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All the data was abtained under the support of the National Science Foundation of China (Grand No: 41430317, 41402136) and the Scientific Research Foundation of Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process, Ministry of Education (China University of Mining and Technology) (Grand No: 2019-009). The data file from mercury intrusion method is named by "data of MIP", the data file from N2 and CO2 adsorption tests was entitled "data of N2 adsorption" and "data of CO2 adsorption", and the data file from permeability test is named by "20160910". The data listed here can be cited and interpreted for researching.
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  • Tabular Data
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The simulation program for the paper: Robust control of desalination plants using sliding mode control synthesis with observer
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This is a data set of 4,335,880 public posts published in Instagram, in the New York City area for 190 days.
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More than 200 gravity reading points along two profiles at location 1 and 2 were carried out perpendicular to the main traces of the fault in west-east profiles. The detail needed with separation between stations was 5 m. The 2D ERT data were collected along two profiles. The dipole-dipole array configuration was used with electrode spacing 3 and 5 m and the length is 141 and 235 m respectively. The 3D ERT data were collected along two profiles. The pole-pole array configuration was used with electrode and line spacing at 5 m respectively. The survey area was 115X15 m.
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  • Dataset
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This data set contains all data used to build the urban ecosystem service model for predicting the the PM2.5 removal service in Beijing under three policy scenarios by 2035. It has two parts: (1) python script of the entire model and associated data including attribute data of sociaoeconomic status and TIFF files of land use change restirctions; (2) testing data and simulation results including TIFF files of LAI, land use distribution, PM2.5 concentration, wind speed.
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  • File Set
This data set comprise the data generated during elaboration of the manuscript entitled "Design of a Micro-Machined Flow Sensor for Aircraft Air Data Systems Application: Mechanical Considerations". These data are COMSOL and ANSYS files.
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  • Dataset
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A reliable and unobtrusive quantification of changes in cortical activity during short-term memory (STM) task can be used to evaluate the efficacy of interfaces and to provide real-time user-state information. In this dataset, we record electroencephalogram (EEG) signals in STM and baseline activity.
Data Types:
  • Software/Code
  • Tabular Data
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  • File Set
Nutrient transporters can be rapidly removed from cell surface via substrate-stimulated endocytosis as a way to control nutrient influx, but the molecular underpinnings have not been well understood. In this work, we focused on zinc-dependent endocytosis of human ZIP4 (hZIP4), a zinc transporter essential for dietary zinc uptake. Structure-guided mutagenesis and internalization assay revealed that hZIP4 per se acts as the exclusive zinc sensor with the transport site being responsible for zinc sensing. In an effort of seeking sorting signal, a scan of the longest cytosolic loop (L2) led to identification of a conserved LQL motif essential for endocytosis. Partial proteolysis of purified hZIP4 demonstrated a structural coupling between the transport site and the L2 upon zinc binding, which supports a working model of how zinc ions at physiological concentration trigger a conformation-dependent endocytosis of the zinc transporter. This work provides a new paradigm on post-translational regulation of nutrient transporters.
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Raw data for 2-choice food preference assays, FLIC assays, immunofluorescence staining, pharyngeal calcium imaging, and optogenetics.
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  • Dataset
  • File Set
Large surveys of peptides naturally presented on major histocompatibility class I (MHC I) proteins have enabled improved MHC I ligand prediction by dramatically expanding the available data for many MHC I alleles. However, it is unclear to what extent antigen processing signals can also be learned from these datasets. Here, we developed a predictor of antigen processing by training neural networks to discriminate mass spec-identified MHC I ligands from unobserved peptides, where both classes of peptides are predicted to be strong MHC I binders. The resulting predictor shows qualitative consistency with established preferences for the transporter associated with antigen processing, proteasomal cleavage, and endoplasmic reticulum aminopeptidases. When we combined the antigen processing predictor with a novel pan-allele MHC I binding predictor in a logistic regression model, the combination model significantly outperformed the two components alone as well as the NetMHCpan 4.0 and MixMHCpred 2.0.2 tools at predicting mass spec-identified MHC I ligands. Our predictors are implemented in the open source MHCflurry package, version 1.6.0 (github.com/openvax/mhcflurry).
Data Types:
  • Tabular Data
  • Dataset
  • File Set
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