Objective The aim of this study was to test associations of prepregnancy BMI, gestational weight gain, oral glucose challenge test results, and postpartum weight loss as predictors of breast milk leptin, insulin, and adiponectin concentrations and whether these relationships vary over time. Methods Milk was collected at 1 and 3 months from 135 exclusively breastfeeding women from the longitudinal Mothers and Infants Linked for Healthy Growth (MILk) study. Hormones were assayed in skimmed samples using ELISA. Mixed‐effects linear regression models were employed to assess main effects and effect‐by‐time interactions on hormone concentrations. Results In adjusted models, BMI was positively associated with milk leptin (P < 0.001) and insulin (P = 0.03) and negatively associated with milk adiponectin (P = 0.02); however, the association was stronger with insulin and weaker with adiponectin at 3 months than at 1 month (time interaction P = 0.017 for insulin and P = 0.045 for adiponectin). Gestational weight gain was positively associated and postpartum weight loss was negatively associated with milk leptin (both P < 0.001), independent of BMI. Oral glucose challenge test results were not associated with these milk hormone concentrations. Conclusions Maternal weight status before, during, and after pregnancy contributes to interindividual variation in human milk composition. Continuing work will assess the role of these and other milk bioactive factors in altering infant metabolic outcomes.

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Recent national reports call for increasing the quantitative acumen of biology students. The BioSQuaRE represents an assessment tool based on such reports. The iterative development of the instrument by science and mathematics faculty in collaboration with educational psychologists is described, and the tool’s psychometric properties are summarized.

SERJ has provided a high quality professional publication venue for researchers in statistics education for close to a decade. This paper presents a review of the articles published to explore what they suggest about the field of statistics education, the researchers, the questions addressed, and the growing knowledge base on teaching and learning statistics. We present a detailed analysis of these articles in order to address the following questions: What is being published and why, who is publishing research in SERJ, how is the research being carried out, and what do the results suggest about future research? Implications for future directions in statistics education research are suggested. First published November 2011 at Statistics Education Research Journal: Archives

One of the first simulation-based introductory statistics curricula to be developed was the NSF-funded Change Agents for Teaching and Learning Statistics curriculum. True to its name, this curriculum is constantly undergoing change. This article describes the story of the curriculum as it has evolved at the University of Minnesota and offers insight into promising new future avenues for the curriculum to continue to affect radical, substantive change in the teaching and learning of statistics. Supplementary materials for this article are available online.

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The influx of data and the advances in computing have led to calls to update the introductory statistics curriculum to better meet the needs of the contemporary workforce. To this end, we developed the COMputational Practices in Undergraduate TEaching of Statistics  (COMPUTES) instrument, which can be used to measure the extent to which computation practices—specifically data, simulation, and coding practices—are included in the introductory statistics curriculum. Data from 236 instructors were used in a psychometric analysis to evaluate the latent structure underlying instructors’ response patterns and  understand the quality of the items. We also examined whether computational practices are being emphasized differently across institutional settings. Results suggest that the latent structure is best captured using a correlated multidimensional model and that most items were contributing information to the measurement process. Across institutional settings, curricular emphasis related to data and simulation practices seem quite similar, while emphasis on coding practices differs.

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