Biology subjects

Aung, T.

Publications and source records attributed to Aung, T..

3 recordsLinked to original sources

bioRxiv2018

Optic Nerve Tortuosity and Globe Proptosis in Normal and Glaucoma Subjects

PurposeTo assess the difference in optic nerve tortuosity during eye movements and globe proptosis between primary open angle glaucoma and normal subjects using orbital magnetic resonance imaging.\n\nMethods10 Chinese subjects matched for age, ethnicity and refractive errors were recruited, including five normal controls and five patients with primary open angle glaucoma. All subjects underwent magnetic resonance imaging to assess their optic nerves and globes for three eye positions: primary gaze, adduction and abduction. Optic nerve tortuosity (optic nerve length divided by the distance between two ends) and globe proptosis (maximum distance between cornea and interzygomatic line) were measured from magnetic resonance imaging images.\n\nResultsIn adduction, the tortuosity of normal eyes was significantly larger than that of the glaucomatous eyes. Optic nerve tortuosity in adduction in the control and glaucoma groups were 1.004{+/-}0.003 (mean {+/-} standard deviation) and 1.001{+/-}0.001, respectively (p=0.037). Globe proptosis (primary gaze) in glaucoma subjects (19.14{+/-}2.11 mm) was significantly higher than that in control subjects (15.32{+/-}2.79 mm; p = 0.046).\n\nConclusionsIn this sample, subjects with glaucoma exhibited more taut optic nerves and more protruding eye globes compared to normal eyes. This may impact optic nerve head deformations in anatomically predisposed patients.\n\nPrecisEyes with glaucoma have tauter optic nerves compared with normal eyes, which may exert more force on the optic nerve head tissues during eye movements.

pathology↗

bioRxiv2018

Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

Asian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7x, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

genetics↗

bioRxiv2017

Classifying smartphone-based accelerometer data to obtain validated measures of subject activity status, step count, and gait speed

BackgroundThe ubiquitous spread of smartphone technology throughout global societies offers an unprecedented opportunity to ethically obtain long-term, highly accurate measurements of individual physical activity. For example, the smartphone intrinsic 3-D accelerometer can be queried during normal phone operation to save time series of acceleration magnitudes (in each of the component directions) for near-real time or post processing.\n\nObjectiveWe describe simple, straightforward algorithms (based on windowed Fourier analysis) for accelerometer data quality control and behavioral classification.\n\nMethodsTo maximize the clinical utility of our classifications, we focused on differentiating the following conditions: forgotten phone, subject resting, low physical activity, high physical activity. We further differentiated high physical activity into epochs of walking and climbing stairs, and further quantified walking to infer step count and gait speed.\n\nResultsWe validated these algorithms in 75 individuals, in both laboratory (treadmill) and naturalistic settings. Our algorithm performance was quite satisfactory, with accuracies of 92-99% for all behavioral categories, and 87-90% for gait metrics in naturalistic settings.\n\nConclusionsWe conclude that smartphones are valid and accurate platforms for measuring day-to-day physical activity in ambulatory, community dwelling individuals.

bioengineering↗