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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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Data and code for Mitchell and Rácz 2020
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Author: Hares Khan 2020, (hkhan.ch@gmail.com) This is the R code used in the study "Khan H., Marcé R., Laas A., Obrador B., "Pelagic calcite precipitation as a major component of the global carbon cycle in lakes and reservoirs" The code was used for assessing the effect of calcite precipitation on pCO2. The outputs of this model were then used with the global distribution of lakes per alkalinity level (data from Marcé et al., 2015) for upscaling at a global level the CO2 emitted by calcification. The model is based on the carbonate equilibra using the AquaEnv package. This code can be used freely and redistributed. This code comes with no warranty
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SynchronicBallot v0.3.0 Diff since v0.2.1 Merged pull requests: Install TagBot as a GitHub Action (#1) (@JuliaTagBot)
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Synthetic dataset and event logs for the paper 'Process Mining from Information-Seeking Conversations' submitted to WCCI 2020.
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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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