We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis. The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive. The proposed solution is valid for any microscope setup, supporting various biological applications. The only necessary element is a shared filesystem, common in any standard laboratory, even in resource-constrained settings. The file watcher functionality in Python can be easily integrated into other python-based software. We demonstrate the proposed solution by performing tiling experiments using the molecular nanoscale live imaging with sectioning ability (MoNaLISA) microscope, a high-throughput super-resolution microscope based on reversible saturable optical fluorescence transitions (RESOLFT).

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself—OME-Zarr—along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain—the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Live-cell imaging of biological structures at high resolution poses challenges in the microscope throughput regarding area and speed. For this reason, different parallelisation strategies have been implemented in coordinate- and stochastictargeted switching super-resolution microscopy techniques. In this line, the molecular nanoscale live imaging with sectioning ability (MoNaLISA), based on reversible saturable optical fluorescence transitions (RESOLFT), offers 45 - 65 nm resolution of large fields of view in a few seconds. In MoNaLISA, engineered light patterns strategically confine the fluorescence to sub-diffracted volumes in a large area and provide optical sectioning, thus enabling volumetric imaging at high speeds. The optical setup presented in this paper extends the degree of parallelisation of the MoNaLISA microscope by more than four times, reaching a field-of-view of (100 - 130 mu m)(2). We set up the periodicity and the optical scheme of the illumination patterns to be power-efficient and homogeneous. In a single recording, this new configuration enables super-resolution imaging of an extended population of the post- synaptic density protein Homer1c in living hippocampal neurons. 

The constant development of novel microscopy methods with an increased number of dedicated hardware devices poses significant challenges to software development. On the onehand, software should control complex instruments, provide flexibility to adapt between different microscope modalities, and be open and resilient to modification and extension byusers and developers. On the other hand, the community needs software that can satisfy therequirements of the users, such as a user-friendly interface and robustness of the code. In this context, we present ImSwitch, based on the model-view-presenter (MVP) design pattern (Potel, 1996), with an architecture that uses polymorphism to provide a generalized solutionto microscope control. Consequently, ImSwitch makes it possible to adapt between different modalities and aims at satisfying the needs of both users and developers. We have alsoincluded a scripting module for microscope automation applications and a structure to efficiently share data between different modules, such as hardware control and image processing. Currently, ImSwitch provides support for light microscopy techniques but could be extendedto other microscopy modalities requiring multiple hardware synchronization. 

The performance of fluorescence microscopy and nanoscopy is often discussed by the effective point spread function and the optical transfer function. However, due to the complexity of the fluorophore properties such as photobleaching or other forms of photoswitching, which introduce a variance in photon emission, it is not trivial to choose optimal imaging parameters and to predict the spatial resolution. In this paper, we analytically derive a theoretical framework for estimating the achievable resolution of a microscope depending on parameters such as photoswitching, labeling densities, exposure time and sampling. We developed a numerical simulation software to analyze the impact of reversibly switchable probes in RESOLFT imaging.

This paper describes an Arduino Due based platform for digital signal processing (DSP) education. The platform consists of an in-house developed shield for robust interfacing with analog audio signals and user inputs, and an off-the-shelf Arduino Due that executes the students' DSP code. This combination enables direct use of the Arduino integrated development environment (IDE), with its low barrier to entry for students, its low maintenance need and cross platform interoperability, and its large user base. Relevant hardware and software features of the platform are discussed throughout, as are design choices made in relation to learning objectives, and the planned use of the platform in our own DSP course.