Institution
University of Bath
Education•Bath, Bath and North East Somerset, United Kingdom•
About: University of Bath is a education organization based out in Bath, Bath and North East Somerset, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 15830 authors who have published 39608 publications receiving 1358769 citations. The organization is also known as: Bath University.
Papers published on a yearly basis
Papers
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TL;DR: A 2.1 Å X-ray crystal structure of the murine μOR bound to the morphinan agonist BU72 and a G protein mimetic camelid antibody fragment is reported, revealing an extensive polar network between the ligand-binding pocket and the cytoplasmic domains appears to play a similar role in signal propagation for all three G-protein-coupled receptors.
Abstract: Activation of the μ-opioid receptor (μOR) is responsible for the efficacy of the most effective analgesics. To shed light on the structural basis for μOR activation, here we report a 2.1 A X-ray crystal structure of the murine μOR bound to the morphinan agonist BU72 and a G protein mimetic camelid antibody fragment. The BU72-stabilized changes in the μOR binding pocket are subtle and differ from those observed for agonist-bound structures of the β2-adrenergic receptor (β2AR) and the M2 muscarinic receptor. Comparison with active β2AR reveals a common rearrangement in the packing of three conserved amino acids in the core of the μOR, and molecular dynamics simulations illustrate how the ligand-binding pocket is conformationally linked to this conserved triad. Additionally, an extensive polar network between the ligand-binding pocket and the cytoplasmic domains appears to play a similar role in signal propagation for all three G-protein-coupled receptors.
704 citations
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TL;DR: A recursive algorithm for computing the Dirichlet tessellation in a highly efficient way is described, and the problems which arise in its implementation are discussed.
Abstract: A finite set of distinct points divides the plane into polygonal regions, each region containing one of the points and comprising that part of the plane nearer to its defining point than to any other. The resultant planar subdivision is called the Dirichlet tessellation; it is one of the most useful constructs associated with such a point configuration. The regions, which we call tiles, are also known as Voronoi or Thiessen polygons. We describe a recursive algorithm for computing the tessellation in a highly efficient way, and discuss the problems which arise in its implementation. Samples of graphical output demonstrate the application of the program on a modest scale; its efficiency allows its application to large sets of data, and detailed discussion of space and time considerations is given, based in part on theoretical predictions and in part on test runs on up to 10,000 points.
703 citations
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Swedish University of Agricultural Sciences1, Commonwealth Scientific and Industrial Research Organisation2, University of Exeter3, University of Bath4, George Washington University5, Ghent University6, Centre for DNA Fingerprinting and Diagnostics7, University of Copenhagen8, Kansas State University9, University of Montpellier10, Max Planck Society11, University of Warsaw12, University of Georgia13, National Autonomous University of Mexico14, Australian National University15, University of Valencia16, Wageningen University and Research Centre17, University of Saskatchewan18, Agriculture and Agri-Food Canada19, Stockholm University20, Eötvös Loránd University21, University of Tokyo22, National Institute of Advanced Industrial Science and Technology23, Plant & Food Research24, Oregon State University25, Agricultural Research Service26, Leiden University27, University of Manitoba28
TL;DR: Despite a large variation in the data, trends that are found are that RNAi is particularly successful in the family Saturniidae and in genes involved in immunity and that gene expression in epidermal tissues seems to be most difficult to silence.
698 citations
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TL;DR: In this paper, a systematic analysis of the influence of preparation conditions in the alkali hydrothermal synthesis on the morphology of TiO2 nanotubes is performed using HRTEM and low temperature nitrogen adsorption.
Abstract: A systematic analysis of the influence of preparation conditions in the alkali hydrothermal synthesis on the morphology of TiO2 nanotubes is performed using HRTEM and low temperature nitrogen adsorption. The possible mechanisms of nanotube formation are reviewed and a mechanism based on the key stage of wrapping of intermediate multilayered titanate nanosheets is suggested. The driving force for wrapping is considered to be the mechanical stress arising during crystallisation/dissolution. The average diameter of the nanotubes was found to depend on the temperature and on the ratio of weight of TiO2 to the volume of sodium hydroxide solution. An increase in the temperature from 120 to 150 °C results in an increase in the average nanotube diameter. Subsequent increases in the temperature result in the formation of non-hollow TiO2 nanofibers with an average diameter of 75 nm, a wide distribution in diameter and a length in excess of 10 µm. The increase of the TiO2 : NaOH molar ratio results in an increase in the average diameter of nanotubes and a decrease of surface area. The average inner diameter of TiO2 nanotubes varied between 2 and 10 nm. The pore-size distribution was evaluated from TEM, and low-temperature nitrogen adsorption data using the BJH method. It was shown that nitrogen adsorption is a suitable method for characterisation of the pore morphology of nanotubes.
694 citations
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TL;DR: The strong association between both genetic barrier defects and environmental insults to the barrier with AD suggests that epidermal barrier dysfunction is a primary event in the development of this disease.
694 citations
Authors
Showing all 16056 results
Name | H-index | Papers | Citations |
---|---|---|---|
Michael Grätzel | 248 | 1423 | 303599 |
Brenda W.J.H. Penninx | 170 | 1139 | 119082 |
Amartya Sen | 149 | 689 | 141907 |
Gilbert Laporte | 128 | 730 | 62608 |
Andre K. Geim | 125 | 445 | 206833 |
Matthew Jones | 125 | 1161 | 96909 |
Benoît Roux | 120 | 493 | 62215 |
Stephen Mann | 120 | 669 | 55008 |
Bruno S. Frey | 119 | 900 | 65368 |
Raymond A. Dwek | 118 | 603 | 52259 |
David Cutts | 114 | 778 | 64215 |
John Campbell | 107 | 1150 | 56067 |
David Chandler | 107 | 424 | 52396 |
Peter H.R. Green | 106 | 843 | 60113 |
Huajian Gao | 105 | 667 | 46748 |