Fusion of Color Correction and HSV Segmentation Techniques for Automated Segmentation of Acute Lymphoblastic Leukemia.

This article presents an enhanced segmentation methodology for the accurate detection of acute lymphoblastic leukemia (ALL) in blood smear images. The proposed approach integrates color correction techniques with HSV color space segmentation to improve white blood cell analysis. Our method addresses common challenges in microscopic image processing, including sensor nonlinearity, uneven illumination, and color distortions. The key objectives of this study are to develop a robust preprocessing pipeline that normalizes blood smear images for consistent analysis, implement an HSV-based segmentation technique optimized for leukocyte detection, and validate the method's effectiveness across various ALL subtypes using clinical samples. The proposed technique was evaluated using real-world blood smear samples from ALL patients. Quantitative analysis demonstrates significant improvements in segmentation accuracy compared to traditional methods. Our approach shows strong capability in reliably detecting and segmenting ALL subtypes, offering the potential for enhanced diagnostic support in clinical settings.

Novel EBT3 film calibration using an integrated HSV color space analysis.

PURPOSE: Radiochromic EBT3 films are commonly used as dosimeter for clinical practice and research on radiotherapy. In principle, they are associated with a flatbed scanner to determine the optical density change, which can be correlated to the absorbed dose after calibration. Several approaches have been proposed to reduce the uncertainties during acquisition and to compensate the lighting inhomogeneities, thus improving the dose measurement. Those works have shown that good accuracy can be achieved for absorbed dose using EBT3 films, at the expense of complex data processing and time-consuming acquisition protocols. METHOD: We introduce the new method to determine the calibration curve based on the HSV color space analysis, which provides robustness and invariance to illumination changes. RESULTS: With this new approach, it allows to compute the calibration curve by performing only a single scan of film pieces regardless either the lateral positions or control points on the scanner bed. Using the hue channel in HSV color space, we prove that the dose can be accurately reconstructed with a much simpler protocol than when using RGB channels with blank scans rectification. Our HSV approach includes comparative gamma index for conventional film analysis. It achieves a gamma index (3%/3mm) over 99% when comparing measurement and AAA computation for a modulated beam. CONCLUSION: Compared to most existing approaches, our approach does not rely on complex mathematical reconstructions or additional scans. Instead, it uses another color model representation to rectify the scanner response, coping the dose measurement homogeneity problem over the scanner window. It facilitates the overall scan calibration to be much simpler, save time, and less manipulations, which also decreases the risk of human error.

Paper-based multicolor sensor for on-site quantitative detection of organophosphate pesticides based on acetylcholinesterase-mediated paper-based Au3+-etching of gold nanobipyramids and CIELab color space.

On-site quantitative detection of organophosphorus pesticides (OPs) is crucial for safeguarding food and public safety. This study presents a novel acetylcholinesterase (AChE)-mediated paper-based Au3+-etching of gold nanobipyramids (AuNBPs) system. The system employs a long-term storable AuNBPs-deposited nylon membrane embedded within a portable and temperature-controlled paper-based analytical device. This system, coupled with a colorimeter-based quantitative method, enables the development of a practical paper-based multicolor sensor (PMS) for on-site quantitative detection of three common OPs (paraoxon, dichlorvos, and trichlorfon). In the absence of OPs, AChE hydrolyzes acetylthiocholine to thiocholine, which reduces Au3+ to Au+. The presence of OPs inhibits AChE activity, thereby preserving Au3+ to etch AuNBPs on nylon membranes, accompanied by multicolor changes. These color changes can be simply quantified by measuring the a∗ parameter of the CIELab color space using a portable colorimeter. Under optimal conditions, the PMS displayed eight OPs-corresponding color changes with a minimum detectable concentration of 1.0-10 µg/L (visual observation) and limits of detection of 0.8-7.2 µg/L (colorimeter) and 0.2-3.4 µg/L (UV-vis spectrometry). The PMS successfully determined the OPs in vegetable and rice samples with recoveries of 89.0-109 % and RSDs (n = 5) of <6 %. These results were consistent with those obtained using the HPLC-MS method. The PMS demonstrates excellent portability, AuNBPs stability, detection sensitivity, and reproducibility, making it a promising tool for the on-site quantitative detection of OPs residues in food. Furthermore, the paper-based etching system coupled with the colorimeter-based quantitative method provides a valuable reference to develop practical PMSs for various targets in diverse fields.

A Cross-Sectional Observational Study of Gingival Color Patterns in the South Indian Population.

BACKGROUND: Gingival aesthetics or pink aesthetics requires a prosthodontic approach to ensure an appealing smile with an optimal muco-gingival appearance by the use of colored materials with gingival shades to match adjacent soft tissues. However, the selection of this adhesive gingival-colored material becomes complex owing to the wide range of gingival color guides and shade tabs currently available on the market. AIM: The study aims to assess the variation in gingival color between two specific regions on the anterior gingival surface through the use of a digital color assessment method. Furthermore, the study seeks to investigate the potential requirements for an innovative soft tissue dual shade guide system. METHODOLOGY: Fifteen participants were examined with an external light source set up in a 45-degree optical configuration. The Frontal view intraoral photographs were taken with a digital Canon 70D camera using a cheek retractor. The photo was white balanced using the color sorter tool in the software (Adobe Photoshop CS6®), and the second quadrant was cropped, two regions were selected (free gingival margin and marginal gingiva) and used for all samples for standardization. The color data were represented in terms of L*, a*, and b* coordinate axes values following the CIELAB color system. The recorded color coordinates were then examined using SPSS software, version 24 (IBM Corp., Armonk, NY). RESULTS: The mean and standard deviation of the coordinate axes were as follows: for L1, 52.33 ± 12.92; for a1, 30.06 ± 4.81; for b1, 18.00 ± 3.89; for L2, 44.53 ± 11.01; for a2, 36.13 ± 7.92; and for b2, 18.26 ± 6.70. Statistically significant differences were found between the L*, a*, and b* color coordinates with a color difference (ΔE) beyond the clinical acceptance (ΔE > 3.7) threshold of ΔE = 4.88, mainly for a* values. CONCLUSIONS: Within the limitations of this study, significant color differences were observed between the selected regions. The a* coordinate was found to be statistically significant (+6.07), indicating a shift towards a lighter shade of redness (+a) in the color-opponent dimensions of redness-greenness within the CIELAB color space system.

ResNeXt-CC: a novel network based on cross-layer deep-feature fusion for white blood cell classification.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5-20.43% accuracy, 3.6-23.56% F1-score, 3.5-25.71% AUROC and 8.1-36.98% specificity, respectively.

"Shock kidney-like Appearance": Objective evaluation of renal color changes in hemorrhagic shock deaths.

Severe bleeding due to various traumatic injuries can cause hemorrhagic shock, which is difficult to diagnose using forensic medicine. Therefore, we defined the difference in color between the renal cortex and medulla observed in hemorrhagic shock deaths as "shock kidney-like appearance (SKLA)" and digitally analyzed the color difference with a digital camera and color analysis software. The aim of this study was to develop and evaluate a method for objectively determining SKLA and improve the accuracy of forensic diagnosis. We examined the kidneys of 122 cases (83 males and 39 females; average age, 64.8 years) autopsied at our facility. Using Image J, we analyzed the color of the cortex and medulla from photographs of bisected kidneys. We defined the color difference between the cortex and medulla in the L*a*b* color space as cortical-medullary color difference and performed a comparative analysis between the hemorrhage and control groups. Significant differences were observed in ΔL* and Δa* values between the two groups (p < 0.05 and p < 0.001, respectively). Analysis of Δa* values showed that the cortex was less reddish than the medulla in the hemorrhage group. The cutoff value for determining SKLA was set at Δa* = -1.33 (sensitivity, 0.79; specificity, 0.81; AUC, 0.859). Traditional evaluations of color rely on subjective assessments, which raise issues of reliability and reproducibility. This study successfully overcame the limitations of subjective evaluation by objectively assessing cortical-medullary color difference in the kidneys. Our results represent an important step towards improving the objectivity of color evaluations.

CropGCNN: color space-based crop disease classification using group convolutional neural network.

Classifying images is one of the most important tasks in computer vision. Recently, the best performance for image classification tasks has been shown by networks that are both deep and well-connected. These days, most datasets are made up of a fixed number of color images. The input images are taken in red green blue (RGB) format and classified without any changes being made to the original. It is observed that color spaces (basically changing original RGB images) have a major impact on classification accuracy, and we delve into the significance of color spaces. Moreover, datasets with a highly variable number of classes, such as the PlantVillage dataset utilizing a model that incorporates numerous color spaces inside the same model, achieve great levels of accuracy, and different classes of images are better represented in different color spaces. Furthermore, we demonstrate that this type of model, in which the input is preprocessed into many color spaces simultaneously, requires significantly fewer parameters to achieve high accuracy for classification. The proposed model basically takes an RGB image as input, turns it into seven separate color spaces at once, and then feeds each of those color spaces into its own Convolutional Neural Network (CNN) model. To lessen the load on the computer and the number of hyperparameters needed, we employ group convolutional layers in the proposed CNN model. We achieve substantial gains over the present state-of-the-art methods for the classification of crop disease.

Citric acid treatment inhibits fading of sorghum (Sorghum bicolor) by modulating the accumulation of flavonoids.

Sorghum seeds can discolor during storage. Treatment of seeds with citric acid improves sensory quality and antioxidant activity. This study compared the differences in phenotypic and antioxidant activity between citric acid-treated and water-treated sorghum seeds. The study used transcriptomics and metabolomics approaches to investigate the regulatory mechanisms. The ∆a, ∆b and ∆l values of citric acid-treated sorghum seeds significantly increased after 6 months of storage. The SOD, POD and CAT enzyme activities of the citric acid-treated group were 1.94, 1.91 and 2.45 times higher than those of the control, respectively. The joint transcriptome and metabolome analysis showed that the citric acid-induced changes were mainly focused on the flavonoid biosynthetic pathway. Citric acid treatment up-regulated CHS, ANR, MYB and bHLH genes and promoted flavonoid accumulation. In conclusion, citric acid treatment promotes flavonoid accumulation, delays sorghum seed discoloration, and enhances antioxidant activity and storage life.

The effect of heat treatment on zircon color and its enhancement mechanism.

The effect of heat treatment on zircon color was studied from a new perspective of chromaticity of gemstones. The mechanism behind zircon color enhancement was investigated through the combination of infrared spectroscopy, X-ray diffraction, Raman spectroscopy, and UV-vis spectroscopy. The study reveals that the color of zircon has no significant correlation to the degree of metamorphism, which decreases as heating temperature rises. Type I and Type II defects in zircon are characterized by the deletion of an oxygen atom at the nearest site to Y3+ occupied by the Zr4+ and the loss of electrons at the next nearest oxygen atom to Y3+, respectively. These defects lead to broad absorption bands in the UV-vis spectra ranging from 340 nm and 500 nm, respectively. Additionally, there is a correlation between the color of zircon and the strength of the relationship between the two absorption bands. After undergoing heat treatment, the first defects to be repaired were type II, followed by type I. Subsequently, the zircon appeared colorless after both types of defects had been corrected.

Impact of Color Space and Color Resolution on Vehicle Recognition Models.

In this study, we analyze both linear and nonlinear color mappings by training on versions of a curated dataset collected in a controlled campus environment. We experiment with color space and color resolution to assess model performance in vehicle recognition tasks. Color encodings can be designed in principle to highlight certain vehicle characteristics or compensate for lighting differences when assessing potential matches to previously encountered objects. The dataset used in this work includes imagery gathered under diverse environmental conditions, including daytime and nighttime lighting. Experimental results inform expectations for possible improvements with automatic color space selection through feature learning. Moreover, we find there is only a gradual decrease in model performance with degraded color resolution, which suggests the need for simplified data collection and processing. By focusing on the most critical features, we could see improved model generalization and robustness, as the model becomes less prone to overfitting to noise or irrelevant details in the data. Such a reduction in resolution will lower computational complexity, leading to quicker training and inference times.

Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer.

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.

Integrating Image Analysis and Machine Learning for Moisture Prediction and Appearance Quality Evaluation: A Case Study of Kiwifruit Drying Pretreatment.

The appearance of dried fruit clearly influences the consumer's perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer.

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

Evaluation of semi-quantitative colorimetric assays based on loop-mediated isothermal amplification indicators by using image analysis.

Colorimetric assays are some of the most convenient detection methods, creating discoloration in solutions that is visible to the naked eye. However, colorimetric reactions have some limitations regarding the variability in the color perception of individuals caused by factors such as color blindness, experience, and gender. Semi-quantitative chromatic analysis has been used as an alternative method to differentiate between two colors and accurately interpret the results from a numerical value, with high confidence. Therefore, we developed and determined the optimal model between Red-Green-Blue (RGB) and Commission Internationale de l'Eclairage (CIE) Lab color spaces to establish a semi-quantitative colorimetric assay via image analysis by the ImageJ program for loop-mediated isothermal amplification (LAMP), using the dyes malachite green and phenol red. The semi-quantitative colorimetric assays using the color distance values of the CIELab color space (ΔEab) were more suitable than those using the RGB color space (ΔERGB) for chromatic differentiation between positive and negative reactions in both indicator dyes, demonstrating the feasibility of this assay to be applied in the detection of a wide range of pathogens and infectious diseases.

Selective Partitioned Regression for Accurate Kidney Health Monitoring.

The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

The Evolution of Multiple Color Mechanisms Is Correlated with Diversification in Sunbirds (Nectariniidae).

How and why certain groups become speciose is a key question in evolutionary biology. Novel traits that enable diversification by opening new ecological niches are likely important mechanisms. However, ornamental traits can also promote diversification by opening up novel sensory niches and thereby creating novel inter-specific interactions. More specifically, ornamental colors may enable more precise and/or easier species recognition and may act as key innovations by increasing the number of species-specific patterns and promoting diversification. While the influence of coloration on diversification is well-studied, the influence of the mechanisms that produce those colors (e.g., pigmentary, nanostructural) is less so, even though the ontogeny and evolution of these mechanisms differ. We estimated a new phylogenetic tree for 121 sunbird species and combined color data of 106 species with a range of phylogenetic tools to test the hypothesis that the evolution of novel color mechanisms increases diversification in sunbirds, one of the most colorful bird clades. Results suggest that: (1) the evolution of novel color mechanisms expands the visual sensory niche, increasing the number of achievable colors, (2) structural coloration diverges more readily across the body than pigment-based coloration, enabling an increase in color complexity, (3) novel color mechanisms might minimize trade-offs between natural and sexual selection such that color can function both as camouflage and conspicuous signal, and (4) despite structural colors being more colorful and mobile, only melanin-based coloration is positively correlated with net diversification. Together, these findings explain why color distances increase with an increasing number of sympatric species, even though packing of color space predicts otherwise.

An Efficient Attentional Image Dehazing Deep Network Using Two Color Space (ADMC2-net).

Image dehazing has become a crucial prerequisite for most outdoor computer applications. The majority of existing dehazing models can achieve the haze removal problem. However, they fail to preserve colors and fine details. Addressing this problem, we introduce a novel high-performing attention-based dehazing model (ADMC2-net)that successfully incorporates both RGB and HSV color spaces to maintain color properties. This model consists of two parallel densely connected sub-models (RGB and HSV) followed by a new efficient attention module. This attention module comprises pixel-attention and channel-attention mechanisms to get more haze-relevant features. Experimental results analyses can validate that our proposed model (ADMC2-net) can achieve superior results on synthetic and real-world datasets and outperform most of state-of-the-art methods.

Chroma-dependence of CIEDE2000 acceptability thresholds for dentistry.

OBJECTIVES: Determine visual 50:50% color difference acceptability thresholds (AT) for regions of the dental color space with varying chromaticity. METHODS: A 40-observer panel belonging to two different groups (dentists and laypersons) evaluated 144 dental resin composites pairs (divided in three different sets of 48 pairs according to chroma value: Low Chroma (LC), Medium Chroma (MC) and High Chroma (HC) placed 40 cm away and inside of a viewing cabinet (D65 Standard light source; diffuse/0° geometry). A Takagi-Sugeno-Kang (TSK) fuzzy approximation was used for fitting the data points and calculate the 50:50% acceptability thresholds in CIEDE2000. A paired t-test was used to evaluate the statistical significance between thresholds differences and Bonferroni correction was applied. RESULTS: The CIEDE2000 50:50% AT were ∆E00 = 2.84, ∆E00 = 2.31 and ∆E00 = 1.80 for LC, MC and HC sets of sample pairs, respectively. The 50:50% AT values were statistically significant between the different sets of sample pairs, as well as the 50:50% AT values obtained for different observer groups. CONCLUSIONS: 50:50% CIEDE2000 acceptability thresholds for dentistry are significantly different depending on the chromaticity of the samples. Observers show higher acceptability for more achromatic samples (low chroma value) than for more chromatic samples. CLINICAL SIGNIFICANCE: The difference in the AT for distinct regions of the dental color space can assist professionals as a quality control tool to assess clinical performance and interpret visual and instrumental findings in clinical dentistry, dental research, and subsequent standardization processes.

Refine localizations of functional variants affecting eggshell color of Lueyang black-boned chicken in the SLCO1B3.

Table eggs with color-uniformity shell are visually attractive for consumers. Lueyang black-boned chicken (LBC) lays colorful eggs, which is undesirable for sale of table eggs, but provides a segregating population for mapping functional variants affecting eggshell color. SLCO1B3 was identified as the causative gene for blue eggs in the Dongxiang and Araucana chickens. The aim of this study is to map functional variants associated with chicken eggshell color in the SLCO1B3. Eggshell color of LBC (n = 383) was measured using the L*a*b color space. SLCO1B3 was resequencing using a subset (n = 30) of 383 samples. Linkage disequilibrium among 139 SNP was analyzed. Association of 16 SNP in the SLCO1B3 and 8 in CPOX, ALAS1, and ABCG2 genes with L*a*b were tested by a polygenic model (LMM) and a polygenic/oligogenic mixed model (BSLMM). Chromatin state annotations were retrieved from the UCSC database. Effect of SLCO1B3 variants distributed in mapping and upstream 1.6-kb regions on promoter activities were analyzed using dual-luciferase reporter assay. One hundred and thirty-nine variants maintained low linkage disequilibrium with 80% of r2 less than 0.226. Fifteen SLCO1B3 variants were significantly associated with a*, of which 1B3_SNP108 was showed the strongest association and the largest effect on a*. In the BSLMM, 1B3_SNP108 alone appeared in the Markov chain Monte Carlo as major variants in 100% of posterior inclusion probability. None of variants in CPOX, ALAS1, and ABCG2 were significantly associated with color indexes except that 2 ALAS1 variants were associated with L*. 1B3_SNP108 distributes in the Intron4 where 6 active enhancers and 1 ATAC island were enriched. However, 1B3_SNP108-containing constructs showed negligible activities in the reporter assay. No significant differences of activities between haplotypes were found for five 5'-deleted promoter constructs. The data recognizes 1B3_SNP108 as a valuable marker for breeding of eggshell color. Functional variants are localized in the region adjacent to the 1B3_SNP108 due to low linkage disequilibrium in the LBC. Our findings extend the role of SLCO1B3 from a causative gene for blue eggs to a major regulator driving continuous variation of LBC eggshell color.