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We describe Quanti.us, a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10–50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.

We describe Quantius, a crowd-based image annotation platform that provides an accurate alternative to task-specific computational algorithms for difficult image analysis problems. We use Quantius to quantify a variety of computationally challenging medium-throughput tasks with ~50x and 30x savings in analysis time and cost respectively, relative to a single expert annotator. We show equivalent deep learning performance for Quantius- and expert-derived annotations, bridging towards scalable integration with tailored machine-learning algorithms.

Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary time scales. Building on our previous work (Mavor et al. 2016), we used deep mutational scanning to determine how twelve new chemicals reveal novel mutational sensitivities of ubiquitin residues. We found sensitization of Lys63 in eight new conditions. In total, our experiments have uncovered a highly sensitizing condition for every position in Ub except Ser57 and Gln62. By determining the Ubiquitin fitness landscape under different chemical constraints, our work helps to resolve the inconsistencies between deep mutational scanning experiments and sequence conservation over evolutionary timescales.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Half a trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Haifa trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Half a trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Haifa trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Haifa trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Haifa trillion dollars later, Microsoft's products are the standards of the personal computer software industry.

Bill Gates started Microsoft with the insight that people valued personal computer software and that they could be made to buy software that they were previously getting for free. Haifa trillion dollars later, Microsoft's products are the standards of the personal computer software industry.