1 Background 2 An important task of macromolecular structure determination by cryo-electron 3 microscopy (cryo-EM) is the identification of single particles in micrographs (particle 4 picking). Currently, particle picking is laborious, time consuming, and potentially biased 5 due to the need of human intervention to initialize the particle picking. The results 6 typically include many false positives and negatives. Adjusting the parameters to 7 eliminate false positives often excludes true particles in certain orientations. The 8 supervised machine learning (e.g. deep learning) methods for particle picking often 9 need a large training dataset, which requires extensive manual annotation. Other 10 reference-dependent methods rely on low-resolution templates for particle detection, 11 matching and picking, and therefore, are not fully automated. These issues motivate 12 us to develop a fully automated, unbiased framework for particle picking. 13Results 14 We design a fully automated, unsupervised approach for single particle picking in cryo-15 EM micrographs. Our approach consists of three stages: image preprocessing, particle 16 clustering, and particle picking. The image preprocessing is based on image 17 averaging, normalization, cryo-EM image contrast enhancement correction (CEC), 18 histogram equalization, restoration, adaptive histogram equalization, guided image 19 filtering, and morphological operations significantly improves the quality of original 20 cryo-EM images. Our particle clustering method is based on an intensity distribution 21 model which is much faster and more accurate than traditional K-means and Fuzzy C-22 Means (FCM) algorithms for single particle clustering. Our particle picking method, 23 based on image cleaning and shape detection with a modified Circular Hough 24 Transform algorithm, effectively detects the shape and the center of each particle and 25 creates a bounding box encapsulating the particles. 26 Conclusions 27AutoCryoPicker can automatically and effectively recognizes particle-like objects from 28 in noisy cryo-EM micrographs without the need of labeled training data and human 29 intervention and therefore is a useful tool for cryo-EM protein structure determination. 30 Keywords 31Clustering, Intensity Based Clustering (IBC), micrograph, Cryo-EM, singe particle 32 pickling, protein structure determination. -3 - Background 34For decades, X-ray crystallography has been the dominant technique for obtaining 35 high-resolution structures of macromolecules. Single-particle cryo-electron 36 microscopy (cryo-EM) was traditionally used to provide low resolution structural 37 information on large protein complexes that resisted crystallization (e.g., highly 38 symmetric particles of viruses). Though the basic workflow of cryo-EM has not 39 changed considerably over the years, recent technological advances in sample 40 preparation, computation and especially instrumentation have revolutionized the field 41 of structural biology [1] [2] [3], allowing it to solve large protein struct...

Background An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking. Results We design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles. Conclusions AutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination. Electronic supplementary material The online version of this article (10.1186/s12859-019-2926-y) contains supplementary material, which is available to authorized users.

Structure determination of proteins and macromolecular complexes by single-particle cryo-electron microscopy (cryo-EM) is poised to revolutionize structural biology. An early challenging step in the cryo-EM pipeline is the detection and selection of particles from two-dimensional micrographs (particle picking). Most existing particle-picking methods require human intervention to deal with complex (irregular) particle shapes and extremely low signal-to-noise ratio (SNR) in cryo-EM images. Here, we design a fully automated super-clustering approach for single particle picking (SuperCryoEMPicker) in cryo-EM micrographs, which focuses on identifying, detecting, and picking particles of the complex and irregular shapes in micrographs with extremely low signal-to-noise ratio (SNR). Our method first applies advanced image processing procedures to improve the quality of the cryo-EM images. The binary mask image-highlighting protein particles are then generated from each individual cryo-EM image using the super-clustering (SP) method, which improves upon base clustering methods (i.e., k-means, fuzzy c-means (FCM), and intensity-based cluster (IBC) algorithm) via a super-pixel algorithm. SuperCryoEMPicker is tested and evaluated on micrographs of β-galactosidase and 80S ribosomes, which are examples of cryo-EM data exhibiting complex and irregular particle shapes. The results show that the super-particle clustering method provides a more robust detection of particles than the base clustering methods, such as k-means, FCM, and IBC. SuperCryoEMPicker automatically and effectively identifies very complex particles from cryo-EM images of extremely low SNR. As a fully automated particle detection method, it has the potential to relieve researchers from laborious, manual particle-labeling work and therefore is a useful tool for cryo-EM protein structure determination.

Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

Abstract-A distortion-less ultra-wideband tapered slot antenna is designed to achieve wide band impedance matching and high gain without requiring coupling liquids. The antenna is embedded in a suitable dielectric material for compact size and performance improvement. The near-field test is simulated by placing several field probes near the antenna to plot the radiation pattern and polarization isolation. The antenna exhibits a highly directive pattern and polarization isolation in near field. The time domain antenna distortion is tested by calculating the fidelity and group delay. The results show low distortion and also show the importance of covering the antenna by dielectric layers for bandwidth increment and distortion reduction. To evaluate the antenna performance in breast cancer detection, three breast phantoms are imaged by using the raster scan imaging method. Two approaches are proposed to detect tumors without the need of breast background data. The approaches based on the effect of the tumor on transmission and reflection parameters on the frequency band allowed for medical applications. The obtained images show the antenna to be a strong candidate for breast imaging as well as in tumor detection for different scenarios that include complex multi-layer phantom and small tumor.