News

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)
doi: https://doi.org/10.1371/journal.pone.0228334

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)
doi: https://doi.org/10.1038/s42256-020-0146-9

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)
doi: https://doi.org/10.1177/2047487319898951

Aims

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.

Conclusion

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.

Seminar on polymers under local active forces: a simplified stochastic model for motor-induced translocations by Laura Natali from Università di Roma “La Sapienza”, Nexus, 22 January 2020

Polymers under local active forces: a simplified stochastic model for motor-induced translocations.
Seminar by Laura Natali from the University of Rome “La Sapienza”, Rome, Italy.

Molecular motors are a wide class of enzymes that can transport even large macromolecules by converting chemical into mechanical energy, through the process of ATP-hydrolysis. Among those, active nanopore translocation is a common mechanism to carry proteins across biological membranes. The phospholipid bilayer is impenetrable for the folded protein, that requires to be unfolded and pulled across channels such as alpha-hemolysin [1]. Exploiting the translocation process, we can acquire information about the target proteins’ structure, such as the intermediate configurations between the folded and denatured structures [2]. Here follows the interest in modeling the motor proteins complex in a simple simulation setup.
First of all, we characterized the active polymer’s features in free space and subsequently, we confined it inside a nanopore model. The study in free space aims to investigate how activity affects both the global and the local polymer properties. The chosen model for the active force is an Active Ornstein-Uhlenbeck Particle, a model closely related to Active Brownian Particles [3]. The presence of the catalytic head affects the end-to-end distance of the polymer, which describes its degree of compactness. The active chain can be studied through the Rouse mode decomposition [4], which allowed us to analyze the dependence of the second moment of the end-to-end distance as a function of the persistence time of the activity. We considered also the distance between consecutive monomers, which provides an insight into the local effects of the active force and how it is transmitted along the chain.
In this work, we were inspired by an experiment employing the ClpXP complex [5], a protein-degradation machine responsible for unfolding and digesting malfunctioning proteins through unidirectional transport across the nanopore. In the model we propose, we enclose the polymer chain in a confining potential, simulating the effect of the nanopore. The energy generated by the molecular motor translocates the polymers, which means they cross the pore from side to side. In our setup, the effect of the molecular motor [6] is represented as a potential barrier combined with a region that makes active the monomers that are crossing it. The translocation pathways have a step-wise profile typical of their biological counterparts.

References:

  1. J. Nivala, D. B. Marks and M. Akeson, Nature Biotechnol., 2013, 31, 247
  2. M. Bacci, M. Chinappi, C. M. Casciola and F. Cecconi, J. Phys.Chem. B, 2012, 116, 4255–4262
  3. L. Caprini and U. M. B. Marconi, Soft matter, 2018, 14, 9044–9054.
  4. P. E. Rouse, J. Chem. Phys., 1953, 21, 1272.
  5. T. A. Baker and R. T. Sauer, Biochimica et Biophysica Acta (BBA) – Molecular Cell Research, 2012, 1823, 15–28.
  6. L. Caprini, U. Marini Bettolo Marconi, A. Puglisi and A. Vulpiani, J. Chemi. Phys., 2019, 150, 024902.

Place: Nexus room, Fysik Origo, Fysik
Time: 22 January, 2020, 11:00

Hillevi Wachtmeister joins the Soft Matter Lab

Hillevi Wachtmeister joined the Soft Matter Lab on 21 January 2020.

Hillevi Wachtmeister is a Master student in Physics at Chalmers University of Technology.

During her Master thesis work she will work on characterizing and tracking different particles and micro organisms using deep learning. She is supervised by Daniel Midtvedt.

Harshith Bachimanchi joins the Soft Matter Lab

Harshith Bachimanchi. (Photo by A. Argun)
Harshith Bachimanchi starts his PhD at the Physics Department of the University of Gothenburg on 20th January 2020.

Harshith has a Master degree in physics from the Indian Institute of Science Education and Research, Pune, India, where he submitted a Master thesis in optics, whose results can be found here.

In his PhD, he will focus on microscopy and deep learning.

Start-up “Lucero” Semi-finalist in SPIE Startup Challenge

Our idea Lucero, has reached the semi-final for the SPIE Start-up challenge, where will pitch in front of a jury at Photonics West in San Francisco, CA, USA on the 4th of February 2020.

Lucero will compete, among other 41 semifinalists, for cash prizes and business support.

In addition, Lucero was awarded one of the three Early Stage Entrepreneurship Travel Grants to attend the semi-final.

The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.

In December, Lucero was ranked among the best 5 business ideas in West Sweden.

Team components: Christopher Jacklin, Rich Zapata Rosas, Felix Mossberg, Falko Schmidt, Alejandro Diaz Tormo and Martin Mojica-Benavides.

Links: Lucero Homepage