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1. Smoothing the Rough Edges: Evaluating Automatically Generated Multi-Lattice Transitions

   https://doi.org/10.1089/3dp.2023.0008  

   Baldwin, Martha; Meisel, Nicholas A.; McComb, Christopher (June 2023, 3D Printing and Additive Manufacturing)

   Additive manufacturing is advantageous for producing lightweight components while addressing complex design requirements. This capability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be beneficial to use multiple, distinct lattice cell types, resulting in multi-lattice structures. In such structures, abrupt transitions between unit cell topologies may cause stress concentrations, making the boundary between unit cell types a primary failure point. Thus, these regions require careful design to ensure the overall functionality of the part. Although computational design approaches have been proposed, smooth transition regions are still difficult to achieve, especially between lattices of drastically different topologies. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells, examining the factors that contribute to smooth transitions. Through computational experimentation, it was found that the smoothness of transition regions was strongly predicted by how closely the endpoints were in the latent space, whereas the number of transition intervals was not a sole predictor. 

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2. Hybrid Geometry/Property Autoencoders for Multi-Lattice Transitions

   Baldwin, M; Meisel, N. A.; McComb, C. (October 2023, 2023 International Solid Freeform Fabrication Symposium)

   Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures. The performance of these structures heavily relies on the detailed design of mesostructural elements. Many current approaches use data-driven design to generate multi-lattice transition regions, making use of models that jointly address the geometry and properties of the mesostructures. However, it remains unclear whether the integration of mechanical properties into the data set for generating multi-lattice interpolations is beneficial beyond geometry alone. To address this issue, this work implements and evaluates a hybrid geometry/property machine learning model for generating multi-lattice transition regions. We compare the results of this hybrid model to results obtained using a geometry-only model. Our research determined that incorporating physical properties decreased the number of variables to address in the latent space, and therefore improves the ability of generative models for developing transition regions of multi-lattice structures. 

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3. Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

   https://doi.org/10.1073/pnas.2316256121  

   Du, Jin-Hong; Chen, Tianyu; Gao, Ming; Wang, Jingshu (September 2024, Proceedings of the National Academy of Sciences)

   Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE’s efficacy in integrative analyses of multiomic datasets with continuous cell population structures. 

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4. Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacity

   https://doi.org/10.1038/s41598-021-98015-7  

   Challapalli, Adithya; Li, Guoqiang (December 2021, Scientific Reports)

   Abstract Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures. 

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5. Integrated analysis of multimodal single-cell data with structural similarity

   https://doi.org/10.1093/nar/gkac781  

   Cao, Yingxin; Fu, Laiyi; Wu, Jie; Peng, Qinke; Nie, Qing; Zhang, Jing; Xie, Xiaohui (September 2022, Nucleic Acids Research)

   Abstract Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios. 

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