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arXiv 2022年

作者： Zhang, Ruitao Han, Xueying Gul, Ijaz Zhai, Shiyao Liu, Ying Zhang, Yongbing Dong, Yuhan Ma, Lan Yu, Dongmei Zhou, Jin Qin, Peiwu Institute of Biomedical and Health Engineering Tsinghua Shenzhen International Graduate School China Precision Medicine and Public Health Tsinghua-Berkeley Shenzhen Institute United States The First Affiliated Hospital of Harbin Medical University China Food Inspection & Quarantine Center Shenzhen Custom China Department of Computer Science Harbin Insitute of Technology Shenzhen China School of Mc chanical Electrical & Information Engineering Shandong University China

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. Copyright © 2022, The Authors. All rights reserved.

关键词： T-cells

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ADAPTIVE AGGREGATION WEIGHTS FOR FEDERATED SEGMENTATION OF PANCREAS MRI

arXiv

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arXiv 2024年

作者： Pan, Hongyi Durak, Gorkem Zhang, Zheyuan Taktak, Yavuz Keles, Elif Aktas, Halil Ertugrul Medetalibeyoglu, Alpay Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Keswani, Rajesh N. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Goggins, Michael G. Wallace, Michael B. Xu, Ziyue Bagci, Ulas Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Northwestern University ChicagoIL United States Department of Biomedical Engineering and Radiology University of Wisconsin MadisonWI United States Division of Gastroenterology and Hepatology Mayo Clinic JacksonvilleFL United States Division of Gastroenterology and Hepatology New York University New YorkNY United States Division of Gastroenterology and Hepatology Johns Hopkins University BaltimoreMD United States NVIDIA BethesdaMD United States

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). © 2024, CC BY.

关键词： Federated learning

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Comparative analysis of hysteresis modulation, adaptive and PWM based sliding mode control of a buck converter

Comparative analysis of hysteresis modulation, adaptive and ...

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2019 IASTED International Conference on Control and Optimization of Renewable Energy Systems, CORES 2019

作者： Shourov, Erfan C. Sabzehgar, Reza Rasouli, Mohammad Fajri, Poria Department of Electrical and Computer Engineering San Diego State University San DiegoCA United States Department of Electrical and Computer Engineering Penn State Behrend EriePA United States Department of Electrical and Biomedical Engineering University of Nevada RenoNV United States

ISBN: (纸本)9780889869967

In this paper, three Sliding Mode Control (SMC) strategies namely the Hysteresis Modulation (HM), the adaptive, and the Pulse Width Modulation (PWM) techniques are designed and utilized to accomplish output voltage regulation of a DC-DC power converter. Simulation studies are conducted to evaluate the performance of each controller, and to demonstrate which controller is better suited for the output voltage control of DC-DC power converter in the presence of sudden changes to both load and input voltage level. © Proceedings of the IASTED International Conference on Control and Optimization of Renewable Energy Systems, CORES 2019. All rights reserved.

关键词： Sliding mode control

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Reinforcement learning enabled automatic impedance control of a robotic knee prosthesis to mimic the intact knee motion in a co-adapting environment

arXiv

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arXiv 2021年

作者： Wu, Ruofan Li, Minhan Yao, Zhikai Si, Jennie Huang, He Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Biomedical Engineering North Carolina State University RaleighNC27695 United States University of North Carolina at Chapel Hill Chapel HillNC27599 United States

Automatically configuring a robotic prosthesis to fit its user’s needs and physical conditions is a great technical challenge and a roadblock to the adoption of the technology. Previously, we have successfully developed reinforcement learning (RL) solutions toward addressing this issue. Yet, our designs were based on using a subjectively prescribed target motion profile for the robotic knee during level ground walking. This is not realistic for different users and for different locomotion tasks. In this study for the first time, we investigated the feasibility of RL enabled automatic configuration of impedance parameter settings for a robotic knee to mimic the intact knee motion in a co-adapting environment. We successfully achieved such tracking control by an online policy iteration. We demonstrated our results in both OpenSim simulations and two able-bodied (AB) subjects. Copyright © 2021, The Authors. All rights reserved.

关键词： Reinforcement learning

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Analysis of growing tumor on the flow velocity of cerebrospinal fluid in human brain using computational modeling and fluid-structure interaction

arXiv

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arXiv 2021年

作者： Uzair-Ul-Haq, Muhammad Ahmed, Ali Mustansar, Zartasha Shaukat, Arslan Margetts, Lee Waris, Asim Nadeem, Faizan Department of Biomedical Engineering & Sciences School of Mechanical & Manufacturing Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Computational Engineering Research center of Modelling and Simulation National University of Sciences and Technology Islamabad44000 Pakistan Department of Computer & Software Engineering College of Electrical & Mechanical Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Mechanical Aerospace and Civil Engineering University of Manchester United Kingdom Nuclear Medicine specialist associated with INMOL cancer hospital Lahore Pakistan

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles. © 2021, CC BY-SA.

关键词： Brain

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Unique material modifications of Ga2O3 enabled by ultrafast laser irradiation 11

Unique material modifications of Ga2O3 enabled by ultrafast ...

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Oxide-based Materials and Devices XI 2020

作者： Ahn, Minhyung Sarracino, Alex Ansari, Abdul Torralva, Ben Yalisove, Steven Phillips, Jamie Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Climate and Space Sciences and Engineering University of Michigan Ann ArborMI48109 United States Materials Science and Engineering Department University of Michigan Ann ArborMI48109 United States

ISBN: (纸本)9781510633254

Ultrafast (femtosecond pulse) laser irradiation provides unique laser-material interactions associated with high instantaneous electric fields and short timescales that lead to a nonequilibrium state between excited electrons and phonons. These interactions result in material modifications that differ from conventional chemical, physical, and thermal processing at much longer timescales, and an opportunity to address material and device processing challenges associated with wide-bandgap materials such as Ga2O3. In this work, we explore ultrafast laser irradiation (Ti:sapphire, 150 fs pulse width) of bulk (010) Sn-doped β-Ga2O3 under two different wavelengths, fundamental (780 nm) and frequency-doubled (390 nm), and a range of laser fluences. We identified a regime for laser-induced damage threshold resulting in material ablation, thermally-induced straight crack formation, and recrystallization. Rastering on a β-Ga2O3 substrate created surface nanostructures including laser-induced periodic surface structures at a high spatial frequency (period ∼250 nm). These highly aligned periodic structures can be controlled by laser polarization and wavelength, presenting a means for direct writing of surface nanostructures. Enhanced atomic movement associated with a transient metallic state can provide a means for intentional generation of point defects via the laser irradiation. These point defects may offer a means of electrical modification, which was demonstrated as more than five orders of the magnitude enhancement of lateral conductance on rastered β-Ga2O3. Moreover, a hydrophobic surface of β-Ga2O3was achieved by ultrafast laser irradiation. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Hydrophobicity

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Nanoparticle localization within chiral liquid crystal defect lines and nanoparticle interactions

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Physical Review E 2023年第3期107卷 034701-034701页

作者： Mykola Tasinkevych Sungoh Park Haridas Mundoor Ivan I. Smalyukh SOFT Group School of Science and Technology Nottingham Trent University Clifton Lane Nottingham NG11 8NS United Kingdom Departamento de Física and Centro de Física Teórica e Computacional Faculdade de Ciências Universidade de Lisboa 1749-016 Lisboa Portugal International Institute for Sustainability with Knotted Chiral Meta Matter Hiroshima University Higashihiroshima 739-8511 Japan Department of Physics University of Colorado Boulder Colorado 80309 USA Soft Materials Research Center Department of Electrical Computer and Energy Engineering and Materials Science and Engineering Program and Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder Colorado 80309 USA

Self-assembly of colloidal particles into predefined structures is a promising way to design inexpensive manmade materials with advanced macroscopic properties. Doping of nematic liquid crystals (LCs) with nanoparticles has a series of advantages in addressing these grand scientific and engineering challenges. It also provides a very rich soft matter platform for the discovery of unique condensed matter phases. The LC host naturally allows the realization of diverse anisotropic interparticle interactions, enriched by the spontaneous alignment of anisotropic particles due to the boundary conditions of the LC director. Here we demonstrate theoretically and experimentally that the ability of LC media to host topological defect lines can be used as a tool to probe the behavior of individual nanoparticles as well as effective interactions between them. LC defect lines irreversibly trap nanoparticles enabling controlled particle movement along the defect line with the use of a laser tweezer. Minimization of Landau–de Gennes free energy reveals a sensitivity of the ensuing effective nanoparticle interaction to the shape of the particle, surface anchoring strength, and temperature, which determine not only the strength of the interaction but also its repulsive or attractive character. Theoretical results are supported qualitatively by experimental observations. This work may pave the way toward designing controlled linear assemblies as well as one-dimensional crystals of nanoparticles such as gold nanorods or quantum dots with tunable interparticle spacing.

关键词： Charged colloids Complex fluids Liquid crystals

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Multi-Niche Human Bone Marrow On-a-Chip for Studying the Interactions of Adoptive Car-T Cell Therapies with Multiple Myeloma

SSRN

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SSRN 2024年

作者： Ghoshal, Delta Petersen, Ingrid Ringquist, Rachel Kramer, Liana Bhatia, Eshant Hu, Thomas Richard, Ariane Park, Reda Corbin, Jenna Agarwal, Savi Thomas, Abel Ramirez, Sebastian Tharayil, Jacob Downey, Emma Ketchum, Frank Ochal, Abigail Sonthi, Neha Lonial, Sagar Kochenderfer, James N. Tran, Reginald Zhu, Mandy Lam, Wilbur A. Coskun, Ahmet F. Roy, Krishnendu The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States The Parker H. Petit Institute for Bioengineering & Bioscience Georgia Institute of Technology AtlantaGA30332 United States School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA30332 United States The George W. Woodruff Department of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biochemistry Georgia Institute of Technology AtlantaGA30332 United States Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Department of Hematology and Medical Oncology Emory University School of Medicine AtlantaGA30322 United States Surgery Branch National Cancer Institute Bethesda MD20892 United States Division of Pediatric Hematology/Oncology Department of Pediatrics Aflac Cancer Center Blood Disorders Service of Children’s Healthcare of Atlanta Emory University School of Medicine AtlantaGA30322 United States School of Engineering Vanderbilt University NashvilleTN37235 United States

Multiple myeloma (MM), a cancer of bone marrow plasma cells, is the second-most common hematological malignancy. However, despite immunotherapies like chimeric antigen receptor (CAR)-T cells, relapse is nearly universal. The bone marrow (BM) microenvironment influences how MM cells survive, proliferate, and resist treatment. Yet, it is unclear which BM niches give rise to MM pathophysiology. Here, we present a 3D microvascularized culture system, which models the endosteal and perivascular bone marrow niches, allowing us to study MM-stroma interactions in the BM niche and model responses to therapeutic CAR-T cells. We demonstrated the prolonged survival of cell line-based and patient-derived multiple myeloma cells within our in vitro system and successfully perfused in donor-matched CAR-T cells. We then measured T cell survival, differentiation, and cytotoxicity against MM cells using a variety of analysis techniques. Our MM-on-a-chip system could elucidate the role of the BM microenvironment in MM survival and therapeutic evasion and inform the rational design of next-generation therapeutics. © 2024, The Authors. All rights reserved.

关键词： T-cells

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Digital filter effect of the ventilator tester parameters to identify inflation phases

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AIP Conference Proceedings 2022年第1期2537卷

作者： Tomy Abuzairi Ahli Irfan Basari 1Electrical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 3Research Center for Biomedical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia 2Biomedical Engineering Study Program Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Kampus UI Depok West Java 16424 Indonesia

A ventilator is a breathing support device that serves as a therapeutic tool or treatment tool for patients with respiratory system failure caused by various health problems or diseases. On ventilators, there are crucial parameters that need to be monitored in advance, such as inspiration–expiration (I:E) ratio, respiration rate, tidal volume, peak inspiration pressure (PIP), positive end-expiratory pressure (PEEP), and oxygen concentration. To monitor those parameters, a ventilator tester is needed. In this paper, we analyzed the effect of a digital filter in the ventilator tester, COVENT-Tester, to identify the inflation phase in the ventilator parameters. The results show that the digital filter is not effective in measuring I:E ratio and respiration rate because of the delay in inspiration and expiration time. However, the digital filter is effective in measuring tidal volume, PIP, PEEP, and oxygen concentration.

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Re-identification of Individuals in Genomic Datasets Using Public Face Images

arXiv

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arXiv 2021年

作者： Venkatesaramani, Rajagopal Malin, Bradley A. Vorobeychik, Yevgeniy Washington University in St. Louis Department of Computer Science and Engineering St. Louis63130 United States Vanderbilt University Department of Electrical Engineering and Computer Science Nashville37232 United States Vanderbilt University Medical Center Department of Biomedical Informatics Nashville37203 United States Vanderbilt University Medical Center Department of Biostatistics Nashville37203 United States

DNA sequencing is becoming increasingly commonplace, both in medical and direct-to-consumer settings. To promote discovery, collected genomic data is often de-identified and shared, either in public repositories, such as OpenSNP, or with researchers through access-controlled repositories. However, recent studies have suggested that genomic data can be effectively matched to high-resolution three-dimensional face images, which raises a concern that the increasingly ubiquitous public face images can be linked to shared genomic data, thereby re-identifying individuals in the genomic data. While these investigations illustrate the possibility of such an attack, they assume that those performing the linkage have access to extremely well-curated data. Given that this is unlikely to be the case in practice, it calls into question the pragmatic nature of the attack. As such, we systematically study this re-identification risk from two perspectives: first, we investigate how successful such linkage attacks can be when real face images are used, and second, we consider how we can empower individuals to have better control over the associated re-identification risk. We observe that the true risk of re-identification is likely substantially smaller for most individuals than prior literature suggests. In addition, we demonstrate that the addition of a small amount of carefully crafted noise to images can enable a controlled trade-off between re-identification success and the quality of shared images, with risk typically significantly lowered even with noise that is imperceptible to humans. © 2021, CC BY.

关键词： DNA sequences

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