Scalar Electroweak Multiplet Dark Matter

We revisit the theory and phenomenology of scalar electroweak multiplet thermal dark matter. We derive the most general, renormalizable scalar potential, assuming the presence of the Standard Model Higgs doublet, $H$, and an electroweak multiplet $\Phi$ of arbitrary SU(2$)_L$ rank and hypercharge, $Y$. We show that, in general, the $\Phi$-$H$ Higgs portal interactions depend on three, rather than two independent couplings as has been previously considered in the literature. For the phenomenologically viable case of $Y=0$ multiplets, we focus on the septuplet and quintuplet cases, and consider the interplay of relic density and spin-independent direct detection cross section. We show that both the relic density and direct detection cross sections depend on a single linear combination of Higgs portal couplings, $\lambda_{\rm eff}$. For $\lambda_{\rm eff}\sim \mathcal{O}(1)$, present direct detection exclusion limits imply that the neutral component of a scalar electroweak multiplet would comprise a subdominant fraction of the observed DM relic density.Comment: 15 pages, 4 figure

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V

Mutation of SLC35D3 causes metabolic syndrome by impairing dopamine signaling in striatal D1 neurons

We thank Dr. Ya-Qin Feng from Shanxi Medical University, Dr. Tian-Yun Gao from Nanjing University and Dr. Yan-Hong Xue from Institute of Biophysics (CAS) for technical assistance in this study. We are very thankful to Drs. Richard T. Swank and Xiao-Jiang Li for their critical reading of this manuscript and invaluable advice. Funding: This work was partially supported by grants from National Basic Research Program of China (2013CB530605; 2014CB942803), from National Natural Science Foundation of China 1230046; 31071252; 81101182) and from Chinese Academy of Sciences (KSCX2-EW-R-05, KJZD-EW-L08). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD