Nankai University

About: Nankai University is a education organization based out in Tianjin, China. It is known for research contribution in the topics: Catalysis & Enantioselective synthesis. The organization has 42964 authors who have published 51866 publications receiving 1127896 citations. The organization is also known as: Nánkāi Dàxué.

A Highly Sensitive Fluorescence Probe for Fast Thiol-Quantification Assay of Glutathione Reductase†

Abstract: Fast detection of cellular thiols in aqueous medium was achieved using a newly developed fluorescence probe (see picture). Based on this probe, a high-throughput fluorescence assay for glutathione reductase was developed.

The Calcium Store Sensor, STIM1, Reciprocally Controls Orai and CaV1.2 Channels

Abstract: Calcium signals, pivotal in controlling cell function, can be generated by calcium entry channels activated by plasma membrane depolarization or depletion of internal calcium stores. We reveal a regulatory link between these two channel subtypes mediated by the ubiquitous calcium-sensing STIM proteins. STIM1 activation by store depletion or mutational modification strongly suppresses voltage-operated calcium (CaV1.2) channels while activating store-operated Orai channels. Both actions are mediated by the short STIM-Orai activating region (SOAR) of STIM1. STIM1 interacts with CaV1.2 channels and localizes within discrete endoplasmic reticulum/plasma membrane junctions containing both CaV1.2 and Orai1 channels. Hence, STIM1 interacts with and reciprocally controls two major calcium channels hitherto thought to operate independently. Such coordinated control of the widely expressed CaV1.2 and Orai channels has major implications for Ca2+ signal generation in excitable and nonexcitable cells.

Hierarchically Porous ZnO Architectures for Gas Sensor Application

Abstract: Hierarchically three-dimensional (3D) porous ZnO architectures were synthesized by a template-free, economical hydrothermal method combined with subsequent calcination. First, a precursor of hierarchical basic zinc carbonate (BZC) nanostructures self-assembled by sheet-like blocks was prepared. Then calcination of the precursor produced hierarchically 3D porous ZnO architectures composed of interconnected ZnO nanosheets with high porosity resulting from the thermal decomposition of the precursor. The products were characterized by X-ray diffraction, Fourier tranform infrared spectroscopy, thermogravimetric−differential thermalgravimetric analysis, scanning electron microscopy, transmission electron microscopy, and Brunauer−Emmett−Teller N2 adsorption−desorption analyses. Control experiments with variations in solvent and reaction time respectively revealed that ethanol was responsible for the formation of the BZC precursor, and the self-assembly of BZC nanosheets into hierarchically 3D architectures was h...

BING: Binarized normed gradients for objectness estimation at 300fps

Abstract: Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersection-over-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image.