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The spatial zonation of the murine placental vasculature is specified by epigenetic mechanisms

Developmental Cell 2025年第10期60卷 1467-1482.e8页

作者： Gehrs, Stephanie Jakab, Moritz Gutjahr, Ewgenija Gu, Zuguang Weichenhan, Dieter Mallm, Jan-Philipp Mogler, Carolin Schlesner, Matthias Plass, Christoph Schlereth, Katharina Augustin, Hellmut G. Division of Vascular Oncology and Metastasis German Cancer Research Center (DKFZ) Heidelberg 69120 Germany European Center for Angioscience (ECAS) Medical Faculty Mannheim Heidelberg University Mannheim 68167 Germany Faculty of Biosciences Heidelberg University Heidelberg 69120 Germany Institute of Pathology University Clinic Heidelberg Heidelberg 69120 Germany Computational Oncology Group Molecular Precision Oncology Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Cancer Epigenomics German Cancer Research Center (DKFZ) Heidelberg 69120 Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and Bioquant Heidelberg 69120 Germany Institute of Pathology TUM School of Medicine Technical University of Munich Munich 80333 Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg 86159 Germany

The labyrinthian fetoplacental capillary network is vital for proper nourishment of the developing embryo. Dysfunction of the maternal-fetal circulation is a primary cause of placental insufficiency. Here, we show that the spatial zonation of the murine placental labyrinth vasculature is controlled by flow-regulated epigenetic mechanisms. Spatiotemporal transcriptomic profiling identified a gradual change in the expression of epigenetic enzymes, including the de novo DNA methyltransferase 3a (DNMT3A). Loss of Dnmt3a resulted in DNA hypomethylation and perturbation of zonated placental gene expression. The resulting global DNA hypomethylation impaired the angiogenic capacity of endothelial cells. Global or endothelium-predominant deletion of Dnmt3a resulted in impaired placental vascularization and fetal growth retardation (FGR). Human placental endothelial gene expression profiling associated preeclampsia with reduced DNMT3A expression. Collectively, our study identified DMNT3A as critical methylome-regulator of placental endothelial gene expression and function with clinical implications for placental dysfunction, as it occurs during preeclampsia or FGR. © 2024 The Authors

关键词： angiogenesis DNA methylation DNMT3A epigenetic regulation growth retardation placental endothelium spatial zonation

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Author Correction: Macroscopic transition metal dichalcogenides monolayers with uniformly high optical quality

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Nature communications 2023年第1期14卷 6241页

作者： Qiuyang Li Adam Alfrey Jiaqi Hu Nathanial Lydick Eunice Paik Bin Liu Haiping Sun Yang Lu Ruoyu Wang Stephen Forrest Hui Deng Department of Physics University of Michigan Ann Arbor MI 48109 USA. Applied Physics Program University of Michigan Ann Arbor MI 48109 USA. Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA. Michigan Center for Materials Characterization College of Engineering University of Michigan Ann Arbor MI 48109 USA. Department of Physics University of Michigan Ann Arbor MI 48109 USA. dengh@umich.edu. Applied Physics Program University of Michigan Ann Arbor MI 48109 USA. dengh@umich.edu.

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Silica nanocone array as a template for fabricating a plasmon induced hot electron photodetector

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Photonics Research 2019年第3期7卷 294-299页

作者： ZHIQIANG YANG KANG DU FANFAN LU YANG PANG SHIJIA HUA XUETAO GAN WENDING ZHANG SOO JIN CHUA TING MEI Key Laboratory of Space Applied Physics and Chemistry Ministry of Education and Shaanxi Key Laboratory of Optical Information TechnologySchool of Science Northwestern Polytechnical University Department of Electrical and Computer Engineering National University of Singapore LEES Program Singapore-MIT Alliance for Research & Technology (SMART)

Plasmon induced hot electrons have attracted a great deal of interest as a novel route for photodetection and lightenergy harvesting. Herein, we report a hot electron photodetector in which a large array of nanocones deposited sequentially with aluminum, titanium dioxide, and gold films can be integrated functionally with nanophotonics and microelectronics. The device exhibits a strong photoelectric response at around 620 nm with a responsivity of 180 μA/W under short-circuit conditions with a significant increase under 1 V reverse bias to 360 μA/W. The increase in responsivity and a red shift in the peak value with increasing bias voltage indicate that the bias causes an increase in the hot electron tunneling effect. Our approach will be advantageous for the implementation of the proposed architecture on a vast variety of integrated optoelectronic devices.

关键词： Silica nanocone array plasmon induced hot electron photodetector

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Quantitative Trading Machine Learning Using Differential Evolution Algorithm

Quantitative Trading Machine Learning Using Differential Evo...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Napas Vinitnantharat Narit Inchan Thatthai Sakkumjorn Kitsada Doungjitjaroen Chukiat Worasucheep Engineering Science Classroom King Mongkut's University of Technology Thonburi Thailand Applied Computer Science Program King Mongkut's University of Technology Thonburi Thailand

Technical analysis, which is commonly used world-wide, employs trading price data to calculate technical indicators. These indicators assist investors in making decision on buy/sell timing for the highest profit. However, there are no such golden rules to guarantee the highest profit from using technical indicators despite hundreds of research in this field. In this research, a quantitative trading machine learning (QTML) was proposed to suggest buying or selling time for a stock. QTML employs Differential Evolution algorithm to analyze trading signals from a set of widely-used technical indicators. Daily stock trading information from different industries during 2014 - 2019 from Stock Exchange of Thailand (SET) are used for evaluating its performance compared with the buy-and-hold strategy. The experiment is with 0.2% commission fee for all buying and selling transactions. The result shows that the proposed QTML model can assist investors in making a higher profit, particularly in stocks with fluctuated or downward trends.

关键词： Market research Testing Training Machine learning Machine learning algorithms Data models Stock markets

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Topological Data Analysis of Black Hole Images

arXiv

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arXiv 2022年

作者： Christian, Pierre Chan, Chi-Kwan Hsu, Anthony Özel, Feryal Psaltis, Dimitrios Natarajan, Iniyan Physics Department Fairfield University 1073 N Benson Rd FairfieldCT06824 United States Department of Astronomy University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Computer Science University of Arizona 1040 4th St TucsonAZ85721 United States Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg2050 South Africa South African Radio Astronomy Observatory Observatory Cape Town7925 South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images into a form that is suitable for topological analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Black holes

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Torsional periodic lattice distortions and diffraction of twisted 2D materials

arXiv

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arXiv 2022年

作者： Sung, Suk Hyun Goh, Yin Min Yoo, Hyobin Engelke, Rebecca Xie, Hongchao Zhang, Kuan Li, Zidong Ye, Andrew Deotare, Parag B. Tadmor, Ellad B. Mannix, Andrew J. Park, Jiwoong Zhao, Liuyan Kim, Philip Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Physics Sogang University Seoul04107 Korea Republic of Department of Physics Harvard University CambridgeMA02138 United States Aerospace Engineering and Mechanics University of Minnesota MinneapolisMN55455 United States Electrical and Computer Engineering Department University of Michigan Ann ArborMI48109 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Chemistry University of Chicago ChicagoIL60637 United States

Twisted 2D materials form complex moiré structures that spontaneously reduce symmetry through picoscale deformation within a mesoscale lattice. We show twisted 2D materials contain a torsional displacement field comprised of three transverse periodic lattice distortions (PLD). The torsional PLD amplitude provides a single order parameter that concisely describes the structural complexity of twisted bilayer moirés. Moreover, the structure and amplitude of a torsional periodic lattice distortion is quantifiable using rudimentary electron diffraction methods sensitive to reciprocal space. In twisted bilayer graphene, the torsional PLD begins to form at angles below 3.89° and the amplitude reaches 8 pm around the magic angle of 1. 1°. At extremely low twist angles (e.g. below 0.25°) the amplitude increases and additional PLD harmonics arise to expand Bernal stacked domains separated by well defined solitonic boundaries. The torsional distortion field in twisted bilayer graphene is analytically described and has an upper bound of 22.6 pm. Similar torsional distortions are observed in twisted WS2, CrI3, and WSe2/MoSe2 © 2022, CC BY.

关键词： Graphene

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BPS2025 - Structural basis of potassium selectivity in kalium channelrhodopsins

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Biophysical Journal 2025年第3期124卷 64a-65a页

作者： Seiya Tajima Yoon Seok Kim YoungJu Jo Peter Y. Wang Joseph M. Paggi Charu Ramakrishnan Shunki Takaramoto Takashi Nagata Masae Konno Kota Katayama Hideki Kandori Ron O. Dror Keiichi Inoue Karl Deisseroth Hideaki Kato Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Cracking the Neural Code Program Stanford University Stanford CA USA The Institute for Solid State Physics The University of Tokyo Kashiwa Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Department of Life Science and Applied Chemistry Nagoya Institute of Technology Nagoya Japan OptoBioTechnology Research Center Nagoya Institute of Technology Nagoya Japan Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

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A Secure and Fault-Tolerant Architecture for LoRaWAN Based on Blockchain

A Secure and Fault-Tolerant Architecture for LoRaWAN Based o...

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Cyber Security in Networking Conference (CSNet)

作者： Victor Ribeiro Raimir Holanda Filho Alex Ramos Graduate Program in Applied Informatics University of Fortaleza (UNIFOR) Fortaleza Brazil Computer Science Department Federal Institute of Ceará (IFCE) Fortaleza Brazil

ISBN: (数字)9781728139494

ISBN: (纸本)9781728139500

The Internet of Things (IoT) paradigm aims to bring continuous interaction between small embedded devices and humans. The IoT has the potential to affect our daily lives and bring many benefits to society. Low-Power Wide-Area Networks (LPWAN) is a new IoT technology that offers long distance connectivity for a massive number of devices. LPWAN is a promising solution to enable complex IoT scenarios, such as smart cities and smart healthcare. LoRa is currently one of the leading LPWAN solutions available for public use. Due to the great number of connected devices and, in some cases, sensitive data transmitted in IoT networks, security is one of the main concerns in LPWAN. In this paper, we focus on the issues of key management in LoRaWAN. We propose a secure architecture for key management based on private blockchain and smart contracts in order to increase the levels of security and availability in LoRaWAN environments. In order to show the feasibility of the proposed architecture, a working prototype was implemented using open-source tools and commodity hardware.

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Reliable Collaborative Semi-infrastructure Vehicle-to-Vehicle Communication for Local File Sharing 15th

Reliable Collaborative Semi-infrastructure Vehicle-to-Vehicl...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Mokhtar, Bassem Azab, Mohamed Fathalla, Efat Ghourab, Esraa M. Magdy, Mohamed Eltoweissy, Mohamed Electrical Engineering Department Alexandria University Alexandria Egypt Computer Engineering Program School of Engineering and Applied Sciences Nile University Giza Egypt Computer and Information Sciences Virginia Military Institute LexingtonVA24450 United States Department of Electrical and Computer Engineering Old Dominion University NorfolkVA United States College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

ISBN: (纸本)9783030301453

Recently, Vehicular Cloud Communication (VCC) has been gaining momentum targeting intelligent and efficient data transmission. VCC is a type of mobile ad-hoc network comprising heterogeneous vehicles sharing their resources to perform collaborative activities. In this paper, we propose a new semi-infrastructure file-browsing in order to provide Network as a service (NaaS) enabling internet-independent browsing. In our scenario, a central management platform plays the role of controlling and managing the selection of relaying vehicles supporting the source to destination file transmission procedure. Nagel-Schreckenberg rules for traffic cellular automata (CA) are used as the basis for our scenario simulation. Nagel-Schreckenberg rules simulate the behavior of a group of hypothetical vehicles moving across a highway. We study the reliability and efficiency of file transfer in such settings. Simulation results show that the number of selected relays required to establish the network highly impacts the probability of successfully sending the requested files. In addition, the distances between the selected relays influence the network throughput and the probability of network failure. Moreover, the density of relays strongly affects the overall delay that occurs due to the continuous re-transmission of the selected files among different hops. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Vehicle to vehicle communications

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Identifying learning rules from neural network observables

arXiv

引用

arXiv 2020年

作者： Nayebi, Aran Srivastava, Sanjana Ganguli, Surya Yamins, Daniel L.K. Neurosciences Ph.D. Program Stanford University United States Department of Computer Science Stanford University United States Department of Applied Physics Stanford University United States Department of Psychology Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States

The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Neural networks

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