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Shenyang, China

Shenyang Normal University is a broad-based university in Shenyang, Liaoning Province, China under the provincial government. Wikipedia.

Wen Z.L.,CAS National Astronomical Observatories | Han J.L.,CAS National Astronomical Observatories | Liu F.S.,Shenyang Normal University
Astrophysical Journal, Supplement Series | Year: 2012

Using the photometric redshifts of galaxies from the Sloan Digital Sky Survey III (SDSS-III), we identify 132,684 clusters in the redshift range of 0.05 ≤ z < 0.8. Monte Carlo simulations show that the false detection rate is less than 6% for the whole sample. The completeness is more than 95% for clusters with a mass of M 200 > 1.0 × 10 14 M in the redshift range of 0.05 ≤ z < 0.42, while clusters of z > 0.42 are less complete and have a biased smaller richness than the real one due to incompleteness of member galaxies. We compare our sample with other cluster samples, and find that more than 90% of previously known rich clusters of 0.05 ≤ z < 0.42 are matched with clusters in our sample. Richer clusters tend to have more luminous brightest cluster galaxies (BCGs). Correlating with X-ray and the Planck data, we show that the cluster richness is closely related to the X-ray luminosity, temperature, and Sunyaev-Zel'dovich measurements. Comparison of the BCGs with the SDSS luminous red galaxy (LRG) sample shows that 25% of LRGs are BCGs of our clusters and 36% of LRGs are cluster member galaxies. In our cluster sample, 63% of BCGs of r petro < 19.5 satisfy the SDSS LRG selection criteria. © 2012 The American Astronomical Society. All rights reserved.

Mei S.,Shenyang Normal University
Journal of Theoretical Biology | Year: 2012

Protein sub-organelle localization, e.g. submitochondria, seems more challenging than general protein subcellular localization, because the determination of protein's micro-level localization within organelle by fluorescent imaging technique would face up with more difficulties. Up to present, there are far few computational methods for protein submitochondria localization, and the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance. Recent researches have demonstrated that gene ontology (GO) is a convincingly effective protein feature for protein subcellular localization. However, the GO information may not be available for novel proteins or sparsely annotated protein subfamilies. In allusion to the problem, we transfer the homology's GO information to the target protein and propose a multi-kernel transfer learning model for protein submitochondria localization (MK-TLM), which substantially extends our previously published work (gene ontology based transfer learning model for protein subcellular localization, GO-TLM). To reduce the risk of performance overestimation, we conduct a more comprehensive survey of the model performance in optimistic case, moderate case and pessimistic case according to the abundance of target protein's GO information. The experiments on submitochondria benchmark datasets show that MK-TLM significantly outperforms the baseline models, and demonstrates excellent performance for novel mitochondria proteins and those mitochondria proteins that belong to the subfamily we know little about. © 2011 Elsevier Ltd.

Yu F.,Shenyang Normal University
Physics Letters, Section A: General, Atomic and Solid State Physics | Year: 2011

Some integrable coupling systems of existing papers are linear integrable couplings. In the Letter, beginning with Lax pairs from special non-semisimple matrix Lie algebras, we establish a scheme for constructing real nonlinear integrable couplings of continuous soliton hierarchy. A direct application to the AKNS spectral problem leads to a novel nonlinear integrable couplings, then we consider the Hamiltonian structures of nonlinear integrable couplings of AKNS hierarchy with the component-trace identity. © 2011 Elsevier B.V.

Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research. © 2013 Suyu Mei.

Bi S.,CAS Institute of Vertebrate Paleontology and Paleoanthropology | Bi S.,Indiana University of Pennsylvania | Wang Y.,CAS Institute of Vertebrate Paleontology and Paleoanthropology | Guan J.,Beijing Natural History Museum | And 2 more authors.
Nature | Year: 2014

The phylogeny of Allotheria, including Multituberculata and Haramiyida, remains unsolved and has generated contentious views on the origin and earliest evolution of mammals. Here we report three new species of a new clade, Euharamiyida, based on six well-preserved fossils from the Jurassic period of China. These fossils reveal many craniodental and postcranial features of euharamiyidans and clarify several ambiguous structures that are currently the topic of debate. Our phylogenetic analyses recognize Euharamiyida as the sister group of Multituberculata, and place Allotheria within the Mammalia. The phylogeny suggests that allotherian mammals evolved from a Late Triassic (approximately 208 million years ago) Haramiyavia-like ancestor and diversified into euharamiyidans and multituberculates with a cosmopolitan distribution, implying homologous acquisition of many craniodental and postcranial features in the two groups. Our findings also favour a Late Triassic origin of mammals in Laurasia and two independent detachment events of the middle ear bones during mammalian evolution. © 2014 Macmillan Publishers Limited. All rights reserved.

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