Microscopy Imaging Methods Research Articles

Comparison of Tool Wear, Surface Roughness, Cutting Forces, Tool Tip Temperature, and Chip Shape during Sustainable Turning of Bearing Steel.

In this study, a comparison of measured cutting parameters is discussed while machining AISI 52100 low-alloy hardened steel under two different sustainable cutting environments, those in which a dry and minimum quantity lubrication (MQL) medium are used. A two-level full factorial design method has been utilized to specify the effect of different experimental inputs on the turning trials. Experiments were carried out to investigate the effects of three basic defining parameters of turning operation which are namely cutting speed, cutting depth, feed rate effects and also the effects of the cutting environment. The trials were repeated for the combination of different cutting input parameters. The scanning electron microscopy imaging method was used to characterize the tool wear phenomenon. The macro-morphology of chips was analyzed to define the influence of cutting conditions. The optimum cutting condition for high-strength AISI 52100 bearing steel was obtained using the MQL medium. The results were evaluated with graphical representations and they indicated the superiority of the pulverized oil particles on tribological performance of the cutting process with application of the MQL system.

Hypoxia-Preconditioned Bone Marrow Mesenchymal Stem Cells Improved Cerebral Collateral Circulation and Stroke Outcome in Mice.

Adequate collateral circulation can remarkably improve patient prognoses for patients experiencing ischemic stroke. Hypoxic preconditioning enhances the regenerative properties of bone marrow mesenchymal stem cells (BMSCs). Rabep2 (RAB GTPase binding effector protein 2) is a key protein in collateral remodeling. We investigated whether BMSCs and hypoxia-preconditioned BMSCs (H-BMSCs) augment collateral circulation poststroke, particularly through Rabep2 regulation. BMSCs or H-BMSCs (1×106) were delivered intranasally in ischemic mice with distal middle cerebral artery occlusion at 6 hours poststroke. Two-photon microscopic imaging and vessel painting methods were used to analyze collateral remodeling. Blood flow, vascular density, infarct volume, and gait analysis were assessed to evaluate poststroke outcomes. Expressions of proangiogenic marker VEGF (vascular endothelial growth factor) and Rabep2 were determined by Western blotting. Western blot, EdU (5-ethynyl-2'-deoxyuridine) incorporation, and tube formation assays were conducted on cultured endothelial cells treated with BMSCs. BMSCs were more effectively transplanted in the ischemic brain after hypoxic preconditioning. The ipsilateral collateral diameter was increased by BMSCs and strengthened by H-BMSCs (P<0.05). BMSCs increased peri-infarct blood flow and vascular density and reduced infarct volume, gait deficits (P<0.05), and furthermore by H-BMSCs (P<0.05). VEGF and Rabep2 protein expression was increased by BMSCs (P<0.05), which was enhanced by preconditioning (P<0.01). Additionally, BMSCs increased Rabep2 expression, proliferation, and tube formation of endothelial cells in vitro (P<0.05). H-BMSCs enhanced these effects (P<0.05), which were annulled by Rabep2 knockdown. BMSCs increased collateral circulation and improved poststroke outcomes, through the upregulation of Rabep2. These effects were enhanced by hypoxic preconditioning.

A high temporal-spatial resolution three-dimensional imaging method using arrayed-focus structured illumination with differential axial enhancement

High precision and fast three-dimensional (3D) microscopic imaging technology plays an extremely important role in MEMS, micro optics, microelectronics, biomedicine and other fields. In this paper, a confocal microscopic 3D imaging method based on differential array focusing structured light illumination (DAF-SIM) is proposed. Theoretical analysis and experimental results show that under the 100x/NA 0.95 objective lens, the lateral resolution of this method reaches 173 nm, exceeding the optical diffraction limit, the axial sensitivity is better than 10 nm, and the time resolution is about 3 Hz@2.3 M Pixel, achieving the goal of high horizontal and axial resolution and time resolution. The high spatial and temporal resolution of DAF-SIM 3D micro imaging method provides a highly applicable, efficient and high-precision online micro terrain detection scheme for intelligent manufacturing and other fields.

Classification between live and dead foodborne bacteria with hyperspectral microscope imagery and machine learning

Identifying live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. This study investigated the use of hyperspectral microscope imaging and deep learning methods to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models, Fusion-Net I, II, and III, were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). The models utilized both morphological and spectral characteristics of the bacterial cells, with inputs of average spectra and 546 nm band images. Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92.9% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains with 96.9% accuracy, making it a dual-task model with potential applications in identifying live foodborne bacteria prior to foodborne outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning methods could provide a new tool for quickly and accurately identifying bacterial viability, thereby improving the efficiency and reliability of food safety inspection.

In vivo ocular microvasculature imaging in rabbits with 3D ultrasound localization microscopy

Morphological and hemodynamic changes in the ocular vasculature are important signs of various ocular diseases. The evaluation of the ocular microvasculature with high resolution is valuable in comprehensive diagnoses. However, it is difficult for current optical imaging techniques to visualize the posterior segment and retrobulbar microvasculature due to the limited penetration depth of light, particularly when the refractive medium is opaque. Thus, we have developed a 3D ultrasound localization microscopy (ULM) imaging method to visualize the ocular microvasculature in rabbits with micron-scale resolution. We used a 32 × 32 matrix array transducer (center frequency: 8 MHz) with a compounding plane wave sequence and microbubbles. Block-wise singular value decomposition spatiotemporal clutter filtering and block-matching 3D denoising were implemented to extract the flowing microbubble signals at different imaging depths with high signal-to-noise ratios. The center points of microbubbles were localized and tracked in 3D space to achieve the micro-angiography. The in vivo results demonstrate the ability of 3D ULM to visualize the microvasculature of the eye in rabbits, where vessels down to 54 μm were successfully revealed. Moreover, the microvascular maps indicated the morphological abnormalities in the eye with retinal detachment. This efficient modality shows potential for use in the diagnosis of ocular diseases.

Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods

Visible/near-infrared hyperspectral microscope imaging (HMI) has provided spectral-spatial features to identify pathogenic bacteria with high accuracy. But the bacterial detection with dark-field HMI requires accurate segmentation of single-cell bacteria from hyperspectral image (hypercube). In this study a robust technique was developed and evaluated to automatically segment single-cell pathogenic bacteria using deep learning and image processing. The proposed method consists of two steps as 1) bacterial segmentation with a deep learning model and 2) single-cell identification by ellipse fitting. Bacterial strains including Escherichia coli, Listeria, Salmonella, and Staphylococcus were prepared to obtain hyperspectral images of bacterial cells under different growth conditions with a Fabry–Perot Interferometer (FPI) HMI system. Based on the hypercube, four deep learning models including U-Net, residual U-Net (ResU-Net), attention gate residual U-Net (AGResU-Net), and attention-gated recurrent residual U-Net (AGR2U-Net) were employed for bacterial cell segmentation. AGR2U-Net with deblurred input images and −1 image padding performed better than other models with 94.1% mean intersection over union and visual inspection confirmed that segmented images with the model were identical to the ground-truth mask images. Also, ellipse fitting and goodness-of-fit evaluation were accurate in 97.4% of 6,426 examined cases. In addition, the robustness of the proposed method was confirmed because its segmentation accuracy and quality were moderately invariant with image blurriness and sample growth conditions. This accurate and robust auto-segmentation technique streamlined the detection of pathogenic bacteria with FPI-HMI by reducing processing time from raw hypercube acquisition to classification with 15 sec.

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

Developing a Quality Evaluation System for Color Reproduction of Color 3D Printing Based on MATLAB Multi-Metrics.

Color 3D printing has been widely used in many fields such as cultural, medical, industrial, and food. The color reproduction accuracy of 3D printed products in these fields is becoming increasingly demanding, which requires more reproduction methods and practical tools. At present, most color 3D printing devices use one quantitative index, that is, color difference, to directly predict the color reproduction quality. However, this single quantitative index is not optimal for the curved surface of 3D printed color objects. Based on color evaluation principles, in this study, five new quantitative metrics consisting of color gamut comparison index, color SSIM index, color FSIM index, iCID index, and subjective scaling values are proposed for comparison, and the corresponding GUI design and code implementation of new color quality evaluation system are performed by MATLAB. Moreover, the comprehensive color assessment of color 3D printed products is confirmed by utilizing standard image acquisition and microscopic imaging methods that are not limited to printing materials and sampling locations. The operation of this system is validated to provide interactivity, simplicity and high efficiency. As a result, the system can provide new valuable feedback for color separation and output calibration of color 3D printing devices.

Single-shot color-coded LED microscopy for quantitative differential phase contrast imaging

Common quantitative differential phase contrast microscopy imaging usually uses multistep circular or annular illuminations and color multiplexed illuminations, bringing about low capture efficiency and poor phase reconstruction, respectively. We present a new single-shot quantitative differential phase contrast microscopy imaging method, which uses R/B-G annular LED multiplexed illumination to achieve better phase reconstruction by capturing 1 colored image. By deriving the phase transfer function of the illumination and simulation verification, the superiority of it is confirmed. Through phase reconstruction simulations for circular step and gradual samples, we verify that the R/B-G annular multiplexed illumination has better axial phase reconstruction accuracy and weaker lateral anisotropy than other color multiplexed illuminations. As a demonstration, quantitative phase imaging is performed on Quantitative Phase Target, validating the axial accuracy, lateral resolution, and lateral anisotropy of our method. Imaging and phase reconstruction were also performed on mouse kidney tissue slice and motional silica microspheres to visually express the utility and practicability of this method. Our research provides a better color multiplexed avenue for the real-time quantitative phase imaging in dispersion-free, weak-phase samples, especially living biological tissues.

Study on the transparent dFC-Tesos microscopic imaging method for large tissues in human brain

Objective: Human brain has features with high fat, high density, difficulty in deep and even labeling, and strong background fluorescence, which made it uneasy to get high-resolution imaging. To solve the above problems, this study successfully explored a new method for human brain transparency called dFC-Tesos by steps including human brain tissue electrophoresis degrease, antibody labeling, decolorization, transparency, three-dimensional high-resolution imaging, to clearly observe the spatial relationships and pathological changes between microglia and Aβ plaques in Alzheimer's disease. Methods: The 3 mm thick slices of the superior temporal gyrus of the human brain were sectioned by a vibration slicer, then degreased in 4% SDS solution with 3 V, 0.01 A electric field until the brain slices were lightly permeable and the interface between gray matter and white matter was fuzzy. Antibodies of microglia were incubated and Aβ plaques were stained by thioflavin S. Additionally, to exclude endogenous fluorescence interference of the samples, the brain slices were decolorized by 8% ammonia, hydrogen peroxide containing 0.08% KOH and 25% N,N,N,N-tetra(2-hydroxypropyl) ethylenediamine (w/v), respectively, which successfully reduced the interference of vascular auto-fluorescence and improved the imaging quality. Human brain tissue transparency was accomplished by tert-butanol gradient degreasing, tert-butanol, and tetraethylenediamine dehydration, benzyl benzoate, bisphenol A ethoxylate diacrylate and tetraethylenediamine for refractive index matching. They were then imaged by LS18 lightsheet micro-imaging instrument with a resolution of 1.65 μm × 1.65 μm × 3.5 μm. Results dFC-Tesos method was sequentially performed by electrophoretic degreasing of human brain samples, electrophoretic antibody labeling, decolorization, transparent human brain sample preparation, light-sheet microscopy, and splicing of big data to successfully obtain ultrastructural information and spatial location relationships of microglia and Aβ plaques in Alzheimer's disease (AD) brain, which provided methodological support for the study of new mechanisms of the pathology of AD. The dFC-Tesos method also provided an important tool for the study of macroscopic and ultrastructural structures of the pathological brain, the mechanism of major brain diseases, as well as the study of drug targets, and so on.

Guidelines for visualization and analysis of DC in tissues using multiparameter fluorescence microscopy imaging methods.

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Here, we provide detailed procedures for a variety of multiparameter fluorescence microscopy imaging methods to explore the spatial organization of DC in tissues and to dissect how DC migrate, communicate, and mediate their multiple functional roles in immunity in a variety of tissue settings. The protocols presented here entail approaches to study DC dynamics and T cell cross-talk by intravital microscopy, large-scale visualization, identification, and quantitative analysis of DC subsets and their functions by multiparameter fluorescence microscopy of fixed tissue sections, and an approach to study DC interactions with tissue cells in a 3D cell culture model. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

An ultrasound-based monitoring of kaolinite flocculent settling processes

In order to implement real-time monitoring of flocculent settling processes, an ultrasonic method based on the ECAH model was proposed and studied, particularly by considering the influence of particle irregularity on sound attenuation. Numerical simulations were conducted to evaluate the influence of critical physical parameters’ variation on the attenuation coefficient. Meanwhile, an integrated experimental system including ultrasonic measurement, interface settling velocity measurement, microscopic imaging and online sampling methods was established. The experiments of kaolinite particle suspensions with four different initial mass concentrations were carried out, and the phenomenon of flocculation and settling was observed accordingly. The duration of each settling stage and its change rate were analyzed simultaneously by both the interface settling velocity and ultrasonic attenuation coefficient. The analysis results show that for the same flocculent settling stage, the relative deviation of duration obtained by two methods above were 0%, 7.32%, −10.57%, 1.62% and 1.63%, respectively. The mechanisms and characteristics of each stage in the precipitation process were discussed in detail. At the same time, the volume concentrations obtained by ultrasonic method were compared with the results of weighing after on-line sampling. The deviations under the four initial mass concentrations were 18.6%, 12.1%, 11.0% and 13.2% respectively. The correlation between the process of flocculation and ultrasonic parameters was established accordingly, which provided data support for the application of ultrasonic technique to the online detection of kaolinite particle flocculent settling processes.

Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks

Large-Field Microscopic Imaging Method Based on Cycle Generative Adversarial Networks

Synthesis of Piperine-Based Ester Derivatives with Diverse Aromatic Rings and Their Agricultural Bioactivities against Tetranychus cinnabarinus Boisduval, Aphis citricola Van der Goot, and Eriosoma lanigerum Hausmann.

Exploration of plant secondary metabolites or by using them as leads for development of new pesticides has become one of the focal research topics nowadays. Herein, a series of new ester derivatives of piperine were prepared via the Vilsmeier−Haack−Arnold (VHA) reaction, and their structures were characterized by infrared spectroscopy (IR), melting point (mp), proton nuclear magnetic resonance spectroscopy (1H NMR), and carbon nuclear magnetic resonance spectroscopy (13C NMR). Notably, the steric configurations of compounds 6 and 7 were confirmed by single-crystal analysis. Against T. cinnabarinus, compounds 9 and 11 exhibited 47.6- and 45.4-fold more pronounced acaricidal activity than piperine. In particular, compounds 9 and 11 also showed 2.6-fold control efficiency on the fifth day of piperine. In addition, compound 6 (>10−fold higher than piperine) displayed the most potent aphicidal activity against A. citricola. Furthermore, some derivatives showed good aphicidal activities against E. lanigerum. Moreover, the effects of compounds on the cuticles of T. cinnabarinus were investigated by the scanning electron microscope (SEM) imaging method. This study will pave the way for future high value added application of piperine and its derivatives as botanical pesticides.

A particle morphology characterization system and its method based on particle scattering image recognition

The morphology of particles is usually detected by microscopic imaging methods such as scanning electron microscope (SEM). However, this type of direct observation relies on expensive equipment and requires complex preparations. Inspired by the transit method in astronomy, a characterization system based on the scattering patterns change due to the relative motion between the particle and facula was proposed in this work. The scattering patterns of particles could be recognized and analyzed automatically to detect the particle shape. The flow trajectory was studied by the optical fluid field couple (OFFC) model proposed in this work to calculate the size and specific surface area of the particle. For getting more accurate results, particle surface roughness was measured by image recognition to correct the results of specific surface areas. The accuracy of the classification machine learning (CML) model was about 97%, and the errors of the specific surface area results were lower than 10%. Comparing with the previous work, the proportions of different particles in mix sample could be detected successfully by this system with a maximum error less than 6%. This work can offer a valuable reference for the fields of characterization morphology systems based on scattering.

High Value-Added Application of Natural Plant Products in Crop Protection: Construction and Pesticidal Activities of Piperine-Type Ester Derivatives and Their Toxicology Study

To discover new potential pesticide candidates, recently, structural modification of natural bioactive products has received much attention. In this work, a series of new piperine-type ester derivatives were regio- and stereoselectively synthesized based on a natural alkaloid piperine isolated from Piper nigrum. Their structures were characterized by IR, mp, 1H NMR (13C NMR), and high-resolution mass spectrometry (HRMS). Against Tetranychus cinnabarinus Boisduval (Acari: Tetranychidae), compounds 4e, 4f, 4u, and 4v displayed the most significant acaricidal activity with LC50 values of 0.155, 0.117, 0.177, and 0.164 mg/mL, respectively. Particularly, compound 4f showed >120-fold higher acaricidal activity than piperine (LC50: 14.198 mg/mL). Notably, the acaricidal activity of 4f was equivalent to that of the commercial acaricide spirodiclofen (LC50: 0.115 mg/mL). Additionally, against Eriosoma lanigerum Hausmann (Hemiptera: Aphididae), compounds 4w and 4b' showed 1.8-fold aphicidal activity of piperine. Furthermore, via the scanning electron microscope (SEM) imaging method, the obvious destruction of the construction of the cuticle layer of 4f-treated T. cinnabarinus was observed. Compound 4f could be further studied as a lead acaricidal agent.

The molecular organization of differentially curved caveolae indicates bendable structural units at the plasma membrane

Caveolae are small coated plasma membrane invaginations with diverse functions. Caveolae undergo curvature changes. Yet, it is unclear which proteins regulate this process. To address this gap, we develop a correlative stimulated emission depletion (STED) fluorescence and platinum replica electron microscopy imaging (CLEM) method to image proteins at single caveolae. Caveolins and cavins are found at all caveolae, independent of curvature. EHD2 is detected at both low and highly curved caveolae. Pacsin2 associates with low curved caveolae and EHBP1 with mostly highly curved caveolae. Dynamin is absent from caveolae. Cells lacking dynamin show no substantial changes to caveolae, suggesting that dynamin is not directly involved in caveolae curvature. We propose a model where caveolins, cavins, and EHD2 assemble as a cohesive structural unit regulated by intermittent associations with pacsin2 and EHBP1. These coats can flatten and curve to enable lipid traffic, signaling, and changes to the surface area of the cell.

Piperazine Derivative Stabilizes Actin Filaments in Primary Fibroblasts and Binds G-Actin In Silico.

Alzheimer's disease (AD) is characterized by synaptic dysfunction, which is expressed through the loss of dendritic spines and changes in their morphology. Pharmacological compounds that are able to protect spines in the AD brain are suggested to be novel drugs that would be able to slow down the disease progression. We have recently shown that a positive modulator of transient receptor potential cation channel subfamily C member 6 (TRPC6), the compound N-(2-chlorophenyl)-2-(4-phenylpiperazine-1-yl) acetamide (51164), causes the upregulation of postsynaptic neuronal store-operated calcium entry, maintains mushroom spine percentage, and recovers synaptic plasticity in amyloidogenic mouse models of Alzheimer's disease. Here, using confocal microscopy and calcium imaging methods, we present the experimental data indicating that 51164 possesses an alternative mechanism of action. We demonstrated that 51164 can increase the mushroom spine percentage in neurons with the downregulated activity of TRPC6-dependent neuronal store-operated calcium entry. Moreover, we report the binding of 51164 to G-actin in silico. We observed that 51164 interacts with Lys 336, Asp157, and Ser14 of G-actin, amino acids involved in the stabilization/polymerization of the G-actin structure. We showed that interactions of 51164 with G-actin are much stronger in comparison to the well-characterized F-actin stabilizing and polymerizing drug, jasplakinolide. The obtained results suggest an alternative protective mechanism of 51164 that is related to the preservation of actin filaments in vitro.

Low-energy scanning transmission electron microscopy applied to ice-embedded biological macromolecules.

In this report, we applied annular bright-field and annular dark-field low-energy (30 keV) scanning transmission electron microscopy imaging to a vitreous ice-embedded biological macromolecule, T4 phage, to investigate the applicability of these methods for morphological investigation and sample screening. Multiple camera lengths were examined to find the optimal acceptance angle for both modes. Image clarity differed substantially between the modes, with the presence of ice also strongly influencing the quality of acquired micrographs. In annular dark-field mode, the proper discrimination of electrons scattered by the specimen from those scattered by the background ice was found to be difficult due to the severe overlap of the scattered electrons. The resulting micrographs lacked clarity, and the ice-embedded phage particles could only be discerned after post-processing image adjustment. However, in annular bright-field mode, despite similar overlapping of the scattered electrons, it was possible to assess the morphology and intactness of the specimen in the embedding ice, suggesting that this mode may find utility in low-energy cryo-scanning transmission electron microscopy imaging methods.

A strategy to quantify myofibroblast activation on a continuous spectrum

Myofibroblasts are a highly secretory and contractile cell phenotype that are predominant in wound healing and fibrotic disease. Traditionally, myofibroblasts are identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers, leading to a binary classification: “activated” or “quiescent (non-activated)”. More recently, however, myofibroblast activation has been considered on a continuous spectrum, but there is no established method to quantify the position of a cell on this spectrum. To this end, we developed a strategy based on microscopy imaging and machine learning methods to quantify myofibroblast activation in vitro on a continuous scale. We first measured morphological features of over 1000 individual cardiac fibroblasts and found that these features provide sufficient information to predict activation state. We next used dimensionality reduction techniques and self-supervised machine learning to create a continuous scale of activation based on features extracted from microscopy images. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of myofibroblast activation and provide an imaging-based strategy to quantify the position of a cell on that spectrum.