Catalytic Asymmetric Cascade Dearomatization of Indoles via a Photoinduced Pd-Catalyzed 1,2-Bisfunctionalization of Butadienes.

Photoinduced Pd-catalyzed bisfunctionalization of butadienes with a readily available organic halide and a nucleophile represents an emerging and attractive method to assemble versatile alkenes bearing various functional groups at the allylic position. However, enantiocontrol and/or diastereocontrol in the C-C or C-X bond-formation step have not been solved due to the open-shell process. Herein, we present a cascade asymmetric dearomatization reaction of indoles via photoexcited Pd-catalyzed 1,2-biscarbonfunctionalization of 1,3-butadienes, wherein asymmetric control on both the nucleophile and electrophile part is achieved for the first time in photoinduced bisfunctionalization of butadienes. This method delivers structurally novel chiral spiroindolenines bearing two contiguous stereogenic centers with high diastereomeric ratios (up to >20 : 1 dr) and good to excellent enantiomeric ratios (up to 97 : 3 er). Experimental and computational studies of the mechanism have confirmed a radical pathway involving excited-state palladium catalysis. The alignment and non-covalent interactions between the substrate and the catalyst were found to be essential for stereocontrol.

Open Long-Tailed Recognition In A Dynamic World.

Real world data often exhibits a long-tailed and open-ended (i.e., with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (tail) classes, generalize across the distribution, and acknowledge novelty upon the instances of unseen classes (open classes). We define Open Long-Tailed Recognition++ (OLTR++) as learning from such naturally distributed data and optimizing for the classification accuracy over a balanced test set which includes both known and open classes. OLTR++ handles imbalanced classification, few-shot learning, open-set recognition, and active learning in one integrated algorithm, whereas existing classification approaches often focus only on one or two aspects and deliver poorly over the entire spectrum. The key challenges are: 1) how to share visual knowledge between head and tail classes, 2) how to reduce confusion between tail and open classes, and 3) how to actively explore open classes with learned knowledge. Our algorithm, OLTR++, maps images to a feature space such that visual concepts can relate to each other through a memory association mechanism and a learned metric (dynamic meta-embedding) that both respects the closed world classification of seen classes and acknowledges the novelty of open classes. Additionally, we propose an active learning scheme based on visual memory, which learns to recognize open classes in a data-efficient manner for future expansions. On three large-scale open long-tailed datasets we curated from ImageNet (object-centric), Places (scene-centric), and MS1M (face-centric) data, as well as three standard benchmarks (CIFAR-10-LT, CIFAR-100-LT, and iNaturalist-18), our approach, as a unified framework, consistently demonstrates competitive performance. Notably, our approach also shows strong potential for the active exploration of open classes and the fairness analysis of minority groups.

c-JUN inhibits mTORC2 and glucose uptake to promote self-renewal and obesity.

Metabolic syndrome is associated with obesity, insulin resistance, and the risk of cancer. We tested whether oncogenic transcription factor c-JUN metabolically reprogrammed cells to induce obesity and cancer by reduction of glucose uptake, with promotion of the stemness phenotype leading to malignant transformation. Liquid alcohol, high-cholesterol, fat diet (HCFD), and isocaloric dextrin were fed to wild-type or experimental mice for 12 months to promote hepatocellular carcinoma (HCC). We demonstrated 40% of mice developed liver tumors after chronic HCFD feeding. Disruption of liver-specific c-Jun reduced tumor incidence 4-fold and improved insulin sensitivity. Overexpression of c-JUN downregulated RICTOR transcription, leading to inhibition of the mTORC2/AKT and glycolysis pathways. c-JUN inhibited GLUT1, 2, and 3 transactivation to suppress glucose uptake. Silencing of RICTOR or c-JUN overexpression promoted self-renewal ability. Taken together, c-JUN inhibited mTORC2 via RICTOR downregulation and inhibited glucose uptake via downregulation of glucose intake, leading to self-renewal and obesity.

Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation.

Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concepts. This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. As shown in Fig. 1, the deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images. It also enables diverse image manipulation including random jittering, image morphing, and category transfer. Such highly flexible restoration and manipulation are made possible through relaxing the assumption of existing GAN inversion methods, which tend to fix the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature images, and thus lead to more precise and faithful reconstruction for real images. Code is available at https://github.com/XingangPan/deep-generative-prior.

Discovery of novel benzofuran scaffold as 4-hydroxyphenylpyruvate dioxygenase inhibitors.

BACKGROUND: 4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays an important role in addressing the issue of plant protection research. This study sheds new light on the differences in molecular scaffold from commercialized HPPD inhibitors. RESULTS: The compounds A1-A18 and B1-B27 were synthesized for in vitro and greenhouse experiments. The greenhouse experiment data indicated that compounds B14 and B18 displayed excellent herbicidal activity, which was higher compared to that of mesotrione. In vitro testing indicated that the compounds were HPPD inhibitors. Moreover, molecular simulation results show that the compounds B14, B18, and mesotrione shared similar interplay with surrounding residues, which led to a perfect interaction with the active site of Arabidopsis thaliana HPPD. Based on crop selectivity results, compounds B14 and B18 were selected for maize studies (injury≤10%), indicating its potential for weed control in maize fields. CONCLUSION: These results showed that the pyrazole-benzofuran structure could be used as possible lead compounds for the development of HPPD inhibitors. © 2020 Society of Chemical Industry.

Discovery of novel arylthioacetic acid derivatives as 4-hydroxyphenylpyruvate dioxygenase inhibitors.

BACKGROUND: 4-Hydroxyphenylpyruvate dioxygenase (HPPD) plays an important role in addressing the issue of plant protection research. In a continuing effort to discover novel HPPD inhibitors, we adopted a bioisosterism strategy to design a series of novel arylthioacetic acid scaffold based on the previously discovered aryloxyacetic acid scaffold. This study sheds new light on the discovery of novel HPPD inhibitors. RESULTS: The compounds A1-A30 and B1-B39 were prepared through an efficient synthetic route for in vitro and glasshouse experiments (herbicidal activities, herbicidal activity spectrum, and crop selectivity). Preliminary bioassay results reveal that these derivatives are promising Arabidopsis thaliana HPPD inhibitors, compounds A11 (Ki = 0.021 µmol L-1 ) and B20 (Ki = 0.022 µmol L-1 ), which exhibit similar activities to that of mesotrione (Ki = 0.020 µmol L-1 ). The glasshouse experiments data indicated that compounds B34 displayed excellent herbicidal activity, which was higher compared to that of mesotrione. Moreover, molecular simulation results show that the compounds B20, B34, and mesotrione shared similar interplay with surrounding residues, which led to a perfect interaction with the active site of Arabidopsis thaliana HPPD. Based on herbicidal results, compound B34 was selected for crop selectivity studies (corn injury ≤ 10%), indicating its potential for weed control in corn fields. CONCLUSION: These bioassay results showed that the compound B34 could be used as a possible lead compound for the development of HPPD inhibitors. © 2020 Society of Chemical Industry.