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1.
129th ASEE Annual Conference and Exposition: Excellence Through Diversity, ASEE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2045759

ABSTRACT

It has become increasingly important for K-12 students to learn how to investigate patterns, correlations, and significance in data. The Berkeley Engineering Research Experiences for Teachers plus Data (BERET+D) pairs undergraduate pre-service teachers and experienced in-service science and mathematics teachers (PSTs and ISTs) to engage in engineering and data science research, exploring and analyzing data sets drawn from a variety of STEM fields and laboratories across the UC Berkeley campus. In addition to conducting independent summer research projects with guidance from university research faculty, the program provides opportunities for: (1) PSTs to develop data science-based lessons inspired by their research and aligned to the Next Generation Science Standards (NGSS), (2) ISTs to create data science-based curricula designed to inspire middle and high school students to see STEM classes as exciting and with real-life applications, and (3) ISTs to collaborate with and mentor PSTs preparing to enter K-12 STEM classrooms. Contributing towards broader impacts, CalTeach recruits a racially and socioeconomically diverse population of PSTs, and all ISTs were recruited from local public schools, in order to educate, prepare, and encourage more minority and female K-12 students to consider higher education and careers in STEM. During the first two summers of this project (2020-2021), participants completed over forty data-science related projects, developed over thirty K-12 data-science related lesson plans in math, science, and engineering, and created six classroom-ready and publicly accessible (teachengineering.org) curricular units showcasing data science. As an example of these curricular units, and as further evidence of the project's broader impact, one IST has developed an ongoing partnership between their classroom and a research laboratory on campus allowing high school physics students to learn data science techniques by analyzing and interpreting distant satellite signals collected by radio telescopes. Preliminary evaluation of this ongoing project revealed that participants viewed data science as important and essential in K-12 curriculum, that data analysis is a critical and useful skill for youth, and that data science aligns closely with the science and engineering practices called forth by NGSS. Though constrained by work-from-home restrictions due to COVID during the first two years, participants described their experience as positive and valuable, particularly in conceiving of ways to engage young learners with data-science through remote instruction. © American Society for Engineering Education, 2022

2.
Cereb Cortex ; 32(22): 5163-5174, 2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-1684532

ABSTRACT

It is unclear how different diets may affect human brain development and if genetic and environmental factors play a part. We investigated diet effects in the UK Biobank data from 18,879 healthy adults and discovered anticorrelated brain-wide gray matter volume (GMV)-association patterns between coffee and cereal intake, coincidence with their anticorrelated genetic constructs. The Mendelian randomization approach further indicated a causal effect of higher coffee intake on reduced total GMV, which is likely through regulating the expression of genes responsible for synaptic development in the brain. The identified genetic factors may further affect people's lifestyle habits and body/blood fat levels through the mediation of cereal/coffee intake, and the brain-wide expression pattern of gene CPLX3, a dedicated marker of subplate neurons that regulate cortical development and plasticity, may underlie the shared GMV-association patterns among the coffee/cereal intake and cognitive functions. All the main findings were successfully replicated. Our findings thus revealed that high-cereal and low-coffee diets shared similar brain and genetic constructs, leading to long-term beneficial associations regarding cognitive, body mass index (BMI), and other metabolic measures. This study has important implications for public health, especially during the pandemic, given the poorer outcomes of COVID-19 patients with greater BMIs.


Subject(s)
COVID-19 , Coffee , Adult , Humans , Mendelian Randomization Analysis , Edible Grain/genetics , Risk Factors , Cognition , Brain , Genome-Wide Association Study
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