The Mesozoic gymnosperms

Features needing elucidation in Mesozoic Cycadales and Bennettitales are given and the Ginkgo—Czekanowskia assemblage is surveyed. Florin's interpretation of the conifer cone scale is discussed in relation to Schweizer's work on Pseudovoltzia. Taxus jurassica Florin is revised and shown to differ generically from Taxus but to share features seen among other Taxaceae: it thus helps to unify the family, but it does not relate the Taxaceae to ordinary conifers. A possible way is offered by which early conifers like Walchia could shift their ovules from a lateral to a terminal position and if this were acceptable, then the class Taxopsida would become unnecessary.     

Die flavonolglykoside von Equisetum silvaticum

Ten glycosides of kaempferol and quercetin, including the hitherto unknown kaempferol and quercetin 3-rutinoside-7-rhamnosides, have been isolated from Equisetum silvaticum L.     

Flavonol methyl ethers from Chrysothamnus viscidiflorus

Two new flavonols, 5,7,4′-trihydroxy-3,6,8,3′-tetramethoxyflavone and quercetagetin 3,5,6,3′-tetramethyl ether, were identified in leaves of Chrysothamnus viscidiflorus. Eight known methyl ethers based on kaempferol, quercetin or their 6-hydroxy derivatives were also detected.     

Systematic implicationsofflavonoid patterns in Haplopappus segregates

Complex flavonoid patterns are correlated with primitive woody taxa and high base chromosome numbers among Haplopappus segregates. Variability among flavonoid complements supports the polyphyletic interpretation of the genus.     

Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets

Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case–control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14 000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.