Optical trapping and surgery of living yeast cells using a single laser

We present optical trapping and surgery of living yeast cells using two operational modes of a single laser. We used a focused laser beam operating in continuous-wave mode for noninvasive optical trapping and manipulation of single yeast cell. We verified that such operational mode of the laser does not cause any destructive effect on yeast cell wall. By changing the operation of the laser to femtosecond-pulsed mode, we show that a tightly focused beam dissects the yeast cell walls via nonlinear absorption. Lastly, using the combined technique of optical microsurgery and trapping, we demonstrate intracellular organelle extraction and manipulation from a yeast cell. The technique established here will be useful as an efficient method for both surgery and manipulation of living cells using a single laser beam.The project has been funded by the Philippine Council for Advanced Science and Technology Research and Development PCASTRD. J. Ando acknowledges the support of the Japan Student Services Organization JASSO for the short-term student exchange promotion program

Stimulated Raman scattering microscopy with spectral focusing of 2-ps laser pulses for higher spectral resolution and signal-to-background ratio

We propose the use of 14 ps chirped laser pulses in stimulated Raman scattering (SRS) microscopy to improve the spectral resolution and signal-to-background ratio (SBR) in SRS imaging. We developed a single-grating-based pulse chirper and implemented it into an intensity-modulation SRS microscope to stretch the excitation pulse width from 2 to 14 ps. We confirmed that the 14 ps pulses provide a spectral resolution of 2 cm-1 by measuring the SRS spectra of diamond crystals. We found that the 14 ps pulses have smaller nonlinear background signals and improve SBR in SRS imaging of various samples due to the instantaneous narrow-band excitation and low peak power. Our technique can broaden the application of the 2 ps intensity modulation SRS microscopy by improving the spectral resolution and sensitivity.SPIE BIOS, 22 January - 28 February 2022, San Francisco, California, United State

Deflectometry based calibration of a deformable mirror for aberration correction and remote focusing in microscopy

Adaptive optics (AO) techniques enhance the capability of optical microscopy through precise control of wavefront modulations to compensate phase aberrations and improves image quality. However, the aberration correction is often limited due to the lack of dynamic range in existing calibration methods, such as interferometry or Shack-Hartmann (SH) wavefront sensors. Here, we use deflectometry (DF) as a calibration method for a deformable mirror (DM) to extend the available range of aberration correction. We characterised the dynamic range and accuracy of the DF-based calibration of DMs depending on the spatial frequency of the test pattern used in DF. We also demonstrated the capability of large magnitude phase control for remote-focusing over a range larger than was possible with SH sensing

Photoinitiator-free micro/nano fabrication of biomaterials with nonlinear deep UV excitation

Two-photon fabrication is expected to be a technique for fabricating biological tissues for regenerative medicine and drug discovery because of its capability of fabricating 3D structures on a subcellular scale. In this study, we conducted two-photon fabrication of biocompatible materials without photoinitiators. By using a visible-wavelength femtosecond pulsed laser as excitation light, two-photon polymerization is induced in deep UV absorbing moieties without the use of photo-initiators. We performed 3D micro/nanofabrication of a biocompatible hydrogel material. By using Raman spectral change, we investigated the photo-chemical process of the biocompatible upon the irradiation of visible pulsed laser light.SPIE OPTO, 22 January - 28 February 2022, San Francisco, California, United State

Gaussian Process Classification Bandits

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset