News

210419: Congratulation Michelle Zhu and Richard Kang for receiving CMU Summer Undergraduate Research Fellowships on our research projects!

200921: AMD Provides Computing Resources To Support COVID-19 Research at Xu Lab

200609: Our lab is awarded an NIH NIGMS R01 grant for developing novel methods for improving structural discrimination in cryo-electron tomography.

200530: Our lab is awarded an NSF IIS grant for developing novel methods for improving automation and speed of macromolecule recognition and localization in cryo-electron tomography using unsupervised deep learning.

200320: Our lab is awarded an NSF IIBR grant for developping novel methods to significantly reduce the amount of annotations for supervised deep learning based cryo-electron tomography analysis.

200224: Congratulations to Xiangrui Zeng for his first authored paper gets accepted by CVPR 2020, the best Computer Vision conference!

191218: Congratulations to Kai Wen Wang for Honorable Mention for the Computing Research Association's (CRA) Outstanding Undergraduate Researcher Award for 2020!

190519: Congratulations Yixiu Zhao for being awarded 2019 Elizabeth W. Jones Award for Excellence in Undergraduate Research in Experimental or Computational Biology.

190415: Congratulations Xiangrui Zeng for being awarded the CMLH fellowship!

181104: Congratulations Bo Zhou for having the following paper accepted by BIBM-MLHRM 2018: Feature Decomposition based Saliency Detection in Electron Cryo-Tomograms

180917: Congratulations Ran Li, Xiangrui Zeng and other students for having the following paper accepted by APBC2019: Automatic Localization and Identification of Mitochondria in Cellular Electron Cryo-Tomography using Faster-RCNN

180822: Our following paper got accepted by Structure. De novo structural pattern mining in cellular electron cryo-tomograms.

180702: Congratulations Chang Liu and other students for having following paper accepted: Liu C, Zeng X, Guo Q, Wang K, Xu M. Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography. British Machine Vision Conference (BMVC) 2018. arXiv:1805.06332

180702: Congratulations Kaiwen Wang and other students for having following paper accepted: Wang K, Zeng X, Liang X, Huo Z, Xing E, Xu M. Image-derived generative modeling of pseudo-macromolecular structures - towards statistical assessment of electron cryotomography template matching. British Machine Vision Conference (BMVC) 2018. arXiv:1805.04634

180525: Congratulations to Guannan Zhao, Bo Zhou and other students for having following paper accepted by MICCAI, a prestigious conference in biomedical image analysis! Zhao G, Zhou B, Wang K, Jiang R, Xu M. Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations. Medical Image Computing and Computer Assisted Intervention (MICCAI) 2018. arXiv:1806.00102

180518: Congratulations to Chengqian Che and other students for having following paper accepted: Che C, Lin R, Zeng X, Elmaaroufi K, Galeotti J, Xu M. Improved deep learning based macromolecules structure classification from electron cryo tomograms. Machine Vision and Applications. arXiv:1707.04885

180516: Our new preliminary working report is online: Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography. arXiv:1805.06332

180515: Our new preliminary working report is online: Image-derived generative modeling of pseudo-macromolecular structures - towards statistical assessment of electron cryotomography template matching. arXiv:1805.04634

180504: Congratulations to Chang Liu and Xiangrui Zeng and other students for having following paper accepted: Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms. IEEE International Conference on Image Processing (ICIP) 2018. Preprint: arXiv:1802.04087

180402: Congratulations Yixiu Zhao and Xiangrui Zeng on their co-first authored paper being accepted by ISMB 2018! Title: An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification.

180321: Congratulations Jialiang Guo and Bo Zhou for their paper being accepted by ICIAR!

180212: Our new preliminary working report is online: Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms

180131: Our new preliminary working report is online: Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography

171227: Congratulations Xiangrui Zeng for his first authored paper being accepted by Journal of Structural Biology: A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

170717: Our new preliminary working report is online: "Improved deep learning based macromolecules structure classification from electron cryo tomograms"

170616: Our new preliminary working report is online: "A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation"

170511: Kaiwen Wang just got awarded CMU Summer Undergraduate Research Apprenticeship on one of our collaborative research projects. Congratulations Kaiwen!

170406: Our manuscript entitled: "Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms" will appear on ISMB 2017. Here is a preprint version.

170129: Our new manuscript is online: "Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms"

Papers & Preprints

See here for the full list. See here for corresponding citations in latex bib format.

Cryo-ET analysis

  1. Zeng X, Kahng A, Xue L, Mahamid J, Chang Y, Xu M. DISCA: high-throughput cryo-ET structural pattern mining by deep unsupervised clustering. bioRxiv. doi:10.1101/2021.05.16.444381
  2. Gao S, Han R, Zeng X, Xu M, Zhang F. Macromolecules Structural Classification with a 3D Dilated Dense Network in Cryo-electron Tomography. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi:10.1109/TCBB.2021.3065986
  3. Du X, Wang H, Zhu Z, Zeng X, Chang Y, Zhang J, Xing E, Xu M. Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography. Bioinformatics. doi:10.1093/bioinformatics/btab123 arXiv:2102.12040
  4. Zhou B, Yu H, Zeng X, Yang X, Zhang J, Xu M. One-shot Learning with Attention-guided Segmentation in Cryo-Electron Tomography. Frontiers in Molecular Biosciences. doi:10.3389/fmolb.2020.613347
  5. Liu S, Ma Y, Ban X, Zeng X, Nallapareddy V, Chaudhari A, Xu M. Efficient Cryo-Electron Tomogram Simulation of Macromolecular Crowding with Application to SARS-CoV-2. BIBM 2020
  6. Li R, Yu L, Zhou B, Zeng X, Wang Z, Yang X, Zhang J, Gao X, Jang R, Xu M. Few-shot learning for classification of novel macromolecular structures in cryo-electron tomograms. PLOS Computational Biology. doi:10.1371/journal.pcbi.1008227
  7. Yu L, Li R, Zeng X, Wang H, Jin J, Yang G, Jiang R, Xu M. Few Shot Domain Adaptation Macromolecule Classification]{Few Shot Domain Adaptation for in situ Macromolecule Structural Classification in Cryo-electron Tomograms. Bioinformatics (2020). doi:10.1093/bioinformatics/btaa671 . arXiv:2007.15422
  8. Liu S, Ban X, Zeng X, Zhao F, Gao Y, Wu W, Zhang H, Chen F, Hall T, Gao X, Xu Min. A Unified Framework for Packing Deformable and Non-deformable Subcellular Structures in Crowded Cryo-electron Tomogram Simulation. BMC Bioinformatics (2020).
  9. Gubins I, et al. SHREC'20 Benchmark: Classification in cryo-electron tomograms. Computers & Graphics. 2020. doi:10.1016/j.cag.2020.07.010
  10. Chen F, Jiang Y, Zeng X, Zhang J, Gao X, Xu M. PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation. Algorithms. 2020. doi:10.3390/a13050126
  11. Gao S, Han R, Zeng X, Xu M, Zhang F. Dilated-denseNet for macromolecule classification in cryo-electron tomography. International Symposium on Bioinformatics Research and Applications (ISBRA 2020). doi:10.1007/978-3-030-57821-3_8
  12. Zeng X, Xu M. Gum-Net: Unsupervised geometric matching for fast and accurate 3D subtomogram image alignment and averaging. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2020). Paper
  13. Shi J, Zeng X, Jiang R, Jiang T, Xu M. A simulated annealing approach for resolution guided homogeneous cryo electron microscopy image selection. Quantitative Biology. doi:10.1007/s40484-019-0191-8. Paper.
  14. Zeng X, Xu M. AITom: Open-source AI platform for cryo-electron tomography data analysis. (2019) arXiv:1911.03044
  15. Lu Y, Zeng X, Tian X, Shi X, Wang H, Zheng X, Liu X, Zhao X, Gao X, Xu M. Spark-based parallel calculation of 3D Fourier shell correlation for macromolecule structure local resolution estimation. BMC Bioinformatics. doi:10.1186/s12859-020-03680-6
  16. Wu X, Zeng X, Zhu Z, Gao X, Xu M. (2019) Template-Based and Template-Free Approaches in Cellular Cryo-Electron Tomography Structural Pattern Mining. In: Computational Biology, (Husi H ed), Codon Publications, doi:10.15586/computationalbiology.2019.ch11
  17. Che C, Xian Z, Zeng X, Gao X, Xu M. Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyro-tomograms. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
  18. Wu X, Mao Y, Wang H, Zeng X, Gao X, Xing E, Xu M. Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019)
  19. Du X, Zeng X, Zhou B, Singh A, Xu M. Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss. British Machine Vision Conference (BMVC 2019 spotlight with acceptance rate < 5%). pdf
  20. Liu S, Du X, Xi R, Xu F, Zeng X, Zhou B, Xu M. Semi-supervised Macromolecule Structural Classification in Cellular Electron Cryo-Tomograms using 3D Autoencoding Classifier. British Machine Vision Conference (BMVC 2019). pdf
  21. Lu Y, Zeng X, Zhao X, Li S, Li H, Gao X, Xu M. Fine-grained Alignment of Cryo-electron Subtomograms Based on MPI Parallel Optimization. BMC Bioinformatics (2019) 20:443. doi:10.1186/s12859-019-3003-2
  22. Lin R, Zeng X, Kitani K, Xu M. Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms. ISMB 2019. Bioinformatics. 2019 Jul 5; 35(14); i260–i268. doi:10.1093/bioinformatics/btz364
  23. Han R, Bao Z, Zeng X, Niu T, Zhang F, Xu M, Gao X. A joint method for marker-free alignment of tilt series in electron tomography. ISMB 2019. Bioinformatics. 2019. doi:10.1093/bioinformatics/btz323
  24. Luo Z, Zeng X, Bao Z, Xu M. Deep Learning-Based Strategy For Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography. International Joint Conference on Neural Networks (IJCNN 2019). arXiv:1908.09993
  25. Li R, Zeng X, Siegmund S, Lin R, Zhou B, Liu C, Wang K, Jiang R, Freyberg Z, Lv H, Xu M. Automatic Localization and Identification of Mitochondria in Cellular Electron Cryo-Tomography using Faster-RCNN. BMC Bioinformatics. 201920 (Suppl 3) :132 doi:10.1186/s12859-019-2650-7
  26. Gubins et al. Classification in Cryo-Electron Tomograms. Eurographics Workshop on 3D Object Retrieval. doi: 10.2312/3dor.20191061
  27. Zhao G, Zhou B, Wang K, Jiang R, Xu M. Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations. Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. arXiv:1806.00102
  28. Liu C, Zeng X, Guo Q, Wang K, Xu M. Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography. British Machine Vision Conference (BMVC) 2018. arXiv:1805.06332
  29. Wang K, Zeng X, Liang X, Huo Z, Xing E, Xu M. Image-derived generative modeling of pseudo-macromolecular structures - towards statistical assessment of electron cryotomography template matching. British Machine Vision Conference (BMVC) 2018. pdf . arXiv:1805.04634
  30. Liu C, Zeng X, Lin R, Liang X, Freyberg Z, Xing E, Xu M. Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms. IEEE International Conference on Image Processing (ICIP) 2018. arXiv:1802.04087
  31. Zhao Y, Zeng X, Guo Q, Xu M. An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification. ISMB 2018. Bioinformatics. 2018 Jul 1; 34(13): i227–i236. doi:10.1093/bioinformatics/bty267. arXiv:1804.01203
  32. Guo J, Zhou B, Zeng X, Freyberg Z, Xu M. Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography. arXiv:1801.10597. International Conference on Image Analysis and Recognition (ICIAR) 2018
  33. Zhou B, Guo Q, Zeng X, Gao X, Xu M. Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms. arXiv:1801.10562. IEEE International Conference on Bioinformatics & Biomedicine, Workshop on Machine Learning in High Resolution Microscopy (BIBM-MLHRM 2018)
  34. Che C, Lin R, Zeng X, Elmaaroufi K, Galeotti J, Xu M. Improved deep learning based macromolecules structure classification from electron cryo tomograms. Machine vision and applications 29.8 (2018): 1227-1236. doi:10.1007/s00138-018-0949-4. arXiv:1707.04885
  35. Zeng X, Leung M, Zeev-Ben-Mordehai T, Xu M. A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation . Journal of Structural Biology. 2018 May;202(2):150-160. doi:10.1016/j.jsb.2017.12.015 arXiv:1706.04970 [code]
  36. Xu M, Chai X, Muthakana H, Liang X, Yang G, Zeev-Ben-Mordehai T, Xing E. Deep learning based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms. 2017. arXiv:1701.08404. ISMB 2017 (acceptance rate 16%), Bioinformatics doi:10.1093/bioinformatics/btx230 [code]
  37. Xu M, Singla J, Tocheva E, Chang Y, Stevens R, Jensen G, Alber F. De novo structural pattern mining in cellular electron cryo-tomograms. Structure. 2019 Apr 2;27(4):679-691.e14. doi:10.1016/j.str.2019.01.005. (Appeared on Structure volume cover and highlighted in Nature Methods 16, page 285 (2019), doi:10.1038/s41592-019-0382-2)
  38. Frazier Z, Xu M, Alber F. TomoMiner and TomoMiner Cloud: A software platform for large-scale subtomogram structural analysis. Structure 2017. (doi: 10.1016/j.str.2017.04.016, co-corresponding author).
  39. Pei L, Xu M, Frazier Z, Alber F. Simulating Cryo-Electron Tomograms of Crowded Mixtures of Macromolecular Complexes and Assessment of Particle Picking. BMC Bioinformatics. 2016; 17: 405.
  40. Xu M, Alber F. Automated target segmentation and real space fast alignment methods for high-throughput classification and averaging of crowded cryo-electron subtomograms. ISMB/ECCB 2013, Bioinformatics. 2013 Jul 1;29(13):i274-82.
  41. Thalassinos K, Pandurangan AP, Xu M, Alber F, Topf M. Conformational States of macromolecular assemblies explored by integrative structure calculation. Structure. 2013 Sep 3;21(9):1500-8.
  42. Xu M, Beck M, Alber F. High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching. Journal of Structural Biology. 2012 May;178(2):152-64. Epub 2012 Mar 7.
  43. Xu M, Alber F. High precision alignment of cryo-electron subtomograms through gradient-based parallel optimization. BMC Systems Biology. 2012, 6(Suppl 1):S18
  44. Xu M, Beck M, Alber F. Template-free detection of macromolecular complexes in cryo-electron tomograms. Bioinformatics (ISMB 2011). 2011 Jul 1;27(13):i69-i76.
  45. Beck M, Topf M, Frazier Z, Tjong H, Xu M, Zhang S, Alber F. Exploring the Spatial and Temporal Organization of a Cell’s Proteome. Journal of Structural Biology. 2011 Mar; 173(3):483-496.
  46. Zhang S, Vasishtan D, Xu M, Topf M, Alber F. A fast mathematical programming procedure for simultaneous fitting of assembly components into cryo-EM density maps. Bioinformatics (ISMB 2010). 2010 Jun 15;26(12):i261-8.
  47. Xu M, Zhang S, Alber F. 3D rotation invariant features for characterization of molecular density map images. IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2009)

Computer vision & Machine learning

  1. Yang Y, Ma Y, Zhang J, Gao X, Xu M. AttPNet: Attention-Based Deep Neural Network for 3D Point Set Analysis. Sensors. doi:10.3390/s20195455
  2. Pang J, Yi K, Yin W, Xu M. Experimental Analysis of Legendre Decomposition in Machine Learning. arXiv:2008.05095

Biomedical image analysis

  1. Dong N, Kampffmeyer M, Liang X, Xu M, Voiculescu I, Xing E. Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels. arXiv:2011.14164
  2. Dong N, Xu M, Liang X, Jiang Y, Dai W, Xing E. Neural Architecture Search for Adversarial Medical Image Segmentation. Medical Image Computing & Computer Assisted Intervention (MICCAI 2019).
  3. Xiao Q, Zou J, Yang M, Gaudio A, Kitani K, Smailagic A, Costa P, Xu M. Improving Lesion Segmentation for Diabetic Retinopathy Using Adversarial Learning. International Conference on Image Analysis and Recognition (ICIAR 2019)
  4. Wang W, Taft D, Chen Y, Zhang J, Wallace C, Xu M, Watkins S, Xing J. Learn to segment single cells with deep distance estimator and deep cell detector. Computers in Biology and Medicine. doi:10.1016/j.compbiomed.2019.04.006
  5. Wang Z, Dong N, DRosario S, Xu M, Xie P, Xing E. Ellipse detection of optic disc-and-cup boundary in fundas image with unsupervised domain adaptation. IEEE International Symposium on Biomedical Imaging (ISBI 2019)

Genomics

  1. Chen Z, Zhang J, Liu J, Zhang Z, Zhu J, Xu M, Gerstein M. SCAN-ATAC-Sim: a scalable and efficient method for simulating single-cell ATAC-seq data from bulk-tissue experiments. Bioinformatics.
  2. Zheng Y, Wang H, Zhang Y, Gao X, Xing E, Xu M. Poly(A)-DG: a deep-learning-based domain generalization method to identify cross-species Ploy(A) signal without prior knowledge from target species. PLOS Computational Biology. doi:10.1371/journal.pcbi.1008297
  3. Xu M, Li W, James GM, Mehan MR, Zhou XJ. Automated multidimensional phenotype profiling using large public microarray repositories. Proc Natl Acad Sci U S A. (PNAS). 2009; 106(30), 12323 - 12328. (Highlighted in Nature Methods 6, 632; Selected and re-published in 2010 International Medical Informatics Association Yearbook of Medical Informatics)
  4. Xu M, Kao MJ, Nunez-Iglesias J, Nevins JR, West M, Zhou XJ. An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer. BMC Genomics. 2008; 9 Suppl 1:S12.
  5. Xu M, Zhu M, Zhang L. A stable iterative method for refining discriminative gene clusters. BMC Genomics. 2008 Sep 16;9 Suppl 2:S18

Computational biology

  1. Wang H, Wei Y, Cao M, Xu M, Wu W, Xing E. Deep Inductive Matrix Completion for Biomedical Interaction Prediction. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019, acceptance rage 18%)

Group

PhD students

Xiangrui Zeng

Mostofa Rafid Uddin

Research assistants

Gregory Howe (MLD, MSML)

Students in collaboration

(Please see our publication)

Alumni

Sima Behpour (Former Postdoctoral Associate)
Xiaoyu Zhu (LTI, MSAII)
Xindi Wu (RI, MSCV)
Yuchen Zeng (CBD)
Kai Wen Wang (ECE, CS)
Yixiu Zhao (CSD)
Bo Zhou (RI, MSCV)
Chang Liu (ECE)

About Min Xu

Min Xu, Ph.D.
Assistant Professor, Computational Biology Department
Training faculty, Master of Science in Computer Vision, Robotics Institute
School of Computer Science Carnegie Mellon University

Office: GHC 7709, 5000 Forbes Avenue, Pittsburgh, PA 15213
Email: mxu1 @ cs.cmu.edu

Curriculum Vitae, Google Scholar, DBLP, Research Gate, and Linkedin pages.

Biography

Dr. Min Xu is an Assistant Professor at the Computational Biology Department in the School of Computer Science at Carnegie Mellon University. He serves as training faculty at the Joint CMU-Pitt Ph.D. Program in Computational Biology. He also serves as a training faculty Master of Science in Computer Vision Program at Robotics Institute. He is an investigator at the National Center for Multiscale Modeling of Biological Systems.

Dr. Xu’s career has centered on developing computational methods for the study of cellular systems using imaging and omics data. He started his research career in the field of Computational Biology and Bioinformatics since 2000. He developed machine learning methods for gene selection for classifying cancer samples, for cancer gene network module discovery, and for sample phenotype prediction through integration of hundreds of gene expression datasets. Since 2008, he started working in the field of computational analysis of Cellular Cryo-Electron Tomography (Cryo-ET) data. He designed structural pattern mining methods and first demonstrated the feasibility of De Novo extraction of structures and spatial organizations of macromolecular complexes in single cells using Cryo-ET data. His current research focus on Cryo-ET derived modelling of cellular organization at molecular resolution.

Dr. Xu has published over 60 research papers. He is currently serving in the editorial board of Statistical Methods in Medical Research.
Dr. Xu received an B.E. in Computer Science from the Beihang University, M.Sc from School of Computing at the National University of Singapore, M.A. in Applied Mathematics from the University of Southern California (USC), and Ph.D. in Computational Biology and Bioinformatics from USC. He was a postdoctoral researcher at USC.

Dr. Xu does research for fun, and he enjoys working with highly talented and motivated students.

Positions

  1. PostDoc positions avaliable!
  2. We are looking for highly self motivated PhD students to join our lab! To the perspective PhD applicants: you are encouraged to read our publications and discuss with Prof. Xu on any new ideas based on these publications.
  3. We have funded research assistant positions available!
  4. We are looking for motivated students to join our lab for graduate or undergraduate level collaborative research projects!
  5. We welcome international research interns! Remote interns are welcome!
  6. We also welcome remote collaborations!

  7. Interested please email Dr. Xu for further information.

Software

The source code associated with our individual papers can be found at our papers page, following with each paper. All source code are deposited to our github portal. We are also in the process of integrating the algorithms into our AITom platform.

Research

The cell is the basic structural, functional, and biological unit of all known living organisms. It is a tiny but very complicated "living machine" that can do a lot of amazing things. However, so far we have very limited knowledge about its complicated molecular machinery due to lack of high resolution and systems level data of individual cells. Cellular Cryo-Electron Tomography is an emerging imaging technique that captures the 3D electron density distribution of cells at sub-molecular resolution and at close-to-native state. Our lab aims to use cutting-edge computation, mathematics and artificial intelligence techniques, particularly those related to computer vision, machine learning and big data, to build structural organization models using such imaging data. Such modeling would be useful for getting new insights into the machinery of cellular systems.

The figure above shows extracted structural patterns in cellular tomograms: (a) Slices of 3D tomogram images of two bacterial cells. Image data are from the Jensen Lab at Caltech. (b) Isosurfaces of instances of extracted structural patterns embedded into the original images. (c) Embedded instances, zooming in on a particular region. (d) Isosurfaces of one example structural pattern from each experiment. (e) Spatial distributions of instances of different structural patterns: left: the Ribosome like patterns distributed outside the nucleoid region; middle: patterns distributed on the nucleoid region; right: patterns distributed at the tip of the cell. For details, see our paper on Structure.