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The dataset is related to linear rock cutting experiments on concrete samples that consisted of different concrete mixtures. It is a supplement to scoping study using a procedural evaluation routine to analyse cutting Force Component Ratios (FCR) that could be used for the identification of changing mechanical rock properties during mechanical excavation. It focuses on the use of FCR in conjunction with point attack picks. The cutting depth and the spacing-to-depth ratio were varied at three levels each. 6, 8 and 12 mm for cutting depth (Coded as DZ in Dataset), the spacing ration at levels of 2, 3 and 4. In the dataset, the resulting spacing is stored as DY. Two artificial rock samples were tested. The samples were composed of two respective three different zones of concrete. The first block’s zones had a nominal cubic Uniaxial Compressive Strength (UCS) of 85 MPa (Zone 3) and 45 MPa (Zone 2). The zones of the second block were 85 (Zone 3), 45 (Zone 2) and 25 MPa (Zone 1). For each combination of cutting parameters, a whole layer of a specimen was cut in such a way that each cut groove crossed the different zones. In total, 414 single cuts were conducted to achieve reliable results. A procedural evaluation process was developed to assess the potential of Force Compononent Ratios for material differenciation based on statistical descriptors. The descriptors used where: arithmetic mean, geometric mean, median, interquartile range, 0.95-quantile, variance coefficient. A classification algorithm implemented in R was utilized to classify all combinations of statistical descriptors and Force Component Ratio. In total 72, combinations of Force Component Ratio and statistical descriptor were classified, 9 combinations were classified as suitable, 10 as partially suitable and the rest as unsuitable. The results imply that an FCR material differenciation based an raw cutting force measurements could be a tool for material diferenciation during mechanical excavation
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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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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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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.
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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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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).
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The Zr-1.05Nb, Zr-0.85Nb-0.20Ta and Zr-0.85Nb-0.40Ta alloys with an alpha plus beta equilibrium microstructure (annealing 570 °C for 3840 h) were measured by EDS-TEM. The measurements of beta phase (precipitates) was carried out in carbon extraction replicas and alpha phase (matrix) was carried out in thin films. The EDS-TEM spectra were obtained with a field emission TEM FEI Tecnai F20 G2 (FEG) operated at 200 kV with an EDAX EDS detector, take-off angle 14.8°, sample rotation 15° and data collected from a focused spot of 15 nm diameter (spot3). The EDS-TEM measurements were made by Dra. Eugenia Zelaya in the Centro Atómico Bariloche, Comisión Nacional de Energía Atómica, Argentina. The group of measurements in this dataset will be the main part of the manuscript: P.A. Ferreirós, P.R. Alonso, D.P. Quirós, E. Zelaya, G.H. Rubiolo, Accurate quantitative EDS-TEM analysis of precipitates and matrix in equilibrium (Alpha+Beta) Zr-1Nb alloys with Ta addition. Manuscript to be sent to Journal of Nuclear Materials (March 2020)
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In the current data article, we present detailed characteristics of voids in carbon/epoxy composite laminates as well as the original image stacks, obtained via X-ray micro-Computed Tomography (micro-CT) . Five different lay-ups are produced with altering the recommended cure cycle in order to intentionally induce voids in the material. For each lay-up, an image stack (consisting of tomographic slices) and a dataset are provided. The image slices are in 8-bit TIF format. The datasets (spreadsheets) include the volume, size parameters, shape parameters, orientation, and location of all the detected voids in the specimen. The segmentation of the images and quantification of voids are performed in VoxTex, an in-house software for processing of micro-CT results. The data is linked to a Data in Brief article "Mehdikhani et al., A dataset of voids’ characteristics in multidirectional carbon fiber/epoxy composite laminates, obtained using X-ray micro-computed tomography, DIB 27 (2019) 104686" and linked to the article "Mehdikhani et al. Detailed characterization of voids in multidirectional carbon fiber/epoxy composite laminates using X-ray micro-computed tomography. Comp Part A 125 (2019) 105532".
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The repository includes the dataset for the manuscript entitled A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy submitted to WRR. Currently, we only deposit the data for plotting Fig4. Upon acceptance, we will store all the data in this repository.
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