The influence of visual flow and perceptual load on locomotion speed

Visual flow is used to perceive and regulate movement speed during locomotion. We assessed the extent to which variation in flow from the ground plane, arising from static visual textures, influences locomotion speed under conditions of concurrent perceptual load. In two experiments, participants walked over a 12-m projected walkway that consisted of stripes that were oriented orthogonal to the walking direction. In the critical conditions, the frequency of the stripes increased or decreased. We observed small, but consistent effects on walking speed, so that participants were walking slower when the frequency increased compared to when the frequency decreased. This basic effect suggests that participants interpreted the change in visual flow in these conditions as at least partly due to a change in their own movement speed, and counteracted such a change by speeding up or slowing down. Critically, these effects were magnified under conditions of low perceptual load and a locus of attention near the ground plane. Our findings suggest that the contribution of vision in the control of ongoing locomotion is relatively fluid and dependent on ongoing perceptual (and perhaps more generally cognitive) task demands

Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone

INTRODUCTION AND OBJECTIVES: The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. METHODS: We identified patients from two centers that had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a CT available. HUm and HUsd of the stones were compared with ANOVA. ROC analysis with Area Under the Curve (AUC), Youden Index and likelihood ratio calculations were performed. RESULTS: Data was available for 466 patients. The major component was CalciumOxalate Monohydrate (COM), Uric Acid, HydroxyApatite, Struvite, Brushite, Cystine and CO Dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4% and 4.7% of the patients respectively. The HUm of UA and Br was respectively significantly lower and higher than the HUm of any other stone type. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851 respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone\u27s mineral composition for all stone types but COM. CONCLUSIONS: To the best of our knowledge this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability