Biology subjects

Doherty, A.

Publications and source records attributed to Doherty, A..

5 recordsLinked to original sources

bioRxiv2018

Hazard detection with monocular bioptic telescopes in a driving simulator

ABSTRACT\n\nPurpose: Recently we developed a driving simulator paradigm to evaluate detection of road hazards when using a bioptic telescope and conducted an initial study using normally-sighted observers with simulated vision loss. We now extend our investigation to quantifying the extent to which visually impaired bioptic users are able to use their fellow (non-telescope) eye to compensate for the ring scotoma of a monocular bioptic telescope. We tested the hypothesis that detection rates would be higher in binocular viewing (fellow eye could potentially compensate) than monocular viewing (fellow eye patched so it could not compensate) for pedestrian hazards present in the scene only while the telescope was being used.\n\nMethods: Sixteen bioptic telescope users (17-80 y) completed six test drives, including three with binocular viewing interleaved between three with monocular viewing. While driving, they used their own monocular bioptic telescopes to read information on highway road signs (n = 71) and pressed the horn when they saw a pedestrian hazard (n = 50). Twenty-six of the pedestrians were programed to appear, run on the road ahead of the driver for 1s within the ring scotoma and then disappear, within the period when participants were reading signs through the bioptic. The timing of the head movement to look into and out of the bioptic was determined and events were then categorized by whether or not the pedestrian hazard was present in the scene only while using the bioptic.\n\nResults: When pedestrian hazards were in the scene only while subjects were using the bioptic to read a sign, detection rates were significantly higher in binocular than monocular viewing (68% vs. 40%). However, when pedestrians when subjects had a brief view of the pedestrian either beforeor after looking through the bioptic, then detection rates did not differ in binocular and monocular viewing (78% vs. 79%). By comparison, when not using the bioptic detection rates were higher (> 90%) and reaction times were shorter (without 0.95 s vs. with 1.25 s)\n\nConclusions: Our results suggest that under binocular viewing conditions the fellow eye was able to compensate for the ring scotoma to a certain extent when subjects used a monocular telescope to read road signs; however, performance was not as good as without the bioptic.

neuroscience↗

bioRxiv2018

Genome-wide association study of circadian rhythmicity in 71 500 UK Biobank participants and polygenic association with mood instability

BackgroundCircadian rhythms are fundamental to health and are particularly important for mental wellbeing. Disrupted rhythms of rest and activity are recognised as risk factors for major depressive disorder and bipolar disorder.\n\nMethodsWe conducted a genome-wide association study (GWAS) of low relative amplitude (RA), an objective measure of circadian rhythmicity derived from the accelerometer data of 71 500 UK Biobank participants. Polygenic risk scores (PRS) for low RA were used to investigate potential associations with psychiatric phenotypes.\n\nOutcomesTwo independent genetic loci were associated with low RA, within genomic regions for Neurofascin (NFASC) and Solute Carrier Family 25 Member 17 (SLC25A17). A secondary GWAS of RA as a continuous measure identified a locus within Meis Homeobox 1 (MEIS1). There were no significant genetic correlations between low RA and any of the psychiatric phenotypes assessed. However, PRS for low RA was significantly associated with mood instability across multiple PRS thresholds (at PRS threshold 0{middle dot}05: OR=1{middle dot}02, 95% CI=1{middle dot}01-1{middle dot}02, p=9{middle dot}6x10-5), and with major depressive disorder (at PRS threshold 0{middle dot}1: OR=1{middle dot}03, 95% CI=1{middle dot}01-1{middle dot}05, p=0{middle dot}025) and neuroticism (at PRS threshold 0{middle dot}5: Beta=0{middle dot}02, 95% CI=0{middle dot}007-0{middle dot}04, p=0{middle dot}021).\n\nInterpretationOverall, our findings contribute new knowledge on the complex genetic architecture of circadian rhythmicity and suggest a putative biological link between disrupted circadian function and mood disorder phenotypes, particularly mood instability, but also major depressive disorder and neuroticism.

genomics↗

bioRxiv2018

GWAS identifies 10 loci for objectively-measured physical activity and sleep with causal roles in cardiometabolic disease.

Physical activity and sleep disorders are established risk factors for many diseases, but their etiology is poorly understood, partly due to a reliance on self-reported evidence. Here we report a genome-wide association study (GWAS) of wearable-defined and machine-learned physical activity and sleep phenotypes in 91,112 UK Biobank participants, and self-reported physical activity in 351,154 UK Biobank participants. While the self-reported activity analysis resulted in no significant (p<5x10-9) loci, the analysis of objectively-measured traits identified 10 loci, 6 of which are novel. These 10 loci account for 0.05% of activity and 0.33% of sleep phenotype variation, but genome-wide estimates suggest that common variation accounts for ~12% of phenotypic variation, indicating high polygenicity. Heritability was higher in women than in men for overall activity ({Delta}h2 = 4%, p=6.3x10-5), moderate intensity activity (6%, p=6.7x10-8), and walking (5%, p=2.6x10-6). Heritability partitioning, enrichment and pathway analyses all indicate the central nervous system plays a role in activity behaviours. Mendelian randomization in publicly available GWAS data and in 278,367 UK Biobank participants, who were not included in our discovery analyses, suggest that overall activity might be causally related to lowering body fat percentage (beta per SD higher overall activity: -0.44, SE=0.047, p=2.70x10-21) and systolic blood pressure (beta per SD: -0.71, SE=0.125, p=1.38x10-8). Our current results advocate the value of physical activity for the reduction of adiposity and blood pressure.

genetics↗

bioRxiv2017

Automated detection of sleep-boundary times using wrist-worn accelerometry

ObjectiveCurrent polysomnography-validated measures of sleep status from wrist-worn accelerometers cannot be used in fully automated analysis as they rely on self-reported sleep-onset and -end (sleep-boundary) information. We set out to develop an automated, data-driven approach to sleep-boundary detection from wrist-worn accelerometer data.\n\nMethodsOn three separate occasions, participants were asked to wear a GENEActiv(R) wrist-worn accelerometer for nine days and concurrently complete sleep diaries with lights-off, asleep and wake-up information. We developed and evaluated three data-driven methods for sleep-boundary detection: a change-point detection based method, a thresholding method and a random forest classifier based method. Mean absolute errors between automatically-derived and self-reported sleep-onset and wake-up times were recorded in addition to kappa statistics for the minute-by-minute performance of each of the methods.\n\nResults46 participants provided 972 days of accelerometer recordings with corresponding self-reported sleep information. The three sleep-boundary detection methods resulted in mean absolute errors in sleep-onset and wake-up times per individual of 36 min, 34 min and 33 min and kappa statistics of 0.87, 0.89 and 0.89, respectively.\n\nConclusionOur methods provide a data-driven approach to detect sleep-onset and -end times without the need for self-reported sleep-boundary information. The methods are likely to be of particular use for large-scale studies where the collection of self-reported sleep diaries is impractical.\n\nSignificanceObjective measures of sleep are needed to reliably detect associations with health outcomes. This work lays the foundation for studies of objectively measured sleep duration and its health consequences in large studies.

epidemiology↗

bioRxiv2017

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,609 UK Biobank participants

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

bioinformatics↗