50 research outputs found

    Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

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    In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

    The effect of Olig1 deficiency on T cell proliferation capability.

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    <p>(A) Proliferative responses of MOG-specific T cells isolated from WT and Olig1<sup>−/−</sup> EAE mice (<i>n</i> = 4). (B) RT-PCR analysis of Olig1 expression in brain, spinal cord, lymph node, spleen and thymus of WT mice.</p

    The effect of Olig1 deficiency on the number of oligodendrocytes and their progenitor cells during EAE.

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    <p>(A) Representative immunostaining of CC1 and PDGFR-α in the white matter of the spinal cord. Scale bar: 40 µm and applies to all panels. (B) Quantitative analysis of mature oligodendrocytes and their progenitor cells. *<i>P</i><0.001. OLG, oligodendrocyte; OPC, oligodendrocyte progenitor cell.</p

    Representative histopathology of the optic nerves in EAE mice.

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    <p>(A) Optic nerves were stained with luxol fast blue (LFB) and hematoxylin and eosin (HE) (upper two panels) or NF200 and toluidine blue for the semithin transverse sections (lower two panels). The arrows point to the degenerating axons. Scale bar: 100 µm for the first and third panels, 75 µm for the second panels and 15 µm for the lower panels. (B) Quantitative analysis of cell infiltrates in the longitudinal section of the optic nerve. (C) Quantitative analysis of degenerating axons in the transverse section of the optic nerve. *<i>P</i><0.01.</p

    Histopathology of the spinal cords in EAE mice.

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    <p>Representative histology of the spinal cords of WT (A) and Olig1<sup>−/−</sup> mice (B) in d10, d20 and d60. Lumbar spinal cords were stained with luxol fast blue (LFB) and hematoxylin and eosin (HE) (upper panels) and either an anti-GFAP (middle panels) or anti-iba1 antibody (lower panels). Scale bar: 200 µm and applies to all panels.</p

    Delayed EAE disease onset in Olig1<sup>−/−</sup> mice.

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    <p>(A) EAE disease incidence in wild-type (WT; <i>n</i> = 17) and Olig1<sup>−/−</sup> (<i>n</i> = 20) mice. (B) Clinical evaluation of WT and Olig1<sup>−/−</sup> EAE mice during a period of 60 days after MOG immunization. (C) Clinical scores of WT and Olig1<sup>−/−</sup> EAE mice in different stages.</p

    Quantification of histopathology of the spinal cords in EAE mice.

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    <p>Quantitative analysis of the extent of demyelination (A), GFAP-positive (B) and iba1-positive (C) cells in the spinal cord. The areas of demyelinated regions in the white matter were measured by ImageJ 1.43u and expressed as a percentage of the whole area of the white matter. GFAP- and iba1-positive cells were counted per unit area (0.143 mm<sup>2</sup>) in the middle region of the ventral horn. ***<i>P</i><0.001; **<i>P</i><0.01; *<i>P</i><0.05.</p

    Rate of change in subfoveal choroidal thickness (CT).

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    <p>A: Changes of the subfoveal CT of amblyopic eyes in patients with anisohypermetropic amblyopia. The thicker choroids became thinner and thinner choroids became thicker. There was a negative correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.59, <i>P</i> = 0.003; Pearson’s correlation coefficient). B: Changes of the subfoveal CT of the fellow eyes in patients with anisohypermetropic amblyopia. The thicker choroid became thinner and thinner choroid became thicker. There was a negative correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.48, <i>P</i> = 0.02; Pearson’s correlation coefficient). C: Changes of the subfoveal CT of control eyes. There was no correlation between the rate of change in the subfoveal choroidal thickness and the baseline subfoveal choroidal thickness. (<i>r</i> = -0.15, <i>P</i> = 0.49; Pearson’s correlation coefficient).</p
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