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Cryo-Electron Ptychography of Proteins: 4D-STEM, or Diffractive Imaging, bears great potential for high-contrast imaging of biological specimens. We determined the Apoferritin structure by cryo-electron ptychography at 5.8Å resolution (Berk Küçükoglu and colleagues), and by cryo-electron microscopy at 1.09Å resolution (Alex Myasnikov and his colleagues from the Dubochet Center for Imaging). Cryo-EM is still (much!) better, but ptychography is rapidly catching up. Collaboration with the labs of Colin Ophus, Carsten Sachse and Knut Müller-Caspary. https://lnkd.in/eT6yCqdQ

🤔 Have you ever wondered what the spot, blob, or strange shape in your cryo-TEM image is? 🔬 Learn from our experienced #cryoTEM scientist, Dr. Brent Wood, about the 5 most common artifacts observed in TEM images of nanoparticle formulations, why they occur, and how NIS scientists overcome each imaging obstacle. 📖 Check out our guide:

A big thank you to my colleagues Dmitry Dzhigaev, PhD and Alex Björling, my collaborators Michael Stuckelberger, Fabian Westermeier and Mikhail Lyubomirskiy, and also my supervisor Jesper Wallentin! In this paper, we present a pre-processing algorithm that recovers the 3D datasets from the BCDI dataset measured under the impact of large angular distortions. The correction provides marked improvements in the subsequent phase retrieval. We believe that this method could open the possibility for using BCDI under more challenging conditions with smaller and less attached particles, and to fully exploit the intensity of beams from fourth-generation synchrotrons. 

🤔 Have you ever wondered what the spot, blob, or strange shape in your cryo-TEM image is? 🔬 Learn from experienced #cryoTEM scientist, Dr. Brent Wood, about the 5 most common artifacts observed in TEM images of nanoparticle formulations, why they occur, and how NIS scientists overcome each imaging obstacle. 📖 Check out our guide:

🔬 Is Automated Microscopy Worth the Investment? How to Prioritize? Many microscopy techniques share a virtually identical control hyperlanguage, including STEM, TEM, various SPM modalities, and ToF-SIMS. This commonality raises a critical question: How should we prioritize and invest in automated microscopy? We need to compare three criteria: anticipated benefit, complexity, and risk. Anticipated Benefit: It's essential to have a clear understanding of what ML is expected to achieve. For instance, in-line data analysis is valuable only if it accelerates decision-making (by human or ML agent) during experiments. Currently, ML-driven decision-making remains relatively simplistic - so we have to have either human in the loop and share decision makign between human and ML, or have relatively simple policies. From this perspective, electron microscopy stands out. These methods can generate vast volumes of data very fast, enabling significant acceleration in decision-making. In contrast, AFM and STM data acquisition is slower, limiting its benefit. Complexity: Atomic resolution offers the advantage of readily interpretable contrast, whereas mesoscopic imaging is more complex. This makes it easier to implement ML for atomic resolution techniques. For mesossocpic imaging, data interpretation and decision makign tend to be vastly more complicated, necessitating human decision making. Risk: Electron beam probes have a strong advantage because sample damage does not affect the probe state. However, in scanning probe microscopy, tip state changes can be irreversible, adding an extra layer of risk. This is particularly the case for STM. Conclusion: STEM should be the first choice for automation due to its high benefit, manageable complexity, and lower risk. AFM and STM follow as second and third choices, respectively. Interestingly, this prioritization aligns with historical progress in microscopy - even though the logic at a time was simpler. Invest wisely, and let's accelerate discovery! 🚀🔍 #Microscopy #Automation #MachineLearning #ResearchInnovation

I am happy to share that we recently published the main results from one of my PhD projects in an open-access journal. I have grown high-density hafnate films using liquid phase epitaxy with different elemental compositions. But before anything was growing, it took many hours in the lab, many frustrating hours to investigate and understand why I was at first not able to grow these films. In the publication, we report on the results of only one of the compositions, Lu2Hf2O7:Eu, the most promising one for high-resolution X-ray imaging. We share some of the challenges we encountered on the way and discuss how to overcome them. Give it a read if you are interested in the topic, or else at least see the radiograph we did on an insect we found in the dust on the floor. I have named him Herman. Thanks to Herman and all the co-authors who helped with discussions, measurements, and finalizing this manuscript. https://lnkd.in/dCV-CehD

Thanks to #research led by INRiM in Florence, we now know more about how #light behaves inside anisotropic materials that scatter light with different rates along different directions. Lorenzo Pattelli, lead researcher on the study, emphasizes the long-standing challenge of dealing with transport anisotropy: 👉 "Almost all scattering materials show some form of anisotropy. Yet, many studies have ignored this aspect, assuming materials are isotropic for simplicity".  👉 “Due to this oversimplification, previously reported scattering coefficients in structurally anisotropic media may be quantitatively inaccurate due to the systematic errors introduced by isotropic modeling". 🔽 Find out more about this work by reading the SPIE, the international society for optics and photonics highlight 🔽 . . . Sony Europe Università degli Studi di Firenze University of Ljubljana LENS - European Laboratory for Non-linear Spectroscopy CNR-INO Cnr-National Institute of Optics I-PHOQS Infrastructure _ #INRiM #Metrology #APNexus

🚀 Unlocking the potential: 3D CoSeedis in chip communication technology™! 🌐🔬   Exciting results from one of our recent studies led by Julia Schueler et al. reveal revolutionary applications in 3D cell culture technology: 🔍 Key discoveries: ·       Compound Testing: Gain unprecedented insights into safety and effectiveness. ·       Live-Cell Imaging: Observe dynamic processes in real-time. ·       High-Quality Biological Material: Enhance reliability in experiments. ·       Pathway Studies: Uncover crucial molecular insights. ·       Histology and Proteomics: Precision exploration of tissues and proteins. ·       Cell Painting: Detailed cellular profiles for informed decisions. ·       Quantitative Image Analysis: Streamline extraction of meaningful data from complex images. 📚 Explore further: Delve into our comprehensive collection of research papers on our website. Discover the future of 3D cell culture technology and unlock a new era of possibilities! 🌐🚀 #ResearchRevelations #InnovationInScience #abcbiopply #3DCellCultures

Multi-bandpass filters transmit specific wavelength bands simultaneously, crucial in biomedical imaging for capturing and analyzing multiple fluorescence channels or spectral components in a single experiment. These filters are carefully chosen to match the emission wavelengths of fluorophores or spectral bands of interest. Proper filter selection and calibration are vital to efficiently capture desired spectral information while minimizing crosstalk. In life science image processing, multi-bandpass filters enhance capabilities by enabling simultaneous acquisition of multiple channels from a single sample, offering researchers valuable data with effective segregation of different wavelength bands. https://lnkd.in/eW8EaWMS #biomedicalimaging #bandpass #microscopy #fluorescence #imaging

I'm thrilled to announce that our latest manuscript, titled "Enhancing Resolution and Contrast in Fibre Bundle-Based Fluorescence Microscopy Using Generative Adversarial Network" has been published in the Journal of Microscopy! Fibre bundle (FB)-based endoscopes play a vital role in biology and medical science due to their minimally invasive nature. However, limitations in resolution and contrast have been a challenge, primarily due to the characteristics of FBs, such as low numerical aperture (NA) and individual fibre core sizes. In our study, we addressed this challenge by leveraging the power of deep learning. We developed a novel approach utilizing Generative Adversarial Networks (GANs) to enhance resolution and contrast in fluorescence images obtained using high-NA FBs. Our methodology involved training a GAN model using data from an FB-based multifocal structured illumination microscope (MSIM), significantly improving resolution and contrast compared to conventional FB-based systems. The results were remarkable. Our GAN model effectively transformed wide-field images into high-resolution MSIM images, surpassing the limitations of traditional optical hardware. The enhanced images substantially improved both contrast and resolution, showcasing the immense potential of GAN-based models trained using Microscopic data. This breakthrough opens up new possibilities for fibre bundle-based fluorescence microscopy, paving the way for more precise and detailed imaging in various biomedical applications. You can access the full manuscript here: https://lnkd.in/dYeXpxRc #Microscopy #FluorescenceMicroscopy #GenerativeAdversarialNetworks #DeepLearning #BiomedicalResearch #SciencePublication