Interracial Dialogues in Dixie: Expressing Emotions to Promote Racial Reconciliation

Given the legacy of racial injustice and mistrust that continues to plague race relations in the United States, it is important that citizens of different racial backgrounds come together to share their feelings and thoughts about race issues in order to advance racial reconciliation in their own communities. Saunders (1999) asserts that such dialogues can transform interracial relationships that could inspire the larger community to change itself. This study presents the results of nine interracial focus groups from two dialogues on race relations events held in Dothan, Alabama in 2015 and 2016. Our findings illustrate that many Black respondents displayed both anger and sadness as they provided stories of the institutionalized racism (e.g., racial profiling, educational inequality, residential segregation, etc.) as well as the more personalized racism that they had experienced. White respondents too demonstrated anger and sadness when relating their own experiences of strained race relations. This mixed-methods study also employed API analysis to further strengthen our original qualitative exploration of emotions. We argue that interracial dialogues can hold the potential for racial reconciliation as participants’ stories elucidate our most intransient race problems while also highlighting the emotions that connect discussants through the dialogic process

Feature analysis for classification of trace fluorescent labeled protein crystallization images

Abstract Background Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated. Results Features are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu’s, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu’s thresholding, region features using green percentile G 90 thresholding, region features using green percentile G 99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier. Contributions The feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis