Results 51 to 60 of about 907,184 (299)

Biophysical analysis of angiotensin II and amyloid‐β cross‐interaction in aggregation and membrane disruption

FEBS Letters, EarlyView.
Angiotensin II (AngII), a neuropeptide, interacts with amyloid‐β (Aβ), a key player in Alzheimer's disease. This study reveals that AngII reduces Aβ aggregation and membrane disruption in vitro. Biophysical assays and molecular modeling suggest AngII binds disordered Aβ forms, potentially modulating early amyloidogenic events and contributing to ...
Mohsen Habibnia, Eric Catalina‐Hernandez, Maialen Cabrerizo‐Idiazabal, Ramon Barnadas‐Rodríguez, Mario Lopez‐Martin, Alex Peralvarez‐Marin   +5 more
wiley   +1 more source

A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning [PDF]

, 2011
In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov ...
Cui, Ying, Huang, Huang, Lau, Vincent K. N., Wang, Rui, Zhang, Shunqing   +4 more
core   +2 more sources

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Distributed ARTMAP [PDF]

, 1999
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open ...
Carpenter, Gail A., Milenova, Boriana L.
core   +1 more source

Low Complexity Regularization of Linear Inverse Problems [PDF]

, 2014
Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it.
A Girard, A. Barron, A. Beck, A. Beck, A. Chambolle, A. Chambolle, A. Daniilidis, A. Montanari, A.N. Tikhonov, A.N. Tikhonov, A.S. Bandeira, A.S. Lewis, A.S. Lewis, A.S. Lewis, A.S. Lewis, B. Efron, B. Efron, B. Recht, B.A. Turlach, B.C. Vũ, B.D. Rao, B.F. Svaiter, B.K. Natarajan, B.S. Mordukhovich, C. Chaux, C. Deledalle, C. Dossal, C. Dossal, C. Lemaréchal, C. Vonesch, C.-A. Deledalle, C.-A. Deledalle, C.L. Mallows, C.M. Stein, D. Gabay, D. Gabay, D. Gross, D. Needell, D. Ville Van De, D. Ville Van De, D.A. Lorenz, D.A. Spielman, D.L. Donoho, D.L. Donoho, D.L. Donoho, D.L. Donoho, E. Grave, E. Hale, E. Harchaoui, E. J. Candès, E. Richard, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, E.J. Candès, F. Bach, F. Bach, F. Luisier, F. Santosa, G. Chen, G. Davis, G. Obozinski, G. Peyré, G. Steidl, G.B. Passty, G.H. Golub, H. Akaike, H. Jégou, H. Jégou, H. Raguet, H. Zou, H.H. Bauschke, H.H. Bauschke, H.L. Taylor, H.M. Hudson, I. Daubechies, J. Allen, J. Bolte, J. Chen, J. Douglas, J. Eckstein, J. Eckstein, J. Eckstein, J. Mairal, J. Tropp, J. Ye, J.-B. Hiriart-Urruty, J.-C. Pesquet, J.-F. Aujol, J.-J. Fuchs, J.-J. Fuchs, J.-L. Starck, J.-L. Starck, J.A. Tropp, J.C. Dunn, J.E. Vogt, J.F. Claerbout, J.M. Lee, K. Bredies, K. Bredies, K. Bredies, K. Kato, K. Knight, K.-C. Li, L. Birgé, L. Birgé, L. Borup, L. Condat, L.I. Rudin, L.M. Briceño Arias, M Coste, M. Elad, M. Elad, M. Fazel, M. Fortin, M. Frank, M. Golbabaee, M. Grasmair, M. Jaggi, M. Meyer, M. Rudelson, M. Yuan, M.J. Wainwright, M.R. Osborne, M.V. Solodov, N. Parikh, O. Scherzer, P. Hall, P. Hall, P. Tseng, P. Zhao, P. Zhao, P.L. Combettes, P.L. Combettes, P.L. Combettes, P.L. Lions, R. Ciak, R. Giryes, R. Glowinski, R. Gribonval, R. Gribonval, R. Gribonval, R. Gribonval, R. Jenatton, R. Jenatton, R. Refregier, R. Tibshirani, R. Tibshirani, R.J. Tibshirani, R.J. Tibshirani, R.L. Dykstra, R.T. Rockafellar, S. Nam, S. Negahban, S. Ramani, S. Ramani, S. Shalev-Shwartz, S. Vaiter, S. Vaiter, S.F. Cotter, S.G. Lingala, S.G. Mallat, S.G. Mallat, S.G. Mallat, S.J. Wright, S.N. Negahban, S.P. Boyd, S.S. Chen, T. Blu, T. Blumensath, T. Strohmer, T.T. Cai, T.T. Cai, T.T. Cai, V. Chandrasekaran, V. Duval, V. Solo, W.L. Hare, X. Shen, Y. Castro de, Y. Censor, Y. Chen, Y. Lyubarskii, Y. Nesterov, Y. Nesterov, Y.C. Eldar, Y.C. Pati   +197 more
core   +3 more sources

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source

Decrypting cancer's spatial code: from single cells to tissue niches

Molecular Oncology, EarlyView.
Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik, Shi Pan, Eloise Withnell, Hou Wang Lam, Maria Secrier   +4 more
wiley   +1 more source

Dynamical stability and chaos in artificial neural network trajectories along training

Frontiers in Complex Systems
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network’s prediction, when confronted with a learning task.
Kaloyan Danovski, Miguel C. Soriano, Lucas Lacasa   +2 more
doaj   +1 more source

From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior [PDF]

, 2018
The study of neuronal interactions is currently at the center of several neuroscience big collaborative projects (including the Human Connectome, the Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the entire brain matrix. Under
Chialvo, Dante R., Gudowska-Nowak, Ewa, Nowak, Maciej A., Ochab, Jeremi K., Tarnowski, Wojciech   +4 more
core   +2 more sources

Current trends in single‐cell RNA sequencing applications in diabetes mellitus

FEBS Open Bio, EarlyView.
Single‐cell RNA sequencing is a powerful approach to decipher the cellular and molecular landscape at a single‐cell resolution. The rapid development of this technology has led to a wide range of applications, including the detection of cellular and molecular mechanisms and the identification and introduction of novel potential diagnostic and ...
Seyed Sajjad Zadian, Khodakaram Jahanbin, Shekoofeh Nikooei, Marzieh Rostaminejad, Arezoo Rahimi, Pedram Abdizadeh, Behnam Alipoor   +6 more
wiley   +1 more source