A Description of Variability of Pacing in Marathon Distance Running

The purpose of this study was twofold: 1) to describe variability of pacing during a marathon and 2) to determine if there is a relationship between variability of pacing and marathon performance. Publically available personal global positioning system profiles from two marathons (Race 1 n = 116, Race 2 n = 169) were downloaded (http://connect.garmin.com) for analysis. The coefficient of variation of velocity (Velcov) was calculated for each profile. Each profile was categorized as finishing in under 3.9 hours, between 3.9 and 4.6 hours, or longer than 4.6 hours. Linear and quadratic lines of best fit were computed to describe the relationship between marathon finish time and Velcov. A 2 (Race) x 3 (bin) analysis of variance (ANOVA) was used to compare the dependent variable (Velcov) between races and the marathon bin finish times. Velcov was not influenced by the interaction of finish time bin and Race (p\u3e0.05) and was not different between races (Race 1: 16.6 ± 6.4%, Race 2: 16.8 ± 6.6%, p\u3e0.05). Velcov was different between finish time categories (p\u3c0.05) for each race such that Velcov was lower for faster finish times. Using combined data from both races, linear (marathon finish time = marathon finish time = 0.09Velcov + 2.9, R^2 = 0.46) and quadratic (marathon finish time = -0.0006 Velcov 2 + 0.11 Velcov + 2.7, R^2 = 0.46) lines of best fit were significant (p\u3c0.05). Slower marathon finishers had greater variability of pace compared to faster marathoner finishers

Keep off the grass?:Cannabis, cognition and addiction

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.In an increasing number of states and countries, cannabis now stands poised to join alcohol and tobacco as a legal drug. Quantifying the relative adverse and beneficial effects of cannabis and its constituent cannabinoids should therefore be prioritized. Whereas newspaper headlines have focused on links between cannabis and psychosis, less attention has been paid to the much more common problem of cannabis addiction. Certain cognitive changes have also been attributed to cannabis use, although their causality and longevity are fiercely debated. Identifying why some individuals are more vulnerable than others to the adverse effects of cannabis is now of paramount importance to public health. Here, we review the current state of knowledge about such vulnerability factors, the variations in types of cannabis, and the relationship between these and cognition and addiction.This work was supported by grants from the US National Institutes of Health to L.H.P. (AA020404, AA006420, AA022249 and AA017447) and by grants from the UK Medical Research Council to H.V.C. and C.J.A.M. (G0800268; MR/K015524/1)

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software