Investigating the micro-rheology of an aging colloidal clay suspension using an optical tweezer
Raman Research Institute, Bangalore, India.
1 June 2022, 12:30 CET
Optical tweezers (OT) can be employed to measure pico-Newton forces acting on a colloidal particle trapped in a medium and have been used to successfully probe complex systems having fragile structures. In this work, we use an optical tweezer setup to study aging aqueous suspensions of Laponite clay particles of different concentrations. Laponite particles in aqueous suspension form fragile networks whose rigidities grow with time due to the gradual evolution of inter-particle electrostatic interactions. Using OT, we study the displacements of a trapped micron-sized colloidal bead in a Laponite suspension medium during the evolution of the underlying structures. By analyzing the power spectrum, we demonstrate that the viscosity of the aging Laponite suspension increases with time. Furthermore, we perform active micro-rheology experiments wherein we apply a sinusoidal oscillation to the sample cell while keeping the particle trapped in the Laponite suspension. Simultaneously, the force response of the trapped particle is recorded during the controlled applied oscillation. The phase lag between the applied oscillatory signal and the force experienced by the trapped particle due to the oscillatory deformation is calculated. A range of frequencies is applied to estimate the elastic (G’) and viscous (G”) moduli of the Laponite suspension over a broad range of time scales and at different suspension ages. It is found that G’ is lower than G” at the lower frequencies and eventually crosses G” at a frequency that depends on the Laponite concentration. We change the size of the trapped particle to study how the probe particle size affects the micro-rheological measurements of the viscoelastic gel-like medium. We next investigate the concentration- and aging-dependences of the fragile structures in Laponite suspensions of different concentrations using cryogenic electron microscopy. The average pore areas of these structures are seen to decrease with increasing Laponite concentrations. We show that the crossover frequency of G’ and G”, obtained from micro-rheological measurements, is proportional to the average diameter of the pores in the Laponite gel measured using electron microscopy.
Rajkumar Biswas is currently doing his PhD in Raman Research Institute (RRI), India. Before joining RRI in 2016, he completed his bachelor’s and masters in Physics from St. Xavier’s college, Kolkata and Indian Institute of Technology, Guwahati (IITG) respectively. In RRI he is working in the Soft Condensed Matter group with Prof. Ranjini Bandyopadhyay. His research focuses on rheological and dynamical properties of different soft matter systems. He has worked on various projects which includes rheological studies of Laponite gels using falling ball viscometer and optical tweezer. Along with that, he has been studying the dynamical heterogeneities in colloidal and granular systems.
Yanuar Rizki Pahlevi will defended his Master thesis in MPCAS at the Chalmers University of Technology on 9 June 2022 at 17:00.
Title: Deep Learning for Optical Tweezers. DeepCalib Implementation for Brownian Motion with Delayed Feedback
Brownian motion with delayed feedback theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming variance methods in order to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.
Name of the master programme: MPCAS – Complex Adaptive Systems Examiner: Giovanni Volpe Supervisor: Aykut Argun Opponent: Ivan Gentile Japiassu
As a transparent animal and with powerful light-based tools to monitor the brain, the larval zebrafish offers a perfect window into functioning neural circuits. We can image the whole brain of zebrafish with cellular resolution, as they respond to various stimuli and record the activity of thousands of neurons. I will focus on two recent studies, one in collaboration with optical physicists, using optical tweezers to move otolith in the inner ear and simulate acceleration. We identified several salient response types, and showed the fish can respond to unnatural stimuli. The other used a microfluidics device to apply water flow to the fish and stimulate the lateral line. The fish’s brain could encode the speed, duration and direction of the water flow, but we also showed that the circuit was biased towards one specific direction of flow. Finally, I will briefly present the new focus of my lab, the gut-brain axis is a physiological communication network between the microbiome, enteric and central nervous system. We are using light sheet microscopy to image the activity of the ENS neurons from 3 to 7 days post fertilization fish. We observed that the spontaneous neuronal activity increases from 3 to 5 dpf, before dropping suddenly at day 7.
I received my PhD in 2012 from the Universite Libre de Bruxelles where I worked on the Trypanosoma brucei parasite. We discovered a key role of Trypanosoma brucei adenylate cyclases in host-pathogen interactions, as well as the mechanisms through which the human APOL1 can trigger the parasite’s death. In 2014, I was awarded an EMBO long-term fellowship, to shift my focus to neuroscience and the use of optogenetics in larval zebrafish. My work since has spanned several sensory modalities in the zebrafish, including optical traps for vestibular stimulation, visual loom responses, auditory processing, and water flow perception.
I am an assistant professor at Aarhus University since October 2021, where I will focus on the gut-brain axis, I am especially interested in the interactions between neurons, bacteria and the immune system.
Medical image segmentation using deep learning.
3 May 2022, 11:00
Image segmentation and synthesis of CT image based on deep learning: Deep learning methods for medical image segmentation are hindered by the lack of training data. This thesis aims to develop a method that overcomes this problem. Basic U-net trained on XCAT phantom data was tested first. The segmentation results were unsatisfactory even when artificial quantum noise was added. As a workaround, CycleGAN was used to add tissue textures to the XCAT phantom images by analyzing patient CT images. The generated images were used to train the network. The textures introduced by CycleGAN improved the segmentation, but some errors remained. Basic U-net was replaced with Attention U-net, which further improved the segmentation. More work is needed to fine-tune and thoroughly evaluate the method. The results obtained so far demonstrate the potential of this method for the segmentation of medical images. The proposed algorithms may be used in iterative image reconstruction algorithms in multi-energy computed tomography.
3D Cell nuclei segmentation using digital nuclei phantom and 3D deep learning methods : The analysis of microscopy image is helpful to pathological analysis. Nowadays, deep learning has shown the capabilities of processing the medical imaging data. However, developing deep learning methods in microscopy image analysis can be challenging because of the lack of ground truth and various resolution of microscopy image data. This project aims to build a digital nuclei phantom that simulates the actual microscopy images, including mitotic rate, nucleus size, noise, point spread function, and diverse resolutions. The phantom images were used to train 3D deep neural network for nuclei segmentation. The trained neural network was tested for segmentation on datasets with different resolutions. The neural network successfully performed segmentation on most resolutions in our dataset, and the segmentation results reflect the morphology and density of nuclei in microscopy images. The future work will focus on improving the nuclei phantom to generate more realistic phantom images, thereby further helping with segmentation.
My name is Hang, I took my bachelor’s in China on radiophysics, and master’s at Linköping University on biomedical imaging. After I graduate, I joined Karolinska as a research assistant on 3D microscopy image processing. Now, I am working at Linköping university as a full time research engineer, contributing to cardiovascular MR image processing, supervised by Petter Dyverfeldt.
How fundamental physics leads the way to a better understanding of life’s complexity
Georg August University Göttingen
20 April 2022, 12:30 CEST
Many biological systems rely on fundamental physical principles for their proper function. Here, mechanical processes such as force generation and adaptation of stiffness and viscosity have been very successfully used to explain complex biomedical questions with physical concepts. Such advances have been largely driven by new methods that allow to quantify biological processes and to construct theoretical models with high predictive power. I will present our recent approaches that allow to study active force generation and mobility in different biological systems over several length scales. Starting with active motion of membranes and intracellular particles in oocytes followed by cytosolic fluidification during cell division we will construct a surprisingly general description of active motion inside the cytoplasm. In a further direction, similar principles are used to study the flow of whole tissue. Here we analyze pressure driven outbursts of cancer cells from model tumors, but also the collective motion during zebrafish development that eventually shapes a whole new animal. The tools we use are largely based on continuum mechanics and statistical mechanics, and give deep insights into the physical principles that are exploited by cells and living objects to perform their intriguing function.
Clogging, Dynamics and Reentrant Fluid for Active Matter on Periodic Substrates
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
6 April 2022, 16:00 CEST
We explore the interactions between substrate length scales and correlation length scales of run-and-tumble active matter disks. For the case of a Casimir geometry of two plates placed a distance d apart, we show that an effective active-matter-mediated attraction arises due to a geometric shadowing effect . Next we shrink the plates down to columns and consider connections to jamming  and clogging effects  found in passive granular matter. The active particles are driven with an external force through columns placed in a square periodic array . When the drive is applied along a symmetry direction of the array, we find a clog-free uniform liquid state for low activity, while at higher activity, the density becomes increasingly heterogeneous and an active clogged state emerges in which the mobility is strongly reduced. For driving along non-symmetry or incommensurate directions, there are two different clogging behaviors consisting of a drive dependent clogged state in the low activity thermal limit and a drive independent clogged state at high activity. These regimes are separated by a uniform flowing liquid at intermediate activity. There is a critical activity level above which the thermal clogged state does not occur, as well as an optimal activity level that maximizes the disk mobility. Thermal clogged states are dependent on the driving direction while active clogged states are not .
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Laura Natali completed the first half of her doctoral studies and she defended her half-time on the 1st of April 2022.
The presentation was held in hybrid format, with part of the audience in the Von Bahr room and the rest connected through zoom. The half-time consisted in a presentation about her past and planned projects and it was followed by a discussion and questions proposed by her opponent Bernhard Mehlig.
Giuseppe Pesce starts his employment as a researcher at the Physics Department of the University of Gothenburg on 30th March 2022.
Giuseppe has a PhD degree in Physics from the University of Naples, Italy, where he was working for several years. His field of expertise is laser spectroscopy and optical tweezers used for several experiments, in particular for microrheology and statistical mechanics.
During his employment, Giuseppe will work on a project about optical tweezers combined with deep learning for construction of scalable quantum dots arrays and on automatisation of a double optical tweezers system to stretch biomolecules.
In the event, held on Tuesday, 15 March 2022, 16:00-19:00, the ten teams that had gone through the training at the Startup Camp and developed their company ideas, pitched their companies on stage to a panel of entrepreneur experts, the other nine teams, and all business coaches at Chalmers Ventures. DeepTrack obtained the first place among the ten participants. Congrats!
Here a few pictures from the final pitching event of the Startup Camp.