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In multicolor fluorescence microscopy, it is crucial to orient biological structures at a single-cell resolution based on precise anatomical annotations of cytoarchitecture images. However, during synchronous multicolor imaging, due to spectral mixing, the crosstalk from the blue signals of 4′,6-diamidino-2-phenylindole (DAPI)-stained cytoarchitecture images to the green waveband hinders the visualization and identification of green signals. Here, we proposed a deep learning-based framework named the crosstalk elimination and cytoarchitecture enhancement pipeline (CECEP) to simultaneously acquire crosstalk-free signals in the green channel and high-contrast DAPI-stained cytoarchitecture images during multicolor fluorescence imaging. For the CECEP network, we proposed an unsupervised learning algorithm named the cytoarchitecture enhancement network (CENet), which increased the signal-to-background ratio (SBR) of the cytoarchitecture images from 1.5 to 15.0 at a reconstruction speed of 25 Hz for 1800 × 1800 pixel images. The CECEP network is widely applicable to images of different quality, different types of tissues, and different multicolor fluorescence microscopy. In addition, the CECEP network can also facilitate various downstream analysis tasks, such as cell recognition, structure tensor calculation, and brain region segmentation. With the CECEP network, we simultaneously acquired two specific fluorescence-labeled neuronal distributions and their colocated high-SBR cytoarchitecture images without crosstalk throughout the brain. Experimental results demonstrate that our method could potentially facilitate multicolor fluorescence imaging applications in biology, such as revealing and visualizing different types of biological structures with precise locations and orientations. [ABSTRACT FROM AUTHOR]

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. EMDS-5 has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. Image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. Image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object segmentation. We extract nine features from the images in EMDS-5 and use the Support Vector Machine (SVM) classifier for testing. In terms of image classification, we select the VGG16 feature to test SVM, k-Nearest Neighbors, Random Forests. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of VGG16 network and ResNet50 network as feature vectors. We use mean average precision as the evaluation index for retrieval. EMDS-5 is available at the URL:https://github.com/NEUZihan/EMDS-5.git. [ABSTRACT FROM AUTHOR]

To prevent corrosion of PZT (Lead zirconate titanate) and electrodes in printheads as they are exposed to ink, Parylene C was selected as the protective layer. The deposition thickness, patterning method, and TGA (thermogravimetric analysis) of Parylene C, as well as its adhesive strength on the substrate and its aging property were studied experimentally. The results of the experiments showed that when the deposition thickness of Parylene C was 450 nm, it fully met the voltage-tolerance requirement, with a reduction in vibration displacement of only 12.89%. To pattern the Parylene C, the oxygen plasma etching method was employed, and the dependence of the etch rate on chamber power and chamber pressure were investigated. TGA experiments with Parylene C indicate that it can withstand the working temperature of a wire-bonding process. Pretreating the substrate with silane coupling agent improved the adhesive strength between Parylene C and the substrate significantly. The dielectric constant of Parylene C was reduced only 10% after 23 days in ink, which indicates the excellent aging resistance of Parylene C. [ABSTRACT FROM AUTHOR]

Inverted light-sheet microscopy (ILSM) is widely employed for fast large-volume imaging of biological tissue. However, the scattering especially in an uncleared sample, and the divergent propagation of the illumination beam lead to a trade-off between axial resolution and imaging depth. Herein, we propose naturally modulated ILSM (NM-ILSM) as a technique to improve axial resolution while simultaneously maintaining the wide field-of-view (FOV), and enhancing imaging contrast via background suppression. Theoretical derivations, simulations, and experimental imaging demonstrate 15% axial resolution increases, and fivefold greater image contrast compared with conventional ILSM. Therefore, NM-ILSM allows convenient imaging quality improvement for uncleared tissue and could extend the biological application scope of ILSM., Competing Interests: JY, ZW, TJ, WQ, and HG have filed a patent application based on this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Qiao, Jiang, Chen, Lu, Ning, Jin, Gong and Yuan.)

Visualizing the relationships and interactions among different biological components in the whole brain is crucial to our understanding of brain structures and functions. However, an automatic multicolor whole-brain imaging technique is still lacking. Here, we developed a multicolor wide-field large-volume tomography (multicolor WVT) to simultaneously acquire fluorescent signals in blue, green, and red channels in the whole brain. To facilitate the segmentation of brain regions and anatomical annotation, we used 4', 6-diamidino-2-phenylindole (DAPI) to provide cytoarchitecture through real-time counterstaining. We optimized the imaging planes and modes of three channels to overcome the axial chromatic aberration of the illumination path and avoid the crosstalk from DAPI to the green channel without the modification of system configuration. We also developed an automatic contour recognition algorithm based on DAPI-staining cytoarchitecture to shorten data acquisition time and reduce data redundancy. To demonstrate the potential of our system in deciphering the relationship of the multiple components of neural circuits, we acquired and quantified the brain-wide distributions of cholinergic neurons and input of ventral Caudoputamen (CP) with the anatomical annotation in the same brain. We further identified the cholinergic type of upstream neurons projecting to CP through the triple-color collocated analysis and quantified its proportions in the two brain-wide distributions. Both accounted for 0.22%, implying CP might be modulated by non-cholinergic neurons. Our method provides a new research tool for studying the different biological components in the same organ and potentially facilitates the understanding of the processing mechanism of neural circuits and other biological activities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ding, Zhao, Luo, Lu, Zhang, Chen, Li, Jia, Zhang, Chen, Chen, Sun, Li, Gong and Yuan.)