249 research outputs found
Estradiol regulation of leucine-rich repeat immunoglobulin-like domains protein 1 (LRIG 1) and roles of estrogen receptor in translational regulation in breast cancer cells
No full textAmong the first diagnostic tests performed upon finding a tumor in the breast is the determination of the expression of three proteins: the Estrogen Receptor α (ERα), the Progesterone Receptor (PR) and Heregulin 2 (HER2). The ER, though often considered responsible for driving tumor growth, is usually indicative of a good prognosis as ER-positive tumors are typically more indolent in nature and responsive to endocrine therapy. Long after estrogens were known to drive the growth of many breast tumors, the estrogen receptor was identified. The ER is a modular transcription factor known to regulate the expression of over a thousand genes in breast cancer cells.
As a master regulator of transcription, ER can impact the transcription and subsequent expression of many proteins that ultimately determine the growth and invasiveness of a tumor. As a transcriptional regulator, activation of ER with its cognate ligand, estradiol, results in a cascade of changes beginning with chromatin remodeling of estradiol-regulated genes.
In an attempt to identify ER-interacting proteins that aid in this chromatin unfolding activity, we performed a yeast two-hybrid screen using amino acids 420-534 of ERα as bait and identified Eukaryotic translation initiation factor 3 subunit F (eIF3f) as an interaction partner. Fluorescence microscopy suggests that in the presence of estradiol, the location of ERα-eIF3f changes. Though unclear if eIF3f, a translation factor, plays a role in chromatin unfolding, as a translation factor, we suggest that the interaction between eIF3f and ERα establishes a link through which estradiol can regulate translation. In support of this link, we find ERα in polysome profile fractions. Knockdown of eIF3f also appears to change the solubility of ERα within the nucleus. eIF3f knockdown also results in changes in steady-state mRNA levels of specific estradiol-target genes. Upon isolation of mRNA following polysome fractionation, we observe differences in the rate of translation of several estradiol-regulated mRNAs. Unlike other ERα-interacting partners such as Jab1 and E6-AP, eIF3f interacts with a shorter form of ERα.
The effects of estradiol on cellular function are pleiotropic. As a transcriptional regulator, ER regulates many genes that can have a subsequent impact on other cellular functions. These so-called secondary effects are often associated as the principle actions of estradiol. We identify leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) as an estradiol-regulated gene. LRIG1 is a negative regulator of Receptor Tyrosine Kinase (RTK) signaling. Up-regulation of LRIG1 mRNA and protein is mediated by the estrogen receptor-α (ERα) and we identified the regions of the LRIG1 gene to which ER binds. LRIG1 regulation by estradiol helps to explain how tumors typically utilize either estradiol or growth factor pathways, but rarely both, as mitogenic stimuli. Increased LRIG1, in response to estradiol, results in decreased signaling through RTK pathways. The impact of LRIG1 regulation by estradiol is cell-type specific. LRIG1 protein levels are important for both the growth of cells as well as colony formation and invasiveness of ERα-positive and HER2-positive BT-474 and ERα-positive and HER2-negative MCF-7 breast cancer cell lines. LRIG1 regulation by estradiol may be important for breast cancer etiology and phenotypic properties by influencing signaling pathways such as the AKT and MAPK pathways, which help to determine the breast tumor subtype as well as responsiveness to cancer treatments.Item withdrawn by Mark Zulauf ([email protected]) on 2010-12-01T18:19:42Z
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A Modern Approach and Analysis of the Electric Guitar in Funk, R&B, and Jazz Fusion
No full textWithin the contemporary and commercial music realm the electric guitar has been a driving force in the Funk genre since its inception and emergence from the late 1950s and early 1960s. Although the instrument has been given considerable attention in the style of Funk, there is far less information relative to its evolution and influence from the late twentieth to the twenty first century. The small number of publications on early Funk, R&B, and Jazz Fusion electric guitar has done little to highlight the guitarists discussed herein. The purpose of this report is to discuss the approach of guitarists Robben Ford, Mark Lettieri, Ole Børud, Cory Wong, KC Roberts, Tom McGuire, and their influence in contemporary Funk, R&B, and Jazz Fusion. The reader will acknowledge and form an understanding of the styles, techniques, and musicality these artists have contributed to the performance of contemporary guitar. By permeating a detailed background into their work, I will breakdown the history, theory, musical and harmonic analysis pertained to a total of nine songs that utilize a modern-day rhythm section with horns. The research, transcriptions, arrangements, analyses, and live recording session included herein will present, recognize, and conclusively prove why these musicians are both undervalued and among some of the most brilliant, virtuosic, and talented performers within the popular music realm of our time
Advisory committee process and program design : low carbon fuel standards
No full textFinal Report -- Appendix A. Summary of Advisory Committee Input -- Appendix B. Lifecycle Analysis -- Appendix C. Infrastructure Cost Assumptions Memorandum -- Appendix D. Economic Analysis -- Appendix E. Comparable Economic Studies in Other States -- Appendix F. Compliance Scenario Documentation -- Appendix G. Indirect Land Use Change Comparative Analysis -- Appendix H. Fuels Assessment Discussion Paper -- Appendix I. Oregon Biomass Assessment -- Appendix J. Credit and Deficit Calculations -- Appendix K. Review of Biodiesel and Renewable Diesel Use Considerations.principal authors: Sue Langston, David Collier, Cory Ann Wind, Dave Nordberg, Carrie Ann Capp, Wendy Simons.Title from PDF cover (viewed on April 20, 2020)."11-AQ-004."This archived document is maintained by the State Library of Oregon as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Includes bibliographical references.Mode of access: Internet from the Oregon Government Publications Collection.Text in English
Estradiol Regulation of Leucine-Rich Repeat Immunoglobulinlike Domains Protein 1 (Lrig 1) and Roles of Estrogen Receptor in Translational Regulation in Breast Cancer Cells
No full textThe effects of estradiol on cellular function are pleiotropic. As a transcriptional regulator, ER regulates many genes that can have a subsequent impact on other cellular functions. These so-called secondary effects are often associated as the principle actions of estradiol. We identify leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) as an estradiol-regulated gene. LRIG1 is a negative regulator of Receptor Tyrosine Kinase (RTK) signaling. Up-regulation of LRIG1 mRNA and protein is mediated by the estrogen receptor-alpha (ERalpha) and we identified the regions of the LRIG1 gene to which ER binds. LRIG1 regulation by estradiol helps to explain how tumors typically utilize either estradiol or growth factor pathways, but rarely both, as mitogenic stimuli. Increased LRIG1, in response to estradiol, results in decreased signaling through RTK pathways. The impact of LRIG1 regulation by estradiol is cell-type specific. LRIG1 protein levels are important for both the growth of cells as well as colony formation and invasiveness of ERalpha-positive and HER2-positive BT-474 and ERalpha-positive and HER2-negative MCF-7 breast cancer cell lines. LRIG1 regulation by estradiol may be important for breast cancer etiology and phenotypic properties by influencing signaling pathways such as the AKT and MAPK pathways, which help to determine the breast tumor subtype as well as responsiveness to cancer treatments.Made available in DSpace on 2015-09-28T15:03:23Z (GMT). No. of bitstreams: 2
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Previous issue date: 2010Embargo set by: Seth Robbins for item 87611
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only111 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2010
Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation
No full textUser Agreement, Public Domain Dedication, and Disclaimer of Liability.
By accessing or downloading the data or work provided here, you, the User, agree that you have read this agreement in full and agree to its terms.
The person who owns, created, or contributed a work to the data or work provided here dedicated the work to the public domain and has waived his or her rights to the work worldwide under copyright law. You can copy, modify, distribute, and perform the work, for any lawful purpose, without asking permission.
In no way are the patent or trademark rights of any person affected by this agreement, nor are the rights that any other person may have in the work or in how the work is used, such as publicity or privacy rights.
Pacific Science & Engineering Group, Inc., its agents and assigns, make no warranties about the work and disclaim all liability for all uses of the work, to the fullest extent permitted by law.
When you use or cite the work, you shall not imply endorsement by Pacific Science & Engineering Group, Inc., its agents or assigns, or by another author or affirmer of the work.
This Agreement may be amended, and the use of the data or work shall be governed by the terms of the Agreement at the time that you access or download the data or work from this Website.
Description
This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017.
Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files.
Each dataframe contains 55 columns:
Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions).
Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping).
Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively.
Columns 4 to 55 contain the process variables; the column names retain the original variable names.
Acknowledgments. This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government
Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation
No full textUser Agreement, Public Domain Dedication, and Disclaimer of Liability.
By accessing or downloading the data or work provided here, you, the User, agree that you have read this agreement in full and agree to its terms.
The person who owns, created, or contributed a work to the data or work provided here dedicated the work to the public domain and has waived his or her rights to the work worldwide under copyright law. You can copy, modify, distribute, and perform the work, for any lawful purpose, without asking permission.
In no way are the patent or trademark rights of any person affected by this agreement, nor are the rights that any other person may have in the work or in how the work is used, such as publicity or privacy rights.
Pacific Science & Engineering Group, Inc., its agents and assigns, make no warranties about the work and disclaim all liability for all uses of the work, to the fullest extent permitted by law.
When you use or cite the work, you shall not imply endorsement by Pacific Science & Engineering Group, Inc., its agents or assigns, or by another author or affirmer of the work.
This Agreement may be amended, and the use of the data or work shall be governed by the terms of the Agreement at the time that you access or download the data or work from this Website.
Description
This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017.
Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files.
Each dataframe contains 55 columns:
Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions).
Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping).
Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively.
Columns 4 to 55 contain the process variables; the column names retain the original variable names.
Acknowledgments. This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government
Localism the American way
No full textRichard S Grayson suggests that in making democratic localism work, the UK could look more at the United States' radically decentralised system. Using the example of Newark's pioneering mayor Cory Booker, he argues that strong elected mayors can bring about significant change, even in difficult circumstances. Copyright (c) 2010 The Author. Public Policy Research (c) 2010 ippr.
Systems modeling of metabolic dysregulation in neurodegenerative diseases
No full textNeurodegenerative diseases (NDDs) encompass a wide range of conditions that arise due to progressive degeneration and ultimate loss of nerve cells in the brain and peripheral nervous system. NDDs such as Alzheimer’s, Parkinson’s and Huntington’s disease negatively impact both length and quality of life, without effective disease-modifying treatments. Herein, we review the use of genome-scale metabolic models, network-based approaches and integration with multi-omics data to identify key biological processes that characterize NDDs. We describe powerful systems biology approaches for modeling NDD pathophysiology by leveraging in silico models that are informed by patient-derived multi-omics data. These approaches can enable mechanistic insights into NDD-specific metabolic dysregulations that can be leveraged to identify potential metabolic markers of disease and pre-disease states
Specificity of Baculorivus P6.9 Basic DNA-Binding Proteins and Critical Role of the C Terminus in Virion Formation
No full textThe majority of double-stranded DNA (dsDNA) viruses infecting eukaryotic organisms use host- or virus-expressed histones or protamine-like proteins to condense their genomes. In contrast, members of the Baculoviridae family use a protamine-like protein named P6.9. The dephosphorylated form of P6.9 binds to DNA in a non-sequence-specific manner. By using a p6.9-null mutant of Autographa californica multiple nucleopolyhedrovirus (AcMNPV), we demonstrate that P6.9 is not required for viral DNA replication but is essential for the production of infectious virus. Virion production was rescued by P6.9 homologs from a number of Alphabaculovirus species and one Gammabaculovirus species but not from the genus Betabaculovirus, comprising the granuloviruses, or by the P6.9 homolog VP15 from the unrelated white spot syndrome virus of shrimp. Mutational analyses demonstrated that AcMNPV P6.9 with a conserved 11-residue deletion of the C terminus was not capable of rescuing p6.9-null AcMNPV, while a chimeric Betabaculovirus P6.9 containing the P6.9 C-terminal region of an Alphabaculovirus strain was able to do so. This implies that the C terminus of baculovirus P6.9 contains sequence elements essential for virion formation. Such elements may possibly interact with species- or genus-specific domains of other nucleocapsid proteins during virus assembly
Climate change impacts on food security in Sub-Saharan Africa: Insights from comprehensive climate change scenarios
No full textClimate change impacts vary significantly, depending on the scenario and the Global Circulation Model (GCM) chosen. This is particularly true for Sub-Saharan Africa. This paper uses a comprehensive climate change scenario (CCC) based on ensembles of 17 GCMs selected based on their relative performance regarding past predictions of temperature and precipitation at the level of 2o x 2o grid cells, generated by a recently developed entropy-based downscaling model. Based on past performance, the effects of temperature and precipitation across the 17 GCMs are incorporated into a global hydrological model that is linked with IFPRI's IMPACT water and food projections model to assess the effects of climate change on food outcomes for the region. For Sub-Saharan Africa, the paper finds that the CCC scenario predicts consistently higher temperatures and mixed precipitation changes for the 2050 period. Compared to historic climate scenarios, climate change will lead to changes in yield and area growth, higher food prices and therefore lower affordability of food, reduced calorie availability, and growing childhood malnutrition in Sub-Saharan Africa.Climate change, hydrology, crop yield, food security,
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