Shenyang, China

Shenyang Normal University
Shenyang, China

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

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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.

Recent years have witnessed much progress in computational modeling for protein subcellular localization. However, there are far few computational models for predicting plant protein subcellular multi-localization. In this paper, we propose a multi-label multi-kernel transfer learning model for predicting multiple subcellular locations of plant proteins (MLMK-TLM). The method proposes a multi-label confusion matrix and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which we further extend our published work MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for plant protein subcellular multi-localization. By proper homolog knowledge transfer, MLMK-TLM is applicable to novel plant protein subcellular localization in multi-label learning scenario. The experiments on plant protein benchmark dataset show that MLMK-TLM outperforms the baseline model. Unlike the existing models, MLMK-TLM also reports its misleading tendency, which is important for comprehensive survey of model's multi-labeling performance. © 2012 Elsevier Ltd.

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.

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

From the super-matrix Lie algebras, we consider a super-extension of the CKdV equation hierarchy in the present Letter, and propose the super-CKdV hierarchy with self-consistent sources. Furthermore, we establish the infinitely many conservation laws for the integrable super-CKdV hierarchy. © 2011 Elsevier B.V. All rights reserved.

Gao P.,Shenyang Normal University
International Journal of Digital Content Technology and its Applications | Year: 2012

With advances in technology, optical fiber sensing technology is increasingly used in civil engineering applications. Using optical fiber sensing technology is to monitor the displacement deformation of deep underground of landslide (high slope) make up for many shortcomings of traditional monitoring methods. In this paper, a case of seepage simulation and monitoring system based on distributed optical fiber sensor is presented. Possible failures of the subunits of monitoring system are analyzed using Fault Tree Analysis (FTA) method. The reliability analysis helps to disclose potential uncertainty and risk, such as fake information that would be non-effective if it is executed by the processor. The failures of optical path configurations and the instrument including software and hardware are analyzed.

Zhao C.,Shenyang Normal University | Tang H.,Shenyang Normal University
Computers and Operations Research | Year: 2012

This note considers a single machine scheduling and due-window assignment problem, in which the processing time of a job is a linear function of its starting time and the job-independent deterioration rates are identical for all jobs. We allow an option for performing a rate-modifying activity for changing the normal processing times of the jobs following this activity. The objective is to schedule the jobs, the due-window and the rate-modifying activity so as to minimize the sum of earliness, tardiness and due-window starting time and due-window size costs. We introduce a polynomial solution for the problem. © 2010 Elsevier Ltd. All rights reserved.

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.

Mei S.,Shenyang Normal University
PLoS ONE | Year: 2012

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at © 2012 Suyu Mei.

News Article | November 30, 2015

The newly-described fossil species, Xiaochelys ningchengensis, from northeastern China’s Jehol Biota; right: an illustration of the skeleton incorporating positive and negative impressions preserved in the layers of stone. Credit: Chang-Fu Zhou Today's sea turtles are the sole survivors of a once diverse ecosystem of marine reptiles from the age of dinosaurs. Sea turtles first appeared during the Cretaceous period, 130-140 million years ago and likely evolved from freshwater ancestors. However, these ancestors have yet to be discovered. In a new study, Dr. Chang-Fu Zhou of the Shenyang Normal University of Liaoning and Dr. Márton Rabi of the Biogeology Workgroup of the University of Tübingen and the Hungarian Academy of Sciences seek to identify the ancestors of modern sea turtles among fossils of the Jehol Biota in northeastern China. Their findings were published in Scientific Reports this November. The Jehol Biota is a rich Cretaceous ecosystem preserved in a multi-layered rock formation cropping out in the Chinese provinces of Liaoning, Hebei and Inner Mongolia. A vast number and variety of organisms became fossilized there about 125 million years ago. The region is famous for its feathered dinosaur fossils which demonstrate that today's birds descended from dinosaurs. However, the first vertebrate of the Jehol Biota to be described in 1942 was one of the many turtles found there. Zhou and Rabi described a new species of Jehol turtle, Xiaochelys ningchengensis, and investigated its possible relationship with today's chelonians. They applied comparative morphological techniques, looking at the creatures' structure and shape as well as using genetic data of living species, compiling a comprehensive family tree of fossil and extant turtles. The researchers wanted to test an earlier hypothesis that the Jehol turtles belong to a lineage that eventually gave rise to today's sea turtles. "According to our findings, the Jehol turtles are instead found on the lineage leading to the cryptodiran turtles," says Zhou. The cryptodires – which also include sea turtles – are able to pull their heads and necks vertically into their shells using an S-shaped motion. "However, a placement of the Jehol turtles close to sea turtles on the family tree is only slightly less supported statistically" adds Rabi. Therefore, the researchers say, the earliest known sea turtles are likely to have looked much like the species found in the Jehol Biota. "These well-preserved fossils give us insights into the origin of cryptodires. About three-quarters of today's turtles belong to that group," says Zhou. It remains unclear, however, just how the main adaptations of sea turtles arose for a marine habitat. These evolutionary changes included the reduction of their skeleton and the development of large and rigid paddles which enable the creatures to swim in a style which is best described as underwater flight. "The origin of sea turtles was a major morphological transition in vertebrate evolution but we still don't really understand how it happened. It was a highly successful adaptation and it is truly depressing to see that the last surviving marine reptiles are threatened with extinction after more than 130 million years," says Rabi. Explore further: More than 500 baby sea turtles released off Fla. More information: Chang-Fu Zhou et al. A sinemydid turtle from the Jehol Biota provides insights into the basal divergence of crown turtles, Scientific Reports (2015). DOI: 10.1038/srep16299

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