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
Sci. Rep. 11, 12350 (2021)
doi: 10.1038/s41598-021-91726-x
biorXiv: 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.
Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)Improving epidemic testing and containment strategies using machine learning. Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe. Submitted to SDS2020 Date: 11 June Time: 16:15 (CEST)
Abstract:
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
Deep learning for particle tracking. (Image by Aykut Argun.)Quantitative Digital Microscopy with Deep Learning Giovanni Volpe
Seminar Vi2 Seminar (Visual Information and Interaction)
University of Uppsala
17 May 2021, 14:15 CEST
Online
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.
Bio: Giovanni Volpe is Professor at the Physics Department at the University of Gothenburg (Gothenburg, Sweden), where he has been leading the Soft Matter Lab since 2016. He has established a strong research group of 18 people (3 postdocs, 12 PhD students, 3 Master students, http://www.softmatterlab.org ) with an externally-funded, ambitious and interdisciplinary research program that combines soft condensed matter, optical manipulation, nanotechnology, and machine learning. He has attracted external funding exceeding 6M €, including several national and European grants such as the ERC-StG ComplexSwimmers (2016-2021) and the ERC-CoG MAPEI (2021-2026). He is a co-funder of the startup companies Lucero Bio and IFLAI.
Link: Vi2 Seminars (zoom link included in the webpage)
Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology, 2 035007 (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
Deep learning for particle tracking. (Image by Aykut Argun.)Photonics, Brain Connectivity, Deep Learning, and Entrepreneurship at GU Physics Giovanni Volpe
Seminar NINa Digital Symposium
11 May 2021, 13:40 CEST
The Soft Matter Lab at Gothenburg University focuses on research at the nexus between photonics, brain connectivity and deep learning. In this presentation, I’ll briefly show our activities along these research directions that can be most interesting for an industry-academia partnership. These include: (1) The development of tools for quantitative digital microscopy enhanced by deep learning, in particular with the recent launch of the Python-based software platform DeepTrack 2.0. (2) The development of tools for the study of brain connectivity, especially within the context of the development of diagnostic and therapeutic tools for neurodegenerative diseases, in particular with the upcoming launch of the Matlab-based software platform Braph 2.0. (3) The development of tools of tools bridging photonics and machine learning. Finally, I’ll briefly present our new startup companies Lucerio Bio and IFLAI.
Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)Machine Learning against Epidemics Giovanni Volpe
Seminar Appunti di Fisica ’21
(online at) IPCF-Messina, Italy
6 May 2021, 16:00 CET
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these predictions, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.
Deep learning for particle tracking. (Image by Aykut Argun)Quantitative Digital Microscopy with Deep Learning
Giovanni Volpe
Colloquium
(online at) NTNU, Norway
23 April 2021, 14:15 CET
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.
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
NeuroImage 236, 118070 (2021)
biorXiv: 10.1101/2020.10.09.333567
doi: 10.1016/j.neuroimage.2021.118070
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.
Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)
The Soft Matter Lab is involved in six presentations at the OSA Biophotonic Congress: Optics in the Life Sciences 2021, topical meeting of Optical Manipulation and its Applications.
Moreover, three of the presentations were selected as finalists for the best student paper in the topical meeting of Optical Manipulation and its Applications.
