Agreement and repeatability of objective systems for assessment of the tear film

Purpose: To assess the agreement and repeatability of two objective systems for measuring the tear film stability. Methods: Retrospective analysis of the tear film stability of 99 healthy right eyes measured with a videokeratoscope (VK) and the Optical Quality Analysis System (OQAS, Visiometrics). Two consecutive measures were taken with both systems, with an interval of 10 min between them. Variables included in the study were first and mean non-invasive break-up times (NIBUT and MNIBUT) measured with VK, and mean and standard deviation of the optical scattering index (OSIm and OSIsd) measured with OQAS. The agreement and repeatability of grading scales provided by both devices were also evaluated using the Cohen’s k with quadratic weights. The Ocular Surface Disease index (OSDI) questionnaire was also passed out to all subjects. Correlations and associations between subjective and objective metrics were analyzed. Results: Significant differences were found between consecutive measurements of NIBUT (p = 0.04) and MNIBUT (p = 0.01), but not for OSIm (p = 0.11) and OSIsd (p = 0.50). Grading scales resulted in fair (k = 0.20) or poor agreement (k = 0.04) between systems depending if the first or second trial was considered. The repeatability of the grading scale was good for OQAS (k = 0.59) and fair for VK (k = 0.37). No significant correlations or associations were found between OSDI and any of the metrics obtained with both devices (p ≥ 0.36). Conclusions: The two devices evaluated cannot be used interchangeably for the assessment of tear film stability. Good intrasession repeatability was obtained for tear film grading of the OQAS whereas it was fair for VK

Clinical Analysis of Central Islands after Small Incision Lenticule Extraction (SMILE)

Purpose: To evaluate the incidence of central islands after 6-month follow-up of Small Incision Lenticule Extraction (SMILE) and to assess their role in safety and accuracy. Methods: Analysis of the preoperative and postoperative corneal tomography, best spectacle refraction and corrected distance visual acuity of 82 subjects that underwent SMILE. Incidence of central islands was assessed through total corneal spherical aberration (SA) over 4 mm of central diameter and the SA was compared between two groups with and without safety loss (CDVA difference ≥0.1 logMAR from preoperative). The cut-off value for detecting the risk of postoperative central island development was calculated. The influence in accuracy was calculated through magnitude of error of the spherical equivalent and astigmatism, both for spectacle refraction at corneal plane (SE-Rx and AST-Rx) and for total corneal refractive power at 3 mm (SE-TCRP3 and AST-TCRP3). Results: Five from 82 eyes resulted in a loss of safety, obtaining significant differences in SA, both preoperatively (p = .01) and postoperatively (p = .007) after stratification by safety loss. A preoperatively cut-off value ≤0.012 μm of SA predicted the appearance of central islands with sensitivity of 100% and specificity of 75%. Despite postoperative SA being related to the preoperative spherical equivalent, for both SE-Rx and SE-TCRP3, this tendency disappeared after readjusting results according to a nomogram. Conclusions: Central islands in SMILE, despite being a rare adverse event, can affect the safety of the procedure and are related to preoperative central steepness, not corrected by the spherical lenticule, which is clearly visible postoperatively

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype × environment (G×E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson’s correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe