Polycyclic polyprenylated acylphloroglucinols (PPAPs) are a class of hybrid natural products sharing the mevalonate/methylerythritol phosphate and polyketide biosynthetic pathways and showing considerable structure and bioactivity diversity. This review discusses the progress of research into the chemistry and biological activity of 421 natural PPAPs in the past 11 years as well as in-depth studies of biological activities and total synthesis of some PPAPs isolated before 2006. We created an online database of all PPAPs known to date at http://www.chem.uky.edu/research/grossman/PPAPs . Two subclasses of biosynthetically related metabolites, spirocyclic PPAPs with octahydrospiro[cyclohexan-1,5'-indene]-2,4,6-trione core and complicated PPAPs produced by intramolecular [4 + 2] cycloadditions of MPAPs, are brought into the PPAP family. Some PPAPs' relative or absolute configurations are reassigned or critically discussed, and the confusing trivial names in PPAPs investigations are clarified. Pharmacologic studies have revealed a new molecular mechanism whereby hyperforin and its derivatives regulate neurotransmitter levels by activating TRPC6 as well as the antitumor mechanism of garcinol and its analogues. The antineoplastic potential of some type B PPAPs such as oblongifolin C and guttiferone K has increased significantly. As a result of the recent appearances of innovative synthetic methods and strategies, the total syntheses of 22 natural PPAPs including hyperforin, garcinol, and plukenetione A have been accomplished.

Abstract

Abstract-In many applications, we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples. Often the pairwise similarities (which are also called affinities) are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples. As observed in several recent approaches, more reliable similarities can be obtained if the original similarities are diffused in the context of other data points, where the context of each point is a set of points most similar to it. Compared to the existing methods, our approach differs in two main aspects. First, instead of diffusing the similarity information on the original graph, we propose to utilize the tensor product graph (TPG) obtained by the tensor product of the original graph with itself. Since TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities. However, it comes at the price of higher order computational complexity and storage requirement. The key contribution of the proposed approach is that the information propagation on TPG can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph. We prove that a graph diffusion process on TPG is equivalent to a novel iterative algorithm on the original graph, which is guaranteed to converge. After its convergence we obtain new edge weights that can be interpreted as new, learned affinities. We stress that the affinities are learned in an unsupervised setting. We illustrate the benefits of the proposed approach for data manifolds composed of shapes, images, and image patches on two very different tasks of image retrieval and image segmentation. With learned affinities, we achieve the bull's eye retrieval score of 99.99 percent on the MPEG-7 shape dataset, which is much higher than the state-of-the-art algorithms. When the data points are image patches, the NCut with the learned affinities not only significantly outperforms the NCut with the original affinities, but it also outperforms state-of-the-art image segmentation methods.

Shape retrieval/matching is a very important topic in computer vision. The recent progress in this domain has been mostly driven by designing smart features for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape matching algorithms. It learns a better metric through graph transduction by propagating the model through existing shapes, in a way similar to computing geodesics in shape manifold. However, the proposed method does not require learning the shape manifold explicitly and it does not require knowing any class labels of existing shapes. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91% on the MPEG-7 data set, which is the highest ever reported in the literature.