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https://www.selleckchem.com/products/azd-1208.html However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola-radish (crop-weed) discrimination using a subset extracted from the "bccr-segset" dataset, and for the "mixed-plants" dataset. Moreover, the real-time weed-plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models.This study is aimed to determine the distribution, diversity and bioprospecting aspects of marine pigmented bacteria (MPB) isolated from pristine Andaman Islands, India. A total of 180 samples including seawater, sediment, marine plants, invertebrates, and vertebrates were collected and investigated for isolating pigmented bacteria. Results revealed that sediment, invertebrates, and seawater samples were colonized with a greater number of pigmented bacteria pertains to 27.9 × 103 CFU/mL, 24.1 × 103 CFU/mL and 6.7 × 103 CFU/mL respectively. Orange (21.6 × 103 CFU/mL) and red (8.0 × 103 CFU/mL) MPB were predominant than other pigmented bacteria. Fourteen potential MPB were selected based on their intense pigmentation and tested for bioactive nature and food colorant applications. Out of 14, two red pigmented strains BSE6.1 & S2.1 displayed potential multifaceted applications, such as antibacterial, antioxidant, food colorant, and staining properties. Brown pigmented strains CO8 and yellow pigmented strain SQ2.3 have displayed staining properties. Chemical characterization of red pigment using TLC, HP-LC, GC-MS, FT-IR and 1H-NMR analysis rev

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