Results 231 to 240 of about 2,932,686 (296)

Ofatumumab in Myelin Oligodendrocyte Glycoprotein Antibody–Associated Disease: A Comparison With Rituximab

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate the efficacy and safety of ofatumumab in patients with myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD), and compare it with rituximab. Methods We conducted a single–center, observational study including 22 MOGAD patients treated with ofatumumab and 21 treated with rituximab.
Yuxin Fan   +5 more
wiley   +1 more source

Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani   +10 more
wiley   +1 more source

Context Engineering for AI-Assisted Pharmacometrics: A Practical Tutorial. [PDF]

open access: yesCPT Pharmacometrics Syst Pharmacol
Pritchard-Bell A   +3 more
europepmc   +1 more source

Shaping Efficiency: Parametric Design for Schwedler Domes. [PDF]

open access: yesMaterials (Basel)
Ibrahim AFAO   +2 more
europepmc   +1 more source

Whole-Body Dynamic Positron Emission and Computed Tomography (WBD-PET/CT): Latest Developments, Challenges and Opportunities. [PDF]

open access: yesDiagnostics (Basel)
Vatalis A   +6 more
europepmc   +1 more source

Signal-Analytics Modeling of Fluorescence Time-to-Detection for <i>E. coli</i> in Treated Wastewater: A Joint Censoring and Sensor Model Approach. [PDF]

open access: yesACS Omega
Haab CA   +10 more
europepmc   +1 more source

Optimization on parametric model

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, 2018
Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with large number of weights consume considerable storage and memory bandwidth. To address this limitation, prun­ing is an effective way to compress neural networks with high accuracy.
Fenfen Huang, Wenbin Yao
openaire   +1 more source

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