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Miyagi, N., Kimura, M., Shoji, H., Saima, A., Ho, C.-M., Tung, S., and Tai, Y.-C., “Statistical Analysis on Wall Shear Stress of Turbulent Boundary Layer in a Channel Flow using Micro-Shear Stress Imager”, International Journal of Heat and Fluid Flow, vol. 21, pp. 576 – 581, 2000.

Leu, T. S. and Ho, C.M., “Control of Global Instability in a Non-Parallel Near Wake”, Journal of Fluid Mechanics, vol. 404, pp. 345-378, 2000.

Jiang, F., Lee, G.B, Tai, Y.C. and Ho, C.M., “A Flexible Micromachine-Based Shear-Stress Sensor Array and its Application to Separation-Point Detection”, Sensors and Actuators A: Physical, 79(3), pp. 194-203, 2000.

Lee, G.B., Huang, P.H., Ho, C.H., Jiang, F., Grosjean, C., and Tai, Y.C., “Sensing and Control of Aerodynamic Separation by MEMS”, The Chinese Journal of Mechanics, Vol. 16, No. 1, pp. 45-52, March 2000.

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Lee, Y.K, Tabeling, P., Shih, C. and Ho, C.M., “Characterization of a MEMS-Fabricated Mixing Device”, Proceedings of MEMS, ASME International Mechanical Engineering Congress and Exposition, pp. 505-511, Orlando, Florida, November 2000.

J.J. Lan, E. H. Dunn, B. Ho, C.M. “Enzyme-Based Electrochemical Biosensor with DNA Array Chip”, Proceedings of Micro Total Analysis System 2000, The Netherlands, 14-18 May, pp.509-511, 2000.

Jiang, F., Xu, Y., Weng, T., Han, Z., Tai, Y.C., Huang, A., Ho, C.M., and Newbern, S., “Flexible Shear Stress Sensor Skin for Aerodynamics Applications,” MEMS-2000, Miyazaki, Japan, pp. 465-470, 2000.

Kimura, M., Takei, M., Miyaki, N., Ho, C.H., Saito, Y., and Horii, K., “High Shear Stress Detection with Micro Imaging Chip and Discrete Wavelets Transform”, Proceedings of ASME FEDSM’00, Boston, Massachusetts, June 11-15, 2000.

Lin, Q., Jiang, X.Q., Han, Z., Tai, Y.C., Lew, J. and Ho, C.M., “MEMS Thermal Shear-Stress Sensors: Experiments, Theory and Modeling”, Technical Digest, Solid State Sensor and Actuator Workshop (SSAW ’00), pp. 304-307, Hilton Head Island, South Carlina, June 2000.

Huang, P.H., Ho, C.M., Jiang, F. and Tai, Y.C., “MEMS Transducers for Aerodynamics – A Paradym Shift”, AIAA Paper No. 00-0249, Reno, Nevada, January 10-13, 2000.

T. Nick Pornsin-Sirirak, S.W. Lee, H. Nassef, J. Grasmeyer, Y.C. Tai, C.M. Ho, M. Keennon,” MEMS Wing Technology for A Battery-Powered Ornithopter,” The 13th IEEE International Conference on Micro Electro Mechanical Systems (MEMS ’00), Miyazaki, Japan, Jan 23-27, 2000, pp. 799-804

Chen, Y. F., Yang, J. M., Gau, J. J., Ho C.M. and Tai, Y. C., “Microfluidic Detection System for Biological Agent Detection”, The 3rd International Conference on The Interaction of Art and Fluid Mechanics, Zurich, Switzerland, 2000.

Wang, T.H, Chen, Y.F, Masset, S., Ho, C.M., and Tai, Y.C., “Molecular Beacon Based Micro Biological Detection System”, Proceedings of International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, 2000.

Lee, G. B., Chiang, S., Tai, Y. C., Tsao, T., Liu, C., Huang, P. H. and Ho, C. M., “Robust Vortex Control of a Delta Wing by Distributed MEMS Actuators”, Journal of Aircraft, Vol. 37, No. 4, pp. 697, July-August, 2000.

Takei, M., Kimura, M., Ho, C.H., Saito, Y., and Horii, K., “Visualization of Shear Stress with Micro Imaging Chip and Discrete Wavelets Transform”, Proceedings of ASME FEDSM’00, Boston, Massachusetts, June 11-15, 2000.


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Kimura, M., Tung, S., Liu, J., Ho, C.M., Jiang, F. and Tai, Y.C., “Measurements of Wall Shear Stress of Turbulent Boundary Layer Using Micro Shear Stress Imaging Chip”, Fluid Dynamics Research, Vol. 24, pp. 329-342, 1999.

Tsao, T., Jiang,f., Liu, C., Miller, R., Tung, s., Huang, J.B., Gupta, B., Babcock, D., Lee, C., Tai, Y.C., Ho, C. M., Kim, J. and Goodman, R., “MEMS based Active Drag Reduction in Turbulent Boundary Layers”, Microengineering Aerospace Systems, ed. Helvajian, H., pp. 553-580, 1999.

Huang, J.B, Jiang, F.K., Tai, Y.C., and Ho, C.M., “A Micro-Electro-Mechanical-System-Based Thermal Shear-Stress Sensor with Self-Frequency Compensation”, Measurement Science and Technology, Vol. 10, pp. 687-696, 1999.

Liu, C., J. Huang, J., Zhu, Z., Jiang, F., Tung, S., Tai, Y.C. and Ho, C.M., “A Micromachined Flow Shear-stress Sensor Based on Thermal Transfer Principles,Journal of MEMS, Vol. 8, No. 1, pp.90-99, 1999.

Yang, X., Yang J.M., Tai, Y.C. and Ho, C.-M., “Micromachined Membrane Particle Filters”, Sensors and Actuators, Vol.73, pp.184-191, 1999.

C. Liu, T. Tsao, V. Lee, J. Lu, Y. Yi, Y.C. Tai, C.M. Ho, “Out of Plane Magnetic Actuators with Electroplated Permalloy for Fluid Dynamic Control,J. Sensors and Actuators, Vol. 78, Issue 2-3, pp. 190-197, 1999.

Li, W. J., Mai, J.D. and Ho, C.M., “Sensors and actuators on non-planar substrates”, Sensors and Actuators, Vol.73, pp.80-88, 1999.


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EB sorting (Peter Lillehoj)

EB sorting

(Peter Lillehoj)

With their developmental potential to differentiate into all three of the germ layers (endoderm, mesoderm and
ectoderm), embryonic stem (ES) cells provide a unique opportunity to study lineage commitment and can potentially
serve as a source of specialized cells for regenerative medicine. Among the various published ES cell differentiation
protocols, the formation of embryoid bodies (EBs), spherical aggregates of spontaneously differentiating ES cells,
is commonly utilized as a critical intermediate step. EBs appear to recapitulate embryonic development, facilitating
induction of differentiation and commitment into specific cell types and obtaining EBs of homogeneous size appears
to be a key factor for successful ES cell research.

Current pipetting methods for separating EBs are time consuming, inefficient and result in poor size uniformity.
Additionally, the separation of EBs through external force fields, such as dielectrophoretic (DEP), acoustic or
magnetic, may raise potential issues in damaging the fragile cellular entities, thereby affecting subsequent cell
differentiation. To overcome these obstacles, we have developed a microfluidic device for sorting embryoid bodies
(EBs) with large dynamic size ranges up to 300 μm. The proposed separation scheme utilizes appropriately spaced
pillars within a microchannel to alter the fluid flow pathway, thus allowing particles of defined sizes to be diverted
towards specific flow paths. We demonstrate for the first time on-chip separation of mouse EBs, which were
separated into three size groups with separation efficiencies approaching 80%. The ability to extract specific size
ranges of EBs will greatly facilitate their subsequent differentiation studies.

Feedback System Control (FSC) for HSV (Xianting Ding)

Feedback System Control (FSC) for HSV (Xianting Ding)

In human disease, many molecular assemblies and pathways within the cell’s signaling and regulatory network function aberrantly, resulting in a complex disease state. Therefore, the most effective way to treat such complex diseases is to use multiple approaches. For example, treating HIV infection and AIDS with a combination of multiple drugs is more effective than single drug therapy. However, determining the optimal combination of several drugs at specific dosages to reach an objective endpoint result (e.g., high efficacy and low toxicity) is a major challenge. The combination of N drugs at M dosage levels results in MN possible combinations, a testing pool size that makes complete screening cost prohibitive and infeasible. Furthermore, many high efficacy drug combinations will also exhibit high toxicity, making these inappropriate for clinical use. We addressed the challenge of identifying high efficacy/low toxicity drug combinations by introducing a feedback system control (FSC) method. With this method, we identified the optimal drug combination that inhibits all HSV-1 infection in vitro and attain limited dosage of the highly toxic drug Ribavirin. This study demonstrates this FSC platform is capable of rapidly screening a large search space using multiple parameters to identify optimal drug combinations and doses for the treatment of a viral infection.

The FSC platform technology consists of four modules. The first module is the input stimulations, namely, the drug combinations. The second module is the bio-complex system of interest, which in this case is the virus and host cell. The third module is the objective function readout, which is the goal for optimization, such as efficacy, toxicity, drug resistance, etc. The fourth module is the search algorithm, which provides the next set of stimulant dosages for directing the bio-complex system toward the desired phenotype (See figure above). For the FSC approach, we started with a set of drugs at arbitrarily chosen concentrations to stimulate the cells infected with HSV-1. The percentage of the host cells that become infected is used as the endpoint readout of the objective function in the third FSC module, and will most likely not be satisfactory in the first permutation. The fourth module of the FSC will use a search algorithm to determine a selection of drug concentrations with potentially better performance that will be used in the next iteration of the experiment and be fed back into the bio-complex system. Iterations of this feedback will continue until the optimal drug combination is reached (i.e., when the system objective function becomes satisfactory).


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