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Abstract Motivation Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. Results We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
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miRNAs and mRNA expression spectrum in tree shrew fungal keratitis
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Changes: Delete superfluous file and add DOI badge
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X-ray diffraction data analysis for high pressure and high temperature experiments
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1) DensityGrid.dat = map of density of pore-free surface rocks (Figure 1a) - Units = g/cm3 - 720 x 1440 pixels corresponding to 0.25-degree pixels in an equirectangular cylindrical projection with the equator as central parallel. - The first row represents 90 to 89.75 degrees of latitude north; the last row represents 89.75 to 90 degrees of latitude south. - The first column represents 0 to 0.25 degree of longitude; the last column represents 359.75 to 360 degrees of longitude. 2) MeltGrid.dat = map of degree of partial melting (Figure S1a) - Units: mass fraction - 720 x 1440 pixels as described above 3) CrustalThickness_ModelU0.dat = map of crustal thickness if laterally uniform density, Model U0 (Figure 2a) CrustalThickness_ModelVn.dat = map of crustal thickness if laterally variable density, Model Vn (Figure 2c, Figures S6 to S10) - Units = km - generated with MakeGridDH of SHTOOLS (https://shtools.github.io/SHTOOLS/) - 720 x 1440 coordinate points corresponding to 0.25-degree spacing in an equirectangular cylindrical projection with the equator as central parallel. - The first row corresponds to 90 degrees of latitude north; the last row corresponds to 89.75 degrees of latitude south. - The first column corresponds to 0 degree of longitude; the last column corresponds to 359.75 degrees of longitude. Note: Figures refer to the following paper (Geophysical Research Letters, 2020): `Mercury's crustal thickness correlates with lateral variations in mantle melt production’, by M. Beuthe, B. Charlier, O. Namur, A. Rivoldini, and T. Van Hoolst
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Matlab analysis code for analysing the data from the Contingent Motivation saccade task presented in Grogan et al., 2020. John P Grogan, Tim R. Sandhu, Sanjay G. Manohar.
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The Libre Multilingual Analyzer, a Natural Language Processing (NLP) C++ toolkit.
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Supplemental analysis code for "Selective inhibition of TGFβ1 activation overcomes primary resistance to checkpoint blockade therapy by altering tumor immune landscape" The following code generates the results necessary for Figure 1 and S1A and additionally generates other figures not used in the manuscript.
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This example describes the calibration of a conventional mass of a weight W against a reference weight R with a nominal mass of 100 g. The example builds on that given in JCGM 101:2008. This time a Bayesian evaluation of the measurement is performed. A Bayesian approach differs from the Monte Carlo method (MCM) of JCGM 101:2008 and the law of propagation of uncertainty (LPU) in JCGM 100:2008 in that it combines prior knowledge about the measurand with the data obtained during calibration. From the joint posterior probability density function which is obtained from this combination, a value and a coverage interval for the measurand are obtained. Files contained in the dataset are: - EMUEActivity113_MassCalibration.pdf: report “Bayesian approach applied to the mass calibration example in JCGM 101:2008”; - EMUEActivity113_MassCalibration.tex: LaTeX source file to be compiled in order to produce EMUEActivity113_MassCalibration.pdf; - Compendium.bib: bibliography file; - conjugateBayesKnownV.pdf: image contained in the report; - MCMvsBayesNI.pdf: image contained in the report; - JCGM101_Mass_calibration_code.R : R code to run the example from the report.
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This is the first version of the Phenobottle. The Phenobottle is an open-source photobioreactor designed to assess growth and photophysiology in microalgae.
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