Visualization 1.mp4

This is a video for the Fig.3 of the article. It implies the SIMPLE-LSFM can acquire the firing sequence of the neurons distributed in various depths of the larval zebrafish

Media 1: Pulse compression in two-photon excitation fluorescence microscopy

Originally published in Optics Express on 05 July 2010 (oe-18-14-14893

Video_1_High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.mp4

Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.</p

Quantitative analysis of the collagen fibers in the subcutaneous pancreatic tumor xenografts.

<p>(A) The collagen density decreases as the tumor xenografts grow. The density of the tumors at 5 days is significantly higher in comparison to those at 20 days (*, p = 0.033, ANOVA linear contrast) and 30 days (**, p = 0.005, ANOVA linear contrast). +, outliers on the box-and-whisker diagram; <i>bars</i>, total extent of the data. (B) The organization of collagen fibers changes during the growth of the subcutaneous pancreatic tumor xenografts. An overall comparison of the correlation values shows the greatest difference between the tumor xenografts harvested at 5 days and those harvested after 10 days, as indicated by the Corr₅₀ value, the distance where the correlation crossed 50% of the initial correlation. *, p = 0.035, ANOVA linear contrast. The sample size is 5 for each group (5 mice). Error bars are one standard deviation above and below each data point.</p

Visualization of the cancerous rat pancreatic samples using nonlinear optical microscopy and conventional histology.

<p>(A) The pancreatic cancer cells with various size and shape as well as linear collagen fibers can be identified in the nonlinear optical image. The red color-coded structure is collagen and the green color for fluorescent component. Scale bar is 30 µm. (B) The hematoxylin and eosin image and (C) the Masson's trichrome image show the morphology of cancerous pancreatic tissues in correspondence with (A).</p

The nonlinear optical images and corresponding histology of subcutaneous pancreatic tumor xenografts.

<p>The pancreatic tumor xenografts harvested at different stages, including (A) 5 days, (B) 10 days, (C) 20 days, and (D) 30 days after implantation, were imaged and related with conventional histology. The SHG images (red color-coded), the TPEF images (green color-coded), and the 3-D superimposed SHG/TPEF images are shown in the first three columns respectively, while the hematoxylin and eosin images and the Masson's trichrome images are displayed in the last two columns. All the 3-D images are 211 µm×211 µm×50 µm. Scale bar is 30 µm.</p

Schematic of a nonlinear optical microscopic system.

<p>Schematic of a nonlinear optical microscopic system.</p

The nuclear sizes for the subcutaneous pancreatic tumor xenografts harvested at different stages.

<p>The nuclear size at 5 days is significantly lower than those at 20 days and 30 days. *, p<0.001, ANOVA linear contrast. The sample size is 5 for each group (5 mice). +, outliers on the box-and-whisker diagram; <i>bars</i>, total extent of the data.</p

Media 1: Nonlinear optical endoscopy based on a double-clad photonic crystal fiber and a MEMS mirror

Originally published in Optics Express on 06 February 2006 (oe-14-3-1027

<i>In vitro</i> assay of XimC.

<p>GC-MS profiles from the <i>in </i><i>vitro</i> assay of XimC. (A) 4HB; (B) heat-inactivated XimC incubated with chorismate; (C) XimC incubated with chorismate.</p