Development and Cross-Validation of a Cadence-Based Metabolic Equation for Walking

The ACSM Metabolic Equation is a widely recognized equation for predicting metabolic intensity from walking speed. However, an equation that uses an observable metric (i.e., cadence [steps/min]), accounts for individual characteristics, and is validated across walking conditions may enable more accessible and accurate predictions of walking intensity. PURPOSE: To develop metabolic equations that predict metabolic intensity (oxygen consumption; mL/kg/min) from cadence using a large treadmill walking dataset (Study One) and cross-validate these equations during overground unconstrained and cadence-constrained walking conditions (Study Two). METHODS: In Study One, 193 adults (21-81 years) completed treadmill walking bouts while oxygen consumption was measured with indirect calorimetry (converted to metabolic equivalents [METs]; 1 MET=3.5 mL/kg/min=1 kcal/kg/min). Directly-observed step counts divided by bout duration produced cadence. The least squares regression of the cadence-intensity relationship produced a simple equation and a full equation was developed using best subsets regression (additional possible predictors of leg length, body mass, BMI, percent body fat, sex, and age). Predictive accuracy and bias of each cadence-based metabolic equation and the ACSM Metabolic Equation was evaluated through k-fold cross-validation. In Study Two, these three metabolic equations were applied to data collected from 20 young adults during overground walking at self-selected paces (unconstrained) and with foot-strikes entrained to music tempos (cadence-constrained). RESULTS: In Study One, the simple equation predicted walking intensity within 0.5 METs, on average, and approximately no bias (CONCLUSIONS: The simple equation performed comparably to the full equation (which accounted for individual characteristics) and appreciably better than the ACSM Metabolic Equation. The simple cadence-based metabolic equation is an improved, user-friendly tool for predicting and prescribing walking intensity with reasonable accuracy (within ~0.5 METs; 45 kcals/hr for the average American)

Money and happiness : rank of income, not income, affects life satisfaction

Does money buy happiness, or does happiness come indirectly from the higher rank in society that money brings? Here we test a rank hypothesis, according to which people gain utility from the ranked position of their income within a comparison group. The rank hypothesis contrasts with traditional reference income hypotheses, which suggest utility from income depends on comparison to a social group reference norm. We find that the ranked position of an individual’s income predicts general life satisfaction, while absolute income and reference income have no effect. Furthermore, individuals weight upward comparisons more than downward comparisons. According to the rank hypothesis, income and utility are not directly linked: Increasing an individual’s income will only increase their utility if ranked position also increases and will necessarily reduce the utility of others who will lose rank

Controlling the False Discovery Rate in Astrophysical Data Analysis

The False Discovery Rate (FDR) is a new statistical procedure to control the number of mistakes made when performing multiple hypothesis tests, i.e. when comparing many data against a given model hypothesis. The key advantage of FDR is that it allows one to a priori control the average fraction of false rejections made (when comparing to the null hypothesis) over the total number of rejections performed. We compare FDR to the standard procedure of rejecting all tests that do not match the null hypothesis above some arbitrarily chosen confidence limit, e.g. 2 sigma, or at the 95% confidence level. When using FDR, we find a similar rate of correct detections, but with significantly fewer false detections. Moreover, the FDR procedure is quick and easy to compute and can be trivially adapted to work with correlated data. The purpose of this paper is to introduce the FDR procedure to the astrophysics community. We illustrate the power of FDR through several astronomical examples, including the detection of features against a smooth one-dimensional function, e.g. seeing the ``baryon wiggles'' in a power spectrum of matter fluctuations, and source pixel detection in imaging data. In this era of large datasets and high precision measurements, FDR provides the means to adaptively control a scientifically meaningful quantity -- the number of false discoveries made when conducting multiple hypothesis tests.Comment: 15 pages, 9 figures. Submitted to A

Derivation of coarse-grained potentials via multistate iterative Boltzmann inversion

In this work, an extension to the standard iterative Boltzmann inversion (IBI) method to derive coarse-grained potentials is proposed. It is shown that the inclusion of target data from multiple states yields a less state-dependent potential, and is thus better suited to simulate systems over a range of thermodynamic states than the standard IBI method. The inclusion of target data from multiple states forces the algorithm to sample regions of potential phase space that match the radial distribution function at multiple state points, thus producing a derived potential that is more representative of the underlying potential interactions. It is shown that the algorithm is able to converge to the true potential for a system where the underlying potential is known. It is also shown that potentials derived via the proposed method better predict the behavior of n-alkane chains than those derived via the standard method. Additionally, through the examination of alkane monolayers, it is shown that the relative weight given to each state in the fitting procedure can impact bulk system properties, allowing the potentials to be further tuned in order to match the properties of reference atomistic and/or experimental systems

Adrenal suppression due to an interaction between ritonavir and injected triamcinolone: a case report

Two HIV-1 infected patients developed signs and symptoms consistent with adrenal suppression after being exposed to intra-articular triamcinolone acetate while also receiving ritonavir as part of their highly active antiretroviral therapy. Laboratory evaluation confirmed secondary adrenal suppression in both cases. Both patients recovered without the need for chronic replacement steroids. Adrenal suppression has been described as an adverse outcome in patients treated with fluticasone and concomitant ritonavir. In the reported cases, the adrenal suppression likely developed as a result of increased systemic concentrations of triamcinolone due to an inhibition of cytochrome p450 3A4 metabolism. Practitioners of HIV medicine should be aware of the potential negative interaction of injected triamcinolone and ritonavir

Statistical Computations with AstroGrid and the Grid

We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, AstroGrid) and the computational grid. We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We present our planned usage of the VOTechBroker in computing a huge number of n-point correlation functions from the SDSS, as well as fitting over a million CMBfast models to the WMAP data.Comment: Invited talk to appear in "Proceedings of PHYSTAT05: Statistical Problems in Particle Physics, Astrophysics and Cosmology