Results 71 to 80 of about 275,780 (304)

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

Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition

AIP Advances, 2017
In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal ...
Yuya Ohmichi
doaj   +1 more source

Data-Driven Pulsatile Blood Flow Physics with Dynamic Mode Decomposition

Fluids, 2020
Dynamic mode decomposition (DMD) is a purely data-driven and equation-free technique for reduced-order modeling of dynamical systems and fluid flow. DMD finds a best fit linear reduced-order model that represents any given spatiotemporal data.
Milad Habibi, Scott T. M. Dawson, Amirhossein Arzani   +2 more
doaj   +1 more source

Characterization of WAC interactions with R2TP and TTT chaperone complexes linking glucose and glutamine availability to mTORC1 activity

FEBS Open Bio, EarlyView.
TTT and R2TP chaperone complexes are required for the assembly and activation of mTORC1. WAC directly interacts with components of TTT, R2TP, and mTORC1, and these interactions are affected by the availability of glucose and glutamine, correlating with changes in mTORC1 activity.
Sofía Cabezudo, Natalia Cuervo, Carmen García‐Martín, Andrés López‐Perrote, Clara Reglero, Adrián Maqueda‐Real, Ana González‐Corpas, Marina Serna, Diego Megías, Alejo Efeyan, Solip Park, Oscar Llorca   +11 more
wiley   +1 more source

Estimation of Power System Inertia Using Nonlinear Koopman Modes

, 2018
We report a new approach to estimating power system inertia directly from time-series data on power system dynamics. The approach is based on the so-called Koopman Mode Decomposition (KMD) of such dynamic data, which is a nonlinear generalization of ...
Hamasaki, Ryo, Ishigame, Atsushi, Susuki, Yoshihiko   +2 more
core   +1 more source

Dynamic mode decomposition for plasma diagnostics and validation [PDF]

Review of Scientific Instruments, 2018
We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the
Roy Taylor, Brian Nelson, J. Nathan Kutz, Kyle Morgan   +3 more
openaire   +4 more sources

Dynamic Mode Decomposition with Control Liouville Operators

IFAC-PapersOnLine, 2021
This paper builds the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are introduced to separate the drift dynamics from the input dynamics.
Joel A. Rosenfeld, Rushikesh Kamalapurkar   +1 more
openaire   +3 more sources

Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics

FEBS Open Bio, EarlyView.
Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini, Nicolò Antonelli, Gianluca Molla   +2 more
wiley   +1 more source

Dynamic Mode Decomposition via Polynomial Root-Finding Methods

Mathematics
Dynamic mode decomposition (DMD) is a powerful data-driven tool for analyzing complex systems that has gained significant attention in various scientific and engineering disciplines.
Gyurhan Nedzhibov
doaj   +1 more source

Compressed dynamic mode decomposition for background modeling [PDF]

Journal of Real-Time Image Processing, 2016
We introduce the method of compressed dynamic mode decomposition (cDMD) for background modeling. The dynamic mode decomposition (DMD) is a regression technique that integrates two of the leading data analysis methods in use today: Fourier transforms and singular value decomposition.
N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz   +2 more
openaire   +6 more sources