A5012105916- Cell Image Analysis Techniques
- Image Processing Techniques and Applications
- Digital Holography and Microscopy
- Advanced Fluorescence Microscopy Techniques
- Microfluidic and Bio-sensing Technologies
- Microbial Community Ecology and Physiology
- Modular Robots and Swarm Intelligence
- Pickering emulsions and particle stabilization
- Mechanical and Optical Resonators
- Isotope Analysis in Ecology
- Micro and Nano Robotics
- AI in cancer detection
- Electrostatics and Colloid Interactions
- Quantum Electrodynamics and Casimir Effect
- Spectroscopy Techniques in Biomedical and Chemical Research
- Electrohydrodynamics and Fluid Dynamics
- Nanomaterials and Printing Technologies
- Minerals Flotation and Separation Techniques
- Coral and Marine Ecosystems Studies
- Photoreceptor and optogenetics research
- SARS-CoV-2 and COVID-19 Research
- Characterization and Applications of Magnetic Nanoparticles
- Orbital Angular Momentum in Optics
- COVID-19 Impact on Reproduction
- Carbon Nanotubes in Composites
University of Gothenburg
2020-2024
Chalmers University of Technology
2019-2020
Video microscopy has a long history of providing insight and breakthroughs for broad range disciplines, from physics to biology. Image analysis extract quantitative information video data traditionally relied on algorithmic approaches, which are often difficult implement, time-consuming, computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved digital microscopy, potentially offering automatized, accurate, fast image analysis. However,...
Abstract The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Owing recent advances microscopy techniques, it is now possible routinely record the motion cells, organelles individual molecules at multiple spatiotemporal scales physiological conditions. However, automated analysis dynamics occurring crowded complex environments still lags behind acquisition microscopic image sequences....
Abstract The manipulation of microscopic objects requires precise and controllable forces torques. Recent advances have led to the use critical Casimir as a powerful tool, which can be finely tuned through temperature environment chemical properties involved objects. For example, these been used self-organize ensembles particles counteract stiction caused by Casimir-Liftshitz forces. However, until now, potential torques has largely unexplored. Here, we demonstrate that efficiently control...
Characterization of suspended nanoparticles in their native environment plays a central role wide range fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via diffusion constant and, as consequence, require long trajectories that medium has known uniform viscosity. However, most biological applications, only short are available, while simultaneously,...
Determination of size and refractive index (RI) dispersed unlabeled subwavelength particles is growing interest in several fields, including biotechnology, wastewater monitoring, nanobubble preparations. Conventionally, the distribution such samples determined via Brownian motion particles, but simultaneous determination their RI remains challenging. This work demonstrates nanoparticle tracking analysis (NTA) an off-axis digital holographic microscope (DHM) enabling both particle individual...
Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where interest are labeled by chemical staining techniques. However, these techniques often invasive sometimes even toxic to the cells, in addition being time consuming, labor intensive, expensive. Here, we introduce an alternative deep-learning–powered based bright-field images a conditional generative adversarial neural network...
Abstract Active matter comprises self-driven units, such as bacteria and synthetic microswimmers, that can spontaneously form complex patterns assemble into functional microdevices. These processes are possible thanks to the out-of-equilibrium nature of active-matter systems, fueled by a one-way free-energy flow from environment system. Here, we take next step in evolution active realizing two-way coupling between particles their environment, where act back on giving rise formation...
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to quantitative tool with ever-increasing resolution throughput. Artificial intelligence, deep neural networks, machine learning are all niche terms describing computational methods that have gained pivotal role in microscopy-based research over past decade. This Roadmap is written collectively by prominent researchers encompasses selected aspects how...
Recent advancements in deep learning (DL) have propelled the virtual transformation of microscopy images across optical modalities, enabling unprecedented multimodal imaging analysis hitherto impossible. Despite these strides, integration such algorithms into scientists' daily routines and clinical trials remains limited, largely due to a lack recognition within their respective fields plethora available methods. To address this, we present structured overview cross-modality transformations,...
The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks recent advances microscopy techniques, it is now possible routinely record the motion cells, organelles, individual molecules at multiple spatiotemporal scales physiological conditions. However, automated analysis dynamics occurring crowded complex environments still lags behind acquisition microscopic image sequences. Here, we...
The marine microbial food web plays a central role in the global carbon cycle. Our mechanistic understanding of ocean, however, is biased towards its larger constituents, while rates and biomass fluxes are mainly inferred from indirect measurements ensemble averages. Yet, resolution at level individual microplankton required to advance our oceanic web. Here, we demonstrate that, by combining holographic microscopy with deep learning, can follow microplanktons throughout their lifespan,...
The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity transport processes and interactions between cell components. These characteristics are seen as motion changes in particle trajectories. Despite existence multiple approaches to carry out this type analysis, no objective assessment these methods has been performed so far. Here, we have designed a competition characterize rank performance when analyzing dynamic behavior single...
The manipulation of microscopic objects requires precise and controllable forces torques. Recent advances have led to the use critical Casimir as a powerful tool, which can be finely tuned through temperature environment chemical properties involved objects. For example, these been used self-organize ensembles particles counteract stiction caused by Casimir-Liftshitz forces. However, until now, potential torques has largely unexplored. Here, we demonstrate that efficiently control alignment...
Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly behaviour due to quadrupolar capillary interactions induced by meniscus deformation. These cause attract each other in either tip-to-tip or side-to-side configurations. However, controlling their interfacial is challenging because it difficult predict which of these two states will be preferred. In this study, we demonstrate that introducing a soft shell around hard ellipsoidal provides means control the...
Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly due to quadrupolar capillary interactions, favouring either tip-to-tip or side-to-side configurations. However, predicting and controlling which structure forms remains challenging. We hypothesize that introducing a polymer-based soft shell around the will modulate these providing means tune preferred configuration based on particle geometry properties.
The interaction between metallic and biological nanoparticles (NPs) is widely used in various biotechnology biomedical applications. However, detailed characterization of this type challenging due to a lack high-throughput techniques that can quantify both size composition suspended NP complexes. Here, we introduce technique called ``twilight nanoparticle tracking analysis'' (tNTA) demonstrate it provides quantitative relationship the measured optical signal dielectric-metal We assess...
DeepTrack is an all-in-one deep learning framework for digital microscopy, attempting to bridge the gap between state of art solutions and end-users. It provides tools designing samples, simulating optical systems, training networks, analyzing experimental data. Moreover, packaged with easy-to-use graphical user interface, designed solve standard microscopy problems no required programming experience. By specifically modularity extendability in mind, we allow new methods easily be...
We present LodeSTAR, an unsupervised, single-shot object detector for microscopy. LodeSTAR exploits the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. demonstrate that is comparable state-of-the-art, supervised deep learning methods, despite training on orders magnitude less data, no annotations. Moreover, we achieves near theoretically optimal results in terms sub-pixel positioning objects various shapes. Finally, show can...
Video microscopy has a long history of providing insights and breakthroughs for broad range disciplines, from physics to biology. Image analysis extract quantitative information video data traditionally relied on algorithmic approaches, which are often difficult implement, time consuming, computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved digital microscopy, potentially offering automatized, accurate, fast image analysis....
Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where interest are labeled by chemical staining techniques. However, these techniques often invasive sometimes even toxic to the cells, in addition being time-consuming, labor-intensive, expensive. Here, we introduce an alternative deep-learning-powered based brightfield images a conditional generative adversarial neural network...
Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides overcoming the limitations of more classical approaches. The training state-of-the-art methods almost universally relies on either vast amounts labeled experimental data or ability to numerically simulate realistic datasets. However, produced by experiments are often challenging label and cannot be easily reproduced numerically. Here, we propose novel deep-learning...
We present LodeSTAR, a label-free, single-shot particle tracker. design method for exploiting the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. demonstrate that LodeSTAR outperforms traditional methods in terms accuracy it reliably tracks experimental data packed cells. Finally, we show can exploit additional extend measurable properties axial position objects polarizability.