Stain-free nucleus identification in holographic learning flow cyto-tomography

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

A New Annealing Method to Obtain High Quality Poly-Si

ABSTRACTA new annealing method, a combination of rapid thermal annealing (RTA) and furnace annealing, has been developed to obtain a high quality poly-Si from a-Si deposited by LPCVD. This method produces a large grain poly-Si with good uniformity, which may result from the growth of relatively defect-free nucleus generated at a high temperature by RTA. Poly-Si thin film transistors fabricated by this new annealing method have higher field effect mobility and better uniformity compared with those by the conventional furnace annealing.