Determining the composition of a mixed material is an open problem that has attracted the interest of researchers in many fields. In our recent work, we proposed a novel approach to determine the composition of a mixed material using convolutional neural networks (CNNs). In machine learning, a model “learns” a specific task for which it is designed through data. Hence, obtaining a dataset of mixed materials is required to develop CNNs for the task of estimating the composition. However, the proposed method instead creates the synthetic data of mixed materials generated from using only images of pure materials present in those mixtures. Thus, it eliminates the prohibitive cost and tedious process of collecting images of mixed materials. The motivation for this study is to provide mathematical details of the proposed approach in addition to extensive experiments and analyses. We examine the approach on two datasets to demonstrate the ease of extending the proposed approach to any mixtures. We perform experiments to demonstrate that the proposed approach can accurately determine the presence of the materials, and sufficiently estimate the precise composition of a mixed material. Moreover, we provide analyses to strengthen the validation and benefits of the proposed approach.

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

In this research, atomic force microscopy (AFM) with a flat tip cantilever is utilized to measure Young's modulus of a whole yeast cell (Saccharomyces cerevisiae BY4741). The results acquired from AFM are similar to those obtained using a microfluidic chip compression system. The mechanical properties of single yeast cells are important parameters which can be examined using AFM. Conventional studies apply AFM with a sharp cantilever tip to indent the cell and measure the force-indentation curve, from which Young's modulus can be calculated. However, sharp tips introduce problems because the shape variation can lead to a different result and cannot represent the stiffness of the whole cell. It can lead to a lack of broader meaning when evaluating Young's modulus of yeast cells. In this report, we confirm the differences in results obtained when measuring the compression of a poly(dimethylsiloxane) bead using a commercial sharp tip versus a unique flat tip. The flat tip effectively avoids tip-derived errors, so we use this method to compress whole yeast cells and generate a force–deformation curve. We believe our proposed method is effective for evaluating Young's modulus of whole yeast cells.

One of the primary uses for transmission electron microscopy (TEM) is to measure diffraction pattern images in order to determine a crystal structure and orientation. In nanobeam electron diffraction (NBED), we scan a moderately converged electron probe over the sample to acquire thousands or even millions of sequential diffraction images, a technique that is especially appropriate for polycrystalline samples. However, due to the large Ewald sphere of TEM, excitation of Bragg peaks can be extremely sensitive to sample tilt, varying strongly for even a few degrees of sample tilt for crystalline samples. In this paper, we present multibeam electron diffraction (MBED), where multiple probe-forming apertures are used to create multiple scanning transmission electron microscopy (STEM) probes, all of which interact with the sample simultaneously. We detail designs for MBED experiments, and a method for using a focused ion beam to produce MBED apertures. We show the efficacy of the MBED technique for crystalline orientation mapping using both simulations and proof-of-principle experiments. We also show how the angular information in MBED can be used to perform 3D tomographic reconstruction of samples without needing to tilt or scan the sample multiple times. Finally, we also discuss future opportunities for the MBED method.

Obesity is characterized by excess adipose tissue and chronic inflammation and promotes extensive changes that can compromise skeletal muscles’ structural and functional integrity. Obesity can seriously impact the force transmission region between the muscle and the tendon, the myotendinous junction (MTJ). The present study aimed to investigate the plasticity of muscle fibers and MTJ regions in high-fat diet-induced obesity in rat tibialis anterior (TA) and soleus (SO) muscles. Wistar rats were divided into control and obese groups (induced by a high-fat diet). The samples of TA and SO muscles were prepared for histochemical and ultrastructural analysis (sarcomeres and MTJ projection). In the muscle fiber, similar adaptations were observed between the muscles of the smaller fiber (types I and IIa) in the obesity results. The MTJ region demonstrated different adaptations between the analyzed muscles. The TA–MTJ region has shorter ultrastructures, while in the SO–MTJ region, the ultrastructures were larger. We conclude that obesity induced by a high-fat diet promotes similar adaptation in the muscle fibers; however, in the MTJ region, the sarcoplasmatic projections and adjacent sarcomere demonstrate different adaptations according to distinct muscle phenotypes.

Manual selection of targets in experimental or diagnostic samples by transmission electron microscopy (TEM), based on single overview and detail micrographs, has been time-consuming and susceptible to bias. Substantial information and throughput gain may now be achieved by the automated acquisition of virtually all structures in a given EM section. Resulting datasets allow the convenient pan-and-zoom examination of tissue ultrastructure with preserved microanatomical orientation. The technique is, however, critically sensitive to artifacts in sample preparation. We, therefore, established a methodology to prepare large-scale digitization samples (LDS) designed to acquire entire sections free of obscuring flaws. For evaluation, we highlight the supreme performance of scanning EM in transmission mode compared with other EM technology. The use of LDS will substantially facilitate access to EM data for a broad range of applications.

Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

Atomic force microscopy (AFM) can determine mechanical properties, associated with surface topography and structure, of a material at the nanoscale. Force–indentation curves that depict the deformation of a target specimen as a function of an applied force are widely used to determine the elastic modulus of a material based on a contact model. However, a hysteresis may arise due to friction between the AFM tip and a specimen. Consequently, the normal force detected using a photodetector during extension and retraction could be underestimated and overestimated, respectively, and the extension/retraction data could result in a significant difference in the elastic modulus measurement result. In this study, elastic modulus and friction coefficient values were determined based on an in situ theoretical model that compensated for the effect of friction on force–indentation data. It validated the proposed model using three different polymer specimens and colloidal-tipped probes for the force–indentation curve and friction loop measurements. This research could contribute to the accurate measurement of mechanical properties using AFM by enhancing the interpretation of force–indentation curves with friction-induced hysteresis. Furthermore, the proposed approach may be useful for analyzing in situ relationships between mechanical and frictional properties from a fundamental tribological perspective.

Amyloid plaques, one of the main hallmarks of Alzheimer's disease (AD), are classified into diffuse (associated with cognitive impairment) and dense-core types (a common finding in brains of people without Alzheimer's disease (non-AD) and without impaired cognitive function) based on their morphology. We tried to determine the usability of gray-level co-occurrence matrix (GLCM) texture parameters of homogeneity and heterogeneity for the differentiation of amyloid plaque images obtained from AD and non-AD individuals. Images of amyloid-β (Aβ) immunostained brain tissue samples were obtained from the Aging, Dementia and Traumatic Brain Injury Project. A total of 1,039 plaques were isolated from different brain regions of 69 AD and non-AD individuals and used for further GLCM analysis. Images of Aβ stained plaques show higher values of heterogeneity parameters and lower values of homogeneity parameters in AD patients, and vice versa in non-AD patients. Additionally, GLCM analysis shows differences in Aβ plaque texture between different brain regions in non-AD patients and correlates with variables that characterize patient's dementia status. The present study shows that GLCM texture analysis is an efficient method to discriminate between different types of amyloid plaques based on their morphology and thus can prove as a valuable tool in the neuropathological investigation of dementia.

A focused ion beam (FIB) technique describing the preparation of specimens for in situ thermal and electrical transmission electron microscopy is presented in detail. The method can be applied to a wide range of materials and allows the sample to be thinned down to electron transparency while it is attached to the in situ chip. This offers the advantage that the specimen can have a quality in terms of contamination and damage due to the ion beam that is comparable to samples prepared by means of conventional FIB preparation. Additionally, our technique can be performed by most commercially available FIB devices and only requires three simple, custom stubs for the procedure. This should enable a large userbase for this type of sample fabrication. One further benefit of our technique is that the in situ chip can be reused to create another sample on the same chip. The quality of the samples is demonstrated by high-resolution transmission electron microscopy as well as electron energy loss spectroscopy.

Listeriosis is a disease that is induced by infection with the Gram-positive bacterium Listeria monocytogenes. Much is still unknown about the pathogenesis of encephalitic listeriosis. We aimed to identify the contribution of glial fibrillary acidic protein (GFAP), epithelial cadherin (E-cadherin), S100, and acute-phase proteins (APPs) in pathogenesis, clinical and preclinical diagnosis of natural cases of encephalitic listeriosis. Of 1,325 Ossimi sheep, 64 were suspected of having listeriosis from 2018 to 2020. Prospective cohort evaluation of clinical and postmortem findings was performed, in addition to bacterial isolation, the measurement of APPs in serum and cerebrospinal fluid (CSF), and the histopathological and immunohistochemical evaluation of GFAP, S100, and E-cadherin. Infected sheep showed nervous symptoms ranging from neck stretching to complete paralysis. APPs were significantly increased in the CSF of both clinically and preclinically diseased animals; however, serum APPs were only significantly increased in clinically diseased animals. Histopathological evaluation revealed microabscesses, meningoencephalitis, and perivascular cuffing of the brainstem of infected sheep. Immunohistochemical investigations revealed strong expression of GFAP and S100 in necrotic areas and negative expression of E-cadherin. The measurement of CSF APPs could be useful in the preclinical diagnosis of sheep listeriosis. GFAP and S100 proteins could be involved in the pathogenesis of listeriosis; however, E-cadherin does not appear to be involved.

GBF1 [Golgi brefeldin A (BFA) resistance factor 1] is a member of the guanine nucleotide exchange factors Arf family. GBF1 localizes at the cis-Golgi and endoplasmic reticulum (ER)-Golgi intermediate compartment where it participates in ER-Golgi traffic by assisting in the recruitment of the coat protein COPI. However, the roles of GBF1 in oocyte meiotic maturation are still unknown. In the present study, we investigated the regulatory functions of GBF1 in mouse oocyte organelle dynamics. In our results, GBF1 was stably expressed during oocyte maturation, and GBF1 localized at the spindle periphery during metaphase I. Inhibiting GBF1 activity led to aberrant accumulation of the Golgi apparatus around the spindle. This may be due to the effects of GBF1 on the localization of GM130, as GBF1 co-localized with GM130 and inhibiting GBF1 induced condensation of GM130. Moreover, the loss of GBF1 activity affected the ER distribution and induced ER stress, as shown by increased GRP78 expression. Mitochondrial localization and functions were affected, as the mitochondrial membrane potential was altered. Taken together, these results suggest that GBF1 has wide-ranging effects on the distribution and functions of Golgi apparatus, ER, and mitochondria as well as normal polar body formation in mouse oocytes.

Current reconstruction methodologies for atom probe tomography (APT) contain serious geometric artifacts that are difficult to address due to their reliance on empirical factors to generate a reconstructed volume. To overcome this limitation, a reconstruction technique is demonstrated where the analyzed volume is instead defined by the specimen geometry and crystal structure as determined by transmission electron microscopy (TEM) and diffraction acquired before and after APT analysis. APT data are reconstructed using a bottom-up approach, where the post-APT TEM image is used to define the substrate upon which APT detection events are placed. Transmission electron diffraction enables the quantification of the relationship between atomic positions and the evaporated specimen volume. Using an example dataset of ZnMgO:Ga grown epitaxially on c-plane sapphire, a volume is reconstructed that has the correct geometry and atomic spacings in 3D. APT data are thus reconstructed in 3D without using empirical parameters for the reverse projection reconstruction algorithm.