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Programmable cell aggregation by a synthetic biosilicification approach

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iScience 2025年第6期28卷

作者： Wang, Qing Sun, Jing Zang, Lei Yao, Huaxiong Wang, Lin Zhu, Runtao Li, Jie Zeng, Simin Tang, Hongting Wang, Teng Liu, Ji Wang, Bo Li, Bo Liu, Zhiyuan Dai, Zhuojun State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Center of Neural Engineering CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Civil Engineering University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 China

Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts have focused on modifying cells with designed organic-based adhesive modules and assembling cells into defined patterns. Here, we present a different approach to guide cell assembly by tuning the organic-inorganic interactions. Our method involves engineering cells to express a silicifying peptide on their surfaces, which promotes silica deposition on the cell walls. This peptide simultaneously binds to the silica synthesized on adjacent cells, triggering cell clustering. The engineered cells exhibit rapid aggregation, with approximately 95% of cells assembling within 15 min. We further show that this capability can facilitate materials assembly and chemical production. Our biosilicification-based approach offers novel insights into natural multicellularity mechanisms and holds potential for applications in biomanufacturing and materials engineering. © 2025 The Author(s)

关键词： Bioengineering Biomaterials Synthetic biology

DATA AUGMENTATION FOR END-TO-END CODE-SWITCHING SPEECH RECOGNITION

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arXiv 2020年

作者： Du, Chenpeng Li, Hao Lu, Yizhou Wang, Lan Qian, Yanmin MoE Key Lab of Artificial Intelligence SpeechLab Department of Computer Science and Engineering AI Institute Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

A Robust Light-Curve Diagnostic for Electron-Capture Supernovae and Low-Mass Fe-Core-Collapse Supernovae

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arXiv 2024年

作者： Sato, Masato Tominaga, Nozomu Blinnikov, Sergei I. Potashov, Marat Sh. Moriya, Takashi J. Hiramatsu, Daichi Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Tokyo Mitaka181-8588 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Department of Physics Faculty of Science and Engineering Konan University 8-9-1 Okamoto Hyogo Kobe658-8501 Japan NRC Kurchatov Institute Moscow123182 Russia Moscow127055 Russia Keldysh Institute of Applied Mathematics RAS 4 Miusskaya Square Moscow125047 Russia School of Physics and Astronomy Monash University ClaytonVIC3800 Australia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138-1516 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States

Core-collapse supernovae (CCSNe) are the terminal explosions of massive stars. While most massive stars explode as iron-core-collapse supernovae (FeCCSNe), slightly less massive stars explode as electron-capture supernovae (ECSNe), shaping the low-mass end of CCSNe. ECSNe was proposed ∼ 40 years ago and first-principles simulations also predict their successful explosions. Observational identification and investigation of ECSNe are important for the completion of stellar evolution theory. To date, only one promising candidate has been proposed, SN 2018zd, other than the historical progenitor of the Crab Nebula, SN 1054. We present representative synthetic light curves of low-mass FeCCSNe and ECSNe exploding with energies in circumstellar media (CSM) estimated with theoretically or observationally plausible methods. The plateaus of the ECSNe are shorter, brighter, and bluer than those of the FeCCSNe. To investigate the robustness of their intrinsic differences, we adopted various explosion energies and CSM. Although they may have similar bolometric light-curve plateaus, ECSNe are bluer than FeCCSNe in the absence of strong CSM interaction, illustrating that multicolor observations are essential to identify ECSNe. This provides a robust indicator of ECSNe because the bluer plateaus stem from the low-density envelopes of their super-asymptotic-giant-branch progenitors. Furthermore, we propose a distance-independent method to identify ECSNe: (g − r)tPT/2 PT − 0.4, i.e., blue g − r at the middle of the plateau (g − r)tPT/2, where tPT is the transition epoch from plateau to tail. Using this method, we identified SN 2018zd as an ECSN, which we believe to be the first ECSN identified with modern observing techniques. © 2024, CC BY.

关键词： Supernovae

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

Assessing Trustworthy ai in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy ai in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy ai' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an ai system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The ai system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the ai system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Accurate lane detection with atrous convolution and spatial pyramid pooling for autonomous driving

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Accurate lane detection with atrous convolution and spatial ...

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Sun, Yuxiang Wang, Lujia Chen, Yongquan Liu, Ming Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Institute of Robotics and Intelligent Manufacturing Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781728163215

Lane detection is a fundamental capability for autonomous driving. Many effective lane detection algorithms based on traditional computer vision and recent deep learning technologies have been proposed. However, the current state-of-the-art lane detection accuracy is still not satisfactory for realizing fully autonomous driving. Thus, this paper proposes a new lane detection network using atrous convolution and spatial pyramid pooling techniques to improve the lane detection accuracy. We address the detection problem with pixel-wise semantic segmentation. Our network consists of one encoder and two decoders, which outputs a binary segmentation map and an embedded feature map, respectively. The embedded feature map is employed for clustering algorithms to separate segmented lane pixels into different lanes. The experimental results on the public Tusimple dataset show that our network outperforms the state-of-the-arts. © 2019 IEEE.

关键词： Convolution

Soft yet Effective Robots via Holistic Co-Design

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arXiv 2025年

作者： Stölzle, Maximilian Pagliarani, Niccolò Stella, Francesco Hughes, Josie Laschi, Cecilia Rus, Daniela Cianchetti, Matteo Santina, Cosimo Della Zardini, Gioele Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Cognitive Robotics Delft University of Technology Mekelweg 2 CD Delft2628 Netherlands The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Embodied AI AG Lausanne1015 Switzerland CREATE Lab EPFL Lausanne1015 Switzerland Advanced Robotics Centre Department of Mechanical Engineering National University of Singapore 117575 Singapore Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Yet, their development is challenging because it requires integrating materials, geometry, actuation, and autonomy into complex mechatronic systems. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability—a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. We then identify three key shortcomings that limit the broader adoption of such co-design methods within the soft robotics domain. First, many rely on simulation-based evaluations focusing on a single metric, while real-world designs must satisfy diverse criteria. Second, current methods emphasize computational modeling without ensuring feasible realization, risking sim-to-real performance gaps. Third, high computational demands limit the exploration of the complete design space. Finally, we propose a holistic co-design framework that addresses these challenges by incorporating a broader range of design values, integrating real-world prototyping to refine evaluations, and boosting efficiency through surrogate metrics and model-based control strategies. This holistic framework, by simultaneously optimizing functionality, durability, and manufacturability, has the potential to enhance reliability and foster broader acceptance of soft robotics, transforming human-robot interactions. © 2025, CC BY.

关键词： Intellectual property core

Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN

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arXiv 2020年

作者： Suzuki, Kanata Mori, Hiroki Ogata, Tetsuya Artificial Intelligence Laboratories Fujitsu Laboratories LTD. Kanagawa211-8588 Japan Department of Intermedia Art and Science School of Fundamental Science and Engineering Waseda University Tokyo169-8555 Japan Hiroki Mori Is with Institute for Ai and Robotics Future Robotics Organization Waseda University Tokyo169-8555 Japan National Institute of Advanced Industrial Science and Technology Tokyo100-8921 Japan

Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot collaborative task, the robot may adopt an unexpected posture due to collisions and other unexpected events. Therefore, robots should be able to recover from disturbances for completing the execution of the intended task. We propose a compensation method for undefined behaviors by switching between two controllers. Specifically, the proposed method switches between learning-based and model-based controllers depending on the internal representation of a recurrent neural network that learns task dynamics. We applied the proposed method to a pickand- place task and evaluated the compensation for undefined behaviors. Experimental results from simulations and on a real robot demonstrate the effectiveness and high performance of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks