Single-molecule Biophysics: Machine Learning for Automated Data Processing

Abstract-Single-molecule fluorescence microscopy has been greatly successful in understanding biophysics at molecular level. This technique has been advancing toward higher throughput, which creates a need for a data-analysis tool to distinguish molecule of interest from other fluorescence signals. Here, we have used supervised machine-learning approaches to filter biological events of our interest, and present three approaches applicable to different data set size

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

In-situ observation of the soot deposition process on a solid wall with a diffusion flame along the wall

Experiments at the Japan Microgravity Center (JAMIC) have investigated the interaction between diffusion flames and solid surfaces placed near flames. The fuel for the flames was C2H4 and the surrounding oxygen concentration 35%, with surrounding air temperatures of T-a = 300 and 600 K. The effects of these parameters on soot distribution in diffusion flames and soot deposition on solid walls were studied. Direct images of the whole flame and shadow images of the flame with back light were recorded and used to calculate the soot volume fraction by the Abel transformation method. Results show that at the higher surrounding air temperature the soot particle distribution region is closer to the wall and results in more deposition. Numerical simulation was also performed to determine the motion of soot particles in the flames and the soot deposition characteristics. The results are in good agreement with the observed soot behavior in microgravity

Estimation of Elbow Wall Thinning Using Ensemble-Averaged Mel-Spectrogram with ResNet-like Architecture

An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with curved pipe specimens with artificial wall thinning. The vibration characteristics of wall-thinned elbows were extracted by using a mel-spectrogram in which modal characteristic variation shifting can be expressed. To reduce the deviation of the model’s prediction values, the ensemble mean value of the mel-spectrogram was used to emphasize stationary signals and reduce noise signals. A convolutional neural network (CNN) regression model with residual blocks was proposed and showed improved performance compared to the models without the residual block. The proposed regression model predicted the thinning thickness of the elbow excluded in training dataset

Effects of fibrous cap thickness on mechanical stress and flow characteristics

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