arXiv subjects

Katsumasa Fujita

Publications and source records attributed to Katsumasa Fujita.

4 recordsLinked to original sources

arXiv2024

Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging Across Multiple Devices

Cytological diagnosis of follicular thyroid carcinoma (FTC) is one of major challenges in the field of endocrine oncology due to absence of evident morphological indicators. Morphological abnormalities in the nucleus are typically key indicators in cancer cytopathology and are attributed to a range of biochemical alterations in nuclear components. Consequently, Raman spectroscopy has been widely used to detect cancer in various cytological samples, often identifying biochemical changes prior to observable morphological alterations. However, in the case of FTC, cytoplasmic features such as carotenoids, cytochromes, and lipid droplets have shown greater diagnostic relevance compared to nuclear features. This study leverages single-cell Raman imaging to explore the spatial origin of diagnostic signals in FTC and normal thyroid (NT) cells, assessing the contributions of the nucleus and cytoplasm independently. Our results demonstrate that Raman spectra from the cytoplasmic region can distinguish between FTC and NT cells with an accuracy of 84% under co-culture conditions, maintaining robustness across multiple devices. In contrast, classification based on nuclear spectra achieved only 53% accuracy, suggesting that biochemical alterations in the cytoplasm play a more significant role in FTC detection than those in the nucleus. Our work elevates the promise of Raman-based cytopathology by providing complementary organelle-dependent information to traditional diagnostic methods and demonstrating transferability across different devices.

q-bio.QM↗

arXiv2022

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.

cs.LG↗

arXiv2020

Giant optical nonlinearity in single silicon nanostructure: ultrasmall all-optical switch and super-resolution imaging

Silicon photonics has attracted significant interest in recent years due to its potential in integrated photonics components (1,2) as well as all-dielectric meta-optics elements.(3) Strong photon-photon interactions, aka optical nonlinearity, realizes active control of aforementioned photonic devices.(4,5) However, intrinsic nonlinearity of Si is too weak to envision practical applications. To boost the nonlinear response, long interaction-length structures such as waveguides, or resonant structures such as microring resonators or photonic crystals have been adopted.(6,7) Nevertheless, their feature sizes are typically larger than 10 $μ$m, much larger than their electronic counterparts. Here we discover, when reducing the size of Si resonator down to ~100 nm, a giant photothermal nonlinearity that yields 400% reversible and repeatable deviation from linear scattering response at low excitation intensity (mW/$μ$m$^2$). The equivalent nonlinear index n$_2$ at nanoscale is five-order larger than that of bulk, due to Mie resonance enhanced absorption and high-efficiency heating in the thermally isolated nanostructure. In addition, the nanoscale thermal relaxation time reaches nanosecond, implying GHz modulation speed. This large and fast nonlinearity enables applications toward all-optical control in nanoscale, as well as super-resolution imaging of silicon.

physics.optics↗

arXiv2019

Multiphoton-excited DUV photolithography for 3D nanofabrication

Light-matter interactions in the deep ultraviolet (DUV) wavelength region exhibits a variety of optical effects such as luminescence, photoisomerization, and polymerization in many materials. Despite the rich photochemistry and high spatial resolution due to the short wavelength, the notorious lack of DUV-compatible optical components and devices precludes use of DUV light in microscopy and lithography as a routine laboratory tool. Here, we present the use of two-photon excitation with visible laser light to realizes photo-polymerization of molecules with an excitation energy equivalent to DUV light. Using standard optics for visible light, methacrylate oligomers were polymerized with 400 nm femtosecond pulses without any addition of photo-initiators and sensitizers. By scanning the laser focus in 3D, a series of fine 3D structures were created with the smallest resolved line-space features of 80 nm. We found DUV polymerizations induced by two-photon absorption is surprisingly efficient and requires laser intensity only on the order of 100 kW/cm^2. With the variety of successful demonstrations including organic- and inorganic-material-made-structures presented, our direct nano-3D-printing method would be a valuable tool for nanofabrication in 3D.

physics.app-ph↗