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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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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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Model Technology Service E-Participation - Voting (E-PV) for Political Communication between the Council of Regional Representatives (DPRD) and the Citizens Using the Framework Information Technology Infrastructure Library (ITIL V.3) Transition Domain Service 7

Model Technology Service E-Participation - Voting (E-PV) for...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Hilabi, Shofa Shofiah Harso Supangat, Suhono Hendric Spits Warnars, Harco Leslie Prabowo, Harjanto Mursitama, Tirta Nugraha Universitas Buana Perjuangan Faculty of Engineering and Computer Science Karawang Indonesia Institute of Technology Bandung School of Electrical Engineering and Informatics Bandung Indonesia Nusantara University Doctor of Computer Science Bina Computer Science Department BINUS Graduate Program Jakarta11530 Indonesia Bina Nusantara University Doctor of Computer Science Jakarta11530 Indonesia Bina Nusantara University Faculty of Humanities International Relations Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

E-voting is an electronic voting information technology service, while E-participation is an information technology service for citizens of the public to provide reports, polls, or suggestions on government policies electronically. The Technology Services E-Participation-VOT (EPV) model is a combined E-Participation and E-Voting service model. The concept of this service model is that citizens transform from E-participation and E-voting services into an EPV model. The purpose of this model is to provide information to participants (citizens) about online-based voting services. This method of developing the EP-Voting Anticipation (EPV) technology model uses agile methods that are effective and flexible in various cases of designing Information Technology models for Information Technology (IT) services that apply a Framework Information Technology Infrastructure Library (ITIL). The results obtained from this study are an Information Technology service system model that provides recommendations for participation in conducting electronic elections (E-Voting). © 2020 IEEE.

关键词： Surveys

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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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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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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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Hybrid KLT-LSTM Tracking for Robust Organ Motion Monitoring in 2D Ultrasound-Guided End-Organ Therapies

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IEEE Transactions on biomedical engineering 2025年 PP卷 PP页

作者： Zebarjadi, Maryam Organ, Anna J. Zachs, Daniel P. Lim, Hubert H. University of Minnesota Department of Electrical and Computer Engineering MinneapolisMN55455 United States University of Minnesota Department of Otolaryngology-Head and Neck Surgery MinneapolisMN55455 United States University of Minnesota Department of Biomedical Engineering Department of Otolaryngology-Head and Neck Surgery Institute for Translational Neuroscience MinneapolisMN55455 United States

Objective: Recent research highlights the potential of ultrasound (US) stimulation as a noninvasive tool for modulating neural and cellular signaling in the spleen and liver to treat inflammatory diseases and diabetes. However, challenges like nerve activation failures, off-target stimulation, and organ motion during respiration can affect treatment efficacy. This study introduces a novel tracking framework for accurate liver and spleen motion tracking using US imaging to overcome these challenges. Methods: The tracking framework integrates an enhanced Kanade–Lucas–Tomasi (EKLT) tracker with a long short-term memory (LSTM) predictor. The EKLT tracker provides precise annotations that improve LSTM training, while the LSTM compensates for occlusions and noise by making predictions based on prior data and dynamically adjusting the region of interest (ROI). Spleen motion tracking was evaluated using 40 recordings from 10 participants, each undergoing four distinct breathing patterns. Additionally, the method was evaluated on a liver motion dataset from MICCAI, collected from 9 subjects. Results: Spleen tracking was most accurate during slow, shallow breathing, with an average error of 0.4 ± 0.4 mm, and had an average error of 1.37 ± 0.9 mm during fast, deep breathing. Liver tracking results showed high accuracy with an average error of 0.3 ± 0.2 mm. Conclusion: The EKLT-LSTM framework offers advantages over previous tracking models, providing high accuracy in tracking liver and spleen motion under occlusion and noisy conditions. Significance: The EKLT-LSTM is suitable for end-organ modulation applications and can be adapted to other ultrasound-guided therapies and bioelectronic medicine. © 1964-2012 IEEE.

关键词： Noninvasive medical procedures

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QUBIQ: Uncertainty Quantification for biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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

arXiv

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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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Multi-Agent Trust Region Policy Optimization

arXiv

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arXiv 2020年

作者： Li, Hepeng He, Haibo The Department of Electrical Computer and Biomedical Engineering University of Rhode Island South KingstownRI United States

We extend trust region policy optimization (TRPO) [1] to cooperative multi-agent reinforcement learning (MARL) for partially observable Markov games (POMG). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents’ observation is sufficient. By using local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratio with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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