Publications by authors named "Yingqiu Cao"

4 Publications

  • Page 1 of 1

Simultaneous optical and electrical in vivo analysis of the enteric nervous system.

Nat Commun 2016 06 7;7:11800. Epub 2016 Jun 7.

School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853, USA.

The enteric nervous system (ENS) is a major division of the nervous system and vital to the gastrointestinal (GI) tract and its communication with the rest of the body. Unlike the brain and spinal cord, relatively little is known about the ENS in part because of the inability to directly monitor its activity in live animals. Here, we integrate a transparent graphene sensor with a customized abdominal window for simultaneous optical and electrical recording of the ENS in vivo. The implanted device captures ENS responses to neurotransmitters, drugs and optogenetic manipulation in real time.
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http://dx.doi.org/10.1038/ncomms11800DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899629PMC
June 2016

A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.

J Neurosci Methods 2016 Mar 21;261:97-109. Epub 2015 Dec 21.

School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.

Background: Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability.

New Method: Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for "fastDTW" in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings.

Results: Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude.

Comparison With Existing Method(s): In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability.

Conclusions: We have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.
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http://dx.doi.org/10.1016/j.jneumeth.2015.12.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749467PMC
March 2016

Non-Faradaic Electrochemical Detection of Exocytosis from Mast and Chromaffin Cells Using Floating-Gate MOS Transistors.

Sci Rep 2015 Dec 21;5:18477. Epub 2015 Dec 21.

Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.

We present non-faradaic electrochemical recordings of exocytosis from populations of mast and chromaffin cells using chemoreceptive neuron MOS (CνMOS) transistors. In comparison to previous cell-FET-biosensors, the CνMOS features control (CG), sensing (SG) and floating gates (FG), allows the quiescent point to be independently controlled, is CMOS compatible and physically isolates the transistor channel from the electrolyte for stable long-term recordings. We measured exocytosis from RBL-2H3 mast cells sensitized by IgE (bound to high-affinity surface receptors FcεRI) and stimulated using the antigen DNP-BSA. Quasi-static I-V measurements reflected a slow shift in surface potential () which was dependent on extracellular calcium ([Ca]o) and buffer strength, which suggests sensitivity to protons released during exocytosis. Fluorescent imaging of dextran-labeled vesicle release showed evidence of a similar time course, while un-sensitized cells showed no response to stimulation. Transient recordings revealed fluctuations with a rapid rise and slow decay. Chromaffin cells stimulated with high KCl showed both slow shifts and extracellular action potentials exhibiting biphasic and inverted capacitive waveforms, indicative of varying ion-channel distributions across the cell-transistor junction. Our approach presents a facile method to simultaneously monitor exocytosis and ion channel activity with high temporal sensitivity without the need for redox chemistry.
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http://dx.doi.org/10.1038/srep18477DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685269PMC
December 2015

Programmable ion-sensitive transistor interfaces. III. Design considerations, signal generation, and sensitivity enhancement.

Phys Rev E Stat Nonlin Soft Matter Phys 2014 May 30;89(5):052817. Epub 2014 May 30.

Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853, USA.

We report on factors that affect DNA hybridization detection using ion-sensitive field-effect transistors (ISFETs). Signal generation at the interface between the transistor and immobilized biomolecules is widely ascribed to unscreened molecular charges causing a shift in surface potential and hence the transistor output current. Traditionally, the interaction between DNA and the dielectric or metal sensing interface is modeled by treating the molecular layer as a sheet charge and the ionic profile with a Poisson-Boltzmann distribution. The surface potential under this scenario is described by the Graham equation. This approximation, however, often fails to explain large hybridization signals on the order of tens of mV. More realistic descriptions of the DNA-transistor interface which include factors such as ion permeation, exclusion, and packing constraints have been proposed with little or no corroboration against experimental findings. In this study, we examine such physical models by their assumptions, range of validity, and limitations. We compare simulations against experiments performed on electrolyte-oxide-semiconductor capacitors and foundry-ready floating-gate ISFETs. We find that with weakly charged interfaces (i.e., low intrinsic interface charge), pertinent to the surfaces used in this study, the best agreement between theory and experiment exists when ions are completely excluded from the DNA layer. The influence of various factors such as bulk pH, background salinity, chemical reactivity of surface groups, target molecule concentration, and surface coatings on signal generation is studied. Furthermore, in order to overcome Debye screening limited detection, we suggest two signal enhancement strategies. We first describe frequency domain biosensing, highlighting the ability to sort short DNA strands based on molecular length, and then describe DNA biosensing in multielectrolytes comprising trace amounts of higher-valency salt in a background of monovalent saline. Our study provides guidelines for optimized interface design, signal enhancement, and the interpretation of FET-based biosensor signals.
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http://dx.doi.org/10.1103/PhysRevE.89.052817DOI Listing
May 2014