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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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This application used the library tada-hdt-entity-experiment and applied it on the T2Dv2 dataset using dbpedia HDT.
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This distribution includes MIDI and PDF files for the anonymous and doubtfully attributed mass movements of the Coimbra Manuscripts, edited by María Elena Cuenca. This distribution also includes the "Renaissance-safe" features extracted by Cory McKay using jSymbolic 2.2 (http://jmir.sourceforge.net) from the MIDI encodings of these Coimbra mass movements, as well as from a secure reference set of Franco-Flemish and Iberian Renaissance mass movements and motets. The MIDI encodings of the latter two datasets cannot be included here due to legal constraints. Those interested in accessing the MIDI files directly should consult The Josquin Research Project (https://josquin.stanford.edu) and The Archive of Iberian Polyphony (https://iberianpolyphony.fcsh.unl.pt), respectively.
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This is part of CLIJ 1.5.7 https://github.com/clij/clij/releases/tag/1.5.7
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No description provided.
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Fixed Set default axis bounds for case where all values are equal (Issue #36) Optimise tick generation to jump straight to range (Issue #42) Fix text rendering of scatter plots
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First version of Morph-CSV, used for evaluating the engine for the submission of Special Issue on Storing, Querying and Benchmarking the Web of Data at the Semantic Web Journal 2020
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parameters’ primary goal is to provide utilities for processing the parameters of various statistical models (see here for a list of supported models). Beyond computing p-values, CIs, Bayesian indices and other measures for a wide variety of models, this package implements features like bootstrapping of parameters and models, feature reduction (feature extraction and variable selection).
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Objectives: To efficiently handle the continuous flow of information to which the attentional system is exposed, humans are equipped with filters like the Attentional Blink (i.e., a failure to detect a second target when it is presented between 200-500 ms after the first one). The aim of this study was to examine whether the practice of two standardized meditation programs (i.e. mindfulness and compassion) could modify the allocation of attentional resources towards emotional information. Methods: A sample of 90 participants (43 in the mindfulness group and 47 in the compassion group) performed a variant of the Emotional Attentional Blink Task using negative, positive and neutral faces, before and after the 8-week meditation programs. Results: Both programs significantly decreased the standard AB effect (F (1.65, 145.58) = 39.79, p<.001, η2partial = .31) with only minor differences between them. Furthermore, the AB reduction after the programs varied according to the different emotional faces used (F (3.10, 272.83) = 4.44, p<.05, η2partial = .05). Conclusions: Results suggest that standardized 8-week meditation programs may significantly change early stages of emotional stimuli processing while promoting a more balanced distribution of attentional resources toward emotional information. Keywords: Mindfulness; Compassion; Attentional Blink; Attention; Emotional Processing.
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original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'
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