WebCalEM: a browser-based tool for routine and accurate pixel-size calibration in cryo-EM.

BioImageIT: A novel python-based architecture for reproducible bio-image workflows.

Recent advances in light microscopy have transformed the scale and complexity of biological imaging data, creating an urgent need for sophisticated computational pipelines capable of extracting meaningful biological insights. However, the current software ecosystem for bio-image analysis remains highly fragmented, with researchers needing to integrate tools written in disparate programming languages and architectural paradigms. This fragmentation gives rise to 'dependency hell' and creates significant technical barriers for life scientists. We present the novel and flexible architecture of BioImageIT, a lightweight open-source workflow management system designed to bridge the gap between advanced computational tools and end-user bio-image analysts. Built upon Python, BioImageIT has evolved into a dual interface architecture: a node-based visual programming GUI alongside a comprehensive Python Application Programming Interface (API). The system features the Wetlands environment management system for automatic dependency resolution, and adopts pandas DataFrames as the universal data structure for inter-node communication. BioImageIT enforces adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) throughout the analysis lifecycle, automatically capturing comprehensive metadata for every processing step. The architecture abstracts the underlying computational infrastructure, laying the groundwork for seamless scaling from local workstations to high-performance computing (HPC) clusters-a capability currently under active development.

ONE microscopy.

AutoSPy: A software of automated image processing for time-lapse scanning tunneling microscopy.

Time-lapse scanning tunneling microscopy (STM) captures how surface structures evolve in real time, but extracting quantitative kinetics from the resulting image series is hampered by drifts and the labor of frame-by-frame processing. We present AutoSPy, a modular software package that automates the conversion of raw time-lapse STM image stacks into analyzable time-series measurements. AutoSPy aligns consecutive frames using scale-invariant feature transform feature matching with RANSAC-based outlier rejection and an affine registration model, correcting not only translational drift but also drift-induced rotation and anisotropic distortion. An adaptive flattening routine combining Sobel gradient detection, Otsu thresholding, region growing, and least-squares plane fitting standardizes background subtraction across large datasets. For analysis, it integrates the Segment Anything Model 2 to segment and track user-selected features across registered frames, yielding trajectories, displacement statistics, and time-dependent area changes for clusters and vacancies with minimal manual input. AutoSPy, thus, provides a generalizable toolkit for quantitative studies of dynamic surface evolution and kinetics under working conditions.

New Approaches to Fast Plate Composition, with a Software Implementation for Taxonomy

New approaches are proposed to simplify and formalize multi-panel figure composition, emphasizing rapid layout construction and experimentation while maintaining explicit control over publication parameters.

Development of a low-cost microscope slide scanner for creating digital library of histological slides

Background: Glass slide microscopy is essential for histology teaching but is limited by fragility, fading stains, preparation costs, and restricted accessibility. Commercial whole-slide scanners are costly and difficult to maintain in low- and middle-income countries. This study developed and evaluated a low-cost locally fabricated slide scanner for digital microscopy. Methods: This 18-month development project was conducted at Maharajgunj Medical Campus using archived anonymized histology slides. The scanner integrated an Arduino Uno microcontroller, stepper motors, joystick control, 3D-printed brackets, and an eyepiece-mounted digital camera attached to an Olympus CX41 microscope. Automated stage movement enabled sequential overlapping image capture. Validity was assessed by expert subjective comparison with direct microscopy for tissue identification, clarity, color accuracy, and visual discrimination. Scanning time, noise output, and cost were recorded. The images were taken at 150 pixels per inch. Results: On scanning five slides, mean scanning time was 5 minutes 26 seconds per slide. Noise levels ranged from 32–69 dB and were not disturbing. The scanner produced images at resolution of 150 pixels per inch with excellent clarity, good color consistency, and visual discrimination. Total development cost was NPR 200,000. Conclusion: A reliable low-cost whole-slide scanner can be locally developed using accessible materials and open-source technology, supporting affordable digital microscopy for medical education and future telepathology applications in resource-limited settings.

WAFMGAN: A frequency‐aware CycleGAN for structure‐consistent virtual staining of transmission electron microscopy images

Conventional transmission electron microscopy (TEM) staining improves image contrast but requires additional sample preparation, consumes reagents and tissue, and may introduce deformation, local mismatch, or tissue loss. When strictly paired stained and unstained sections are difficult to obtain, unpaired virtual staining provides a practical route for improving the readability of unstained TEM images while retaining correspondence with source‐image structural cues. This paper presents WAFMGAN, a frequency‐aware CycleGAN framework that combines Wavelet Asymmetric Frequency Mamba (WAFM) blocks for low‐frequency‐dominant global staining transfer and long‐range structural coordination with a Frequency‐Adaptive Gated Refinement (FAGR) block for later‐stage boundary and texture refinement. On the mouse renal TEM test set, WAFMGAN achieved an FID of 14.8064 and a KID( ×100$\times 100$ ) of 0.5074 ±$\pm$ 0.0417, representing reductions of 23.0% and 47.3% relative to CycleGAN. At the original‐micrograph‐pair level, WAFMGAN achieved SSIM, MS‐SSIM, CSS, and Gradient‐SSIM values of 0.9796, 0.9874, 0.9825, and 0.9889, respectively, and all four structure‐related metrics were significantly higher than those of every evaluated baseline after Holm–Bonferroni correction. Output‐level frequency analysis further showed lower wavelet‐domain discrepancy than CycleGAN on all four tissue sets and lower high‐frequency Fourier discrepancy on the renal, heart, and liver sets. These results demonstrate that WAFMGAN improves stained‐domain distribution and frequency matching while maintaining strong source‐image structural consistency. WAFMGAN therefore provides a useful frequency‐aware approach for improving the readability of unstained TEM images while maintaining source‐image structural cues.