Seed transmission of turnip yellow mosaic virus in winter turnip and winter oilseed rapes

This is the first record of seed transmission of turnip yellow mosaic virus (TYMV) in oilseed and turnip rapes. The seed transmission of TYMV in a naturally infected winter turnip rape (Brassica napus var. silvestris) cultivar Perko PVH was investigated. By ELISA 1.6%, 3.2% and 8.3% seed transmission of the virus was found in seed of plants from three localities. The proportion of infected seeds produced by artificially infected plants of winter oilseed rape (Brassica napus ssp. oleifera) and winter turnip rape cultivars was determined. The virus transmission rate, expressed as the proportion of virus-infected plants which germinated from the seed was for the oilseed rape cvs Jet Neuf 0.1%, Solida 0.4%, Silesia 0.8%, Darmor 1.2%, SL-507 0.2%, SL-509 0.0% and for the winter turnip rape cv. Perko 1.5%. ELISA cannot be used in direct tests on bulk seed lots to estimate proportion of infected seed, but must be used on germinated seedlings.     

Aspects of hadrosaurian cranial anatomy

Supraorbital bones in Saurolophus angustirostris are described, and their presence in all hadrosaurs is suggested. Frontal-nasal and premaxillar-nasal fontanellae are distinguished in hadrosaurs; their presence is explained as connected with growth and considered to he responsible for the variability of crest structures. New data indicating the presence of a cartilaginous diverticulum nasi within the circumnarial depression in Saurobphus ongustirostris are presented. A physiological (respiratory and/or thermoregulatory) function of the nasal diverticulum is proposed.     

European checkerspots (Melitaeini: Lepidoptera,Nymphalidae) are not aposematic – the point of view of great tits (Parus major)

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Multiplex panels of polymorphic microsatellite loci for two cryptic bat species of the genus Pipistrellus,developed by cross‐species amplification within the family Vespertilionidae

The paper presents multiplex panels of polymorphic microsatellites for two closely related cryptic species Pipistrellus pipistrellus and Pipistrellus pygmaeus. We tested the cross‐species amplification of 34 microsatellite loci, originally developed for five vespertilionid bat species. Ten and nine polymorphic loci in P. pipistrellus (mean number of alleles per locus = 10.5) and P. pygmaeus (8.1), respectively, in three multiplex polymerase chain reactions per species were amplified. All loci can be analysed in a single fragment analysis and can be used as markers to the study of evolution and the ecology of structured populations of socially living bats.     

The "Goldilocks Effect" in Cystic Fibrosis: identification of a lung phenotype in the <Emphasis Type="Italic">cftr</Emphasis> knockout and heterozygous mouse

Background  

Cystic Fibrosis is a pleiotropic disease in humans with primary morbidity and mortality associated with a lung disease phenotype. However, knockout in the mouse of cftr, the gene whose mutant alleles are responsible for cystic fibrosis, has previously failed to produce a readily, quantifiable lung phenotype.     

Living polypeptides

Block copolypeptides, which combine the self-assembly of block copolymers and the highly ordered 3D structures of proteins, are potential candidates for novel supramolecular structures and biotech applications, such as biosensors, tissue engineering, and selective drug delivery. The synthesis of model block copolypeptides through living nucleophilic/basic polymerization of alpha-amino acid N-carboxyanhydrides (NCAs) has been a challenge for more than fifty years, most probably due to traces of impurities in the system. This problem has been overcome, using high vacuum techniques in order to create and maintain the conditions necessary for the living polymerization of NCAs with primary amines. This method is a general one and opens avenues leading to novel, well-defined polypeptides with various architectures.     

Expanding the understanding of biases in development of clinical-grade molecular signatures: a case study in acute respiratory viral infections

Background

The promise of modern personalized medicine is to use molecular and clinical information to better diagnose, manage, and treat disease, on an individual patient basis. These functions are predominantly enabled by molecular signatures, which are computational models for predicting phenotypes and other responses of interest from high-throughput assay data. Data-analytics is a central component of molecular signature development and can jeopardize the entire process if conducted incorrectly. While exploratory data analysis may tolerate suboptimal protocols, clinical-grade molecular signatures are subject to vastly stricter requirements. Closing the gap between standards for exploratory versus clinically successful molecular signatures entails a thorough understanding of possible biases in the data analysis phase and developing strategies to avoid them.

Methodology and Principal Findings

Using a recently introduced data-analytic protocol as a case study, we provide an in-depth examination of the poorly studied biases of the data-analytic protocols related to signature multiplicity, biomarker redundancy, data preprocessing, and validation of signature reproducibility. The methodology and results presented in this work are aimed at expanding the understanding of these data-analytic biases that affect development of clinically robust molecular signatures.

Conclusions and Significance

Several recommendations follow from the current study. First, all molecular signatures of a phenotype should be extracted to the extent possible, in order to provide comprehensive and accurate grounds for understanding disease pathogenesis. Second, redundant genes should generally be removed from final signatures to facilitate reproducibility and decrease manufacturing costs. Third, data preprocessing procedures should be designed so as not to bias biomarker selection. Finally, molecular signatures developed and applied on different phenotypes and populations of patients should be treated with great caution.     

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

MOTIVATION: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types. RESULTS: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets. AVAILABILITY: The software system GEMS is available for download from http://www.gems-system.org for non-commercial use. CONTACT: alexander.statnikov@vanderbilt.edu.