Results 71 to 80 of about 549,786 (309)

Clinically Relevant Outcome Measures in Women With Adrenoleukodystrophy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Adrenoleukodystrophy is a rare inherited peroxisomal disease caused by pathogenic variants in the ABCD1 gene located on the X chromosome. Although the most severe central nervous system and adrenal complications typically affect only men with adrenoleukodystrophy, the majority of women develop myeloneuropathy symptoms in adulthood.
Chenwei Yan, Elizabeth I. Pierpont, Amena S. Fine, Reena V. Kartha   +3 more
wiley   +1 more source

BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping [PDF]

, 2022
Gasser Elbanna, Neil Scheidwasser, Mikolaj Kegler, Pierre Beckmann, Karl El Hajal, Miloš Cerňak   +5 more
openalex   +1 more source

Remote Assessment of Ataxia Severity in SCA3 Across Multiple Centers and Time Points

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Spinocerebellar ataxia type 3 (SCA3) is a genetically defined ataxia. The Scale for Assessment and Rating of Ataxia (SARA) is a clinician‐reported outcome that measures ataxia severity at a single time point. In its standard application, SARA fails to capture short‐term fluctuations, limiting its sensitivity in trials.
Marcus Grobe‐Einsler, Vivian Maas, Arian Taheri Amin, Jennifer Faber, Tamara Schaprian, Katharina Hill, Matthis Synofzik, Lisa H. Graf, Heike Jacobi, Manuela Lima, Ana F. Ferreira, Bart P. van de Warrenburg, Ilse H. J. Willemse, Dagmar Timmann, Andreas Thieme, Paola Giunti, Hector Garcia‐Moreno, Magda M. Santana, Jeannette Hübener‐Schmid, Elin H. Davies, Thomas Klockgether   +20 more
wiley   +1 more source

Exploiting map information for self-supervised learning in motion forecasting [PDF]

, 2022
Caio Azevedo, Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou   +3 more
openalex   +1 more source

Normal‐Appearing White Matter Injury Mediates Chronic Deep Venous Hypoxia and Disease Progression in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore how cerebral hypoxia and Normal‐Appearing White Matter (NAWM) integrity affect MS lesion burden and clinical course. Methods Seventy‐nine MS patients, including 13 clinically isolated syndrome (CIS) patients and 66 relapsing–remitting multiple sclerosis (RRMS) patients, and 44 healthy controls (HCs) were recruited from ...
Xinli Wang, Huiying Wang, Zhizheng Zhuo, Ai Guo, Ke Lv, Decai Tian, Chao Chai, Yunyun Duan, Shuang Xia   +8 more
wiley   +1 more source

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

BMC Medical Imaging
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data.
Blake VanBerlo, Jesse Hoey, Alexander Wong   +2 more
doaj   +1 more source

A Simple Data Mixing Prior for Improving Self-Supervised Learning [PDF]

, 2022
Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie   +6 more
openalex   +1 more source

A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials

Advanced Engineering Materials, EarlyView.
This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline, Matthew Santer, Robert Hewson   +2 more
wiley   +1 more source

Weighted Ensemble Self-Supervised Learning

, 2022
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explore how ensemble methods can improve
Ruan, Yangjun, Singh, Saurabh, Morningstar, Warren, Alemi, Alexander A., Ioffe, Sergey, Fischer, Ian, Dillon, Joshua V.   +6 more
openaire   +2 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source