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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Microenvironmental regulation of stem cells injected in the area at risk of neurodegenerative diseases

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BIOCELL 2022年第10期46卷 2231-2234页

作者： JU HYUNG LEE IL-KWON KIM SANG WOO KIM SOYEON LIM SEAHYOUNG LEE KI-CHUL HWANG BYEONG-WOOK SONG Graduate Program of Biomedical Engineering Yonsei UniversitySeoul03722Korea Institute for Bio-Medical Convergence College of MedicineCatholic Kwandong UniversityGangwon-do25601Korea Department for Medical Science College of MedicineCatholic Kwandong UniversityGangwon-do25601Korea

The complex mechanism of degenerative diseases and the non-specific modulation of regenerative targets aretopics that need to be elucidated in order to advance the use of stem cells in improvement of neurodegenerative *** pre-transplantation through post-transplantation, there are many changes in the conditions, both inside andoutside of the stem cells that have not been carefully considered. This has hindered development in the field of celltherapy and regeneration. This viewpoint highlights the potential implications of intracellular and extracellularalterations of stem cells in transplanted areas at risk of neurodegenerative disease.

关键词： Cell therapy Microenvironment Neurodegenerative disease Stem cells

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Z-Laminating Assembly of Graphene Nanoflakes for Super-Strong Membranes and Functional Coatings

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ACS Applied Nano Materials 2024年第13期7卷 14957-14963页

作者： Santos, Ian Daniell Tjarks, Patrick Muthu, Jeyavelan Lin, You-Chen Yen, Zhi-Long Chand, Pradyumna Kumar Raman, Radha Nguyen, Dinh Tuan Rouhani, Mehdi Jeng, Yeau-Ren Hsieh, Ya-Ping Hofmann, Mario Department of Physics National Taiwan University Taipei 106 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei 106 Taiwan J. Heyrovsky Institute of Physical Chemistry of the Czech Academy of Sciences Prague 18200 Czech Republic Institute of Atomic and Molecular Sciences Academia Sinica Taipei 106 Taiwan Department of Chemistry National Taiwan University Taipei 106 Taiwan Department of Physics National Central University Taoyuan 320 Taiwan Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Central University Taipei 106 Taiwan Department of Biomedical Engineering National Cheng Kung University 1 University Road Tainan 70101 Taiwan Department of Materials Science and Engineering National Cheng Kung University Tainan 70101 Taiwan Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng-Kung University (NCKU) Tainan 70101 Taiwan

We demonstrate the strengthening of graphene, an atomically thin carbon allotrope, by out-of-plane folding. Through lateral confinement of graphene at the water-air interface, nanoscale buckling was induced in suspended flakes, leading to an unexpected folding transition beyond a critical surface pressure. The emergence of folding was confirmed by in situ Brewster angle reflectivity and ex situ microscopy, showing a unique “z-laminated” nanostructure. Molecular dynamics simulations indicate that z-lamination yields an enhanced adhesion between neighboring flakes compared to lateral sliding, which was confirmed by a surface pressure hysteresis during the folding process. Mechanical testing reveals superior Young’s modulus and yield strength when compared to conventional graphene assemblies and even compared to composites. We demonstrate the potential of the z-lamination approach for applications in graphene-based structural materials, tribological layers, and functional electrochemical coatings. Finally, the complete recyclability of z-laminated graphene opens up new routes toward sustainable nanostructured materials. © 2024 The Authors. Published by American Chemical Society

关键词： 2D materials composites laminated graphene membranes recyclable

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Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

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SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Deepepiil13: Deep Learning for Prediction of Il-13 Inducing Epitopes Using Pre-Trained Language Models and Multi-Window Convolutional Neural Networks

SSRN

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SSRN 2024年

作者： Chuang, Cheng-Che Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

Interleukin-13 (IL-13) is a key cytokine involved in allergic inflammation and the cytokine storm associated with severe COVID-19. Identifying antigenic epitopes capable of inducing IL-13 holds therapeutic potential for developing epitope-based vaccines and immunomodulatory treatments. However, conventional methods for epitope prediction are often inefficient and inaccurate. This study presents DeepEpiIL13, a novel deep learning framework that integrates pre-trained language models and multi-window convolutional neural networks (CNNs) to rapidly and accurately predict IL-13-inducing epitopes from protein sequences. The ProtTrans pre-trained language model was employed to generate high-dimensional embeddings that capture rich contextual information from protein sequences. These embeddings were then inputted into a multi-window CNN architecture, enabling the efficient exploration of local and global sequence patterns associated with IL-13 induction. The proposed approach was rigorously evaluated using benchmark datasets and an independent SARS-CoV-2 dataset. DeepEpiIL13 achieved superior performance compared to traditional methods, with an impressive Matthews correlation coefficient (MCC) of 0.52 and an area under the receiver operating characteristic curve (AUC) of 0.86 on the benchmark dataset. On the independent SARS-CoV-2 dataset, DeepEpiIL13 demonstrated remarkable robustness, attaining an MCC of 0.63 and an AUC of 0.92, highlighting its potential applicability in the context of COVID-19 research. This study introduces a powerful deep learning framework for accurate epitope prediction, offering new avenues for the development of epitope-based vaccines and immunotherapies targeting IL-13-mediated disorders. The successful identification of IL-13-inducing epitopes paves the way for novel therapeutic interventions against allergic diseases, inflammatory conditions, and potentially severe viral infections like COVID-19. © 2024, The Authors. All rights reserved.

关键词： COVID-19

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3D Image Generation from X-Ray Projections Using Generative Adversarial Networks

3D Image Generation from X-Ray Projections Using Generative ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Mohammad AlShurbaji Salam Dhou Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0°&90° to 60°&150°, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections.

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

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Identifying Prognostic Markers of Non-small Cell Lung Carcinoma Using Bioinformatics

Identifying Prognostic Markers of Non-small Cell Lung Carcin...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Khan, Siladitya Chakraborty, Debamitra Department of Biomedical Engineering University of Rochester RochesterNY14627 United States Goergen Institute for Data Science University of Rochester RochesterNY14627 United States Materials Science Graduate Program University of Rochester RochesterNY14627 United States

ISBN: (纸本)9789811670107

We present a transcriptomics pipeline for performing the functional analysis of array expression profiling data of normal and adenocarcinoma lung tissue. Our aims are twofold, firstly to elucidate molecular processes that transform normal tissue, to a lung adenocarcinoma. Following this, we perform functional analysis to screen potential hub genes and demonstrate their value in cancer prognosis. Analysis was performed on a total of 500 differentially expressed genes (DEG), screened for their regulation and transcriptional modulation in a female population carrying non–small cell lung carcinoma (NSCLC). Key pathways like cytokine-cytokine receptors, ECM receptor interactions, and TNF signaling were identified suggesting a potential role in lung tumorigenesis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bioinformatics

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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