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CLEO: Applications and Technology, A and T 2022

作者： Li, Jingxi Garfinkel, Jason Zhang, Xiaoran Wu, Di Zhang, Yijie de Haan, Kevin Wang, Hongda Liu, Tairan Bai, Bijie Rivenson, Yair Rubinstein, Gennady Scumpia, Philip O. Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Dermatology and Laser Centre Studio CityCA91604 United States Computer Science Department University of California Los AngelesCA90095 United States Division of Dermatology University of California Los AngelesCA90095 United States Department of Dermatology Veterans Affairs Greater Los Angeles Healthcare System Los AngelesCA90073 United States Department of Surgery University of California Los AngelesCA90095 United States

We report in vivo virtual histology of skin without a biopsy, where deep learning is used to virtually stain tissue and generate hematoxylin and eosin (H&E)-like microscopic images of skin using a reflectance conf... 详细信息

ISBN: (纸本)9781957171050

关键词： Reflection

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Inventing the Potential of a High-Frequency EEG, Namely Dodecanogram (DDG): Human Subjects’ Study 5th

Inventing the Potential of a High-Frequency EEG, Namely Dode...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Singh, Pushpendra Sarkar, Jhimli Dey, Parama Sarkar, Sounak Pattanaya, Anindya Nag, Sudipa Pramanik, Sudeshna Sahoo, Pathik Saxena, Komal Krishnanda, Soami Daya Dutta, Tanusree Ghosh, Subrata Bandyopadhyay, Anirban NIMS 1-2-1 Sengen Ibaraki Tsukuba3050047 Japan Department Electronics and Electrical Communication Engineering IIT Kharagpur West Bengal Kharagpur721302 India Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati Assam781039 India Organizational Behavior and Human Resource Management Indian Institute of Management Ranchi834008 India Amity School of Applied Science Amity University Rajasthan Kant Kalwar NH-11C Jaipur Delhi Highway Rajasthan Jaipur303007 India Microwave Physics Laboratory Department of Physics and Computer Science Dayalbag Educational Institute Uttar Pradesh Agra282005 India Chemical Science and Technology Division CSIR-North East Institute of Science and Technology NEIST Assam Jorhat785006 India Uttar Pradesh Ghaziabad201002 India

ISBN: (纸本)9789819719228

The conventional EEG machine measures brain signal frequencies between 1 and 300 Hz on the scalp. By utilizing Fourier transform (FFT), it generates a 2D frequency profile. Our innovative EEG, known as Dodecanogram (DDG), operates in two modes. In the first mode, it detects a wide range of frequencies from 6 THz to 1 milliHz, creating a brain scan based on resonance. In the second mode, it uses picosecond pulses to capture potential surges, measuring their duration, intensity, and phase variation, revealing brain activity patterns. Both modes work simultaneously in the DDG device. To minimize environmental interference, we use interconnected DDG devices on eight human brains. The detected results from DDG show that DDG is a fundamental technology for understanding various mental states. Potential applications include neuro-disease diagnosis and exploring treatments involving electrical and electromagnetic pulses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electromagnetic pulse

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Quantitative particle agglutination assay using mobile holographic imaging and neural networks

Quantitative particle agglutination assay using mobile holog...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yi Luo Hyou-Arm Joung Sarah Esparza Jingyou Rao Omai Garner Aydogan Ozcan Electrical and Computer Engineering Department University of California Los Angeles CA USA Bioengineering Department University of California Los Angeles CA USA California NanoSystems Institute (CNSI) Los Angeles CA USA Computer Science Department University of California Los Angeles CA USA Department of Pathology and Laboratory Medicine University of California Los Angeles CA USA

A quantitative particle agglutination assay using mobile holography and neural networks is demonstrated to rapidly and accurately measure C-reactive protein (CRP) concentration in human serum samples.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Proteins Atmospheric measurements Neural networks Measurement by laser beam Lasers and electrooptics Holography Particle measurements

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SrvfNet: A generative network for unsupervised multiple diffeomorphic shape alignment

arXiv

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arXiv 2021年

作者： Nunez, Elvis Lizarraga, Andrew Joshi, Shantanu H. Department of Electrical and Computer Engineering UCLA CA90095 United States UCLA Brain Mapping Center Department of Neurology and Bioengineering UCLA CA90095 United States

—We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is capable of aligning to a predefined template as well as jointly predicting an optimal template from data while simultaneously achieving alignment. Our network is constructed as a generative encoder-decoder architecture comprising fully-connected layers capable of producing a distribution space of the warping functions. We demonstrate the strength of our framework by validating it on synthetic data as well as diffusion profiles from magnetic resonance imaging (MRI) data. © 2021, CC BY-NC-ND.

关键词： Deep neural networks

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CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments

arXiv

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arXiv 2024年

作者： Huang, Kaixuan Qu, Yuanhao Cousins, Henry Johnson, William A. Yin, Di Shah, Mihir Zhou, Denny Altman, Russ Wang, Mengdi Cong, Le Department of Electrical and Computer Engineering Princeton University United States Department of Pathology Department of Genetics Stanford University School of Medicine United States Cancer Biology Program Stanford University School of Medicine United States Department of Medicine Stanford University School of Medicine United States Medical Scientist Training Program Stanford University School of Medicine United States Google DeepMind United Kingdom Department of Bioengineering Department of Genetics Stanford University United States Center for Statistics and Machine Learning Princeton University United States

The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes. We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent’s effectiveness in a real-world use case. Furthermore, we explore the ethical and regulatory considerations associated with automated gene-editing design, highlighting the need for responsible and transparent use of these tools. Our work aims to bridge the gap between beginner biological researchers and CRISPR genome engineering techniques, and demonstrate the potential of LLM agents in facilitating complex biological discovery tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Genes

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Fully Portable Wireless Soft Stethoscope and Machine Learning for Continuous Real-Time Auscultation and Automated Disease Detection

Fully Portable Wireless Soft Stethoscope and Machine Learnin...

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Electronic Components and Technology Conference (ECTC)

作者： Sung Hoon Lee Yun Soung Kim Woon-Hong Yeo School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology Atlanta Georgia USA IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta Georgia USA George W. Woodruff School of Mechanical Engineering College of Engineering Georgia Institute of Technology Atlanta Georgia USA Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Georgia Institute of Technology Atlata Georgia USA

Modern computer-aided auscultation using digital stethoscopes has advantages over traditional acoustic auscultation, but current devices are too bulky for continuous monitoring and suffer from motion artifacts. A new study presents a soft, wireless, imperceptible wearable system that can continuously monitor heart and lung sounds, allowing for more accurate diagnosis of various lung diseases. The system uses machine learning to capture relevant physiological sounds and precisely analyze core mechanics, providing better performance than commercial digital stethoscopes. The soft stethoscope system successfully overcomes the limitations of traditional stethoscopes and commercial digital ones, offering skin-friendly, robust adhesion to the body while minimizing motion artifacts. The system can detect high-quality cardiopulmonary sounds even during daily activities and is capable of diagnosing seven different types of lung diseases with 85% accuracy for seven classes and 96.7% accuracy for normal versus abnormal. The soft stethoscope system's form factor, portability, and high-quality sound recording offer potential applications in sleep studies and biometric security systems. The study concludes that the soft device has the potential for more accurate at-home sleep monitoring and lung disease detection, paving the way for advancements in digital and smart healthcare. Future studies will focus on a large-group clinical trial with the soft stethoscope system to automatically diagnose cardiopulmonary diseases while providing continuous, digital, real-time auscultation. Additionally, integrating the system with other sensing modalities would expand its applications, such as personalized physiological signals for next-generation biometric security systems.

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Alteration of Cortical Somatosensory Feedback in Post-Stroke Movement Control

Alteration of Cortical Somatosensory Feedback in Post-Stroke...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Rita Huan-Ting Peng Nishaal Parmar Jordan N. Williamson Parikshat Sirpal Beni E. Mulyana Mahmood Rajabtabar Darvish Joohwan Sung Evgeny V. Sidorov Yuan Yang Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign Urbana IL USA Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital Urbana IL USA Department of Electrical and Computer Engineering University of Oklahoma Tulsa OK USA Department of Neurology University of Oklahoma Health Sciences Center Oklahoma City OK USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign Urbana IL USA Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain.

关键词： Somatosensory Neuroimaging Motor drives Magnetic resonance imaging Process control Motors Particle measurements Time measurement Indexes engineering in medicine and biology

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Residual block Convolutional Auto Encoder in Content-Based Medical Image Retrieval

Residual block Convolutional Auto Encoder in Content-Based M...

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IEEE IVMSP Workshop

作者： Zahra Tabatabaei Adriá n Colomer Kjersti Engan Javier Oliver Valery Naranjo Dept. of Artificial Intelligence Tyris Tech S.L. Valencia Spain Institute of Research and Innovation in Bioengineering Universitat Polit&#x00E9 cnica de Val&#x00E9 ncia Valencia Spain Department of Electrical Engineering and Computer Science University of Stavanger Norway

Approximately 12 percent of men will suffer from prostate cancer during their lifetime. While some types of this cancer grow slowly and need minimal treatment, others are aggressive and spread quickly. In order to be able to provide a fast and accurate diagnosis, computer vision techniques are widely used to help pathologists in the diagnosis and prognosis tasks. In this work, a new Content-Based Medical Image Retrieval (CBMIR) method is presented. The method, which is named ResCAE, presents a modified Convolutional Auto-Encoder (CAE) with a residual block and a skip layer to extract the relevant features of prostate cancer in Whole Slide Images (WSIs) in SICAPv2 data set. To the best of the author’s knowledge, this is the first study of using a Res-CAE to perform a search engine in prostate cancer images. After extracting features, Euclidean and Cosine have been applied to calculate the similarity of the query image and the gallery. The experimental results show that the proposed method achieves 0.85, 0.78 of accuracy by computing 7-top and 5-top accuracy, respectively.

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Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion

arXiv

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arXiv 2024年

作者： Wei, Xinxu Zhao, Kanhao Jiao, Yong Carlisle, Nancy B. Xie, Hua Fonzo, Gregory A. Zhang, Yu Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Bioengineering Lehigh University BethlehemPA18015 United States Department of Psychology Lehigh University BethlehemPA18015 United States Center for Neuroscience Research Children's National Hospital WashingtonDC20010 United States Center for Psychedelic Research and Therapy Department of Psychiatry and Behavioral Sciences Dell Medical School The University of Texas at Austin AustinTX78712 United States

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domain or modality, neglecting the valuable complementary information offered by multiple domains from both fMRI and EEG, which is crucial for a comprehensive representation of disorder pathology. This limitation poses a challenge in effectively leveraging the synergistic information derived from these modalities. To address this, we propose a Multimodal Cross-domain Self-supervised Pre-training Model (MCSP), a novel approach that leverages self-supervised learning to synergize multi-modal information across spatial, temporal, and spectral domains. Our model employs cross-domain self-supervised loss that bridges domain differences by implementing domain-specific data augmentation and contrastive loss, enhancing feature discrimination. Furthermore, MCSP introduces cross-modal self-supervised loss to capitalize on the complementary information of fMRI and EEG, facilitating knowledge distillation within domains and maximizing cross-modal feature convergence. We constructed a large-scale pre-training dataset and pretrained MCSP model by leveraging proposed self-supervised paradigms to fully harness multimodal neuroimaging data. Through comprehensive experiments, we have demonstrated the superior performance and generalizability of our model on multiple classification tasks. Our study contributes a significant advancement in the fusion of fMRI and EEG, marking a novel integration of cross-domain features, which enriches the existing landscape of neuroimaging research, particularly within the context of mental disorder studies. © 2024, CC BY-NC-SA.

关键词： Electroencephalography

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An MRI-Conditional Flexible Endoscopic Robot with a Hydraulic Tendon-Driven Actuation System

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Journal of Medical Robotics Research 2024年第1-2期9卷

作者： Qi, Boshen Chen, Hengjie Langley, Jason Badie, Behnam Hu, Xiaoping Sheng, Jun Department of Mechanical Engineering University of California Riverside 900 University Avenue Riverside 92521 CA United States Department of Electrical and Computer Engineering University of California Riverside 900 University Avenue Riverside 92521 CA United States Department of Bioengineering University of California Riverside 900 University Avenue Riverside 92521 CA United States Division of Neurosurgery City of Hope National Medical Center 1500 E Duarte Road Duarte 91010 CA United States

This paper presents the conceptualization, mathematical modeling, and experimental validation of a magnetic resonance imaging (MRI)-conditional, flexible endoscopic robot for MRI-guided neurosurgery. The robotic assembly encompasses a straight rigid shaft, a tendon-driven steerable tip with two degrees of freedom, and a compact hybrid actuation system made of nonmagnetic modules. The robot’s small diameter, approximately 3 mm, allows for robot navigation through narrow cavities, such as nasal passages and the densely packed anatomy of the skull base. By filling the robot with deionized water, the MRI contrast of the robot can be improved, therefore enabling intra-operative tracking of the robot’s movement. In this paper, we present the design and fabrication process of the robotic system, kinematic analysis of the robot, and experimental studies to validate our model, characterize the robot, and test the feedback robot control. Furthermore, we demonstrate the MRI compatibility of the robot, along with the intra-operative tracking capability. © World Scientific Publishing Company.

关键词： endoscopic robot MRI-conditional robot neurosurgery tendon-driven robot

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