1,720,963 research outputs found
3D reconstruction of protein complex structures using synthesized multi-view AFM images
Full text linkRecent developments in deep learning-based methods demonstrated its potential to predict the 3D protein structures using inputs such as protein sequences, Cryo-Electron microscopy (Cryo-EM) images of proteins, etc. However, these methods struggle to predict the protein complexes (PC), structures with more than one protein. In this work, we explore the atomic force microscope (AFM) assisted deep learning-based methods to predict the 3D structure of PCs. The images produced by AFM capture the protein structure in different and random orientations. These multi-view images can help train the neural network to predict the 3D structure of protein complexes. However, obtaining the dataset of actual AFM images is time-consuming and not a pragmatic task. We propose a virtual AFM imaging pipeline that takes a 'PDB' protein file and generates multi-view 2D virtual AFM images using volume rendering techniques. With this, we created a dataset of around 8K proteins. We train a neural network for 3D reconstruction called Pix2Vox++ using the synthesized multi-view 2D AFM images dataset. We compare the predicted structure obtained using a different number of views and get the intersection over union (IoU) value of 0.92 on the training dataset and 0.52 on the validation dataset. We believe this approach will lead to better prediction of the structure of protein complexes.This is a preprint of the proceedings from Rade, Jaydeep, Soumik Sarkar, Anwesha Sarkar, and Adarsh Krishnamurthy. "3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images." arXiv preprint arXiv:2211.14662 (2022). doi: https://doi.org/10.48550/arXiv.2211.14662. Copyright 2022 The Authors. CC BY
3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs)
Full text linkRecent advancements in deep learning for predicting 3D protein structures have shown promise, particularly when leveraging inputs like protein sequences and Cryo-Electron microscopy (Cryo-EM) images. However, these techniques often fall short when predicting the structures of protein complexes (PCs), which involve multiple proteins. In our study, we investigate using atomic force microscopy (AFM) combined with deep learning to predict the 3D structures of PCs. AFM generates height maps that depict the PCs in various random orientations, providing a rich information for training a neural network to predict the 3D structures. We then employ the pre-trained UpFusion model (which utilizes a conditional diffusion model for synthesizing novel views) to train an instance-specific NeRF model for 3D reconstruction. The performance of UpFusion is evaluated through zero-shot predictions of 3D protein structures using AFM images. The challenge, however, lies in the time-intensive and impractical nature of collecting actual AFM images. To address this, we use a virtual AFM imaging process that transforms a `PDB' protein file into multi-view 2D virtual AFM images via volume rendering techniques. We extensively validate the UpFusion architecture using both virtual and actual multi-view AFM images. Our results include a comparison of structures predicted with varying numbers of views and different sets of views. This novel approach holds significant potential for enhancing the accuracy of protein complex structure predictions with further fine-tuning of the UpFusion network.This is a preprint from Rade, Jaydeep, Ethan Herron, Soumik Sarkar, Anwesha Sarkar, and Adarsh Krishnamurthy. "3D Reconstruction of Protein Structures from Multi-view AFM Images using Neural Radiance Fields (NeRFs)." arXiv preprint arXiv:2408.06244 (2024). doi: https://doi.org/10.48550/arXiv.2408.06244. CC-BY-NC-SA
Deep learning-based 3D multigrid topology optimization of manufacturable designs
No full textStructural topology optimization is a compute-intensive process due to several iterations of simulations required to evaluate the performance of the component during optimization. Deep learning (DL) based approaches can address this challenge, but these methods were demonstrated mainly using 2D shapes and, at best, in low-resolution 3D geometries (typically 323). Further, due to non-manufacturable geometric features, the predicted optimal geometries from DL may not be manufacturable, even using additive manufacturing. In this paper, we develop a DL framework
using a multigrid convolutional neural network (CNN) to generate high-resolution topology-optimized 3D geometries with additional checks on the manufacturability of the predicted shapes. Our framework predicts the final optimal topology using the initial strain energy (objective function of structural topology optimization) and target volume fraction (material fraction to be preserved after optimization) as input. We train the network using a multigrid approach, which enables topology optimization at 1283 resolution, which was previously computationally challenging. We first train the multigrid CNN at a lower resolution and then transfer the learned network to continue training at higher resolutions. We use a distributed deep learning framework on a GPU supercomputing cluster to further speed up the training time. Distributed DL significantly speeds up the training time by more than 4x while achieving similar model performance. Finally, we check the optimal geometries for manufacturability using fused deposition modeling (FDM)-specific manufacturability constraints. The large training dataset (> 60,000 high-resolution topology optimization examples) will be released with the paper to enable further research on this topic.This is a manuscript of the article published as Rade, Jaydeep, Anushrut Jignasu, Ethan Herron, Ashton Corpuz, Baskar Ganapathysubramanian, Soumik Sarkar, Aditya Balu, and Adarsh Krishnamurthy. "Deep learning-based 3D multigrid topology optimization of manufacturable designs." Engineering Applications of Artificial Intelligence 126 (2023): 107033.
doi: https://doi.org/10.1016/j.engappai.2023.107033
AI Guided Measurement of Live Cells Using AFM
Full text linkAtomic force microscopy (AFM), a member of the ‘scanning probe microscopy’ family, is an excellent platform for high-resolution imaging and mechanical characterization of a wide range of samples, including live cells, proteins, and other biomolecules. AFM is also beneficial for measuring interaction forces and binding kinetics for protein-protein or receptor-ligand interactions on live cells at a single-molecule level. However, high-resolution imaging and force measurements performed with AFM and data analytics are time-consuming and require specific skill sets and constant human supervision. It also involves problems such as cantilever tip breakage after prolonged functionalization and damage to the live cell samples due to lack of optimization of the loading forces, making this a low throughput method. Although remarkable progress in the area of AI and machine learning (ML) over the past few years has left its mark on bio-imaging as well, the potential of AI-AFM strategies in a live cell characterization has been mostly unexplored. In this paper, we developed an ML framework to perform automatic sample selection for AFM navigation during AFM biomechanical mapping. We established ML-based closed-loop scanner trajectory and force tracking algorithms for precise AFM positioning during sample navigation and biomechanical mapping at high speed. Our innovation will directly address state-of-the-art AFM operation via AI-driven intelligent automation, including intelligent navigation and image data analysis.This is a proceeding article published as Rade, Jaydeep, Juntao Zhang, Soumik Sarkar, Adarsh Krishnamurthy, Juan Ren, and Anwesha Sarkar. "AI Guided Measurement of Live Cells Using AFM." IFAC-PapersOnLine 54, no. 20 (2021): 316-321. doi: https://doi.org/10.1016/j.ifacol.2021.11.193. Copyright 2021 The Authors. This is an open access article under the CC BY-NC-ND License (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Deep learning frameworks for structural topology optimization
Full text linkTopology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend the current approaches to higher resolutions.
In this thesis, we explore deep learning-based frameworks that are consistent with traditional topology optimization algorithms for three-dimensional topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each trying to learn a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better than current ML-based topology optimization methods
Deep learning frameworks for structural topology optimization
Full text linkTopology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current methods are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training. These challenges make it non-trivial to extend the current approaches to higher resolutions.
In this thesis, we explore deep learning-based frameworks that are consistent with traditional topology optimization algorithms for three-dimensional topology optimization with a reasonably fine (high) resolution. We achieve this by training multiple networks, each trying to learn a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm. We demonstrate the application of our framework on both 2D and 3D geometries. The results show that our approach predicts the final optimized design better than current ML-based topology optimization methods.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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