A microfabricated high-throughput cell electrofusion chip with 1,368 pairs of high aspect ratio silicon microelectrodes is presented. These microelectrodes, which were distributed in six individual microscale cell-fusion chambers, were covered with titanium and gold thin film to improve their electric conductivity as well as surface hydrophobility. Six chambers having different electrode distances make the chip highly suitable for fusing cells with different sizes. A microfluidic platform was set up for flowing control, cell manipulation and also experimental observation. Cells for electrofusion were first aligned at the prearranged locations by the dielectrophoretic force between two counter-electrodes, which benefits the traverse of electric pulse through the cell-cell contacting point for electroporation. Several on-chip cell electrofusion experiments have been carried out on different kinds of animal cells and plant protoplasts. Compared with conventional electrofusion methods, higher fusion efficiency was achieved on this device for precisely forming micropores on the proximate membranes of two contacting cells, and high throughput was also obtained due to the use of a large number of microelectrodes for cell manipulation and fusion. Moreover, a much lower power supply was required for the shorter distance between two counter-electrodes.
For wireless multimedia sensor networks (WMSNs) deployed in noisy and unattended environments, it is necessary to establish a comprehensive framework that protects the accuracy of the gathered multimedia information. In this paper, we jointly consider data aggregation, information trust, and fault tolerance to enhance the correctness and trustworthiness of collected information. Based on the multilayer aggregation architecture of WMSNs, we design a trust-based framework for data aggregation with fault tolerance with a goal to reduce the impact of erroneous data and provide measurable trustworthiness for aggregated results. By extracting statistical characteristics from different sources and extending Josang's trust model, we propose how to compute self-data trust opinion, peer node trust opinion, and peer data trust opinion. According to the trust transfer and trust combination rules designed in our framework, we derive the trust opinion of the sink node on the final aggregated result. In particular, this framework can evaluate both discrete data and continuous media streams in WMSNs through a uniform mechanism. Results obtained from both simulation study and experiments on a real WMSN testbed demonstrate the validity and efficiency of our framework, which can significantly improve the quality of multimedia information as well as more precisely evaluate the trustworthiness of collected information.
Purpose:Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively.
Conclusions:The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation.
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