53,261 research outputs found
Subunit composition of minK potassium channels.
Expression of minK protein in Xenopus oocytes induces a slowly activating, voltage-dependent, potassium-selective current. Point mutations in minK that alter current gating kinetics, ion selectivity, pharmacology, and response to protein kinase C all support the notion that minK is a structural protein for a channel-type transporter. Yet, minK has just 130 amino acids and a single transmembrane domain. Though larger cloned potassium channels form functional channels through tetrameric subunit association, the subunit composition of minK is unknown. Subunit stoichiometry was determined by coexpression of wild-type minK and a dominant lethal point mutant of minK, which reaches the plasma membrane but passes no current. The results support a model for complete minK potassium channels in which just two minK monomers are present, with other, as yet unidentified, non-minK subunits
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MinK residues line a potassium channel pore.
MinK has neither the P region nor signature sequence that characterizes pore-forming subunits of all known K+ channels. A specific minK region has now been identified that affects external blockade by 2 common probes of K+ channel pores. When mutated to cysteine, residues in this region render minK susceptible to covalent blockade by methanethiosulfonate ethylsulfonate and alter reversible inhibition by tetraethylammonium. The 2 blockers are found to share overlapping binding site determinants and to interact. Since inhibition by external tetraethylammonium is sensitive to voltage and to the internal concentration of permeant ions, we argue that tetraethylammonium blocks by occluding the external end of a water-filled transmembrane pore. These findings support the view that minK is directly involved in forming a K+-selective ion conduction pathway
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Sequence and function of the two P domain potassium channels: implications of an emerging superfamily.
A new superfamily of K+ channels has emerged in the past 2 years. Notable for possessing two pore-forming P domains in each subunit, members of the superfamily have been recognized through phylogeny from micro-organisms to humans. Four subfamilies of two P domain channels have been isolated thus far; among these are the first cloned examples of outward rectifier and open rectifier (or leak) K+ channels. The two P domain K+ channels offer a new perspective from which to glimpse the molecular basis for function and dysfunction of K+-selective ion channels
Energy and centrality dependences of charged multiplicity density in relativistic nuclear collisions
Using a hadron and string cascade model, JPCIAE, the energy and centrality
dependences of charged particle pseudorapidity density in relativistic nuclear
collisions were studied. Within the framework of this model, both the
relativistic experimental data and the PHOBOS and PHENIX
data at =130 GeV could be reproduced fairly well without retuning
the model parameters. The predictions for full RHIC energy collisions
and for collisions at the ALICE energy were given. Participant nucleon
distributions were calculated based on different methods. It was found that the
number of participant nucleons, for distinguishing various theoretical models.Comment: 10 pages, 4 figures, submitted to Phy. Lett.
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Modeling singular mineralization processes due to fluid pressure fluctuations
Mineralization in the Earth's crust can be regarded as a singular process resulting in large amounts of mass accumulation and element enrichment over short time or space scales. The elemental concentrations modeled by fractals and multifractals show self-similarity and scale-invariant properties. We take the view that fluid-pressure variations in response to earthquakes or fault rupture are primarily responsible for changes in solubility and trigger transient physical and chemical variations in ore-forming fluids that enhance the mineralization process. Based on this general concept, we investigated mineral precipitation processes driven by rapid fluid pressure reductions by coupling mineralization to a cellular automaton model to reveal the nonlinear mechanism of the orogenic gold mineralization process using simulation. In the model, fluid pressure can increase to the rock failure condition, which was set as lithostatic pressure at a depth of 10 km (270 MPa), due to either porosity reduction or dehydration reactions. Rapid drops in pressure resulting from fault rupture or local hydrofracture may induce repeated gold precipitation. The geochemical patterns generated by the model evolve from depletion to enrichment patterns, and from spatially random to spatially clustered structures quantified by multifractal models and geostatistics. Results show how metal elements self-organize to form high metal concentration patterns displaying self-similarity and scale-invariance. These transitions are attributed to the growth and coalescence of sub-networks with different fluid pressures up to the percolation threshold, resulting in a wide range of fluid pressure reductions and gold precipitation in the form of clusters. The results suggest that cyclic evolution of fluid pressure and its effects on gold precipitation systems can effectively mimic the repeated mineralization superposition process, and generate complex geochemical patterns characterized by a multifractal model. The nonlinear behavior exhibits scale-invariance and self-organized critical threshold, where mineral phase separations result from fluid pressure reductions associated with fault failure
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Context plays an important role in human language understanding, thus it may
also be useful for machines learning vector representations of language. In
this paper, we explore an asymmetric encoder-decoder structure for unsupervised
context-based sentence representation learning. We carefully designed
experiments to show that neither an autoregressive decoder nor an RNN decoder
is required. After that, we designed a model which still keeps an RNN as the
encoder, while using a non-autoregressive convolutional decoder. We further
combine a suite of effective designs to significantly improve model efficiency
while also achieving better performance. Our model is trained on two different
large unlabelled corpora, and in both cases the transferability is evaluated on
a set of downstream NLP tasks. We empirically show that our model is simple and
fast while producing rich sentence representations that excel in downstream
tasks
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Cosmogenic-neutron activation of TeO2 and implications for neutrinoless double- β decay experiments
Flux-averaged cross sections for cosmogenic-neutron activation of natural tellurium were measured using a neutron beam containing neutrons of kinetic energies up to ∼800 MeV and having an energy spectrum similar to that of cosmic-ray neutrons at sea level. Analysis of the radioisotopes produced reveals that Ag110m will be a dominant contributor to the cosmogenic-activation background in experiments searching for neutrinoless double-β decay of Te130, such as the Cryogenic Underground Observatory for Rare Events (CUORE) and the Sudbury Neutrino Observatory Plus (SNO+). An estimate of the cosmogenic-activation background in the CUORE experiment has been obtained using the results of this measurement and cross-section measurements of proton activation of tellurium. Additionally, the measured cross sections in this work are also compared with results from semiempirical cross-section calculations
Rethinking Skip-thought: A Neighborhood based Approach
We study the skip-thought model with neighborhood information as weak
supervision. More specifically, we propose a skip-thought neighbor model to
consider the adjacent sentences as a neighborhood. We train our skip-thought
neighbor model on a large corpus with continuous sentences, and then evaluate
the trained model on 7 tasks, which include semantic relatedness, paraphrase
detection, and classification benchmarks. Both quantitative comparison and
qualitative investigation are conducted. We empirically show that, our
skip-thought neighbor model performs as well as the skip-thought model on
evaluation tasks. In addition, we found that, incorporating an autoencoder path
in our model didn't aid our model to perform better, while it hurts the
performance of the skip-thought model
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