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Direct measurement of DNA bending by quantum magnetic imaging of a nanomechanical torque balance

Physical Review Applied 2024年第6期22卷 064044-064044页

作者： Zeeshawn Kazi Isaac M. Shelby Ruhee Nirodi Joseph Turnbull Hideyuki Watanabe Kohei M. Itoh Paul A. Wiggins Kai-Mei C. Fu Department of Physics University of Washington Seattle Washington 98105 USA Present address: Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08540 USA. National Institute of Advanced Industrial Science and Technology Tsukuba Central 2 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan School of Fundamental Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Department of Bioengineering University of Washington Seattle Washington 98105 USA Department of Electrical and Computer Engineering University of Washington Seattle Washington 98105 USA Physical Sciences Division Pacific Northwest National Laboratory Richland Washington 99354 USA

DNA flexibility is a key determinant of biological function, from nucleosome positioning to transcriptional regulation, motivating a direct measurement of the bend-torque response of individual DNA molecules. In this work, DNA bending is detected with the use of a nanomechanical torque balance formed by the tethering of a ferromagnetic nanoparticle probe by an individual DNA molecule to a diamond magnetic field imager. The torque exerted by the DNA in response to bending caused by an applied magnetic torque is measured by wide-field imaging of quantum defects near the surface of the diamond. Qualitative measurements of differences in DNA biomechanical binding configuration are demonstrated, and as a proof of principle, a quantitative measurement of the bend response is made for individual DNA molecules. This quantum-enabled measurement approach could be applied to characterize the bend response of biophysically relevant short DNA molecules as well as the sequence dependence of DNA bending energy.

关键词： Molecules

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Gibbs-Preserving Operations Requiring Infinite Amount of Quantum Coherence

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Physical Review Letters 2025年第17期134卷 170201-170201页

作者： Hiroyasu Tajima Ryuji Takagi Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo 182-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama 332-0012 Japan Present address: Information Science and Electrical Engineering Department of Informatics Kyushu University 744 Motooka Nishi-ku Fukuoka 819-0395 Japan Department of Basic Science The University of Tokyo 3-8-1 Komaba Meguro-ku Tokyo 153-8902 Japan

Gibbs-preserving operations have been studied as one of the standard free processes in quantum thermodynamics. Although they admit a simple mathematical structure, their operational significance has been unclear due to the potential hidden cost to implement them using an operationally motivated class of operations, such as thermal operations. Here, we show that this hidden cost can be infinite—we present a family of Gibbs-preserving operations that cannot be implemented by thermal operations aided by any finite amount of quantum coherence. Our result implies that there are uncountably many Gibbs-preserving operations that require unbounded thermodynamic resources to implement, raising a question about employing Gibbs-preserving operations as available thermodynamics processes. This finding is a consequence of the general lower bounds we provide for the coherence cost of approximately implementing a certain class of Gibbs-preserving operations with a desired accuracy. We find that our lower bound is almost tight, identifying a quantity—related to the energy change caused by the channel to implement—as a fundamental quantifier characterizing the coherence cost for the approximate implementation of Gibbs-preserving operations.

关键词： Diffusion

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Structural inversion asymmetry in epitaxial ultrathin films of Bi(111)/InSb(111)B

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Physical Review Materials 2025年第5期9卷 054202-054202页

作者： Hadass S. Inbar Muhammad Zubair Jason T. Dong Aaron N. Engel Connor P. Dempsey Yu Hao Chang Shinichi Nishihaya Shoaib Khalid Alexei V. Fedorov Anderson Janotti Chris J. Palmstrøm Materials Department UC Santa Barbara California 93106 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Electrical & Computer Engineering UC Santa Barbara California 93106 USA Present address: Department of Physics Tokyo Institute of Technology Tokyo 152-8551 Japan. Princeton Plasma Physics Laboratory Princeton New Jersey 08540 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA

Bismuth (Bi) films hold potential for spintronic devices due to strong spin-orbit coupling. Understanding the growth, surface states, and interactions with the substrate is key to their functionalization. Large-area high-quality (111) Bi ultrathin films were grown on InSb (111)B substrates by molecular beam epitaxy (MBE). Strong film-substrate interactions epitaxially stabilize the (111) orientation and lead to nonequivalent interface potentials. Analysis of angle-resolved photoemission spectroscopy (ARPES) measurements, employed to characterize the evolution of the surface states with film thickness, indicate a crossing at the M¯ point, suggesting a topologically trivial phase in the thin film. The results show the presence of interfacial bonds to the substrate breaks inversion symmetry, preventing the semimetal-to-semiconductor transition predicted for freestanding bismuth layers, highlighting the importance of controlled functionalization and surface passivation of two-dimensional materials.

关键词： Ultrathin films

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N-way parametric frequency beamsplitter for quantum photonics

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Physical Review Research 2025年第2期7卷 023108-023108页

作者： Richard Oliver Miri Blau Chaitali Joshi Xingchen Ji Ricardo Gutiérrez-Jáuregui Ana Asenjo-Garcia Michal Lipson Alexander L. Gaeta Department of Applied Physics and Applied Mathematics Columbia University 500 West 120th Street New York New York 10027 USA Present address: Google Quantum AI Santa Barbara CA 93111 USA. Department of Electrical Engineering Columbia University 500 West 120th Street New York New York 10027 USA Present address: John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China. Department of Physics Columbia University 538 West 120th Street New York New York 10027 USA Departamento de Física Cuántica y Fotónica Instituto de Física Universidad Nacional Autónoma de México Ciudad de México 04510 Mexico

Optical networks are the leading platform for the transfer of information due to their low loss and ability to scale to many information channels using optical frequency modes. To fully leverage the quantum properties of light in this platform, it is desired to manipulate higher-dimensional superpositions by orchestrating linear, beamsplitter-type interactions between several channels simultaneously. We propose a method of achieving simultaneous, all-to-all coupling between N optical frequency modes via N-way Bragg-scattering four-wave mixing. By exploiting the frequency degree of freedom, additional modes can be multiplexed in an interaction medium of fixed volume and loss while avoiding the introduction of excess noise. We generalize the theory of the frequency-encoded two-mode interaction to N modes under this four-wave-mixing approach, finding that arbitrary unitary transformations are possible through successive four-wave-mixing pump configurations. We experimentally verify the quantum nature of this scheme by demonstrating three-way multiphoton interference. Two input photons are shared among three frequency modes and display interference differing from that of two classical (coherent-state) inputs. These results show the potential of our approach for the scalability of photonic quantum information processing to general N-mode systems in the frequency domain.

关键词： Four wave mixing

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Machine learning for improved current-density reconstruction from two-dimensional vector magnetic images

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Physical Review Applied 2025年第3期23卷 034035-034035页

作者： Niko R. Reed Danyal Bhutto Matthew J. Turner Declan M. Daly Sean M. Oliver Jiashen Tang Kevin S. Olsson Nicholas Langellier Mark J.H. Ku Department of Physics University of Maryland College Park Maryland USA Quantum Technology Center University of Maryland College Park Maryland USA Joint Quantum Institute University of Maryland College Park Maryland USA Department of Biomedical Engineering Boston University Boston Massachusetts USA A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown Massachusetts USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA The MITRE Corporation McLean Virginia 22102 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Department of Physics Harvard University Cambridge Massachusetts USA Present address: VideaHealth Inc. Boston MA. Department of Materials Science and Engineering University of Delaware Newark Delaware 19716 USA Department of Physics and Astronomy University of Delaware Newark Delaware 19716 USA Present address: Northrop Grumman Mission Systems Linthicum Maryland 21090 USA.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high-spatial-frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional images of vector magnetic fields acquired by a quantum diamond microscope . Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower-signal-to-noise-ratio data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources.

关键词： Signal to noise ratio

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Bringing sensation to prosthetic hands-chronic assessment of implanted thin-film electrodes in humans

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npj Flexible Electronics 2023年第1期7卷 32-45页

作者： PaulČvančara Giacomo Valle Matthias Müller Inga Bartels Thomas Guiho Arthur Hiairrassary Francesco Petrini Stanisa Raspopovic Ivo Strauss Giuseppe Granata Eduardo Fernandez Paolo M.Rossini Massimo Barbaro Ken Yoshida Winnie Jensen Jean-Louis Divoux David Guiraud Silvestro Micera Thomas Stieglitz Laboratory for Biomedical Microtechnology Department of Microsystems Engineering-IMTEKAlbert-Ludwig-University FreiburgFreiburgGermany Center for Neuroprosthetics and Institute of Bioengineering School of EngineeringÉcole Polytechnique Fédérale de Lausanne(EPFL)LausanneSwitzerland The Biorobotics Institute Scuola Superiore Sant’AnnaPisaItaly Camin Team INRIAUniversity of MontpellierMontpellierFrance Institute of Neurology Catholic University of the Sacred HeartRomaItaly Institute of Neurosurgery Catholic University of the Sacred HeartRomaItaly Department of Neuroscience&Neurorehabilitation IRCCS San Raffaele Roma00166 RomeItaly Department of Electrical and Electronic Engineering University of CagliariCagliariItaly Biomedical Engineering Department Indiana University-Purdue UniversityIndianapolisINUSA SMI Dept.Health Science and TechnologyAalborg UniversityAalborgDenmark AXONIC/Groupe MXM VallaurisFrance BrainLinks-BrainTools Albert-Ludwig-University FreiburgFreiburgGermany Bernstein Center Freiburg Albert-Ludwig-University FreiburgFreiburgGermany Present address:Laboratory for Neuroengineering Department of Health Sciences and TechnologyInstitute for Robotics and Intelligent SystemsETH ZürichZürichSwitzerland

Direct stimulation of peripheral nerves with implantable electrodes successfully provided sensory feedback to amputees while using hand *** of the electrodes is key to success,which we have improved for the polyimide-based transverse intrafascicular multichannel electrode(TIME).The TIMEs were implanted in the median and ulnar nerves of three trans-radial amputees for up to six *** present a comprehensive assessment of the electrical properties of the thin-film metallization as well as material status post *** TIMEs stayed within the electrochemical safe limits while enabling consistent and precise amplitude *** lead to a reliable performance in terms of eliciting *** signs of corrosion or morphological change to the thin-film metallization of the probes was observed by means of electrochemical and optical *** presented longevity demonstrates that thin-film electrodes are applicable in permanent implant systems.

关键词： film electrodes sensation

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engineering exchange bias at the interface of self-polarized ultrathin ferroelectric BaTiO3 and ferromagnetic La0.67Sr0.33MnO3

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Physical Review Applied 2024年第5期22卷 054035-054035页

作者： Tuhin Maity Manisha Bansal Nives Strkalj Kapildeb Dolui Di Zhang Zihao He Guillaume F. Nataf Adam Lovett Massimo Ghidini Department of Materials Science and Metallurgy University of Cambridge Cambridge CB3 0FS UK School of Physics Indian Institute of Science Education and Research Thiruvananthapuram 695551 India CAMRIE and CHPC Indian Institute of Science Education and Research Thiruvananthapuram 695551 India Center for Advanced Laser Techniques Institute of Physics 10000 Zagreb Croatia School of Materials Engineering Purdue University West Lafayette Indiana USA Present address: GREMAN UMR7347 CNRS University of Tours INSA Centre Val de Loire 37000 Tours France Diamond Light Source Harwell Science and Innovation Campus Chilton Didcot OX11 0DE UK Department of Mathematics Physics and Computer Science University of Parma 43124 Parma Italy

We investigate the emergence and optimization of conventional exchange bias (EB) in ultrathin (<10 nm) ferroelectric (FE) BaTiO3 (BTO)/ferromagnetic (FM) La0.67Sr0.33MnO3 (LSMO) epitaxial bilayers without an antiferromagnetic (AFM) material. The EB originates from the electronic orbital reconstruction at the FE-FM interface due to the ferroelectric polarization. We achieve maximum EB of approximately 42 Oe with single-domain polarization in nine-unit-cell-thick BTO, setting the BTO thickness above the critical threshold for ferroelectricity yet below the thickness of strain relaxation and multidomain breakdown. Furthermore, the LSMO layer needs to be thick enough to sustain both the FM layer and polarization-induced AFM spin configuration at the LSMO/BTO interface, yet as thin as possible to enable the EB loop shift. The temperature, training, field, and thickness dependence of the EB confirm that the LSMO/BTO interface exhibits conventional EB despite its unconventional origin. Using x-ray magnetic circular dichroism, scanning transmission electron microscopy, and density-functional-theory calculations, we confirm that the macroscopic EB effect originates from the interfacial AFM spin configuration in LSMO driven by FE-induced d-orbital modifications in interfacial Mn ions. Thus, we engineer strong interfacial EB coupling in artificial multiferroics without a conventional AFM material by controlling FE polarization, highlighting the potential for advanced spintronic applications.

关键词： Exchange bias Spintronics Surface & interfacial phenomena Ferroelectrics Ferromagnets Magnetic thin films Multiferroics Transition metal oxides

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A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning

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Discover data 2023年第1期1卷 4页

作者： Paul K Mvula Paula Branco Guy-Vincent Jourdan Herna L Viktor Present Address: School of Electrical Engineering and Computer Science (EECS) University of Ottawa 800 King Edward Avenue Ottawa K1N 6N5 ON Canada.

In Machine Learning, the datasets used to build models are one of the main factors limiting what these models can achieve and how good their predictive performance is. Machine Learning applications for cyber-security or computer security are numerous including cyber threat mitigation and security infrastructure enhancement through pattern recognition, real-time attack detection, and in-depth penetration testing. Therefore, for these applications in particular, the datasets used to build the models must be carefully thought to be representative of real-world data. However, because of the scarcity of labelled data and the cost of manually labelling positive examples, there is a growing corpus of literature utilizing Semi-Supervised Learning with cyber-security data repositories. In this work, we provide a comprehensive overview of publicly available data repositories and datasets used for building computer security or cyber-security systems based on Semi-Supervised Learning, where only a few labels are necessary or available for building strong models. We highlight the strengths and limitations of the data repositories and sets and provide an analysis of the performance assessment metrics used to evaluate the built models. Finally, we discuss open challenges and provide future research directions for using cyber-security datasets and evaluating models built upon them.

关键词： Cyber-security Datasets Intrusion detection Malware detection Performance metrics Phishing detection

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Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias

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The journal of prevention of Alzheimer's disease 2025年 100169页

作者： Sonia Akter Zhandi Liu Eduardo J Simoes Praveen Rao Institute for Data Science and Informatics University of Missouri USA. Department of Electrical Engineering and Computer Science University of Missouri USA. Department of Biomedical Informatics Biostatics and Medical Epidemiology University of Missouri USA. Institute for Data Science and Informatics University of Missouri USA Department of Electrical Engineering and Computer Science University of Missouri USA. Electronic address: praveen.rao@missouri.edu.

BACKGROUND:Over 6 million patients in the United States are affected by Alzheimer's Disease and Related Dementias (ADRD). Early detection of ADRD can significantly improve patient outcomes through timely treatment. OBJECTIVE:To develop and validate machine learning (ML) models for early ADRD diagnosis and prediction using de-identified EHR data from the University of Missouri (MU) Healthcare. DESIGN:Retrospective case-control study. SETTING:The study used de-identified EHR data provided by the MU NextGen Biomedical Informatics, modeled with the PCORnet Common Data Model (CDM). PARTICIPANTS:An initial cohort of 380,269 patients aged 40 or older with at least two healthcare encounters was narrowed to a final dataset of 4,012 ADRD cases and 119,723 controls. METHODS:Six ML classifier models: Gradient-Boosted Trees (GBT), Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), eXtreme Gradient-Boosting (XGBoost), Logistic Regression (LR), and Adaptive Boosting (AdaBoost) were evaluated using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), accuracy, sensitivity, specificity, and F1 score. SHAP (SHapley Additive exPlanations) analysis was applied to interpret predictions. RESULTS:The GBT model achieved the best AUC-ROC scores of 0.809-0.833 across 1- to 5-year prediction windows. SHAP analysis identified depressive disorder, age groups 80-90 yrs and 70-80 yrs, heart disease, anxiety, and the novel risk factors of sleep apnea, and headache. CONCLUSION:This study underscores the potential of ML models for leveraging EHR data to enable early ADRD prediction, supporting timely interventions, and improving patient outcomes. By identifying both established and novel risk factors, these findings offer new opportunities for personalized screening and management strategies, advancing both clinical and informatics science.

关键词： Alzheimer's disease Dementias Early prediction Electronic health record data Machine learning (ML)

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Norepinephrine changes behavioral state through astroglial purinergic signaling

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science (New York, N.Y.) 2025年第6748期388卷 769-775页

作者： Chen, Alex B. Duque, Marc Rymbek, Altyn Dhanasekar, Mahalakshmi Wang, Vickie M. Mi, Xuelong Tocquer, Loeva Narayan, Sujatha Legorreta, Emmanuel Marquez Eddison, Mark Yu, Guoqiang Wyart, Claire Prober, David A. Engert, Florian Ahrens, Misha B. Howard Hughes Medical Institute Janelia Research Campus Ashburn VA United States Department of Molecular and Cellular Biology Harvard University Cambridge MA United States Graduate Program in Neuroscience Harvard Medical School Boston MA United States Tianqiao and Chrissy Chen Institute for Neuroscience Division of Biology and Biological Engineering California Institute of Technology Pasadena CA United States Sorbonne Université Paris Brain Institute (Institut du Cerveau Institut National de la Santé et de la Recherche Médicale U1127 Centre National de la Recherche Scientifique Unité Mixte de Recherche 7225 Assistance Publique-Hôpitaux de Paris Campus Hospitalier Pitié-Salpêtrière Paris France Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA United States Present address: Allen Institute for Brain Science Seattle WA United States Department of Automation Tsinghua University Beijing China

Both neurons and glia communicate through diffusible neuromodulators;however, how neuron-glial interactions in such neuromodulatory networks influence circuit computation and behavior is unclear. During futility-induced behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine (NE) drives fast excitation and delayed inhibition of behavior and circuit activity. We found that astroglial purinergic signaling implements the inhibitory arm of this motif. In larval zebrafish, NE triggers astroglial release of adenosine triphosphate (ATP), extracellular conversion of ATP into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. Our results suggest a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in NE-mediated behavioral and brain state transitions and position astroglia as important effectors in neuromodulatory signaling.

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