Journal of microscopy最新文献

Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs.

To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers – regardless of coding experience – to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation.

The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.

Fourier ptychography microscopy (FPM) has made significant progress since its invention in 2013, thanks to its adaptable nature, high resolution, and vast field-of-view capabilities. FPM is used in various medical applications across multiple optical wavelengths, from automated digital pathology to radiology and ultraviolet label-free imaging. This review explores the fundamental physical and computational concepts that have driven advancements in digital pathology using FPM. A crucial part of the progress has been the development of computational algorithms, which have directly contributed to the improvements in FPM. We evaluate early-stage algorithms like the Gerchberg–Saxton and highlight how phase-retrieval and deep-learning advancements have propelled FPM forward. Additionally, we discuss the impact of these algorithms on digital pathology for potential automated diagnosis, providing a comprehensive explanation of their influence on medical imaging and offering insights into future research directions.

Background Remover (BGR) is a novel software tool developed as a plugin to the well-known ImageJ program and designed to address the challenges of analysing fluorescent microscopy images characterised by low signal-to-noise ratios and heterogeneous backgrounds. The used algorithm effectively differentiates between signal and noise pixels, preserving the signal while eliminating noise. This functionality enables the analysis of images with objects of varying intensities, allowing for reliable identification even in low signal-to-noise ratio conditions. Furthermore, BGR offers the capability to determine the intensity of identified objects, enhancing its utility for researchers in the field. The paper describes the algorithm and the program functioning, as well as the carried out tests of its performance. The program is freely downloadable from the website https://kilianna.github.io/background-remover/

In this paper, in-line phase-contrast synchrotron microtomography was used to study the morphology of adult Aedes aegypti. These specimens are vectors of several arboviruses, causing dengue, chikungunya, Zika and yellow fever. The morphological details of this insect species are still incomplete and insufficient. To address this gap, this study examined whole specimens of Aedes aegypti in the adult phase at high resolution. For this, the adult samples were scanned in the microtomography beamline (SYRMEP) at the Italian Synchrotron Light Laboratory (ELETTRA). The phase-contrast technique allowed us to obtain high-quality images, which made it possible to evaluate the segmentation of structures on the rendered volume by the Dragonfly software. The combination of high-quality images and segmentation process provide adequate visualisation of different organs which could serve in assessing the effectiveness of innovative control population methods as a basis for future control studies of the insect vector.

The recovery of critical raw materials from water electrolysers, which are used to produce green hydrogen, is essential to keep the raw materials with limited availability in the material cycle and to facilitate the expansion of production of this technology, which is supposed to be essential for the decarbonisation of our industrial society. Proton exchange membrane water electrolysers (PEMWE) use precious metals such as Ir and Pt as catalysts, which require a high recycling rate due to their natural scarcity. In order to investigate at an early-stage mechanical recycling technologies, such as shredding for liberation and milling for decoating of these complex materials, it becomes necessary to develop small-scale experimental methods. This is due to the low availability of End-of-Life samples and the high price of pristine electrolyser components. Especially decoating has shown huge potential for a highly selective separation of defined material layers; nevertheless, until now, there is no method to determine the success of decoating of the flexible polymer membrane, which is coated on both sides with particle-based electrodes. One possible concept is presented here, using scanning electron microscope images and micro-X-ray fluorescence elemental maps. Image processing and segmentation is performed using the WEKA software and a simple thresholding method. This allows the efficiency of the decoating process to be determined with an accuracy of ±0.5 percentage points for decoated PEMWE cell samples. The high accuracy of the presented method framework provides the necessary tool for any further quantitative development of improved mechanical stressing for decoating.

Highly oriented pyrolytic graphite (HOPG) is one of the most used host materials for obtaining and investigating graphite intercalated compounds, because of the high degree structural order of this polycrystal. Experiments on electrochemically intercalated HOPG in sulphuric acid have a model character, as the results obtained can be usefully generalised, not only with respect to other graphite compounds but also for the intercalation of other layered host lattices. In addition, the HOPG/H₂SO₄ system has an attractive potential for the possibility of electrochemically producing graphite oxide, ideally, by reversible oxidation/reduction cycles, which is of interest for energy storage and graphene production on an industrial scale. However, the oxidation/reduction cycles in such electrochemical intercalation process are not reversible and topotactic, so that the HOPG structure is considerably altered. This alteration may affect, for instance, the quality of the electrochemically produced graphene. In particular, the impact the electrochemical intercalation has on the conductivity of basal planes of HOPG, and so on graphene sheets, is still debated. In this work, we investigated both the macroscopic and microscopic electron transfer (ET) kinetics of the HOPG surface, before and after the intercalation of 1 M H₂SO₄ to obtain graphite intercalated compound, by using cyclic voltammetry (CV) and scanning electrochemical microscopy (SECM), respectively. The heterogeneous kinetic constant (k⁰) of the HOPG was evaluated quantitatively by using the redox systems [Fe(CN)₆]^3-/4- and [Ru(NH₃)₆]^3+/2+. The morphology of the samples was also investigated by atomic force microscopy (AFM), which revealed a widespread formation of blisters and precipitates during the HOPG intercalation process. The CV and SECM results indicate that, upon intercalation, the electrochemical behaviour of the HOPG changes sensibly and the ET decreases sensibly. However, this effect depends on the redox mediators employed and it results more dramatic for the [Fe(CN)₆]^3-/4- system, for which a decrease of k⁰ by orders of magnitude was obtained. The decrease of ET can be correlated to the blisters and precipitates, which occur during the HOPG intercalation, as observed by AFM.

Microscopy and image analysis play a vital role in parasitology research; they are critical for identifying parasitic organisms and elucidating their complex life cycles. Despite major advancements in imaging and analysis, several challenges remain. These include the integration of interdisciplinary data; information derived from various model organisms; and data acquired from clinical research. In our view, artificial intelligence—with the latest advances in machine and deep learning—holds enormous potential to address many of these challenges. This review addresses how artificial intelligence, machine learning and deep learning have been used in the field of parasitology—mainly focused on Apicomplexan, Diplomonad, and Kinetoplastid groups. We explore how gaps in our understanding could be filled by AI in future parasitology research and diagnosis in the field. Moreover, it addresses challenges and limitations currently faced in implementing and expanding the use of artificial intelligence across biomedical fields. The necessary increased collaboration between biologists and computational scientists will facilitate understanding, development, and implementation of the latest advances for both scientific discovery and clinical impact. Current and future AI tools hold the potential to revolutionise parasitology and expand One Health principles.

To enhance the indexing rate of conventional electron backscatter diffraction (EBSD), this study employed EBSD to collect and analyse the mapping data of cubic phase materials. Kikuchi bands were identified using Hough transform, and the pattern centre was optimised through a genetic algorithm. Four objective functions were designed to investigate the influence of varying population sizes on the convergence of the algorithm. The results revealed that the calculation stabilised when the population size reached 400, with the HMAE (H-mean angular error) objective function exhibiting superior performance in screening by integrating the number of matched Kikuchi bands and mean angular error (MAE). Furthermore, to address indexing errors resulting from overlapping Kikuchi patterns at grain boundaries, an indexing optimisation method based on pattern similarity matching was proposed, significantly improving the indexing rate of EBSD mapping data. Finally, neighbourhood search strategy was implemented to further refine the indexing process, ensuring high indexing accuracy while substantially reducing computational time. This study offers novel methodologies and insights for improving the efficiency and precision of EBSD mapping data acquisition and analysis.