Results 111 to 120 of about 9,360,077 (298)

Integrated PANoptosis Profiling Identifies Immunosuppressive Subtypes and a Prognostic Signature With Functional Validation of MLKL in Glioblastoma

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective The prognosis of glioblastoma (GBM) remains highly unfavorable, largely due to high tumor heterogeneity and an immunosuppressive microenvironment. However, the functional role of PANoptosis in this context is poorly understood. Methods Patients were stratified via K‐means clustering. A risk score model was constructed using prognosis‐
Langfei Tian, Minghui Zhao, Yuanbo Hu, Kaiyue Wang, Haiguang Liu, Zetong Bai, Kebin Zheng   +6 more
wiley   +1 more source

Angle-based Graph Neural Network Method for Anomaly Detection in High Dimensional Data [PDF]

Jisuanji gongcheng
In high-dimensional data spaces, most data are located at the edges of the high-dimensional space and distributed sparsely, resulting in the problem of "curse of dimensionality", which makes existing anomaly detection methods unable to ensure ...
Jun WANG, Huixia LAI, Yue WAN, Shi ZHANG
doaj   +1 more source

Anti‐CD20 Discontinuation Versus Continuation in People Aged Over 50 With Non‐Active Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To determine whether discontinuing anti‐CD20 therapy in people with relapsing‐onset MS aged over 50 is associated with an increased risk of relapse, inflammatory activity, confirmed disability accrual, and serious infection compared with continuing therapy.
Alexia Moukhine, Fabien Rollot, Romain Casey, Nicolas Collongues, Sandra Vukusic, Guillaume Mathey, Anne Laure Dubessy, Jonathan Ciron, Jérôme De Seze, Aurélie Ruet, Pierre Labauge, Arnaud Kwiatkowski, Hélène Zephir, Caroline Papeix, Gilles Defer, Christine Lebrun‐Frenay, David Axel Laplaud, Eric Berger, Eric Thouvenot, Jean Pelletier, Abdullatif Al Khedr, Olivier Casez, Bertrand Bourre, Abir Wahab, Laurent Magy, Jean‐Philippe Camdessanché, Ines Doghri, Solène Moulin, Mariana Sarov Rivière, Karolina Hankiewicz, Amélie Dos Santos, Corinne Pottier, Eric Manchon, Jennifer Aboab, Maia Tchikviladze, Thomas David, Gilles Edan, Emmanuelle Le Page, Laure Michel, Anne Kerbrat, OFSEP Investigators   +40 more
wiley   +1 more source

Latching chains in K-nearest-neighbor and modular small-world networks

, 2015
Latching dynamics retrieve pattern sequences successively by neural adaption and pattern correlation. We have previously proposed a modular latching chain model in Song et al.
Simonov, Alexander Yurievich, Song SM(宋三明), Yao HX(姚鸿勋)   +2 more
core  

Arterial Spin‐Labeling MRI at the Cortical‐CSF Interface: A Novel Biomarker in Alzheimer Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background/Objective Arterial spin‐labeling (ASL) MRI can measure perfusion signal adjacent to CSF spaces and may provide information regarding CSF‐adjacent water transport physiology. We developed an automated pipeline to extract cortical‐CSF interface (IF) perfusion for comparison between Alzheimer disease (AD) and cognitively normal ...
Mona Asghariahmadabad, Pouya Metanat, Michael Thomas, Luke W. Bonham, Tyson Lam, Mehmet Kurt, Hong Nguyen, Tara Ellingson, Mauro Zucchelli, Stefano Casagranda, Quin Lu, Andreas M. Rauschecker, Leo Sugrue, Luis E. Savastano, Christopher P. Hess, Kamalini Ranasinghe, S. Andrew Josephson, Bruce L. Miller, Jennifer S. Yokoyama, Gil D. Rabinovici, Peter A. Ljubenkov, Lawren VandeVrede, Kambiz Nael   +22 more
wiley   +1 more source

The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.)

, 1979
A two-stage iterative algorithm for selecting a subset of a training set of samples for use in a condensed nearest neighbor (CNN) decision rule is introduced. The proposed method uses the concept of mutual nearest neighborhood for selecting samples close
Krishna, G, Gowda, K
core  

K nearest neighbor reinforced expectation maximization method

, 2011
K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based
Mehmet Aci, Avci M., Aci M., Mutlu Avci
core   +1 more source

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

An adaptive multiclass nearest neighbor classifier

, 2020
We consider a problem of multiclass classification, where the training sample S_n={(Xi,Yi)}ni=1 is generated from the model ℙ(Y = m|X = x) = ηm(x), 1 ≤ m ≤ M, and η1(x), …, ηM(x) are unknown α-Holder continuous functions.
Puchkin, Nikita, Vladimir Spokoiny, Nikita Puchkin, Spokoiny, Vladimir   +3 more
core   +1 more source

Clinical Impact of MGMT Promoter Methylation in IDH‐Mutant Gliomas: Influence of Threshold Selection and Clinical Confounding

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background The clinical relevance of MGMT promoter methylation in IDH‐mutant gliomas remains controversial in the era of molecular classification. We aimed to systematically evaluate its clinical relevance by integrating quantitative assessment, cutoff exploration, and adjustment for clinical confounding.
Haihui Jiang, Linxi Cao, Xiaodong Chen, Siqi Tong, Qinghui Zhu, Jun Yang, Song Lin, Yong Cui   +7 more
wiley   +1 more source