Highlights of Supersymmetric Hypercharge ±1\pm1 Triplets

The discovery of a standard model (SM)-like Higgs boson with a relatively heavy mass $m_h$ and hints of di-photon excess has deep implication to supersymmetric standard models (SSMs). We consider the SSM extended with hypercharge $\pm1$ triplets, and investigate two scenarios of it: (A) Triplets significantly couple to the Higgs doublets, which can substantially raise $m_h$ and simultaneously enhance the Higgs to di-photon rate via light chargino loops; (B) Oppositely, these couplings are quite weak and thus $m_h$ can not be raised. But the doubly-charged Higgs bosons, owing to the gauge group structure, naturally interprets why there is an excess rather than a deficient of Higgs to di-photon rate. Additionally, the pseudo Dirac triplet fermion is an inelastic non-thermal dark matter candidate. Light doubly-charged particles, especially the doubly-charged Higgs boson around 100 GeV in scenario B, are predicted. We give a preliminary discussion on their search at the LHC.Comment: JHEP version. Typos fixed, comments, references and acknowledge adde

Using natural language processing to improve biomedical concept normalization and relation mining

This thesis concerns the use of natural language processing for improving biomedical concept normalization and relation mining. We begin with introducing the background of biomedical text mining, and subsequently we will continue by describing a typical text mining pipeline, some key issues and problems in mining biomedical texts, and the possibility of using natural language procesing to solve the problems. Finally we end an outline of the work done in this thesis

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe