Inverse dynamical photon scattering (IDPS): an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy.

To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy (OM), immunofluorescence microscopy (IM), and transmission electron microscopy (TEM). We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi- instance model for classification of 3 immune-mediated glomerular diseases, namely immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN), and lupus nephritis (LN). This model adopts an instance-level multi-instance learning (I-MIL) method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient. By comparing this model with unimodal and bimodal models, we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion. The multi-modal multi-instance model combining OM, IM, and TEM images had a disease classification accuracy of (88.34±2.12)%, superior to that of the optimal unimodal model [(87.08±4.25)%] and that of the optimal bimodal model [(87.92±3.06)%]. This multi- modal multi- instance model based on OM, IM, and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Microstructural information acquired from image analysis can be used in cell modeling. In order to obtain more precise Solid Oxide Fuel Cell (SOFC) microstructure parameters, an adaptive fuzzy approach is developed for three-phase identification of YSZ/Ni anode Optical Microscopic (OM) images. A new quantum-inspired clique potential Markov random field (MRF) function is proposed to considerate spatial information in fuzzy logic model, where the space distance based weight is introduced to reflect the influence of neighborhood pixels. Simulated images and real SOFC anode OM images are used to compare the effectiveness and practicability of the proposed algorithm with others. Experiment results demonstrate that the proposed methods can accurately separate there-phase of SOFC OM images, which lays the foundation of subsequent microstructural parameters extracting.

Fused Filament Fabrication (FFF) is the most popular and widely used additive manufacturing process for printing polymer and composite products. Various production factors influenced the strength and stiffness of the part manufactured by 3D printing. A comprehensive experimental analysis was conducted in this study to examine the effect of FFF process parameters (infill density, pattern, and layer thickness) on mechanical properties and failure mechanism. The tensile, flexural, and impact test specimens were printed using ABS and carbon fibre reinforced ABS filaments in accordance with ASTM standards. Furthermore, dynamic properties are studied using dynamic mechanical analysis to estimate the loss factor and glass transition temperature under the impact of temperature and frequency in addition to static properties. Further, the results showed the addition of carbon fiber in ABS increases the mechanical properties. The failure modes are studied using optical microscopy and Scanning Electron Microscopy images and it has been visualized that due to improper layer deposition, poor bonding between the previous layer and low infill density creates a void in the specimen which results in poor mechanical properties. The Dynamic Mechanical Analysis showed that at higher frequency the molecular movement decreases which in turn stabilizes the composite behavior and reduces the loss factor.

.We present a multimodal visible light optical coherence microscopy (OCM) and fluorescence imaging (FI) setup. Specification and phantom measurements were performed to characterize the system. Two applications in neuroimaging were investigated. First, curcumin-stained brain slices of a mouse model of Alzheimer’s disease were examined. Amyloid-beta plaques were identified based on the fluorescence of curcumin, and coregistered morphological images of the brain tissue were provided by the OCM channel. Second, human brain tumor biopsies retrieved intraoperatively were imaged prior to conventional neuropathologic work-up. OCM revealed the three-dimensional structure of the brain parenchyma, and FI added the tumor tissue-specific contrast. Attenuation coefficients computed from the OCM data and the florescence intensity values were analyzed and showed a statistically significant difference for 5-aminolevulinic acid (5-ALA)-positive and -negative brain tissues. OCM findings correlated well with malignant hot spots within brain tumor biopsies upon histopathology. The combination of OCM and FI seems to be a promising optical imaging modality providing complementary contrast for applications in the field of neuroimaging.

Design and realization of high resolution optical micro-scanning thermal microscope imaging system

Reconstruction of 3-dimensional objects in Scanning Acoustic Microscopy (SAM) is now of great interest. From the mathematical point of view, the simplest kind of 3- dimensional objects are spherical particles, Investigations of the image formation of spheres have revealed different phenomena, which are of current scientific concern. First theoretical treatments1 showed that the image formation of spheres in transmission configuration of SAM is easily to understand, and is close to the imaging in optical microscopy. In transmission acoustic microscopy, the dimension of the image is only slightly dependent on the scanning plane position. The strongest image contrast arises when the focal plane coincides with the center of the sphere. The diameter of the image in this position is nearly equal to the particle size. In contrary, in reflection acoustic microscopy, image formation of spherical particles has revealed extraordinary properties2,3. The purpose of this report is to present an interpretation of the images of spheres at different focal plane positions, using Fourier spectrum approach in reflection SAM.

Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, high-resolution microscopy with automatic image processing.

Summary: Soluble hyperbranched acrylate copolymers were prepared by the copolymerization of divinylbenzene (0.10 mol · L−1) and ethyl acrylate (0.50 mol · L−1) using dimethyl 2,2′‐azoisobutyrate of high concentrations (0.30–0.50 mol · L−1) as initiator at 70 and 80 °C in benzene. The copolymer formed at 80 °C for 1 h showed the weight‐average molecular weight of 2.5 × 105, the small radius of gyration of 10 nm, the low second virial coefficient of 5.7 × l0−7 mL · g−2 as shown by the MALLS measurements at 25 °C in tetrahydrofuran, and also the very low intrinsic viscosity of 0.10 dL · g−1 at 30 °C in benzene. The hyperbranched copolymer exhibited an upper critical solution temperature (35 °C on cooling) in an acetone‐water (60:11 v/v). The copolymer showed an ability to encapsulate and transfer Rhodamine 6G as a dye probe and could stabilize Ag nanoparticles. The porous film was prepared by simply casting an acetone solution of the hyperbranched copolymer on a cover glass. The copolymer molecules radially arranged on the surface layer of the spherical pores as observed by the polarized optical microscope. The hyperbranched acrylate copolymer was hydrolyzed by KOH to yield poly(carboxylic acid).Optical microscope image (crossed polarizers) of a porous film from copolymer solution in acetone.imageOptical microscope image (crossed polarizers) of a porous film from copolymer solution in acetone.

Optical microscopy imaging provides cellular-level visualization of tissues, which has therefore become the gold standard for the diagnosis of cancers. Despite the significant contributions of optical microscopy to pathology, a fundamental pre-requist is to capture high-quality and all-in-focus images at high speed. In this work, we propose a Kernel Distillation AutoFocus (KDAF) method to predict the defocus distance based on a single-frame image. This work takes the advantages from both previous virtual-refocusing and single-shot focus estimation methods. During the training phase, the focus distance is formulated as a latent variable to determine the kernel of defocus blurring. The in-focus samples are processed via the kernel to generate the defocused images via the conditional convolutional neural networks. During the testing phase, we directly predict the focus distance via the latent features. Compared with previous works, the proposed method introduces the paired focused/defocused samples as strong supervision to learn the representative features for focus estimation. Experiments on BBBC and Incoherent datasets demonstrate that the proposed can effectively estimate the optimal focus distance. This method can accurately estimate the focus distance based on single-shot imaging. Accurate and automated focus estimation may potentially improve the imaging quality of microscopes, which can benefit the robust diagnosis.

Fabrication of nanocrystalline austenitic stainless steel with superior strength and ductility via binder assisted extrusion method

Snow is a divided medium that can adsorb atmospheric trace gases. Evaluating the impact of the snow cover on atmospheric chemistry therefore requires the knowledge of the specific surface area (SSA) of snow. This paper compares the results of three methods used to measure or estimate the SSA of four snow samples: CH4 adsorption at 77 K, optical microscopy (OM), and scanning electron microscopy (SEM, used only on two samples). Within error bars, CH4 adsorption and OM yield similar results on three of the four snow samples. Values for the 4th sample are within a factor of 2. For both samples where CH4 adsorption, OM, and SEM are used, all three methods yield similar results, but CH4 adsorption always has a better accuracy and a much better precision. Thus, despite its ease of use, estimates from OM images are often not accurate enough to monitor the evolution of snow SSA. The main sources of error in the OM method are the difficulty to determine snow crystal thicknesses and to take into account the topography of the snow crystal surface. The combination of CH4 adsorption and OM or SEM can provide useful information on the evolution of both the SSA and the shape of snow crystals. This will be useful to evaluate the respective contributions of adsorption/desorption and sublimation/condensation processes to the impact of the snow cover on atmospheric chemistry.

We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally change both the hardware and image reconstruction methods used in optical microscopy in a holistic manner.

Many diseases in rural areas and developing countries are detected late at an advanced stage when treatment might involve complications and higher cost, resulting in a greater number of fatalities. This study aims to make early disease detection simpler and affordable for people living in remote areas and developing countries. A new age optical microscope with high sensitivity diagnosis can revolutionize this gap in disease detection. Here, a smartphone-based imaging device (SID) using optics and a smartphone interface was developed to speedup the process of diagnosis in areas that do not have easy access to health centers and diagnostic clinics. The device was built using acrylic sheets to make it less bulky and customizable and three-dimensional (3D) printed mechanical parts were used to increase stability. The study includes calibration, and testing the device with various samples to determine its capabilities. Images were acquired using the various types of BLIPS lens integrated onto the smartphone camera lens and compared with optical microscope images. The device can visualize single human blood cell which is 8 μm in size using ultra-BLIPS lens and magnification is comparable to an objective lens used in an optical microscope.

The research on nano-photonics has been on the increase recently. Among the materials that exhibit stunning optical properties are the nano-photonic materials found in nature such as those on butterfly wings. The wing scales of papilio crino was analysed using the Scanning Electron Microscopy (SEM), UV-Vis-NIR spectrometer, and Optical Microscopy (OPM). Under the OPM, the wing scales was imaged both in normal and polarized (45°) light. The SEM images reveal concave surfaces separated by repeating pattern of ridges and cross ribs. The variation in colour across the concave surface modulation was evident from the optical microscopy images. Through the numerical simulation and CIELab colour space, it was shown that the green colour seen on the wings is a combination of yellow and blue light reflected from different microscopic regions on the wing concave surface. This shows application in the bio-mimicks of retroflection sensors for the detection and classification of optical systems.

In this study, Hardox 400 steel used as substrate material was coated through solid media Thermoreactive Diffusion (TRD) method using Ferro Niobium and Ferro Boron powders from carbide forming element powders. Coating was carried out in three different temperatures ([Formula: see text]C, [Formula: see text]C and [Formula: see text]C) and three different time intervals (1, 2 and 3[Formula: see text]h). Microstructures of the coated specimens were examined by optical microscope, Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDX) and X-ray Diffraction (XRD); and hardness values were measured. The effects of coating parameters on coating thickness and hardness were analyzed by ANOVA. In addition, specimens were subjected to wear tests to determine the effect of hardness and coating parameters on wear. In the wear tests, Taguchi test design setup was used. The obtained results were compared with the Hardox 400 steel used under current conditions. It was seen from optical microscope and SEM images that Hardox 400 steel surface could be coated with TRD method depending on coating parameters. The average thickness of NbC–B coating ranged from 1.797[Formula: see text][Formula: see text]m to 5.596[Formula: see text][Formula: see text]m under different process temperature and time. Rising the coating time and temperature increased the coating thickness by 311.40%. EDX analysis showed that the coating layer was composed of B, C, Fe and Nb elements, and XRD analysis also showed that the phase in the coating layer is NbC–B. The NbC–B phase was determined to be an important factor in increasing the hardness. The coating hardness is enhanced by 320.80% depending on the coating parameters. Optimum coating thickness, hardness and wear results were obtained from high coating temperature and time. Uncoated Hardox 400 steels were worn out more compared to the coated Hardox 400 steels. The contribution of coating temperature and time to wear resistance was 1.46% and 8.02%, respectively. It was observed that the important parameter for wear volume was the applied load.