Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks

High-Temperature Electronic Materials: Silicon Carbide and Diamond

The physical and chemical properties of wide-band-gap semiconductors make these materials an ideal wide bandgapsemiconductor choice for device fabrication for applications in many different areas, e.g. light emitters, high-temperature and high-power electronics, high-power microwave devices, micro-electromechanical system (MEM) technology, and substrates for semiconductor preparation. These semiconductors have micro-electromechanical system (MEMS) been recognized for several decades as being suitable for these applications, but until recently the low material quality has not allowed the fabrication of high-quality devices. In this material quality chapter, we review the wide-band-gap semiconductors, silicon carbide and diamond. Silicon carbide electronics is advancing from the research stage to commercial production. The commercial availability of single-crystal SiC substrates during the early 1990s gave rise to intense activity in the development of silicon carbide devices. The commercialization started with the release of blue light-emitting diode (LED). The recent release of high-power Schottky diodes was a further demonstration of the progress made towards defect-free SiC substrates. Diamond has superior physical and chemical properties. Silicon-carbide- and diamond-based diamondsilicon carbide (SiC) electronics are at different stages of development. The preparation of high-quality single-crystal substrates of wafer size has allowed recent significant progress in the fabrication of several types of devices, and the development has reached many important milestones. However, high-temperature studies are still scarce, and diamond-based electronics is still in its infancy