Biological Engineering publications (MU)The items in this collection are the scholarly output of the faculty, staff, and students of the Department of Biological Engineering.https://hdl.handle.net/10355/82012024-03-28T22:37:08Z2024-03-28T22:37:08ZAngular Distribution of Diffuse Reflectance in Biological TissueXia, Jinjun, 1971-Yao, Gang, Ph. D.https://hdl.handle.net/10355/90452017-04-25T17:45:12Z2007-09-01T00:00:00ZAngular Distribution of Diffuse Reflectance in Biological Tissue
Xia, Jinjun, 1971-; Yao, Gang, Ph. D.
We measured angular-resolved diffuse reflectance in tissue samples of different anisotropic characteristics. Experimental measurements were compared with theoretical results based on the diffusion approximation.
The results indicated that the angular distribution in isotropic tissue was the same as in isotropic phantoms. Under normal incidence, the measured angular profiles of diffuse reflectance approached the Lambertian distribution when the evaluation location was far away from the incident point. The skewed angular profiles observed under oblique incidence could be explained using the diffuse model. The anisotropic tissue structures in muscle showed clear effects on the measurements especially at locations close to the light incidence. However, when measuring across the muscle fiber orientations, the
results were in good agreement with those obtained in isotropic samples.
doi:10.1364/AO.46.006552
2007-09-01T00:00:00ZApplying the Polarization Memory Effect in Polarization-gated Subsurface ImagingNothdurft, RalphYao, Gang, Ph. D.https://hdl.handle.net/10355/90032017-04-25T17:45:17Z2006-05-01T00:00:00ZApplying the Polarization Memory Effect in Polarization-gated Subsurface Imaging
Nothdurft, Ralph; Yao, Gang, Ph. D.
Polarization memory is a well established phenomenon occurring when circularly polarized light propagates in turbid media of larger particles. Recent studies have demonstrated that the circularly crosspolarized imaging can significantly improve subsurface reflection contrast due to the polarization memory effect. We have found that such improvement is strongly influenced by the optical properties of the media. Circularly cross-polarized light provides superior image enhancement in low scattering media, but becomes inferior in high scattering media. Our experiments also demonstrate that polarization imaging provides no significant improvement to image resolution.
doi:10.1364/OE.14.004656
2006-05-01T00:00:00ZComputational model of extracellular glutamate in the nucleus accumbens predicts neuroadaptations by chronic cocainePendyam, SandeepMohan, Ashwin, 1978-Kalivas, Peter W., 1952-Nair, Satish S., 1960-https://hdl.handle.net/10355/97872020-06-23T21:31:54Z2009-02-01T00:00:00ZComputational model of extracellular glutamate in the nucleus accumbens predicts neuroadaptations by chronic cocaine
Pendyam, Sandeep; Mohan, Ashwin, 1978-; Kalivas, Peter W., 1952-; Nair, Satish S., 1960-
Chronic cocaine administration causes instability in extracellular glutamate in the nucleus accumbens that is thought to contribute to the vulnerability to relapse. A computational framework was developed to model glutamate in the extracellular space, including synaptic and nonsynaptic glutamate release, glutamate elimination by glutamate transporters and diffusion, and negative feedback on synaptic release via metabotropic glutamate receptors (mGluR2/3). This framework was used to optimize the geometry of the glial sheath surrounding excitatory synapses, and by inserting physiological values, accounted for known stable extracellular, extrasynaptic concentrations of glutamate measured by microdialysis and glutamatergic tone on mGluR2/3. By using experimental values for cocaine-induced reductions in cystine-glutamate
exchange and mGluR2/3 signaling, the computational model successfully represented the experimentally observed increase in glutamate that is seen in rats during cocaine-seeking. This model provides a mathematical framework for describing how pharmacological or pathological conditions influence glutamate transmission measured by microdialysis.
Notice: this is the author's version of a work that was accepted for publication in Neuroscience. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neuroscience, Vol. 158, Issue #4 (2008) doi:10.1016/j.neuroscience.2008.11.014 . http://journals.elsevier.com/03064522/neuroscience/
2009-02-01T00:00:00ZCoregulation of Ion Channel Conductances Preserves Output in a Computational Model of a Crustacean Cardiac Motor NeuronBall, John M., 1982-Franklin, Clarence C.Tobin, Anne-EliseSchulz, DavidNair, Satish S., 1960-https://hdl.handle.net/10355/97612020-06-19T21:37:37Z2010-01-01T00:00:00ZCoregulation of Ion Channel Conductances Preserves Output in a Computational Model of a Crustacean Cardiac Motor Neuron
Ball, John M., 1982-; Franklin, Clarence C.; Tobin, Anne-Elise; Schulz, David; Nair, Satish S., 1960-
Similar activity patterns at both neuron and network levels can arise from different combinations of membrane and synaptic conductance values. A strategy by which neurons may preserve their electrical output is via cell type-dependent balances of inward and outward currents. Measurements of mRNA transcripts that encode ion channel proteins within motor neurons in the crustacean cardiac ganglion recently revealed correlations between certain channel types. To determine whether balances of intrinsic currents potentially resulting from such correlations preserve certain electrical cell outputs, we developed a nominal biophysical model of the crustacean cardiac ganglion using biological data. Predictions from the nominal model showed that coregulation of ionic currents may preserve the key characteristics of motor neuron activity. We then developed a methodology of sampling a multidimensional parameter space to select an appropriate model set for meaningful comparison with variations in correlations seen in biological datasets.
This item also falls under Society for Neuroscience copyright. For more information, please visit http://www.jneurosci.org/cgi/content/full/30/25/8637?maxtoshow=&hits=10&RESULTFORMAT=1&author1=nair&andorexacttitle=and&andorexacttitleabs=and&andorexactfulltext=and&searchid=1&FIRSTINDEX=0&sortspec=relevance&resourcetype=HWCIT&eaf .
Link active as of 1/29/2011. Link maintenance is the responsibility of the Society for Neuroscience.; Digital Object Identifier 10.1523/JNEUROSCI.6435-09.2010
2010-01-01T00:00:00Z