CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages
ABSTRACT Backgrounds Alzheimer's disease (AD) is characterized by amyloid‐beta plaques, tau tangles, and neuroinflammation. C‐X3‐C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies.
Ling Wang +6 more
wiley +1 more source
StackDPP: a stacking ensemble based DNA-binding protein prediction model
Background DNA-binding proteins (DNA-BPs) are the proteins that bind and interact with DNA. DNA-BPs regulate and affect numerous biological processes, such as, transcription and DNA replication, repair, and organization of the chromosomal DNA.
Sheikh Hasib Ahmed +3 more
doaj +1 more source
No unbiased Estimator of the Variance of K-Fold Cross-Validation [PDF]
In statistical machine learning, the standard measure of accuracy for models is the prediction error, i.e. the expected loss on future examples. When the data distribution is unknown, it cannot be computed but several resampling methods, such as K-fold ...
Yoshua Bengio, Yves Grandvalet
core
Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment
ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang +22 more
wiley +1 more source
Longitudinal variable selection by cross-validation in the case of many covariates [PDF]
Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (Marginal Models, Mixed Effects Models, etc.), far fewer options appear to exist for the ...
Eva Cantoni +3 more
core
A relation between log-likelihood and cross-validation log-scores
It is shown that the log-likelihood of a hypothesis or model given some data is equal to an average of all leave-one-out cross-validation log-scores that can be calculated from all subsets of the data. This relation can be generalized to any k-fold cross-
PierGianLuca Porta Mana
core +1 more source
Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3
ABSTRACT Objective To examine the value of MRI outcomes as endpoints for preventive and early‐stage trials of two polyglutamine spinocerebellar ataxias (SCAs). Methods A cohort of 100 participants (23 SCA1, 63 SCA3, median Scale for the Assessment and Rating of Ataxia (SARA) score = 5, 42% preataxic, and 14 gene‐negative controls) was scanned at 3T up ...
Thiago J. R. Rezende +26 more
wiley +1 more source
Comparison of Statistical Approaches for Modelling Land-Use Change
Land-use change can have local-to-global environment impacts such as loss of biodiversity and climate change as well as social-economic impacts such as social inequality.
Bo Sun, Derek T. Robinson
doaj +1 more source
FDG‐PET Associations With Disease Severity and Outcomes in NMDA‐Receptor IgG Autoimmune Encephalitis
ABSTRACT Background Patients with N‐methyl‐D‐aspartate (NMDA) receptor‐immunoglobulin G (IgG) autoimmune encephalitis (NMDAR‐IgG AE) demonstrate occipital lobe hypometabolism on baseline brain fluorodeoxyglucose‐positron emission tomography (bFDG‐PET).
Jonathan K. Lee +7 more
wiley +1 more source
Let \(X_ i\) be independent random variables with means \(m_ i\), \(1\leq i\leq n\), and common variance \(\sigma^ 2\). The mean vector m is to be estimated by an element of a given finite set of linear estimators \(\hat m,\) \(\hat m\) shall be chosen such that \(L_ n(\hat m)=n^{-1} \| m- \hat m\|^ 2\) is a minimum.
openaire +3 more sources

