Myxococcus xanthus colonies develop different strategies to adapt to their environment, leading to the formation of macroscopic patterns from microscopic entities. (Image by the Authors of the manuscript.)Tutorial for the growth and development of Myxococcus xanthus as a Model System at the Intersection of Biology and Physics
Jesus Manuel Antúnez Domínguez, Laura Pérez García, Natsuko Rivera-Yoshida, Jasmin Di Franco, David Steiner, Alejandro V. Arzola, Mariana Benítez, Charlotte Hamngren Blomqvist, Roberto Cerbino, Caroline Beck Adiels, Giovanni Volpe
arXiv: 2407.18714
Myxococcus xanthus is a unicellular organism whose cells possess the ability to move and communicate, leading to the emergence of complex collective properties and behaviours. This has made it an ideal model system to study the emergence of collective behaviours in interdisciplinary research efforts lying at the intersection of biology and physics, especially in the growing field of active matter research. Often, challenges arise when setting up reliable and reproducible culturing protocols. This tutorial provides a clear and comprehensive guide on the culture, growth, development, and experimental sample preparation of M. xanthus. Additionally, it includes some representative examples of experiments that can be conducted using these samples, namely motility assays, fruiting body formation, predation, and elasticotaxis.
The book Deep Learning Crash Course, authored by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo, has been published online by No Starch Press in July 2024.
Citation
Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo. Deep Learning Crash Course. No Starch Press.
ISBN-13: 9781718503922
(Photo by A. Argun.)Marcel Rey has been appointed as an assistant professor at the University of Münster, Department of Physical Chemistry. Congrats! He will start his new appointment on July 3rd 2024.
Artist rendition of a disk-shaped microparticle trapped above a circular uncoated pattern within a thin gold layer coated on a glass surface. (Image by the Authors of the manuscript.)Nanoalignment by Critical Casimir Torques
Gan Wang, Piotr Nowakowski, Nima Farahmand Bafi, Benjamin Midtvedt, Falko Schmidt, Agnese Callegari, Ruggero Verre, Mikael Käll, S. Dietrich, Svyatoslav Kondrat, Giovanni Volpe
Nature Communications, 15, 5086 (2024)
DOI: 10.1038/s41467-024-49220-1
arXiv: 2401.06260
The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.
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, currently at version 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.
(Photo by A. Ciarlo.)Jason Lewis started to work as a researcher at the Physics Department of the University of Gothenburg on 1st June 2024.
Jason received his Ph.D. degree in Complexity Science from the University of Warwick, UK, with a thesis titled “Topological models of swarming”, which studied the dynamics of bird flocks, specifically under topological constraints, via theory and numerical simulation.
After his PhD, he undertook a postdoc in the group of Joakim Stenhammar at Lund University, Sweden, where he investigated chemotaxis and the collective behaviour of microswimmers, known as active turbulence, in addition to other projects at the interface of machine learning and active matter.
His research focuses on the theory and simulation of active matter systems at all scales, specifically on modelling the structure and dynamics of self-organising groups of motile robots.
Real-time control of optical tweezers with deep learning. (Image by the Authors of the manuscript.)Deep learning for optical tweezers
Antonio Ciarlo, David Bronte Ciriza, Martin Selin, Onofrio M. Maragò, Antonio Sasso, Giuseppe Pesce, Giovanni Volpe and Mattias Goksör
Nanophotonics, 13(17), 3017-3035 (2024)
doi: 10.1515/nanoph-2024-0013
arXiv: 2401.02321
Optical tweezers exploit light–matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nanotechnology. Recently, the use of deep learning has started to enhance optical tweezers by improving their design, calibration, and real-time control as well as the tracking and analysis of the trapped objects, often outperforming classical methods thanks to the higher computational speed and versatility of deep learning. In this perspective, we show how cutting-edge deep learning approaches can remarkably improve optical tweezers, and explore the exciting, new future possibilities enabled by this dynamic synergy. Furthermore, we offer guidelines on integrating deep learning with optical trapping and optical manipulation in a reliable and trustworthy way.
Rendering of the absorption of optical power by iron-oxide nanocores in a super-paramagnetic particle. (Image by A. Lech.)Alex Lech defended his Master Thesis on 16 May 2024 at 15:45. Congrats!
Title: Simulation of light-absorbing microparticles in an optical landscape
Abstract:
Simulating the dynamics of active particles play a key role in understanding the many behaviours active matter can exhibit. Experimental studies are more costly than simulations in this regard, as there is much work that needs to be performed with setups and observation time. Computer simulations are a powerful and cost-effective alternative to experiments. One topic of study within active matter is light-absorbing microparticles which are commonly made of silica with a light-absorbing metallic compound such as iron oxide or gold. One such microparticle is the Janus particle, a silica particle with a hemispherical coating of gold as the absorbing compound. When illuminated with a laser, the coating absorbs the light and heats up rapidly, generating a temperature gradient which allows the Janus particle to exhibit self-propulsion and clustering with other Janus particles due to thermophoresis and Brownian motion.
In this thesis, I introduce a simulation model which simulates light-absorbing microparticles with a desired distribution of iron oxide in an optical landscape. In particular, I will consider the case of an optical landscape characterized by a periodical sinusoidal intensity profile of a given spatial periodicity.
The results show that for a hemispherical distribution (Janus particle) there is self-propulsion originating at the side of the cap, with super-diffusive characteristics. When the laser periodicity is similar to the particle radius, it becomes confined between two high intensity peaks. A particle with uniform distribution diffuses with Brownian motion, with no self-propulsion. Clustering behaviour arises when multiple particles are in close proximity to each other, as observed in experiments.
The agreement with experimental results opens up for the opportunity to simulate other light-absorbing particles with different distributions of absorbing compounds.
Supervisor: Agnese Callegari Examiner: Giovanni Volpe Opponent: John Klint, Niphredil Klint
He will start his new appointment on May 6th 2024. His research will focus on nanooptics technology for electronic paper, optical neural networks, and intelligent microparticles.
Correlated photons in superresolution imaging and correlated motions in biophysical interaction
Alexander Rohrbach
15 May 2024
12:30
Nexus
Abstract
Our research concentrates on light scattering at small biological structures enabling image formation and particle tracking in biophysics.
Coherent light, i.e. correlated photons enable higher scattering cross-sections than for instance incoherent fluorescence light. Thereby laser light enables to acquire images with millisecond integration times and small motion blur of dynamic particles, such as viruses in the cell periphery. The inherent speckle formation in coherent imaging is avoided by a novel technique called Rotating Coherent Scattering (ROCS) microscopy, which is the only technique that can image diffusing viruses and thereby allows to investigate their binding behavior to the cell periphery.
In the second part of my talk I discuss correlated particle motions, i.e. timescale dependent memory effects in viscoelastic media such as the cell periphery. Using a frequency decomposition of the tracked particle motions, apparently invisible binding of particles to the cell can be made visible.
Short CV
I studied physics at the university of Erlangen-Nürnberg (Germany), where I did my diploma in 1994 at the institute of optics. During my PhD in physics in Heidelberg I investigated different kinds of light scattering at the University, as well as evanescent wave microscopy at the Max-Planck-Institute for medical research. In both cases I worked on applications in cell biology. After my PhD in 1998, I continued my research as a Post-Doc at the European Molecular Biology Laboratory (EMBL) in Heidelberg. I intensified my studies on microscopy, light scattering and optical forces. In 2001 I became project leader of the photonic force microscopy group at EMBL, where I concentrated on the further technical development of this scanning probe microscopy and on applications in biophysics and soft matter physics. In 2005 I was awarded with the habilitation in physics at the university of Heidelberg. Since January 2006 I have been a full professor for Bio- and Nano-Photonics at IMTEK, Faculty of engineering and since 2007 also a member of the physics faculty, University of Freiburg.
I love mathematical models and I hate when the performance of scientists is squeezed into metric numbers.
