New or rare Madagascar tiger beetles-21. Physodeutera (Microlepidia) propripenis sp. nov., Ph. (M.) marginemaculata (W. Horn) and Ph. (M.) peyrierasi Rivalier (Coleoptera: Cicindelidae).

A new species of the Madagascan endemic genus Physodeutera Lacordaire, 1842 is described from northern Madagascar as Physodeutera (Microlepidia) propripenis sp. nov. The new species is compared to similar Physodeutera (Microlepidia) marginemaculata (W. Horn, 1934) and Physodeutera (Microlepidia) peyrierasi Rivalier, 1967. Apart from a detailed description of the new species, illustrations in colour photographs of its habitus, diagnostic characters and habitat are introduced. Differential diagnoses of the two similar species, as well as illustrations of their habitus and distinguishing characters in colour photographs are presented with references to their redescriptions and illustrations based on type and other relevant specimens in the monograph of the genus (Moravec 2002a). A revised key to the subgenus Microlepidia Rivalier, 1967 is presented in order to supplement the key previously published in the monograph. Essential maps of the distribution of the three species are also given.

New or rare Madagascar tiger beetles-18. Pogonostoma (Pogonostoma) ondravybirali sp. nov., elaborated redescriptions of P. (P.) atrorotundatum, P. (P.) densisculptum and P. (Microstenocera) fabiocassolai (Coleoptera: Cicindelidae).

Pogonostoma (Pogonostoma) ondravybirali sp. nov. from southwestern Madagascar is described as new to science. The new species is placed to a large P. (Pogonostoma) elegans species-group (sensu Moravec 2007) along with seven other mutually similar species which are recognized within this species-group as a species-complex of P. (P.) alluaudi W. Horn, 1898. An elaborated redescription of the most similar P. (P.) atrorotundatum W. Horn, 1934 is given. A revised key to the P. (P.) elegans species-group is presented in order to supplement the key in the monograph of the genus Pogonostoma Klug, 1835 by Moravec (2007). First description of male characters of P. (Pogonostoma) densisculptum Moravec, 2003 (belonging to P. (P.) gibbosum species-group sensu Moravec 2007) and first description of female characters of P. (Microstenocera) fabiocassolai Moravec, 2003 (of the P. (M.) minimum species-group sensu Moravec 2007) are introduced. Type and other specimens of the presented species are illustrated in colour photographs of their habitus, diagnostic characters and variability (including two diagnostic characters of P. (P.) gibbosum Rivalier, 1970). Essential maps of the distribution of the treated species in Madagascar are given.

New or rare Madagascar tiger beetles-17. Description of Pogonostoma (Microstenocera) noheli sp. nov. and redescription of P. (M.) flavomaculatum W. Horn (Coleoptera: Cicindelidae).

Pogonostoma (Microstenocera) noheli sp. nov. from central-eastern Madagascar is described as new to science. The new species was previously (in the monograph of the genus by Moravec 2007) partly confused and included within the externally very similar P. (M.) flavomaculatum W. Horn, 1892, a hitherto rare species based on a holotype only. A rectified redescription of the genuine P. (M.) flavomaculatum also is presented, complemented with external and internal diagnostic characters gained from a great number of recently caught adults. A revised key to Pogonostoma (Microstenocera) pusillum species-group and illustrations of the habitus, diagnostic characters and variability of these two species are provided.

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.

BACKGROUND: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. RESULTS: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

Big data of materials science: critical role of the descriptor.

Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.