Evaluation of Isokinetic Single-Leg Cycling as a Rehabilitation Exercise Following Anterior Cruciate Ligament Reconstruction Surgery

The anterior cruciate ligament (ACL) is one of the most commonly injured ligaments, with over 250,000 injuries per year in the United States. Previous studies have found that ACL-deficient individuals avoid use of the quadriceps in the injured limb as a means of limiting anterior movement of the tibia in the absence of a functioning ACL. From these results, a study was designed to investigate the effectiveness of isokinetic single-leg cycling in increasing quadriceps muscle recruitment and activation. Ten control and seven ACL-reconstructed subjects completed a series of 15 s cycling trials in isokinetic mode at 75 rpm, while kinematic, kinetic, and electromyographic data of the lower limbs were collected, with the trials including both double-leg and single-leg cycling. It was hypothesized that there would be an increase in quadriceps muscle activity, peak knee extensor moment, and knee joint power in single-leg cycling when compared to double-leg cycling. The results of the study suggest that single-leg cycling may be an effective exercise in increasing the strength of the quadriceps following anterior cruciate ligament reconstruction surgery. Although no significant changes occurred, the results indicate that, given a specific limb power, more muscle force will be generated from the quadriceps muscle group in single-leg cycling than double-leg cycling

Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees

We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that these star/galaxy classifications are expected to be reliable for approximately 22 million objects with r < ~20. The general machine learning environment Data-to-Knowledge and supercomputing resources enabled extensive investigation of the decision tree parameter space. This work presents the first public release of objects classified in this way for an entire SDSS data release. The objects are classified as either galaxy, star or nsng (neither star nor galaxy), with an associated probability for each class. To demonstrate how to effectively make use of these classifications, we perform several important tests. First, we detail selection criteria within the probability space defined by the three classes to extract samples of stars and galaxies to a given completeness and efficiency. Second, we investigate the efficacy of the classifications and the effect of extrapolating from the spectroscopic regime by performing blind tests on objects in the SDSS, 2dF Galaxy Redshift and 2dF QSO Redshift (2QZ) surveys. Given the photometric limits of our spectroscopic training data, we effectively begin to extrapolate past our star-galaxy training set at r ~ 18. By comparing the number counts of our training sample with the classified sources, however, we find that our efficiencies appear to remain robust to r ~ 20. As a result, we expect our classifications to be accurate for 900,000 galaxies and 6.7 million stars, and remain robust via extrapolation for a total of 8.0 million galaxies and 13.9 million stars. [Abridged]Comment: 27 pages, 12 figures, to be published in ApJ, uses emulateapj.cl

Relational efficacy beliefs in physical activity classes: a test of the tripartite model

This study explored the predictive relationships between students’ (N = 516, Mage = 18.48, SD = 3.52) tripartite efficacy beliefs and key outcomes in undergraduate physical activity classes. Students reported their relational efficacy perceptions (i.e., other-efficacy and relation-inferred self-efficacy, or RISE) with respect to their instructor before a class, and instruments measuring self-efficacy, enjoyment, and effort were administered separately following the class. The following week, an independent observer assessed student achievement. Latent variable path analyses that accounted for nesting within classes revealed (a) that students were more confident in their own ability when they reported favorable other-efficacy and RISE appraisals, (b) a number of direct and indirect pathways through which other-efficacy and RISE predicted adaptive in-class outcomes, and (c) that self-efficacy directly predicted enjoyment and effort, and indirectly predicted achievement. Although previous studies have examined isolated aspects within the tripartite framework, this represents the first investigation to test the full range of direct and indirect pathways associated with the entire model

Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry

We identify 885,503 type 1 quasar candidates to i<22 using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-Field Infrared Survey Explorer (WISE) "ALLWISE" data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically-confirmed type-1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high probability potential quasars with 3.5<z<5 (of which 6779 are new photometric candidates). Our algorithm is more complete to z>3.5 than the traditional mid-IR selection "wedges" and to 2.2<z<3.5 quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggests that the resulting catalog is relatively complete to known quasars and is identifying new high-z quasars at z>3. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.Comment: 54 pages, 17 figures; accepted by ApJS Data for tables 1 and 2 available at http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/master_quasar_catalogs.011414.fits.bz2 and http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/optical_ir_quasar_candidates.052015.fits.bz

Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog

We present the SDSS-XDQSO quasar targeting catalog for efficient flux-based quasar target selection down to the faint limit of the Sloan Digital Sky Survey (SDSS) catalog, even at medium redshifts (2.5 <~ z <~ 3) where the stellar contamination is significant. We build models of the distributions of stars and quasars in flux space down to the flux limit by applying the extreme-deconvolution method to estimate the underlying density. We convolve this density with the flux uncertainties when evaluating the probability that an object is a quasar. This approach results in a targeting algorithm that is more principled, more efficient, and faster than other similar methods. We apply the algorithm to derive low-redshift (z < 2.2), medium-redshift (2.2 <= z 3.5) quasar probabilities for all 160,904,060 point sources with dereddened i-band magnitude between 17.75 and 22.45 mag in the 14,555 deg^2 of imaging from SDSS Data Release 8. The catalog can be used to define a uniformly selected and efficient low- or medium-redshift quasar survey, such as that needed for the SDSS-III's Baryon Oscillation Spectroscopic Survey project. We show that the XDQSO technique performs as well as the current best photometric quasar-selection technique at low redshift, and outperforms all other flux-based methods for selecting the medium-redshift quasars of our primary interest. We make code to reproduce the XDQSO quasar target selection publicly available