Implementing and Evaluating a Course-Based Undergraduate Research Experience (CURE) at a Hispanic-Serving Institution

We are examining the impact of a course-based undergraduate research experience (CURE) at a land-grant, Hispanic-serving institution in the southwestern United States. Students in our CURE completed one or two extended research projects over a single semester. Our CURE enrolled a high proportion of underrepresented minority students (70.3%), including 60.2% Hispanic students. One year after CURE completion, 31.5% of CURE students had graduated with a STEM degree, and 54.3% were enrolled in a STEM major. Pre- and postcourse surveys of indicators of persistence including scientific self-efficacy, scientific identity, valuing scientific community objectives, and intention to persist showed positive shifts. Impacts on STEM persistence have implications for the role of our CURE in diversifying the STEM pipeline, particularly for students historically underrepresented in STEM

Satisfaction With Psychology Training In the Veterans Healthcare Administration

Given that VA is the largest trainer of psychologists in the United States, this study sought to understand satisfaction with VA psychology training and which elements of training best predict trainees\u27 positive perceptions of training (e.g., willingness to choose training experience again, stated intentions to work in VA). Psychology trainees completed the Learners\u27 Perceptions Survey (LPS) from 2005 to 2017 (N = 5,342). Satisfaction was uniformly high. Trainee satisfaction was significantly associated with level of training, facility complexity, and some patient-mix factors. Learning environment (autonomy, time with patients, etc.), clinical faculty/preceptors (teaching ability, accessibility, etc.), and personal experiences (work/life balance, personal responsibility for patient care, etc.) were the biggest drivers of stated willingness to repeat training experiences in VA and seek employment there. Results have implications for psychologists involved in the provision of a training experience valued by trainees

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the WU-Minn-Ox HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The HCP-style neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the “WU-Minn-Ox” HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The “HCP-style” neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

distal antenna and distal antenna-related function in the retinal determination network during eye development in Drosophila

AbstractDrosophila eye specification occurs through the activity of the retinal determination (RD) network, which includes the Eyeless (Ey), Eyes absent (Eya), Sine oculis (So) and Dachshund (Dac) transcription factors. Based on their abilities to transform antennal precursors towards an eye fate, the distal antenna (dan) and distal antenna-related (danr) genes encode two new RD factors. Dan and Danr are probable transcription factors localized in nuclei of eye precursors and differentiating eye tissue. Loss-of-function single and double dan/danr mutants have small, rough eyes, indicating a requirement for wild-type eye development. In addition, dan and danr participate in the transcriptional hierarchy that controls expression of RD genes, and Dan and Danr interact physically and genetically with Ey and Dac. Eye specification culminates in differentiation of ommatidia through the activities of the proneural gene atonal (ato) in the founding R8 photoreceptor and Egfr signaling in additional photoreceptors. Danr expression overlaps with Ato during R8 specification, and Dan and Danr regulate Ato expression and are required for normal R8 induction and differentiation. These data demonstrate a role for Dan and Danr in eye development and provide a link between eye specification and differentiation

Investigating Student Understanding of Histograms: Pre-test Results

Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. Histograms are included in introductory statistics texts, research methods texts, and in the popular press. Students often have difficulty interpreting what a histogram conveys. For example, delMas, Garfield and Ooms (2005) report that students tend to confuse bar graphs and time plots with histograms. This is especially problematic because histograms are important building blocks in student understanding of statistics. This research aims to further knowledge on how students perceive histograms and what we can do as instructors to help students spot misconceptions and correct them. In Phase 1 of our study, we collected data from winter 2012 classes at Grand Valley State University. We seek to answer the following research question: What is the extent of student misconceptions both before and after instruction in an introductory statistics class?This presentation will discuss findings from Phase 1 of our study