Line-scan Imaging Research Articles

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

Residual Mulching Film Detection in Seed Cotton Using Line Laser Imaging

Due to the widespread use of mulching film in cotton planting in China, residual mulching film mixed with machine-picked cotton poses a significant hazard to cotton processing. Detecting residual mulching film in seed cotton has become particularly challenging due to the film’s semi-transparent nature. This study constructed an imaging system combining an area array camera and a line scan camera. A detection scheme was proposed that utilized features from both image types. To simulate online detection, samples were placed on a conveyor belt moving at 0.2 m/s, with line lasers at a wavelength of 650 nm as light sources. For area array images, feature extraction was performed to establish a partial least squares discriminant analysis (PLS-DA) model. For line scan images, texture feature analysis was used to build a support vector machine (SVM) classification model. Subsequently, image features from both cameras were merged to construct an SVM model. Experimental results indicated that detection methods based on area array and line scan images had accuracies of 75% and 79%, respectively, while the feature fusion method achieved an accuracy of 83%. This study demonstrated that the proposed method could effectively improve the accuracy of residual mulching film detection in seed cotton, providing a basis for reducing residual mulching film content during processing.

Line-scanning microscopy with laterally symmetric imaging using simultaneous cross-line illumination

Using an on-the-fly scanning scheme, line confocal microscopy can obtain complex structures of large biological tissues with high throughput. Yet, it suffers from lateral imaging asymmetry and thus introduces the potential deformations of the observation results. Here, we propose cross-line illumination microscopy (cLIM) that acquires the imaging data of two perpendicular directions simultaneously through the same objective lens in a line scanning and utilizes two-direction deconvolution fusion to achieve lateral symmetric imaging performance. Imaging fluorescence beads indicates that cLIM reduces lateral resolution asymmetry from 46.1% to 2.5% and improves lateral resolution by 31.0%, compared with traditional line-scanning imaging. Compared with commercial point-confocal microscopy, the cLIM has a 25.84× increase in imaging speed and 1.93× better background-suppressing ability when imaging an 11,306 μm×7783 μm×100 μm mouse kidney slice. We also show the advantages of the cLIM in observing direction-sensitive texture features by imaging a muscular tissue slice. cLIM offers a novel solution to achieve laterally symmetric line-scanning imaging with simple modifications while maintaining high throughput and accuracy for imaging large-scale samples.

Detection of different adulteration in cinnamon powder using hyperspectral imaging and artificial neural network method

Cinnamon is one of the medicinal spices that is important in point of economic and human health. The goal of the present research was to classify the levels of different adulterations in the spice using hyperspectral imaging technology. In the present research, three adulterants were investigated including sea foam powder and chickpea and wheat flour with 0, 5, 15, 30, and 50% adulteration levels. After sample preparation, the hyperspectral images of them were acquired using a line scan imaging system. The effective wavelengths were selected and image features were extracted. The effective features were selected and classified using the artificial neural network method. The classification accuracies of the classifier to identify sea foam powder and chickpea and wheat flour adulterants were equal to 98.9, 100, and 100%, respectively. The results showed the high ability of hyperspectral imaging combined with artificial neural networks method to detect adulteration in cinnamon with high reliability.

Atrial fibrillation is perpetuated by human heart secretome in ex-vivo langendorff-perfused hearts

Abstract Introduction Atrial fibrillation (AF) confers high morbidity and mortality, yet parts of the underlying pathophysiology remain elusive. Left atrial (LA) pressure is altered by the presence of AF and vice-versa. The cardiac secretome might be influenced by LA pressure and potentially alters the susceptibility of the atria to AF. We investigated the effect of the human cardiac secretome on atrial electrophysiology in a translational ex-vivo setting. Methods We obtained venous (coronary sinus) serum samples during AF and in sinus rhythm (SR) in patients hospitalized for pulmonary vein isolation (PVI). Moreover, LA tissue was collected from patients undergoing cardiac surgery and coronary sinus serum samples were collected before (CTRL) and after intraoperative volume challenge (VC). Single cardiomyocytes were isolated from human LA tissue and Ca2+ dependent excitation-contraction coupling (ECC) was studied using confocal line-scan imaging with CTRL or VC. Moreover, a modified Langendorff-Setup with the ability to monitor and control intra-cardiac pressure was utilized to perfuse mouse hearts with AF and SR serum. A multielectrode array (MEA) was placed on the epicardial aspect of the left atrium to measure electrical signals in a spatiotemporal fashion. External bipolar burst pacing protocols were applied to test AF inducibility. Results Ca2+ dependent ECC (i.e. Ca2+ amplitude and release) in human cardiomyocytes was unaltered with exposure to VC serum. Upon introducing a modified version of the Langendorff setup with the ability of linear pressure adjustment, a burst pacing protocol to test for AF inducibility was successfully implemented in mice atria. Atria perfused with human AF serum showed significantly increased AF inducibility as compared to SR serum. Moreover, both, total AF duration as well as number of AF episodes (i.e. AF burden) were increased in AF vs. SR serum upon several different pacing protocols. Conclusion Cardiac secretome during increased LA pressure had no significant effect on Ca2+ dependent ECC in human cardiomyocytes. However, secretome during AF conferred pro-arrhythmogenic properties as compared to SR, further strengthening the concept of "AF begets AF". Ongoing experiments test the hypothesis of potential differences in the multi-level omics profile of AF serum as the underlying mechanism.

Imaging of human parafoveal area with large field of view in adaptive optics line scanning ophthalmoscope

The parafoveal area, with its high concentration of photoreceptors and fine retinal capillaries, is crucial for central vision and often exhibits early signs of pathological changes. The current adaptive optics scanning laser ophthalmoscope (AOSLO) provides an excellent tool to acquire accurate and detailed information about the parafoveal area with cellular resolution. However, limited by the scanning speed of two-dimensional scanning, the field of view (FOV) in the AOSLO system was usually less than or equal to 2∘, and the stitching for the parafoveal area required dozens of images, which was time-consuming and laborious. Unfortunately, almost half of patients are unable to obtain stitched images because of their poor fixation. To solve this problem, we integrate AO technology with the line-scan imaging method to build an adaptive optics line scanning ophthalmoscope (AOLSO) system with a larger FOV. In the AOLSO, afocal spherical mirrors in pairs are nonplanar arranged and the distance and angle between optical elements are optimized to minimize the aberrations, two cylinder lenses are orthogonally placed before the imaging sensor to stretch the point spread function (PSF) for sufficiently digitizing light energy. Captured human retinal images show the whole parafoveal area with [Formula: see text] FOV, 60[Formula: see text]Hz frame rate and cellular resolutions. Take advantage of the 5∘ FOV of the AOLSO, only 9 frames of the retina are captured with several minutes to stitch a montage image with an FOV of [Formula: see text], in which photoreceptor counting is performed within approximately 5∘ eccentricity. The AOLSO system not only provides cellular resolution but also has the capability to capture the parafoveal region in a single frame, which offers great potential for noninvasive studying of the parafoveal area.

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1–3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400–1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.

Neural Network-Based Investigation of Periodic Noise Reduction Methods for High-Resolution Infrared Line Scanning Images

In the realm of neural network-based noise reduction, conventional models predominantly address Gaussian and blur artifacts across entire images. However, they encounter notable challenges when directly applied to periodic noise characteristics of high-resolution infrared sequential imagery. The high resolution also complicates the construction of suitable datasets. Our study introduces an innovative strategy that transforms two-dimensional images into one-dimensional signals, eliminating the need for processing the full image. We have developed a simulated dataset that closely mirrors natural infrared line scanning images derived from the FLIR dataset. To address low-frequency periodic noise, we propose two neural-network-based denoising approaches. The first employs a neural network to deduce noise from the one-dimensional signal, while the second utilizes discrete Fourier transforms for noise prediction within the frequency domain. Our experimental results highlight the Restormer model’s exemplary performance in direct noise prediction, where denoised images attain a PSNR of around 41 and an SSIM close to 0.9 on simulated data, leaving minimal residual noise in the actual denoised images. Further, we investigate the influence of Fourier coefficients, as predicted by neural networks, on the denoising process in the second approach. Employing 12 frequency coefficients, the Restormer and NAFNet models both reach a PSNR near 34 and an SSIM of approximately 0.842.

Terahertz 3-D fast line-scanning imaging using 3-D printed devices.

This article presents a terahertz (THz) fast line-scanning imaging system with three-dimensional (3-D) focus-steering capability operating at 0.1 THz. The system comprises a 3-D printed rotating multi-prism plate and a dual-device structure consisting of a negative ridge pyramid and a column ridge pyramid. The simulation and experimental results demonstrate that the system generates a sheet-shaped diffraction-free beam with a projection distance of approximately 175 mm and a diffraction-free distance of approximately 200 mm. Moreover, the system maintains a resolution greater than 4 mm within the diffraction-free range. Furthermore, the proposed THz lens-less line-scanning imaging system enables 3-D scanning imaging within a set range of ±22°. The proposed approach can be extended to cover other frequencies within the THz range by appropriately adjusting the parameters. The system has the advantages of long working distance and long depth of field, making it a very attractive candidate for low-cost, easy-fabrication, and easy-adjustment solutions for the next generation of THz fast detection and imaging technology.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

Development of track component health indices using image-based railway track inspection data

The primary role of the US Department of Transportation (USDOT) Federal Railroad Administration (FRA) is ensuring the safe operation of railway rolling stock and infrastructure by way of regulatory oversight. FRA regulations require US railroads to conduct visual track inspections as often as twice per week depending on a specific track segment’s FRA track class, which also governs maximum train operating speed. Such inspections are often subjective due to the inherent limitations of human visual inspection and cognition. Additionally, human visual inspections require some level of risk given the need for inspectors to be on track while also consuming valuable network capacity. As a result, and the desire to collect objective data to improve both safety and maintenance planning, railroads are pursuing new means and methods to assess track condition and evaluate track component health. This paper presents a numerical method to define track component health using field data collected on the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in Pueblo, Colorado, USA. Line scan laser and image data of the track were captured using a 3D Laser Triangulation system and were subsequently processed using Deep Convolutional Neural Networks (DCNNs). The track heath quantification method proposed establishes benchmarks that were developed based on the understanding of railway track mechanics, high axle load (HAL) railroad engineering instructions, and FRA regulations. The novel metrics presented are referred to as Track Component Heath Indices (TCHIs) and are quantitative values that objectively assess track condition and provide a means to monitor condition change with time and tonnage. These data can be used in conjunction with traditional track geometry and other forms of track heath data (e.g. GPR and rail profile) to more holistically assess the condition of the track structure and its components and ultimately predict its future state.

Three-dimensional visualization and detection of early bruise in apple based on near-infrared hyperspectral imaging coupled with geometrical influence correction

Visible-near-infrared (Vis-NIR) spectral imaging holds great promise for the automatic detection of fruit defects. However, uneven brightness resulting from fruit geometry and the limitations of one-directional imaging significantly restrict the current detection to a limited area. This study presents a rotation measurement system that combines a line-scan NIR-hyperspectral imaging (HSI) camera with a laser profile. A total of 72 apple samples with bruises in the central and edge regions were prepared. A 360° scanning approach was employed to collect HSI and shape data from the entire surface of the samples over a 6-h post-bruising period. Height and angle corrections were applied to eliminate the surface geometric influences on the HSI data, resulting in improved reflectance spectrum uniformity. A two-step principal component analysis method was employed for image enhancement, followed by a straightforward bruise detection technique using global segmentation and connected-domain selection. The results demonstrated an overall improvement in bruise detection over time. Moreover, the correction significantly enhanced the detection accuracy. After 6 h of bruising, the corrected data achieved a classification accuracy of 90.3% and an identification rate of 83.3% for central bruises and 61.1% for edge bruises, whereas the uncorrected data yielded 70.8%, 58.3%, and 31.9%, respectively. Thus, this study successfully detected early bruising across the entire surface of apples and improved the detection in low-intensity edge areas. The proposed method has the potential to contribute to the comprehensive evaluation of agricultural products with irregular geometries.

A novel error model for the line scan imaging system

Abstract The factors influencing the accuracy of the line scan imaging system (LSIS) are highly complex, and their error propagation mechanisms are poorly clarified. To reasonably and effectively mitigate the impact of mechanical factors on imaging accuracy, a novel comprehensive error model for LSIS is proposed in this paper, revealing the relationship between imaging errors and various mechanical error sources from manufacturing, assembly, and movement. Based on the analysis of the generation and propagation relationship of mechanical errors in LSIS, the integration of the mechanical system and the imaging system is accomplished to form a comprehensive multibody system, thus establishing a complete linear transmission mechanism for mechanical errors in the imaging process within the model. The experiments demonstrate that this model can provide a reference for the localization of mechanical error sources by utilizing the changes in extrinsic parameters during the calibration process. Furthermore, when the sensitivities of error sources are not exceptionally low, and certain cases with systematic errors are excluded, the model can make accurate estimations of the sensitivities of imaging errors to those errors generated by various mechanical sources. The estimated relative errors do not exceed 6%. The results confirm the effectiveness of this model, which can be utilized to ascertain the range of error sources and assess the extent to which errors from various sources impact imaging accuracy. This model provides a basis for the precision optimization of LSIS.

Joint geometric and piecewise photometric line-scan image registration

Joint geometric and piecewise photometric line-scan image registration

Stainless steel cylindrical pot outer surface defect detection method based on cascade neural network

Addressing issues such as surface geometric distortion, high reflection, and the challenge of detecting minor defects on the external surfaces of stainless steel pots, this paper presents a novel approach for detecting defects on the external surfaces of cylindrical stainless steel pots based on high-resolution line-scan imaging. The method begins by creating a real-time inspection system that includes a line-scan camera and a strip light source. This system achieves distortion-free, high-resolution image acquisition of the outer surfaces of stainless steel cylindrical pots by establishing linear constraints on pot size, rotary table rotation speed, and line-scan frame rate. Subsequently, a You Only Look Once and fully convolutional network cascade neural network surface defect detection strategy is introduced, utilizing dual-channel images of the original and enhanced images as inputs. This approach enables the characterization of subtle defects in high-resolution image data. Finally, we conducted experiments using the proposed method on the provided dataset, and the results demonstrate the effectiveness of this approach in detecting various types of product defects. The mean pixel accuracy achieved a remarkable 91.69%, while the mean intersection over union score reached an impressive 83.59%. These findings provide an effective technical means for the qualitative detection of the types of defects on the surface of stainless steel pots and the quantitative measurement of the size of the defects.

The Role of P-Selectin in Microvascular Hemodynamics and Cerebral Brain Volumes in Aging Sickle Cell Mice

Cerebral vasculopathy has been linked to the development of cognitive impairment in sickle cell disease (SCD). We have previously demonstrated that 13-month-old sickle cell mice recapitulate features of cerebral vasculopathy such as tortuous vessels, microthrombi, and hypoperfusion seen in adults with SCD. Histologically, we also showed these mice have increased cerebral microvascular deposition of P-selectin and more frequent and larger microinfarcts compared to matched controls. We further showed that these mice developed evidence of cognitive deficit compared to matched controls. Based on these observations, we hypothesized P-selectin may play a critical role in development of cerebral micro vasculopathy and downstream pathologic sequalae such as microinfarcts by enhancing leukocyte-endothelial interactions. Furthermore, abundant evidence from non-sickle mice shows that microinfarcts can exacerbate small vessel changes including gray matter volume loss. Building on our prior work, in this study, we examined the effect of P-selectin deficiency on cerebral microvascular hemodynamics and leukocyte-endothelial interaction and well as changes in cortical and subcortical gray matter volume in aged sickle mice. Male, P-selectin knockout (Psel KO) or intact (Psel WT) chimeric mice were generated by transplanting control (AA) or sickle (SS) bone marrow into P-selectin knockout (Psel -/-) or C57BL/6 (B6) recipients. To assess the impact of vasculopathy and brain gray matter volume changes longitudinally, male mice were assessed at 12-, 14- or 16-month post-transplant. Hemodynamics (mean velocity, red blood cell (RBC) flux, white blood cell stalls in capillaries as well as venules) were obtained from line-scan images acquired using in-vivo two-photon microscopy, using custom and validated MATLAB scripts. Gray matter volumes in 26 cortical and subcortical brain regions with relevance to cognitive function, were derived from parcellation of T2-weighted magnetic resonance imaging after non-linear registration to Allen brain atlas T1 anatomical space. Parcellation was accomplished by registering the Paxinos atlas to the mouse T2 now in Allen space. Brain volume analyses was accomplished using Analyze 12 software. After the completion of in-vivo studies, brains were extracted for immunohistochemistry analysis. Statistical analysis was performed using two-way ANOVA. The breakdown of the mice included in this study are; 14 B6 AA, 12 B6 SS, 12 Psel KO AA and 13 Psel KO SS. Given the high attrition rate in older mice, data from 12-, 14- and 16-month-old cohorts were combined. Mean age of B6 AA cohort was 13.1 months, 12.8 months for B6 SS, 13. 9 months for Psel KO AA and 13.6 months for Psel KO SS mice and were not statistically different. Psel KO sickle mice had a significantly lower mean capillary RBC velocity (1.733 mm/s vs 4.717 mm/s, p < 0.01) compared to Psel WT SS mice. In addition, Psel KO SS mice had relatively smaller (more normal) capillary diameter compared to Psel WT SS mice (6.248 um vs 8.613 um, p < 0.001). Interestingly, there was no statistically significant difference in RBC flux between Psel KO SS and Psel WT SS mice, however Psel KO SS mice show a lower red blood cell flux similar to that seen in Psel WT AA mice ( Figure 1). Of the 26 brain regions that were assessed, Psel KO SS mice had larger measured gray matter volume in the frontal association cortex (3.01 ± 0.77 vs 3.66± 0.33 mm 3, p= 0.0083), prelimbic cortex (1.72 ± 0.27 vs 1.95 ± 0.17 mm 3, p=0.0079), hippocampus (17.67 ± 1.56 vs 16.21± 1.97 mm 3, p=0.01)( Figure 1) including other regions such as lateral (2.12 ± 0.19 vs 1.88 ± 0.31 vs mm 3 p=0.0096) medial (1.84± 0.16 vs 1.64 ± 0.26 mm 3, p=0.01), and ventral orbital cortex( 1.95± 0.17 vs1.72 ± 0.27 mm 3, p=0.01) and dorsolateral olfactory tract (1.46 6± 0.13 vs 1.31 ± 0.20 mm 3, p=0.023 compared to the Psel WT SS mice (data not shown). In conclusion, non-hematopoetic P-selectin deficiency improves cerebral vasculopathy as demonstrated by normalization of hemodynamics in sickle mice. We also show that Psel KO SS mice had larger brain gray matter volume in regions involved in cognitive function. Additional studies are needed to identify the underlying mechanism of improvement in brain gray matter volume as well as whether P-selectin could be potential target for reducing SCD associated neurological complications.

Towards Ultraviolet Microbeam Scanning and Lens-Less UV Microbeam Microscopy with Mirror Galvanometric Scanners: From the History of Research Instrumentation to Engineering of Modern Mechatronic Optical Systems

This article aims to ensure continuity between classical methods of ul-traviolet microscopy or/and micromanipulation using ultraviolet mi-crobeam and lens-less ultraviolet microscopy and microbeam exposure of cells and tissues. Considering the history of the development of the method and the possibility of working with different methods of mechanical scanning, the authors propose to use mirror galvanometers and an electromechanical scanning system in the mechanical engineering of lensless microbeam installations. These installations make it possible to provide both scanning with an ultraviolet microbeam to obtain a line scan image, and precision micromanipulation at the level of individual cells or individual organelles (in the case of large cells). We propose to extrapolate mathematical models previously developed for galvanic mirrors of light-beam oscilloscopes for microbeam scanning systems, position-sensitive micromanipulation, and real-time microphotometric cell analysis.

Spatial resolution enhancement with line-scan light-field imaging.

This Letter proposes a line-scan-based light-field imaging framework that records lines of a light-field image successively to improve its spatial resolution. In this new, to the best of our knowledge, light-field imaging method, a conventional square or hexagonal microlens array is replaced with a cylindrical one. As such, the spatial resolution along the cylindrical axis remains unaffected, but angular information is recorded in the direction perpendicular to the cylindrical axis. By sequentially capturing multiple rows of light-field images with the aid of a translation device, a high-resolution two-dimensional light-field image can then be constructed. As a proof of concept, a prototype line-scan light-field camera was built and tested with the 1951 USAF resolution chart and the high-precision calibration dot array. Good measurement accuracies in the x, y, and z directions are demonstrated and prove that line-scan light-field imaging can significantly improve spatial resolutions and could be an alternative for fast three-dimensional inspections in the production line.

Microstructural evolution and analysis of Zr–Co/Cu–Al glassy composites and crystallized alloys via indentation and MD simulations

B2-containing bulk metallic glass (BMG) composites are still attractive due to their unique deformation mechanism and positive effect on work-hardening and ductility. As stated in review [1], the competition between B2 phase formation, retention of amorphous matrix and intermetallic compounds depends on several parameters, such as alloy composition and cooling rate. For a given BMG composite containing B2 phase, the glass matrix, B2 phase, and brittle compounds generally coexist. As a result, the relationship between microstructures such as atomic diffusion and micro-indentations must be investigated. In this work, nano-indentation, micro-hardness, and molecular dynamics (MD) simulations were performed on two Zr–Co/Cu–Al BMG composites with certain differences in amorphous matrix and crystalline phases such as their distribution, volume fraction, or types. The results indicated that the (1) formation of B2 phase, and precipitation of brittle compounds substantially differed; (2) the atomic transfer in these indented positions can be observed in simulated experiments and line scanning images (energy dispersive spectroscopy) for alloy compositions; (3) the degree of atomic diffusion in BMG composites functioned as the depth of nano-indentation via MD simulations; (4) the differences in nano-deformation via MD simulations can be observed for Zr–Cu alloys with various crystals; (5) the micro-indentation may stimulate transformation or B2 phase formation in localized regions upon indented stress; (6) the simulated results and experimental outcomes were mutually corroborated. These results also give a hint that atomic diffusion and phase formation in multiple mingled microstructures really existed, but they were very complicated and different in localized areas. This research is important and provides a clue for further exploration of the relationship between deformation and atomic diffusion.

SparkMaster 2: A New Software for Automatic Analysis of Calcium Spark Data.

Calcium (Ca) sparks are elementary units of subcellular Ca release in cardiomyocytes and other cells. Accordingly, Ca spark imaging is an essential tool for understanding the physiology and pathophysiology of Ca handling and is used to identify new drugs targeting Ca-related cellular dysfunction (eg, cardiac arrhythmias). The large volumes of imaging data produced during such experiments require accurate and high-throughput analysis. We developed a new software tool SparkMaster 2 (SM2) for the analysis of Ca sparks imaged by confocal line-scan microscopy, combining high accuracy, flexibility, and user-friendliness. SM2 is distributed as a stand-alone application requiring no installation. It can be controlled using a simple-to-use graphical user interface, or using Python scripting. SM2 is shown to have the following strengths: (1) high accuracy at identifying Ca release events, clearly outperforming previous highly successful software SparkMaster; (2) multiple types of Ca release events can be identified using SM2: Ca sparks, waves, miniwaves, and long sparks; (3) SM2 can accurately split and analyze individual sparks within spark clusters, a capability not handled adequately by prior tools. We demonstrate the practical utility of SM2 in two case studies, investigating how Ca levels affect spontaneous Ca release, and how large-scale release events may promote release refractoriness. SM2 is also useful in atrial and smooth muscle myocytes, across different imaging conditions. SparkMaster 2 is a new, much-improved user-friendly software for accurate high-throughput analysis of line-scan Ca spark imaging data. It is free, easy to use, and provides valuable built-in features to facilitate visualization, analysis, and interpretation of Ca spark data. It should enhance the quality and throughput of Ca spark and wave analysis across cell types, particularly in the study of arrhythmogenic Ca release events in cardiomyocytes.