1,616 research outputs found

    Fucosyltransferase 1 and 2 play pivotal roles in breast cancer cells.

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    FUT1 and FUT2 encode alpha 1, 2-fucosyltransferases which catalyze the addition of alpha 1, 2-linked fucose to glycans. Glycan products of FUT1 and FUT2, such as Globo H and Lewis Y, are highly expressed on malignant tissues, including breast cancer. Herein, we investigated the roles of FUT1 and FUT2 in breast cancer. Silencing of FUT1 or FUT2 by shRNAs inhibited cell proliferation in vitro and tumorigenicity in mice. This was associated with diminished properties of cancer stem cell (CSC), including mammosphere formation and CSC marker both in vitro and in xenografts. Silencing of FUT2, but not FUT1, significantly changed the cuboidal morphology to dense clusters of small and round cells with reduced adhesion to polystyrene and extracellular matrix, including laminin, fibronectin and collagen. Silencing of FUT1 or FUT2 suppressed cell migration in wound healing assay, whereas FUT1 and FUT2 overexpression increased cell migration and invasion in vitro and metastasis of breast cancer in vivo. A decrease in mesenchymal like markers such as fibronectin, vimentin, and twist, along with increased epithelial like marker, E-cadherin, was observed upon FUT1/2 knockdown, while the opposite was noted by overexpression of FUT1 or FUT2. As expected, FUT1 or FUT2 knockdown reduced Globo H, whereas FUT1 or FUT2 overexpression showed contrary effects. Exogenous addition of Globo H-ceramide reversed the suppression of cell migration by FUT1 knockdown but not the inhibition of cell adhesion by FUT2 silencing, suggesting that at least part of the effects of FUT1/2 knockdown were mediated by Globo H. Our results imply that FUT1 and FUT2 play important roles in regulating growth, adhesion, migration and CSC properties of breast cancer, and may serve as therapeutic targets for breast cancer

    The Assessment for Sensitivity of a NO2 Gas Sensor with ZnGa2O4/ZnO Core-Shell Nanowires—a Novel Approach

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    The application of novel core-shell nanowires composed of ZnGa2O4/ZnO to improve the sensitivity of NO2 gas sensors is demonstrated in this study. The growth of ZnGa2O4/ZnO core-shell nanowires is performed by reactive evaporation on patterned ZnO:Ga/SiO2/Si templates at 600 °C. This is to form the homogeneous structure of the sensors investigated in this report to assess their sensitivity in terms of NO2 detection. These novel NO2 gas sensors were evaluated at working temperatures of 25 °C and at 250 °C, respectively. The result reveals the ZnGa2O4/ZnO core-shell nanowires present a good linear relationship (R2 > 0.99) between sensitivity and NO2 concentration at both working temperatures. These core-shell nanowire sensors also possess the highest response (<90 s) and recovery (<120 s) values with greater repeatability seen for NO2 sensors at room temperature, unlike traditional sensors that only work effectively at much higher temperatures. The data in this study indicates the newly-developed ZnGa2O4/ZnO core-shell nanowire based sensors are highly promising for industrial applications

    Library Assessment and Data Analytics in the Big Data Era: Practice and Policies

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    Emerging technologies have offered libraries and librarians new ways and methods to collect and analyze data in the era of accountability to justify their value and contributions. For example, Gallagher, Bauer and Dollar (2005) analyzed the paper and online journal usage from all possible data sources and discovered that users at the Yale Medical Library preferred the electronic format of articles to the print version. After this discovery, they were able to take necessary steps to adjust their journal subscriptions. Many library professionals advocate such data-driven library management to strengthen and specify library budget proposals

    Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation

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    Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it is more imperative to provide ultra-dense LEO constellation with enhanced on-board computation capability. Benefited from it, we have proposed a collaborative federated learning over LEO satellite constellation (FedLEO). We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The GS initially selects an LEO server, whereas its LEO clients are all determined by clustering mechanism and communication capability through the optical inter-satellite links (ISLs). The re-clustering of changing LEO server will be executed once with low communication quality of FedLEO. In the simulations, we have numerically analyzed the proposed FedLEO under practical Walker-based LEO constellation configurations along with MNIST training dataset for classification mission. The proposed FedLEO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing

    Genome-wide gene-based association study

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    Genome-wide association studies, which analyzes hundreds of thousands of single-nucleotide polymorphisms to identify disease susceptibility genes, are challenging because the work involves intensive computation and complex modeling. We propose a two-stage genome-wide association scanning procedure, consisting of a single-locus association scan for the first stage and a gene-based association scan for the second stage. Marginal effects of single-nucleotide polymorphisms are examined by using the exact Armitage trend test or logistic regression, and gene effects are examined by using a p-value combination method. Compared with some existing single-locus and multilocus methods, the proposed method has the following merits: 1) convenient for definition of biologically meaningful regions, 2) powerful for detection of minor-effect genes, 3) helpful for alleviation of a multiple-testing problem, and 4) convenient for result interpretation. The method was applied to study Genetic Analysis Workshop 16 Problem 1 rheumatoid arthritis data, and strong association signals were found. The results show that the human major histocompatibility complex region is the most important genomic region associated with rheumatoid arthritis. Moreover, previously reported genes including PTPN22, C5, and IL2RB were confirmed; novel genes including HLA-DRA, BTNL2, C6orf10, NOTCH4, TAP2, and TNXB were identified by our analysis

    Construction of endophenotypes for complex diseases in the presence of heterogeneity

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    Endophenotypes such as behavior disorders have been increasingly adopted in genetic studies for complex traits. For efficient gene mapping, it is essential that an endophenotype is associated with the disease of interest and is inheritable or co-segregating within families. In this study, we proposed a strategy to construct endophenotypes to analyze the Genetic Analysis Workshop 14 simulated dataset. Initially, generalized estimating equation models were employed to identify phenotypes that were correlated to the disease (affected status) in combination with the family structures in data. Endophenotypes were then constructed with consideration of heterogeneity as functions of the identified phenotypes. Genome scans on the constructed endophenotypes were carried out using family-based association analysis. For comparison, genome scans were also performed with the original affected status. The family-based association analysis using the endophenotypes correctly identified the same susceptible gene in about 80 of the 100 replicates
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