Keynote talk by G. Volpe at the Online Conference Motile Active Matter, 26 October 2020

Active Matter Meets Machine Learning: Opportunities and Challenges
Giovanni Volpe
26 October 2020, 13:45 CEST
Keynote talk (Online) at the Online Conference Motile Active Matter, Jülich Förschungszentrum, 26 October 2020

Abstract: Machine-learning methods are starting to shape active-matter research. Which new trends will this start? Which new groundbreaking insight and applications can we expect? More fundamentally, what can this contribute to our understanding of active matter? Can this help us to identify unifying principles and systematise active matter? This presentation addresses some of these questions with some concrete examples, exploring how machine learning is steering active matter towards new directions, offering unprecedented opportunities and posing practical and fundamental challenges. I will illustrate some most successful recent applications of machine learning to active matter with a slight bias towards work done in my research group: enhancing data acquisition and analysis [1, 2]; providing new data-driven models; improving navigation and search strategies [3, 4]; offering insight into the emergent dynamics of active matter in crowded and complex environments. I will discuss the opportunities and challenges that are emerging: implementing feedback control; uncovering underlying principles to systematise active matter; understanding the behaviour, organisation and evolution of biological active matter; realising active matter with embodied intelligence. Finally, I will highlight how active matter and machine learning can work together for mutual benefit.

References
[1] S. Helgadottir, A. Argun, G. Volpe, Digital video microscopy enhanced by deep learning. Optica 6, 506–513 (2019)
[2] S. Bo, F. Schmidt, R. Eichhorn, G. Volpe, Measurement of anomalous diffusion using recurrent neural networks. Phys. Rev. E 100, 010102(R) (2019)
[3] G. Volpe, G. Volpe, The topography of the environment alters the optimal search strategy for active particles. Proc. Natl. Acad. Sci. 114, 11350–11355 (2017)
[4] S. Colabrese, K. Gustavsson, A. Celani, L. Biferale, Flow navigation by smart microswimmers via reinforcement learning. Phys. Rev. Lett. 118, 158004 (2017).

Online seminar by G. Volpe at DiSTAP, Singapore-MIT Alliance for Research and Technology (SMART) Centre

Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
22 October 2020, 14:00 CEST
Invited Seminar (Online) at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore & Boston (MA)

Abstract: Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

References:
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe, “Quantitative Digital Microscopy with Deep Learning”, arXiv:2010.08260 (2020)

Dendritic spines are lost in clusters in patients with Alzheimer’s disease on biorXiv

Combined confocal microscopy picture showing a neuron with a soma free of PHF-tau.
Dendritic spines are lost in clusters in patients with Alzheimer’s disease
Mite Mijalkov, Giovanni Volpe, Isabel Fernaud-Espinosa, Javier DeFelipe, Joana B. Pereira, Paula Merino-Serrais
doi: 10.1101/2020.10.20.346718

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a deterioration of neuronal connectivity. The pathological accumulation of tau protein in neurons is one of the hallmarks of AD and has been connected to the loss of dendritic spines of pyramidal cells, which are the major targets of cortical excitatory synapses and key elements in memory storage. However, the detailed mechanisms underlying the loss of dendritic spines in patients with AD are still unclear. Here, comparing dendrites with and without tau pathology of AD patients, we show that the presence of tau pathology determines the loss of dendritic spines in blocks, ruling out alternative models where spine loss occurs randomly. Since memory storage has been associated with synaptic clusters, the present results provide a new insight into the mechanisms by which tau drives synaptic damage in AD, paving the way to memory deficits by altering spine organization.

Quantitative Digital Microscopy with Deep Learning on ArXiv

Particle tracking and characterization in terms of radius and refractive index.

Quantitative Digital Microscopy with Deep Learning
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe
arXiv: 2010.08260

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Presentation by F. Schmidt on Career Transition from Research to Entrepreneurship, 14 October 2020

Falko Schmidt, founder of Lucero Bio AB.

From basic research to founding a startup company: A personal journey to the unknown

Falko Schmidt
Career Seminar, Online, University of Gothenburg, Sweden
14 October 2020, 11:30-13:00
26 November 2020, 11:30-13:00

I am currently a PhD student at the Physics Department of the University of Gothenburg and will defend at the beginning of 2021. For a couple of years I have been playing with different ideas for a startup already, involving technical solutions developed during my time as PhD and was looking for their applications in the real world. After a couple of failures I have recently founded my own startup company Lucero AB. We will develop automated optical manipulation solutions for single cell analysis with applications in research on longevity, viral diseases and in the pharmaceutical industry. During this seminar I will share my insights on how to find ideas, how to validate them and the process towards creating a startup company.

This seminar is a recurring seminar and it is part of the Career Development and Entrepreneurship series initiated by the Faculty of Science. It will take place during the autumn 2020 and spring 2021.

Please check the links below for the planned dates:

14 October: Career seminars for PhD students at the Dept of Biological and Environmental Sciences and the Dept of Marine Sciences

26 November: Career seminars for PhD students at the Dept of Mathematical Sciences (GU and Chalmers)

Additional dates will be added later.

Age-related differences in network structure and dynamic synchrony of cognitive control on biorXiv

Gamma efficiency for older adults.
Age-related differences in network structure and dynamic synchrony of cognitive control
T. Hinault, M. Mijalkov, J.B. Pereira, Giovanni Volpe, A. Bakker, S.M. Courtney
doi: 10.1101/2020.10.09.333567

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we propose a mechanistic framework of cognitive variability in older adults, linking the influence of white matter microstructure on fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.

Invited talk by G. Volpe at GSJP, 1 October 2020

Logo of GSJP2020 – First Global Symposium on Janus Particles.

Giovanni Volpe will give an online invited presentation at the First Global Symposium on Janus Particles (GSJP) 2020.

GSJP will bring together a collection of experts who are in the vanguard of scientific and engineering investigations on Janus particles all around the globe.

The contribution of Giovanni Volpe will be presented according to the following schedule:

Giovanni Volpe
Light-controlled Assembly of Active Colloidal Molecules

Activity and life have emerged from a primordial broth of simple building blocks when the presence of energy flows made these blocks come together and interact in non-trivial ways. Here, we use experiments and simulations demonstrating that active molecules can be created and controlled by light. Shining light on a primordial broth containing passive particles of two different species, we create active colloidal molecules of increasing complexity, which behave as migrators, spinners and rotators. This demonstrates a powerful new route for nonequilibrium self-assembly, which may help explaining the emergence of complex systems in living matter and may also proof useful as a design principle for the construction of flexible micromotors and cargo transport in health care applications.

Date: 1 October 2020
Time: 10:10 (EST)
Place: Online