The Experts below are selected from a list of 189714 Experts worldwide ranked by ideXlab platform
Ed Hurt - One of the best experts on this subject based on the ideXlab platform.
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sem1 is a Functional Component of the nuclear pore complex associated messenger rna export machinery
Journal of Cell Biology, 2009Co-Authors: Marius Boulos Faza, Stefan Kemmler, Sonia Jimeno, Cristina Gonzalezaguilera, Andres Aguilera, Ed Hurt, Vikram Govind PanseAbstract:The evolutionarily conserved protein Sem1/Dss1 is a subunit of the regulatory particle (RP) of the proteasome, and, in mammalian cells, binds the tumor suppressor protein BRCA2. Here, we describe a new function for yeast Sem1. We show that sem1 mutants are impaired in messenger RNA (mRNA) export and transcription elongation, and induce strong transcription-associated hyper-recombination phenotypes. Importantly, Sem1, independent of the RP, is Functionally linked to the mRNA export pathway. Biochemical analyses revealed that, in addition to the RP, Sem1 coenriches with Components of two other multisubunit complexes: the nuclear pore complex (NPC)-associated TREX-2 complex that is required for transcription-coupled mRNA export, and the COP9 signalosome, which is involved in deneddylation. Notably, targeting of Thp1, a TREX-2 Component, to the NPC is perturbed in a sem1 mutant. These findings reveal an unexpected nonproteasomal function of Sem1 in mRNA export and in prevention of transcription-associated genome instability. Thus, Sem1 is a versatile protein that might stabilize multiple protein complexes involved in diverse pathways.
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sus1 a Functional Component of the saga histone acetylase complex and the nuclear pore associated mrna export machinery
Cell, 2004Co-Authors: Susana Rodrigueznavarro, Tamas Fischer, Mingjuan Luo, Oreto Antunez, Susanne Brettschneider, Johannes Lechner, Jose E Perezortin, Robin Reed, Ed HurtAbstract:Gene expression is a coordinated multistep process that begins with transcription and RNA processing in the nucleus followed by mRNA export to the cytoplasm for translation. Here we report the identification of a protein, Sus1, which functions in both transcription and mRNA export. Sus1 is a nuclear protein with a concentration at the nuclear pores. Biochemical analyses show that Sus1 interacts with SAGA, a large intranuclear histone acetylase complex involved in transcription initiation, and with the Sac3-Thp1 complex, which functions in mRNA export with specific nuclear pore proteins at the nuclear basket. DNA macroarray analysis revealed that Sus1 is required for transcription regulation. Moreover, chromatin immunoprecipitation showed that Sus1 is associated with the promoter of a SAGA-dependent gene during transcription activation. Finally, mRNA export is impaired in sus1 mutants. These data provide an unexpected connection between the SAGA histone acetylase complex and the mRNA export machinery.
Youngmin Kwon - One of the best experts on this subject based on the ideXlab platform.
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effect of postural changes on in vivo pelvic tilt and Functional Component anteversion in total hip arthroplasty patients with lumbar disc degenerations
Journal of Bone and Joint Surgery-british Volume, 2020Co-Authors: Christian Klemt, Sakkadech Limmahakhun, Georges Bounajem, Liang Xiong, Ingwon Yeo, Youngmin KwonAbstract:Aims The complex relationship between acetabular Component position and spinopelvic mobility in patients following total hip arthroplasty (THA) renders it difficult to optimize acetabular Component...
David A Orsinelli - One of the best experts on this subject based on the ideXlab platform.
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platypnea orthodeoxia syndrome a story of weight gain unmasking congenital heart disease
Journal of the American College of Cardiology, 2019Co-Authors: Beth Foreman, Kristopher P Kline, Saurabh Rajpal, Aarthi Sabanayagam, David A OrsinelliAbstract:Platypnea orthodeoxia syndrome (POS) describes a rare condition of dyspnea and hypoxemia induced by upright posture that resolves when supine. Two conditions must coexist: an interatrial communication and a Functional Component that redirects shunt flow in the upright position. A 65 year old lady
David J Mayman - One of the best experts on this subject based on the ideXlab platform.
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pelvic tilt in patients undergoing total hip arthroplasty when does it matter
Journal of Arthroplasty, 2015Co-Authors: Joseph Maratt, Christina Esposito, Alexander S Mclawhorn, Seth A Jerabek, Douglas E Padgett, David J MaymanAbstract:Pelvic tilt (PT) affects the Functional anteversion and inclination of acetabular Components in total hip arthroplasty (THA). One-hundred and thirty-eight consecutive patients who underwent unilateral primary THA were reviewed. Most cases had some degree of pre-operative PT, with 17% having greater than 10° of PT on standing pre-operative radiographs. There was no significant change in PT following THA. A computer model of a hemispheric acetabular Component implanted in a range of anatomic positions in a pelvis with varying PT was created to determine the effects of PT on Functional anteversion and inclination. Based on the study results, tilt-adjustment of the acetabular Component position based on standing pre-operative imaging will likely improve Functional Component position in most patients undergoing THA.
Michael Levine - One of the best experts on this subject based on the ideXlab platform.
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Robust Functional estimation in the multivariate partial linear model
Annals of the Institute of Statistical Mathematics, 2019Co-Authors: Michael LevineAbstract:We consider the problem of adaptive estimation of the Functional Component in a partial linear model where the argument of the function is defined on a q -dimensional grid. Obtaining an adaptive estimator of this Functional Component is an important practical problem in econometrics where exact distributions of random errors and the parametric Component are mostly unknown. An estimator of the Functional Component that is adaptive over the wide range of multivariate Besov classes and robust to a wide choice of distributions of the linear Component and random errors is constructed. It is also shown that the same estimator is locally adaptive over the same range of Besov classes and robust over large collections of distributions of the linear Component and random errors as well. At any fixed point, this estimator attains a local adaptive minimax rate.
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robust Functional estimation in the multivariate partial linear model
arXiv: Statistics Theory, 2017Co-Authors: Michael LevineAbstract:We consider the problem of adaptive estimation of the Functional Component in a multivariate partial linear model where the argument of the function is defined on a $q$-dimensional grid. Obtaining an adaptive estimator of this Functional Component is an important practical problem in econometrics where exact distributions of random errors and the parametric Component are mostly unknown and cannot safely assumed to be normal. An estimator of the Functional Component that is adaptive in the mean squared sense over the wide range of multivariate Besov classes and robust to a wide choice of distributions of the linear Component and random errors is constructed. It is also shown that the same estimator is locally adaptive over the same range of Besov classes and robust over large collections of distributions of the linear Component and random errors as well. At any fixed point, this estimator also attains a local adaptive minimax rate. The procedure needed to obtain such an estimator turns out to depend on the choice of the right shrinkage approach in the wavelet domain. We show that one possible approach is to use the multivariate version of the classical BlockJS method. The multivariate version of BlockJS is developed in the manuscript and is shown to represent an independent interest. Finally, the Besov space scale over which the proposed estimator is locally adaptive is shown to depend on the dimensionality of the domain of the Functional Component; the higher the dimension, the larger the smoothness indicator of Besov spaces must be.
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a semiparametric multivariate partially linear model a difference approach
Journal of Statistical Planning and Inference, 2016Co-Authors: Lawrence D Brown, Michael Levine, Lie WangAbstract:A multivariate semiparametric partial linear model for both xed and random design cases is considered. The xed design case is shown to be, in eect, a semiparametric random eld model. In either case, the model is analyzed us- ing a dierence sequence approach. The linear Component is estimated based on the dierences of observations and the Functional Component is estimated using a multivariate Nadaraya-Watson kernel smoother of the residuals of the linear t. We show that both Components can be asymptotically estimated as well as if the other Component were known. The estimator of the linear Component is shown to be asymptotically normal and ecient if the length of the dierence sequence used goes to innity at a certain rate. The Functional Component estimator is shown to be rate optimal if the Lipschitz smoothness index exceeds half the dimensionality of the Functional Component argument. We also develop a test for linear combi- nations of regression coecients whose asymptotic power does not depend on the Functional Component. All of the proposed procedures are easy to implement. Fi- nally, numerical performance of all the procedures is studied using simulated data.
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minimax rate of convergence for an estimator of the Functional Component in a semiparametric multivariate partially linear model
Journal of Multivariate Analysis, 2015Co-Authors: Michael LevineAbstract:Abstract A multivariate semiparametric partial linear model for both fixed and random design cases is considered. Earlier, in Brown et al. (2014), the model has been analyzed using a difference sequence approach. In particular, the Functional Component has been estimated using a multivariate Nadaraya–Watson kernel smoother of the residuals of the linear fit. Moreover, this Functional Component estimator has been shown to be rate optimal if the Lipschitz smoothness index exceeds half the dimensionality of the Functional Component domain. In the current manuscript, we take this research further and show that, for both fixed and random designs, the rate achieved is the minimax rate under both risk at a point and the L 2 risk. The result is achieved by proving lower bounds on both pointwise risk and the L 2 risk of possible estimators of the Functional Component.
