Neuron’s spikes noise level classification using hidden markov models

Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information

Multinozzle Emitter Arrays for Nanoelectrospray Mass Spectrometry

Mass spectrometry (MS) is the enabling technology for proteomics and metabolomics. However, dramatic improvements in both sensitivity and throughput are still required to achieve routine MS-based single cell proteomics and metabolomics. Here, we report the silicon-based monolithic multinozzle emitter array (MEA), and demonstrate its proof-of-principle applications in high-sensitivity and high-throughput nanoelectrospray mass spectrometry. Our MEA consists of 96 identical 10-nozzle emitters in a circular array on a 3-inch silicon chip. The geometry and configuration of the emitters, the dimension and number of the nozzles, and the micropillar arrays embedded in the main channel, can be systematically and precisely controlled during the microfabrication process. Combining electrostatic simulation and experimental testing, we demonstrated that sharpened-end geometry at the stem of the individual multinozzle emitter significantly enhanced the electric fields at its protruding nozzle tips, enabling sequential nanoelectrospray for the high-density emitter array. We showed that electrospray current of the multinozzle emitter at a given total flow rate was approximately proportional to the square root of the number of its spraying-nozzles, suggesting the capability of high MS sensitivity for multinozzle emitters. Using a conventional Z-spray mass spectrometer, we demonstrated reproducible MS detection of peptides and proteins for serial MEA emitters, achieving sensitivity and stability comparable to the commercial capillary emitters. Our robust silicon-based MEA chip opens up the possibility of a fully-integrated microfluidic system for ultrahigh-sensitivity and ultrahigh-throughput proteomics and metabolomics

Microfluidic Device for the Selective Chemical Stimulation of Neurons and Characterization of Peptide Release with Mass Spectrometry

Neuropeptides are synthesized in and released from neurons and are involved in a wide range of physiological processes, including temperature homeostasis, learning, memory, and disease. When working with sparse neuronal. net-works, the ability to collect and characterize small sample volumes is important as neurons often release only a small proportion of their mass limited content Microfluidic systems are well suited for the study of neuropeptides. They offer the ability to control and manipulate the extracellular environment and small sample volumes, thereby reducing the dilution of peptides following release. We present an approach for the culture and stimulation of a neuronal network within a microfluidic device, subsequent collection of the released peptides, and their detection via mass spectrometry. The system employs microvalve-controlled stimulation channels to selectively stimulate a low-density neuronal culture, allowing us to determine the temporal onset of peptide release. Released peptides from the well-characterized, peptidergic bag cell neurons of Aplysia californica were collected and their temporal pattern of release was characterized with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. We show a robust difference in the timing of release for chemical solutions containing elevated K+ (7 +/- 3 min), when compared to insulin (19 +/- 7 min) (p < 0.00001).close