Microscopy & Microtechniques 71 Premium AFM for Medium-sized Samples Launched
Tosca 200 is the latest addition to the Tosca series from Anton Paar, which now consists of Tosca 400, a large-sample, premium AFM, and Tosca 200, an AFM for medium-sized samples and budget-sensitive research. Both devices feature the same high level of automation, increasing the efficiency and significantly simplifying the handling of AFM measurements.
Following the introduction of Tosca 400 in 2017 as the first AFM that combined premium technology with time-efficient operation for large samples, Anton Paar now launches Tosca 200, an AFM for medium-sized samples and budget-sensitive research but providing the same quality and flexibility.
The features that make the difference are: the Probemaster is a unique tool specifically developed for cantilever handling that saves time and guarantees the fast, safe, and correct placement of the cantilever; Tosca carries out laser alignment automatically with just two clicks in the control software; navigation on an overview map only by mouse-click which facilitates the localisation of the right measuring area, especially when working with more than one sample; the integrated side-view camera enables live tracking of the cantilever position with respect to the surface on the main screen, so the coarse approach of the probe is fast and safe and auto-engagement is then performed within seconds; the magnetic sample carrier lock for fast sample mounting means that multiple samples can be placed on the sample carrier outside of the instrument and the lock guarantees a stable position of the samples; the workflow-oriented Tosca Control software guides the user thorough every step of the measurement so that the final results are obtained faster and the user can focus on the interpretation and further research.
With their focus on performance and measurement efficiency, both instruments in the Tosca series are the perfect nanotechnology tools for scientists as well as industrial users.
More information online:
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AI Meets Live Cell Imaging
Olympus’ newly launched scanR 3.1 high-content screening (HCS) station fully embraces the capabilities of artificial intelligence (AI) to enable cutting-edge life science research. It combines the modularity and flexibility of a microscope-based setup with the automation, speed, throughput and reproducibility of HCS applications. Using the ‘self-learning microscopy’ concept, scanR 3.1 makes it easy to gather data quickly from large live cell populations for reliable, well-supported experimental results.
To minimise setup time, the self-learning approach makes use of a short, one-time training phase in which the software uses a quickly acquired set of images to generate ‘ground truth’ data without requiring human annotations. It then uses convolutional neural networks to autonomously create robust algorithms that can rapidly analyse large sets of images.
One application that clearly shows the power of AI in HCS is label-free quantification of live cells. Olympus’ scanR HCS software can reliably derive nuclei positions in micro wells solely from brightfield transmission images, with an accuracy that rivals fluorescence. Quantifying live cells from brightfield images instead of fluorescence shortens exposure times, avoids using genetic modifications or nucleus markers, and saves fluorescence channels for other markers. These benefits reduce phototoxicity and lead to simpler, faster image acquisition and better cell viability.
Reliable low light analysis has also become possible thanks to scanR’s AI-based imaging software. Olympus has shown that its software accurately detects DAPI-labelled cells at only 0.2% of the optimal light intensity. It can even distinguish different stages of the cell cycle, based on the intensity of the signal, providing better insights and improving reproducibility.
Dr Manoel Veiga, Application Specialist at Olympus Soft Imaging Solutions, commented on the launch, saying: “This exciting new scanR system is good news for a whole range of life science applications. Low light and label-free analysis are only examples of how automated detection using an AI-based approach can improve the accuracy and reproducibility of HCS. Olympus’ self-learning microscopy approach for scanR 3.1 combines these benefits with a user-friendly workflow that requires little human interaction during the training stage. This makes it easy for life science researchers to harness the power of HCS data while benefitting from a fast, simple setup.”
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