Extracting quantitative biological information from brightfield cell images using deep learning on ArXiv

Virtually-stained generated image for lipid-droplet.
Extracting quantitative biological information from brightfield cell images using deep learning
Saga Helgadottir, Benjamin Midtvedt, Jesús Pineda, Alan Sabirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe
arXiv: 2012.12986

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.

Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces on ArXiv

Metavehicles.
Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces
Daniel Andrén, Denis G. Baranov, Steven Jones, Giovanni Volpe, Ruggero Verre, Mikael Käll
arXiv: 2012.10205

Nanostructured dielectric metasurfaces offer unprecedented opportunities to manipulate light by imprinting an arbitrary phase-gradient on an impinging wavefront. This has resulted in the realization of a range of flat analogs to classical optical components like lenses, waveplates and axicons. However, the change in linear and angular optical momentum associated with phase manipulation also results in previously unexploited forces acting on the metasurface itself. Here, we show that these optomechanical effects can be utilized to construct optical metavehicles – microscopic particles that can travel long distances under low-power plane-wave illumination while being steered through the polarization of the incident light. We demonstrate movement in complex patterns, self-correcting motion, and an application as transport vehicles for microscopic cargo, including unicellular organisms. The abundance of possible optical metasurfaces attests to the prospect of developing a wide variety of metavehicles with specialized functional behavior.

Giovanni Volpe awarded new ERC Consolidator Grant

ERC logo.
Giovanni Volpe has been awarded a new European Research Council (ERC) Consolidator Grant on Wednesday, December 9th 2020.

The title of his project is “Microscopic Active Particles with Embodied Intelligence”.

Active particles and active matter research tries to understand and replicate the characteristics of living microorganisms in artificial systems. Over billions of years of evolution, living organisms have developed complex strategies to survive and thrive. The artificial active particles are still incapable of autonomous information processing.

Giovanni Volpe’s project aims to address three main challenges in the current research on active matter:

  • Make active particles capable of autonomous information processing.
  • Optimize the behavioral strategies of individual active particles.
  • Optimize the interactions between active particles.

Links
Giovanni Volpe awarded new ERC Consolidator Grant
Official press release (ERC): CoG Recipients 2020
14 forskare verksamma i Sverige får ERC Consolidator grant

Lecture by G. Volpe: Graph Theory Concepts, 25 November 2020

On 25 November 2020, Giovanni Volpe gave an online lecture on Graph Theory Concepts, in the scope of Karolinska Institute graduate course 3064: Imaging in Neuroscience: With a focus on structural MRI methods

The lecture is published online on youtube.

Link:
Imaging in Neuroscience: Graph Theory Concepts

Project “Active matter goes smart” featured on KAW Foundation website

Giovanni Volpe, photo of Johan Wingborg.
Giovanni Volpe’s new project “Active matter goes smart” has been featured on the website of the Knut and Alice Wallenberg (KAW) Foundation.

The feature article explains the project and its main aim of creating smart particles that react to their environment to a general audience.

The article is available both in English and in Swedish.

Links:
Skapar smarta partiklar med naturen som förebild (Swedish)
Creating smart particles modeled on nature (English)

Photos of Johan Wingborg, taken from Creating smart particles modeled on nature

Improving epidemic testing and containment strategies using machine learning on ArXiv

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Enhanced force-field calibration via machine learning featured in AIP SciLight

The article Enhanced force-field calibration via machine learning
has been featured in: “Machine Learning Outperforms Standard Force-Field Calibration Techniques”, AIP SciLight (November 6, 2020).

Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.

Scilight is published weekly (52 issues per year) by AIP Publishing.

Enhanced force-field calibration via machine learning published in Applied Physics Reviews

Representation of a particle in a force field
Enhanced force-field calibration via machine learning
Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, Giovanni Volpe
Applied Physics Reviews 7, 041404 (2020)
doi: 10.1063/5.0019105
arXiv: 2006.08963

The influence of microscopic force fields on the motion of Brownian particles plays a fundamental role in a broad range of fields, including soft matter, biophysics, and active matter. Often, the experimental calibration of these force fields relies on the analysis of the trajectories of these Brownian particles. However, such an analysis is not always straightforward, especially if the underlying force fields are non-conservative or time-varying, driving the system out of thermodynamic equilibrium. Here, we introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific applications.