Results 101 to 110 of about 4,573,641 (310)
Developmental, Neuroanatomical and Cellular Expression of Genes Causing Dystonia
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Dystonia is one of the most common movement disorders, with variants in multiple genes identified as causative. However, an understanding of which developmental stages, brain regions, and cell types are most relevant is crucial for developing relevant disease models and therapeutics.
Darren Cameron +5 more
wiley +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Glioma recurrence severely impacts patient prognosis, with current treatments showing limited efficacy. Traditional methods struggle to analyze recurrence mechanisms due to challenges in assessing tumor heterogeneity, spatial dynamics, and gene networks.
Lei Qiu +10 more
wiley +1 more source
An Outlier Fuzzy Detection Method Using Fuzzy Set Theory
IEEE Access, 2019 Outlier mining task is to discover some unusual objects, and however, most existing methods and their mining results lack pertinence. To address the pertinence of outlier results, we propose a novel outlier detection approach, namely, FOD, which aims at ...
Lizhong Jin, Junjie Chen, Xiaobo Zhang
doaj +1 more source
Traumatic Microhemorrhages Are Not Synonymous With Axonal Injury
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Diffuse axonal injury (DAI) is caused by acceleration‐deceleration forces during trauma that shear white matter tracts. Susceptibility‐weighted MRI (SWI) identifies microbleeds that are considered the radiologic hallmark of DAI and are used in clinical prognostication.
Karinn Sytsma +9 more
wiley +1 more source
Detecting Outliers in Data with Correlated Measures
, 2018 Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults.
Kifer, Daniel +2 more
core +1 more source
Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
wiley +1 more source
, 2018 We propose mean-shift to detect outlier points. The method processed every point by calculating its k-nearest neighbors (k-NN), and then shifting the point to the mean of its neighborhood. This is repeated three times. The bigger the movement, the more likely the point is an outlier. Boundary points are expected to move more than inner points; outliers
Yang, Jiawei +2 more
openaire +3 more sources
Complementarity of Long‐Reads and Optical Mapping in Parkinson's Disease for Structural Variants
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Long‐read sequencing and optical genome mapping technologies have the ability to detect large and complex structural variants. This has led to the discovery of novel pathogenic variants in neurodegenerative movement disorders. Thus, we aimed to systematically compare the SV detection capabilities of OGM and ONT in Parkinson's disease.
André Fienemann +17 more
wiley +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Outlier Detection for Time Series with Recurrent Autoencoder Ensembles
International Joint Conference on Artificial Intelligence, 2019 We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs).
Tung Kieu +3 more
semanticscholar +1 more source