Results 181 to 190 of about 7,487,886 (340)
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
One System, Two Rules: Asymmetrical Coupling of Speech Production and Reading Comprehension in the Trilingual Brain. [PDF]
Wang Y, Meng Y, Yang Q, Wang R.
europepmc +1 more source
Learning and change in a dual lexicon model of speech production. [PDF]
Davis M, Redford MA.
europepmc +1 more source
A unidirectional cerebral organoid–organoid neural circuit is established using a microfluidic platform, enabling controlled directional propagation of electrical signals, neuroinflammatory cues, and neurodegenerative disease–related proteins between spatially separated organoids.
Kyeong Seob Hwang +9 more
wiley +1 more source
Evaluating the temporal order of motor and auditory systems in speech production using intracranial EEG. [PDF]
Li S +7 more
europepmc +1 more source
Predictive Coding and Internal Error Correction in Speech Production. [PDF]
Teghipco A, Okada K, Murphy E, Hickok G.
europepmc +1 more source
Two‐photon lithography (TPL) enables 3D magnetic nanostructures with unmatched freedom in geometry and material choice. Advances in voxel control, deposition, and functionalization open pathways to artificial spin ices, racetracks, microrobots, and a number of additional technological applications.
Joseph Askey +5 more
wiley +1 more source
Mimed speech as an intermediary state between overt and imagined speech production in an electrocorticography study. [PDF]
Kwon J +4 more
europepmc +1 more source
Microplastics from Wearable Bioelectronic Devices: Sources, Risks, and Sustainable Solutions
Bioelectronic devices (e.g., e‐skins) heavily rely on polymers that at the end of their life cycle will generate microplastics. For research, a holistic approach to viewing the full impact of such devices cannot be overlooked. The potential for devices as sources for microplastics is raised, with mitigation strategies surrounding polysaccharide and ...
Conor S. Boland
wiley +1 more source

