Results 241 to 250 of about 2,479,210 (291)
FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
Mass spectrometry based identification of AMP‐O‐Tris generated by Thermococcus onnurineus Cas10
FEBS Open Bio, EarlyView.Isolated Thermococcus onnurineus Cas10 generates the noncanonical ATP‐derived product AMP‐O‐Tris while in Tris‐containing buffer as identified via mass spectrometry, revealing relaxed nucleophile selectivity under isolated conditions. These findings suggest that multiprotein Csm complex assembly restricts Cas10 reactivity toward canonical cyclic ...
Su‐Jin Lee +6 more
wiley +1 more source
FEBS Open Bio, EarlyView.This paper reveals how human lactoferrin–albumin fusion (hLF‐HSA) potently suppresses lung adenocarcinoma cell migration. hLF‐HSA upregulates NHE7, leading to Golgi alkalization, disruption of the Golgi secretome, downregulation of MMP1, and reversal of EMT. These findings suggest a novel Golgi‐targeting strategy to suppress cancer cell migration.
Hana Nopia +3 more
wiley +1 more source
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Improvement of mixed predictors in linear mixed models
Journal of Applied Statistics, 2020In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean ...
Özge Kuran, M. Revan Özkale
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2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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2020
This chapter introduces linear mixed models, which have wide applicability in small area estimation due to their flexibility to combining different types of information and explaining sources of errors. Three of the most used fitting methods are presented under two parametrizations.
Domingo Morales +3 more
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This chapter introduces linear mixed models, which have wide applicability in small area estimation due to their flexibility to combining different types of information and explaining sources of errors. Three of the most used fitting methods are presented under two parametrizations.
Domingo Morales +3 more
openaire +2 more sources
2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
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2001
Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
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Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
openaire +1 more source
Linear and Generalized Linear Mixed Models and Their Applications
Technometrics, 2008(2008). Linear and Generalized Linear Mixed Models and Their Applications. Technometrics: Vol. 50, No. 1, pp. 93-94.
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