mass ◽

Characteristic Length ◽

Data Set ◽

Mass Data ◽

Skeletal Musculature ◽

Length Data ◽

Unexplained Variance ◽

Parameter Values ◽

The Mass, Metabolism and Length Explanation (MMLE) was advanced in 1984 to explain the relationship between metabolic rate and body mass for birds and mammals. This paper reports on a modernized version of MMLE. MMLE predicts the absolute value of Basal Metabolic Rate (BMR) for individual animals rather than parameters in the power law relationship BMR = a(body mass)b. Beginning with the proposition that BMR is proportional to the number of mitochondria in an animal, two primary equations are derived that predict BMR and body mass as functions of an individual animal’s characteristic length and sturdiness factor. The characteristic length is a measureable skeletal length associated with an animal’s means of propulsion. The sturdiness factor expresses how sturdy or gracile an animal is. Eight other parameters occur in the equations that vary little among animals in the same phylogenetic group. The present paper modernizes MMLE by explicitly treating Froude and Strouhal dynamic similarity of mammals’ skeletal musculature, revising the treatment of BMR and using new data to estimate numerical values for the parameters that occur in the equations. A mass and length data set with 575 entries from the orders Rodentia, Chiroptera, Artiodactyla, Carnivora, Perissodactyla and Proboscidae is used. A BMR and mass data set with 436 entries from the orders Rodentia, Chiroptera, Artiodactyla and Carnivora is also used. With the estimated parameter values MMLE can exactly predict every BMR and mass datum from the BMR and mass data set without any unexplained variance. Furthermore MMLE can exactly predict every body mass and length datum from the mass and length data set without any unexplained variance. Whether or not MMLE can simultaneously exactly predict an individual animal’s BMR and body mass given its characteristic length awaits analysis of a data set that simultaneously reports all three of these items for individual animals.

Body mass data set for 1317 bird and 270 mammal species from Colombia

Ecology ◽

10.1002/ecy.3273 ◽

2020 ◽

Author(s):

David Ocampo ◽

Kevin G. Borja‐Acosta ◽

Julián Lozano‐Flórez ◽

Sebastián Cifuentes‐Acevedo ◽

Enrique Arbeláez‐Cortés ◽

...

Keyword(s):

Body Mass ◽

Mammal Species ◽

Data Set ◽

Mass Data

The impact of late-career job loss and genetic risk on body mass index: Evidence from variance polygenic scores

Scientific Reports ◽

10.1038/s41598-021-86716-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lauren L. Schmitz ◽

Julia Goodwin ◽

Jiacheng Miao ◽

Qiongshi Lu ◽

Dalton Conley

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Complex Traits ◽

Job Loss ◽

Past Research ◽

Long Term Effects ◽

Polygenic Scores ◽

Show Evidence ◽

Using Data ◽

AbstractUnemployment shocks from the COVID-19 pandemic have reignited concerns over the long-term effects of job loss on population health. Past research has highlighted the corrosive effects of unemployment on health and health behaviors. This study examines whether the effects of job loss on changes in body mass index (BMI) are moderated by genetic predisposition using data from the U.S. Health and Retirement Study (HRS). To improve detection of gene-by-environment (G × E) interplay, we interacted layoffs from business closures—a plausibly exogenous environmental exposure—with whole-genome polygenic scores (PGSs) that capture genetic contributions to both the population mean (mPGS) and variance (vPGS) of BMI. Results show evidence of genetic moderation using a vPGS (as opposed to an mPGS) and indicate genome-wide summary measures of phenotypic plasticity may further our understanding of how environmental stimuli modify the distribution of complex traits in a population.

Using Data Analytics to Create a Digital Strategy That Drives Engagement and Views on Social Media

Case Studies in Sport Management ◽

10.1123/cssm.2019-0028 ◽

2020 ◽

Vol 9 (S1) ◽

pp. S9-S12

Author(s):

David Pierce ◽

Geoffre Sherman

Keyword(s):

Social Media ◽

Digital Literacy ◽

Large Data ◽

Sport Marketing ◽

Data Set ◽

Content Creation ◽

New Strategy ◽

Sports Organization ◽

Using Data ◽

Creativity And Innovation

Students are placed into a consulting role with SPT, a sport marketing agency hired to help a sports organization create a new strategy for video content creation on social media. Students are provided a large data set in Tableau with analytics that hold the key to increasing the team’s engagement and views of videos on social media. Can your students find the insights in the data to drive a new video strategy for social media? Can they turn those insights into a creative content plan that will engage and win fans in the future? Students will have the opportunity to demonstrate creativity and innovation, data-based decision making, and digital literacy.

Tumor mutational burden (TMB) may be a promising predictive biomarker of response to PD-1/PD-L1 targeting in MSI-H colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.8_suppl.43 ◽

2019 ◽

Vol 37 (8_suppl) ◽

pp. 43-43 ◽

Cited By ~ 2

Author(s):

Marwan Fakih ◽

Jaideep Singh Sandhu ◽

Ching Ouyang ◽

Ethan Sokol ◽

Jeffrey S. Ross ◽

...

Keyword(s):

Clinical Investigation ◽

Objective Response ◽

Predictive Biomarkers ◽

Progression Free Survival ◽

Large Data ◽

Predictive Biomarker ◽

Continuous Variable ◽

Data Set ◽

Cut Point ◽

43 Background: PD-1 targeting with pembrolizumab or nivolumab leads to durable clinical benefits in patients (pts) with microsatellite instability-high (MSI-H) tumors. However, 30-35% of mCRC pts with MSI-H tumors will experience progressive disease (PD) as a best response when treated with anti-PD1 agents, highlighting the need of additional predictive biomarkers. Methods: We performed a retrospective multi-center clinical investigation to evaluate the impact of TMB, age, gender, stage at initial presentation, pattern of metastatic disease, tumor grade, and RAS/RAF status on response to anti-PD1/PD-L1 in MSI-H mCRC. TMB and MSI status were determined by hybrid capture-based next-generation sequencing (Foundation Medicine [FM]). The TMB distribution in MSI-H CRC was estimated from a large data set from FM. Results: 22 eligible MSI-H mCRC pts were identified across 5 cancer centers: 19 pts received pembrolizumab, 1 pt received nivolumab, 1 pt received nivolumab + ipilimumab, and 1 pt received durvalumab + tremelimumab. Among tested variables, TMB (as a continuous variable) showed the strongest association with an objective response (OR; p < 0.001). Also, both univariate and multivariate analyses supported that TMB serves as an independent prognostic variable in predicting progression-free survival (PFS; p < 0.001 and p < 0.01, respectively). Using log-rank statistics, the optimal predictive cut-point for TMB was estimated between 37-41 mutations/Mb to dichotomize pts into TMBhigh and TMBlow groups. All 13 pts (100%) with TMBhigh had an OR, while only 2/9 (22%) pts with TMBlow had an OR and 6/9 had PD. The median PFS for TMBhigh pts has not been reached (no progressors, median follow-up > 18 mos), while the median PFS for TMBlow pts was 2 mos. Amongst 821 MSI-H CRC cases tested at FM, the 25th, 35th, 50th and 75th percentile TMB cutoffs were 33.1, 37.4, 46.1, and 61.8 mutations/Mb, respectively. Our optimal TMB cut-point range suggests that MSI-H mCRC with the lowest 35th percentile of TMB have a low likelihood of benefit from anti-PD1. Conclusions: These TMB findings require validation in prospective trials and may guide the sequencing of PD-1 inhibitor monotherapy in MSI-H mCRC.

Changes in body fluid and energy compartments during prolonged hunger strike

Revista do Hospital das Clínicas ◽

10.1590/s0041-87812000000200003 ◽

2000 ◽

Vol 55 (2) ◽

pp. 47-54 ◽

Cited By ~ 8

Author(s):

Joel Faintuch ◽

Francisco Garcia Soriano ◽

José Paulo Ladeira ◽

Mariano Janiszewski ◽

Irineu Tadeu Velasco ◽

...

Keyword(s):

Lean Body Mass ◽

Body Mass ◽

Body Fat ◽

Simultaneous Increase ◽

Average Weight ◽

Prognostic Impact ◽

Starvation Period ◽

Total Resistance ◽

Fat Percentage ◽

Prolonged total food deprivation in non-obese adults is rare, and few studies have documented body composition changes in this setting. In a group of eight hunger strikers who refused alimentation for 43 days, water and energy compartments were estimated, aiming to assess the impact of progressive starvation. Measurements included body mass index (BMI), triceps skinfold (TSF), arm muscle circumference (AMC), and bioimpedance (BIA) determinations of water, fat, lean body mass (LBM), and total resistance. Indirect calorimetry was also performed in one occasion. The age of the group was 43.3±6.2 years (seven males, one female). Only water, intermittent vitamins and electrolytes were ingested, and average weight loss reached 17.9%. On the last two days of the fast (43rd-44th day) rapid intravenous fluid, electrolyte, and vitamin replenishment were provided before proceeding with realimentation. Body fat decreased approximately 60% (BIA and TSF), whereas BMI reduced only 18%. Initial fat was estimated by BIA as 52.2±5.4% of body weight, and even on the 43rd day it was still measured as 19.7±3.8% of weight. TSF findings were much lower and commensurate with other anthropometric results. Water was comparatively low with high total resistance, and these findings rapidly reversed upon the intravenous rapid hydration. At the end of the starvation period, BMI (21.5±2.6 kg/m²) and most anthropometric determinations were still acceptable, suggesting efficient energy and muscle conservation. Conclusions: 1) All compartments diminished during fasting, but body fat was by far the most affected; 2) Total water was low and total body resistance comparatively elevated, but these findings rapidly reversed upon rehydration; 3) Exaggerated fat percentage estimates from BIA tests and simultaneous increase in lean body mass estimates suggested that this method was inappropriate for assessing energy compartments in the studied population; 4) Patients were not morphologically malnourished after 43 days of fasting; however, the prognostic impact of other impairments was not considered in this analysis.