Results 191 to 200 of about 386,074 (314)

Functional Precision Oncology Approach Using Nanoliter Droplet Array for Drug Sensitivity Testing in Lung Cancer

open access: yesAdvanced Healthcare Materials, EarlyView.
A miniaturized drug sensitivity and resistance testing (DSRT) workflow based on the Droplet Microarray (DMA) platform enables functional drug testing using minimal patient‐derived tumor material. By screening nanoliter‐scale droplets containing as few as 300 cells, this approach generates reproducible and tumor‐specific drug response profiles ...
Maryam Salarian   +7 more
wiley   +1 more source

A brief history of dopamine prediction errors. [PDF]

open access: yesFront Comput Neurosci
Dudhabhate BB, Costa KM.
europepmc   +1 more source

PolyGraph – Flexible, Biocompatible & Electrically Optimized Graphene‐Polymer Composites for Next‐Generation Neural Interfaces

open access: yesAdvanced Healthcare Materials, EarlyView.
PolyGraph, a flexible graphene‐polycaprolactone nanocomposite, unites conductivity, biocompatibility, and processability for next‐generation neural interfaces. Fabricated into microneedle arrays with ultra‐flexible backings, PolyGraph enables bidirectional neuronal recording and stimulation in brain tissue, advancing brain‐computer interface (BCI) and ...
Jack Maughan   +12 more
wiley   +1 more source

Microengineered Gradient Hydrogels for Mechanobiology

open access: yesAdvanced Healthcare Materials, EarlyView.
Gradient hydrogels are used to mimic the mechanical heterogeneity in native tissues, offering powerful in vitro platforms to study cell‐material interactions in diverse pathophysiological contexts. Here, we present a comprehensive review of the design and experimental considerations for stiffness gradient hydrogels, discussing exemplary achievements ...
Shin Wei Chong   +4 more
wiley   +1 more source

Causal attributions shape the formation of novel ability self-beliefs. [PDF]

open access: yesCommun Psychol
Mayer AV   +7 more
europepmc   +1 more source

Training error, generalization error and learning curves in neural learning

open access: yesTraining error, generalization error and learning curves in neural learning
A neural network is trained by using a set of available examples to minimize the training error such that the network parameters fit the examples well. However, it is desired to minimize the generalization error to which no direct access is possible. There are discrepancies between the training error and the generalization error due to the statistical ...
openaire  

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