Results 101 to 110 of about 5,911,579 (291)

Dynamic Causal Modeling of Gender Differences in Emotion: Implications for Augmented Cognition [PDF]

, 2020
The goal of this study is to investigate the neural basis of gender difference in emotion processing. Elec- troencephalogram (EEG) signals were recorded when the same set of emotion-eliciting images was shown to male and female participants.
Nam, Chang S., Huang, Jiali, Lindquist, Kristen   +2 more
core   +1 more source

Dynamic Causal Modeling of Subcortical Connectivity of Language

The Journal of Neuroscience, 2011
Subcortical–cortical interactions in the language network were investigated using dynamic causal modeling of magnetoencephalographic data recorded during auditory comprehension. Participants heard sentences that either were correct or contained violations. Sentences containing violations had syntactic or prosodic violations or both.
David, O., Maess, B. ; https://orcid.org/0000-0002-7857-291X, Eckstein, K., Friederici, A. ; https://orcid.org/0000-0002-6328-865X   +3 more
openaire   +4 more sources

Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation

FEBS Letters, EarlyView.
Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang, Ruslana Romaniuk, Hristo L. Svilenov   +2 more
wiley   +1 more source

Causal Interpretation of DBSCAN Algorithm: A Dynamic Modeling for Epsilon Estimation

Entropy
DBSCAN is widely used to identify structured regions in unlabeled data, but its performance depends critically on the selection of the neighborhood parameter ε.
K. Garcia-Sanchez, J.-L. Perez-Ramos, S. Ramirez-Rosales, A.-M. Herrera-Navarro, H. Jiménez-Hernández, D. Canton-Enriquez   +5 more
doaj   +1 more source

Liver organoids: modelling complexity in homeostasis and disease

FEBS Letters, EarlyView.
Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley   +1 more source

Dynamic causal weighting-based risk propagation modeling for airport movement areas

Scientific Reports
To analyze how dynamic operational conditions (e.g., environment changes, traffic surges) drive risk propagation in airport movement areas, this study proposes a model integrating complex network theory and reinforcement learning to decode causal ...
Wei Wu, Jiayi Lin, Ming Wei, Xinglong Wang, Longfei Zhu   +4 more
doaj   +1 more source

MODELING THE DEMAND FOR DURABLE INPUTS: DISTRIBUTED LAGS AND CAUSALITY [PDF]

Vector-autoregressive-moving-average (VARMA) modeling was used to identify distributed lag relationships among farm tractor derived demand variables and to provide a basis for formally testing the hypothesis that the price of new tractor horsepower is ...
Mui, H.W., Ali, Mukhtar M., Bradford, Garnett L.   +2 more
core  

Autophagy and mitophagy in pancreatic β‐cell homeostasis and their involvement in diabetes pathophysiology

FEBS Letters, EarlyView.
This review focuses on the role of autophagy and mitophagy in maintaining pancreatic β‐cell function and homeostasis. We discuss how genetic defects affecting these pathways contribute to the development of type 1, type 2, monogenic, and gestational diabetes. We further explore their potential as therapeutic targets. Created in BioRender.
Yunkyeong Lee, Mingming Tong, Anna L. Gloyn   +2 more
wiley   +1 more source

Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

NeuroImage: Clinical, 2017
Mirror visual feedback (MVF) is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke.
Soha Saleh, Mathew Yarossi, Thushini Manuweera, Sergei Adamovich, Eugene Tunik   +4 more
doaj   +1 more source

Parametric dynamic causal modelling

, 2020
This technical note introduces parametric dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions ...
Jafarian, Amirhossein, Zeidman, Peter, Wykes, Rob. C, Walker, Matthew, Friston, Karl J.   +4 more
openaire   +2 more sources