arXiv subjects

Soichi Wakatsuki

Publications and source records attributed to Soichi Wakatsuki.

5 recordsLinked to original sources

arXiv2026

Fast Wide-field Light Sheet Electro-optic FLIM

We demonstrate volumetric fluorescence lifetime microscopy (FLIM) using the electro-optic FLIM technique. Images acquired in a selective plane illumination microscope are gated using a Pockels cell driven at 80 MHz, enabling light sheet FLIM acquisition with up to 800 μm field of view. Volume acquisitions are demonstrated on fluorescent bead mixtures and in live Arabidopsis thaliana root samples using both genetically encoded fluorescent proteins and endogenous autofluorescence.

physics.optics↗

arXiv2025

Quantum-optimal nonlinear microscopy with classical light

Nonlinear optical processes are used in biological microscopy to surpass the diffraction limit on resolution, image deeper into brain tissues, and identify biomolecules without exogenous labels. These techniques typically require high optical intensities to increase the strength of the nonlinear interactions, which can perturb native biochemistry and damage or kill living samples. Stimulated Raman scattering (SRS) microscopy visualizes the spatial distribution of molecules using a nonlinear interaction between light and chemically specific molecular vibrations. However, the detection of biomolecules at low concentrations is limited by the total photon dose that can be applied before photodamage alters the sample, and photon shot noise sets the minimum achievable noise floor for most microscopes. Here we demonstrate a cavity-enhanced SRS microscope that is more sensitive than an equivalent conventional SRS microscope by up to 8.3(7) dB in spectroscopy and 8.6(1) dB in cell imaging. These results approach quantum limits on sensitivity and demonstrate that quantum states of light are sufficient but not necessary to enhance the sensitivity of microscopy techniques that are limited by photodamage.

physics.optics↗

arXiv2021

An image-incorporated immersed boundary method for diffusion equations

A novel sharp interface ghost-cell based immersed boundary method has been proposed and its parameters have been optimized against an analytical model in diffusion applications. The proposed embedded constrained moving least-squares (ECMLS) algorithm minimizes the error of the interpolated concentration at the image point of the ghost point by applying a moving least-squares method on all internal nodes, near the ghost image point, and the associated mirrored image points of these internal nodes through the corresponding boundary conditions. Using an analytical model as a reference, the ECMLS algorithm is compared to the constrained moving least-squares (CMLS) algorithm and the staircase model using various grid sizes, interpolation basis functions, weight functions, and the penalty parameter of the constraint. It is found that using ECMLS algorithm in the investigated diffusion application, the incomplete quartic basis function yields the best performance while the quadratic, cubic, and bicubic basis functions also produce results better than the staircase model. It is also found that the linear and bilinear basis functions cannot produce results better than the staircase model in diffusion applications. It is shown that the optimal radius of the region of internal nodes used for interpolation scales with the logarithm of the boundary radius of curvature. It is shown that for the diffusion application, the proposed ECMLS algorithm produces lower errors at the boundary with better numerical stability over a wider range of basis functions, weight functions, boundary radius of curvature, and the penalty parameter than the CMLS algorithm.

math.NA↗

arXiv2020

Sequence-guided protein structure determination using graph convolutional and recurrent networks

Single particle, cryogenic electron microscopy (cryo-EM) experiments now routinely produce high-resolution data for large proteins and their complexes. Building an atomic model into a cryo-EM density map is challenging, particularly when no structure for the target protein is known a priori. Existing protocols for this type of task often rely on significant human intervention and can take hours to many days to produce an output. Here, we present a fully automated, template-free model building approach that is based entirely on neural networks. We use a graph convolutional network (GCN) to generate an embedding from a set of rotamer-based amino acid identities and candidate 3-dimensional C$α$ locations. Starting from this embedding, we use a bidirectional long short-term memory (LSTM) module to order and label the candidate identities and atomic locations consistent with the input protein sequence to obtain a structural model. Our approach paves the way for determining protein structures from cryo-EM densities at a fraction of the time of existing approaches and without the need for human intervention.

q-bio.BM↗

arXiv2020

Hybrid real- and reciprocal-space full-field imaging with coherent illumination

We present a novel diffractive imaging method that harnesses a low-resolution real-space image to guide the phase retrieval. A computational algorithm is developed to utilize such prior knowledge as a real-space constraint in the iterative phase retrieval procedure. Numerical simulations and proof-of-concept experiments are carried out, demonstrating our method's capability of reconstructing high-resolution details that are otherwise inaccessible with traditional phasing algorithms. With the present method, we formulate a conceptual design for the coherent imaging experiments at a next-generation X-ray light source.

physics.optics↗