Results 231 to 240 of about 24,324,331 (382)

Machine Learning for Surrogate Modeling in Multi-Objective Wing Optimization

Clemente Carrari, Gabriel, Pereira Gouveia da Silva, Gabriel   +1 more
openalex   +1 more source

Towards using human-surrogate models to optimize training sequences during visual category learning

, 2018
Brett D. Roads, Michael C. Mozer
openalex   +1 more source

On modal decomposition as surrogate for charge-conservative EHD modelling of Taylor Cone jets

, 2023
Sílvio Cândido, José Páscoa
openalex   +1 more source

Promiscuous stimulation of HSP70 ATPase activity by parasite‐derived J‐domains

FEBS Open Bio, EarlyView.
The malaria parasite Plasmodium falciparum exports three highly homologous yet functionally divergent J‐domain proteins into human erythrocytes. Here, we show that J‐domains isolated from all three proteins effectively stimulate the ATPase activity of both endogenous host and exported parasite HSP70 chaperones.
Julian Barth, Moritz Koch, Le‐Han Rössner, Johanna Eichhorn, Denys Pogoryelov, Matthias P. Mayer, Jude M. Przyborski   +6 more
wiley   +1 more source

Surrogate Modeling of Resonant Behavior in Scattering Problems Through Adaptive Rational Approximation and Sketching. [PDF]

J Sci Comput
Pradovera D, Hiptmair R, Perugia I.
europepmc   +1 more source

Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model [PDF]

Yuya Hamamatsu, Pavlo Kupyn, Roza Gkliva, Asko Ristolainen, Maarja Kruusmaa   +4 more
openalex   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture. [PDF]

PLoS Comput Biol
Comlekoglu T, Toledo-Marín JQ, Comlekoglu T, DeSimone DW, Peirce SM, Fox G, Glazier JA.   +6 more
europepmc   +1 more source

Surfing on Fitness Landscapes: A Boost on Optimization by Fourier Surrogate Modeling. [PDF]

Entropy (Basel), 2020
Manzoni L, Papetti DM, Cazzaniga P, Spolaor S, Mauri G, Besozzi D, Nobile MS.   +6 more
europepmc   +1 more source

Chameleon sequences reveal structural effects in proteins representing micelle‐like distribution of hydrophobicity

FEBS Open Bio, EarlyView.
Amino acids sequence of two different proteins with the same sequence (chameleon sequence—black boxes) represent in 3D structure of the proteins different secondary structures: HHHH—helical and BBB—Beta‐structural. The chains folded in water environment adopt different III‐order structures in which the chameleon fragments appear to adopt similar status
Irena Roterman, Katarzyna Stapor, Leszek Konieczny, Krzysztof Gądek, Piotr Nowakowski   +4 more
wiley   +1 more source