Author: Agnese Callegari

Flash Talk by A. Callegari at 729. WE Heraeus Seminar on Fluctuation Induced Forces, Online, 14 February 2022

Potential energy landscape for a flake suspended on a patterned substrate. (Image by A. Callegari.)
Theoretical and numerical study of the interplay of Casimir-Lifshitz and critical Casimir force for a metallic flake suspended on a metal-coated substrate
Agnese Callegari
729. WE-Heraeus Stiftung Seminar on Fluctuation-induced Forces
14 February 2022, 14:50 CET

Casimir-Lifshitz forces arise between uncharged metallic objects because of the confinement of the electromagnetic fluctuations. Typically, these forces are attractive, and they are the main cause of stiction between microscopic metallic parts of micro- and nanodevices. Critical Casimir forces emerge between objects suspended in a critical binary liquid mixture upon approaching the critical temperature, can be made either attractive or repulsive by choosing the appropriate boundary conditions, and dynamically tuned via the temperature.
Experiments show that repulsive critical Casimir forces can be used to prevent stiction due to Casimir-Lifshitz forces. In a recent work, a microscopic metallic flake was suspended in a liquid solution above a metal-coated substrate [1]. By suspending the flake in a binary critical mixture and tuning the temperature we can control the flake’s hovering height above the substrate and, in the case of repulsive critical Casimir forces, prevent stiction.
Here, we present the model for the system of the metallic flake suspended above a metal-coated substrate in a binary critical mixture and show that repulsive critical Casimir forces can effectively counteract Casimir-Lifshitz forces and can be used to control dynamically the height of the flake above the surface. This provides a validation of the experimental results and a base to explore and design the behavior of similar systems in view of micro- and nanotechnological applications.

References
[1] F. Schmidt, A. Callegari, A. Daddi-Moussa-Ider, B. Munkhbat, R. Verre, T. Shegai, M. Käll, H. Löwen, A. Gambassi and G. Volpe, to be submitted (2022)

Invited Talk by G. Volpe at UFS Day 10.02.22

DeepTrack 2.0 Logo. (Image from DeepTrack 2.0 Project)

Deep learning for experimental soft matter
Giovanni Volpe
Invited Talk at UFS Day 10.02.22
Online
10 February 2022
14:00 CET

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in experimental soft matter.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease published in Cerebral Cortex

Visual display of the nodes that show significant differences between controls and participants with PD in network measures using the anti-symmetric correlation method. (Image by the Authors.)
Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Cerebral Cortex 32(3), 593–607 (2022)
doi: 10.1093/cercor/bhab237

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Antonio Ciarlo joins the Soft Matter Lab

(Photo by A. Argun.)
Antonio Ciarlo joined the Soft Matter Lab on 31th January 2022.

Antonio is a PhD student at the Physics Department of the University of Naples, Italy.

He will be working on the modelling and the analysis of the data of his experiments with intracavity optical trapping.

He will stay in our lab for six months.

Keynote Talk by G. Volpe at IUPAP Conference on Condensed Matter Physics and Optics, 20 January 2022


Deep learning for microscopy, optical trapping, and active matter
Giovanni Volpe
Keynote Talk at IUPAP conference on Condensed Matter Physics and Optics
Online
20 January 2022
15:00 PST

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.

Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data published in Cerebral Cortex

Brain nodes. (Image taken from the article.)
Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Anna Canal-Garcia, Emiliano Gómez-Ruiz, Mite Mijalkov, Yu-Wei Chang, Giovanni Volpe, Joana B Pereira, Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex, bhab429 (2022)
doi: 10.1093/cercor/bhab429

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Yanuar Rizki Pahlevi joins the Soft Matter Lab

(Photo by A. Argun.)
Yanuar Rizki Pahlevi joined the Soft Matter Lab on 18 January 2022.

Yanuar is a master student in Complex Adaptive Systems at Chalmers University of Technology.

During his time at the Soft Matter Lab, he will focus on implementing deep learning techniques to predict delay in Brownian motion with delayed feedback.

Lukas Niese defended his Master thesis on 17 January 2022. Congrats!

Lukas Niese defended his Master thesis in Physics at the Technische Universität Dresden on 17 January 2022. Congrats!

(Image from Lukas Niese’s Master Thesis)
Title: Application of Deep Learning for Investigation of Chemotactic Behaviour in Marine Microorganisms

Deep learning has recently become a powerful instrument, enhancing research in many fields and profiting from abundant availability of manifold data sets. In active matter research, medicine and biology there is huge demand of robust and accurate methods to track and analyse micro scale particles and cells in microscopy images. The Pyhton based software Deeptrack 2.0 offers a basic toolkit to build customized deep learning methods for particle localization, classification and tracking. In this project Deeptrack 2.0 was used to track marine microorganisms and investigate their motion in response to chemical stimulants, known as chemotaxis. In addition, the accuracy of particle localization and classification was measured by three different benchmark tests, which imitated shapes and movement of real microorganisms. The results were compared with the performance ofthe algorithmic standard method Trackmate by Fiji ImageJ. Deeptrack 2.0 has shown a significantly better performance for particles with complex shapes and with time varying appearance were to be tacked. However Trackmate is slightly more accurate in locating small particles appearing in Gaussian intensity distribution. In the experimental part two test assays have been developed and proven a facile and robust way to study chemoattraction in the autotrophic green alga Dunaliella tertiolecta. Deeptrack was successfully applied create and analyze the cell trajectories according to velocity and spatial distribution in individuals. Based on the developed combination of experiment and computational analysis, further investigations can be carried out to elucidate the chemical and ecological nature of chemotaxis in Dunaliella tertiolecta.

​Adviser: Prof. Giovanni Volpe
Examiner: Prof. Alexander Eychmüller (TU Dresden)
Date: 17 January 2022
Time: 17:00
Place: TU Dresden and Online via Zoom

Book “Simulation of Complex Systems” published at IOP

Book cover. (From the IOP website.)
The book Simulation of Complex Systems, authored by Aykut Argun, Agnese Callegari and Giovanni Volpe, has been published by IOP in December 2021.

The book is available for the students of Gothenburg University and Chalmers University of Technology through the library service of each institution.
The example codes presented in the book can be found on GitHub.

Links
@ IOP Publishing

@ Amazon.com

Citation 
Aykut Argun, Agnese Callegari & Giovanni Volpe. Simulation of Complex Systems. IOP Publishing, 2022.
ISBN: 9780750338417 (Hardback) 9780750338431 (Ebook).