Baby Boomers in Technology-Rich Environments: Using PIAAC to Study the Association of Workplace Learning with Technology Competency

A skill gap in Problem Solving in Technology-Rich Environments (PS-TRE) between U.S. Baby Boomers and younger generations has been documented in previous studies using the Programme for the International Assessment of Adult Competencies (PIAAC) Survey of Adult Skills (Rampey et al., 2016). Bringing this generation of workers up to speed in this competency area is important because older workers are a growing segment of the U.S. workforce with 13 million employees expected to be age 65 or older by 2024 (Toossi & Torpey, 2017). Workplace learning may be a solution, but few studies in adult learning document outcomes of training interventions specifically for this generation, and few if any studies explore the efficacy of informal learning to improve technology competency among Baby Boomers. By using PIAAC to study the association of nonformal and informal workplace learning with PS-TRE competency among U.S. Baby Boomers, this study directly responds to these gaps in the literature. Multiple linear regression was used to conduct this analysis. Results indicate that Baby Boomers may make significant gains in PS-TRE if they participate in an optimal amount of nonformal workplace learning (on-the-job training or seminar/workshop participation). Some caution may be warranted, however, in use of on-the-job training among workers age 60-70. Learning informally from coworkers or supervisors was not associated with significant gains in PS-TRE. An optimal amount of learning-by-doing may be beneficial in large organizations, but findings also indicate too much learning-by-doing may be detrimental. No significant differences were found between men and women, between supervisors and non-supervisors, or between workers in different economic sectors. Since this is a cross-sectional study, findings are not causal; however, future research seems most promising in exploring the impact of seminar or workshop participation on PS-TRE competency for Baby Boomers

Lessons Learned from Implementing Unconscious Bias Training at an Academic Medical Center

Introduction. In 2018, our Midwestern university medical center began offering unconscious bias training. Each session concluded with a standard evaluation. We analyzed two years of data that focused on three areas: 1) whether demographic differences or amount of prior knowledge on the topic influenced the training experience; 2) what participants liked best about the training; and 3) whether participants’ stated intentions to apply their learning at the end of the training aligned with institutional goals of the training. Methods. Participants attended sessions open to the campus community pre-scheduled by the Office for Diversity, Equity, and Inclusion and posted on its website. Chi-square tests were utilized to test associations between outcomes and questionnaire responses. Outcome measures included race/ethnicity, prior knowledge level, and overall rating of the training. Thematic analysis was used to code comments and establish themes from two open-ended survey questions. Results. Significant differences were found by race and ethnicity for all questionnaire responses; each were p < 0.001. Those who reported they had advanced/expert knowledge on the topic were less likely to report the training increased their knowledge, and those who reported their race as White/Caucasian tended to give the training the highest overall rating, as did heterosexuals. Through thematic analysis, participants valued the interactive nature of the training sessions, the use of storytelling, and the safety of the learning environment. Participants’ intention to apply their learning indicated they had gained general awareness of bias and settings where it might influence their work. Conclusions. In an effort to foster a better working and learning environment for those who are underrepresented in the health professions, training was provide that may not have met the expectations of all participants. At the same time, participants who identified as White clearly increased their awareness of bias. Therefore, it is recommended to move away from one-size-fits-all unconscious bias training and develop a robust training continuum to provide ongoing advancement for diverse audiences

Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii

Citation: Raithel, S., Johnson, L., Galliart, M., Brown, S., Shelton, J., Herndon, N., & Bello, N. M. (2016). Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii. Bmc Genomics, 17, 16. doi:10.1186/s12864-016-2442-7Background: Differential expression (DE) analysis of RNA-seq data still poses inferential challenges, such as handling of transcripts characterized by low expression levels. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on transcripts characterized by a small number of read counts, so-called low-count transcripts, as motivated by an ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem exhibits qualitative phenotypic divergence consistent with enhanced drought tolerance, plausibly associated with transcripts of low expression levels. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60 % of which are classified as low-count) collected from leaf tissue of individual plants of big bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the inferential performance of recently developed statistical methods for DE analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Results: Both DE methods seemed to properly control family-wise type 1 error on low-count transcripts, whereas edgeR robust showed greater power and DESeq2 showed greater precision and accuracy. However, specification of the degree of freedom parameter under edgeR robust had a non-trivial impact on inference and should be handled carefully. When properly specified, both DE methods showed overall promising inferential performance on low-count transcripts, suggesting that ad-hoc data filtering steps at arbitrary expression thresholds may be unnecessary. A note of caution is in order regarding the approximate nature of DE tests under both methods. Conclusions: Practical recommendations for DE inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications focused on transcripts of low expression levels

Signalling plasticity and energy saving in a tropical bushcricket

Males of the tropical bushcricket Mecopoda elongata synchronize their acoustic advertisement signals (chirps) in interactions with other males. However, synchrony is not perfect and distinct leader and follower roles are often maintained. In entrainment experiments in which conspecific signals were presented at various rates, chirps displayed as follower showed notable signal plasticity. Follower chirps were shortened by reducing the number and duration of syllables, especially those of low and medium amplitude. The degree of shortening depended on the time delay between leader and follower signals and the sound level of the entraining stimulus. The same signal plasticity was evident in male duets, with the effect that the last syllables of highest amplitude overlapped more strongly. Respiratory measurements showed that solo singing males producing higher chirp rates suffered from higher metabolic costs compared to males singing at lower rates. In contrast, respiratory rate was rather constant during a synchronous entrainment to a conspecific signal repeated at various rates. This allowed males to maintain a steady duty cycle, associated with a constant metabolic rate. Results are discussed with respect to the preference for leader signals in females and the possible benefits males may gain by overlapping their follower signals in a chorus

Ecotypes of an ecologically dominant prairie grass (\u3ci\u3eAndropogon gerardii\u3c/i\u3e) exhibit genetic divergence across the U.S. Midwest grasslands’ environmental gradient

Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation by distance (IBD) vs. isolation by environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas and Illinois. Neighbour-joining tree and PCoA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. We found high genetic variability within prairies (80%) and even fragmented Illinois prairies, surprisingly, contained high within-prairie genetic diversity (92%). Using BAYENV2, 14 topranked outlier loci among ecotypes were associated with temperature and precipitation variables. Six of seven BAYESCAN FST outliers were in common with BAYENV2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate

Data from: Ecotypes of an ecologically dominant prairie grass (Andropogon gerardii) exhibit genetic divergence across the U.S. Midwest grasslands environmental gradient

Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study the nature of population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation-by-distance (IBD) vs. isolation-by-environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection, and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas, and Illinois. Neighbor-joining tree and PCA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. High genetic variability within prairies was found (80%) and even fragmented Illinois prairies, surprisingly, contain high within-prairie genetic diversity (92%). Using Bayenv2, we identified 14 top-ranked outlier loci among ecotypes to be associated with temperature and precipitation variables. Six of seven BayeScan FST-outliers were also found in common with Bayenv2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate

Bacterial but Not Fungal Rhizosphere Community Composition Differ among Perennial Grass Ecotypes under Abiotic Environmental Stress

Environmental change, especially frequent droughts, is predicted to detrimentally impact the North American perennial grasslands. Consistent dry spells will affect plant communities as well as their associated rhizobiomes, possibly altering the plant host performance under environmental stress. Therefore, there is a need to understand the impact of drought on the rhizobiome, and how the rhizobiome may modulate host performance and ameliorate its response to drought stress. In this study, we analyzed bacterial and fungal communities in the rhizospheres of three ecotypes (dry, mesic, and wet) of dominant prairie grass, Andropogon gerardii. The ecotypes were established in 2010 in a common garden design and grown for a decade under persistent dry conditions at the arid margin of the species’ range in Colby, Kansas. The experiment aimed to answer whether and to what extent do the different ecotypes maintain or recruit distinct rhizobiomes after 10 years in an arid climate. In order to answer this question, we screened the bacterial and fungal rhizobiome profiles of the ecotypes under the arid conditions of western Kansas as a surrogate for future climate environmental stress using 16S rRNA and ITS2 metabarcoding sequencing. Under these conditions, bacterial communities differed compositionally among the A. gerardii ecotypes, whereas the fungal communities did not. The ecotypes were instrumental in driving the differences among bacterial rhizobiomes, as the ecotypes maintained distinct bacterial rhizobiomes even after 10 years at the edge of the host species range. This study will aid us to optimize plant productivity through the use of different ecotypes under future abiotic environmental stress, especially drought

BAYENV2 Covariance Matrices

Resulting covariance matrices after removing the 14 top-ranked outliers from the 'control loci' data set. Tabs separate each successive covariance matrix. These matrices result from independent runs of 10^6 iterations

BAYENV2 Input File - All Markers

Use this file as the 'control loci' input for BayEnv2. This file is analogous to the 'SNPSFILE' described in BayEnv2