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Multiplexed Serial Imaging of Immunotherapy Response with Serrs Nanoprobes

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Multiplexed Serial Imaging of Immunotherapy Response with Se...

IEEE International Symposium on Biomedical Imaging

作者： Naxhije Berisha Konstantinos Plakas Matthieu Gigoux Taha Merghoub Chrysafis Andreou Hunter College of the City University of New York New York NY USA PhD Program in Chemistry The Graduate Center of the City University of New York NY USA Memorial Sloan Kettering Cancer Center New York NY USA Department of Chemistry University at Buffalo The State University of New York Buffalo NY USA Weill Cornell Medicine New York NY USA Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

With the advent of new and promising cancer therapies, and with increased interest in precision medicine, there is an urgent need to develop non-invasive technologies that evaluate heterogeneity in treatment response. We demonstrate an 8-plex imaging platform based on surface enhanced resonance Raman scattering nanoparticles (SERRS NPs) to monitor the expression of 4 immunomarkers before and after treatment with checkpoint blockade therapy with anti-CTLA4 and anti-PD-1 antibodies in immunotherapy models of colon (CT26) and breast (4T1) cancers. We found that CT26 tumors showed an early treatment response, while the typically refractory 4T1 tumors demonstrated an unexpected treatment response, suggesting that the nanoparticles may have synergistically contributed to the treatment. Serial imaging of multiple markers during therapy can help predict treatment efficacy and uncover new theranostic adjuvants.

关键词： Nanoparticles Multiplexing Precision medicine Raman scattering Immunotherapy Resonance Surface treatment

A 21.6-bit programmable-Gain Triple-Sampling Incremental ΔΣADC With 4 ppm INL for Bridge Transducers

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IEEE Sensors Journal 2025年

作者： Kang, Junho Rhee, Jooyeol Kim, Suhwan Jun, Jaehoon Seoul08826 Korea Republic of Gachon University Department of Semiconductor Engineering College of Semiconductor Seongnam13120 Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Gwanak Analog Company Ltd Seoul08826 Korea Republic of Inha University Department of Electrical and Computer Engineering Program in Semiconductor Convergence Incheon22212 Korea Republic of

This article presents a high-resolution programmable-gain incremental delta-sigma (ΔΣ) analog-to-digital converter (ADC) optimized for high-accuracy bridge transducer measurements. The proposed design incorporates a triple-sampling (TRS) technique and an auto-zeroing (AZ) integrator that are effectively combined to suppress DC offset and 1/f noise, reduce input-referred noise (IRN), and improve effective resolution (ER). Implemented using a switched-capacitor (SC) ΔΣ modulator architecture, the proposed ADC provides accurate gain control without the need for additional power-hungry amplifiers. With the AZ-TRS integrator, a low IRN of 1.03 μVRMS is achieved. Furthermore, the ADC demonstrates an integral nonlinearity (INL) of ±4 ppm across all gain settings, with a maximum gain of 13. Additionally, it achieves power supply rejection ratio (PSRR) and common-mode rejection ratio (CMRR) values of 117.1 dB and 122.0 dB, respectively. The prototype chip was fabricated using a 0.18-μm CMOS process, and the ΔΣ modulator occupies a compact area of 0.34 mm2. The ADC operates with a supply voltage of 3.0 V and a current consumption of 361 μA for the analog circuitry, while the digital filter consumes 36 μA at 1.8 V. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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Science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

Micromagnetic Neural Stimulation and Spintronic Neural Sensing

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Micromagnetic Neural Stimulation and Spintronic Neural Sensi...

Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Renata Saha Denis Tonini Matthew S. Hopper Abhinav Goyal Jason Yuen Yoonbae Oh Zachary Sanger Sadegh Faramarzi Maple Shiao Dusty Van Helden Robert P. Bloom Onri J. Benally Kai Wu Susan A. Keirstead Theoden I. Netoff Walter C. Low John Osborn Kevin E. Bennet Kendall H. Lee Hojin Shin Jian-Ping Wang Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN United States Department of Neurologic Surgery Mayo Clinic Rochester MN United States Medical Scientist Training Program Mayo Clinic Rochester MN United States IMPACT - the Institute for Mental and Physical Health and Clinical Translation Barwon Health Deakin University School of Medicine Geelong VIC Australia Department of Biomedical Engineering Mayo Clinic Rochester MN United States Department of Biomedical Engineering University of Minnesota Minneapolis MN United States Stem Cell Institute University of Minnesota Minneapolis MN United States Department of Surgery University of Minnesota Minneapolis MN United States Department of Neurosurgery University of Minnesota Minneapolis MN United States Division of Engineering Mayo Clinic Rochester MN United States

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

We report our team's recent magnetic biomedical brain-related research work: 1) micromagnetic neural stimulation $(\boldsymbol{\mu} \mathbf{MS})$ ; 2) calculation of magnetic field generated by neural cells and tissues and spintronic neural sensing. First, the designing, fabrication, and testing of two micromagnetic implants - the Magnetic Pen (MagPen), a solenoid-shaped single $\boldsymbol{\mu} \mathbf{coil}$ prototype, and the Magnetic Patch (MagPatch), a rectangular helix-shaped planar $\boldsymbol{\mu} \mathbf{coil}$ array prototype, is discussed. The efficacy of micromagnetic activation using MagPen has been successfully tested over many rodent models. The MagPen prototype had its caveat in terms of mm-size, lack of multidimensional spatial control and activation at the cellular-level. To bridge this research gap, the MagPatch array was designed and fabricated to study $\boldsymbol{\mu} \mathbf{MS}$ at the single-cell resolution. Second, we used FEM exemplary models and open-source computational libraries and calculated the magnetic fields generated by individual neurons and neuronal networks at micrometer distances. Our results show that the magnetic field generated by a single-neuron action potential can be detected by ultra-high sensitivity sub-pT magnetic field sensors, which opens the door to future in vivo decoding of neuronal activities through neural networks. Room temperature, high endurance, small volume and low power make spintronic sensors one of the promising candidates for neural sensing. On this aspect, the recent experimental progress for the spintronic sensors for neural sensing has been reviewed, with a specific discussion on our effort on spintronic stack design, device fabrication and detection.

关键词： Temperature sensors Fabrication Magnetic sensors Computational modeling Prototypes Micromagnetics Magnetic fields

Linearized frequency domain Landau-Lifshitz-Gilbert equation formulation

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arXiv 2022年

作者： Lin, Zhuonan Lomakin, Vitaliy Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Center for Memory and Recording Research La Jolla CA92093 United States Materials Science and Engineering Program University of California San Diego La Jolla CA92093 United States

We present a general finite element linearized Landau-Lifshitz-Gilbert equation (LLGE) solver for magnetic systems under weak time-harmonic excitation field. The linearized LLGE is obtained by assuming a small deviation around the equilibrium state of the magnetic system. Inserting such expansion into LLGE and keeping only first order terms gives the linearized LLGE, which gives a frequency domain solution for the complex magnetization amplitudes under an external time-harmonic applied field of a given frequency. We solve the linear system with an iterative solver using generalized minimal residual method. We construct a preconditioner matrix to effectively solve the linear system. The validity, effectiveness, speed, and scalability of the linear solver are demonstrated via numerical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

Compressed sensing 3D fluorescence microscopy using optimized phase mask

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Compressed sensing 3D fluorescence microscopy using optimize...

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Yanny, Kyrollos Antipa, Nick Liberti, William Dehaeck, Sam Monakhova, Kristina Liu, Fanglin Linda Shen, Konlin Ng, Ren Waller, Laura UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Brussels1050 Belgium

We demonstrate a single-shot miniature 3D computational microscope with an optimized phase encoder. Our method uses sparsity-based reconstruction to achieve a 2.76µm lateral and 15µm axial resolution across ... 详细信息

ISBN: (纸本)9781557528209

关键词： Fluorescence microscopy

Energy-efficient knowledge distillation for spiking Neural Networks

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arXiv 2021年

作者： Lee, Dongjin Park, Seongsik Kim, Jongwan Doh, Wuhyeong Yoon, Sungroh Department of Electrical and Computer Engineering Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Seoul National University Korea Republic of

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs. Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model. However, analysis on energy efficiency, which is also an important feature of SNN, was absent. In this paper, we thoroughly analyze the performance of the distilled SNN model in terms of accuracy and energy efficiency. In the process, we observe a substantial increase in the number of spikes, leading to energy inefficiency, when using the conventional knowledge distillation methods. Based on this analysis, to achieve energy efficiency, we propose a novel knowledge distillation method with heterogeneous temperature parameters. We evaluate our method on two different datasets and show that the resulting SNN student satisfies both accuracy improvement and reduction of the number of spikes. On MNIST dataset, our proposed student SNN achieves up to 0.09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method. We also compare the results with other SNN compression techniques and training methods. © 2021, CC BY.

关键词： Energy efficiency

Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

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arXiv 2024年

作者： Tan, Myles Joshua Toledo Maravilla, Nicholle Mae Amor Tan Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL32610 United States Biology Program College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Yo-Vivo Corporation Negros Occidental Bacolod City6100 Philippines

This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning experiences,foster intrinsic motivation, and support voluntary behavior change amongstudents. By analyzing established ethical frameworks and educational theoriessuch as deontological ethics, consequentialism, constructivist learning, andSelf-Determination Theory (SDT), the paper argues that GAI, when usedresponsibly, can enhance digital literacy, encourage genuine knowledgeconstruction, and uphold ethical standards in education. This researchhighlights the potential of GAI to create enriching, personalized learningenvironments that prepare students to navigate the complexities of the modernworld ethically and effectively. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

Utilizing probabilistic entanglement between sensors in quantum networks

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arXiv 2024年

作者： Van Milligen, Emily A. Gagatsos, Christos N. Kaur, Eneet Towsley, Don Guha, Saikat Department of Physics The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Cisco Quantum Lab Los Angeles United States College of Computer Science University of Massachusetts AmherstMA United States

One of the most promising applications of quantum networks is entanglement assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete variable case—and quantum squeezing in the continuous variable case distributed amongst all of the sensors in a given network. However, it can be difficult to ensure all sensors pre-share entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled. © 2024, CC BY.

关键词： Quantum entanglement