Comparison of the Performance of a Novel, Smartphone-based, Head-mounted Perimeter (GearVision) With the Humphrey Field Analyzer.

PRECIS: The agreement between a head-mounted perimeter [GearVision (GV)] and Humphrey field analyzer (HFA) for total threshold sensitivity was a mean difference of -1.9 dB (95% limits of agreement -5 to 1). GV was the preferred perimeter in 68.2% of participants. PURPOSE: The purpose of this study was to compare reliability indices and threshold sensitivities obtained using a novel, smartphone-based, head-mounted perimeter (GV) with the HFA in normal, glaucoma suspect and glaucoma patients. A secondary objective was to evaluate the subjective experience participants had with both perimeters using a questionnaire. METHODS: In a prospective, cross-sectional study; 107 eyes (34 glaucoma, 18 glaucoma suspect, and 55 normal) of 54 participants underwent HFA and GV in random order. The main outcome measure was the agreement of threshold sensitivities using Bland and Altman analysis. Participants also completed a questionnaire about their experience with the devices. RESULTS: Median false-positive response rate for GV was 7% (4% to 12%), while for HFA it was 0% (0% to 6%, P<0.001). Median false-negative response rate was similar for both tests. In all, 84 eyes with reliable HFA and GV results were included in the final analysis. Median threshold sensitivity of all 52 points on HFA was 29.1 dB (26.5 to 30.7 dB) and for GV was 30.6 dB (29.1 to 32.6 dB; P<0.001). Mean difference (95% limits of agreement) in total threshold sensitivity between HFA and GV was -1.9 dB (-5 to 1 dB). The 95% limits of agreement were fairly narrow (-8 to 2 dB) across the 6 Garway-Heath sectors. Most participants preferred to perform GV (68.2%) if required to repeat perimetry compared with HFA (20.6%, P<0.001). CONCLUSIONS: There was fairly good agreement between the threshold sensitivities of GV and HFA. GV was also preferred by most patients and could potentially supplement HFA as a portable or home perimeter.

Diabetes and Composition of Weight Lifting and Cardio in Exercise.

The prevalence of type-2 diabetes(T2D) has increased globally. This has led to greater mortality, morbidity and disability in the general population. It is thus crucial to identify methods to prevent its onset among the healthy, and to also discover solutions to adequately manage the complications among those affected. Most research in this area has focused on the role of diet and exercise. More recently, different exercise types and their relationship with T2D has received considerable attention. In our work, we investigate the association between T2D (primary outcome) and two types of exercises: cardio (CR) and weight lifting (WL). Specifically, the relationship between duration of time spent in the two exercises and the odds of T2D is explored. Data are obtained from the Behavioural Risk Factor Surveillance System (BRFSS) survey, USA. Three ethnic populations are considered: White American, Black American and Hispanic American. Both WL and CR are found to be associated with negative log-odds of diabetes across all three ethnicities (WL: p <; 0.0001 and CR: p=0.00431). The association between WL and T2D is found to be modified for females (interaction-term coefficient: -0.096 (p=0.0115)).

Estimation of Spherical Refractive Errors Using Virtual Reality Headset.

Refractive errors are the most common visual defects in humans. They are corrected using lenses whose power is determined using expensive and bulky devices operated by trained professionals. This limits the outreach of eye- health care. We exploit commercial virtual reality (VR) setup to create a portable and inexpensive system for subjective estimation of spherical refractive errors. In doing so, we aim to keep hardware additions simple and to a minimum. We add a plain reflecting mirror in a VR headset to project optotypes on programmable focal planes at varying distances from the subject's eye. An interactive interface uses feedback from the user to estimate accommodation range and spherical refractive errors automatically. We compute the range and precision of our system, and validate them in a user trial study. The proposed setup strongly agrees with clinical subjective refraction.

GearVision: Smartphone Based Head Mounted Perimeter For Detection Of Visual Field Defects.

Automated visual field perimetry is widely used for the evaluation of visual field defects caused by ophthalmological and neurodegenerative diseases. This test is typically performed using the Humphrey Perimeter or Octopus Perimeter. However, their high cost and large footprint limit their use to the clinical setting. This in turn limits their reach especially in the context of screening in remote environments and personal setting. In this paper, we report the development and testing of GearVision, a portable, accessible and compact virtual reality based visual field perimeter. It enables regular visual field testing in a cost-effective and convenient manner. Currently, GearVision is meant to augment the existing perimetry system and hence facilitate the detection of visual field defects early and without expert supervision or the need for hospital visits. In addition to the development of standard 30-2 suprathreshold and full threshold perimetry tests, we have proposed methods to improve test reliability and compliance. These include optional rest intervals during the test to reduce errors caused by strain or fatigue and improved false positive estimation based on statistical analysis of a patient's response times. We have tested the proposed system on 21 subjects and validated its capability to detect visual field defects.

A fast algorithm for exact sequence search in biological sequences using polyphase decomposition.

MOTIVATION: Exact sequence search allows a user to search for a specific DNA subsequence in a larger DNA sequence or database. It serves as a vital block in many areas such as Pharmacogenetics, Phylogenetics and Personal Genomics. As sequencing of genomic data becomes increasingly affordable, the amount of sequence data that must be processed will also increase exponentially. In this context, fast sequence search algorithms will play an important role in exploiting the information contained in the newly sequenced data. Many existing algorithms do not scale up well for large sequences or databases because of their high-computational costs. This article describes an efficient algorithm for performing fast searches on large DNA sequences. It makes use of hash tables of Q-grams that are constructed after downsampling the database, to enable efficient search and memory use. Time complexity for pattern search is reduced using beam pruning techniques. Theoretical complexity calculations and performance figures are presented to indicate the potential of the proposed algorithm.