Protein Structure Data Management System

With advancement in the development of the new laboratory instruments and experimental techniques, the protein data has an explosive increasing rate. Therefore how to efficiently store, retrieve and modify protein data is becoming a challenging issue that most biological scientists have to face and solve. Traditional data models such as relational database lack of support for complex data types, which is a big issue for protein data application. Hence many scientists switch to the object-oriented databases since object-oriented nature of life science data perfectly matches the architecture of object-oriented databases, but there are still a lot of problems that need to be solved in order to apply OODB methodologies to manage protein data. One major problem is that the general-purpose OODBs do not have any built-in data types for biological research and built-in biological domain-specific functional operations. In this dissertation, we present an application system with built-in data types and built-in biological domain-specific functional operations that extends the Object-Oriented Database (OODB) system by adding domain-specific additional layers Protein-QL, Protein Algebra Architecture and Protein-OODB above OODB to manage protein structure data. This system is composed of three parts: 1) Client API to provide easy usage for different users. 2) Middleware including Protein-QL, Protein Algebra Architecture and Protein-OODB is designed to implement protein domain specific query language and optimize the complex queries, also it capsulates the details of the implementation such that users can easily understand and master Protein-QL. 3) Data Storage is used to store our protein data. This system is for protein domain, but it can be easily extended into other biological domains to build a bio-OODBMS. In this system, protein, primary, secondary, and tertiary structures are defined as internal data types to simplify the queries in Protein-QL such that the domain scientists can easily master the query language and formulate data requests, and EyeDB is used as the underlying OODB to communicate with Protein-OODB. In addition, protein data is usually stored as PDB format and PDB format is old, ambiguous, and inadequate, therefore, PDB data curation will be discussed in detail in the dissertation

Metallic Icosahedron Phase of Sodium at Terapascal Pressures

Alkali metals exhibit unexpected structures and electronic behavior at high pressures. Compression of metallic sodium (Na) to 200 GPa leads to the stability of a wide-band-gap insulator with the double hexagonal hP4 structure. Post-hP4 structures remain unexplored, but they are important for addressing the question of the pressure at which Na reverts to a metal. Here we report the reentrant metallicity of Na at the very high pressure of 15.5 terapascal (TPa), predicted using first-principles structure searching simulations. Na is therefore insulating over the large pressure range of 0.2-15.5 TPa. Unusually, Na adopts an oP8 structure at pressures of 117-125 GPa, and the same oP8 structure at 1.75-15.5 TPa. Metallization of Na occurs on formation of a stable and striking body-centered cubic cI24 electride structure consisting of Na12 icosahedra, each housing at its center about one electron which is not associated with any Na ions.Comment: 5 pages, 4 figures, PRL (2015

Data-Driven Event Identification Using Deep Graph Neural Network and PMU Data

Phasor measurement units (PMUs) are being widely installed on power transmission systems, which provides a unique opportunity to enhance wide-area situational awareness. One essential application is to utilize PMU data for real-time event identification. However, taking full advantage of all PMU data in event identification is still an open problem. Hence, we propose a novel event identification method using multiple PMU measurements and deep graph neural network techniques. Unlike the previous models that rely on data from single PMU and ignore the interactive relationships between different PMUs or use multiple PMUs but determine the functional connectivity manually, our method performs interactive relationship inference in a data-driven manner. To ensure the optimality of the interactive inference procedure, the proposed method learns the interactive graph jointly with the event identification model. Moreover, instead of generating a single statistical graph to represent pair-wise relationships among PMUs during different events, our approach produces different event identification-specific graphs for different power system events, which handles the uncertainty of event location. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. The numerical results validate that our method has higher identification accuracy compared to the existing methods

Maintenance mechanism of Enteromorpha prolifera green tide: From perspective of nutrients utilization

293-297Green tide caused by macroalgae is one of the global ocean ecological disasters and nutrients with high concentration are considered as materials base for the outbreak of green tide. Nevertheless, there is no continuous nutrients supply for macroalgae during their floating in sea areas. Thus, there must be special nutrients utilization strategy for the macroalgae to maintain growth and proliferation even if the nutrients in seawater can not supply enough nutrients for them. To verify the hypothesis, Enteromorpha prolifera responsible for green tide was exposed to nutrients with different concentrations. E. prolifera absorbed and stored excrescent nutrients when it encountered nutrient eutrophication, then released and reutilized those stored nutrients for growth and proliferation in the nutrient-shortage seawaters. Thus, the green tide can be maintained by the nutrients regulation ability of E. prolifera. Results of the present work may be helpful to provide enlightenment on prediction and controlling of macroalgae green tide