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The Metropolitan Museum of Art has used TMS (The Museum System) as its collections management system for over twenty years; a subset of the data stored here is fed directly to our Online Collection (https://www.metmuseum.org/art/collection). Over the past decade, the curatorial departments at The Met have worked with the Museum's Digital Department to develop creative ways to use TMS for a variety of purposes: to enhance our online presence, to track important events and processes, to organize and track our archives, and to support research. This paper will provide an overview of several of these initiatives as they are implemented by the Department of Egyptian Art., CIPEG Journal: Ancient Egyptian & Sudanese Collections and Museums, No 3 (2019)

Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections., 3 pages, 4 figures

a Native crRNA-tracrRNA duplex. b Short sgRNA (+48 nucleotides). c Commonly used lone sgRNA (+85 nucleotides). Figure S2. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 1b, c. Figure S3. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 1d. Figure S4. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 2a. Figure S5. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 2b. Figure S6. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 3a. Figure S7. CD4 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 3b. Figure S8. CCR5 knockout efficiency for the indicated sgRNAs with a 4-, 5-, or 6-bp duplex extension. CCR5 expression was determined in the same way as in Fig. 1b. Figure S9. CCR5 knockout efficiency for the indicated sgRNAs with T→A, T→C and T→G mutations for the sgRNAs in Fig. 3c. Figure S10. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 5a. Figure S11. CD4 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 5d, e. Figure S12. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 6. (PDF 2487 kb)

a Native crRNA-tracrRNA duplex. b Short sgRNA (+48 nucleotides). c Commonly used lone sgRNA (+85 nucleotides). Figure S2. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 1b, c. Figure S3. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 1d. Figure S4. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 2a. Figure S5. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 2b. Figure S6. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 3a. Figure S7. CD4 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 3b. Figure S8. CCR5 knockout efficiency for the indicated sgRNAs with a 4-, 5-, or 6-bp duplex extension. CCR5 expression was determined in the same way as in Fig. 1b. Figure S9. CCR5 knockout efficiency for the indicated sgRNAs with T→A, T→C and T→G mutations for the sgRNAs in Fig. 3c. Figure S10. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 5a. Figure S11. CD4 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 5d, e. Figure S12. CCR5 knockout efficiency as determined by flow cytometry for the sgRNAs in Fig. 6. (PDF 2487 kb)