Results 71 to 80 of about 1,563,284 (329)

Quantifying the Impact of Ocrelizumab on Paramagnetic Rim Lesions in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Paramagnetic rim lesions (PRLs) are a subset of chronic active multiple sclerosis (MS) lesions marked by iron‐laden microglia and macrophages. Ocrelizumab, a monoclonal antibody targeting CD20+ B cells, suppresses acute MS activity, but its effect on PRLs remains unclear. In a longitudinal study of 29 ocrelizumab‐treated patients with at least
Kimberly H. Markowitz, Neha V. Safi, Iliana Pliska‐Bloch, Ulrike W. Kaunzner, Ha Luu, Yi Wang, Thanh D. Nguyen, Sandra Hurtado Rúa, Susan A. Gauthier   +8 more
wiley   +1 more source

Addressing Economic Insecurities Can Improve Patient‐Reported Outcomes in Lupus

Arthritis Care &Research, Accepted Article.
Background Economic insecurities, such as food, housing, transportation, and financial challenges, are modifiable risk factors and influence patient‐reported outcomes (PROs) in systemic lupus erythematosus (SLE). We examined: 1) associations between economic insecurities and PROs; 2) the impact of screening and addressing economic insecurities during ...
Jay Patel, Tripti Singh, Meredith Ingersoll, Shelby Gomez, Amanda Weber, Sarah E. Panzer, Sancia Ferguson, Christie M. Bartels, Shivani Garg   +8 more
wiley   +1 more source

Real-time flood forecasting coupling different postprocessing techniques of precipitation forecast ensembles with a distributed hydrological model. The case study of may 2008 flood in western Piemonte, Italy [PDF]

Natural Hazards and Earth System Sciences, 2013
In this work, we compare the performance of an hydrological model when driven by probabilistic rain forecast derived from two different post-processing techniques.
D. Cane, S. Ghigo, D. Rabuffetti, M. Milelli   +3 more
doaj   +1 more source

Bayesian model averaging in astrophysics: a review [PDF]

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2013
AbstractWe review the use of Bayesian model averaging in astrophysics. We first introduce the statistical basis of Bayesian model selection and model averaging. We discuss methods to calculate the model‐averaged posteriors, including Markov chain Monte Carlo (MCMC), nested sampling, population Monte Carlo, and reversible jump MCMC (RJMCMC).
Parkinson, David, Liddle, Andrew
openaire   +3 more sources

Trajectories of Physical Function in Canadian Children with Juvenile Idiopathic Arthritis

Arthritis Care &Research, Accepted Article.
Objectives We describe trajectories of physical function in children newly diagnosed with juvenile idiopathic arthritis (JIA) and identify trajectories with persisting functional impairments and associated baseline characteristics. Methods We included patients enrolled in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry ...
Clare Cunningham, Meghan McPherson, Lillian Lim, Roberta A. Berard, Matthew Berkowitz, Jean‐Philippe Proulx‐Gauthier, Brian M. Feldman, Nicole Johnson, Dax G. Rumsey, Heinrike Schmeling, Lori B. Tucker, Thomas Loughin, Kristin M. Houghton, Jaime Guzman, The CAPRI Registry Investigators   +14 more
wiley   +1 more source

Similarity-Based Multiple Model Adaptive Estimation

IEEE Access, 2018
Multiple model adaptive estimation (MMAE) methods are frequently used to overcome the parametric uncertainty of the system's model. Most MMAE methods approximate the state posteriori (posterior probability) by a weighted arithmetic average of model ...
Akbar Assa, Konstantinos N. Plataniotis
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Bayesian dynamic quantile model averaging

Annals of Operations Research
AbstractThis article introduces a novel dynamic framework to Bayesian model averaging for time-varying parameter quantile regressions. By employing sequential Markov chain Monte Carlo, we combine empirical estimates derived from dynamically chosen quantile regressions, thereby facilitating a comprehensive understanding of the quantile model ...
Bernardi, Mauro, Casarin, Roberto, Maillet, Bertrand B., Petrella, Lea   +3 more
openaire   +4 more sources

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Bayesian networks for constrained location choice modeling using structural restrictions and model averaging

European Journal of Transport and Infrastructure Research, 2018
In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities.
Tai-Yu Ma, Sylvain Klein
doaj   +1 more source