Wages, Skills, and Technology in the United States and Canada

Wages for more- and less-educated workers have followed strikingly different paths in the U.S. and Canada. During the 1980's and 1990's, the ratio of earnings of university graduates to high school graduates increased sharply in the U.S. but fell slightly in Canada. Katz and Murphy (1992) found that for the U.S. a simple supply-demand model fit the pattern of variation in the premium over time. We find that the same model and parameter estimates explain the variation between the U.S. and Canada. In both instances, the relative demand for more-educated labor shifts out at the same, consistent rate. Both over time and between countries, the variation in rate of growth of relative wages can be explained by variation in the relative supply of more-educated workers. Many economists suspect that technological change is causing the steady increases in the relative demand for more-educated labor. If so, these data provide independent evidence on the spatial and temporal variation in the pattern of technological change. Whatever is causing this increased demand for skill, the evidence from Canada suggest that increases in educational attainment and skills can reduce the rate at which relative wages diverge.

Prediction and verification of microRNA targets by MovingTargets, a highly adaptable prediction method

BACKGROUND: MicroRNAs (miRNAs) mediate a form of translational regulation in animals. Hundreds of animal miRNAs have been identified, but only a few of their targets are known. Prediction of miRNA targets for translational regulation is challenging, since the interaction with the target mRNA usually occurs via incomplete and interrupted base pairing. Moreover, the rules that govern such interactions are incompletely defined. RESULTS: MovingTargets is a software program that allows a researcher to predict a set of miRNA targets that satisfy an adjustable set of biological constraints. We used MovingTargets to identify a high-likelihood set of 83 miRNA targets in Drosophila, all of which adhere to strict biological constraints. We tested and verified 3 of these predictions in cultured cells, including a target for the Drosophila let-7 homolog. In addition, we utilized the flexibility of MovingTargets by relaxing the biological constraints to identify and validate miRNAs targeting tramtrack, a gene also known to be subject to translational control dependent on the RNA binding protein Musashi. CONCLUSION: MovingTargets is a flexible tool for the accurate prediction of miRNA targets in Drosophila. MovingTargets can be used to conduct a genome-wide search of miRNA targets using all Drosophila miRNAs and potential targets, or it can be used to conduct a focused search for miRNAs targeting a specific gene. In addition, the values for a set of biological constraints used to define a miRNA target are adjustable, allowing the software to incorporate the rules used to characterize a miRNA target as these rules are experimentally determined and interpreted

Numerical Investigation of PLIF Gas Seeding for Hypersonic Boundary Layer Flows

Numerical simulations of gas-seeding strategies required for planar laser-induced fluorescence (PLIF) in a Mach 10 air flow were performed. The work was performed to understand and quantify adverse effects associated with gas seeding and to compare different flow rates and different types of seed gas. The gas was injected through a slot near the leading edge of a flat plate wedge model used in NASA Langley Research Center's 31- Inch Mach 10 Air Tunnel facility. Nitric oxide, krypton, and iodine gases were simulated at various injection rates. Simulation results showing the deflection of the velocity field for each of the cases are presented. Streamwise distributions of velocity and concentration boundary layer thicknesses as well as vertical distributions of velocity, temperature, and mass distributions are presented for each of the cases. Relative merits of the different seeding strategies are discussed

Assessment of the learning curve in health technologies: a systematic review

Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve:" Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning

Assessing the learning curve effect in health technologies: Lessons from the non-clinical literature

Introduction: Many health technologies exhibit some form of learning effect, and this represents a barrier to rigorous assessment. It has been shown that the statistical methods used are relatively crude. Methods to describe learning curves in fields outside medicine, for example, psychology and engineering, may be better. Methods: To systematically search non–health technology assessment literature (for example, PsycLit and Econlit databases) to identify novel statistical techniques applied to learning curves. Results: The search retrieved 9,431 abstracts for assessment, of which 18 used a statistical technique for analyzing learning effects that had not previously been identified in the clinical literature. The newly identified methods were combined with those previously used in health technology assessment, and categorized into four groups of increasing complexity: a) exploratory data analysis; b) simple data analysis; c) complex data analysis; and d) generic methods. All the complex structured data techniques for analyzing learning effects were identified in the nonclinical literature, and these emphasized the importance of estimating intra- and interindividual learning effects. Conclusion: A good dividend of more sophisticated methods was obtained by searching in nonclinical fields. These methods now require formal testing on health technology data sets

Evaluation of Maize Germplasm from Saint Croix for Resistance to Leaf Feeding by Fall Armyworm

Maize (Zea mays L.) is a preferred host of fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera:Noctuidae), with larvae primarily feeding on developing leaves and ear tissue. The fall armyworm is resistant to several classes of insecticide and Bt-maize grown in certain areas. Native sources of plant resistance to the pest are available for public use, but new sources of resistance need to be discovered and developed. The objective for this study was to test maize germplasm collected from Saint Croix, U.S. Virgin Islands, for resistance to leaf feeding by fall armyworm. Plants were grown in the field and artificially infested at a high level. Scores of damage by fall armyworm feeding on leaves at 7 and 14 days differed significantly for the 13 maize genotypes tested. Scores at 14 days for Saint Croix Group 1 (5.8), Saint Croix Group 3 (5.6), Saint Croix 2 (5.6), and Saint Croix 7 (6.0) were moderately resistant and not significantly different from one another. Individual plants in the populations were variable for resistance to leaf feeding, and scored between 4 and 7. It should be possible to select within the populations for greater resistance to damage by fall armyworms feeding on leaves

Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments

Effective pre-hire assessments impact organizational outcomes. Recent developments in machine learning provide an opportunity for practitioners to improve upon existing scoring methods. This study compares the effectiveness of an empirically keyed scoring model with a machine learning, random forest model approach in a biodata assessment. Data was collected across two organizations. The data from the first sample (N=1,410), was used to train the model using sample sizes of 100, 300, 500, and 1,000 cases, whereas data from the second organization (N=524) was used as an external benchmark only. When using a random forest model, predictive validity rose from 0.382 to 0.412 in the first organization, while a smaller increase was seen in the second organization. It was concluded that predictive validity of biodata measures can be improved using a random forest modeling approach. Additional considerations and suggestions for future research are discussed

Evaluation of XL370A-Derived Maize Germplasm for Resistance to Leaf Feeding by Fall Armyworm

The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is an economically important insect with larvae damaging maize (Zea mays L.) leaves and ear tissue. The pest has become resistant to several classes of insecticide and Bt-maize grown in some geographical areas. Once discovered and characterized, native sources of maize resistance to this pest could be effectively integrated with existing control tactics. The objective for this study was to test experimental lines derived from maize germplasm XL370A for resistance to leaf feeding by fall armyworm. Plants were grown in the field in 2018 and 2019, artificially infested with fall armyworm, and leaf damage scores recorded. Average 14-day scores for experimental maize lines GEMN-0095 (5.8), GEMN-0096 (5.7), and GEMN-0133 (5.6) were moderately resistant and 7- and 14-day scores for these entries were not significantly different across both years. Cuba 94 was not significantly different from the three entries with the exception of having greater 7-day damage scores in 2019. GEMN-0048 was not resistant but variability was observed in 14-day scores between 4 (resistant) and 8 (susceptible) in individual plants. The experimental lines are adapted for growth in temperate regions and might provide maize breeding programs with useful levels of resistance to fall armyworm