Results 81 to 90 of about 1,239,914 (289)

How memory generates heterogeneous dynamics in temporal networks

, 2014
Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading.
Barrat, Alain, Génois, Mathieu, Vestergaard, Christian L.   +2 more
core   +3 more sources

Multi‐omics and low‐input proteomics profiling reveals dynamic regulation driving pluripotency initiation in early mouse embryos

FEBS Open Bio, EarlyView.
Mouse pre‐implantation development involves a transition from totipotency to pluripotency. Integrating transcriptomics, epigenetic profiling, low‐input proteomics and functional assays, we show that eight‐cell embryos retain residual totipotency features, whereas cytoskeletal remodeling regulated by the ubiquitin‐proteasome system drives progression ...
Wanqiong Li, Xi Xiao, Lingbin Qi, Chanyi Li, Sirui Song, Jiaying Qin, Zhigang Xue, Jinfeng Xue, Huaifang Li   +8 more
wiley   +1 more source

Temporal-Difference Networks

, 2015
8 pages, 3 figures, presented at the 2004 conference on Neural Information Processing Systems. in Advances in Neural Information Processing Systems 17 (proceedings of the 2004 conference), Saul, L. K., Weiss, Y., and Bottou, L. (Eds)
Sutton, Richard S., Tanner, Brian
openaire   +2 more sources

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

, 2019
Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage
Betthauser, Joseph L., Fifer, Matthew S., Kaliki, Rahul R., Krall, John T., Thakor, Nitish V.   +4 more
core   +1 more source

A light‐triggered Time‐Resolved X‐ray Solution Scattering (TR‐XSS) workflow with application to protein conformational dynamics

FEBS Open Bio, EarlyView.
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei, Fredrik Orädd, Konstantinos Magkakis, Magnus Andersson   +3 more
wiley   +1 more source

Risk of Coinfection Outbreaks in Temporal Networks: A Case Study of a Hospital Contact Network

Frontiers in Physics, 2017
We study the spreading of cooperative infections in an empirical temporal network of contacts between people, including health care workers and patients, in a hospital.
Jorge P. Rodríguez, Fakhteh Ghanbarnejad, Víctor M. Eguíluz   +2 more
doaj   +1 more source

Temporal overdrive recurrent neural network [PDF]

2017 International Joint Conference on Neural Networks (IJCNN), 2017
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process.
Bianchi, Filippo Maria, Kampffmeyer, Michael C., Maiorino, Enrico, Jenssen, Robert   +3 more
openaire   +3 more sources

Network Localization of Fatigue in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Fatigue is among the most common symptoms and one of the main factors determining the quality of life in multiple sclerosis (MS). However, the neurobiological mechanisms underlying fatigue are not fully understood. Here we studied lesion locations and their connections in individuals with MS, aiming to identify brain networks ...
Olli Likitalo, Jaakko Kungshamn, Albert Bellmunt‐Gil, Silvia Tommasin, Abhineet Ojha, Matias Viitala, Juho Aaltonen, Jyrki Lötjönen, Juha Koikkalainen, Pauli Ylikotila, Patrizia Pantano, Merja Soilu‐Hänninen, Juho Joutsa   +12 more
wiley   +1 more source

Post‐COVID Fatigue Is Associated With Reduced Cortical Thickness After Hospitalization

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Neuropsychiatric symptoms are among the most prevalent sequelae of COVID‐19, particularly among hospitalized patients. Recent research has identified volumetric brain changes associated with COVID‐19. However, it currently remains poorly understood how brain changes relate to post‐COVID fatigue and cognitive deficits.
Tim J. Hartung, Florentin Steigerwald, Amy Romanello, Cathrin Kodde, Matthias Endres, Sandra Frank, Peter Heuschmann, Philipp Koehler, Stephan Krohn, Daniel Pape, Jens Schaller, Sophia Stöcklein, Istvan Vadasz, Janne Vehreschild, Martin Witzenrath, Thomas Zoller, Carsten Finke, on behalf of the NAPKON Study Group, Y. Ahlgrimm, C. Finke, J. Fricke, T. Keil, L. Krist, N. Lisewsky, M. Mittermaier, M. Mueller‐Plathe, C. Pley, S. Schmidt, A. Stege, F. Steinbeis, S. Steinbrecher, C. Wildberg, M. Witzenrath, E. Zessin, T. Zoller, C. Arendt, C. Bellinghausen, S. Cremer, A. Groh, A. Gruenewaldt, Y. Khodamoradi, S. Klinsing, G. Rohde, M. Vehreschild, T. Vogl, S. Frank, J. C. Hellmuth, M. Huber, S. Kaeaeb, O. T. Keppler, E. Khatamzas, C. Mandel, S. Mueller, M. Muenchhoff, L. Reeh, C. Scherer, H. Stubbe, M. von Bergwelt, L. Weiss, B. Zwissler, S. Cleef, M. E. Figuera Basso, J. Franzenburg, K. Franzpoetter, A. Friedrichs, A. Hermes, J. Heyckendorf, C. Kujat, I. Lehmann, C. Maetzler, S. Meier, D. Pape, S. Poick, L. Reinke, A. K. Russ, A. M. Scheer, D. Schunk, T. Tamminga, S. Bohnet, D. Droemann, K. F. Franzen, R. Hoerster, N. Kaeding, M. Nissen, P. Parschke, J. Rupp, S. Caesar, H. Einsele, S. Frantz, A. Frey, A. Grau, K. Haas, C. Haertel, K. G. Haeusler, G. Hein, J. Herrmann, A. Horn, R. Jahns, P. Meybohm, F. A. Montellano, C. Morbach, J. Schmidt, P. Schulze, S. Stoerk, J. Volkmann, T. Bahmer, A. Hermes, M. Krawczak, W. Lieb, S. Schreiber, T. Tamminga, B. Balzuweit, S. Berger, J. Fricke, M. Hummel, A. Krannich, L. Krist, F. Kurth, J. Lienau, R. Lorbeer, C. Pley, J. Schaller, S. Schmidt, C. Thibeault, M. Witzenrath, T. Zoller, I. Bernemann, T. Illig, M. Kersting, N. Klopp, V. Kopfnagel, S. Muecke, M. Kraus, B. Lorenz‐Depiereux, G. Anton, A. Kuehn‐Steven, S. Kunze, M. K. Tauchert, K. Appel, M. Brechtel, I. Broehl, K. Fiedler, R. Geisler, S. M. Hopff, K. Knaub, C. Lee, S. Nunes de Miranda, S. Raquib, G. Sauer, M. Scherer, J. J. Vehreschild, P. Wagner, L. Wolf, J. C. Hellmuth, K. Guenther, F. Haug, J. Haug, A. Horn, M. Kohls, C. Fiessler, P. U. Heuschmann, O. Miljukov, C. Nuernberger, J. P. Reese, L. Schmidbauer, I. Chaplinskaya, S. Hanss, D. Krefting, C. Pape, M. Rainers, A. Schoneberg, N. Weinert, T. Bahls, W. Hoffmann, M. Nauck, C. Schaefer, M. Schattschneider, D. Stahl, H. Valentin, P. Heuschmann, A. L. Hofmann, S. Jiru‐Hillmann, J. P. Reese, S. Herold, P. Heuschmann, R. Heyder, W. Hoffmann, T. Illig, S. Schreiber, J. J. Vehreschild, M. Witzenrath   +190 more
wiley   +1 more source

Temporal Multivariate Networks

, 2014
Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, the structure of the dynamic graph evolves as vertices and edges are added or removed from the graph.
Archambault, Daniel, Abello, James, Kennedy, Jessie, Kobourov, Stephen, Ma, Kwan-Liu, Miksch, Silva, Muelder, Chris, Telea, Alexandru C.   +7 more
openaire   +4 more sources