Active Matter in Complex and Crowded Environments
Giovanni Volpe
Institute of Protein Biochemistry, National Research Council (CNR), Naples, Italy
25 January 2018
News
Dynamic Deposition of Particles in Evaporating Droplets published in J. Phys. Chem. Lett.
Dynamic control of particle deposition in evaporating droplets by an external point source vapor
Robert Malinowski, Giovanni Volpe, Ivan Parkin & Giorgio Volpe
The Journal of Physical Chemistry Letters 9(3), 659—664 (2018)
DOI: 10.1021/acs.jpclett.7b02831
arXiv: 1801.08218
The deposition of particles on a surface by an evaporating sessile droplet is important for phenomena as diverse as printing, thin-film deposition, and self-assembly. The shape of the final deposit depends on the flows within the droplet during evaporation. These flows are typically determined at the onset of the process by the intrinsic physical, chemical, and geometrical properties of the droplet and its environment. Here, we demonstrate deterministic emergence and real-time control of Marangoni flows within the evaporating droplet by an external point source of vapor. By varying the source location, we can modulate these flows in space and time to pattern colloids on surfaces in a controllable manner.
Maximilian Leyman joins the Soft Matter Lab
Maximilian Leyman joined the Soft Matter Lab on 15 January 2018.
He is a Master student in the Complex Adaptive Systems Master at Chalmers University of Technology.
He will work on his Master thesis on the numerical study of the motion of robots with sensorial feedback.
Freddie Ogemark joins the Soft Matter Lab
Freddie Ogemark joined the Soft Matter Lab on 12 January 2018.
He is a Master student in the Complex Adaptive Systems Master at Chalmers University of Technology.
He will work on his Master thesis on the experimental study of the motion of robots with sensorial feedback.
Altered Brain Network in Amyloid Pathology published in Neurobiol. Aging
Altered structural network organization in cognitively normal individuals with amyloid pathology
Olga Voevodskaya, Joana B. Pereira, Giovanni Volpe, Olof Lindberg, Erik Stomrud, Danielle van Westen, Eric Westman & Oskar Hansson
Neurobiology of Aging 64, 15—24 (2018)
DOI: 10.1016/j.neurobiolaging.2017.11.014
Recent findings show that structural network topology is disrupted in Alzheimer’s disease (AD), with changes occurring already at the prodromal disease stages. Amyloid accumulation, a hallmark of AD, begins several decades before symptom onset, and its effects on brain connectivity at the earliest disease stages are not fully known. We studied global and local network changes in a large cohort of cognitively healthy individuals (N = 299, Swedish BioFINDER study) with and without amyloid-β (Aβ) pathology (based on cerebrospinal fluid Aβ42/Aβ40 levels). Structural correlation matrices were constructed based on magnetic resonance imaging cortical thickness data. Despite the fact that no significant regional cortical atrophy was found in the Aβ-positive group, this group exhibited an altered global network organization, including decreased global efficiency and modularity. At the local level, Aβ-positive individuals displayed fewer and more disorganized modules as well as a loss of hubs. Our findings suggest that changes in network topology occur already at the presymptomatic (preclinical) stage of AD and may precede detectable cortical thinning.
Amyloid Network Topology in Alzheimer published in Cerebral Cortex
Amyloid network topology characterizes the progression of Alzheimer’s disease during the predementia stages
Joana B. Pereira, Tor Olof Strandberg, Sebastian Palmqvist, Giovanni Volpe, Danielle van Westen, Eric Westman & Oskar Hansson, for the Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex 28(1), 340—349 (2018)
DOI: 10.1093/cercor/bhx294
There is increasing evidence showing that the accumulation of the amyloid-β (Aβ) peptide into extracellular plaques is a central event in Alzheimer’s disease (AD). These abnormalities can be detected as lowered levels of Aβ42 in the cerebrospinal fluid (CSF) and are followed by increased amyloid burden on positron emission tomography (PET) several years before the onset of dementia. The aim of this study was to assess amyloid network topology in nondemented individuals with early stage Aβ accumulation, defined as abnormal CSF Aβ42 levels and normal Florbetapir PET (CSF+/PET−), and more advanced Aβ accumulation, defined as both abnormal CSF Aβ42 and Florbetapir PET (CSF+/PET+). The amyloid networks were built using correlations in the mean 18F-florbetapir PET values between 72 brain regions and analyzed using graph theory analyses. Our findings showed an association between early amyloid stages and increased covariance as well as shorter paths between several brain areas that overlapped with the default-mode network (DMN). Moreover, we found that individuals with more advanced amyloid accumulation showed more widespread changes in brain regions both within and outside the DMN. These findings suggest that amyloid network topology could potentially be used to assess disease progression in the predementia stages of AD.
Metastable Clusters and Channels published in New J. Phys.
Metastable clusters and channels formed by active particles with aligning interactions
Simon Nilsson & Giovanni Volpe
New Journal of Physics 19, 115008 (2017)
DOI: 10.1088/1367-2630/aa9516
arXiv: 1706.01326
We introduce a novel model for active particles with short-range position-dependent aligning interactions and study their behaviour in crowded environments using numerical simulations. When only active particles are present, we observe a transition from a gaseous state to the emergence of metastable clusters as the level of orientational noise is reduced. When passive particles are also present, we observe the emergence of a network of metastable channels.
Minimal Microscopic Heat Engine published in Phys. Rev. E
Experimental realization of a minimal microscopic heat engine
Aykut Argun, Jalpa Soni, Lennart Dabelow, Stefano Bo, Giuseppe Pesce, Ralf Eichhorn & Giovanni Volpe
Physical Review E 96(5), 052106 (2017)
DOI: 10.1103/PhysRevE.96.052106
arXiv: 1708.07197
Microscopic heat engines are microscale systems that convert energy flows between heat reservoirs into work or systematic motion. We have experimentally realized a minimal microscopic heat engine. It consists of a colloidal Brownian particle optically trapped in an elliptical potential well and simultaneously coupled to two heat baths at different temperatures acting along perpendicular directions. For a generic arrangement of the principal directions of the baths and the potential, the symmetry of the system is broken, such that the heat flow drives a systematic gyrating motion of the particle around the potential minimum. Using the experimentally measured trajectories, we quantify the gyrating motion of the particle, the resulting torque that it exerts on the potential, and the associated heat flow between the heat baths. We find excellent agreement between the experimental results and the theoretical predictions.
Optimal Search Strategy in Complex Topography published in PNAS
The topography of the environment alters the optimal search strategy for active particles
Giorgio Volpe & Giovanni Volpe
Proceedings of the National Academy of Science USA 114(43), 11350—11355 (2017)
DOI: 10.1073/pnas.1711371114
arXiv: 1706.07785
In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search-and- rescue operations in disaster zones, and to genetic computer algo- rithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α ≤ 1 are generally believed to optimize the search process. However, most search spaces present complex to- pographies. What is the best search strategy in these more realistic scenarios? Here we show that the topography of the environment significantly alters the optimal search strategy towards less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for non-regenerating sparse targets in a two-dimensional space with steps drawn from a Lévy distribution with exponent varying from α = 1 to α = 2 (Brownian). We demon- strate that, when boundaries, barriers and obstacles are present, the optimal search strategy depends on the topography of the environ- ment with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher’s size. Our results are relevant for search problems at different length scales, from animal and human foraging, to microswimmers’ taxis, to bio- chemical rates of reaction.
Oleksii Bielikh defended his Master Thesis. Congrats!
Oleksii Bielikh defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 5 October 2017.
Thesis title: Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity
Thesis advisor: Giovanni Volpe
One of the current frontiers in neurosciences is to understand brain connectivity both in healthy subjects and patients. Recent studies suggest that brain connectivity measured with graph theory is a reliable candidate biomarker of neuronal dysfunction and disease spread in neurodegenerative disorders. Widespread abnormalities in the topology of the cerebral networks in patients correlate with a higher risk of developing dementia and worse prognosis.
In order to recognize such abnormalities, brain network graph measures should be compared with the corresponding measures calculated on random graphs with the same degree distribution. However, creating a random graph with prescribed degree sequence that has number of nodes of magnitude of 105 is a recognized problem. Existing algorithms have a variety of shortcomings, among which are slow run-time, non-uniformity of results and divergence of degree distribution with the target one.
The goal of this thesis is to explore the possibility of finding an algorithm that can be used with very large networks. Multiple common algorithms were tested to check their scaling with increasing number of nodes. The results are compared in order to find weaknesses and strengths of particular algorithms, and certain changes are offered that speed up their runtimes and/or correct for the downsides. The degree distributions of the resulting random graphs are compared to those of the target graphs, which are constructed in a way that mimics some of the most common characteristics of brain networks, namely small-worldness and scale-free topology, and it is discussed why some of the models are more appropriate than others in this case. Simulations prove that the majority of algorithms are vastly inefficient in creating random large graphs with necessary limitations on their topology, while some can be adapted to showcase to a certain extent promising results.