The Minneapolis Institute of Art (Mia) is an arts museum located in Minneapolis, Minnesota, United States. Home to more than 90,000 works of art representing 5,000 years of world history, Mia is one of the largest art museums in the United States. Its permanent collection includes world-famous works that embody the highest levels of artistic achievement, spanning about 20,000 years and representing the world’s diverse cultures across six continents. The museum has seven curatorial areas: Arts of Africa & the Americas; Contemporary Art; Decorative Arts, Textiles & Sculpture; Asian Art; Paintings; Photography and New Media; and Prints and Drawings.Mia is one of the largest arts educators in Minnesota. More than a half-million people visit the museum each year, and a hundred thousand more are reached through the museum’s Art Adventure program for elementary schoolchildren. The museum’s free general admission policy, public programs, classes for children and adults, and award-winning interactive media programs have helped to broaden and deepen this museum’s roots in the communities it serves.
We examine the impact of a policy that reduces information about rental housing applicants on racial discrimination. We submitted fictitious email inquiries to publicly advertised rentals using names manipulated on perceived race and ethnicity before and after a policy that restricted the use of background checks, eviction history, income minimums, and credit history in rental housing applications in Minneapolis. After the policy was implemented, discrimination against African American and Somali American men increased. Triple difference analysis shows that discrimination increased in Minneapolis relative to St. Paul after the policy.
Introduction and Objective: Breast milk is a dynamic substance rich in both nutritive and nonnutritive compounds that may influence infant growth, development, and obesity risk. We aimed to identify metabolites in human milk that are associated with infant growth and body composition. Methods: We analyzed breast milk metabolomics from 350 mother-infant pairs at 1 month postpartum using high-sensitivity LC/GC-MS and assessed infant anthropometrics (BMI percentile) and body composition [percent body fat (%BF), Fat Free Mass Index (FFMI)] using air displacement plethysmography (at 1-month) and DXA (at 6-months). We tested associations between milk metabolites and infant anthropometric and body composition measures using linear regressions adjusting for covariates including maternal age, parity, delivery mode, gestational age, infant sex, birth weight, race, ethnicity, and enrollment site. We used Benjamini-Hochberg procedure to adjust for multiple comparisons (FDR <0.05). Results: Semi-quantitative concentrations were obtained for 458 metabolites. A greater number of milk metabolites were associated with %BF (9 with FDR<0.05) and BMI percentile (4 with FDR <0.05) than with FFMI (0 with FDR <0.05). Highest ranking metabolites associated with %BF included gamma-glutamylglutamine (beta=0.056, FDR=0.004) and 7-methylguanine (beta=0.061, FDR=0.03) while docosahexaenoate (beta = -0.020, FDR= 0.02) and p-hydroxybenzoate (beta = -0.012, FDR= 0.02) were the highest ranking metabolites associated with BMI percentile. The purine metabolite 7-methylguanine has been implicated previously in adipogenesis. Conclusion: Infant adiposity measures, but not measures of fat free mass, are associated with differences in the human milk metabolome. Further studies are needed to determine whether differences in human milk metabolites play a mechanistic role in infant growth and obesity risk. A. Uniyal: None. C. Lu: None. J.M. Dreyfuss: None. E.M. Nagel: None. A. Pena: None. M. Rudolph: None. D.A. Fields: None. E.W. Demerath: None. E.M. Isganaitis: None. NIH/NICHD (R01HD080444, R01HD109830)
AbstractWriting is essential for success in academics and everyday tasks, but the development of writing skills depends on consistent access to high-quality instruction, extended practice, and personalized feedback. To address these demands and meet students’ needs, educators and researchers have turned to technology-based writing tools. Ideally, these tools integrate the core components of intelligent tutoring, including a domain model, student model, tutor model, and interface model to engage students with individualized feedback that is linked to adaptive writing instruction. However, the landscape of writing tools still has much room for improvement in terms of incorporating advanced artificial intelligence-enabled features to better approximate intelligent tutoring systems (ITSs). This chapter describes the key elements of ITS technologies and how they can be integrated to further develop ITS tools for writing. To this end, this chapter (1) summarizes evidence-based aspects of successful ITSs and how they might be integrated into computer-based tools for writing, (2) reviews how existing systems have leveraged intelligent tutoring approaches, and (3) articulates how future technology-based writing tools could implement advanced intelligent tutoring features to better meet students’ needs. The chapter concludes with the implications and future directions of intelligent tutoring for the teaching and learning of writing.
A major clinical study raised questions about one of the most celebrated cancer-screening procedures available, but a close look at the data tells a different story. A major clinical study raised questions about one of the most celebrated cancer-screening procedures available, but a close look at the data tells a different story.
