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An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes

Neuron 2025年第10期113卷 1525-1547.e15页

作者： Furlanis, Elisabetta Dai, Min Leyva Garcia, Brenda Tran, Thien Vergara, Josselyn Pereira, Ana Gorissen, Bram L. Wills, Sara Vlachos, Anna Hairston, Ariel Dwivedi, Deepanjali Du, Sarah McMahon, Justin Huang, Shuhan Morabito, Annunziato Vazquez, Arenski Kim, Soyoun Lee, Anthony T. Chang, Edward F. Razzaq, Taha Qazi, Ahmed Vargish, Geoffrey Yuan, Xiaoqing Caccavano, Adam Hunt, Steven Chittajallu, Ramesh McLean, Nadiya Hewitt, Lauren Paranzino, Emily Rice, Haley Cummins, Alex C. Plotnikova, Anya Mohanty, Arya Tangen, Anne Claire Shin, Jung Hoon Azadi, Reza Eldridge, Mark A.G. Alvarez, Veronica A. Averbeck, Bruno B. Alyahyay, Mansour Vallejo, Tania Reyes Soheib, Mohammed Vattino, Lucas G. MacGregor, Cathryn P. Chatain, Carolina Piletti Banks, Emmie Olah, Viktor Janos Naskar, Shovan Hill, Sophie Liebergall, Sophie Badiani, Rohan Hyde, Lili Hanley, Ella Xu, Qing Allaway, Kathryn C. Goldberg, Ethan M. Rowan, Matthew J.M. Nowakowski, Tomasz J. Lee, Soohyun Favuzzi, Emilia Kaeser, Pascal S. Sjulson, Lucas Batista-Brito, Renata Takesian, Anne E. Ibrahim, Leena A. Iqbal, Asim Pelkey, Kenneth A. McBain, Chris J. Dimidschstein, Jordane Fishell, Gord Wang, Yating Harvard Medical School Blavatnik Institute Department of Neurobiology Boston 02115 MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge 02142 MA United States Eunice Kennedy Shriver National Institute of Child Health and Human Development NIH Bethesda 20892 MD United States Dominick P. Purpura Department of Neuroscience Albert Einstein College of Medicine Bronx NY United States Department of Neurological Surgery University of California San Francisco San Francisco CA United States Weill Institute for Neurosciences University of California San Francisco San Francisco CA United States Tibbling Technologies Redmond WA United States National Institute of Mental Health National Institutes of Health Bethesda 20892-4415 MD United States Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Eaton-Peabody Laboratories Massachusetts Eye and Ear Infirmary Boston MA United States Department of Otolaryngology - Head and Neck Surgery Harvard Medical School Boston MA United States Department of Neuroscience Kavli Institute for Neuroscience Yale University School of Medicine New Haven 06510 CT United States Wu Tsai Institute Yale University New Haven 06510 CT United States Department of Cell Biology Emory University School of Medicine Atlanta 30322 GA United States Unit of Functional Neural Circuit National Institute of Mental Health National Institutes of Health Bethesda 20892 MD United States Department of Neuroscience The University of Pennsylvania Perelman School of Medicine Philadelphia 19104 PA United States Genomics & Systems Biology New York University Abu Dhabi Abu Dhabi United Arab Emirates Dominick P. Purpura Department of Psychiatry and Behavioral Sciences Albert Einstein College of Medicine Bronx NY United States

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate specific neuronal subtypes are still limited. Here, we performed systematic analysis of single-cell genomic data to identify enhancer candidates for each of the telencephalic interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic interneurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP), and manipulate (opto-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy. © 2025 Elsevier Inc.

关键词： adeno-associated virus cholinergic cortex enhancer GABAergic interneuron

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Incorporating user expectations and behavior into the measurement of search effectiveness

Incorporating user expectations and behavior into the measur...

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作者： Moffat, Alistair Bailey, Peter Scholer, Falk Thomas, Paul Department of Computing and Information Systems University of Melbourne VIC3010 Australia Microsoft Australia 6 National Circuit BartonACT2600 Australia School of Computer Science and Information Technology RMIT University GPO Box 2476 MelbourneVIC3001 Australia

Information retrieval systems aim to help users satisfy information needs. We argue that the goal of the person using the system, and the pattern of behavior that they exhibit as they proceed to attain that goal, should be incorporated into the methods and techniques used to evaluate the effectiveness of IR systems, so that the resulting effectiveness scores have a useful interpretation that corresponds to the users' search experience. In particular, we investigate the role of search task complexity, and show that it has a direct bearing on the number of relevant answer documents sought by users in response to an information need, suggesting that useful effectiveness metrics must be goal sensitive. We further suggest that user behavior while scanning results listings is affected by the rate at which their goal is being realized, and hence that appropriate effectiveness metrics must be adaptive to the presence (or not) of relevant documents in the ranking. In response to these two observations, we present a new effectiveness metric, INST, that has both of the desired properties: INST employs a parameter T, a direct measure of the user's search goal that adjusts the top-weightedness of the evaluation score;moreover, as progress towards the target T is made, the modeled user behavior is adapted, to reflect the remaining expectations. INST is experimentally compared to previous effectiveness metrics, including Average Precision (AP), Normalized Discounted Cumulative Gain (NDCG), and Rank-Biased Precision (RBP), demonstrating our claims as to INST's usefulness. Like RBP, INST is a weighted-precision metric, meaning that each score can be accompanied by a residual that quantifies the extent of the score uncertainty caused by unjudged documents. As part of our experimentation, we use crowdsourced data and score residuals to demonstrate that a wide range of queries arise for even quite specific information needs, and that these variant queries introduce significant levels o

关键词： Information retrieval systems

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107.5 Gb/s 850 nm multi- and single-mode VCSEL transmission over 10 and 100 m of multi-mode fiber

107.5 Gb/s 850 nm multi- and single-mode VCSEL transmission ...

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2016 Optical Fiber Communications Conference and Exhibition, OFC 2016

作者： Puerta, R. Agustin, M. Chorchos, L. Toeski, J. Kropp, J.-R. Ledentsov, N. Shchukin, V.A. Ledentsov, N.N. Henker, R. Monroy, I. Tafur Olmos, J. J. Vegas Turkiewicz, J.P. Akademivej Building 358 Kgs. Lyngby2800 Denmark VI-Systems GmbH Hardenbergstrasse 7 Berlin10623 Germany Technische Universität Dresden Department for Circuit Design and Network Theory Dresden01062 Germany Institute of Telecommunications Warsaw University of Technology Nowowiejska 15/19 Warsaw00-665 Poland

First time successful 107.5 Gb/s MultiCAP 850 nm OM4 MMF transmissions over 10 m with multi-mode VCSEL and up to 100 m with single-mode VCSEL are demonstrated, with BER below 7% overhead FEC limit measured for each ca... 详细信息

ISBN: (纸本)9781943580071

关键词： Optical fiber communication

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Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control

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BMC Research Notes 2016年第1期9卷 1-7页

作者： Prahm, Cosima Eckstein, Korbinian Ortiz-Catalan, Max Dorffner, Georg Kaniusas, Eugenijus Aszmann, Oskar C. Institute of Electrodynamics Microwave and Circuit Engineering Vienna University of Technology Gusshausstr. 25 Vienna 1040 Austria CD Laboratory for Extremity Reconstruction Division of Plastic and Reconstructive Surgery Medical University of Vienna Währinger Gürtel 18-20 Vienna 1090 Austria Department of Radiology and Nuclear Medicine Medical University of Vienna Lazarettgasse 14 Vienna 1090 Austria Division for Biomedical Engineering Department of Signals and Systems Chalmers University of Technology Campus Johanneberg Gothenburg 41296 Sweden Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Spitalgasse 23 Vienna 1090 Austria

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0). © 2016 The Author(s).

关键词： Machine learning Neural computation Pattern recognition Prosthetics Upper limb amputation

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Signal Processing for Cybersecurity and Privacy

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IEEE SIGNAL PROCESSING MAGAZINE 2013年第5期30卷 14-15页

作者： Sankar, Lalitha Trappe, Wade Ramchandran, Kannan Poor, H. Vincent Debbah, Merouane Department of Transport VÅ B-Technical University Ostrava Department of Circuit Theory Czech Technical University Prague Massachusetts Institute of Technology Laboratory for Information and Decision Systems Cambridge MA USA

The aim of this special issue is to examine information security and privacy methods that complement traditional cryptographic services. The articles chosen survey the challenges associated with achieving security and privacy in both communications networks as well as distributed systems and identify signal processing approaches to protect information.

关键词： Special issues and sections Privacy Computer security Security Cryptography Biometrics Data privacy

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Optimal component selection for energy-efficient systems

Optimal component selection for energy-efficient systems

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2013 16th Forum on Specification and Design Languages, FDL 2013

作者： Sauppe, Matthias Horn, Thomas Markert, Erik Heinkel, Ulrich Sahm, Hans-Werner Otto, Klaus-Holger Department for Systems and Circuit Design Chemnitz University of Technology Chemnitz Germany Alcatel-Lucent AG Nuremberg Germany

ISBN: (纸本)9782953050486

Microelectronics have developed very fast in the past. The design process of those systems is getting more and more complex and new design methods have to be applied continuously. One main observation is the increasing design level over time, hence, the re-use of components is getting more important. A key challenge of IC design is the selection of a system architecture which fulfills all requirements in terms of data throughput, area, timing, power and cost. In this paper, we present a a problem class for the optimal component selection in order to assist in selecting the best available alternatives. We will show how to express top-level constraints and optimisation targets including dependencies between components. In addition, heuristic solving algorithms will be presented. The evaluation section shows that the presented algorithms perform well on typical problem sets. © 2013 European Electronic Chips & systems design Initiative - ECSI.

关键词： Heuristic algorithms

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Active self-assembly of algorithmic shapes and patterns in polylogarithmic time 13

Active self-assembly of algorithmic shapes and patterns in p...

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2013 4th ACM Conference on Innovations in Theoretical Computer Science, ITCS 2013

作者： Woods, Damien Chen, Ho-Lin Goodfriend, Scott Dabby, Nadine Winfree, Erik Yin, Peng Computer Science California Institute of Technology Pasadena CA 91125 United States Center for Mathematics of Information California Institute of Technology Pasadena CA 91125 United States Chemical Engineering California Institute of Technology Pasadena CA 91125 United States Computation and Neural Systems California Institute of Technology Pasadena CA 91125 United States Bioengineering California Institute of Technology Pasadena CA 91125 United States Center for Biological Circuit Design California Institute of Technology Pasadena CA 91125 United States Department of Electrical Engineering National Taiwan University Taiwan Wyss Institute for Biologically Inspired Engineering 3 Blackfan Circle Boston MA 02445 United States

ISBN: (纸本)9781450318594

We describe a computational model for studying the complexity of self-assembled structures with active molecular components. Our model captures notions of growth and movement ubiquitous in biological systems. The model is inspired by biology's fantastic ability to assemble biomolecules that form systems with complicated structure and dynamics, from molecular motors that walk on rigid tracks and proteins that dynamically alter the structure of the cell during mitosis, to embryonic development where large scale complicated organisms efficiently grow from a single cell. Using this active self-assembly model, we show how to efficiently self-assemble shapes and patterns from simple monomers. For example we show how to grow a line of monomers in time and number of monomer states that is merely logarithmic in its length. Our main results show how to grow arbitrary connected two-dimensional geometric shapes and patterns in expected time polylogarithmic in the size of the shape plus roughly the time required to run a Turing machine deciding whether or not a given pixel is in the shape. We do this while keeping the number of monomer types logarithmic in shape size, plus monomers required by the Kolmogorov complexity of the shape or pattern. This work thus highlights the fundamental efficiency advantage of active self-assembly over passive self-assembly and motivates experimental effort to construct self-assembly systems with active molecular components. © 2013 Authors.

关键词： Self assembly

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Tool-supported specification of a wind turbine

Tool-supported specification of a wind turbine

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9th International Multi-Conference on systems, Signals and Devices, SSD 2012

作者： Markert, Erik Shende, Milind Horn, Thomas Wolf, Peter Heinkel, Ulrich Chemnitz University of Technology Department of Circuit and Systems Design Chemnitz Germany

ISBN: (纸本)9781467315906

Requirements management systems are a key technology to raise the efficiency of a design process. This paper presents the current opportunities of the tool SpecScribe in different physical domains to assist the development of a wind turbine health monitoring system. SpecScribe not only offers document organization but also direct design support by code generation and formal verification issues. This includes digital and analog problems which are transformed to FSM and hybrid automata. From these automata a SystemC-AMS model can be generated automatically. Furthermore a VHDL-AMS model is included in the specification as "golden model". © 2012 IEEE.

关键词： Specifications

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