arXiv subjects

David Qin

Publications and source records attributed to David Qin.

2 recordsLinked to original sources

arXiv2026

Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks

Accurate estimation of the relative concentrations of chromophores in a spectroscopic photoacoustic (sPA) image can reveal immense structural, functional, and molecular information about physiological processes. However, due to nonlinearities and ill-posedness inherent to sPA imaging, concentration estimation is intractable. The Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE) aims to address the sPA optical inversion and spectral unmixing problems without assuming linearity. Herein, SPOI-AE was trained and tested on \textit{in vivo} mouse lymph node sPA images with unknown ground truth chromophore concentrations. SPOI-AE better reconstructs input sPA pixels than conventional algorithms while providing biologically coherent estimates for optical parameters, chromophore concentrations, and the percent oxygen saturation of tissue. SPOI-AE's unmixing accuracy was validated using a simulated mouse lymph node phantom ground truth.

cs.LG↗

arXiv2022

Ultrafast reversible self-assembly of living tangled matter

Tangled active filaments are ubiquitous in nature, from chromosomal DNA and cilia carpets to root networks and worm blobs. How activity and elasticity facilitate collective topological transformations in living tangled matter is not well understood. Here, we report an experimental and theoretical study of California blackworms (Lumbriculus variegatus), which slowly form tangles over minutes but can untangle in milliseconds. Combining ultrasound imaging, theoretical analysis and simulations, we develop and validate a mechanistic model that explains how the kinematics of individual active filaments determines their emergent collective topological dynamics. The model reveals that resonantly alternating helical waves enable both tangle formation and ultrafast untangling. By identifying generic dynamical principles of topological self-transformations, our results can provide guidance for designing new classes of topologically tunable active materials.

cond-mat.soft↗