Category: Article

Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis published in Environmental Science: Nano

Optical beam focused into the liquid: the tire particles are pushed away from the laser focus.

Raman Tweezers for Tire and Road Wear Micro- and Nanoparticles Analysis
Pietro Giuseppe Gucciardi, Gillibert Raymond, Alessandro Magazzù, Agnese Callegari, David Bronte Ciriza, Foti Antonino, Maria Grazia Donato, Onofrio M. Maragò, Giovanni Volpe, Marc Lamy de La Chapelle & Fabienne Lagarde
Environmental Science: Nano 9, 145 – 161 (2022)
ChemRxiv: https://doi.org/10.33774/chemrxiv-2021-h59n1
doi: https://doi.org/10.1039/D1EN00553G

Tire and Road Wear Particles (TRWP) are non-exhaust particulate matter generated by road transport means during the mechanical abrasion of tires, brakes and roads. TRWP accumulate on the roadsides and are transported into the aquatic ecosystem during stormwater runoffs. Due to their size (sub-millimetric) and rubber content (elastomers), TRWP are considered microplastics (MPs). While the amount of the MPs polluting the water ecosystem with sizes from ~ 5 μm to more than 100 μm is known, the fraction of smaller particles is unknown due to the technological gap in the detection and analysis of < 5 μm MPs. Here we show that Raman Tweezers, a combination of optical tweezers and Raman spectroscopy, can be used to trap and chemically analyze individual TWRPs in a liquid environment, down to the sub-micrometric scale. Using tire particles mechanically grinded from aged car tires in water solutions, we show that it is possible to optically trap individual sub-micron particles, in a so-called 2D trapping configuration, and acquire their Raman spectrum in few tens of seconds. The analysis is then extended to samples collected from a brake test platform, where we highlight the presence of sub-micrometric agglomerates of rubber and brake debris, thanks to the presence of additional spectral features other than carbon. Our results show the potential of Raman Tweezers in environmental pollution analysis and highlight the formation of nanosized TRWP during wear.

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University of Gothenburg > News and Events: New technology enables the detection of microplastics from road wear
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Objective comparison of methods to decode anomalous diffusion published in Nature Communications

An illustration of anomalous diffusion. (Image by Gorka Muñoz-Gil.)
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March, Erez Aghion, Aykut Argun, Chang Beom Hong, Tom Bland, Stefano Bo, J. Alberto Conejero, Nicolás Firbas, Òscar Garibo i Orts, Alessia Gentili, Zihan Huang, Jae-Hyung Jeon, Hélène Kabbech, Yeongjin Kim, Patrycja Kowalek, Diego Krapf, Hanna Loch-Olszewska, Michael A. Lomholt, Jean-Baptiste Masson, Philipp G. Meyer, Seongyu Park, Borja Requena, Ihor Smal, Taegeun Song, Janusz Szwabiński, Samudrajit Thapa, Hippolyte Verdier, Giorgio Volpe, Arthur Widera, Maciej Lewenstein, Ralf Metzler, and Carlo Manzo
Nat. Commun. 12, Article number: 6253 (2021)
doi: 10.1038/s41467-021-26320-w
arXiv: 2105.06766

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

Active droploids published in Nature Communications

Active droploids. (Image taken from the article.)
Active droploids
Jens Grauer, Falko Schmidt, Jesús Pineda, Benjamin Midtvedt, Hartmut Löwen, Giovanni Volpe & Benno Liebchen
Nat. Commun. 12, 6005 (2021)
doi: 10.1038/s41467-021-26319-3
arXiv: 2109.10677

Active matter comprises self-driven units, such as bacteria and synthetic microswimmers, that can spontaneously form complex patterns and assemble into functional microdevices. These processes are possible thanks to the out-of-equilibrium nature of active-matter systems, fueled by a one-way free-energy flow from the environment into the system. Here, we take the next step in the evolution of active matter by realizing a two-way coupling between active particles and their environment, where active particles act back on the environment giving rise to the formation of superstructures. In experiments and simulations we observe that, under light-illumination, colloidal particles and their near-critical environment create mutually-coupled co-evolving structures. These structures unify in the form of active superstructures featuring a droplet shape and a colloidal engine inducing self-propulsion. We call them active droploids—a portmanteau of droplet and colloids. Our results provide a pathway to create active superstructures through environmental feedback.

The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus published in Science Advances

M. xanthus cell-cell and cell-particle local interactions during cellular aggregation.
The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus
Karla C. Hernández Ramos, Edna Rodríguez-Sánchez, Juan Antonio Arias del Angel, Alejandro V. Arzola, Mariana Benítez, Ana E. Escalante, Alessio Franci, Giovanni Volpe, Natsuko Rivera-Yoshida
Sci. Adv. 7(35), eabh2278 (2021)
bioRxiv: 10.1101/2021.01.27.428527
doi: 10.1126/sciadv.abh2278

The social soil-dwelling bacteria Myxococcus xanthus can form multicellular structures, known as fruiting bodies. Experiments in homogeneous environments have shown that this process is affected by the physico-chemical properties of the substrate, but they have largely neglected the role of complex topographies. We experimentally demonstrate that the topography alters single-cell motility and multicellular organization in M. xanthus. In topographies realized by randomly placing silica particles over agar plates, we observe that the cells’ interaction with particles drastically modifies the dynamics of cellular aggregation, leading to changes in the number, size and shape of the fruiting bodies, and even to arresting their formation in certain conditions. We further explore this type of cell-particle interaction in a minimal computational model. These results provide fundamental insights into how the environment topography influences the emergence of complex multicellular structures from single cells, which is a fundamental problem of biological, ecological and medical relevance.

The Cognitive Connectome in Healthy Aging published in Frontiers in Aging Neuroscience

Age-independent cognitive connectome in the whole cohort.
The Cognitive Connectome in Healthy Aging
Eloy Garcia-Cabello, Lissett Gonzalez-Burgos, Joana B. Pereira, Juan Andres Hernández-Cabrera, Eric Westman, Giovanni Volpe, José Barroso, & Daniel Ferreira
Front. Aging Neurosci. 13, 530 (2021)
doi: 10.3389/fnagi.2021.694254

Objectives: Cognitive aging has been extensively investigated using both univariate and multivariate analyses. Sophisticated multivariate approaches such as graph theory could potentially capture unknown complex associations between multiple cognitive variables. The aim of this study was to assess whether cognition is organized into a structure that could be called the “cognitive connectome,” and whether such connectome differs between age groups.

Methods: A total of 334 cognitively unimpaired individuals were stratified into early-middle-age (37–50 years, n = 110), late-middle-age (51–64 years, n = 106), and elderly (65–78 years, n = 118) groups. We built cognitive networks from 47 cognitive variables for each age group using graph theory and compared the groups using different global and nodal graph measures.

Results: We identified a cognitive connectome characterized by five modules: verbal memory, visual memory—visuospatial abilities, procedural memory, executive—premotor functions, and processing speed. The elderly group showed reduced transitivity and average strength as well as increased global efficiency compared with the early-middle-age group. The late-middle-age group showed reduced global and local efficiency and modularity compared with the early-middle-age group. Nodal analyses showed the important role of executive functions and processing speed in explaining the differences between age groups.

Conclusions: We identified a cognitive connectome that is rather stable during aging in cognitively healthy individuals, with the observed differences highlighting the important role of executive functions and processing speed. We translated the connectome concept from the neuroimaging field to cognitive data, demonstrating its potential to advance our understanding of the complexity of cognitive aging.

Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks published in European Heart Journal – Digital Health

Neural network prediction of mortality and atrial fibrillation. (Image taken from the article’s graphical abstract.)
Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register based medical hospital data and neural networks
Kok Wai Giang, Saga Helgadottir, Mikael Dellborg, Giovanni Volpe, Zacharias Mandalenakis
European Heart Journal – Digital Health (2021)
doi: 10.1093/ehjdh/ztab065

Aims: To improve short-and long-term predictions of mortality and atrial fibrillation (AF) among patients with congenital heart disease (CHD) from a nationwide population using neural networks (NN).

Methods and results: The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with CHD born from 1970 to 2017. A total of 71 941 CHD patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a NN model was obtained to predict mortality and AF. Logistic regression (LR) based on the same data was used as a baseline comparison. Of 71 941 CHD patients, a total of 5768 died (8.02%) and 995 (1.38%) developed AF over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of NN models in predicting the mortality and AF was higher than the performance of LR regardless of the complexity of the disease, with an average area under the receiver operating characteristic of >0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of CHD over time.

Conclusion: We found that NN can be used to predict mortality and AF on a nationwide scale using data that are easily obtainable by clinicians. In addition, NN showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.

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

Differences between controls and PD participants in nodal network measures. (Image taken from the article.)
Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Cerebral Cortex, bhab237 (2021)
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.

Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces published in Nature Nanotechnology

Metavehicles.
Microscopic Metavehicles Powered and Steered by Embedded Optical Metasurfaces
Daniel Andrén, Denis G. Baranov, Steven Jones, Giovanni Volpe, Ruggero Verre, Mikael Käll
Nat. Nanotechnol. (2021)
doi: 10.1038/s41565-021-00941-0
arXiv: 2012.10205

Nanostructured dielectric metasurfaces offer unprecedented opportunities to manipulate light by imprinting an arbitrary phase gradient on an impinging wavefront. This has resulted in the realization of a range of flat analogues to classical optical components, such as lenses, waveplates and axicons. However, the change in linear and angular optical momentum associated with phase manipulation also results in previously unexploited forces and torques that act on the metasurface itself. Here we show that these optomechanical effects can be utilized to construct optical metavehicles – microscopic particles that can travel long distances under low-intensity plane-wave illumination while being steered by the polarization of the incident light. We demonstrate movement in complex patterns, self-correcting motion and an application as transport vehicles for microscopic cargoes, which include unicellular organisms. The abundance of possible optical metasurfaces attests to the prospect of developing a wide variety of metavehicles with specialized functional behaviours.

Extracting quantitative biological information from bright-field cell images using deep learning published in Biophysics Reviews

Virtually-stained generated image for lipid-droplet.
Extracting quantitative biological information from bright-field cell images using deep learning
Saga Helgadottir, Benjamin Midtvedt, Jesús Pineda, Alan Sabirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe
Biophysics Rev. 2, 031401 (2021)
arXiv: 2012.12986
doi: 10.1063/5.0044782

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of bright-field images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the bright-field images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using bright-field images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.

Classification, inference and segmentation of anomalous diffusion with recurrent neural networks published in Journal of Physics A: Mathematical and Theoretical

RANDI architecture to classify the model underlying anomalous diffusion.
Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
Aykut Argun, Giovanni Volpe, Stefano Bo
J. Phys. A: Math. Theor. 54 294003 (2021)
doi: 10.1088/1751-8121/ac070a
arXiv: 2104.00553

Countless systems in biology, physics, and finance undergo diffusive dynamics. Many of these systems, including biomolecules inside cells, active matter systems and foraging animals, exhibit anomalous dynamics where the growth of the mean squared displacement with time follows a power law with an exponent that deviates from 1. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks can be overwhelmingly difficult when only few short trajectories are available, a situation that is common in the study of non-equilibrium and living systems. Here, we present a data-driven method to analyze single anomalous diffusion trajectories employing recurrent neural networks, which we name RANDI. We show that our method can successfully infer the anomalous exponent, identify the type of anomalous diffusion process, and segment the trajectories of systems switching between different behaviors. We benchmark our performance against the state-of-the art techniques for the study of single short trajectories that participated in the Anomalous Diffusion (AnDi) challenge. Our method proved to be the most versatile method, being the only one to consistently rank in the top 3 for all tasks proposed in the AnDi challenge.