Results 131 to 140 of about 1,822,892 (288)

Baseline Neuroinflammation Stratifies TSPO‐PET Response to Disease‐Modifying Therapy in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate which baseline clinical and imaging characteristics best predict TSPO‐PET‐measurable reduction in glial activation following treatment of multiple sclerosis (MS), to utilize this information for designing more efficient biomarker‐based clinical trials targeting glial activation.
Marlene T. Morch, Markus Matilainen, Giulia N. Baldrighi, Marjo Nylund, Maija Saraste, Laura Airas   +5 more
wiley   +1 more source

Computational Differential Privacy for Encrypted Databases Supporting Linear Queries

International audienceDifferential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis.
Alborch Escobar, Ferran, Laguillaumie, Fabien, Phan, Duong Hieu, Canard, Sébastien   +3 more
core   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

Greater consistency required for telecommunications privacy

, 2007
In her detailed submission to the Australian Law Reform Commission\u27s Review of Privacy, the Privacy Commissioner, Karen Curtis, has called for changes to a number of laws to assist in clarifying privacy requirements in the telecommunications industry.
Karen Curtis, Office of the Privacy Commissioner   +1 more
core  

Privacy-Preserving Deep Reinforcement Learning based on Differential Privacy

Deep reinforcement learning, with its extensive applications and remarkable performance, is emerging as a pivotal technology garnering researchers' attention.
Tian, H, Zhao, W, Sang, Y, Xiong, N
core   +1 more source

Disease Course After Anti‐CD20 Discontinuation in Secondary Progressive Multiple Sclerosis—A Multicenter Long‐Term Longitudinal Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To describe long‐term outcomes after anti‐CD20 discontinuation in selected patients with secondary progressive multiple sclerosis (SPMS) who remained without subsequent disease‐modifying therapy (DMT). Methods We retrospectively analyzed data from four centers in Austria and Switzerland.
Ferdinand Otto, Dariia Kliushnikova, Richard Friedrich Radlberger, Sinan Yasaroglu, Tobias Moser, Andrea Harrer, Kitty Kratzer, Wolfgang Hitzl, Christiane Gradl, Martin Schmidauer, Patrick Roth, Harald Hegen, Peter Wipfler   +12 more
wiley   +1 more source

Submission to the Australian Law Reform Commission's review of privacy

, 2007
In this detailed submission to the Australian Law Reform Commission\u27s review of privacy laws, the Privacy Commissioner, Karen Curtis, calls for changes to a number of laws.
Office of the Privacy Commissioner
core  

Safety and Efficacy of GLP‐1 Receptor Agonists in Adults With Epilepsy, Obesity, and Type 2 Diabetes

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Managing obesity in patients with epilepsy is complicated by the weight‐gaining properties of essential antiseizure medications (ASMs) such as valproate and pregabalin. We evaluated the safety and efficacy of initiating glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) in this population.
Hyoshin Son, Inyoung Hwang, Sang Won Lee, Yun Kim   +3 more
wiley   +1 more source

Gradual Release of Sensitive Data under Differential Privacy

, 2017
We introduce the problem of releasing private data under differential privacy when the privacy level is subject to change over time. Existing work assumes that privacy level is determined by the system designer as a fixed value before private data is ...
George J. Pappas, Shuo Han, Fragkiskos Koufogiannis   +2 more
core   +1 more source

Noise Variance Optimization in Differential Privacy: A Game-Theoretic Approach Through Per-Instance Differential Privacy

IEEE Access
The concept of differential privacy (DP) can quantitatively measure privacy loss by observing the changes in the distribution caused by the inclusion of individuals in the target dataset.
Sehyun Ryu, Jonggyu Jang, Hyun Jong Yang
doaj   +1 more source