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PURPOSE:To determine the effects of optically imposed astigmatism on early eye growth in chicks. METHODS:5-day-old (P5) White Leghorn chicks were randomly assigned to either wear, monocularly, a "high magnitude" (H: +4.00DS/-8.00DC) crossed-cylindrical lens oriented at one of four axes (45, 90, 135, and 180; n = 20 in each group), or were left untreated (controls; n = 8). Two additional groups wore a "low magnitude" (L: +2.00DS/-4.00DC) cylindrical lens orientated at either axis 90 or 180 (n = 20 and n = 18, respectively). Refractions were measured at P5 and after 7 days of treatment for all chicks (P12), whereas videokeratography and ex-vivo eyeshape analysis were performed at P12 for a subset of chicks in each group (n = 8). RESULTS:Compared to controls, chicks in the treatment groups developed significant amounts of refractive astigmatism (controls: 0.03 ± 0.22DC; treatment groups: 1.34 ± 0.22DC to 5.51 ± 0.26DC, one-way ANOVAs, p ≤ 0.05) with axes compensatory to those imposed by the cylindrical lenses. H cylindrical lenses induced more refractive astigmatism than L lenses (H90 vs. L90: 5.51 ± 0.26D vs. 4.10 ± 0.16D; H180 vs. L180: 2.84 ± 0.44D vs. 1.34 ± 0.22D, unpaired two-sample t-tests, both p ≤ 0.01); and imposing with-the-rule (H90 and L90) and against-the-rule astigmatisms (H180 and L180) resulted in, respectively, steeper and flatter corneal shape. Both corneal and internal astigmatisms were moderately to strongly correlated with refractive astigmatisms (Pearson's r: +0.61 to +0.94, all p ≤ 0.001). In addition, the characteristics of astigmatism were significantly correlated with multiple eyeshape parameters at the posterior segments (Pearson's r: -0.27 to +0.45, all p ≤ 0.05). CONCLUSIONS:Chicks showed compensatory ocular changes in response to the astigmatic magnitudes imposed in this study. The correlations of changes in refractive, corneal, and posterior eyeshape indicate the involvement of anterior and posterior ocular segments during the development of astigmatism.

As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.