Shortening the Early Treatment Diabetic Retinopathy Study visual acuity test utilizing a novel computer software: reproducibility in control and patient eyes.

Purpose: To describe and compare a method of computerized visual acuity (VA) testing software to the Early Treatment Diabetic Retinopathy Study (ETDRS) chart.

Methods: Setting: Single tertiary institution.

Study Population: Prospective study including right eyes of volunteers (N = 109) and patients (N = 126).

A Comparison of different scoring terminations rules for visual acuity testing: from a computer simulation to a clinical study.

: To compare four visual acuity (VA) scoring termination rules. : A computer simulation generated 30,000 virtual patients who underwent 10 repetitions for each of four termination rules, on both the Snellen and ETDRS charts (2.4 million tests performed in total).

Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests.

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes.

Season of birth, natural light, and myopia.

Purpose: To investigate the possible roles of season of birth and perinatal duration of daylight hours (photoperiod) in the development of myopia.

Design: Retrospective, population-based, epidemiological study.

Participants: A total of 276 911 adolescents (157 663 male, 119 248 female) 16 to 22 years old.

Correcting BLAST e-values for low-complexity segments.

The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper, we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them and generates scores that are more effective in distinguishing true similarities from chance similarities.