担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Microalgae-based methods used in heavy metal (HM)-polluted wastewater treatment have attracted increasing attention in recent decades, due to their eco-friendliness, profitability, and sustainability. Unfortunately, their low HM removal efficiency hinders them from practical use. In this work, we report an AI-on-a-chip method, a combination of AI and lab-on-a-chip technology, for identifying Euglena gracilis (a microalgal species) cells with high HM removal efficiency through a morphological meta-feature. In the near future, the implementation of the morphological meta-feature in a high-throughput cell sorting process will pave the way for realizing directed-evolution-based development of microalgae with extremely high HM removal efficiency for practical wastewater treatment worldwide.

DOI： 10.1109/Transducers50396.2021.9495554

Kyutacar

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = −0.82, P = 2.1 × 10−5) between a morphological meta-feature recognized via our machine learning algorithms and the Cu2+ removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.

DOI： 10.1021/acs.est.0c05278

Kyutacar

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記述言語：英語   掲載種別：研究論文（学術雑誌）

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology. This journal is

DOI： 10.1039/d0lc00080a

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記述言語：英語   掲載種別：研究論文（学術雑誌）

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a potent tool for biomedical applications as it enables high-throughput QPI of numerous cells for large-scale single-cell analysis in a label-free manner. However, there are a few critical limitations that hinder OTS-QPI from being widely applied to diverse applications, such as its costly instrumentation and inherent phase-unwrapping errors. Here, to overcome the limitations, we present a QPI-free OTS-QPI method that generates "virtual" phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images. Specifically, our trained generative adversarial network model generated virtual phase images with high similarity (structural similarity index >0.7) to their corresponding real phase images. This was also supported by our successful classification of various types of leukemia cells and white blood cells via their virtual phase images. The virtual OTS-QPI method is highly reliable and cost-effective and is therefore expected to enhance the applicability of OTS microscopy in diverse research areas, such as cancer biology, precision medicine, and green energy.

DOI： 10.1063/1.5134125

Kyutacar

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Nanoskiving, benefiting from its simple operation and high reproducibility, is a promising method to fabricate nanometer-size electrodes. In this work, we report the fabrication of Au nanowire electrodes with different shapes and well-controlled sizes through nanoskiving. Au nanowire block electrodes, membrane electrodes and tip electrodes are prepared with good reproducibility. Steady-state cyclic voltammograms (CVs) demonstrate that all these electrodes behave well as nanoband ultramicroelectrodes. A fast heterogeneous electron transfer rate constant can be extracted reliably from steady-state CVs at various size Au nanowire block electrodes by the Koutecký-Levich (K-L) method. The Au nanowire membrane electrodes demonstrate good sensitivity toward the oxidation of catecholamine and could monitor catecholamine released from rat adrenal chromaffin cells stimulated by high K+

DOI： 10.1039/c9an00122k

Kyutacar

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