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In active learning, Optimally Balanced Entropy-Based Sampling (OBEBS) method is a selection strategy from unlabelled data. At active zero-shot learning there is not enough information for supervised machine learning method, thus, our sampling strategy was based on unsupervised learning (clustering). The cluster membership likelihoods of the items were essential for the algorithm to connect the clusters and the classes; i.e. to find assignment between them. For best assignment, Hungarian algorithm was used. We developed and implemented adaptive assignment variants of OBEBS method in the software.
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3x2x2 design factor 1: condition (feature-alone color, feature-alone shape, binding) factor 2: block (two experimental runs/condition/participants) factor 3: group (old vs young participants) In this study, we compared the electrophysiological activity associated with the binding process in young (N=22, mean age=28.5) and old (N=22; mean age=67.4) participants in a change detection task. Analysis of event-related brain potentials (ERP) focused on the differences between feature-alone (color or shape) and feature-conjunction (color-shape) conditions in stimulus encoding. Independently of aging, discrimination ability was significantly attenuated in the binding condition. The effect, however, was more pronounced in old participants. ERP components related to the visual feature detection and processing (posterior N1 and frontal P2) were not modulated in the binding condition. Only in old participants, a late positive ERP component (LPC) was increased signaling the allocation of additional resources.
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Primary analysis files for bioRxiv manuscript with id 2019/859603 (https://www.biorxiv.org/content/10.1101/859603v1) to evaluate how common variant effect prediction methods capture effect determined by deep mutational scanning experiments. 'data' contains the deep mutational scanning data in a parsed format. See the manuscript for the original data sources which would then be processed with parseRawDatasets.py, followed by manual sequence mapping (resulting in the mapped_seqs.txt files) and then be processed with parseScores.py to result in the .npz files. 'predictionData' contains predictions from SIFT, PolyPhen-2, SNAP2 and Envision, parsed into .npz files. Additional folders are for dummy methods and while executing the below scripts. 'analysis' will contain most of the output files. See below for sample calls to reproduce e.g. Figure 1 from the paper. The scripts are written in Python3 and require, among others, numpy, pandas, scipy, sklearn, rpy2, svgutils and matplotlib. For all scripts the --normalization-scheme flag describes how the experimental scores are processed to fit on the same scale of values. The scheme used for the final manuscript is 'wt0_del_scaled' for deleterious effect variants and 'wt0_ben_scaled' for beneficial effect variants. For compareBinaryDMSToPredictions.py the --binarization-scheme flag describes how scores are binarized to neutral/effect. Possible values are the schemes outlined in the manuscript 'syn90', 'syn95' and 'syn99'.
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Deregulated expression of MYC induces a dependence on the NUAK1 kinase, but the molecular mechanisms underlying this dependence have not been fully clarified. Here we show that NUAK1 is a predominantly nuclear protein that associates with a network of nuclear protein phosphatase 1 (PP1) interactors and that PNUTS, a nuclear regulatory subunit of PP1, is phosphorylated by NUAK1. Both NUAK1 and PNUTS associate with the splicing machinery. Inhibition of NUAK1 abolishes chromatin association of PNUTS, reduces spliceosome activity and suppresses nascent RNA synthesis. Activation of MYC does not bypass the requirement for NUAK1 for spliceosome activity, but significantly attenuates transcription inhibition. Consequently, NUAK1 inhibition in MYC-transformed cells induces global accumulation of RNAPII both at the pause-site and at the first exon/intron boundary, but does not increase mRNA synthesis. We suggest that NUAK1 inhibition in the presence of deregulated MYC traps non-productive RNAPII due to the absence of correctly assembled spliceosomes.
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Data for "Does Renewable Energy Consumption Improve Sustainable Development? A Panel ARDL Approach"
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Clay enrichment experiments were carried out with the Chinese national standards “SYT 5163-2010” to semi-quantitative study of each type of clay mineral in samples. Contact resonance (CR) technique was performed to characterize micromechanics of shale after exposure to water. A total of 64 plus A16 test areas in samples of terrestrial shale in the Ordos Basin and of marine shale in the Sichuan Basin were selected for testing. The results indicated that the compositions of the terrestrial and marine shales were significantly different. Moreover, strength-related mechanical properties, such as storage modulus, of shale would decrease after shale interacts with water, but with different contributions from brittle minerals and clay, as well as variations depending on bedding plane orientation.
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The simulated and measured data in toroidal dipole metamaterials.
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The dataset contains information on the currents in the upper 15-meter layer of the Caspian Sea in 2003-2005. The velocity fields were reconstructed in an eddy-resolving ocean general circulation model SZ-COMPAS using a realistic forcing. The data arrays have a very high resolution: ~2 km in space and 4 hours in time. This is sufficient to resolve most of the mesoscale features of sea dynamics as well as a wide range of their temporal spectrum, including inertial oscillations, synoptic and seasonal variability. The NetCDF files are: 3 files with instantaneous currents and 3 files with monthly mean currents (suffix “mm” in the file names). Each file corresponds to one of the three horizons (depths): 1 m, 7 m, and 15 m. In the horizontal plane the data are defined on a uniform geographical grid (46.7625–54.2125°E, 36.5092–47.2892°N), dimensions of all of the arrays are 299 by 589. The period covered by the instantaneous data is 2003, time step is 4 hours; the first record corresponds to 2003-01-01 04:00:00 GMT. The period covered by the monthly mean data is 2003-2005 with one vector field for every month, defined on the last day of the month. All data dimension is cm/s. It should be noted that the model describes the upper 30-meter layer of the sea in sigma-coordinate, so the data values are actually defined on the 1st, 4th, and 8th sigma-horizons, rather than 1 m, 7 m, and 15 m depths. This means that, in the sea cells with bottom depth less than 30 m, the actual depth of the data nodes is ~3%, ~23% and 50% of real water column height, while in the rest of cells (with bottom depth greater than 30 m), the data nodes are located at ~1 m, ~7 m, and ~15 m of depth (± few centimeters depending on the local sea level).
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These two files include the heat capacity data for pure iron and magnesium. They include three columns: temperature (Temp), heat capacity (Cp) and reference (Ref). The data are collected from the two following sources: (1) Y. S. Touloukian, E. H. Buyco, Thermophysical properties of matter - the TPRC data series. volume 4. specific heat-metallic elements and alloys, Tech. rep., Thermophysical and Electronic Properties Information Analysis Center Lafayette IN (1971). (2) NIST Thermodynamic Research Center, the NIST alloy data web application, https://trc.nist.gov/metals_data/, (accessed: 13 February 2020).
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Main authors potentially importatn from the chimical epistemic field in byopolimers. (from 2006 to 2019) - Patents registered and papers (Conferences are not included. Database in .bib bibliographic format.
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