Smart fluid manipulation with automatically controlled paper valves will enable automated and multi-step immunoassays on paper-based microfluidic devices. In this work, we present an integrated paper-based microfluidic platform with shape-memory polymer (SMP)-actuated fluid valves capable of automated colorimetric enzyme-linked immunosorbent assays (ELISAs). A single-layer microfluidic paper-based analytical device (μPAD) was designed to store all the reagents on the chip, and sequentially transfer reagents to a paper test zone following a specific ELISA protocol through automatic fluidic flow control by the multiple SMP-actuated valves. The actuation of a paper valve was based on the thermally responsive, duel-state shape transformation of a SMP sheet attached to the root of a paper cantilever beam for driving a hydrophilic paper bridge to connect and disconnect two paper channels. A portable colorimetric reader was developed to control the on-chip valve operations, quantify the colorimetric signal output, display the assay result, and wirelessly transmit the data to a smart phone for the application of telemedicine. Reliable operations of the paper valve and the entire μPAD were demonstrated with success rates of 97% and 93%, respectively. A detection mechanism for valve malfunction was designed and confirmed effective to identify any mal-operation of individual valves, thus rendering our platform reliable in real assays. For device calibration, we conducted direct ELISAs of rabbit IgG in phosphate-buffered saline (PBS), and achieved a low limit of detection (LOD) of 27 pM (comparable to that of standard and paper-based ELISAs). In order to demonstrate the clinical application of our multi-step immunoassay platform, we also conducted sandwich ELISAs to quantify the protein level of an inflammatory cytokine, namely tumor necrosis factor (TNF)-α, in surgically injured laryngeal tissues of rats. The protein levels of TNF-α were shown similar between the conventional and μPAD ELISAs.
Abstract-This paper presents a novel particle allocation approach to particle filtering which minimizes the total tracking distortion for a fixed number of particles over a video sequence. We define the tracking distortion as the variance of the error between the true state and estimated state and use rate-distortion theory to determine the optimal particle number and memory size allocation under fixed particle number and memory constraints, respectively. We subsequently provide an algorithm for simultaneous adjustment of the proposal variance and particle number for optimal particle allocation in video tracking systems. Experimental results are used to evaluate the proposed video tracking system and demonstrate its utility for target tracking in numerical examples and video sequences. We demonstrate the superiority of the proposed dynamic proposal variance and optimal particle allocation algorithm in comparison to traditional particle allocation methods, i.e., a fixed number of particles per frame.
| W e describe a one-joint planar arm which r epeatedly throws and catches parts on its surface, and we demonstrate that proper choice of the throw v elocity a n d arm geometry guarantees that the part will enter a unique recurrent motion pattern from a large set of initial con gurations. The resulting system resembles an open-loop stable juggler of polygonal parts. Combined with a simple one-bit sensor, the system can be used as a parts feeder.
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