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Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies. [ABSTRACT FROM AUTHOR]

A diagnostic model for large‐scale assessment was applied to TIMSS data to compare mathematics performance of eighth graders from three countries—the US, Singapore, and Israel. Compared were attribute mastery probabilities for content, skills and cognitive processes underlying students’ performance on the 1999 TIMSS‐R mathematics test. Also compared were the proportions of students from the three samples in each of eight hierarchically ordered clusters of knowledge states. The results indicated significant differences in favour of the Singaporean sample on most attributes underlying the test. The results were discussed in light of the cultural context of education in the respective countries. [ABSTRACT FROM AUTHOR]

Guided by Shulman's (1986, 1987) tripartate model of teacher expertise (subject matter knowledge (SMK), pedagogical content knowledge (PCK), and general pedagogical knowledge (GPK)), the present study examined 162 U.S. and Chinese 3rd grade mathematics teachers' expertise in teaching fractions. Results show that U.S. teachers lag significantly behind Chinese teachers in SMK (concepts, computations, and word problems) and in some areas of PCK (e.g., such as identifying important points of teaching the fraction concepts and how to ensure students' understanding). Although the Chinese teachers performed poorly in comparison to their U.S. counterparts on a test designed to measure GPK (e.g., psychological and educational theories and applications), its poor reliability calls these results into question. The implications of this study are discussed in terms of practical ideas for teacher training in the U.S. and China. [ABSTRACT FROM AUTHOR]