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Статті в журналах з теми "Particle Picking"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

McSweeney, Donal M., Sean M. McSweeney, and Qun Liu. "A self-supervised workflow for particle picking in cryo-EM." IUCrJ 7, no. 4 (June 23, 2020): 719–27. http://dx.doi.org/10.1107/s2052252520007241.

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Анотація:

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.

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2

Al-Azzawi, Ouadou, Tanner, and Cheng. "A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM." Genes 10, no. 9 (August 30, 2019): 666. http://dx.doi.org/10.3390/genes10090666.

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Анотація:

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.

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3

Yu, Xiaolu. "Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network." Wireless Communications and Mobile Computing 2022 (September 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/6411285.

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Анотація:

In order to solve the logistics optimization problem, an application method of the improved particle swarm optimization algorithm in logistics energy-saving pickup information network is proposed. Firstly, a mathematical model of logistics cycle picking information scheduling optimization is established, logistics and picking paths are encoded as particles, and the optimal logistics cycle picking optimization scheme is found through the cooperation between particles. Secondly, the deficiencies of the particle swarm optimization algorithm are improved accordingly. In order to test the performance of the IPSO algorithm in solving the logistics circulation picking problem, in the simulation environment of P42 core, 2.6 GHz CPU, 4 GB memory, and Windows XP, the simulation experiment was carried out using VC++6.0 programming operating system. The particle number of the IPSO algorithm is 20, ω max = 5 , ω max = 1 . The experimental results show that the improved particle swarm optimization algorithm can effectively bypass the premature convergence of the traditional particle swarm optimization algorithm and ensure that the optimal solution is searched in the global scope, and the optimal probabilistic solution is obtained, which is better than other scheduling algorithms, with more obvious advantages.

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4

Adiga, Umesh, William T. Baxter, Richard J. Hall, Beate Rockel, Bimal K. Rath, Joachim Frank, and Robert Glaeser. "Particle picking by segmentation: A comparative study with SPIDER-based manual particle picking." Journal of Structural Biology 152, no. 3 (December 2005): 211–20. http://dx.doi.org/10.1016/j.jsb.2005.09.007.

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5

Ramani Lata, K., P. Penczek, and J. Frank. "Automatic Particle Picking From Electron Micrographs." Microscopy Today 3, no. 3 (April 1995): 12–13. http://dx.doi.org/10.1017/s1551929500063203.

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Анотація:

The present-day interactive manual selection of biological molecules from digitized micrographs for single particle averaging and reconstruction requires substantial effort and time. Thus a computer algorithm capable of recognition of structural content and selection of particles would be desirable. A few approaches have been proposed in the past. The method by Frank and Wagenknecht is based on the principle of correlation search. Van Heel's method is based on the computation of the local variance over a small area around each point of the image field. The method by Harauz and Fong- Lochovsky is based on edge-detection.

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6

Lata, K. Ramani, P. Penczek, and J. Frank. "Automatic particle picking from electron micrographs." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 122–23. http://dx.doi.org/10.1017/s0424820100168347.

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Анотація:

The present-day interactive manual selection of biological molecules from digitized micrographs for single particle averaging and reconstruction requires substantial effort and time. Thus a computer algorithm capable of recognition of structural content and selection of particles would be desirable.A few approaches have been proposed in the past. The method by Frank and Wagenknecht is based on the principle of correlation search. Van Heel's method is based on the computation of the local variance over a small area around each point of the image field. The method by Harauz and Fong-Lochovsky is based on edge-detection. The present work was focussed on the detection and classification of particlesby exploiting the standard statistical methods of discriminant analysis.The proposed technique is described in the block diagram (Fig.1). As illustrated in the figure, the program consists of three distinct segments devoted to, respectively, preparation of the data, training session and automatic selection based on a discriminant function set up in the training, hi the data preparation segment, the micrograph is (i) reduced four-fold in size, (ii) low-pass filtered and (iii) run through a peak search algorithm.

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7

Ramani Lata, K., P. Penczek, and J. Frank. "Automatic particle picking from electron micrographs." Ultramicroscopy 58, no. 3-4 (June 1995): 381–91. http://dx.doi.org/10.1016/0304-3991(95)00002-i.

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8

Zhou, Xiao Min, Ying De Li, and Yue Peng Yao. "Slotting Optimization Model and Algorithm for Concerning the Correlation in Hybrid Travel Policy." Applied Mechanics and Materials 694 (November 2014): 90–94. http://dx.doi.org/10.4028/www.scientific.net/amm.694.90.

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In the periodic picking background, we researched the influence of the picking correlations between Stock Keeping Units (SKUs), established dynamic location assignment model to minimize the total picking time, developed a particle swarm optimization (PSO) based on the correlated SKUs. We set the cube-per-order-index (COI) solution as initial solution, used correlation strength to update the velocity and position of particles and assigned correlated SKUs to adjacent slots according to the optimal location sequence. The result shows that in zone-based wave-picking system with hybrid touring policy, the solution quality of PSO is always better than COI, the improvement of PSO is 2.50%~13.9% and average improvement is 2.84%~12.53%; the correlation has significant impact on the picking efficiency.

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9

Sanchez-Garcia, Ruben, Joan Segura, David Maluenda, Jose Maria Carazo, and Carlos Oscar S. Sorzano. "Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy." IUCrJ 5, no. 6 (October 30, 2018): 854–65. http://dx.doi.org/10.1107/s2052252518014392.

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Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different `cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%.

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10

Yang, Wei, Xue Lian Li, Hai Gang Wang, and Yu Xiao Du. "Optimization for Order-Picking Path of Carousel in AS/RS Based on Improving Particle Swarm Optimization Approach." Advanced Materials Research 267 (June 2011): 752–56. http://dx.doi.org/10.4028/www.scientific.net/amr.267.752.

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In order to improve the access efficiency of small items storage system in Automatic Storage & Retrieval System, we use the particle swarm optimization approach to analyze and optimize its order-picking path by taking the carousel with double sorting tables as research object. Through the analysis of order-picking process, a mathematical model for solving optimization of order-picking path is brought forward, and a solving process based on particle swarm is designed aiming at this model and testing the effectiveness of this algorithm. The experimental simulation proves that PSO can be the fast, stable and effective solution to the optimization problem of order-picking path for double sorting tables, thereby improving the overall operation efficiency of Automatic Storage & Retrieval System.

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11

Ouyang, Jianquan, Yue Zhang, Kun Fang, Tianming Liu, and Xiangyu Pan. "Urdnet: A Cryo-EM Particle Automatic Picking Method." Computers, Materials & Continua 72, no. 1 (2022): 1593–610. http://dx.doi.org/10.32604/cmc.2022.025072.

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12

Wong, H. "Model-based particle picking for cryo-electron microscopy." Journal of Structural Biology 145, no. 1-2 (January 2004): 157–67. http://dx.doi.org/10.1016/j.jsb.2003.05.001.

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13

Cao, Xiaoman, Hansheng Yan, Zhengyan Huang, Si Ai, Yongjun Xu, Renxuan Fu, and Xiangjun Zou. "A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator." Agronomy 11, no. 11 (November 11, 2021): 2286. http://dx.doi.org/10.3390/agronomy11112286.

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Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

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14

Wan, Yiyang, and Zhenhai Xia. "Self-Cleaning and Controlled Adhesion of Gecko Feet and Their Bioinspired Micromanipulators." MRS Advances 3, no. 29 (2018): 1641–46. http://dx.doi.org/10.1557/adv.2018.51.

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ABSTRACTBioinspired micromanipulators have been made based on gecko dynamic self-cleaning mechanism. Various particles such as spherical SiO2/polystyrene, and short fibrous glass can be captured, transmitted and dropped on glass substrate with precisely predesigned patterns, by using the micromanipulator with the help of atomic force microscope (AFM). It has been demonstrated that particle-pad interface and particle-substrate interface exhibit diverse adhesion behaviors under different z-piezo retracting speed. The particle-substrate adhesion increases faster than the particle-pad adhesion with increasing the detaching velocity, which makes it possible to manipulate the particles by adjusting the retreating speed only. Probability tests was performed to better choose suitable parameters for picking and dropping operations. This work provides a potential solution to manipulation of micro/nano particles for precise assembly.

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15

Vargas, J., V. Abrishami, R. Marabini, J. M. de la Rosa-Trevín, A. Zaldivar, J. M. Carazo, and C. O. S. Sorzano. "Particle quality assessment and sorting for automatic and semiautomatic particle-picking techniques." Journal of Structural Biology 183, no. 3 (September 2013): 342–53. http://dx.doi.org/10.1016/j.jsb.2013.07.015.

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16

Wu, Zhong-huan, Hong-jie Chen, and Jia-jia Yang. "Optimization of Order-Picking Problems by Intelligent Optimization Algorithm." Mathematical Problems in Engineering 2020 (July 23, 2020): 1–12. http://dx.doi.org/10.1155/2020/6352539.

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Анотація:

To improve the efficiency of warehouse operations, reasonable optimization of picking operations has become an important task of the modern supply chain. For the purpose of optimization of order picking in warehouses, a new fruit fly optimization algorithm, particle swarm optimization, random weight, and weight decrease model are used to solve the mathematical model. Further optimization is achieved through the analysis of the warehouse shelves and screening of the optimal solution of the picking time. In addition, simulation experiments are conducted in the MATLAB environment through programming. The shortest picking time is found out and chosen as an optimized method by taking advantage of the effectiveness of these six algorithms in the picking optimization and comparing the data obtained under the simulation. The result shows that the optimization capacity of RWFOA is better and the picking efficiency is the best.

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17

Eldar, Amitay, Boris Landa, and Yoel Shkolnisky. "KLT picker: Particle picking using data-driven optimal templates." Journal of Structural Biology 210, no. 2 (May 2020): 107473. http://dx.doi.org/10.1016/j.jsb.2020.107473.

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18

Merham, Jovany, Alexander Rakowski, and Joesph Patterson. "Particle Picking in Cryo-TEM Images Using Machine Learning." Microscopy and Microanalysis 26, S2 (July 30, 2020): 2102–3. http://dx.doi.org/10.1017/s1431927620020450.

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19

DAHLVIK, PETER, GUILLERMO BLUVOL, KARL-HEINZ KAGERER, MANFRED ARNOLD, and DAN VARNEY. "Influence of topcoat pigment particle size distribution on tail-edge pick resistance in sheet-fed offset printing." June 2012 11, no. 6 (July 1, 2012): 51–58. http://dx.doi.org/10.32964/tj11.6.51.

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This paper describes ground calcium carbonate pigment particle size distribution and its influence on the tail-edge picking of pilot-coated paper as determined in full-scale sheet-fed offset printing. A tailor-made method was developed using a modified printing plate and high-tack inks to assess surface strength in terms of edge picking. In addition to the type, fineness, and particle size distribution of the ground calcium carbonate pigment, we also evaluated the solids content of the coating color, binder level, clay usage, and calendering. The printing test method provided differentiation relative to the investigated parameters, and it was possible to correlate these results with laboratory test data on ink-coating interaction and mercury intrusion porosimetry. Maximizing the solids content of the formulation to some extent compensated for the loss of pick resistance that followed binder reduction. Other laboratory tests showed poor correlation with the observed degree of edge picking.

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20

Noble, Alex, Tristan Bepler, and Bonnie Berger. "Neural network particle picking and denoising in cryoEM with Topaz." Acta Crystallographica Section A Foundations and Advances 76, a1 (August 2, 2020): a221. http://dx.doi.org/10.1107/s0108767320097810.

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21

Liu, Qun, Donal McSweeney, and Sean McSweeney. "A Self-Supervised Workflow for Particle Picking in Cryo-EM." Microscopy and Microanalysis 26, S2 (July 30, 2020): 2314. http://dx.doi.org/10.1017/s1431927620021169.

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22

Kong, Fang, Xirong Li, Qing Liu, Chuangye Yan, and Xinqi Gong. "An automatic particle picking method based on Generative Adversarial Network." Communications in Information and Systems 19, no. 3 (2019): 321–41. http://dx.doi.org/10.4310/cis.2019.v19.n3.a5.

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23

Song, Jin Bao, Jun Yu Li, and Qin Zhang. "The Model Based Behavior Driven Arithmetic Research." Advanced Engineering Forum 6-7 (September 2012): 1066–71. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1066.

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This paper is based on the particle filter for discrete particle track prediction theory, analyses the motion of animation with the methods of picking key points and predicting motion trace by utilizing particle filter. The behavior model has been built for the already existing animation character. During the research, the thesis realized using existed animation motion trace model to drive a similar figure and create a new animation.

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24

Gao, Lei, Zhen-yun Jiang, and Fan Min. "First-Arrival Travel Times Picking through Sliding Windows and Fuzzy C-Means." Mathematics 7, no. 3 (February 27, 2019): 221. http://dx.doi.org/10.3390/math7030221.

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First-arrival picking is a critical step in seismic data processing. This paper proposes the first-arrival picking through sliding windows and fuzzy c-means (FPSF) algorithm with two stages. The first stage detects a range using sliding windows on vertical and horizontal directions. The second stage obtains the first-arrival travel times from the range using fuzzy c-means coupled with particle swarm optimization. Results on both noisy and preprocessed field data show that the FPSF algorithm is more accurate than classical methods.

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25

Song, Jin Bao, Long Ye, and Qin Zhang. "A Behavior Retargeting Algorithm Based on the Model." Applied Mechanics and Materials 668-669 (October 2014): 1021–24. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1021.

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Анотація:

This paper analyses the motion of animation with the methods of picking key points and predicting motion trace based on the particle filter for discrete particle track prediction theory. The behavior model has been built for the already existing animation character. During the research, the thesis realized using existed animation motion trace model to drive a similar figure and create a new animation.

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26

Langlois, Robert, Jesper Pallesen, Jordan T. Ash, Danny Nam Ho, John L. Rubinstein, and Joachim Frank. "Automated particle picking for low-contrast macromolecules in cryo-electron microscopy." Journal of Structural Biology 186, no. 1 (April 2014): 1–7. http://dx.doi.org/10.1016/j.jsb.2014.03.001.

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27

Heimowitz, Ayelet, Joakim Andén, and Amit Singer. "APPLE picker: Automatic particle picking, a low-effort cryo-EM framework." Journal of Structural Biology 204, no. 2 (November 2018): 215–27. http://dx.doi.org/10.1016/j.jsb.2018.08.012.

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28

Varney, Daniel, and Douglas Bousfield. "Discrete element method to predict coating failure mechanisms." January 2018 17, no. 01 (February 1, 2018): 21–28. http://dx.doi.org/10.32964/tj17.01.21.

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Анотація:

The mechanical properties of coating layers are critical for post-application processes such as calendering, printing, and folding. Discrete element methods (DEM) have been used to simulate basic deformations such as tensile and compression, but have not been used as a tool to predict cracking-at-the-fold (CAF) or picking. DEM has the potential to increase our understanding of these failure mechanisms at the particle level. We propose a method to model the three-point bending of a coating layer and also the out-of-plane picking event during printing (using a z-direction scenario and an approach involving a moving force/velocity). Properties of the binder and the binder concentration are input parameters for the simulation. The model predicts the crack formation of the layer, the flexural modulus, and the maximum flexural strain during bending. The model also predicts the forces required for picking to occur. Results are compared with those of complimentary studies.

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29

Gao, Lei, Haokun Jiang, and Fan Min. "Automatic first-arrival picking through convolution kernel construction and particle swarm optimization." Computers & Geosciences 155 (October 2021): 104859. http://dx.doi.org/10.1016/j.cageo.2021.104859.

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30

Bepler, Tristan, Andrew Morin, Micah Rapp, Julia Brasch, Lawrence Shapiro, Alex J. Noble, and Bonnie Berger. "Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs." Nature Methods 16, no. 11 (October 7, 2019): 1153–60. http://dx.doi.org/10.1038/s41592-019-0575-8.

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31

Norousi, Ramin, Stephan Wickles, Christoph Leidig, Thomas Becker, Volker J. Schmid, Roland Beckmann, and Achim Tresch. "Automatic post-picking using MAPPOS improves particle image detection from cryo-EM micrographs." Journal of Structural Biology 182, no. 2 (May 2013): 59–66. http://dx.doi.org/10.1016/j.jsb.2013.02.008.

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32

Wang, Feng, Huichao Gong, Gaochao Liu, Meijing Li, Chuangye Yan, Tian Xia, Xueming Li, and Jianyang Zeng. "DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM." Journal of Structural Biology 195, no. 3 (September 2016): 325–36. http://dx.doi.org/10.1016/j.jsb.2016.07.006.

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33

Hoang, Thai V., Xavier Cavin, Patrick Schultz, and David W. Ritchie. "gEMpicker: a highly parallel GPU-accelerated particle picking tool for cryo-electron microscopy." BMC Structural Biology 13, no. 1 (2013): 25. http://dx.doi.org/10.1186/1472-6807-13-25.

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34

Ohashi, Masataka, Fumio Hosokawa, Takao Shinkawa, and Kenji Iwasaki. "Evaluation of automated particle picking for cryogenic electron microscopy using high-precision transmission electron microscope simulation based on a multi-slice method." Acta Crystallographica Section D Structural Biology 77, no. 7 (June 29, 2021): 966–79. http://dx.doi.org/10.1107/s2059798321005106.

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Анотація:

This work describes the GRIPS automated particle-picking software for cryogenic electron microscopy and the evaluation of this software using elbis, a high-precision transmission electron microscope (TEM) image simulator. The goal was to develop a method that can pick particles under a small defocus condition where the particles are not clearly visible or under a condition where the particles are exhibiting preferred orientation. The proposed method handles these issues by repeatedly performing three processes, namely extraction, two-dimensional classification and positioning, and by introducing mask processing to exclude areas with particles that have already been picked. TEM images for evaluation were generated with a high-precision TEM image simulator. TEM images containing both particles and amorphous ice were simulated by randomly placing O atoms in the specimen. The experimental results indicate that the proposed method can be used to pick particles correctly under a relatively small defocus condition. Moreover, the results show that the mask processing introduced in the proposed method is valid for particles exhibiting preferred orientation. It is further shown that the proposed method is applicable to data collected from real samples.

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35

Wang, Yi Qiang, Chao Fu, Ming Yang Ma, and Lin Bin Wang. "Routing Optimization of High-Level Order Picking Truck Based on Swarm Intelligent Algorithm." Applied Mechanics and Materials 101-102 (September 2011): 414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.414.

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Анотація:

In order to improve the operation efficiency of order picking trucks in the warehouse, the mathematical model of the routing optimization problem is established. Then PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) are used to solve the model. Experimental results show that both of them have good overall search ability and astringency. The operation efficiency is improved to a great extent by using swarm intelligent algorithms.

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36

Ouyang, Jianquan, Jinling Wang, Yaowu Wang, and Tianming Liu. "CenterPicker: An Automated Cryo-EM Single-Particle Picking Method Based on Center Point Detection." Journal of Cyber Security 4, no. 2 (2022): 65–77. http://dx.doi.org/10.32604/jcs.2022.028065.

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37

Volkmann, N. "An approach to automated particle picking from electron micrographs based on reduced representation templates." Journal of Structural Biology 145, no. 1-2 (January 2004): 152–56. http://dx.doi.org/10.1016/j.jsb.2003.11.026.

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38

Wagner, Thorsten, Luca Lusnig, Sabrina Pospich, Markus Stabrin, Fabian Schönfeld, and Stefan Raunser. "Two particle-picking procedures for filamentous proteins: SPHIRE-crYOLO filament mode and SPHIRE-STRIPER." Acta Crystallographica Section D Structural Biology 76, no. 7 (June 17, 2020): 613–20. http://dx.doi.org/10.1107/s2059798320007342.

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Анотація:

Structure determination of filamentous molecular complexes involves the selection of filaments from cryo-EM micrographs. The automatic selection of helical specimens is particularly difficult, and thus many challenging samples with issues such as contamination or aggregation are still manually picked. Here, two approaches for selecting filamentous complexes are presented: one uses a trained deep neural network to identify the filaments and is integrated in SPHIRE-crYOLO, while the other, called SPHIRE-STRIPER, is based on a classical line-detection approach. The advantage of the crYOLO-based procedure is that it performs accurately on very challenging data sets and selects filaments with high accuracy. Although STRIPER is less precise, the user benefits from less intervention, since in contrast to crYOLO, STRIPER does not require training. The performance of both procedures on Tobacco mosaic virus and filamentous F-actin data sets is described to demonstrate the robustness of each method.

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39

Hu, Qinlong, and Yimin Fan. "Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot." Journal of Sensors 2022 (July 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/4268589.

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Анотація:

In order to improve the efficiency of a logistics cycle robot picking up goods, a path planning algorithm based on artificial intelligence was proposed. After analyzing the particle swarm optimization algorithm, the particle swarm optimization algorithm is optimized and improved and the path planning of a single robot is obtained. On this basis, a multipopulation particle swarm optimization (CMMPPSO) algorithm is proposed. The results show that the JMPOPSO algorithm is more accurate than the BPSO algorithm and the maximum fitness optimized by the BPSO algorithm is 1.59, while the maximum fitness optimized by the JMPOPSO algorithm is 1.98. The path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, shortening by about 25% and shortening the time by about 30. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.

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40

Li, Zhenhua, and Mirko van der Baan. "Microseismic event localization by acoustic time reversal extrapolation." GEOPHYSICS 81, no. 3 (May 2016): KS123—KS134. http://dx.doi.org/10.1190/geo2015-0300.1.

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Анотація:

Traditional ray-based methods for microseismic event localization require picking of P- and S-wave first arrivals, which is often time consuming. Polarization analysis for each event is often also needed to determine its absolute location. Location methods based on reverse time extrapolation avoid the need for first-arrival time picking. Traditional reverse time extrapolation only incorporates particle velocity or displacement wavefields. This is an incomplete approximation of the acoustic representation theorem, which leads to artifacts in the back-propagation process. For instance, if the incomplete approximation is used for microseismic event locations using three-component (3C) borehole recordings, it produces a ghost event on the opposite side of the well, which leads to ambiguous interpretations. We have developed representation-theorem-based reverse time extrapolation for microseismic event localization, combining the 3C particle velocities (displacements) and the pressure wavefield. The unwanted ghost location is removed by explicitly incorporating a wavefield and its spatial derivative. Moreover, polarization analysis is not needed, because wavefields will focus at its absolute location during back propagation. Determination of microseismic event locations using wavefield extrapolation also necessitates a robust focusing criterion. The Hough transform allows for accurate determination of source timing and location by summing wavefront energy in the time-space domain. Synthetic examples demonstrated the good performance of the wavefield extrapolation scheme and focusing criterion in complex velocity fields for borehole acquisition geometries.

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41

Adnan, Refed, and Talib M. J. Abbas. "MATERIALIZED VIEWS QUANTUM OPTIMIZED PICKING for INDEPENDENT DATA MARTS QUALITY." Iraqi Journal of Information & Communications Technology 3, no. 1 (April 11, 2020): 26–39. http://dx.doi.org/10.31987/ijict.3.1.88.

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Particular and timely unified information along with quick and effective query response times is the basic fundamental requirement for the success of any collection of independent data marts (data warehouse) which forms Fact Constellation Schema or Galaxy Schema. Because of the materialized view storage area, the materialization of all views is practically impossible thus suitable materialized views (MVs) picking is one of the intelligent decisions in designing a Fact Constellation Schema to get optimal efficiency. This study presents a framework for picking best-materialized view using Quantum Particle Swarm Optimization (QPSO) algorithm where it is one of the stochastic algorithm in order to achieve the effective combination of good query response time, low query handling cost and low view maintenance cost. The results reveals that the proposed method for picking best-materialized view using QPSO algorithm is better than other techniques via computing the ratio of query response time and compare it to the response time of the same queries on the materialized views. Ratio of implementing the query on the base table takes five times more time than the query implementation on the materialized views. Where the response time of queries through MVs access were found 0.084 seconds while by direct access queries were found 0.422 seconds. This outlines that the performance of query through materialized views access is 402.38% better than those directly access via data warehouse-logical.

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42

Carrasco, Miguel, Patricio Toledo, and Nicole D. Tischler. "Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing." Biomolecules 9, no. 12 (November 30, 2019): 809. http://dx.doi.org/10.3390/biom9120809.

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Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

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43

Telford, M., and R. Mooi. "Podial Particle Picking in Cassidulus caribaearum (Echinodermata: Echinoidea) and the Phylogeny of Sea Urchin Feeding Mechanisms." Biological Bulletin 191, no. 2 (October 1996): 209–23. http://dx.doi.org/10.2307/1542924.

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44

Humbert-Droz, Julie, Aurélie Picton, and Anne Condamines. "How to build a corpus for a tool-based approach to determinologisation in the field of particle physics." Research in Corpus Linguistics 7 (2019): 1–17. http://dx.doi.org/10.32714/ricl.07.01.

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Анотація:

This paper discusses corpus design and building issues when dealing with a complex, multidimensional phenomenon such as determinologisation. Its representation in corpus data imposes an original reflection on the process and on some essential concepts of corpus building. This paper focuses on the necessity of representing the progressive aspects of determinologisation in the corpus, i.e. through levels of specialisation and through time, and the practical issues this raises. At the same time, it shows that a representative corpus of determinologisation in a specific domain (in this case, particle physics) implies clear and objective criteria when it comes to picking individual texts. Four principles are established to this end. The discussion leads to the proposal of a solid text selection procedure, which ensures that the peculiarities of determinologisation in the domain of particle physics are reflected in the corpus.

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45

Adewumi, Aderemi Oluyinka, and Akugbe Martins Arasomwan. "Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization." International Journal on Artificial Intelligence Tools 24, no. 05 (October 2015): 1550017. http://dx.doi.org/10.1142/s0218213015500177.

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Анотація:

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

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46

Scaramuzza, Stefano, and Daniel Castaño-Díez. "Step-by-step guide to efficient subtomogram averaging of virus-like particles with Dynamo." PLOS Biology 19, no. 8 (August 26, 2021): e3001318. http://dx.doi.org/10.1371/journal.pbio.3001318.

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Subtomogram averaging (STA) is a powerful image processing technique in electron tomography used to determine the 3D structure of macromolecular complexes in their native environments. It is a fast growing technique with increasing importance in structural biology. The computational aspect of STA is very complex and depends on a large number of variables. We noticed a lack of detailed guides for STA processing. Also, current publications in this field often lack a documentation that is practical enough to reproduce the results with reasonable effort, which is necessary for the scientific community to grow. We therefore provide a complete, detailed, and fully reproducible processing protocol that covers all aspects of particle picking and particle alignment in STA. The command line–based workflow is fully based on the popular Dynamo software for STA. Within this workflow, we also demonstrate how large parts of the processing pipeline can be streamlined and automatized for increased throughput. This protocol is aimed at users on all levels. It can be used for training purposes, or it can serve as basis to design user-specific projects by taking advantage of the flexibility of Dynamo by modifying and expanding the given pipeline. The protocol is successfully validated using the Electron Microscopy Public Image Archive (EMPIAR) database entry 10164 from immature HIV-1 virus-like particles (VLPs) that describe a geometry often seen in electron tomography.

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47

Castaño-Díez, Daniel, Mikhail Kudryashev, and Henning Stahlberg. "Dynamo Catalogue: Geometrical tools and data management for particle picking in subtomogram averaging of cryo-electron tomograms." Journal of Structural Biology 197, no. 2 (February 2017): 135–44. http://dx.doi.org/10.1016/j.jsb.2016.06.005.

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48

Chung, Jeong Min, Clarissa L. Durie, and Jinseok Lee. "Artificial Intelligence in Cryo-Electron Microscopy." Life 12, no. 8 (August 19, 2022): 1267. http://dx.doi.org/10.3390/life12081267.

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Анотація:

Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.

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49

Chen, Yee Ming, and Chun-Ta Lin. "Optimizing the Operation Sequence of a Multihead Surface Mounting Machine Using a Discrete Particle Swarm Optimization Algorithm." Journal of Artificial Evolution and Applications 2008 (April 29, 2008): 1–8. http://dx.doi.org/10.1155/2008/315950.

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Анотація:

The optimization of the nozzle selection, for sequencing component pick and place operations, is very important to the efficiency of multihead surface mounting machine (SMM). The nozzle change operation, that is, choosing the best nozzle head relative pair that is most effective for picking and placing components onto the printed circuit board (PCB), significantly adds to the overall assembly time. In this paper, as a practical application, we focus on a discrete particle swarm optimization (DPSO) algorithm for multihead SMM which is used to minimize the number of nozzle change operations and pick and place operations simultaneously. To evaluate the performance of the proposed algorithm, we test it on assembly tasks of PCBs through simulations. The results of computer experiments show that this DPSO algorithm was superior to the standard PSO algorithm.

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50

Kumar, V. "Robust filtering and particle picking in micrograph images towards 3D reconstruction of purified proteins with cryo-electron microscopy." Journal of Structural Biology 145, no. 1-2 (January 2004): 41–51. http://dx.doi.org/10.1016/j.jsb.2003.09.036.

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