Multiple H-Rearrangements in 10-Benzylthio-dithranol Radical Cations

10-Alkylthio- and 10-arylthio-derivatives of dithranol (anthralin; 1,8-dihydroxy-9-anthrone) are of interest in search for new anti-psoriatic agents2 , 3 ). By working out ms procedures for unequivocal identification of trace amounts of these compounds4 ) it was established that in case of 10-phenylthio-dithranol putative by-products, especially one giving rise to ions at m/z = 226 (dithranol), are artefacts of thermal reaction in the mass spectrometer1). In the EI-MS of those 10-substituted dithranols containing a S-CH2R chain, however, these ions (m/z = 226) arise from M + * as well. Scope and mechanism of their formation was examined by analyzing compound 1 and its D-labelled derivatives 2 and 3

Identification of the dominant diffusing species in silicide formation

Implanted noble gas atoms of Xe have been used as diffusion markers in the growth study of three silicides: Ni2Si, VSi2, and TiSi2. Backscattering of MeV He has been used to determine the displacement of the markers. We found that while Si atoms predominate the diffusion in VSi2 and TiSi2, Ni atoms are the faster moving species in Ni2Si

Absolute differential cross sections for electron-impact excitation of CO near threshold: II. The Rydberg states of CO

Absolute differential cross sections for electron-impact excitation of Rydberg states of CO have been measured from threshold to 3.7 eV above threshold and for scattering angles between 20° and 140°. Measured excitation functions for the b 3Σ+, B 1Σ+ and E 1π states are compared with cross sections calculated by the Schwinger multichannel method. The behaviour of the excitation functions for these states and for the j 3Σ+ and C 1Σ+ states is analysed in terms of negative-ion states. One of these resonances has not been previously reported

Deep learning with convolutional neural networks for decoding and visualization of EEG pathology

We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet architectures recently shown to decode task-related information from EEG at least as well as established algorithms designed for this purpose. In decoding EEG pathology, both ConvNets reached substantially better accuracies (about 6% better, ~85% vs. ~79%) than the only published result for this dataset, and were still better when using only 1 minute of each recording for training and only six seconds of each recording for testing. We used automated methods to optimize architectural hyperparameters and found intriguingly different ConvNet architectures, e.g., with max pooling as the only nonlinearity. Visualizations of the ConvNet decoding behavior showed that they used spectral power changes in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside other features, consistent with expectations derived from spectral analysis of the EEG data and from the textual medical reports. Analysis of the textual medical reports also highlighted the potential for accuracy increases by integrating contextual information, such as the age of subjects. In summary, the ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic.Comment: Published at IEEE SPMB 2017 https://www.ieeespmb.org/2017

Gamma–ray spectroscopy with single–carrier collection in high–resistivity semiconductors

With the standard plane–parallel configuration of semiconductor detectors, good γ–ray spectra can only be obtained when both electrons and holes are completely collected. We show by calculations (and experiments) that with contacts of hemispherical configuration one can obtain γ–ray spectra of adequate resolution and with signal heights of nearly full amplitude even when only one type of carrier is collected. Experiments with CdTe detectors for which the µτ product for electrons is about 10^(3) times that of the holes confirm these calculations. The adoption of hemispherical contacts thus widens the range of high–resistivity semiconductors potentially acceptable for γ–ray detection at room temperature

The PlaNet Consortium: A Network of European Plant Databases Connecting Plant Genome Data in an Integrated Biological Knowledge Resource

The completion of the Arabidopsis genome and the large collections of other plant sequences generated in recent years have sparked extensive functional genomics efforts. However, the utilization of this data is inefficient, as data sources are distributed and heterogeneous and efforts at data integration are lagging behind. PlaNet aims to overcome the limitations of individual efforts as well as the limitations of heterogeneous, independent data collections. PlaNet is a distributed effort among European bioinformatics groups and plant molecular biologists to establish a comprehensive integrated database in a collaborative network. Objectives are the implementation of infrastructure and data sources to capture plant genomic information into a comprehensive, integrated platform. This will facilitate the systematic exploration of Arabidopsis and other plants. New methods for data exchange, database integration and access are being developed to create a highly integrated, federated data resource for research. The connection between the individual resources is realized with BioMOBY. BioMOBY provides an architecture for the discovery and distribution of biological data through web services. While knowledge is centralized, data is maintained at its primary source without a need for warehousing. To standardize nomenclature and data representation, ontologies and generic data models are defined in interaction with the relevant communities.Minimal data models should make it simple to allow broad integration, while inheritance allows detail and depth to be added to more complex data objects without losing integration. To allow expert annotation and keep databases curated, local and remote annotation interfaces are provided. Easy and direct access to all data is key to the project

Variational Calculation for the Equation of State of Nuclear Matter at Finite Temperatures

An equation of state (EOS) for uniform nuclear matter is constructed at zero and finite temperatures with the variational method starting from the realistic nuclear Hamiltonian composed of the Argonne V18 and UIX potentials. The energy is evaluated in the two-body cluster approximation with the three-body-force contribution treated phenomenologically so as to reproduce the empirical saturation conditions. The obtained energies for symmetric nuclear matter and neutron matter at zero temperature are in fair agreement with those by Akmal, Pandharipande and Ravenhall, and the maximum mass of the neutron star is 2.2 Msolar. At finite temperatures, a variational method by Schmidt and Pandharipande is employed to evaluate the free energy, which is used to derive various thermodynamic quantities of nuclear matter necessary for supernova simulations. The result of this variational method at finite temperatures is found to be self-consistent.Comment: Revised Versio