Anisotropic dynamics of a self-assembled colloidal chain in an active bath on ArXiv

Bright-field microscopy image of a magnetic chain trapped at the liquid-air interface in a bacterial bath

Anisotropic dynamics of a self-assembled colloidal chain in an active bath
Mehdi Shafiei Aporvari, Mustafa Utkur, Emine Ulku Saritas, Giovanni Volpe & Joakim Stenhammar
arXiv: 2002.09961

Anisotropic macromolecules exposed to non-equilibrium (active) noise are very common in biological systems, and an accurate understanding of their anisotropic dynamics is therefore crucial. Here, we experimentally investigate the dynamics of isolated chains assembled from magnetic microparticles at a liquid-air interface and moving in an active bath consisting of motile E. coli bacteria. We investigate both the internal chain dynamics and the anisotropic center-of-mass dynamics through particle tracking. We find that both the internal and center-of-mass dynamics are greatly enhanced compared to the passive case, and that the center-of-mass diffusion coefficient D features a non-monotonic dependence as a function of the chain length. Furthermore, our results show that the relationship between the parallel and perpendicular components of D is preserved in the active bath compared to the passive case, with a higher diffusion parallel to the chain direction, in contrast to previous findings in the literature. We argue that this qualitative difference is due to subtle differences in the experimental geometry and conditions and the relative roles played by long-range hydrodynamic interactions and short-range collisions.

Shaping the future of machine learning for active matter

Machine learning has proven to be very useful for the study of active matter, a collective term referring to things like cells and microorganisms. The field is quite new and growing fast. In an attempt to inspire more researchers to try the methods a group of scientists have published a paper in prestigious publication Nature Machine Intelligence reviewing what has been accomplished so far – and what lies ahead. Continue reading (English)

Press release:
Shaping the future of machine learning for active matter (In English)
Formar framtiden för AI-forskning på aktiv materia (In Swedish)

Machine learning for active matter

Invited talks by G. Volpe and A. Magazzù at “SPACE Tweezers” Kick-off Meeting, Messina, Italy, 18-19 February 2020

Alessandro Magazzù and Giovanni Volpe will give invited presentations at the Kick-off meeting of SPACE Tweezers (Spectroscopy of Planetary and AtmospheriC particulatE by optical Tweezers).

SPACE Tweezers proposes research activities to trap and characterise spectroscopically extraterrestrial particles and their analogs. The opportunity to apply optical tweezers to planetary particulate matter can pave the way for space applications for in situ analyses and/or for sample return of particles in pristine conditions, i.e. preventing contamination and alteration, unlike collection methods so far used in space exploration.

The meeting, organised by Maria Grazia Donato, Pietro Guicciardi, Maria Antonia Iatì, and Onofrio M. Maragò, will take place at CNR-IPCF, Messina, on 18-19 February 2020.

The contributions of Giovanni Volpe and Alessandro Magazzù will be presented  according to the following schedule:

Giovanni Volpe
Optical Tweezers Activities in Gothenburg
Date: 19 February 2020
Time: 10:55 CET

Alessandro Magazzù
Controlling the Dynamics of Colloidal Particles by Critical Casimir Forces using Blinking Optical Tweezers
Date: 19 February 2020
Time: 11:20 CET




Delayed correlations improve the reconstruction of the brain connectome published on PLoS ONE

Example of a weighted small-world structural network.

Delayed correlations improve the reconstruction of the brain connectome
Mite Mijalkov, Joana B. Pereira & Giovanni Volpe
PLoS ONE 15(2), e0228334 (2020)

The brain works as a large-scale complex network, known as the connectome. The strength of the connections between two brain regions in the connectome is commonly estimated by calculating the correlations between their patterns of activation. This approach relies on the assumption that the activation of connected regions occurs together and at the same time. However, there are delays between the activation of connected regions due to excitatory and inhibitory connections. Here, we propose a method to harvest this additional information and reconstruct the structural brain connectome using delayed correlations. This delayed-correlation method correctly identifies 70% to 80% of connections of simulated brain networks, compared to only 5% to 25% of connections detected by the standard methods; this result is robust against changes in the network parameters (small-worldness, excitatory vs. inhibitory connection ratio, weight distribution) and network activation dynamics. The delayed-correlation method predicts more accurately both the global network properties (characteristic path length, global efficiency, clustering coefficient, transitivity) and the nodal network properties (nodal degree, nodal clustering, nodal global efficiency), particularly at lower network densities. We obtain similar results in networks derived from animal and human data. These results suggest that the use of delayed correlations improves the reconstruction of the structural brain connectome and open new possibilities for the analysis of the brain connectome, as well as for other types of networks.

Machine learning for active matter published on Nature Machine Intelligence

Neural net with input layer (left), dense internal layers, and output layer (right).

Machine learning for active matter
Frank Cichos, Kristian Gustavsson, Bernhard Mehlig & Giovanni Volpe
Nature Machine Intelligence 2(2), 94–103 (2020)

The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help to disentangle the complexity of biological active matter, helping, for example, to establish a relation between genetic code and emergent bacterial behaviour, to find navigation strategies in complex environments, and to map physical cues to animal behaviours. In this Review, we highlight the current state of the art in the application of machine learning to active matter and discuss opportunities and challenges that are emerging. We also emphasize how active matter and machine learning can work together for mutual benefit.

An algorithm that learns to diagnose a genetic disease

Researchers at the University of Gothenburg, together with researchers from Portugal, have now found a way to estimate the probability that a patient will suffer from a common genetic disease by training an algorithm using patient data. Continue reading (in English)

Press release:
Algoritm lär sig diagnostisera genetisk sjukdom (in Swedish)
An algorithm that learns to diagnose genetic disease (in English)

Article: Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning published in European Journal of Preventive Cardiology

Neural networks consist of a series of connected layers of neurons, whose connection weights are adjusted to learn how to determine the diagnosis from the input data.

Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning
Anna Pina, Saga Helgadottir, Rosellina Margherita Mancina, Chiara Pavanello, Carlo Pirazzi, Tiziana Montalcini, Roberto Henriques, Laura Calabresi, Olov Wiklund, M Paula Macedo, Luca Valenti, Giovanni Volpe, Stefano Romeo
European Journal of Preventive Cardiology (2020)


Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a “virtual” genetic test using machine-learning approaches.

Methods and results

We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data (N = 174) and internal test (N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations.


In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing.