Characterization of nanoparticles generated from drilling activities within a sub-surface mine using a novel sampler

2020 Spring.Includes bibliographical references.This study employed nanoparticle sampling techniques to characterize the aerosol generated from a routine mining activity. A preliminary survey of the particle emission from the feed-leg drilling activity was conducted in the excavations of an experimental mine. The level of particulate exposure was sampled using a novel sampler for respirable and nanometer sized particles; and monitored by direct reading real time instruments. A NanoScan scanning mobility particle sizer (measurement range 10-420 nm) and an optical particle sizer (measurement range 0.3-10 µm) were used. Particulate morphological and structural examination of samples collected with the novel nanoparticle sampler and a thermophoretic sampler was conducted through transmission and scanning electron microscopy and x-ray dispersive analysis. Based on the real-time instrument data, the researchers found high concentrations (> 3.5 x 106 particles/cm3) of ultrafine/nanoparticles generated from the drilling activity. A large amount of submicron silica, spherical primary and agglomerated particles rich in carbon were discovered via analysis of particle sampler specimens with energy-dispersive x-ray spectroscopy. Many particle agglomerates contained primary particles less than 100 nm. Exposure to particles in the nanometer size from various sources within the mining environment has not been well characterized and may be associated with respiratory and systemic disease among miners

Higher-Derivative Gravity in String Theory

We explicitly extract the structure of higher-derivative curvature-squared terms at genus 0 and 1 in the d=4 heterotic string effective action compactified on symmetric orbifolds by computing on-shell S-matrix superstring amplitudes. In particular, this is done within the context of calculating the graviton 4-point amplitude. We also discuss the moduli-dependent gravitational threshold corrections to the coupling associated with the CP even quadratic curvature terms.Comment: 14 pages, 6 Postscript figures, latex and psfi

Randomised controlled trials (RCTs) in sports injury research:authors-please report the compliance with the intervention

Background In randomised controlled trials (RCTs) of interventions that aim to prevent sports injuries, the intention-to-treat principle is a recommended analysis method and one emphasised in the Consolidated Standards of Reporting Trials (CONSORT) statement that guides quality reporting of such trials. However, an important element of injury prevention trials-compliance with the intervention-is not always well-reported. The purpose of the present educational review was to describe the compliance during follow-up in eight large-scale sports injury trials and address compliance issues that surfaced. Then, we discuss how readers and researchers might consider interpreting results from intention-to-treat analyses depending on the observed compliance with the intervention. Methods Data from seven different randomised trials and one experimental study were included in the present educational review. In the trials that used training programme as an intervention, we defined full compliance as having completed the programme within +/- 10% of the prescribed running distance (ProjectRun21 (PR21), RUNCLEVER, Start 2 Run) or time-spent-running in minutes (Groningen Novice Running (GRONORUN)) for each planned training session. In the trials using running shoes as the intervention, full compliance was defined as wearing the prescribed running shoe in all running sessions the participants completed during follow-up. Results In the trials that used a running programme intervention, the number of participants who had been fully compliant was 0 of 839 (0%) at 24-week follow-up in RUNCLEVER, 0 of 612 (0%) at 14-week follow-up in PR21, 12 of 56 (21%) at 4-week follow-up in Start 2 Run and 8 of 532 (1%) at 8-week follow-up in GRONORUN. In the trials using a shoe-related intervention, the numbers of participants who had been fully compliant at the end of follow-up were 207 of 304 (68%) in the 21 week trial, and 322 of 423 (76%), 521 of 577 (90%), 753 of 874 (86%) after 24-week follow-up in the other three trials, respectively. Conclusion The proportion of runners compliant at the end of follow-up ranged from 0% to 21% in the trials using running programme as intervention and from 68% to 90% in the trials using running shoes as intervention. We encourage sports injury researchers to carefully assess and report the compliance with intervention in their articles, use appropriate analytical approaches and take compliance into account when drawing study conclusions. In studies with low compliance, G-estimation may be a useful analytical tool provided certain assumptions are met

Sports injuries and risk factors in youth soccer [Abstract].

peer reviewe

Epidemiology of injuries in high-level youth sport in Luxembourg [Abstract]

peer reviewe

Motivational Interviewing to Increase Physical Activity Behavior in Cancer Patients: A Pilot Randomized Controlled Trials

OBJECTIVE: This pilot randomized controlled trial (RCT) aimed at evaluating the feasibility and potential efficacy of a motivational interviewing (MI) intervention to increase physical activity (PA) behavior in cancer patients. METHODS: Participants were randomly assigned to an experimental group with standard care plus 12 MI sessions within 12 weeks or a control group with standard care only. The number of recruited participants and the modality of recruitment were recorded to describe the reach of the study. The acceptability of the study was estimated using the attrition rate during the intervention phase. The potential efficacy of the intervention was evaluated by analyzing the PA behavior. RESULTS: Twenty-five participants were recruited within the 16-month recruitment period (1.6 participants per month). Five participants (38.5%) from the experimental group (n = 13) and one participant (8.3%) from the control group (n = 12) dropped out of the study before the end of the intervention phase. No group by time interaction effect for PA behavior was observed at the end of the intervention. CONCLUSION: Due to the low recruitment rate and compliance, no conclusion can be drawn regarding the efficacy of MI to increase PA behavior in cancer patients. Moreover, the current literature cannot provide any evidence on the effectiveness of MI to increase PA in cancer survivors. Future RCTs should consider that the percentage of uninterested patients to join the study may be as high as 60%. Overrecruitment (30% to 40%) is also recommended to accommodate the elevated attrition rate

Time-to-event analysis for sports injury research part 2: Time-varying outcomes

BACKGROUND: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. CONTENT: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. CONCLUSION: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: ‘how much change in training load is too much before injury is sustained, among athletes with different characteristics?’ Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward

Time-to-event analysis for sports injury research part 1: Time-varying exposures

BACKGROUND: ‘How much change in training load is too much before injury is sustained, among different athletes?’ is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. AIM: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. CONTENT: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. CONCLUSION: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data