Results 221 to 230 of about 906,571 (334)

Intratumoral Fusobacterium nucleatum Drives Cancer‐Associated Fibroblasts Enrichment and Immune Exclusion in Esophageal Squamous Cell Carcinoma

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
Fusobacterium nucleatum contributes to the progression of ESCC by inducing NF‐κB–mediated inflammatory signaling in tumor cells and promoting CAFs activation. Its presence may facilitate immune exclusion and tumor invasion through stromal remodeling. Furthermore, F.
Takashi Ofuchi   +9 more
wiley   +1 more source

Visual Imagery and False Memory for Pictures: A Functional Magnetic Resonance Imaging Study in Healthy Participants

open access: gold, 2017
Christian Stephan‐Otto   +6 more
openalex   +2 more sources

Inverse Engineering of Mg Alloys Using Guided Oversampling and Semi‐Supervised Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
End‐to‐end design of engineering materials such as Mg alloys must include the properties, structure, and post‐synthesis processing methods. However, this is challenging when destructive mechanical testing is needed to annotate unseen data, and the processing methods for hypothetical alloys are unknown.
Amanda S. Barnard
wiley   +1 more source

False memories for end-of-life decisions. [PDF]

open access: green, 2008
Stefanie J. Sharman   +4 more
openalex   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

open access: yesAdvanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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