Fossil scales illuminate the early evolution of lepidopterans and structural colors

Lepidopteran scales exhibit remarkably complex ultrastructures, many of which produce structural colors that are the basis for diverse communication strategies. Little is known, however, about the early evolution of lepidopteran scales and their photonic structures. We report scale architectures from Jurassic Lepidoptera from the United Kingdom, Germany, Kazakhstan, and China and from Tarachoptera (a stem group of Amphiesmenoptera) from mid-Cretaceous Burmese amber. The Jurassic lepidopterans exhibit a type 1 bilayer scale vestiture: an upper layer of large fused cover scales and a lower layer of small fused ground scales. This scale arrangement, plus preserved herringbone ornamentation on the cover scale surface, is almost identical to those of some extant Micropterigidae. Critically, the fossil scale ultrastructures have periodicities measuring from 140 to 2000 nm and are therefore capable of scattering visible light, providing the earliest evidence of structural colors in the insect fossil record. Optical modeling confirms that diffraction-related scattering mechanisms dominate the photonic properties of the fossil cover scales, which would have displayed broadband metallic hues as in numerous extant Micropterigidae. The fossil tarachopteran scales exhibit a unique suite of characteristics, including small size, elongate-spatulate shape, ridged ornamentation, and irregular arrangement, providing novel insight into the early evolution of lepidopteran scales. Combined, our results provide the earliest evidence for structural coloration in fossil lepidopterans and support the hypothesis that fused wing scales and the type 1 bilayer covering are groundplan features of the group. Wing scales likely had deep origins in earlier amphiesmenopteran lineages before the appearance of the Lepidoptera

Extensive cross-disciplinary analysis of biological and chemical control of Calanus finmarchicus reproduction during an aldehyde forming diatom bloom in mesocosms

Egg and faecal pellet production and egg hatching success of the calanoid copepod Calanus finmarchicus were monitored over a period of 14 days (14-28 April, 2008) while fed water from 4 differently treated mesocosms and ambient water. Two of the mesocosms used were inoculated with the polyunsaturated aldehyde (PUA)-producing diatom Skeletonema marinoi, while 2 received only nutrient additions with or without silica. The mesocosms developed blooms of S. marinoi, mixed diatoms or the haptophyte Phaeocystis pouchetii, respectively. Faecal pellet production of C. finmarchicus increased with increasing food availability. Egg production increased with time in all mesocosms to a maximum single female production of 232 eggs female(-1) day(-1) (average of 90 eggs female(-1) day(-1)) and followed the development of ciliates and P. pouchetii, but was not affected by the observed high (up to 15 nmol L(-1)) PUA production potential of the phytoplankton. The hatching success of the eggs produced on the mesocosm diets was high (78-96%) and was not affected by either aldehydes in the maternal diet or exposure to the dissolved aldehydes in the water

Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables

Chemogenomic Analysis of G-Protein Coupled Receptors and Their Ligands Deciphers Locks and Keys Governing Diverse Aspects of Signalling

Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs) is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI) is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic) or inhibitory (antagonistic) ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs) 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences

Model Selection in an Ensemble Framework

We like to present a method to build ensemble models based on an extended cross-validation approach. The cross-validation puts several model classes in a tournament and selects the best performing model with respect to the validation set. This leads to a model selection strategy and an estimation of the expected modelling error

Iterated Time Series Prediction with Ensemble Models

We describe the use of ensemble methods to build proper models time series prediction. Our approach extends the classical ensemble methods for neural networks by using several different model architectures. We further suggest an iterated prediction procedure to select the final ensemble members. This is an extension of well know the crossvalidation scheme for model validation