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📍Arrived in Finland🇫🇮 together with @henriqueslab.bsky.social for the @sigridjuselius.bsky.social symposium AI Transforming Life Sciences and Medicine🤖💻🧬🩺 What an honour to share the program with the best! 👾🔬🌈🧫 ... and a true excitement to do it as a PI at #NIMSB for the 1st time!💙

🚨Our preprint on DL for single-image super-resolution in microscopy is out!🌟 sl1nk.com/p62yedg Our benchmark recaps an adventure of learning and questioning with @ivanhcenalmor.bsky.social & @iarganda.eurosky.social 🌟 how to objectively assess SISR models & ease the choice for their use in👉 biology🔬

🎉 The new #SDB Merch Store is officially LIVE! 👕🧢☕ Grab your favorite SDB gear including t-shirts, sweatshirts, caps, mugs & more ✨ 🛍️ Visit the store via the SDB homepage or shop directly here: sdb-official-merch.printify.me Show off your dev bio pride 🔬🧬🐟 #DevelopmentalBiology

The legendary @guijacquemet.bsky.social at Sigrid Juselius #AI Symposium. "I'm a cell biologist", but then 1/3 of the talk went to mechanistic cell biology, 1/3 algorithmic technology development and 1/3 AI. Gonna get him a decaf... (legend says he's also coming up soon with adaptive microscopy)

🔬🚀 Preprint update for #VLab4Mic - our super-resolution experiments virtual simulation system. Now installs as one-click desktop app: no Python, no setup, thanks to the magic of the @guijacquemet.bsky.social's lab #LabConstrictor packaging engine www.biorxiv.org/content/10.64898/2026.06.02.729521v2

We are very pleased to announce Dr Ignacio Arganda-Carreras @iarganda.eurosky.social as winner of the RMS Award for Data Analysis in Imaging (DAIM) 🙂 🔬 Read more about Ignacio's achievements: www.rms.org.uk/resource/dr-...

I'm very happy to announce that the "From Postdoc to PI Session" will be sponsored by the @biologists.bsky.social and we will be offering a $$ Prize to the best talk, so now a twofer reason to apply! www.germanfishmeeting.org/from-postoc-... Let me know if you have any question/s! 🐟

Excited to share my first PhD project: LabConstrictor 📒🐍 Have you ever created a Jupyter notebook with all your love 🫶, only for others to be unable to install it 🥲? LabConstrictor, comes to solve this! Check out how it works in the preprint 🔗 arxiv.org/abs/2603.107... or follow this thread ⬇️

Super excited to share a project I’ve worked on since my Master’s thesis, what a journey!🚵 Finally out as a Nature Methods Stage 1 Registered Report preprint doi.org/10.6084/m9.f... We benchmark deep learning-based single-image super-resolution methods for microscopy imaging. Interested? Check out 🧵

Virtual super-resolution deep learning methods provide a powerful solution to overcome the physical and temporal constraints of microscopy imaging. Yet, assessing and choosing an ideal methodological strategy complicates their use in life sciences and creates a lack of trust in these methods. Here we propose an objective comparison of nine popular single-image super-resolution (SISR) models in a collection of publicly available microscopy datasets, including cell components like microtubules, endoplasmic reticulum, and actin, using confocal microscopy, SEM, SIM, SMLM and STED microscopy modalities for fixed and live-cell microscopy data. The proposed models will be assessed quantitatively with a collection of metrics in microscopy and computer vision, and qualitatively by experts in the field. The proposed models will be made accessible through open, user-friendly, containerised notebooks. This systematic assessment of SISR approaches will provide a more comprehensive understanding of these methods' performance and contribute to standardising SISR methods in microscopy.