arXiv subjects

Daiki Matsunaga

Publications and source records attributed to Daiki Matsunaga.

15 recordsLinked to original sources

arXiv2026

Elastohydrodynamic coupling enhances flow generation by coordinated ciliary beating

Ciliary arrays pump fluid at low Reynolds number through non-reciprocal beating and phase coordination between neighbouring cilia. Previous studies have demonstrated that antiplectic metachronal waves are more effective than symplectic waves in enhancing transport, and have proposed several physically intuitive explanations for this preference. What remains incomplete is a predictive analytical understanding of how hydrodynamic coupling and beat geometry determine the flow-maximising phase difference. Here, we address this problem in two steps: we first use reinforcement learning to identify flow-maximising coordination in a bead--spring cilia model, and then introduce an analytically tractable reduced model, termed a tilted-slider model, to analyse the weak-coupling limit. Reinforcement learning identifies antiplectic coordination as the flow-maximising state in linear arrays, and shows that the phase difference between neighbouring cilia accounts for most of the flow enhancement. We then use the tilted-slider model to show that a shift of the time-averaged position opposite to the effective-stroke direction enhances fluid transport through its coupling with the elastic restoring force. The reduced model further reveals that antiplectic coordination can be optimal, consistent with previous studies, whereas symplectic coordination can instead become optimal depending on beat geometry. These results identify a simple elastohydrodynamic mechanism underlying flow-maximising metachronal coordination.

physics.bio-ph↗

arXiv2026

Tuning microswimmer motility by liposome encapsulation: swimming and cargo transport of Chlamydomonas-encapsulating liposome

Inspired by biology's use of vesicles for targeted transport, many studies have propelled liposomes with active matter, creating synthetic systems that can be viewed as microscale biohybrid robots. Nevertheless, the underlying motility mechanisms from a hydrodynamic perspective are often unresolved, and reliable velocity control remains challenging. Here we present a chlamylipo formed by encapsulating the motile alga Chlamydomonas reinhardtii within a giant liposome. We quantify how the characters of swimming change under controlled perturbations and, from a fluid-mechanical perspective, derive a deformation-velocity expression that incorporates liposome radius, beating frequency, and membrane protrusion. We further show that motility can be reversibly switched by incorporating light-responsive lipids, with the liposome acting as a "clutch" that modulates membrane-coupled propulsion. Thus, liposome encapsulation can function not only as a cargo compartment but also as a tunable motility regulator, enabling speed adjustment and reversible transitions between motile and non-motile states.

physics.bio-ph↗

arXiv2026

Modeling bacterial flow field with regularized singularities

The flow field generated by a swimming bacterium serves as a fundamental building block for understanding hydrodynamic interactions between bacteria. Although the flow field generated by a force dipole (stresslet) well captures the fluid motion in the far field limit, the stresslet description does not work in the near-field limit, which can be important in microswimmer interactions. Here we propose the model combining an anisotropically regularized stresslet with an isotropically regularized source dipole, and it nicely reproduces the flow field around a swimming bacterium, which is validated by the experimental measurements of the flow field around \textit{E. coli} and our boundary-element-method simulations of a helical microswimmer, in both cases of the free space and the confined space with a no-slip wall. This work provides a practical tool for obtaining the flow field of the bacterium, and can be utilised to study the collective responses of bacteria in dense suspensions.

cond-mat.soft↗

arXiv2026

Cell-induced wrinkling patterns on soft substrates

Cells exert traction forces on compliant substrates and can induce surface instabilities that appear as characteristic wrinkling patterns. Here, we develop a mechanical description of cell-induced wrinkling on soft substrates using a thin film elastic framework based on the Föppl-von Kármán equations coupled to a phase-field model of a single cell. We model in-plane contractile stresses driven by cellular activity and study how their magnitude, spatial distribution, and symmetry determine the onset of wrinkling and the resulting pattern selection. The theory predicts transitions between distinct morphologies, such as radial, circumferential, and anisotropic wrinkle arrangements, and provides scaling relations for wrinkle wavelength and amplitude as functions of elastic parameters and imposed cellular forcing. We compare these predictions with available experimental observations of cell-driven wrinkling on compliant gels and find good agreement for both qualitative pattern classes and quantitative wavelength trends. Our results offer a minimal modelling framework to interpret wrinkling assays and connect observed surface patterns to underlying cellular forces.

cond-mat.soft↗

arXiv2025

Optimal Undulatory Swimming with Constrained Deformation and Actuation Intervals

In nature, many unicellular organisms are able to swim with the help of beating filaments, where local energy input leads to cooperative undulatory beating motion. Here, we investigate by employing reinforcement learning how undulatory microswimmers modeled as a discretized bead-bend-spring filament actuated by torques which are constrained locally. We show that the competition between actively applied torques and intrinsic bending stiffness leads to various optimal beating patterns characterized by distinct frequencies, amplitudes, and wavelengths. Interestingly, the optimum solutions depend on the action interval, i.e.\ the time scale how fast the microswimmer can \rev{change the applied torques} based on its internal state. We show that optimized stiffness- and action-interval-dependent beating is realized by bang-bang solutions of the applied torques with distinct optimum time-periodicity and phase shift between consecutive joints, which we analyze in detail by a systematic study of possible bang-bang wave solution patterns of applied torques. Our work not only sheds light on how efficient beating patterns of biological microswimmers can emerge based on internal and local constraints, but also offers actuation policies for potential artificial elastic microswimmers.

physics.bio-ph↗

arXiv2023

Asymmetric bistability of chiral particle orientation in viscous shear flows

The migration of helical particles in viscous shear flows plays a crucial role in chiral particle sorting. Attaching a non-chiral head to a helical particle leads to a rheotactic torque inducing particle reorientation. This phenomenon is responsible for bacterial rheotaxis observed for flagellated bacteria as Escherichia coli in shear flows. Here we use a high-resolution microprinting technique to fabricate micro-particles with controlled and tunable chiral shape consisting of a spherical head and helical tails of various pitch and handedness. By observing the fully time-resolved dynamics of these micro-particles in microfluidic channel flow, we gain valuable insights into chirality-induced orientation dynamics. Our experimental model system allows us to examine the effects of particle elongation, chirality, and head-heaviness for different flow rates on the orientation dynamics, while minimizing the influence of Brownian noise. Through our model experiments we demonstrate the existence of asymmetric bistability of the particle orientation perpendicular to the flow direction. We quantitatively explain the particle equilibrium orientations as a function of particle properties, initial conditions and flow rates, as well as the time-dependence of the reorientation dynamics through a theoretical model. The model parameters are determined using boundary element simulations and excellent agreement with experiments is obtained without any adjustable parameters. Our findings lead to a better understanding of chiral particle transport, bacterial rheotaxis and might allow the development of targeted delivery applications.

cond-mat.soft↗

arXiv2021

Field-controlling patterns of sheared ferrofluid droplets

We investigate how ferrofluid droplets suspended in a wall-bounded shear flow can organise when subjected to an external magnetic field. By tuning the magnitude of the external magnetic field, we find that the ferrofluid droplets form chain-like structures in the flow direction when the magnetic field is weak, while forming a crystal-like pattern in a strong magnetic field. We provide the phase diagram and the critical conditions for this chain-to-crystal transition, by applying both numerical simulations and analytic calculations. We also examine how the organised patterns of the ferrofluid droplets can be controlled by simply changing the direction of the magnetic field. This work demonstrates new aspects of field-controllable ferrofluid droplets as a configurable and reprocessable metamaterial.

cond-mat.soft↗

arXiv2021

Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images

Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.

physics.bio-ph↗

arXiv2020

Magnetic microswimmers exhibit Bose-Einstein-like condensation

We study an active matter system comprised of magnetic microswimmers confined in a microfluidic channel and show that it exhibits a new type of self-organized behavior. Combining analytical techniques and Brownian dynamics simulations, we demonstrate how the interplay of non-equilibrium activity, external driving, and magnetic interactions leads to the condensation of swimmers at the center of the channel via a non-equilibrium phase transition that is formally akin to Bose-Einstein condensation. We find that the effective dynamics of the microswimmers can be mapped onto a diffusivity-edge problem, and use the mapping to build a generalized thermodynamic framework, which is verified by a parameter-free comparison with our simulations. Our work reveals how driven active matter has the potential to generate exotic classical non-equilibrium phases of matter with traits that are analogous to those observed in quantum systems.

cond-mat.stat-mech↗

arXiv2020

Degenerate states, emergent dynamics and fluid mixing by magnetic rotors

We investigate the collective motion of magnetic rotors suspended in a viscous fluid under an uniform rotating magnetic field. The rotors are positioned on a square lattice, and low Reynolds hydrodynamics is assumed. For a $3 \times 3$ array of magnets, we observe three characteristic dynamical patterns as the external field strength is varied: a synchronized pattern, an oscillating pattern, and a chessboard pattern. The relative stability of these depends on the competition between the energy due to the external magnetic field and the energy of the magnetic dipole-dipole interactions among the rotors. We argue that the chessboard pattern can be understood as an alternation in the stability of two degenerate states, characterized by striped and spin-ice configurations, as the applied magnetic field rotates. For larger arrays, we observe propagation of slip waves that are similar to metachronal waves. The rotor arrays have potential as microfluidic devices that can mix fluids and create vortices of different sizes.

cond-mat.soft↗

arXiv2019

Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. The main goal of this work is to test the validity of this approach across different markets and longer time horizons for backtesting using rolling window analysis. In this work, we concentrate on the prediction of individual stock prices in the Japanese Nikkei 225 market over a period of roughly 20 years. For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies. Our preliminary results show a 29.5% increase and a 2.2-fold increase in the return ratio and Sharpe ratio, respectively, when compared to the market benchmark, as well as a 6.32% increase and 1.3-fold increase, respectively, compared to the baseline LSTM model.

cs.LG↗

arXiv2019

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet

We propose an image-based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the microscope images, we develop a new CNN (convolutional neural network) architecture SW-UNet (small-world U-Net), which is a CNN that reflects the concept of the small-world network. The SW-UNet shows better performance in wrinkle segmentation task compared to other methods: the error (Euclidean distance) of SW-UNet is 4.9 times smaller than 2D-FFT (fast Fourier transform) based segmentation approach, and is 2.9 times smaller than U-Net. As a demonstration, we compare the contractile force of U2OS (human osteosarcoma) cells and show that cells with a mutation in the KRAS oncogne show larger force compared to the wild-type cells. Our new machine learning based algorithm provides us an efficient, automated and accurate method to evaluate the cell contractile force.

physics.bio-ph↗

arXiv2018

Clustering of Magnetic Swimmers in a Poiseuille Flow

We investigate the collective behavior of magnetic swimmers, which are suspended in a Poiseuille flow and placed under an external magnetic field, using analytical techniques and Brownian dynamics simulations. We find that the interplay between intrinsic activity, external alignment, and magnetic dipole-dipole interactions leads to longitudinal structure formation. Our work sheds light on a recent experimental observation of a clustering instability in this system.

cond-mat.soft↗

arXiv2017

Focusing and sorting of ellipsoidal magnetic particles in microchannels

We present a simple method to control the position of ellipsoidal magnetic particles in microchannel Poiseuille flow using a static uniform magnetic field. The magnetic field is utilized to pin the particle orientation, and the hydrodynamic interactions between ellipsoids and channel walls allow control of the transverse position of the particles. We employ a far-field hydrodynamic theory and simulations using the boundary element method and Brownian dynamics to show how magnetic particles can be focussed and segregated by size and shape. This is of importance for particle manipulation in lab-on-a-chip devices.

cond-mat.soft↗

arXiv2017

Far-field theory for trajectories of magnetic ellipsoids in rectangular and circular channels

We report a method to control the positions of ellipsoidal magnets in flowing channels of rectangular or circular cross section at low Reynolds number.A static uniform magnetic field is used to pin the particle orientation, and the particles move with translational drift velocities resulting from hydrodynamic interactions with the channel walls which can be described using Blake's image tensor.Building on his insights, we are able to present a far-field theory predicting the particle motion in rectangular channels, and validate the accuracy of the theory by comparing to numerical solutions using the boundary element method.We find that, by changing the direction of the applied magnetic field, the motion can be controlled so that particles move either to a curved focusing region or to the channel walls.We also use simulations to show that the particles are focused to a single line in a circular channel.Our results suggest ways to focus and segregate magnetic particles in lab-on-a-chip devices.

cond-mat.soft↗