Low Magnification Images Research Articles

Incorrectly Focused Neodymium:Yttrium-Aluminum-Garnet (Nd:YAG) Laser Beam Leads to Massive Destructive Effects in Small-Aperture (Pinhole) Intraocular Lenses.

Pinhole intraocular lenses (IOLs) were developed to improve reading by compensating for loss of accommodative function. The IC-8® Apthera™ is a small-aperture presbyopia-correcting IOL that combines the proven principle of small-aperture optics with an aspheric monofocal lens to deliver a continuous range of vision for patients withcataractsfrom distance to near vision. Posterior capsule opacification is the most common sequela after cataract surgery. It is effectively treated by laser capsulotomy. However, if the laser beam is incorrectly focused, the IOL can be permanently damaged (pits/shots). In this experimental study, yttrium-aluminum-garnet (YAG) pits were purposefully created. Defects were analyzed and compared between the periphery of the ring in the clear area of the hydrophobic acrylic lens and at the carbon black (CB)-polyvinylidene fluoride (PVDF) filtering component (FilterRing™) of the pinhole lens. All defects were made using identical settings/energy levels (2.6mJ). The damage induced to the IC-8® Apthera™ IOL was examined by low-magnification images, light microscopy, scanning electron microscopy, and micro-computed tomography (micro-CT). YAG defects in the carbon black filter ring were much more severe than those in the clear zone due to the high absorption of the carbon black. Massive defects and destruction of the lens with tearing out of fragments and particles were observed. The missing volume calculated from the micro-CT reconstruction was 0.266 mm3, which is 1.6% of the entire IOL volume, or more than 1000 times the volume damaged in the largest shot in the periphery. Based on the results, we highly recommend using the lowest possible energy levels, posterior offset setting, and circular pattern for maximum safety when performing laser capsulotomy with pinhole implants. Care should be taken to avoid creating irreversible iatrogenic defects that may affect overall quality. The safest area for performing capsulotomy seems to be the periphery of the ring segment. Video available for this article.

Tuning Van Der Waals Heterostructure of WS2/SrTiO3 Via in Situ Transmission Electron Microscopy

Monolayer two-dimensional (2D) materials have been widely researched and discussed in recent years, while multi-layers have received comparatively less attention. Nowadays, combining different materials to create heterostructures has become mainstream. In semiconductor industry, heterostructure can form devices such as lasers, light-emitting diodes, fast transistors and so on. Heterostructures can create well electronic properties by combining two different materials together. Therefore, heterostructures become a great tool to manipulate the electronic structures. In traditional heterostructures, the interfaces between two different materials are formed by covalent bonds, hence it requires similar lattice constants and structures. On the other hand, novel van der Waal heterostructures (vdW-heterostructure), formed by 2D materials, do not require the same strict similarity as traditional ones. VdW-heterostructures, which create 3D structures through van der Waals forces, provide a new perspective. In this study, we created a vdW-heterostructures by stacking tungsten disulfide (WS2) with single-crystal strontium titanate, SrTiO3 (STO), shown in Figure 1a,b. The crystalline atomic-layer WS2 and STO were synthesized by chemical vapor deposition (CVD) and pulsed laser deposition (PLD), respectively. They are analyzed by high-resolution transmission electron microscope (HRTEM) shown in Figure 1c-h. Once the vdW-heterostructures are formed, we conducted in situ heating by TEM. Through the advanced in situ technique, we observed the change of the twisted stacking angle by heating the vdW-heterostructure. Ultimately, the lattices will shift and rotate, minimizing the free energy, so that the vdW-heterostructure will be aligned and formed epitaxially. In addition, we utilized atomic resolution scanning transmission electron microscope (STEM) to identify the stacking images and the moiré pattern of the vdW-heterostructure. During the rotation, we can observed different moiré patterns by various twisted stacking angles. These different moiré patterns can produce different electrical properties, which can be applied to advanced semiconductor devices. Keywords: high-resolution TEM/STEM, in situ heating, 2D materials, van der Waal heterostructures, moiré pattern Figure 1. The vdW-heterostructure of STO/WS2. a) The diagram showing that the formation process of WS2/STO vdW-heterostructures which have different twisted stacking angles. b) The low-magnification SEM image showing that the single crystal STO thin film is transferred onto the TEM heating chips. The circles in the middle are the observation windows for transmission electrons. c-e) HRTEM images. f-g) Diffraction patterns of HRTEM. c,f) Single crystal STO thin film with d-spacing (1−10) = 0.284 nm. d,g) Monolayer single crystal WS2 with d-spacing (−110) = 0.274 nm. e,h) WS2(red)/STO(yellow) vdW-heterostructure with stacking angle 3.6°. Figure 1

Morphology of human sinoatrial node and its surrounding right atrial muscle in the global obesity pandemic-does fat matter?

The sinus node (SN) is the main pacemaker site of the heart, located in the upper right atrium at the junction of the superior vena cava and right atrium. The precise morphology of the SN in the human heart remains relatively unclear especially the SN microscopical anatomy in the hearts of aged and obese individuals. In this study, the histology of the SN with surrounding right atrial (RA) muscle was analyzed from young non-obese, aged non-obese, aged obese and young obese individuals. The impacts of aging and obesity on fibrosis, apoptosis and cellular hypertrophy were investigated in the SN and RA. Moreover, the impact of obesity on P wave morphology in ECG was also analyzed to determine the speed and conduction of the impulse generated by the SN. Human SN/RA specimens were dissected from 23 post-mortem hearts (preserved in 4% formaldehyde solution), under Polish local ethical rules. The SN/RA tissue blocks were embedded in paraffin and histologically stained with Masson's Trichrome. High and low-magnification images were taken, and analysis was done for appropriate statistical tests on Prism (GraphPad, USA). 12-lead ECGs from 14 patients under Polish local ethical rules were obtained. The P wave morphologies from lead II, lead III and lead aVF were analyzed. Compared to the surrounding RA, the SN in all four groups has significantly more connective tissue (P ≤ 0.05) (young non-obese individuals, aged non-obese individuals, aged obese individuals and young obese individuals) and significantly smaller nodal cells (P ≤ 0.05) (young non-obese individuals, aged non-obese individuals, aged obese individuals, young obese individuals). In aging, overall, there was a significant increase in fibrosis, apoptosis, and cellular hypertrophy in the SN (P ≤ 0.05) and RA (P ≤ 0.05). Obesity did not further exacerbate fibrosis but caused a further increase in cellular hypertrophy (SN P ≤ 0.05, RA P ≤ 0.05), especially in young obese individuals. However, there was more infiltrating fat within the SN and RA bundles in obesity. Compared to the young non-obese individuals, the young obese individuals showed decreased P wave amplitude and P wave slope in aVF lead. Aging and obesity are two risk factors for extensive fibrosis and cellular hypertrophy in SN and RA. Obesity exacerbates the morphological alterations, especially hypertrophy of nodal and atrial myocytes. These morphological alterations might lead to functional alterations and eventually cause cardiovascular diseases, such as SN dysfunction, atrial fibrillation, bradycardia, and heart failure.

Multi-scale tissue fluorescence mapping with fiber optic ultraviolet excitation and generative modeling

Cellular imaging of thick samples requires physical sectioning or laser scanning microscopy, which can be restrictive, involved, and generally incompatible with high-throughput requirements. We developed fiber optic microscopy with ultraviolet (UV) surface excitation (FUSE), a portable and quantitative fluorescence imaging platform for thick tissue that enabled quick sub-cellular imaging without thin sections. We substantially advanced prior UV excitation approaches with illumination engineering and computational methods. Optical fibers delivered <300nm light with directional control, enabling unprecedented 50× widefield imaging on thick tissue with sub-nuclear clarity, and 3D topography of surface microstructure. Probabilistic modeling of high-magnification images using our normalizing flow architecture FUSE-Flow (made freely available as open-source software) enhanced low-magnification imaging with measurable localized uncertainty via variational inference. Comprehensive validation comprised multi-scale fluorescence histology compared with standard H&E histology, and quantitative analyses of senescence, antibiotic toxicity, and nuclear DNA content in tissue models via efficient sampling of thick slices from entire murine organs up to 0.4×8×12mm and 1.3 million cells per surface. This technology addresses long-standing laboratory gaps in high-throughput studies for rapid cellular insights.

Automatic Detection of Cast Billet Dendrite Based on Improved Hough Transform

Primary dendrite information is one of the most important metrics to measure the quality of continuous cast slabs. The contrast of low magnification images is very low under the influence of illumination and sampling devices, so the traditional dendrite detection method has the problem of missed detections. We propose an automatic dendrite detection method based on an improved Hough transform, which effectively improves the accuracy and efficiency of primary dendrite detection. By using the local grayscale features of the image, a genetic algorithm-based local contrast enhancement algorithm is proposed. Compared with the traditional contrast enhancement algorithm, it can retain all the information of the dendrites. Combined with the image binarization method based on Hessian matrix, we can obtain more detailed information about the dendrites. According to the continuity and solidification characteristics of dendrites, the Hough transform is improved to extract dendrite information, which effectively reduces the computational cost of the Hough transform. The experimental results show that the method of this paper has versatility, and the error is four pixels compared with the manual method, which can provide a reliable basis for the subsequent judgement of the quality of cast billets.

MamlFormer: Priori-experience guiding transformer network via manifold adversarial multi-modal learning for laryngeal histopathological grading

Pathologic grading of laryngeal squamous cell carcinoma (LSCC) plays a crucial role in diagnosis, prognosis, and migration. However, the grading performance and interpretability of the intelligent grading model based on LSCC low magnification images are poor. This is because it lacks the delicate nuclear information and information more relevant to grading contained in the high magnification images labeled by pathologists. Yet, low magnification images have information such as tissue texture and contours. Thus, we proposed an end-to-end transformer network with manifold adversarial multi-modal learning (MamlFormer). It effectively fuses and learns LSCC high and low magnification pathology image modalities. Firstly, we demonstrate the feasibility and sufficient conditions for modal fusion of LSCC high and low magnification images from Hoeffding's inequality and multimodal co-regularization. Secondly, we design a new manifold block. It constructs the manifold subspace by some principles. Those principles are divisibility, recoverability, and local distance closest of the feature matrix before and after the mapping of the LSCC each magnification image modalities. Meanwhile it can well solve the problems of redundant feature matrix information and weak modal semantic consistency after multimodal learning. Thirdly, we utilize the encoder and the adversarial loss function to implement adversarial block. It can adaptively learn the latent metrics of the modal distributions of LSCC high and low magnification images. Therefore, it also enhances the complementarity of LSCC high and low magnification image modalities. Then, numerous experiments show that MamlFormer outperforms other SOTA models in both grading performance and interpretability. Finally, we also performed generalization experiments on highly prevalent cervix squamous cell carcinoma. The MamlFormer over is superior to other SOTA models in terms of grading performance and interpretability. This indicates its excellent generalization performance and clinical practicability.

A novel deep learning-based method for automatic stereology of microglia cells from low magnification images

Microglial cells mediate diverse homeostatic, inflammatory, and immune processes during normal development and in response to cytotoxic challenges. During these functional activities, microglial cells undergo distinct numerical and morphological changes in different tissue volumes in both rodent and human brains. However, it remains unclear how these cytostructural changes in microglia correlate with region-specific neurochemical functions. To better understand these relationships, neuroscientists need accurate, reproducible, and efficient methods for quantifying microglial cell number and morphologies in histological sections. To address this deficit, we developed a novel deep learning (DL)-based classification, stereology approach that links the appearance of Iba1 immunostained microglial cells at low magnification (20×) with the total number of cells in the same brain region based on unbiased stereology counts as ground truth. Once DL models are trained, total microglial cell numbers in specific regions of interest can be estimated and treatment groups predicted in a high-throughput manner (<1 min) using only low-power images from test cases, without the need for time and labor-intensive stereology counts or morphology ratings in test cases. Results for this DL-based automatic stereology approach on two datasets (total 39 mouse brains) showed >90% accuracy, 100% percent repeatability (Test-Retest) and 60× greater efficiency than manual stereology (<1 min vs. ∼ 60 min) using the same tissue sections. Ongoing and future work includes use of this DL-based approach to establish clear neurodegeneration profiles in age-related human neurological diseases and related animal models.

Modified Fourier Transform Misalignment Analysis multi-rotate method for measuring fibre alignment in stitched glass fabrics

Measurements of fibre alignment are important for assessing the quality of fibre reinforced composite materials. The Fourier-Transform Misalignment Analysis (FTMA) algorithm is able to measure the average alignment of features over an area, and is therefore well-suited to measuring fibre alignment in low-magnification images. In images with few pixels, the precision of the FTMA algorithm depends on the alignment of the image features. Features at 45° can be measured with greater precision than those near 0 or ±90°. To combat this, a new FTMA-MultiRotate method is proposed. This modified algorithm has been assessed against computer-generated images, where it is able to achieve an average accuracy within ±0.5°. It has then further been applied to measure fibre alignment in low-magnification photographs of glass fabrics, and these measurements have been verified against measurements taken manually. In 93% of cases, agreement between the modified FTMA-MultiRotate algorithm and manual measurements is within ±1.5°.

Artifact detection of low-magnification pathology whole-slide images

Artifact detection of low-magnification pathology whole-slide images

Noninvasive In Planta Live Measurements of H2O2 and Glutathione Redox Potential with Fluorescent roGFPs-Based Sensors.

In this protocol, we present a noninvasive in planta bioimaging technique for the analysis of hydrogen peroxide (H2O2) and glutathione redox potential in adult Arabidopsis thaliana plants. The technique is based on the use of stereo fluorescence microscopy to image A. thaliana plants expressing the two genetically encoded fluorescent sensors roGFP2-Orp1 and Grx1-roGFP2. We provide a detailed step-by-step protocol for performing low magnification imaging with mature plants grown in soil or hydroponic systems. This protocol aims to serve the scientific community by providing an accessible approach to noninvasive in planta bioimaging and data analysis.

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

Molecular layer deposition of hybrid silphenylene-based dielectric film

Molecular layer deposition (MLD) offers molecular level control in deposition of organic and hybrid thin films. This article describes a new type of inorganic–organic silicon-based MLD process where Aluminium chloride (AlCl3) and 1,4-bis(triethoxysilyl)benzene (BTEB) were used as precursors. Hybrid films were deposited at a temperature range of 300 to 500 °C and high growth per cycle (GPC) up to 1.94 Å was obtained. Field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) were used to analyze the appearance of the film surface. The hybrid film was amorphous in low-magnification FESEM images but some particulates appeared in high-magnification FESEM images (200 k). Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), Time-of-flight elastic recoil detection analysis (ToF-ERDA), and X-ray photoelectron spectroscopy (XPS) were employed to analyze the structure and composition of the hybrid film. The ratio of Al/Si in the hybrid film was 0.8. The storage environment of the films affected their capacitance, dielectric constant, leakage performance, and breakdown voltage. A film stored in a high vacuum (10–6 mbar) environment had low leakage current density (< 10–6 A × cm−2 at an applied voltage of 28 V) and a dielectric constant of 4.94, which was much smaller than after storing in a humid ambient environment.Graphical abstract

Validity of low-magnification narrow-band imaging in annual endoscopy screening for gastric neoplasms: A case-control study

Previously, I have reported the efficacy of whole stomach observation using magnifying narrow-band imaging at low magnification (LM-NBI) after routine white-light imaging (WLI). LM-NBI can detect lesions overlooked by WLI. However, the effectiveness of annual examinations remains unexplored. I conducted this case-control study at a single institution. In LM-NBI group, patients with chronic gastritis underwent a LM-NBI scan, defined as the minimal magnification offering maximal visual field coverage to unveil to the microsurface pattern of gastric mucosa, of the whole stomach following WLI. Historical control was used as the conventional magnifying endoscopy (CE) group. In both groups, index endoscopy, which equalizes the status, was performed first, and subsequently annually endoscopies up to five times were conducted. The first annual examination was performed from April 2019 to March 2020 in LM-NBI group and from April 2015 to March 2016 in CE group. The detection of gastric neoplasia was analyzed according to medical records. Among 388 patients in the LM-NBI group, and 381 in the CE group, 15 and 5 patients with gastric neoplasia were respectively identified. Except for one mucosa-associated lymphoid tissue lymphoma, all were epithelial neoplasias. All endoscopic examinations were performed safely without complications which needed additional medical interventions. Through a Cox proportional hazards model, the hazard ratio of 2.78 (95% CE, 1.01–7.64) was determined. Kaplan–Meier analysis (p ​= ​0.039, log-rank test) revealed superior efficacy of annual LM-NBI over CE in detecting gastric neoplasia. This is the first study to report the efficacy of annual endoscopy using LM-NBI.

(Digital Presentation) Ni-Doped PrBaFe2O5+ δ As Symmetrical Electrode for Solid Oxide Cells

The development of new redox stable electrode materials for Solid Oxide Cells (SOFs) have attracted great attention in recent years, since finding suitable compositions to operate efficiently in both fuel cell and electrolyzer modes is crucial for their wide commercialization [1]. For this reason, research on new doping strategies [2] and advanced deposition techniques [3] for symmetrical electrode materials have significantly increased with the aim of simplifying cell fabrication and extending cell durability. In this sense, double perovskites are gaining great interest due to their excellent electrical properties and high redox and long-term stability in different atmospheres [4].In this study, the double-perovskite (PrBa)0.95Fe2O5+δ (PBF) is proposed as symmetrical electrode for SOCs [5]. Two different strategies are implemented: on the one hand, Ni-doping in order to improve the electrical properties under reducing atmosphere and, on the other hand, the preparation of nanostructured electrodes in one-step by spray-pyrolysis deposition. A thorough characterization of the material is performed regarding its crystal structure, microstructure and electrochemical properties in both oxidizing and reducing atmospheres.With that purpose, PBF and (PrBa)0.95Fe2-xNixO5+ δ (PBFNx) powders are prepared by the freeze-drying precursor method. XRD patterns show the formation of a single perovskite-type phase in both air and H2 atmospheres, without the presence of any secondary phases. As for conductivity tests, they reveal increased values for the Ni-doped material over the pristine PBF material in air and 5% H2 at 600 ºC. These materials were thereafter screen-printed onto La0.9Sr0.1Ga0.8Mg0.2O2.85 (LSGM) electrolyte and calcined at 1100 ºC for 1h to ensure adequate adhesion to the electrolyte.Alternatively, spray-pyrolysis deposition method is employed to infiltrate PBFNx, with a 0.02 M precursor solution of the corresponding nitrate salts, into a porous Ce0.9Gd0.1O1.95 (CGO) layer previously fixed onto LSGM electrolyte (PBFNx-CGO). SEM images reveal that the screen-printed PBFNx has a thickness of 30 µm and a particle size of around 1 µm, while the same composition deposited by spray-pyrolysis shows a nanometric particle size and it is well-infiltrated in the 10 µm-thick CGO layer (Figure 1a). These electrode microstructures are maintained after ongoing a reduction process, where exsolved Ni particles are observed on the surface.The impedance spectra show lower polarization resistance (Rp) values for PBFNx with respect to PBF, i.e. 0.47 and 0.62 Ω cm2 at 650 ºC in air, respectively. But most significantly, further Rp reduction is observed for PBFNx-CGO prepared by spray-pyrolysis, values as low as 0.17 Ω cm2 at 650 ºC in air (Figure 1b). In reducing atmosphere, the PBFNx-CGO electrode also has lower Rp values (0.23 Ω cm2 at 650 ºC) when compared to the pristine PBF (0.52 Ω cm2 at 650 ºC).All in all, the nanostructured Ni-doped PBF electrodes exhibit high redox stability and low polarization resistance values under both oxidizing and reducing atmospheres, demonstrating their potential application as symmetrical electrodes for SOCs. These findings show that the optimization of the electrode composition but mostly its microstructure is key when developing new electrode materials for solid oxide cells.This research is supported by Ministerio de Ciencia, Innovación y Universidades through the RTI2018-093735-B-I00 and UMA18-FEDERJA-033 projects (Spain), and the University of Malaga through the B1-2021_03 project.[1] J. Zamudio-García, L. Caizán-Juanarena, J.M. Porras-Vázquez, E.R. Losilla, D. Marrero-López, A review on recent advances and trends in symmetrical electrodes for solid oxide cells, J. Power Sources. 520 (2022). https://doi.org/10.1016/j.jpowsour.2021.230852.[2] T. Su, Y. Li, Y. Yang, Z. Xu, N. Shi, Y. Wan, Y. Xie, D. Huan, S. Xue, C. Xia, Effect of tungsten doping on strontium ferrite electrode for symmetrical solid oxide electrochemical cell, Int. J. Hydrogen Energy. 45 (2020) 23401–23410.[3] Y. Kwon, S. Kang, J. Bae, Development of a PrBaMn2O5+δ-La0.8Sr0.2Ga0.85Mg0.15O3-δ composite electrode by scaffold infiltration for reversible solid oxide fuel cell applications, Int. J. Hydrogen Energy. 45 (2019) 1748–1758.[4] B. Zhang, Y. Wan, Z. Hua, K. Tang, C. Xia, Tungsten-Doped PrBaFe2O5+δ Double Perovskite as a High-Performance Electrode Material for Symmetrical Solid Oxide Fuel Cells, ACS Appl. Energy Mater. 4 (2021) 8401–8409.[5] D. Kim, S.J. Son, M. Kim, H.J. Park, J.H. Joo, PrBaFe2O5+δ promising electrode for redox-stable symmetrical proton-conducting solid oxide fuel cells, J. Eur. Ceram. Soc. 41 (2021) 5939–5946. Figure 1. a) PBFNx-CGO electrode deposited by spray-pyrolysis onto LSGM electrolyte: (i) Low magnification image of the electrode and electrolyte; (ii) Zoomed image of the pure PBFN top layer and the PBFNx layer infiltrated in CGO; and (iii) Zoomed image of PBFNx particles infiltrated on CGO scaffold. b) Polarization resistance (Rp) in air of PBF, PBFNx and PBFNx-CGO electrodes as a function of temperature (450-800 ºC). Figure 1

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images.

Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.

Dynamic evolution mechanism of scanning moiré fringes

Scanning moiré fringes (SMFs) in scanning transmission electron microscopy (STEM) have a broad application prospect owing to the low-magnification imaging and hereto the low electron irritation damage, especially in defects localization, strain analysis etc. However, the dynamic evolution mechanism of SMFs is still not clear. In this paper, we carry out in-depth study of SMFs with ferroelectric material GeSe as an example. With the help of combination of aberration-corrected STEM imaging and geometrical model, we discuss the evolution of SMFs with variation of scanning step (magnification), and explain its quasiperiodic behavior in the experiments. Our results will deepen the understanding of SMFs, and may widen their applications under the guidance of the new formation mechanism.

Borrelia Infected Neoplastic Glial Cells in Five Patients With Glioblastoma Multiformae

Glioblastoma multiformae (GBM) is a primary malignancy of brain astrocytes. Transcranial stereotactic biopsy specimens from five deceased and anonymous patients with GBM were retrieved from archival storage two years after the death of the patients. All patients were residents of the Long Island, New York metropolitan region where borrelia burgdorferi and borrelia miyamotoi infections are hyperendemic. Fluorescence in situ hybridization (FISH) using two DNA probes specific for genes of burgdorferi borrelia and an additional FISH method study with two DNA probes specific for miyamotoi borrelia each and individually produced high signal borrelia DNA hybridizations inside malignant cells in brain biopsies from all five decedents. Bright signal bacterial DNA hybridizations inside live borrelia spirochetes were found at all levels in serial sections of tumor biopsy fragments in low magnification imaging and outright invasion of the nuclei of individual tumor cells in high resolution imaging. Herein is the first report of a cluster of GBM patients with universal borrelia microbe invasion of neoplastic glial cells.

Performance improvement of macroscopical film of gold nanoparticles as temperature sensor derived from its mono-crystallinity

Macroscopically monocrystalline, self-assembled monolayers (M2-SAM) of gold nanoparticles coated with oleylamine (Au@OLA NPs) are achieved by optimizing their surface charges, which can create proper long-rang order electrostatic repulsive interactions to help balance the strong van der Waals attractive force and the hydrophobic repulsion force between neighboring Au NPs. The mono-crystallinity of the resulting M2-SAM of Au@OLA NPs is embodied in unconventional Moiré patterns beyond the low magnification imaging limit of scanning electron microscopy. The mono-crystallinity not only makes the M2-SAM of Au@OLA NPs be SERS substrates for quantitative analysis, but also enables them to fabricate temperature sensors with high precision and broad measurement span. Both the electrical resistivity-based temperature sensor with a precision of 0.001 °C in the range of −243 to 77 °C and the SERS-based temperature sensor with a precision of 0.5 °C in a range of 33 to 130 °C are implemented.

Multiple-image super-resolution of cryo-electron micrographs based on deep internal learning.

Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest. The sampling requirements of high-resolution imaging impose limitations on the pixel sizes that can be used for acquisition, limiting the size of the field of view and requiring data collection sessions of several days to accumulate sufficient numbers of particles. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state-of-the-art performance on natural images. Building on these advances, here, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to single-particle datasets of apoferritin and T20S proteasome, we show that the resolution of the 3D structure obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with in silico image SR has the potential to accelerate cryo-EM data collection by virtue of including more particles in each exposure and doing so without sacrificing resolution.

Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention

Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the clinical diagnosis, mainly because of the commonly used low-magnification pathological images lack fine cellular structure information and accurate localization, the diagnosis results of pathologists are different from those of attentional convolutional network -based methods, and the gradient-weighted class activation mapping method cannot be optimized to create the best visualization map. To address this problem, we propose an end-to-end depth domain adaptive network (DDANet) with integration gradient CAM and priori experience-guided attention to improve the tumor grading performance and interpretability by introducing the pathologist's a priori experience in high-magnification into the depth model. Specifically, a novel priori experience-guided attention (PE-GA) method is developed to solve the traditional unsupervised attention optimization problem. Besides, a novel integration gradient CAM is proposed to mitigate overfitting, information redundancies and low sparsity of the Grad-CAM graphs generated by the PE-GA method. Furthermore, we establish a set of quantitative evaluation metric systems for model visual interpretation. Extensive experimental results show that compared with the state-of-the-art methods, the average grading accuracy is increased to 88.43% (↑4.04%), the effective interpretable rate is increased to 52.73% (↑11.45%). Additionally, it effectively reduces the difference between CV-based method and pathology in diagnosis results. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and our model outperforms pathologists with different levels of experience.