28 research outputs found

    Quality Improvement to Meet Competitive Fringe

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    Chalcogenide Glass-on-Graphene Photonics

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    Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of high-performance glass-on-graphene devices including ultra-broadband on-chip polarizers, energy-efficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators

    Quality Improvement to Meet Competitive Fringe

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    <i>N</i>‑Hydroxycinnamide Derivatives of Osthole Presenting Genotoxicity and Cytotoxicity against Human Colon Adenocarcinoma Cells in Vitro and in Vivo

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    Osthole is extracted from the Chinese herbs <i>Cnidium monnieri</i> and <i>Angelica pubescens</i>, and it was found to have antitumor activity in vitro and in vivo. A series of osthole derivatives have been synthesized, and the <i>N</i>-hydroxycinnamide derivatives of osthole, WJ1376-1 and WJ1398-1 were found to have the greatest potential against human colon adenocarcinoma cells. In contrast to the parental osthole, both WJ1376-1 and WJ1398-1 were found to induce multinucleation and polyploidy by microscopic observation and flow cytometry. WJ1376-1 and WJ1398-1 significantly activated ataxia telangiectasia and rad3 related (ATR) kinase, which triggered activation of the checkpoint kinase 2 (Chk2) signaling pathway and then down regulated Cdc25 phosphatase and Cdc2/cyclin B kinase activities. WJ1376-1 and WJ1398-1 also inhibited the phosphorylation of Aurora A kinase, which is associated with important processes during mitosis. The presence of a “comet” DNA fragment and phosphorylation of p53 at Ser 15 clearly indicated that DNA damage occurred with WJ1376-1 and WJ1398-1 treatment. WJ1376-1 and WJ1398-1 ultimately induced apoptosis as evidenced by the upregulation of Bad and activation of caspases-3, -7, and -9. Furthermore, WJ1376-1 and WJ1398-1 also showed a great effect in attenuating tumor growth without affecting the body weight of xenograft nude mice. Taken together, these results suggest that the toxic activities of WJ1376-1 and WJ1398-1 were dissimilar to that of the parental osthole, which can induce cell polyploidy and G<sub>2</sub>/M cell cycle arrest in colon adenocarcinoma cells and may provide a potential therapeutic target for colon cancer treatment in the future

    Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support

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    In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring. To address this concern, the data rotational method by PCA in tree-based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre-defined number of subset features (free pre-defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real-world medical data sets, Random RotBoost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks
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