He will visit us for 5 months from March 1, 2024, to July 31, 2024.
He completed his Ph.D. at the University of Illinois at Urbana-Champaign, and was a Marie Skłodowska-Curie Research Fellow at the Institut Curie in Paris, France. He started his group in 2016 in California and is now moving his research activities to Toulouse France. His research interests are in cellular biophysics, soft and active matter physics, and bio-inspired materials with a theme towards understanding emergent behavior.
Anoop is a Senior Fellow at the Singapore-MIT Alliance for Research & Technology (SMART) center, based in the National University Singapore campus, Singapore.
He is working on computational analysis for precision agriculture at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), SMART, Singapore. As a part of this work, he is also working on the BRAPH-2 platform for spectral analysis applications at DiSTAP, SMART, and Temasek Life Sciences Laboratory (TLL), Singapore.
Presentation in group meeting of Prof. Michael Strano, Department of Chemical Engineering, Massachusetts Institute of Technology, USA and DiSTAP, Singapore-MIT Alliance for Research and Technology, Singapore.
Date: 23 February 2024
Neural network training requires complete data. We have introduced GapNet, which can train neural networks with incomplete data, using medical data. This approach can be generalized for integrating spectrum data across different frequency ranges, allowing the neural network to combine important information from diverse spectrum datasets.
Erik Olsén, Berenice García Rodríguez, Fredrik Skärberg, Petteri Parkkila, Giovanni Volpe, Fredrik Höök, and Daniel Sundås Midtvedt
Nano Letters, 24(6), 1874-1881 (2024)
doi: 10.1021/acs.nanolett.3c03539
arXiv: 2309.07572
Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.
Giovanni Volpe
Mini-Symposium with Giovanni Volpe and Pawel Sikorski, Lund, Sweden, 11 January 2024
Date: 11 January 2024
Time: 15:15
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.
The presentation, with title: “Holographic characterization of biological nanoparticles using deep learning”, was held in hybrid format, with part of the audience in the Nexus room and the rest connected through zoom. The half-time consisted in a presentation about his past and planned projects and it was followed by a discussion and questions proposed by his opponent Prof. Rebecka Jörnsten.
The presentation started with a short background to characterization of biological particles inside cells and an introduction to the papers included in the half-time.
It continued with images and videos of various particle types inside cells, both tracked and characterized, followed by a description of the LodeSTAR-model.
In the last section, he outlined the proposed continuation of his PhD, with an ongoing project for monitoring lipid droplets during long timescales and a neural network for 3D rotation parameter estimation of rotating biological samples.
PhD Student: Fredrik Skärberg
Supervisor: Daniel Midtvedt
Co-supervisors: Giovanni Volpe, Fredrik Höök
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.
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.
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.
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.
