Results 51 to 60 of about 8,978,880 (312)

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

open access: yesJournal of Advanced Transportation, 2023
To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were
Congcong Bai   +6 more
doaj   +1 more source

Testing homogeneity in Weibull error in variables models [PDF]

open access: yesAnnals of the Institute of Statistical Mathematics, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Valença, Dione Maria, Bolfarine, Heleno
openaire   +2 more sources

Valosin‐containing protein counteracts ATP‐driven dissolution of FUS condensates through its ATPase activity in vitro

open access: yesFEBS Letters, EarlyView.
Biomolecular condensates formed by fused in sarcoma (FUS) are dissolved by high ATP concentrations yet persist in cells. Using a reconstituted system, we demonstrate that valosin‐containing protein (VCP), an AAA+ ATPase, counteracts ATP‐driven dissolution of FUS condensates through its D2 ATPase activity.
Hitomi Kimura   +2 more
wiley   +1 more source

Some recent advances in measurement error models and methods [PDF]

open access: yes, 2005
Measurement errors, error in variables, misclassification, efficiency comparison, survival analysis. JEL C13, C20, C24, C25,
Augustin, Thomas   +3 more
core   +1 more source

Diversity and complexity in neural organoids

open access: yesFEBS Letters, EarlyView.
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley   +1 more source

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework [PDF]

open access: yesHydrology and Earth System Sciences, 2015
Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure.
M. S. Raleigh   +2 more
doaj   +1 more source

Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation

open access: yesFEBS Letters, EarlyView.
Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz   +11 more
wiley   +1 more source

Incorporating remote sensing measurement error for forest inventory

open access: yesBig Earth Data
Remotely sensed data are increasingly used with model-based inference to estimate forest population characteristics (e.g., areal means). However, at-sensor radiance affected by measurement errors has inherent variations over time, space and spectrum ...
Qing Xu   +3 more
doaj   +1 more source

pH‐mediated activation of the lysosomal arginine sensor SLC38A9

open access: yesFEBS Letters, EarlyView.
Cells monitor nutrient levels via the lysosomal transporter SLC38A9 to activate the mechanistic target of rapamycin complex 1 (mTORC1). This study reveals that SLC38A9 function is regulated by pH. We identified histidine 544 as a critical pH sensor that undergoes conformational changes to control amino acid efflux from lysosomes; therefore, it ...
Xuelang Mu, Ampon Sae Her, Tamir Gonen
wiley   +1 more source

Asymptotic Properties for Estimators in a Semiparametric EV Model with NA Errors and Missing Responses

open access: yesDiscrete Dynamics in Nature and Society, 2022
This article deals with the semiparametric errors-in-variables (EV) model yi=ξiβ+gti+ϵi, xi=ξi+μi, where yi are the random missing response variables, ξi,ti are the design points, ξi are the potential variables observed with measurement errors μi, and ...
Jing-jing Zhang, Cheng-Liang Liu
doaj   +1 more source

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