The textile industry has caused severe water pollution by using many toxic chemicals for producing fabric dyes. In response to this problem, indigoidine has attracted attention as an alternative natural blue dye, but it is necessary to achieve a high-level production to compete with synthetic blue dyes. Here we report a metabolically engineered Corynebacterium glutamicum capable of producing indigoidine to a high concentration with high productivity. First, the blue-pigment indigoidine synthetase (bpsA) gene from Streptomyces lavendulae was expressed in C. glutamicum, which carries strong fluxes toward L-glutamate, a precursor of indigoidine. Production performance of this base strain, already producing 7.3 ± 0.3 g/L indigoidine from the flask, was further improved by streamlining the intracellular supply of the precursors L-glutamate and L-glutamine, strengthening the phosphotransferase system-independent glucose uptake system, channeling carbon fluxes from glycolysis to the tricarboxylic acid (TCA) cycle, and minimizing byproducts formation. Fedbatch fermentation of the final strain BIRU11 produced 49.30 g/L indigoidine with a productivity of 0.96 g/L/h, the highest titer and productivity to date. Finally, indigoidine from the fed-batch fermentation of the BIRU11 strain was used to dye white cotton fabrics to examine its color and performance. This study demonstrates the potential of producing fabric dyes in a sustainable manner by using a metabolically engineered bacterium.

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Although it is widely known that the foundation color gradually changes over time after applying the makeup, no scientific clues are available, thereby remaining the “darkening” phenomenon equivocal. This study measured the discoloration of liquid foundations and suggested indices to describe the discoloration over time. Four liquid foundations were applied to opacity charts with a thickness of 100 μm, the surface color was obtained using a non‐contact spectrophotometer, and reported in the CIE 1976 L*a*b* color space. A long‐term measurement for 24 hours was conducted in that the initial 2 hours were measured with a 60‐second interval. The discoloration was observed as the decrease of both lightness and hue angle (h* of CIE L*C*h*) of liquid foundation over time. Different degrees and speed of color change were observed across the four foundations. Also, the discoloration was tested on three healthy human skins in 2‐hour intervals in repeated measurements. Positive correlations in changes of lightness and hue were found among the opacity charts and human skin. Furthermore, the study suggests indices, “Dt” and “TΔE” to describe the quantitative amount of discoloration over time and time duration of discoloration, respectively.

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It is difficult to describe facial skin color through a solid color as it varies from region to region. In this article, the authors utilized image analysis to identify the facial color representative region. A total of 1052 female images from Humanae project were selected as a solid color was generated for each image as their representative skin colors by the photographer. Using the open CV-based libraries, such as EOS of Surrey Face Models and DeepFace, 3448 facial landmarks together with gender and race information were detected. For an illustrative and intuitive analysis, they then re-defined 27 visually important sub-regions to cluster the landmarks. The 27 sub-region colors for each image were finally derived and recorded in L∗, a∗, and b∗. By estimating the color difference among representative color and 27 sub-regions, we discovered that sub-regions of below lips (low Labial) and central cheeks (upper Buccal) were the most representative regions across four major ethnicity groups. In future study, the methodology is expected to be applied for more image sources.