Dental materials are mainly tested in vitro, so laboratory conditions must reproduce the oral environment to ensure the validity of their results. This study aimed to evaluate the influence of backgrounds on the color of gingiva-colored resin-based composites (GCRBC). Three discs of each of 20 shades of GCRBCs and each thickness (1 and 2 mm) were prepared. Diffuse reflectance was measured on-air and over three natural teeth (0M3/B1, 3M3/B3, and 5M3/B4 shades of Vita 3D Master/Vita Classical guides, respectively) using a calibrated spectroradiometer, CIE D65 illuminant and the CIE 45°/0° geometry. CIEDE2000 color difference formula and its 50:50% perceptibility and acceptability thresholds have been used to calculate and interpret the results. It can be stated that the background influences the color of all GCRBCs tested, although the effect is more pronounced for 1 mm thick samples. L*, a* and b* coordinates values of GCRBCs on air were significantly different from those obtained on natural teeth backgrounds, and the total color differences were greater than the acceptability thresholds. Since GCRBCs are placed on a dental substrate in clinical conditions, it is not advisable to perform color measurements of GCRBCs on-air because of the high color differences found. This recommendation is especially relevant for thin specimens.
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http://dx.doi.org/10.3390/ma15103712 | DOI Listing |
JMIR Med Inform
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Institute of History and Ethics in Medicine, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.
View Article and Find Full Text PDFPLoS One
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Nanjing University of Information Science and Technology, Nanjing, China.
The forensic examination of AIGC(Artificial Intelligence Generated Content) faces poses a contemporary challenge within the realm of color image forensics. A myriad of artificially generated faces by AIGC encompasses both global and local manipulations. While there has been noteworthy progress in the forensic scrutiny of fake faces, current research primarily focuses on the isolated detection of globally and locally manipulated fake faces, thus lacking a universally effective detection methodology.
View Article and Find Full Text PDFAnnu Rev Entomol
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Department of Biology and Molecular Sciences Research Center, University of Puerto Rico, San Juan, Puerto Rico.
Novel traits in the order Lepidoptera include prolegs in the abdomen of larvae, scales, and eyespot and band color patterns in the wings of adults. We review recent work that investigates the developmental origin and diversification of these four traits from a gene-regulatory network (GRN) perspective. While prolegs and eyespots appear to derive from distinct ancestral GRNs co-opted to novel body regions, scales derive from in situ modifications of a sensory bristle GRN.
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