Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.113Ysciescopu

Regional characteristics of fine aerosol mass increase elucidated from long-term observations and KORUS-AQ campaign at a Northeast Asian background site

Funding Information: This research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT) (NRF2020M3G1A111499813). S. Lim was supported by the National Research Foundation of Korea (NRF) from the Ministry of Science and ICT (2018R1D1A1B07050849 and 2021R1C1C2011543). M. Lee thanks to the support by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF2020R1A2C301459213). S.-W. Kim was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2017R1D1A1B06032548). Funding to K.-S. Kang was provided by the National Institute of Environmental Research (NIER-RP2017-166). Funding Information: This research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT) (NRF2020M3G1A111499813). S. Lim was supported by the National Research Foundation of Korea (NRF) from the Ministry of Science and ICT (2018R1D1A1B07050849 and 2021R1C1C2011543). M. Lee thanks to the support by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF2020R1A2C301459213). S.-W. Kim was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (2017R1D1A1B06032548). Funding to K.-S. Kang was provided by the National Institute of Environmental Research (NIER-RP2017-166). Publisher Copyright: © 2022 The Author(s)Northeast Asia has suffered from severe PM2.5 pollution and the exact mechanisms have yet to be fully understood. Here, we investigated the transformation processes of submicron aerosols using a 4-year data set obtained at Jeju, a Northeast Asian background site. The diurnal-cycle constrained empirical orthogonal function analysis of nanoparticle size-number distribution distinguished 2 modes: burst of nucleation-Aitken particles and increase in accumulation mode particles, representing “new particle formation and growth” and “PM2.5 mass increase,” respectively. In these events, aerosol and meteorological characteristics changed progressively over several days, revealing that the PM2.5 mass increase is an episodic event occurring on a regional scale. The increase in PM2.5 mass was accompanied by an increase in aerosol liquid water content, which correlated well with SO4-2 and NO3, and a decrease in incoming solar radiation (-14.1 Wm-2 day-1) constituting a positive feedback. The “transport/haze” episode of KOREA-U.S. Air Quality campaign corresponds to “PM2.5 mass increase,” during which the vertical evolution of particles demonstrates that nanoparticles ≥3.5 nm were entrained into the shallow boundary layer upon vertical mixing and converted to accumulation-mode particles ≥0.3 mm at relative humidity (RH) exceeding the deliquescence RH of secondary inorganic aerosol (SIA). Coincidently, at ground, the coating thickness of refractory black carbon (rBC) (48 ± 39 nm) and SIA concentration increased. Furthermore, the diameter of rBC (180-220 nm)-containing particle in core-shell configuration linearly increased with PM2.5 mass, reaching 300-400 nm at PM2.5 ≥ 40 mg m-3.This observational evidence suggests that the thick coating of rBCs resulted from the active conversion of condensable gases into the particulate phase on the rBC surface, thereby increasing the mass of the accumulation-mode aerosol. Consequently, this result complies with the strategy to reduce primary emissions of gaseous precursors for SIA and particulates such as rBC as a way to effectively mitigate haze pollution as well as climate change in Northeast Asia.Peer reviewe

Tuning-free controller to accurately regulate flow rates in a microfluidic network

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.open1142sciescopu

Factors affecting the simple febrile seizures in COVID-19 children: a case–control study from China

ObjectiveThe over-age phenomenon of simple febrile seizures (SFS) was found during the epidemic in COVID-19, but there was no clear explanation, especially in China. This study aimed to analyze the clinical and auxiliary examination features of SFS in children infected with the coronavirus disease 2019 (COVID-19).MethodsIn total, 78 patients with SFS in the Department of Pediatric and Neurology of Qujing First People's Hospital were enrolled and divided into the COVID-19-positive group (case group) and the COVID-19-negative group (control group). The clinical characteristics, auxiliary examinations, and risk factors were analyzed.ResultsThere were significant differences in age stratification between the two groups. The proportion of children aged over 5 years old in the case group (47.4%) was higher than that of the control group (5%) (p < 0.0001). In terms of sex distribution, the proportion of males in the case group was higher than that in the control group (71.1% vs. 50%), but the difference was not statistically significant (p = 0.0678). For blood cell analysis, the values of white blood cells (WBC), lymphocytes (LY), and monocytes (MN) in the case group were significantly lower than those in the control group (p < 0.01). Serum electrolyte analysis showed the greatest difference in blood sodium. The proportion of hyponatremia in the case group was higher than that in the control group (36.8% vs. 17.5%), but the difference did not reach statistical significance (p = 0.0745). A multivariate logistic regression analysis showed that the history of FS was a independent protective factors for SFS in children with COVID-19 (OR = 0.115, p = 0.009), and age was an independent risk factor for SFS in children with COVID-19 (OR = 1.042, p = 0.001).ConclusionAge distribution, sex a previous history of FS and hyponatremia were different between children with and without COVID-19 in SFS. The history of FS was an independent protective factors for SFS in children with COVID-19

임의 분포된 촉각 센서로부터 응력 지도를 추정하기 위한 심층 신경망 구조

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Memory-Efficient Deep Neural Network Architecture to Estimate Stress Map from Randomly Distributed Tactile Sensors

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Transcriptional Programs and Regeneration Enhancers Underlying Heart Regeneration

The heart plays the vital role of propelling blood to the entire body, which is essential to life. While maintaining heart function is critical, adult mammalian hearts poorly regenerate damaged cardiac tissue upon injury and form scar tissue instead. Unlike adult mammals, adult zebrafish can regenerate injured hearts with no sign of scarring, making zebrafish an ideal model system with which to study the molecular mechanisms underlying heart regeneration. Investigation of heart regeneration in zebrafish together with mice has revealed multiple cardiac regeneration genes that are induced by injury to facilitate heart regeneration. Altered expression of these regeneration genes in adult mammals is one of the main causes of heart regeneration failure. Previous studies have focused on the roles of these regeneration genes, yet the regulatory mechanisms by which the expression of cardiac regeneration genes is precisely controlled are largely unknown. In this review, we will discuss the importance of differential gene expression for heart regeneration, the recent discovery of cardiac injury or regeneration enhancers, and their impact on heart regeneration

Improvement in Error Recognition of Real-Time Football Images by an Object-Augmented AI Model for Similar Objects

In this paper, we analyze the recognition error of the general AI recognition model and propose the structure-modified and object-augmented AI recognition model. This model has object detection features that distinguish specific target objects in target areas where players with similar shapes and characteristics overlapped in real-time football images. We implemented the AI recognition model by reinforcing the training dataset and augmenting the object class. In addition, it is shown that the recognition rate increased by modifying the model structure based on the recognition errors analysis of general AI recognition models. Recognition errors were decreased by applying the modules of HSV processing and differentiated classes (overlapped player groups) learning to the general AI recognition model. We experimented in order to compare the recognition error reducing performance with the general AI model and the proposed AI model by the same real-time football images. Through this study, we have confirmed that the proposed model can reduce errors. It showed that the proposed AI model structure to recognize similar objects in real-time and in various environments could be used to analyze football games