The perceptions of parents in Australia had a significant impact on their children’s future, and many youngsters took careful note of these before making career choices. Mothers tended to hold their sons’ mathematical ability in higher regard. Their thoughts about their daughters’ maths ability were much more tied to actual maths achievements, such as test scores. These findings point to a gender bias in mothers’ judgments about sons versus daughters, or might reflect differences in the self-concept that male and female adolescents hold of themselves. These therefore highlight the impact that parents can have in encouraging or discouraging their children’s STEM career aspirations. ### Reference: Watt, H.M.G. et al. (2016). Mathematics – A Critical Filter for STEM-related Career Choices? A longitudinal examination among Australian and U.S.
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Its objective is to map to clinical parameters each of these patients myeloma cells genomic profiles, generated from specimens collected at first presentation and at progression events, to develop a more complete understanding of patient responses to treatments. Read More The findings announced today underscore the power of causal models to discover drug targets and pathways from the nearly infinite number of possibilities, said Iya Khalil, PhD, Chief Commercial Officer, Executive Vice President and Co-Founder of GNS. In combination with the MMRFs CoMMpass data, one of the richest datasets of its kind, we are not only accelerating the discovery and development of new therapeutics, but also helping patients and their providers with decisions about the optimal use of existing and future therapies. a job interviewGNS and the MMRF have made a number of significant discoveries since the organizations began collaborating approximately two years ago, in the fall of 2014. The 2016 ASH Annual Meeting & Exposition presentation marks the second time in as many years that this effort combining data from the MMRF CoMMpass Sudy with causal machine learning and simulation technology has been featured at ASH. At the 2015 event, GNS and the MMRF revealed the discovery of novel drivers of clinical outcomes. About GNS Healthcare GNS Healthcare applies causal machine learning and simulation technology to predict which treatments will work for which patients, improving individual patient outcomes and the health of populations, while reducing the total cost of care. The GNS technology is based on its MeasureBase data integration architecture and patented REFS (Reverse Engineering and Forward Simulation) causal inference and simulation engine. Health plans, bio-pharmaceutical companies, healthcare providers, foundations, academic medical centers, and self-insured employers use these cloud-based solutions to solve pressing and costly problems including metabolic syndrome, medication adherence, end-of-life care, preterm birth, personalized care pathways in specialty care, oncology, and diabetes, new drug target discovery, patient stratification in clinical trials, and more. GNS solutions focus on reducing adverse events, slowing disease progression, and improving therapeutic effectiveness through precision matching that maximizes impact on individual patient health outcomes while reducing wasteful spending and downstream medical costs.
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