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The role of cognitive engagement in promoting deep learning is well established. This deep learning fosters attributes of success such as self-efficacy, motivation and persistence. However, the traditional chalk-and-talk teaching and learning environment is not conducive to engage students cognitively. The biggest impediment to implementing an environment for deep learning such as active-learning is the limited duration of a typical class period most of which is consumed by lecturing. In this paper, best practices and strategies for cognitive engagement of students in the classroom are discussed. Several lower level math and aerospace engineering courses were redesigned and offered during the academic year at a historically black university. The learning strategies in these redesigned courses included the “flipped” pedagogical model which allowed the integration of the active-learning strategy in the classroom. The research study is to determine the impact of these redesigned courses on student academic performance and persistence in STEM courses. The efficacy of the design of the flipped approach was also investigated. A between-group quasi-experimental research design was used for comparing student academic performance in traditional classroom (control group) and redesigned classroom (intervention group). A within-subject, repeated measures design was also used to assess the impact on the students’ self-regulated learning. A validated instrument was used to measure the effect of the redesigned learning environment on the motivational beliefs and self-regulated learning. Data on the academic performance of the students were collected. Analyses of these data indicated a significant impact on student academic performance. A positive change in student motivation and self-regulated learning was observed. Data analysis also validated the design of the intervention. This research is supported by NSF Grant# 1712156. 

ABSTRACT Susceptibility to breast cancer is significantly increased in individuals with germ line mutations in RECQ1 (also known as RECQL or RECQL1 ), a gene encoding a DNA helicase essential for genome maintenance. We previously reported that RECQ1 expression predicts clinical outcomes for sporadic breast cancer patients stratified by estrogen receptor (ER) status. Here, we utilized an unbiased integrative genomics approach to delineate a cross talk between RECQ1 and ERα, a known master regulatory transcription factor in breast cancer. We found that expression of ESR1 , the gene encoding ERα, is directly activated by RECQ1. More than 35% of RECQ1 binding sites were cobound by ERα genome-wide. Mechanistically, RECQ1 cooperates with FOXA1, the pioneer transcription factor for ERα, to enhance chromatin accessibility at the ESR1 regulatory regions in a helicase activity-dependent manner. In clinical ERα-positive breast cancers treated with endocrine therapy, high RECQ1 and high FOXA1 coexpressing tumors were associated with better survival. Collectively, these results identify RECQ1 as a novel cofactor for ERα and uncover a previously unknown mechanism by which RECQ1 regulates disease-driving gene expression in ER-positive breast cancer cells. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.