28 research outputs found

    Endothelial Cell-Specific Molecule 2 (Ecsm2) Localizes To Cell-Cell Junctions And Modulates Bfgf-Directed Cell Migration Via The Erk-Fak Pathway

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    Background: Despite its first discovery by in silico cloning of novel endothelial cell-specific genes a decade ago, the biological functions of endothelial cell-specific molecule 2 (ECSM2) have only recently begun to be understood. Limited data suggest its involvement in cell migration and apoptosis. However, the underlying signaling mechanisms and novel functions of ECSM2 remain to be explored. Methodology/Principal Findings: A rabbit anti-ECSM2 monoclonal antibody (RabMAb) was generated and used to characterize the endogenous ECSM2 protein. Immunoblotting, immunoprecipitation, deglycosylation, immunostaining and confocal microscopy validated that endogenous ECSM2 is a plasma membrane glycoprotein preferentially expressed in vascular endothelial cells (ECs). Expression patterns of heterologously expressed and endogenous ECSM2 identified that ECSM2 was particularly concentrated at cell-cell contacts. Cell aggregation and transwell assays showed that ECSM2 promoted cell-cell adhesion and attenuated basic fibroblast growth factor (bFGF)-driven EC migration. Gain or loss of function assays by overexpression or knockdown of ECSM2 in ECs demonstrated that ECSM2 modulated bFGF-directed EC motility via the FGF receptor (FGFR)-extracellular regulated kinase (ERK)-focal adhesion kinase (FAK) pathway. The counterbalance between FAK tyrosine phosphorylation (activation) and ERK-dependent serine phosphorylation of FAK was critically involved. A model of how ECSM2 signals to impact bFGF/FGFR-driven EC migration was proposed. Conclusions/Significance: ECSM2 is likely a novel EC junctional protein. It can promote cell-cell adhesion and inhibit bFGF-mediated cell migration. Mechanistically, ECSM2 attenuates EC motility through the FGFR-ERK-FAK pathway. The findings suggest that ECSM2 could be a key player in coordinating receptor tyrosine kinase (RTK)-, integrin-, and EC junctional component-mediated signaling and may have important implications in disorders related to endothelial dysfunction and impaired EC junction signaling. © 2011 Shi et al

    Structural and Functional Characterization of Two Alternative Splicing Variants of Mouse Endothelial Cell-Specific Chemotaxis Regulator (ECSCR)

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    Endothelial cells (ECs) that line the lumen of blood vessels are important players in blood vessel formation, and EC migration is a key component of the angiogenic process. Thus, identification of genes that are specifically or preferentially expressed in vascular ECs and in-depth understanding of their biological functions may lead to discovery of new therapeutic targets. We have previously reported molecular characterization of human endothelial cell-specific molecule 2 (ECSM2)/endothelial cell-specific chemotaxis regulator (ECSCR). In the present study, we cloned two mouse full-length cDNAs by RT-PCR, which encode two putative ECSCR isoform precursors with considerable homology to the human ECSCR. Nucleotide sequence and exon-intron junction analyses suggested that they are alternative splicing variants (ECSCR isoform-1 and -2), differing from each other in the first and second exons. Quantitative RT-PCR results revealed that isoform-2 is the predominant form, which was most abundant in heart, lung, and muscles, and moderately abundant in uterus and testis. In contrast, the expression of isoform-1 seemed to be more enriched in testis. To further explore their potential cellular functions, we expressed GFP- and FLAG-tagged ECSCR isoforms, respectively, in an ECSCR deficient cell line (HEK293). Interestingly, the actual sizes of either ECSCR-GFP or -FLAG fusion proteins detected by immunoblotting are much larger than their predicted sizes, suggesting that both isoforms are glycoproteins. Fluorescence microscopy revealed that both ECSCR isoforms are localized at the cell surface, which is consistent with the structural prediction. Finally, we performed cell migration assays using mouse endothelial MS1 cells overexpressing GFP alone, isoform-1-GFP, and isoform-2-GFP, respectively. Our results showed that both isoforms significantly inhibited vascular epidermal growth factor (VEGF)-induced cell migration. Taken together, we have provided several lines of experimental evidence that two mouse ECSCR splicing variants/isoform precursors exist. They are differentially expressed in a variety of tissue types and likely involved in modulation of vascular EC migration. We have also defined the gene structure of mouse ECSCR using bioinformatics tools, which provides new information towards a better understanding of alternative splicing of ECSCR

    Storm impacts on phytoplankton community dynamics in lakes

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    In many regions across the globe, extreme weather events, such as storms, have increased in frequency, intensity and duration. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. For lake ecosystems, high winds and rainfall associated with storms are linked by short term runoff events from catchments and physical mixing of the water column. Although we have a well-developed understanding of how such wind and precipitation events alter lake physical processes, our mechanistic understanding of how these short-term disturbances 48 translate from physical forcing to changes in phytoplankton communities is poor. Here, we provide a conceptual model that identifies how key storm features (i.e., the frequency, intensity, and duration of wind and precipitation) interact with attributes of lakes and their watersheds to generate changes in a lake’s physical and chemical environment and subsequently phytoplankton community structure and dynamics. We summarize the current understanding of storm-phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions by generating testable hypotheses across a global gradient of lake types and environmental conditions.Fil: Stockwell, Jason D.. University of Vermont; Estados UnidosFil: Adrian, Rita. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Andersen, Mikkel. Dundalk Institute of Technology; IrlandaFil: Anneville, Orlane. Institut National de la Recherche Agronomique; FranciaFil: Bhattacharya, Ruchi. University of Missouri; Estados UnidosFil: Burns, Wilton G.. University of Vermont; Estados UnidosFil: Carey, Cayelan C.. Virginia Tech University; Estados UnidosFil: Carvalho, Laurence. Freshwater Restoration & Sustainability Group; Reino UnidoFil: Chang, ChunWei. National Taiwan University; República de ChinaFil: De Senerpont Domis, Lisette N.. Netherlands Institute of Ecology; Países BajosFil: Doubek, Jonathan P.. University of Vermont; Estados UnidosFil: Dur, Gaël. Shizuoka University; JapónFil: Frassl, Marieke A.. Griffith University; AustraliaFil: Gessner, Mark O.. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Hejzlar, Josef. Biology Centre of the Czech Academy of Sciences; República ChecaFil: Ibelings, Bas W.. University of Geneva; SuizaFil: Janatian, Nasim. Estonian University of Life Sciences; EstoniaFil: Kpodonu, Alfred T. N. K.. City University of New York; Estados UnidosFil: Lajeunesse, Marc J.. University of South Florida; Estados UnidosFil: Lewandowska, Aleksandra M.. Tvarminne Zoological Station; FinlandiaFil: Llames, Maria Eugenia del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Matsuzaki, Shin-ichiro S.. National Institute for Environmental Studies; JapónFil: Nodine, Emily R.. Rollins College; Estados UnidosFil: Nõges, Peeter. Estonian University of Life Sciences; EstoniaFil: Park, Ho-Dong. Shinshu University; JapónFil: Patil, Vijay P.. US Geological Survey; Estados UnidosFil: Pomati, Francesco. Swiss Federal Institute of Water Science and Technology; SuizaFil: Rimmer, Alon. Kinneret Limnological Laboratory; IsraelFil: Rinke, Karsten. Helmholtz-Centre for Environmental Research; AlemaniaFil: Rudstam, Lars G.. Cornell University; Estados UnidosFil: Rusak, James A.. Ontario Ministry of the Environment and Climate Change; CanadáFil: Salmaso, Nico. Research and Innovation Centre - Fondazione Mach; ItaliaFil: Schmitt, François. Laboratoire d’Océanologie et de Géosciences; FranciaFil: Seltmann, Christian T.. Dundalk Institute of Technology; IrlandaFil: Souissi, Sami. Universite Lille; FranciaFil: Straile, Dietmar. University of Konstanz; AlemaniaFil: Thackeray, Stephen J.. Lancaster Environment Centre; Reino UnidoFil: Thiery, Wim. Vrije Unviversiteit Brussel; Bélgica. Institute for Atmospheric and Climate Science; SuizaFil: Urrutia Cordero, Pablo. Uppsala University; SueciaFil: Venail, Patrick. Universidad de Ginebra; SuizaFil: Verburg, Piet. 8National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Williamson, Tanner J.. Miami University; Estados UnidosFil: Wilson, Harriet L.. Dundalk Institute of Technology; IrlandaFil: Zohary, Tamar. Israel Oceanographic & Limnological Research; IsraelGLEON 20: All Hands' MeetingRottnest IslandAustraliaUniversity of Western AustraliaUniversity of AdelaideGlobal Lake Ecological Observatory Networ

    Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

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    This research aims to identify price determinants for sharing economy-based accommodation services and to further use the identified price determinants to predict accommodation prices. A dataset drawn from Airbnb.com, was collected for analysis. We identify price determinants from five categories. The top five price determinants are identified as room type, city, distance to tourist attractions, number of pictures posted, and number of amenities provided. More importantly, we find that interaction effects between variables can also significantly influence price. Finally, a series of price prediction models are built based on the identified price determinants

    Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

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    Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN (K-Nearest Neighbors), DT (Decision Tree), and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises

    Application Research of File Fingerprint Identification Detection Based on a Network Security Protection System

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    A DLP (data loss prevention) system usually arranges network monitors at the network boundary to perform network traffic capture, file parsing, and strategy matching procedures. Strategy matching is a key process to prevent corporate secret-related documents from leaking. This paper adopts the document fingerprint similarity detection method based on the SimHash principle and customizes the KbS (Keyword-based SimHash) fingerprint, PbS (Paragraph-based SimHash) fingerprint, and SoP (SimHash of Paragraph) fingerprint, three different feature extraction SimHash algorithms for strategy matching to detect. The parsed unstructured data is stored as a file type in.txt format, and then a file fingerprint is generated. Matching the established sensitive document library to calculate the Hamming distance between the fingerprints, the Hamming distance values under different modification degrees are summarized. The experimental results reveal that the hybrid algorithmic strategy matching rules with different levels and accuracy are established. This paper has a reference role for the leakage prevention research of enterprise sensitive data

    On Retargeting the AI Programming Framework to New Hardwares

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    International audienceNowadays, a large number of accelerators are proposed to increase the performance of AI applications, making it a big challenge to enhance existing AI programming frameworks to support these new accelerators. In this paper, we select TensorFlow to demonstrate how to port the AI programming framework to new hardwares, i.e., FPGA and Sunway TaihuLight here. FPGA and Sunway TaihuLight represent two distinct and significant hardware architectures for considering the retargeting process. We introduce our retargeting processes and experiences for these two platforms, from the source codes to the compilation processes. We compare the two retargeting approaches and demonstrate some preliminary experimental results
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