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arXiv 2024年

作者： Chen, Fu-Der Sharma, Ankita Xue, Tianyuan Jung, Youngho Govdeli, Alperen Mak, Jason C.C. Chameh, Homeira Moradi Movahed, Mandana Brunk, Michael G.K. Luo, Xianshu Chua, Hongyao Lo, Patrick Guo-Qiang Valiante, Taufik A. Sacher, Wesley D. Poon, Joyce K.S. Max Planck Institute of Microstructure Physics Weinberg 2 Halle06120 Germany Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada Max Planck-University of Toronto Centre for Neural Science and Technology Canada Krembil Brain Institute University Health Network TorontoON Canada Advanced Micro Foundry Pte. Ltd. 11 Science Park Road Singapore Science Park II 117685 Singapore Division of Neurosurgery Department of Surgery Toronto Western Hospital University of Toronto TorontoON Canada Institute of Biomedical Engineering University of Toronto TorontoON Canada

In brain activity mapping experiments using optogenetics, patterned illumination is crucial for deterministic and localized stimulation of neurons. However, due to optical scattering in brain tissue, light-emitting implantable devices are needed to bring precise patterned illumination to deep brain regions. A promising solution is silicon neural probes with integrated nanophotonic circuits that form tailored beam emission patterns without lenses. Here, we demonstrate neural probes with grating-based light emitters that generate a single steerable light beam across > 60% of the steering range with ≥ 4 dB of background suppression for optogenetic photostimulation. The light emitters, optimized for blue or amber light, combine end-fire optical phased arrays with slab gratings to suppress higher-order sidelobes. In vivo optogenetic photostimulation was demonstrated in Thy1-ChR2-YFP mice. These neural probes, capable of continuous single beam scanning, offer a promising avenue for optogenetic stimulation with cellular-level spatial precision in the deep brain. © 2024, CC BY-NC-ND.

关键词： Nanophotonics

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An Interactive Control Approach to 3D Shape Reconstruction

An Interactive Control Approach to 3D Shape Reconstruction

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American Control Conference

作者： Bipul Islam Ji Liu Anthony Yezzi Romeil Sandhu Department of Computer Science & Biomedical Informatics Stony Brook University Stony Brook NY Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY Department of Electrical and Computer Engineering Georgia Tech Atlanta GA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

The ability to accurately reconstruct the 3D facets of a scene is one of the key problems in robotic vision. However, even with recent advances with machine learning, there is no high-fidelity universal 3D reconstruction method for this optimization problem as schemes often cater to specific image modalities and are often biased by scene abnormalities. Simply put, there always remains an "information" gap due to the dynamic nature of real-world scenarios. To this end, we demonstrate a feedback control framework which invokes operator inputs (also prone to errors) in order to augment existing reconstruction schemes. For proof-of-concept, we choose a classical region-based stereoscopic reconstruction approach and show how an ill-posed model can be augmented with operator input to be much more robust to scene artifacts. We provide necessary conditions for stability via Lyapunov analysis and perhaps more importantly, we show that the stability depends on a notion of absolute curvature. Mathematically, this aligns with previous work that has shown Ricci curvature as proxy for functional robustness of dynamical networked systems. We conclude with results that show how our method can improve standalone reconstruction schemes.

关键词： Three-dimensional displays Image reconstruction Two dimensional displays Surface reconstruction Image segmentation Shape Cameras

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Self-organization of mouse embryonic stem cells into reproducible pre-gastrulation embryo models via CRISPRa programming

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Cell Stem Cell 2025年第6期32卷 895-913.e8页

作者： Lodewijk, Gerrald A. Kozuki, Sayaka Han, Clara J. Topacio, Benjamin R. Lee, Seungho Nixon, Lily Zargari, Abolfazl Knight, Gavin Ashton, Randolph Qi, Lei S. Shariati, S. Ali Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz CA United States Genomics Institute University of California Santa Cruz Santa Cruz CA United States Institute for The Biology of Stem Cells University of California Santa Cruz Santa Cruz CA United States Department of Electrical and Computer Engineering University of California Santa Cruz Santa Cruz CA United States Neurosetta LLC Madison WI United States Wisconsin Institute for Discovery Madison WI United States Department of Biomedical Engineering University of Wisconsin-Madison Madison WI United States Department of Bioengineering Stanford University Stanford CA United States Sarafan ChEM-H Stanford University Stanford CA United States Chan Zuckerberg Biohub San Francisco San Francisco CA United States

Embryonic stem cells (ESCs) can self-organize into structures with spatial and molecular similarities to natural embryos. During development, embryonic and extraembryonic cells differentiate through activation of endogenous regulatory elements while co-developing via cell-cell interactions. However, engineering regulatory elements to self-organize ESCs into embryo models remains underexplored. Here, we demonstrate that CRISPR activation (CRISPRa) of two regulatory elements near Gata6 and Cdx2 generates embryonic patterns resembling pre-gastrulation mouse embryos. Live single-cell imaging revealed that self-patterning occurs through orchestrated collective movement driven by cell-intrinsic fate induction. In 3D, CRISPRa-programmed embryo models (CPEMs) exhibit morphological and transcriptomic similarity to pre-gastrulation mouse embryos. CPEMs allow versatile perturbations, including dual Cdx2-Elf5 activation to enhance trophoblast differentiation and lineage-specific activation of laminin and matrix metalloproteinases, uncovering their roles in basement membrane remodeling and embryo model morphology. Our findings demonstrate that minimal intrinsic epigenome editing can self-organize ESCs into programmable pre-gastrulation embryo models with robust lineage-specific perturbation capabilities. © 2025 The Author(s)

关键词： Collective Motion CRISPRa embryo models embryonic patterning epigenome editing synthetic biology

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Dielectric spectroscopy of liver mixture model for millimetre-wave imaging

Dielectric spectroscopy of liver mixture model for millimetr...

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2019 European Microwave Conference in Central Europe, EuMCE 2019

作者： Farhat, Iman Bonello, Julian Farrugia, Lourdes Di Meo, Simona Pollacco, Daphne-Anne Sammut, Charles Department of Physics University of Malta Malta Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9782874870675

In this paper, we present a technique to mimic the dielectric properties for liver at 0.2-50 GHz based on the effective medium theory. A mathematical model based on Bruggeman mixture equation is presented to describe the dielectric properties of liver. The dielectric response of the mixture system considers two dielectric media, referred to as the host and inclusion media. In fact, proposed mixture solution consists of concentrations of Bovine Serum Albumin (BSA) in PBS solution, synthesized to mimic in-vivo liver tissue using physiological saline solutions coupled with protein additions. Generally, when testing an electromagnetic medical device in prototype phase, such solutions are required to assess the feasibility of the technology. The dielectric properties as measured experimentally are correlated to the calculated effective permittivity using Bruggemann equation. Results were then analysed, implying that such solutions can be utilised in the construction of human body phantoms for narrowband and ultra-wideband microwave devices for both millimeter-wave imaging and applications in hyperthermia and other ablation modalities. The frequency range studied could also lead to realistic phantoms for dosimetry studies related to human safety concerns. Hence, a stratified liver mixture model has been proposed which can be used as a theoretical basis for taking further steps in liver cancer research. © 2019 EuMCE.

关键词： Phantoms

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Faster convergence in deep-predictive-coding networks to learn deeper representations

arXiv

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arXiv 2021年

作者： Sledge, Isaac J. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States The Department of Biomedical Engineering University of Florida GainesvilleFL United States

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feed-back connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features. However, this inference is a major computational bottleneck. It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes learning stagnation. We also demonstrate that it permits constructing deep and wide predictive-coding networks. Such convolutional networks implement receptive fields that capture well the entire classes of objects on which the networks are trained. This improves the feature representations compared with our lab's previous non-convolutional and convolutional DPCNs. It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner. © 2021, CC BY.

关键词： Convolution

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Hyperparameter Selection for Bayesian Image Reconstruction by Mimicking Physical Crystallization

Hyperparameter Selection for Bayesian Image Reconstruction b...

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2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020

作者： Gao, Yongfeng Liang, Zhengrong Lu, Siming Shi, Yongyi Chang, Shaojie Hou, Wei The Department of Radiology Stony Brook University Stony Brook NY11794 United States The Department of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony Brook University Stony Brook NY11794 United States The Department of Family Population and Preventive Medicine Stony Brook University Stony Brook NY11794 United States

ISBN: (纸本)9781728176932

Although Bayesian theory has been successfully applied for count-limited medical image reconstruction in the past two decades, its wide applications in clinic has been hampered by its hyperparameter β, which is traditionally determined by a trial-error style. To eliminate the cumbersome style, this work aims to present a selection method by mimicking the physical model of cooling down the temperature adaptively for an economic high-quality crystal. From the basic Bayes' law, the physical meaning of hyperparameter can be interpreted as the ratio of the data uncertainty (or variance α) and the prior tolerance (or σ) by formulating the probability distribution functions (FDFs) of the data fidelity and prior expectation. Inspired by this idea, the prior tolerance σ can be treated as the temperature of the texture patterns, and β can be adjusted according to different texture pattern status by satisfying the condition of each PDF in the Bayes' Law during the iteration. In Simulated phantom study, realistic Poisson noise added to the pre-log transmission data model was used. Both phantom simulation and clinical patient data results show that the proposed method can provide comparable reconstructed image quality comparing to the conventional methods but with much less reconstruction time. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stop the iteration process. © 2020 IEEE

关键词： Phantoms

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Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography

arXiv

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arXiv 2022年

作者： Anderson, Evan D. Wilcox, Ramsey Nayak, Anuj Zwilling, Christopher Robles-Granda, Pablo Kim, Been Varshney, Lav R. Barbey, Aron K. Neuroscience Program University of Illinois UrbanaIL United States Department of Psychology University of Illinois UrbanaIL United States Department of Electrical & Computer Engineering University of Illinois UrbanaIL United States Beckman Institute University of Illinois UrbanaIL United States Department of Computer Science University of Illinois UrbanaIL United States Google Brain Mountan View CA United States

Individual differences in human intelligence can be modeled and predicted from in vivo neurobiological connectivity. Many established modeling frameworks for predicting intelligence, however, discard higher-order information about individual differences in brain network topology, and show only moderate performance when generalized to make predictions in out-of-sample subjects. In this paper, we propose that connectome-based predictive modeling, a common predictive modeling framework for neuroscience data, can be productively modified to incorporate information about brain network topology and individual differences via the incorporation of bagged decision trees and the network based statistic. These modifications produce a novel predictive modeling framework that leverages individual differences in cortical tractography to generate accurate regression predictions of intelligence scores. Network topology-based feature selection provides for natively interpretable networks as input features, increasing the model's explainability. Investigating the proposed modeling framework's efficacy, we find that advanced connectome-based predictive modeling generates neuroscience predictions that account for a significantly greater proportion of variance in general intelligence scores than previously established methods, advancing our scientific understanding of the network architecture that underlies human intelligence. © 2022, CC BY-SA.

关键词： Forecasting

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The challenges of measuring spin Seebeck noise

arXiv

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arXiv 2023年

作者： Luo, Renjie Zhao, Xuanhan Legvold, Tanner J. Chen, Liyang Liu, Changjiang Hong, Deshun Bhattacharya, Anand Natelson, Douglas Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Program Smalley-Curl Institute Rice University HoustonTX77005 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Just as electronic shot noise in driven conductors results from the granularity of charge and the statistical variation in the arrival times of charge carriers, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. Using spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet, we demonstrate the technical challenges inherent in such noise measurements. While there is a small increase in voltage noise in the inverse spin Hall detector at low temperatures associated with adding a magnetic field, the dependence on field orientation implies that this is not due to magnon shot noise. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Further, we show that magnon shot noise detection through the standard inverse spin Hall approach is likely impossible due to geometric factors. Implications for future attempts to measure magnon shot noise are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Shot noise

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SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation

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computers in Biology and Medicine 2025年 194卷 110258页

作者： Chen, Weijie McMillan, Alan B. Department of Electrical and Computer Engineering University of Wisconsin-Madison WI53705 United States Department of Radiology University of Wisconsin-Madison WI53705 United States Department of Medical Physics University of Wisconsin-Madison WI53705 United States Department of Biomedical Engineering University of Wisconsin-Madison WI53705 United States

This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to generate diverse models from a single well-trained checkpoint, facilitating the training of a model family. This involves producing multiple outputs from a single input, fusing them into a final output, and estimating uncertainty based on output disagreements. Implemented using U-Net and UNETR models for segmentation and synthesis tasks, this approach was tested on CT body segmentation and MR-CT synthesis datasets. It achieved a mean Dice coefficient of 0.814 in segmentation and a Mean Absolute Error of 88.17 HU in synthesis, improved from 89.43 HU by pruning. Additionally, the framework was evaluated under image corruption and data undersampling, maintaining correlation between uncertainty and error, which highlights its robustness. These results suggest that the proposed approach not only maintains the performance of well-trained models but also enhances interpretability through effective uncertainty estimation, applicable to both convolutional and transformer models in a range of imaging tasks. © 2025 Elsevier Ltd

关键词： Image segmentation

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Magneto-optics in a van der Waals magnet tuned by self-hybridized polaritons

arXiv

引用

arXiv 2023年

作者： Dirnberger, Florian Quan, Jiamin Bushati, Rezlind Diederich, Geoffrey Florian, Matthias Klein, Julian Mosina, Kseniia Sofer, Zdenek Xu, Xiaodong Kamra, Akashdeep García-Vidal, Francisco J. Alù, Andrea Menon, Vinod M. Department of Physics City College of New York New YorkNY10031 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Electrical Engineering City College of the City University of New York New YorkNY10031 United States Intelligence Community Postdoctoral Research Fellowship Program University of Washington SeattleWA United States Department of Physics Department of Materials Science and Engineering University of Washington SeattleWA United States Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technickaá 5 Prague 6166 28 Czech Republic Universidad Autónoma de Madrid MadridE- 28049 Spain

Controlling quantum materials with light is of fundamental and technological importance. By utilizing the strong coupling of light and matter in optical cavities [1–3], recent studies were able to modify some of their most defining features [4–6]. In this work, we study the magneto-optical properties of a van der Waals magnet that supports strong coupling of photons and excitons even in the absence of external cavity mirrors. In this material – the layered magnetic semiconductor CrSBr – emergent light-matter hybrids called polaritons are shown to significantly increase the spectral bandwidth of correlations between the magnetic, electronic, and optical properties, enabling largely tunable optical responses to applied magnetic fields and magnons. Our results highlight the importance of exciton-photon self-hybridization in van der Waals magnets and motivate novel directions for the manipulation of quantum material properties by strong light-matter coupling. © 2023, CC BY.

关键词： Van der Waals forces

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