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1. Decoding of Code-Multiplexed Coulter Sensor Signals via Deep Learning

   https://doi.org/10.1109/TRANSDUCERS.2019.8808195  

   Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Anandakumar, Dakshitha B; Sarioglu, A. Fatih (June 2019, Transducers 2019)

   Code-multiplexed Coulter sensors can easily be integrated into microfluidic devices and provide information on spatiotemporal manipulations of suspended particles for quantitative sample assessment. In this paper, we introduced a deep learning-based decoding algorithm to process the output waveform from a network of code- multiplexed Coulter sensors on a microfluidic device. Our deep learning-based algorithm both simplifies the design of coded Coulter sensors and increases the signal processing speed. As a proof of principle, we designed and fabricated a microfluidic platform with 10 code-multiplexed Coulter sensors, and used a suspension of human ovarian cancer cells as a test sample to characterize the system. Our deep learning-based algorithm resulted in an 87% decoding accuracy at a sample processing speed of 800 particles/s. 

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2. Processing code-multiplexed Coulter signals via deep convolutional neural networks

   https://doi.org/10.1039/c9lc00597h  

   Wang, Ningquan; Liu, Ruxiu; Asmare, Norh; Chu, Chia-Heng; Sarioglu, A. Fatih (September 2019, Lab on a Chip)

   Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network. 

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3. Analysis and Characterization of Soft-Lithography-Compatible Parallel-Electrode-Sensors in Microfluidic Devices

   https://doi.org/10.1109/TRANSDUCERS.2019.8808217  

   Liu, Ruxiu; Chu, Chia-Heng; Boya, Mert; Lee, Dohwan; Civelekoglu, Ozgun; Chen, Hang; Sarioglu, A. Fatih (June 2019, Transducers 2019)

   Microfluidic devices integrated with Coulter sensors have been widely used in counting and characterizing suspended particles. The electrodes in these devices are mostly arranged in a coplanar fashion due to a simple fabrication process and leads to non-uniform electric fields confined to the floor of the microfluidic channel. We have recently introduced a simple fabrication method that can effortlessly create parallel electrodes in microfluidic devices built with soft-lithography. In this paper, we theoretically and experimentally analyze the developed parallel-electrode Coulter sensor and compare its sensitivity with that of the Coulter sensor built on conventional coplanar electrodes. Both our simulation results and experiments with cell suspensions show that parallel-electrode Coulter sensor can provide as much as ~5× sensitivity improvement over conventional coplanar electrodes. 

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4. Quantitative Measurement of Cell Surface Expression via Magnetophoretic Cytometry

   https://doi.org/10.1109/TRANSDUCERS.2019.8808437  

   Civelekoglu, Ozgun; Wang, Ningquan; Boya, Mert; Ozkaya-Ahmadov, Tevhide; Liu, Ruxiu; Sarioglu, A. Fatih (June 2019, Transducers 2019)

   Identification of membrane antigens and measurement of their expression within a cell population is of fundamental importance to medical and biological studies. In this work, we present a cytometry approach that is based on magnetophoresis and distributed Coulter sensing in a microfluidic system. Our magnetophoretic cytometer offers quantitative analysis of cell membrane antigens on a portable and disposable platform compared to conventional flow cytometers, which are complex, expensive and large systems. Our tests with human breast cancer cells show the utility of our microfluidic device and its potential as a point-of-care instrument for biomedical testing. 

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5. Using Software-Based Performance Counters to Expose Low-Level Open MPI Performance Information

   https://doi.org/10.1145/3127024.3127039  

   Eberius, David; Patinyasakdikul, Thananon; Bosilca, George (September 2017, EuroMPI)

   This paper details the implementation and usage of software-based performance counters to understand the performance of a particular implementation of the MPI standard, Open MPI. Such counters can expose intrinsic features of the software stack that are not available otherwise in a generic and portable way. The PMPI-interface is useful for instrumenting MPI applications at a user level, however it is insufficient for providing meaningful internal MPI performance details. While the Peruse interface provides more detailed information on state changes within Open MPI, it has not seen widespread adoption. We introduce a simple low-level approach that instruments the Open MPI code at key locations to provide fine-grained MPI performance metrics. We evaluate the overhead associated with adding these counters to Open MPI as well as their use in determining bottlenecks and areas for improvement both in user code and the MPI implementation itself. 

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