Serum protein N-glycome patterns reveal alterations associated with endometrial cancer and its phenotypes of differentiation

BackgroundAberrant N-glycosylation and its involvement in pathogenesis have been reported in endometrial cancer (EC). Nevertheless, the serum N-glycomic signature of EC remains unknown. Here, we investigated serum N-glycome patterns of EC to identify candidate biomarkers.MethodsThis study enrolled 34 untreated EC patients and 34 matched healthy controls (HC) from Peking Union Medical College Hospital. State-of-the-art MS-based methods were employed for N-glycans profiling. Multivariate and univariate statistical analyses were used to identify discriminative N-glycans driving classification. Receiver operating characteristic analyses were performed to evaluate classification accuracy.ResultsEC patients displayed distinct differences in serum N-glycome and had abnormal high-mannose and hybrid-type N-glycans, fucosylation, galactosylation, and linkage‐specific sialylation compared with HC. The glycan panel built with the four most discriminative and biologically important derived N-glycan traits could accurately identify EC (random forest model, the area under the curve [AUC]=0.993 [95%CI 0.955-1]). The performance was validated by two other models. Total hybrid-type N-glycans significantly associated with the differentiation types of EC could effectively stratify EC into well- or poorly-differentiated subgroups (AUC>0.8).ConclusionThis study provides the initial evidence supporting the utility of serum N-glycomic signature as potential markers for the diagnosis and phenotyping of EC

Identification of Hub Genes Related to the Recovery Phase of Irradiation Injury by Microarray and Integrated Gene Network Analysis

BACKGROUND: Irradiation commonly causes long-term bone marrow injury charactertized by defective HSC self-renewal and a decrease in HSC reserve. However, the effect of high-dose IR on global gene expression during bone marrow recovery remains unknown. METHODOLOGY: Microarray analysis was used to identify differentially expressed genes that are likely to be critical for bone marrow recovery. Multiple bioinformatics analyses were conducted to identify key hub genes, pathways and biological processes. PRINCIPAL FINDINGS: 1) We identified 1302 differentially expressed genes in murine bone marrow at 3, 7, 11 and 21 days after irradiation. Eleven of these genes are known to be HSC self-renewal associated genes, including Adipoq, Ccl3, Ccnd1, Ccnd2, Cdkn1a, Cxcl12, Junb, Pten, Tal1, Thy1 and Tnf; 2) These 1302 differentially expressed genes function in multiple biological processes of immunity, including hematopoiesis and response to stimuli, and cellular processes including cell proliferation, differentiation, adhesion and signaling; 3) Dynamic Gene Network analysis identified a subgroup of 25 core genes that participate in immune response, regulation of transcription and nucleosome assembly; 4) A comparison of our data with known irradiation-related genes extracted from literature showed 42 genes that matched the results of our microarray analysis, thus demonstrated consistency between studies; 5) Protein-protein interaction network and pathway analyses indicated several essential protein-protein interactions and signaling pathways, including focal adhesion and several immune-related signaling pathways. CONCLUSIONS: Comparisons to other gene array datasets indicate that global gene expression profiles of irradiation damaged bone marrow show significant differences between injury and recovery phases. Our data suggest that immune response (including hematopoiesis) can be considered as a critical biological process in bone marrow recovery. Several critical hub genes that are key members of significant pathways or gene networks were identified by our comprehensive analysis

Geometric Programming with Discrete Variables Subject to Max-Product Fuzzy Relation Constraints

The problem of geometric programming subject to max-product fuzzy relation constraints with discrete variables is studied. The major difficulty in solving this problem comes from nonconvexity caused by these product terms in the general geometric function and the max-product relation constraints. We proposed a 0-1 mixed integer linear programming model and adopted the branch-and-bound scheme to solve the problem. Numerical experiments confirm that the proposed solution method is effective

Products distribution and heavy metals migration during catalytic pyrolysis of refinery oily sludge

The reclamation disposal of oily sludge, which is a hazardous waste from the extraction, transportation, storage, and refining of crude oil, is a paramount challenge for environmental protection and resource recycle. Herein, a catalytic pyrolysis approach with the participation of CaO was adopted for oil resource recovery. The results show that the optimal pyrolysis temperature for recovering oil was 500 °C, in which the pyrolysis oil yield was 44.37%. CaO could act as a catalyst during the pyrolysis process, thus promoting the formation of light components in the pyrolysis oil. The light components in pyrolysis oil increased from 5.08% to 16.67% with the participation of CaO. Meanwhile, the addition of CaO immobilized As, Cr, Pb and Zn into the pyrolysis slag, thus decreasing their migration into pyrolysis oil and gas. The migration of Ni displayed a different trend, and part of Ni entered into the pyrolysis oil and gas. The BCR continuous extraction experiments display that the highly biological-activity heavy metals (i.e., F1, F2 and F3 form) was transformed to a more stable state (i.e., F4 form). These results demonstrate that the catalytic pyrolysis approach with the participation of CaO not only improve the yield and quality of pyrolysis oil, but also reduce the emission and mobility of heavy metals

Theoretical investigation of group-IV binary compounds in the P4/ncc phase

Three direct and two indirect semiconductor materials together with one metallic material for group-IV binary compounds in the P4/ncc phase are investigated in this work, by employing density functional theory (DFT), where the morphology, stability, mechanical anisotropy, electronic properties, effective mass and optical properties are obtained. SiC, SnC and SnSi are all semiconductor materials with direct bandgaps of 3.38 eV, 1.30 eV and 0.67 eV, respectively, while GeC and GeSi have indirect bandgaps of 2.86 eV and 1.14 eV, respectively. The formation energy of P4/ncc-SiC is −133 meV per atom, indicating its excellent thermodynamic stability and great promise for future experimental realization. P4/ncc-SiC is more incompressible than C2/m-SiC and P42/mnm-Si8C4, and P4/ncc-GeSi is more incompressible than h-GeSi. SnSi has the largest anisotropy in the Young’s and shear modulus, SiC and GeC have the largest anisotropy in the Poisson’s ratio. P4/ncc-SnSi and SnC have low electron effective masses of 0.08m0 and 0.09m0, respectively, which may indicate a high carrier transport property. Compared with Fd-3m-Si, SnSi and GeSi show better optical absorption properties for infrared and visible light regions. All these unique properties endow these materials with great promise for application in microelectronic and optoelectronic devices

A sequential subspace learning method and its application to dynamic texture analysis

Conference Name:International Conference on Intelligent Computing. Conference Address: Hefei, PEOPLES R CHINA. Time:AUG 23-26, 2005.Incremental update of subspace has new and interesting research applications in vision such as active recognition, object tracking and dynamic texture analysis. In this paper, a sequential subspace learning method is proposed for dynamic texture analysis. The learning algorithm can update adaptively dynamic texture subspace based on sequential observation data, and has higher computation efficiency and numerical stableness. Also our learning method considers the change of the texture sample mean when each new observation datum arrives, whereas existing subspace learning methods ignore the fact that the sample mean varies over time. Experimental results show the learning method for dynamic texture subspace is efficient and effective. (c) 2006 Elsevier Inc. All rights reserved

Salient Object Detection: A Discriminative Regional Feature Integration Approach

Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. In this paper, we regard saliency map computation as a regression problem. Our method, which is based on multi-level image segmenta-tion, uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the saliency map. The contributions lie in two-fold. One is that we show our approach, which integrates the regional contrast, regional property and regional backgroundness descriptors together to form the master saliency map, is able to produce superior saliency maps to existing algorithms most of which combine saliency maps heuristically computed from different types of fea-tures. The other is that we introduce a new regional fea-ture vector, backgroundness, to characterize the back-ground, which can be regarded as a counterpart of the objectness descriptor [2]. The performance evaluation on several popular benchmark data sets validates that our approach outperforms existing state-of-the-arts. 1