One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)Playing with Active Matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 20 – 24 August 2023 Date: 21 August 2023
In the last 20 years, active matter has been a very successful research field, bridging the fundamental physics of nonequilibrium thermodynamics with applications in robotics, biology, and medicine. This field deals with active particles, which, differently from passive Brownian particles, can harness energy to generate complex motions and emerging behaviors. Most active-matter experiments are performed with microscopic particles and require advanced microfabrication and microscopy techniques. Here, we propose some macroscopic experiments with active matter employing commercially available toy robots, i.e., the Hexbugs. We demonstrate how they can be easily modified to perform regular and chiral active Brownian motion. We also show that Hexbugs can interact with passive objects present in their environment and, depending on their shape, set them in motion and rotation. Furthermore, we show that, by introducing obstacles in the environment, we can sort the robots based on their motility and chirality. Finally, we demonstrate the emergence of Casimir-like activity-induced attraction between planar objects in the presence of active particles in the environment.
The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 20-24 August 2023, with the presentations listed below.
Agnese Callegari: Playing with active matter
21 August 2023 • 4:05 PM – 4:20 PM PDT | Conv. Ctr. Room 6D
Giovanni Volpe is also co-author of the presentations:
Jiawei Sun (KI): (Poster) Assessment of nonlinear changes in functional brain connectivity during aging using deep learning
21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
Blanca Zufiria Gerbolés (KI): (Poster) Exploring age-related changes in anatomical brain connectivity using deep learning analysis in cognitively healthy individuals
21 August 2023 • 5:30 PM – 7:00 PM PDT | Conv. Ctr. Exhibit Hall A
Mite Mijalkov (KI): Uncovering vulnerable connections in the aging brain using reservoir computing
22 August 2023 • 9:15 AM – 9:30 AM PDT | Conv. Ctr. Room 6C
Imaging-based volumetric measures. (Image by the Authors of the manuscript.)CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration
Meera Srikrishna, Nicholas J. Ashton, Alexis Moscoso, Joana B. Pereira, Rolf A. Heckemann, Danielle van Westen, Giovanni Volpe, Joel Simrén, Anna Zettergren, Silke Kern, Lars-Olof Wahlund, Bibek Gyanwali, Saima Hilal, Joyce Chong Ruifen, Henrik Zetterberg, Kaj Blennow, Eric Westman, Christopher Chen, Ingmar Skoog, Michael Schöll
Alzheimer’s & Dementia 20, 629–640 (2024)
arXiv: 2401.06260
doi: 10.1002/alz.13445
INTRODUCTION
Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning–based model that produced accurate and robust cranial CT tissue classification.
MATERIALS AND METHODS
We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition.
RESULTS
CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration.
DISCUSSION
These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation.
Illustration of resting state network activity. (Image by the Authors of the manuscript.)Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis
Paulo Lizano, Chelsea Kiely, Mite Mijalkov, Shashwath A. Meda, Sarah K. Keedy, Dung Hoang, Victor Zeng, Olivia Lutz, Joana B. Pereira, Elena I. Ivleva, Giovanni Volpe, Yanxun Xu, Adam M. Lee, Leah H. Rubin, S Kristian Hill, Brett A. Clementz, Carol A. Tamminga, Godfrey D. Pearlson, John A. Sweeney, Elliot S. Gershon, Matcheri S. Keshavan, Jeffrey R. Bishop
Brain Behavior and Immunity, 114, 3-15 (2023)
doi: 10.1016/j.bbi.2023.07.014
Introduction
High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.
Methods
Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.
Results
Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen’s d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.
Conclusion
These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain’s ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.
Adaptivity across different scientific disciplines (blue) and applications (yellow) as well as its strong inter- linking and interlocking, similar to a system of gears. (Image by the Authors of the manuscript)Perspectives on adaptive dynamical systems
Jakub Sawicki, Rico Berner, Sarah A. M. Loos, Mehrnaz Anvari, Rolf Bader, Wolfram Barfuss, Nicola Botta, Nuria Brede, Igor Franović, Daniel J. Gauthier, Sebastian Goldt, Aida Hajizadeh, Philipp Hövel, Omer Karin, Philipp Lorenz-Spreen, Christoph Miehl, Jan Mölter, Simona Olmi, Eckehard Schöll, Alireza Seif, Peter A. Tass, Giovanni Volpe, Serhiy Yanchuk, Jürgen Kurths
Chaos 33, 071501 (2023)
doi: 10.1063/5.0147231
arXiv: 2303.01459
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.
An illustration of microscopic gold flakes on surface. (Image by F. Schmidt.)Tunable critical Casimir forces counteract Casimir–Lifshitz attraction
Agnese Callegari PIERS 2023, Prague, Czech Republic
3 July 2023, 09:40
Casimir forces in quantum electrodynamics emerge between microscopic metallic objects because of the confinement of the vacuum electromagnetic fluctuations occurring even at zero temperature. Their generalization at finite temperature and in material media are referred to as Casimir–Lifshitz forces. These forces are typically attractive, leading to the widespread problem of stiction between the metallic parts of micro- and nanodevices. Recently, repulsive Casimir forces have been experimentally realized but their reliance on specialized materials prevents their dynamic control and thus limits their further applicability. Here, we experimentally demonstrate that repulsive critical Casimir forces, which emerge in a critical binary liquid mixture upon approaching the critical temperature, can be used to actively control microscopic and nanoscopic objects with nanometer precision. We demonstrate this by using critical Casimir forces to prevent the stiction caused by the Casimir–Lifshitz forces. We study a microscopic gold flake above a flat gold-coated substrate immersed in a critical mixture. Far from the critical temperature, stiction occurs because of dominant Casimir–Lifshitz forces. Upon approaching the critical temperature, however, we observe the emergence of repulsive critical Casimir forces that are sufficiently strong to counteract stiction. Our method provides a novel way of controlling the distances of micro- and nanostructures using tunable critical Casimir forces to counteract forces such as the Casimir–Lifshitz force, thereby preventing stiction and device failure. Due to the simplicity of our design the concept can be adapted to already existing MEMS and NEMS. Moreover, this path opens new possibilities for the dynamic control of MEMS and NEMS where the temperature of the system could be controlled via light illumination, enabling faster transitions and higher selectivity for a new generation of the micromembranes that are found ubiquitously in MEMS and NEMS devices.
References
Falko Schmidt, Agnese Callegari, Abdallah Daddi-Moussa-Ider, Battulga Munkhbat, Ruggero Verre, Timur Shegai, Mikael Käll, Hartmut Löwen, Andrea Gambassi and Giovanni Volpe, Tunable critical Casimir forces counteract Casimir-Lifshitz attraction,
Nature Physics 19, 271-278 (2023)
