Spin accumulation created electrically in an n-type germanium channel using Schottky tunnel contacts

Using high-quality Fe$_{3}$Si/$n^{+}$-Ge Schottky-tunnel-barrier contacts, we study spin accumulation in an $n$-type germanium ($n$-Ge) channel. In the three- or two-terminal voltage measurements with low bias current conditions at 50 K, Hanle-effect signals are clearly detected only at a forward-biased contact. These are reliable evidence for electrical detection of the spin accumulation created in the $n$-Ge channel. The estimated spin lifetime in $n$-Ge at 50 K is one order of magnitude shorter than those in $n$-Si reported recently. The magnitude of the spin signals cannot be explained by the commonly used spin diffusion model. We discuss a possible origin of the difference between experimental data and theoretical values.Comment: 4 pages, 3 figures, To appear in J. Appl. Phy

Epitaxial Growth of a Full-Heusler Alloy Co2_{2}FeSi on Silicon by Low-Temperature Molecular Beam Epitaxy

For electrical spin injection and detection of spin-polarized electrons in silicon, we explore highly epitaxial growth of ferromagnetic full-Heusler-alloy Co2FeSi thin films on silicon substrates using low-temperature molecular beam epitaxy (LTMBE). Although in-situ reflection high energy electron diffraction images clearly show two-dimensional epitaxial growth for growth temperatures T_G of 60, 130, and 200 C, cross-sectional transmission electron microscopy experiments reveal that there are single-crystal phases other than Heusler alloys near the interface between Co_2FeSi and Si for T_G = 130 and 200 C. On the other hand, almost perfect heterointerfaces are achieved for T_G = 60 C. These results and magnetic measurements indicate that highly epitaxial growth of Co_2FeSi thin films on Si is demonstrated only for T_G = 60 C.Comment: 3 pages, 3 figure

Mining metabolites: extracting the yeast metabolome from the literature

Text mining methods have added considerably to our capacity to extract biological knowledge from the literature. Recently the field of systems biology has begun to model and simulate metabolic networks, requiring knowledge of the set of molecules involved. While genomics and proteomics technologies are able to supply the macromolecular parts list, the metabolites are less easily assembled. Most metabolites are known and reported through the scientific literature, rather than through large-scale experimental surveys. Thus it is important to recover them from the literature. Here we present a novel tool to automatically identify metabolite names in the literature, and associate structures where possible, to define the reported yeast metabolome. With ten-fold cross validation on a manually annotated corpus, our recognition tool generates an f-score of 78.49 (precision of 83.02) and demonstrates greater suitability in identifying metabolite names than other existing recognition tools for general chemical molecules. The metabolite recognition tool has been applied to the literature covering an important model organism, the yeast Saccharomyces cerevisiae, to define its reported metabolome. By coupling to ChemSpider, a major chemical database, we have identified structures for much of the reported metabolome and, where structure identification fails, been able to suggest extensions to ChemSpider. Our manually annotated gold-standard data on 296 abstracts are available as supplementary materials. Metabolite names and, where appropriate, structures are also available as supplementary materials

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods