Reading Requirements and Basic Secondary Teacher Certification: An Update

Being involved with pre-service training of content teachers in a state which requires by law courses in reading method, we raised the following question: Since only two such studies appear in the widely circulated professional literature, were the findings reported by Bader truly representative of a positive trend toward a commitment to reading, or had the earlier comments of Estes and Piercey proven to be more prophetic

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

REMAP:An online remote sensing application for land cover classification and monitoring

Recent assessments of progress towards global conservation targets have revealed a paucity of indicators suitable for assessing the changing state of ecosystems. Moreover, land managers and planners are often unable to gain timely access to the maps they need to support their routine decision-making. This deficiency is partly due to a lack of suitable data on ecosystem change, driven mostly by the considerable technical expertise needed to develop ecosystem maps from remote sensing data. We have developed a free and open-access online remote sensing and environmental modelling application, the Remote Ecosystem Monitoring and Assessment Pipeline (Remap; https://remap-app.org), that enables volunteers, managers and scientists with little or no experience in remote sensing to generate classifications (maps) of land cover and land use change over time. Remap utilizes the geospatial data storage and analysis capacity of Google Earth Engine and requires only spatially resolved training data that define map classes of interest (e.g. ecosystem types). The training data, which can be uploaded or annotated interactively within Remap, are used in a random forest classification of up to 13 publicly available predictor datasets to assign all pixels in a focal region to map classes. Predictor datasets available in Remap represent topographic (e.g. slope, elevation), spectral (archival Landsat image composites) and climatic variables (precipitation, temperature) that are relevant to the distribution of ecosystems and land cover classes. The ability of Remap to develop and export high-quality classified maps in a very short (<10 min) time frame represents a considerable advance towards globally accessible and free application of remote sensing technology. By enabling access to data and simplifying remote sensing classifications, Remap can catalyse the monitoring of land use and change to support environmental conservation, including developing inventories of biodiversity, identifying hotspots of ecosystem diversity, ecosystem-based spatial conservation planning, mapping ecosystem loss at local scales and supporting environmental education initiatives

T-Lymphocyte Activation is Not Affected by the Mobilization of Senescent T-Cells into the Peripheral Blood Following an Acute Bout of Exercise

It is well recognized that individuals are at an increased risk of illness following an arduous exercise regime. Exercise may affect activation status of cells and play a pivotal role in defense against pathogenic invasion. CD69 is the earliest known expressed cell surface antigen of T-cell activation and is a reliable marker of cell activation status (Green et al. Med. Sci. Sports Exerc. 35, 582-588: 2003). Exercise is known to alter the frequency of senescent cells in the blood expressing the cell surface glycoprotein killer cell lectin-like receptor G1 (KLRG1), and are antigen-experienced and unable to clonally expand upon further antigenic stimulation (Simpson et al. J. Appl. Phys. 103, 396-401:2007), PURPOSE: To examine the contribution of senescent T-cells mobilized by exercise on the overall activation status of the peripheral blood T-cell pool following an acute bout of exercise. METHODS: Ten moderately trained males (age: 24.6 ± 4.8; height: 183.1 ± 6.7cm; mass: 72.8 ± 7.9kg; ; 61.3 ± 5.9 ml.kg-1.min-1) ran at speeds corresponding to 80% until volitional exhaustion (time: 36.1 ± 5.8 minutes). Blood lymphocytes isolated before (PRE), immediately after (POST) and 1 hour after (1HrPOST) exercise were stimulated for 4 hours in culture with and without the mitogen PMA and assessed for KLRG1 and CD69 expression and co-expression on CD3+, CD3+/CD4+ (CD4+) and CD3+/CD8+ (CD8+) lymphocyte subsets using 4-colour flow cytometry. RESULTS: No changes in CD69 GMFI were observed on total CD3+, CD4+ and CD8+ T-cells POST or 1HrPOST exercise. The proportions of KLRG1+ cells among the total CD3+, CD4+ and CD8+ T-cell populations increased by 172%, 107% and 169% respectively POST exercise and fell below baseline values 1h later (p\u3c0.05). At all sample time points, CD69 GMFI was greater on stimulated KLRG1+ T-cells compared to KLRG1- cells (p\u3c0.05). CONCLUSION: We conclude that exercise does not affect the activation status of the total T-cell pool. Instead, the number of senescent cells expressing CD69 is greater than those that are not senescent at all times. This suggests that upon pathogenic invasion post-exercise

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts

Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure

Separability tests for high-dimensional, low-sample size multivariate repeated measures data

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Valid inference in longitudinal imaging requires enough flexibility of the covariance model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the covariance structure. Separable (Kronecker product) covariance models provide one such parameterization in which the spatial and temporal covariances are modeled separately. However, evaluating the validity of this parameterization in high-dimensions remains a challenge. Here we provide a scientifically informed approach to assessing the adequacy of separable (Kronecker product) covariance models when the number of observations is large relative to the number of independent sampling units (sample size). We address both the general case, in which unstructured matrices are considered for each covariance model, and the structured case, which assumes a particular structure for each model. For the structured case, we focus on the situation where the within subject correlation is believed to decrease exponentially in time and space as is common in longitudinal imaging studies. However, the provided framework equally applies to all covariance patterns used within the more general multivariate repeated measures context. Our approach provides useful guidance for high dimension, low sample size data that preclude using standard likelihood based tests. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the approaches appeal