Application of a new microcantilever biosensor resonating at the air–liquid interface for direct insulin detection and continuous monitoring of enzymatic reactions

Here we describe the application of a recently developed high-resolution microcantilever biosensor resonating at the air–liquid interface for the continuous detection of antigen–antibody and enzyme–substrate interactions. The cantilever at the air–liquid interface demonstrated 50% higher quality factor and a 5.7-fold increase in signal-to-noise-ratio (SNR) compared with one immersed in the purified water. First, a label-free detection of a low molecular weight protein (insulin, 5.8 kDa) in physiological concentration was demonstrated. The liquid facing side of the cantilever was functionalized by coating its surface with insulin antibodies, while the opposite side was exposed to air. The meniscus membrane at the micro-slit around the cantilever sustained the liquid in the microchannel. After optimizing the process of surface functionalization, the resonance frequency shift was successfully measured for insulin solutions of 0.4, 2.0, and 6.3 ng ml^(−1). To demonstrate additional application of the device for monitoring enzymatic protein degradation, the liquid facing microcantilever surface was coated with human recombinant SOD1 (superoxide dismutase 1) and exposed to various concentrations of proteinase K solution, and the kinetics of the SOD1 digestion was continuously monitored. The results showed that it is a suitable tool for sensitive protein detection and analysis

Modeling Realized Variance with Realized Quarticity

This paper proposes a model for realized variance that exploits information in realized quarticity. The realized variance and quarticity measures are both highly persistent and highly correlated with each other. The proposed model incorporates information from the observed realized quarticity process via autoregressive conditional variance dynamics. It exploits conditional dependence in higher order (fourth) moments in analogy to the class of GARCH models exploit conditional dependence in second moments

Jointly Modeling Autoregressive Conditional Mean and Variance of Non-Negative Valued Time Series

This paper considers observation driven models with conditional mean and variance dynamics for non-negative valued time series. The motivation is to relax the restriction imposed on the higher order moment dynamics in standard multiplicative error models driven only by the conditional mean dynamics. The empirical fit of a zero inflated mixture distribution is assessed with trade duration data with a large fraction of zero observations. All authors have read and agreed to the published version of the manuscript

Silicon-based integrated interferometer with phase modulation driven by surface acoustic waves

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研究解説 : Characterization of Silicon Nanocantilevers

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