News

Norma Caridad Palmero Cruz joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Norma Caridad Palmero Cruz starts her PhD at the Physics Department at the University of Gothenburg on 8th January 2024.

Norma has a Master degree in Physics from the University of Havana, Cuba.

In her PhD, Norma will focus on on the study of biological systems using optical tweezers and light sheets techniques.

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning on ArXiv

Schematic illustration of the plasmonic H2 sensing principle, where the sorption of hydrogen into hydride-forming metal nanoparticles induces a change in their localized surface plasmon resonance frequency, which leads to a color change that is resolved in a spectroscopic measurement in the visible light spectral range. (Image by the Authors of the manuscript.)
Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning
Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček, Pernilla Ekborg-Tanner, Sara Nilsson, Giovanni Volpe, Paul Erhart, Christoph Langhammer
arXiv: 2312.15372

The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble Model for Accelerated Sensing (LEMAS) accelerates the response of a state-of-the-art optical plasmonic hydrogen sensor by up to a factor of 40 in an oxygen-free inert gas environment, by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Furthermore, it eliminates the pressure dependence of the response intrinsic to metal hydride-based sensors, while leveraging their ability to operate in oxygen-starved environments that are proposed to be used for inert gas encapsulation systems of hydrogen installations. Moreover LEMAS provides a measure for the uncertainty of the predictions that is pivotal for safety-critical sensor applications. Our results thus advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.

Emiliano Gómez presented his half-time seminar on 29 November 2023

Emiliano Gomez Ruiz during his half-time seminar. (Photo by L. Pérez García.)
Emiliano Gómez completed the first half of his doctoral studies and he defended his half-time on the 29th of November 2023.

The presentation was conducted in a hybrid format, with part of the audience present in the Nexus room and the remainder connected through Zoom. The seminar comprised a presentation covering both his completed and planned projects, followed by a discussion and questions posed by his opponent, Prof. Martin Adiels.

The presentation commenced with an overview of his concluded projects. The first project with title “Brain Analysis using Graph Theory 2” is a software that uses Deep Learning and Graph Theory to analyse brain networks, this software is an open-source MATLAB with github “github.com/braph-software/BRAPH-2” and two projects in which this software was applied, first one on haematopoietic cell structural pattern taken from bone marrow and the second one is of memory capacity of aging brain networks using reservoir computing.

 

 

Symposium on AI, Neuroscience, and Aging featured on ANSA.it

The Symposium on AI, Neuroscience, and Aging has been featured on ANSA.it news, in an article with title: Simposio italo-svedese a Stoccolma sull’IA e la neuroscienza (Italian).

ANSA (an acronym standing for Agenzia Nazionale Stampa Associata) is the leading news agency in Italy and one of the top ranking in the world.

Optimal calibration of optical tweezers with arbitrary integration time and sampling frequencies – A general framework published in Biomedical Optics Express

Different sampling methods for the trajectory of a particle. (Adapted from the manuscript.)
Optimal calibration of optical tweezers with arbitrary integration time and sampling frequencies — A general framework
Laura Pérez-García, Martin Selin, Antonio Ciarlo, Alessandro Magazzù, Giuseppe Pesce, Antonio Sasso, Giovanni Volpe, Isaac Pérez Castillo, Alejandro V. Arzola
Biomedical Optics Express, 14, 6442-6469 (2023)
doi: 10.1364/BOE.495468
arXiv: 2305.07245

Optical tweezers (OT) have become an essential technique in several fields of physics, chemistry, and biology as precise micromanipulation tools and microscopic force transducers. Quantitative measurements require the accurate calibration of the trap stiffness of the optical trap and the diffusion constant of the optically trapped particle. This is typically done by statistical estimators constructed from the position signal of the particle, which is recorded by a digital camera or a quadrant photodiode. The finite integration time and sampling frequency of the detector need to be properly taken into account. Here, we present a general approach based on the joint probability density function of the sampled trajectory that corrects exactly the biases due to the detector’s finite integration time and limited sampling frequency, providing theoretical formulas for the most widely employed calibration methods: equipartition, mean squared displacement, autocorrelation, power spectral density, and force reconstruction via maximum-likelihood-estimator analysis (FORMA). Our results, tested with experiments and Monte Carlo simulations, will permit users of OT to confidently estimate the trap stiffness and diffusion constant, extending their use to a broader set of experimental conditions.

Talk by G. Volpe at the Symposium on AI, Neuroscience, and Aging, Stockholm, 27 November 2023

(Image by A. Argun)
Deep Learning for Imaging and Microscopy
Giovanni Volpe
Symposium on AI, Neuroscience, and Aging, Stockholm, Sweden, 27 November 2023
Date: 27 November 2023
Time: 15:55

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 have introduced a software, DeepTrack 2.1, 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 2.1 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.

Seminar by C. Reichhardt on 30 November 2023

Complex Dynamics in Active Matter Systems, Frustration Effects, Magnus Forces and Synchronization
Charles Reichhardt
Los Alamos National Laboratory

30 November 2023, 16:30, Nexus

Active matter denotes systems with self-propulsion and arises in biological, soft, robotic, and social settings [1]. Here, we outline some of our group’s recent efforts in active systems, including active matter interacting with ordered and disordered substrates, where various kinds of active clogging and commensuration effects can occur that have connections with frustrated systems and Mott physics. We also discuss chiral active systems with a Magnus force, where we find edge currents similar to those found for topological systems or charged particles in magnetic fields. In the presence of quenched disorder, the chiral active system also shows side jump effects with an active matter Hall angle. Finally, we discuss coupled active matter swarmulators where, in addition to activity, the particles have an internal degree of freedom that can become synchronized or antisynchronized. This system shows a variety of new kinds of motility-induced phase-separated states, including active matter stripes, frustrated states, gels, cluster fluids, and glassy states.

[1] Active Brownian particles in complex and crowded environments, Clemens Bechinger, Roberto Di Leonardo, Hartmut Lowen, Charles Reichhardt Giorgio Volpe, and Giovanni Volpe, Reviews of Modern Physics 88 045006 (2016).

Presentation by M.Selin at S3IC, Barcelona, 23 November 2023

3d Visualization of the full Minitweezers 2.0 system. (Illustration by M. Selin.)
Minitweezers 2.0, Paving the way for fully autonomous optical tweezers experiments.
Martin Selin
Single-Molecule Sensors and NanoSystems International Conference – S3IC 2023
23 November 2023, 11:51 (CET)

Since their invention by Ashkin et al. in the 1980s, optical tweezers have evolved into an indispensable tool in physics, especially in biophysics, with applications spanning from cell sorting to stretching single DNA strands. By the 2000s, commercial systems became available. Nevertheless, owing to their unique requirements, many labs prefer to construct their own, often drawing inspiration from existing designs.

A prominent optical tweezers design is the “miniTweezers” system, pioneered by Bustamante’s group in the late 1990s. This system has been widely adopted globally for force spectroscopy experiments on single molecules, including DNA, proteins, and RNA.

In this presentation, we unveil an advanced iteration of the miniTweezers. By enhancing its control and acquisition capabilities, we’ve augmented its versatility, enabling new experiment types. A significant breakthrough is the integration of real-time image feedback, which paves the way for automated procedures via deep learning-based image analysis, the first of which we demonstrate in this presentation.

We showcase this system’s capabilities through three distinct experiments:

  1. A pulling experiment on a λ-DNA strand. By tethering DNA between two polystyrene beads – one anchored in a micropipette and the other manipulated by the tweezer – we illustrate near-complete automation, with the system autonomously handling bead trapping, attachment of the DNA and the pulling procedure.
  2. An exploration of Coulomb interactions between charged particles. Here, one particle remains in a micropipette, while the other orbits the stationary bead, providing a 3D map of the interaction.
  3. A non-contact stretching experiment on red blood cells is conducted under low osmotic pressure conditions. Modulating the laser power induces cell elongation along the laser’s propagation direction. By correlating this elongation with the optical force exerted by the lasers, we present a simple and non-invasive method to measure membrane rigidity.

In summary, these advancements mark a significant leap in the capabilities and applications of optical tweezers in biophysics. As we push the boundaries of automation and precision, we envision a future where such instruments can unravel even more intricate molecular interactions and cellular mechanics, setting the stage for groundbreaking discoveries.

Presentation by A. Ciarlo at S3IC, Barcelona, 22 November 2023

Illustration of a particle trapped in a two-beam optical trap with transverse offset. (Illustration by A. Ciarlo.)
Intracavity dual-beam optical trap with transverse offset
Antonio Ciarlo
Single-Molecule Sensors and NanoSystems International Conference – S3IC 2023
22 November 2023, 17:04 (CET)

Intracavity optical tweezers are a valuable tool for capturing microparticles in water by exploiting the nonlinear feedback effect induced by particle motion when confined in a laser cavity. This feedback effect arises as a consequence of the particle confinement inside a laser cavity, leading to fluctuations in the optical losses of the cavity due to Brownian motion. Our study extends intracavity optical trapping to both single-beam and counter-propagating dual-beam configurations, allowing us to investigate what happens when the two beams are slightly misaligned.
We used a 1030-nm Yb-doped ring fiber laser (pumped at 976 nm) with a hybrid optical path that allows light propagation in both fiber and air. To switch between single-beam and dual-beam configurations, a free-space removable isolator is incorporated, resulting in a single-beam configuration when the isolator is installed and a dual-beam configuration when the isolator is removed. We tracked particle positions in 3D using digital holographic microscopy and simultaneously measured the powers of the two counter-propagating beams, providing insight into the feedback effect. A crucial aspect of our experiment is the ability to introduce a transverse offset between the two optical beams in the two-beam configuration, resulting in periodic particle motion.
Our study has revealed a periodic orbital rotation of the particle that is closely related to the behavior of the two laser beam powers. We investigated the effect of beam separation and laser pump power on this phenomenon.
This phenomenon results from the interplay of gradient force, scattering force, and nonlinear feedback. The trapped particle undergoes periodic transitions between the two traps, causing a periodic variation in the laser power of the two beams. As a result, the particle acts as a micro-isolator, attenuating the beam in which it is trapped and amplifying the other beam. It was also observed that the duration of the transition increases as the pump power decreases and the distance between the two traps increases.
Future research will focus on refining the trapping configurations to exploit their potential for precise particle manipulation in the field of nanothermodynamics.

Y.-W. Chang received the Gun and Bertil Stohnes Foundation Prize for PhD students

Logo of the Gun and Bertil Stohne’s Foundation. (Image from the Foundation’s website.)

Yu-Wei Chang received one of the Gun and Bertil Stohnes Foundation Prizes for PhD students, with his recent research focusing on deep learning analysis of longitudinal tau pathology. The price consists in 100000 SEK given to one – or shared between two – student(s) at a Swedish university.

The Gun and Bertil Stohnes Foundation awards this prize to research projects in geriatrics that the Board deems of exceptional interest and value.

Anna Canal Garcia, from Karolinska Institutet and supervised by Prof. Joana B. Pereira, is the other recipient of this award. Anna’s research focuses on the intricate multilayer network analysis of brain neuroimaging data.