Loss of Gap Junction Delta-2 (GJD2) gene orthologs leads to refractive error in zebrafish.

Myopia is the most common developmental disorder of juvenile eyes, and it has become an increasing cause of severe visual impairment. The GJD2 locus has been consistently associated with myopia in multiple independent genome-wide association studies. However, despite the strong genetic evidence, little is known about the functional role of GJD2 in refractive error development. Here, we find that depletion of gjd2a (Cx35.5) or gjd2b (Cx35.1) orthologs in zebrafish, cause changes in the biometry and refractive status of the eye. Our immunohistological and scRNA sequencing studies show that Cx35.5 (gjd2a) is a retinal connexin and its depletion leads to hyperopia and electrophysiological changes in the retina. These findings support a role for Cx35.5 (gjd2a) in the regulation of ocular biometry. Cx35.1 (gjd2b) has previously been identified in the retina, however, we found an additional lenticular role. Lack of Cx35.1 (gjd2b) led to a nuclear cataract that triggered axial elongation. Our results provide functional evidence of a link between gjd2 and refractive error.

Comparative Chemometric Analysis for Classification of Acids and Bases via a Colorimetric Sensor Array.

With the increasing availability of digital imaging devices, colorimetric sensor arrays are rapidly becoming a simple, yet effective tool for the identification and quantification of various analytes. Colorimetric arrays utilize colorimetric data from many colorimetric sensors, with the multidimensional nature of the resulting data necessitating the use of chemometric analysis. Herein, an 8 sensor colorimetric array was used to analyze select acid and basic samples (0.5 - 10 M) to determine which chemometric methods are best suited for classification quantification of analytes within clusters. PCA, HCA, and LDA were used to visualize the data set. All three methods showed well-separated clusters for each of the acid or base analytes and moderate separation between analyte concentrations, indicating that the sensor array can be used to identify and quantify samples. Furthermore, PCA could be used to determine which sensors showed the most effective analyte identification. LDA, KNN, and HQI were used for identification of analyte and concentration. HQI and KNN could be used to correctly identify the analytes in all cases, while LDA correctly identified 95 of 96 analytes correctly. Additional studies demonstrated that controlling for solvent and image effects was unnecessary for all chemometric methods utilized in this study.

An Improved Comparison of Chemometric Analyses for the Identification of Acids and Bases With Colorimetric Sensor Arrays.

Colorimetric sensor arrays incorporating red, green, and blue (RGB) image analysis use value changes from multiple sensors for the identification and quantification of various analytes. RGB data can be easily obtained using image analysis software such as ImageJ. Subsequent chemometric analysis is becoming a key component of colorimetric array RGB data analysis, though literature contains mainly principal component analysis (PCA) and hierarchical cluster analysis (HCA). Seeking to expand the chemometric methods toolkit for array analysis, we explored the performance of nine chemometric methods were compared for the task of classifying 631 solutions (0.1 to 3 M) of acetic acid, malonic acid, lysine, and ammonia using an eight sensor colorimetric array. PCA and LDA (linear discriminant analysis) were effective for visualizing the dataset. For classification, linear discriminant analysis (LDA), (k nearest neighbors) KNN, (soft independent modelling by class analogy) SIMCA, recursive partitioning and regression trees (RPART), and hit quality index (HQI) were very effective with each method classifying compounds with over 90% correct assignments. Support vector machines (SVM) and partial least squares - discriminant analysis (PLS-DA) struggled with ~85 and 39% correct assignments, respectively. Additional mathematical treatments of the data set, such as incrementally increasing the exponents, did not improve the performance of LDA and KNN. The literature precedence indicates that the most common methods for analyzing colorimetric arrays are PCA, LDA, HCA, and KNN. To our knowledge, this is the first report of comparing and contrasting several more diverse chemometric methods to analyze the same colorimetric array data.

Colorimetric Sensor Arrays for the Detection and Identification of Chemical Weapons and Explosives.

There is a significant demand for devices that can rapidly detect chemical-biological-explosive (CBE) threats on-site and allow for immediate responders to mitigate spread, risk, and loss. The key to an effective reconnaissance mission is a unified detection technology that analyzes potential threats in real time. In addition to reviewing the current state of the art in the field, this review illustrates the practicality of colorimetric arrays composed of sensors that change colors in the presence of analytes. This review also describes an outlook toward future technologies, and describes how they could possibly be used in areas such as war zones to detect and identify hazardous substances.