Contribution of anaerobic energy expenditure to whole body thermogenesis

Heat production serves as the standard measurement for the determination of energy expenditure and efficiency in animals. Estimations of metabolic heat production have traditionally focused on gas exchange (oxygen uptake and carbon dioxide production) although direct heat measurements may include an anaerobic component particularly when carbohydrate is oxidized. Stoichiometric interpretations of the ratio of carbon dioxide production to oxygen uptake suggest that both anaerobic and aerobic heat production and, by inference, all energy expenditure – can be accounted for with a measurement of oxygen uptake as 21.1 kJ per liter of oxygen. This manuscript incorporates contemporary bioenergetic interpretations of anaerobic and aerobic ATP turnover to promote the independence of these disparate types of metabolic energy transfer: each has different reactants and products, uses dissimilar enzymes, involves different types of biochemical reactions, takes place in separate cellular compartments, exploits different types of gradients and ultimately each operates with distinct efficiency. The 21.1 kJ per liter of oxygen for carbohydrate oxidation includes a small anaerobic heat component as part of anaerobic energy transfer. Faster rates of ATP turnover that exceed mitochondrial respiration and that are supported by rapid glycolytic phosphorylation with lactate production result in heat production that is independent of oxygen uptake. Simultaneous direct and indirect calorimetry has revealed that this anaerobic heat does not disappear when lactate is later oxidized and so oxygen uptake does not adequately measure anaerobic efficiency or energy expenditure (as was suggested by the "oxygen debt" hypothesis). An estimate of anaerobic energy transfer supplements the measurement of oxygen uptake and may improve the interpretation of whole-body energy expenditure

Flow transitions in two-dimensional foams

For sufficiently slow rates of strain, flowing foam can exhibit inhomogeneous flows. The nature of these flows is an area of active study in both two-dimensional model foams and three dimensional foam. Recent work in three-dimensional foam has identified three distinct regimes of flow [S. Rodts, J. C. Baudez, and P. Coussot, Europhys. Lett. {\bf 69}, 636 (2005)]. Two of these regimes are identified with continuum behavior (full flow and shear-banding), and the third regime is identified as a discrete regime exhibiting extreme localization. In this paper, the discrete regime is studied in more detail using a model two dimensional foam: a bubble raft. We characterize the behavior of the bubble raft subjected to a constant rate of strain as a function of time, system size, and applied rate of strain. We observe localized flow that is consistent with the coexistence of a power-law fluid with rigid body rotation. As a function of applied rate of strain, there is a transition from a continuum description of the flow to discrete flow when the thickness of the flow region is approximately 10 bubbles. This occurs at an applied rotation rate of approximately $0.07 {\rm s^{-1}}$

The bias of the submillimetre galaxy population: SMGs are poor tracers of the most massive structures in the z ~ 2 Universe

It is often claimed that overdensities of (or even individual bright) submillimetre-selected galaxies (SMGs) trace the assembly of the most-massive dark matter structures in the Universe. We test this claim by performing a counts-in-cells analysis of mock SMG catalogues derived from the Bolshoi cosmological simulation to investigate how well SMG associations trace the underlying dark matter structure. We find that SMGs exhibit a relatively complex bias: some regions of high SMG overdensity are underdense in terms of dark matter mass, and some regions of high dark matter overdensity contain no SMGs. Because of their rarity, Poisson noise causes scatter in the SMG overdensity at fixed dark matter overdensity. Consequently, rich associations of less-luminous, more-abundant galaxies (i.e. Lyman-break galaxy analogues) trace the highest dark matter overdensities much better than SMGs. Even on average, SMG associations are relatively poor tracers of the most significant dark matter overdensities because of 'downsizing': at z < ~2.5, the most-massive galaxies that reside in the highest dark matter overdensities have already had their star formation quenched and are thus no longer SMGs. At a given redshift, of the 10 per cent most-massive overdensities, only ~25 per cent contain at least one SMG, and less than a few per cent contain more than one SMG.Comment: 6 pages, 3 figures, 1 table; accepted for publication in MNRAS; minor revisions from previous version, conclusions unchange

Thermic effect of feeding: orange juice vs. a protein drink (240 kcal)

We investigated the thermic effect of feeding (TEF) equicaloric (1004.16 kJ) portions of randomly provided fresh squeezed orange juice (17.45 oz) and Protein RushTM (40g protein, 17 oz). Eight subjects (5 women, 3 men; 25.8 ± 9.2 yrs, 174.9 ± 12.4 cm, 71.5 ± 17.5 kg) reported to the lab on subsequent mornings and underwent 30-minutes of resting metabolic rate testing, followed by 2-minutes of drink ingestion, followed by 60-minutes of supine rest. Data were collected via a metabolic cart and ventilated hood. Resting data were subtracted from all post-ingestion measures. Within groups the rate of O2 uptake (l min-1) increased significantly for protein (+29%, p = 0.03) but not for orange juice (+21%, p = 0.11); when expressed as ml . kg-1 min-1, both groups had significant increases (p \u3c 0.005). Between groups O2 uptake measurements over the 1-hour period revealed a 21% difference between orange juice (2.66 ± 0.6 liters) and protein (3.36 ± 0.9 liters) that did not reach statistical significance (p = 0.10). Energy expenditure (kJ) determined via the respiratory exchange ratio (RER) revealed orange juice at (60.8 ± 10.1 kJ) and protein (63.7 ± 20.0 kJ) were 5% different, also non-significant (p = 0.72). The RER averaged over the 60-min TEF period was significantly different between orange juice (0.868 ± 0.07) and protein (0.773 ± 0.04) (p = 0.005). Sample size calculations indicate that 14 subjects would reveal statistical significance for O2 uptake yet 163 subjects would be required for energy expenditure differences between drinks. We suggest the potential for bias in selecting a measure of TEF from data within- and between-groups and, O2 uptake vs. energy expenditure

Studying Effects of Primary Care Physicians and Patients on the Trade-Off Between Charges for Primary Care and Specialty Care Using a Hierarchical Multivariate Two-Part Model

Objective. To examine effects of primary care physicians (PCPs) and patients on the association between charges for primary care and specialty care in a point-of-service (POS) health plan. Data Source. Claims from 1996 for 3,308 adult male POS plan members, each of whom was assigned to one of the 50 family practitioner-PCPs with the largest POS plan member-loads. Study Design. A hierarchical multivariate two-part model was fitted using a Gibbs sampler to estimate PCPs\u27 effects on patients\u27 annual charges for two types of services, primary care and specialty care, the associations among PCPs\u27 effects, and within-patient associations between charges for the two services. Adjusted Clinical Groups (ACGs) were used to adjust for case-mix. Principal Findings. PCPs with higher case-mix adjusted rates of specialist use were less likely to see their patients at least once during the year (estimated correlation: –.40; 95% CI: –.71, –.008) and provided fewer services to patients that they saw (estimated correlation: –.53; 95% CI: –.77, –.21). Ten of 11 PCPs whose case-mix adjusted effects on primary care charges were significantly less than or greater than zero (p \u3c .05) had estimated, case-mix adjusted effects on specialty care charges that were of opposite sign (but not significantly different than zero). After adjustment for ACG and PCP effects, the within-patient, estimated odds ratio for any use of primary care given any use of specialty care was .57 (95% CI: .45, .73). Conclusions. PCPs and patients contributed independently to a trade-off between utilization of primary care and specialty care. The trade-off appeared to partially offset significant differences in the amount of care provided by PCPs. These findings were possible because we employed a hierarchical multivariate model rather than separate univariate models

A Hierarchical Multivariate Two-Part Model for Profiling Providers\u27 Effects on Healthcare Charges

Procedures for analyzing and comparing healthcare providers\u27 effects on health services delivery and outcomes have been referred to as provider profiling. In a typical profiling procedure, patient-level responses are measured for clusters of patients treated by providers that in turn, can be regarded as statistically exchangeable. Thus, a hierarchical model naturally represents the structure of the data. When provider effects on multiple responses are profiled, a multivariate model rather than a series of univariate models, can capture associations among responses at both the provider and patient levels. When responses are in the form of charges for healthcare services and sampled patients include non-users of services, charge variables are a mix of zeros and highly-skewed positive values that present a modeling challenge. For analysis of regressor effects on charges for a single service, a frequently used approach is a two-part model (Duan, Manning, Morris, and Newhouse 1983) that combines logistic or probit regression on any use of the service and linear regression on the log of positive charges given use of the service. Here, we extend the two-part model to the case of charges for multiple services, using a log-linear model and a general multivariate log-normal model, and employ the resultant multivariate two-part model as the within-provider component of a hierarchical model. The log-linear likelihood is reparameterized as proposed by Fitzmaurice and Laird (1993), so that regressor effects on any use of each service are marginal with respect to any use of other services. The general multivariate log-normal likelihood is constructed in such a way that variances of log of positive charges for each service are provider-specific but correlations between log of positive charges for different services are uniform across providers. A data augmentation step is included in the Gibbs sampler used to fit the hierarchical model, in order to accommodate the fact that values of log of positive charges are undefined for unused service. We apply this hierarchical, multivariate, two-part model to analyze the effects of primary care physicians on their patients\u27 annual charges for two services, primary care and specialty care. Along the way, we also demonstrate an approach for incorporating prior information about the effects of patient morbidity on response variables, to improve the accuracy of provider profiles that are based on patient samples of limited size