Dataset Information

High-Refractive-Index Chip with Periodically Fine-Tuning Gratings for Tunable Virtual-Wavevector Spatial Frequency Shift Universal Super-Resolution Imaging.

ABSTRACT: Continued research in fields such as materials science and biomedicine requires the development of a super-resolution imaging technique with a large field of view (FOV) and deep subwavelength resolution that is compatible with both fluorescent and nonfluorescent samples. Existing on-chip super-resolution methods exclusively focus on either fluorescent or nonfluorescent imaging, and, as such, there is an urgent requirement for a more general technique that is capable of both modes of imaging. In this study, to realize labeled and label-free super-resolution imaging on a single scalable photonic chip, a universal super-resolution imaging method based on the tunable virtual-wavevector spatial frequency shift (TVSFS) principle is introduced. Using this principle, imaging resolution can be improved more than threefold over the diffraction limit of a linear optical system. Here, diffractive units are fabricated on the chip's surface to provide wavevector-variable evanescent wave illumination, enabling tunable spatial frequency shifts in the Fourier space. A large FOV and resolutions of λ/4.7 and λ/7.1 were achieved for label-free and fluorescently labeled samples using a gallium phosphide (GaP) chip. With its large FOV, compatibility with different imaging modes, and monolithic integration, the proposed TVSFS chip may advance fields such as cell engineering, precision industry inspection, and chemical research.

SUBMITTER: Tang M

PROVIDER: S-EPMC8948578 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-Refractive-Index Chip with Periodically Fine-Tuning Gratings for Tunable Virtual-Wavevector Spatial Frequency Shift Universal Super-Resolution Imaging.

Tang Mingwei M Han Yubing Y Ye Dehao D Zhang Qianwei Q Pang Chenlei C Liu Xiaowei X Shen Weidong W Ma Yaoguang Y Kaminski Clemens F CF Liu Xu X Yang Qing Q

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20220127 9

Continued research in fields such as materials science and biomedicine requires the development of a super-resolution imaging technique with a large field of view (FOV) and deep subwavelength resolution that is compatible with both fluorescent and nonfluorescent samples. Existing on-chip super-resolution methods exclusively focus on either fluorescent or nonfluorescent imaging, and, as such, there is an urgent requirement for a more general technique that is capable of both modes of imaging. In ...[more]

PMID: 35332700

Similar Datasets

Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

High-Refractive-Index Chip with Periodically Fine-Tuning Gratings for Tunable Virtual-Wavevector Spatial Frequency Shift Universal Super-Resolution Imaging.

Publications

High-Refractive-Index Chip with Periodically Fine-Tuning Gratings for Tunable Virtual-Wavevector Spatial Frequency Shift Universal Super-Resolution Imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets