Condensed tannins in extracts from European medicinal plants and herbal products

Medicinal plant materials are not usually analysed for condensed tannins (CT). Thirty commercially available European medicinal plants and herbal products were screened for CT and fourteen CT samples were analysed in detail. This is also the first comprehensive CT analysis of pine buds, walnut leaves, heather flowers and great water dock roots. Acetone/water extracts contained between 3.2 and 25.9 g CT/100 g of extract, had CT with mean degrees of polymerisation of 2.9 to 13.3, procyanidin/prodelphinidin ratios of 1.6/98.4 to 100/0 and cis/trans flavan-3-ol ratios of 17.7/82.3 to 97.3/2.7. The majority of samples contained procyanidins, four contained A-type linkages (blackthorn flowers, heather flowers, bilberry leaves and cowberry leaves) and one sample also had galloylated procyanidins (great water dock roots).

Proteomic analysis of the medicinal plant Artemisia annua: data from leaf and trichome extracts

This article contains raw and processed data related to research published by Bryant et al. [1]. Data was obtained by MS-based proteomics, analysing trichome-enriched, trichome-depleted and whole leaf samples taken from the medicinal plant Artemisia annua and searching the acquired MS/MS data against a recently published contig database [2] and other genomic and proteomic sequence databases for comparison. The processed data shows that an order-of-magnitude more proteins have been identified from trichome-enriched Artemisia annua samples in comparison to previously published data. Proteins known to have a role in the biosynthesis of artemisinin and other highly abundant proteins were found which imply additional enzymatically driven processes occurring within the trichomes that are significant for the biosynthesis of artemisinin.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.