Synthesis of heteroaromatic natural products

The synthesis of biologically active natural products has been used for the discovery of new drugs. Organic synthesis is designed for a target molecule like a novel compound by selecting optimal reactions from optimal starting materials. Each reaction and each step of a synthesis should give a good yield for the product with little work. In this thesis, we explored both total synthesis and methodology of several natural products and analogs, especially heteroaromatic natural compounds. Chapter 1 describes an efficient synthesis of 2-substituted and 2,3-disubstituted indoles via a two-step approach in one pot involving a six-electron ring closure. Chapter 2 describes the direct synthesis of neocryptolepine in four steps in two pots. Chapter 3 is about synthesis studies towards the unique κ opioid receptor agonist salvinorin A. Chapter 4 describes a direct approach to the synthesis of methyllycaconitine, one of the diterpenoid alkaloids

Metabolism and function of hepatitis B virus cccDNA: Implications for the development of cccDNA-targeting antiviral therapeutics.

Persistent hepatitis B virus (HBV) infection relies on the stable maintenance and proper functioning of a nuclear episomal form of the viral genome called covalently closed circular (ccc) DNA. One of the major reasons for the failure of currently available antiviral therapeutics to achieve a cure of chronic HBV infection is their inability to eradicate or inactivate cccDNA. In this review article, we summarize our current understanding of cccDNA metabolism in hepatocytes and the modulation of cccDNA by host pathophysiological and immunological cues. Perspectives on the future investigation of cccDNA biology, as well as strategies and progress in therapeutic elimination and/or transcriptional silencing of cccDNA through rational design and phenotypic screenings, are also discussed. This article forms part of a symposium in Antiviral Research on “An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B.

Actively controlling the topological transition of dispersion based on electrically controllable metamaterials

Topological transition of the iso-frequency contour (IFC) from a closed ellipsoid to an open hyperboloid, will provide unique capabilities for controlling the propagation of light. However, the ability to actively tune these effects remains elusive and the related experimental observations are highly desirable. Here, tunable electric IFC in periodic structure which is composed of graphene/dielectric multilayers is investigated by tuning the chemical potential of graphene layer. Specially, we present the actively controlled transportation in two kinds of anisotropic zero-index media containing PEC/PMC impurities. At last, by adding variable capacitance diodes into two-dimensional transmission-line system, we present the experimental demonstration of the actively controlled magnetic topological transition of dispersion based on electrically controllable metamaterials. With the increase of voltage, we measure the different emission patterns from a point source inside the structure and observe the phase-transition process of IFCs. The realization of actively tuned topological transition will opens up a new avenue in the dynamical control of metamaterials.Comment: 21 pages,8 figure

BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation