Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but the data is often aggregated without regard to users in the Twitter populations of each community. This paper describes a simple yet effective method for building community-level models using Twitter language aggregated by user. Results on four different U.S. county-level tasks, spanning demographic, health, and psychological outcomes show large and consistent improvements in prediction accuracies (e.g. from Pearson r = .73 to .82 for median income prediction or r = .37 to .47 for life satisfaction prediction) over the standard approach of aggregating all tweets. We make our aggregated and anonymized community-level data, derived from 37 billion tweets -over 1 billion of which were mapped to counties, available for research.
Colony quantification is essential in clinical and research settings as well as pedagogy at the college level. Human visual (HV) counting, the most common method, is time consuming and fraught with errors. The time, accuracy and precision of HV counting were compared to a high-end professional automated counter, an inexpensive phone application and a free phone application. Low cost benefits of increased speed and accuracy with automated counting are maximized when counting single round colonies; but much reduced if colonies have irregular morphology or demonstrate haemolysis.
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