Results 61 to 70 of about 621,842 (299)

Transferrin receptor 1‐mediated iron uptake supports thermogenic activation in human cervical‐derived adipocytes

open access: yesFEBS Letters, EarlyView.
In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai   +9 more
wiley   +1 more source

Errors in Variables and the Empirics of Economic Growth [PDF]

open access: yes
We examine cross-sectional empirical evidence on the determinants of economic growth in light of an instrumental variables estimator, based on sample moments of order higher than two, which does not require extraneous instruments and which remains ...
Jean-Louis ARCAND, Marcel DAGENAIS
core   +3 more sources

Spherical Regression with Errors in Variables

open access: yesThe Annals of Statistics, 1989
Suppose \(u_ 1,...,u_ n\), \(v_ 1,...,v_ n\) are random points on the sphere such that for unknown points \(\xi_ 1,...,\xi_ n\) and unknown rotation \(A_ 0\), the distribution of \(u_ i\) depends only on \(u^ t_ i\xi_ i\) and that of \(v_ i\) on \(v^ t_ iA_ 0\xi_ i\).
openaire   +2 more sources

Tau acetylation at K331 has limited impact on tau pathology in vivo

open access: yesFEBS Letters, EarlyView.
We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto   +3 more
wiley   +1 more source

Regression quantiles with errors-in-variables [PDF]

open access: yes, 2003
In a lot of situations, variables are measured with errors. While this problem has been previously studied in the kontext of kernel regression, no work has been done in quantile regression. To estimate this function we use deconvoluting kernel estimators.
Ioannides, D. A., Matzner-Lober, E.
core  

Value and limitations of intracranial recordings for validating electric field modeling for transcranial brain stimulation

open access: yesNeuroImage, 2020
Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping ...
Oula Puonti   +3 more
doaj   +1 more source

Calpain small subunit homodimerization is robust and calcium‐independent

open access: yesFEBS Letters, EarlyView.
Calpains dimerize via penta‐EF‐hand (PEF) domains. Using single‐molecule force spectroscopy, we measured the strength and kinetics of PEF–PEF homodimer binding. The interaction is robust, shows a transient conformational step before dissociation, and remains largely insensitive to Ca2+.
Nesha May O. Andoy   +4 more
wiley   +1 more source

Minimum distance estimation of dynamic models with errors-in-variables [PDF]

open access: yes, 2014
Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation.
Gospodinov, Nikolay   +2 more
core   +1 more source

Prediction of treatments effects in a biased allocation model

open access: yesRevstat Statistical Journal, 2005
Robbins and Zhang [15] provide consistent estimators of multiplicative treatment effects under a biased treatment allocation scheme, and illustrate their methodology within Poisson and binomial models.
Fernando J.M. Magalhães
doaj   +1 more source

Estimation of the mean of the partially linear single-index errors-in-variables model with missing response variables

open access: yesJournal of Inequalities and Applications, 2020
In this paper, we estimate the mean of the partially linear single-index errors-in-variables model with missing response variables. The linear covariate is measured with additive error, therefore missing is not random.
Xin Qi, ZhuoXi Yu
doaj   +1 more source

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