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1. ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

   https://doi.org/10.1093/bioadv/vbae111  

   Lai, Jhih-Siang; Burley, Stephen_K; Duarte, Jose_M; Gromiha, ed., Michael (July 2024, Bioinformatics Advances)

   Abstract MotivationVolumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. ResultsThis application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementationZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License. 

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2. Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics

   https://doi.org/10.1063/5.0101128  

   Du, Pan; Zhu, Xiaozhi; Wang, Jian-Xun (August 2022, Physics of Fluids)

   Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex three-dimensional (3D) patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrary complex 3D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques. 

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3. Automatic Feature Selection for Shape Registration in Additive Manufacturing

   Lin, Weizhi; Dai, Peng; Huang, Qiang (April 2020, IISE ANNUAL CONFERENCE & EXPO)

   null (Ed.)

   There is a growing importance in characterizing 3D shape quality in additive manufacturing (a.k.a. 3D printing). To accurately define the shape deviation between the designed product and actual build, shape registration of scanned point cloud data serves as a prerequisite for a reliable measurement. However, manual registration is currently heavily involved, for example, in obtaining initial matching of the design and the scanned product based on landmark features. The procedure can be inefficient, and more importantly, introduce potentially large operator-to-operator variations for complex geometries and deformation. Finding a sparse shape correspondence before refined registration would be meaningful to address this problem. In that case, automatic landmark selection has been a challenging issue, particularly for complicate geometric shapes like teeth. In this work we present an automatic landmark selection method for complicated 3D shapes. By incorporating subject matter knowledge (e.g., dental biometric information), a 3D shape will be first segmented through a new density-based clustering method. The geodesic distance is proposed as the distance metric in the revised clustering procedure. Geometrically informative features in each segment are automatically selected through the principal component analysis and Hotelling's T2 statistic. The proposed method is demonstrated in dental 3D printing application and could serve as a basis of sparse shape correspondence. 

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4. Constrained Conditional Moment Restriction Models

   https://doi.org/10.3982/ECTA13830  

   Chernozhukov, Victor; Newey, Whitney K.; Santos, Andres (March 2023, Econometrica)

   Shape restrictions have played a central role in economics as both testable implications of theory and sufficient conditions for obtaining informative counterfactual predictions. In this paper, we provide a general procedure for inference under shape restrictions in identified and partially identified models defined by conditional moment restrictions. Our test statistics and proposed inference methods are based on the minimum of the generalized method of moments (GMM) objective function with and without shape restrictions. Uniformly valid critical values are obtained through a bootstrap procedure that approximates a subset of the true local parameter space. In an empirical analysis of the effect of childbearing on female labor supply, we show that employing shape restrictions in linear instrumental variables (IV) models can lead to shorter confidence regions for both local and average treatment effects. Other applications we discuss include inference for the variability of quantile IV treatment effects and for bounds on average equivalent variation in a demand model with general heterogeneity. 

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5. SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators

   Luo, Andrew; Li, Tianqin; Zhang, Wen-Hao; Lee, Tai-Sing (October 2021, Proceedings)

   null (Ed.)

   Recent advances in deep generative models have led to immense progress in 3D shape synthesis. While existing models are able to synthesize shapes represented as voxels, point-clouds, or implicit functions, these methods only indirectly enforce the plausibility of the final 3D shape surface. Here we present a 3D shape synthesis framework (SurfGen) that directly applies adversarial training to the object surface. Our approach uses a differentiable spherical projection layer to capture and represent the explicit zero isosurface of an implicit 3D generator as functions defined on the unit sphere. By processing the spherical representation of 3D object surfaces with a spherical CNN in an adversarial setting, our generator can better learn the statistics of natural shape surfaces. We evaluate our model on large-scale shape datasets, and demonstrate that the end-to-end trained model is capable of generating high fidelity 3D shapes with diverse topology. 

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