Molecular cloning and bacterial expression of a cDNA encoding furostanol glycoside 26-O-β-glucosidase of Costus speciosus

AbstractFurostanol glycoside 26-O-β-glucosidase (F26G) purified from Costus speciosus rhizomes was digested with endoproteinase, and several internal peptide fragments were obtained. Degenerate oligonucleotide primers based on amino acid sequences of the peptides were used for amplification of F26G cDNA fragments by applying nested polymerase chain reactions to cDNAs from in vitro cultured plantlets of C. speciosus. Using primers based on sequences of the cDNA fragments, the 5′- and 3′-end clones were isolated by rapid amplification of cDNA ends (RACE) methods. Finally, the entire coding portion of F26G cDNA was cloned by using primers designed from sequences of the RACE products. The deduced amino acid sequence of CSF26G1, the protein encoded by the cloned cDNA, consists of 562 amino acids and shows high homology to a widely distributed family of β-glucosidases (BGA family). Cell-free homogenate of Escherichia coli expressing CSF26G1 cDNA showed β-glucosidase activity specific for cleavage of the C-26 glucosidic bond of furostanol glycosides

SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer

Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the SAN's effectiveness as compared to usual GANs. Furthermore, we also apply SAN to StyleGAN-XL, which leads to state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256$\times$256.Comment: 24 pages with 12 figure

回腸を利用した膀胱形成術後に回腸部分より発生した腺癌の1例

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