Anti-PLEC monoclonal antibody, clone 10F6

Substitution of Se by Te in argyrodite-like Cu8GeSe6 leads to compounds Cu8GeSe6-xTex (0 <= x <= 2) and involves a structure change from the Cu8GeSe6 type to the Ag8GeTe6 type. The crystal structure of Cu8GeSe4.25Te1.75 between 50 degrees C and 500 degrees C reveals pronounced disorder of Cu atoms that is best described by anharmonic atomic displacement parameters using the Gram-Charlier expansion. Diffusion pathways are visualized by isosurfaces of joint probability density functions. The lattice parameter a increases with temperature in two linear regimes from 10.4682(18) angstrom to 10.609(3) angstrom with a superionic transition at 400 degrees C. The potential barrier for Cu is about 90 meV at 50 degrees C, which is in the range of typical ion conductors. Cu8GeSe4Te2 exhibits a higher thermoelectric figure of merit (zT = 0.65 at 350 degrees C) than Cu8GeSe6 (zT = 0.2 at 350 degrees C). The application of an effective mass model reveals that the charge carrier concentration of 6 x 10(20) cm(-3) is close to optimum. In addition, argyrodite-type precipitates form heterostructures with a Cu- and Se-doped matrix of germanium antimony tellurides in the quinary system Cu/Ge/Sb/Se/Te, whose transport properties were characterized.

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e. g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.