This paper analyzes the nature and perceived effects of mid-stakes testing (known as the EQAO) in Ontario, Canada. Ontario’s mid-stakes tests were meant to ensure accountability and transparency, and ...assure system-wide improvement, while avoiding the negative effects and perverse incentives of their high-stakes counterparts. The paper provides new evidence from two projects covering almost a 10-year time-span in 10 of Ontario’s 72 school districts. It shows that even though mid-stakes testing is milder in its manifestations and effects than high-stakes testing, concerns remain about the need for and side effects of such testing. The findings concern two periods of Ontario educational reform. In the first period, with a specific focus on improving performance in literacy and mathematics, administrators and special education support staff felt that the assessments raised teachers’ expectations and sense of urgency leading to steady improvements in measured achievement, but that there was also evidence of negative effects, especially on paying undue attention to “bubble” students just below the threshold for minimum proficiency. In the second reform period focused on broad excellence, well-being and equity as inclusion, mid-stakes tests were perceived as having more widespread negative effects. These included teaching to the test, cultural bias, avoidance of innovation, dilemmas of whether to include highly vulnerable students in the testing process or not, and emotional ill-being among students and teachers. The paper concludes that Ontario’s twentieth century system of large scale, mid-stakes assessment has not kept pace with its twenty first century commitments to deeper learning and stronger well-being.
Purpose
To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative ...through‐plane interpolation methods.
Methods
We implemented a 3D convolutional neural network entitled DeepResolve to learn residual‐based transformations between high‐resolution thin‐slice images and lower‐resolution thick‐slice images at the same center locations. DeepResolve was trained using 124 double echo in steady‐state (DESS) data sets with 0.7‐mm slice thickness and tested on 17 patients. Ground‐truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state‐of‐the‐art single‐image sparse‐coding super‐resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin‐slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground‐truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann‐Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability.
Results
DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse‐coding super‐resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse‐coding super‐resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73).
Conclusion
DeepResolve was capable of resolving high‐resolution thin‐slice knee MRI from lower‐resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state‐of‐the‐art methods.
Covid-19 lockdown restrictions constitute a population-wide "life-change event" disrupting normal daily routines. It was proposed that as a result of these lockdown restrictions, physical activity ...levels would likely decline. However, it could also be argued that lifestyle disruption may result in the formation of increased physical activity habits. Using a longitudinal design, the purpose of this study was to investigate changes in physical activity of different intensities, across individuals who differed in activity levels prior to lockdown restrictions being imposed, and across three time periods: pre-, during- and post-lockdown. This study also examined the extent to which the experience of daily hassles explained any changes in physical activity. A convenience sample (
= 759) recruited through social media, provided data from an online survey administered during weeks 2-3 of a 5-week lockdown and 231 participants provided complete data again 6 weeks post-lockdown (72% female,
age = 43 years). Participants completed the International Physical Activity Questionnaire-Short Form and the Daily Hassles Scale. Results showed that vigorous and moderate intensity PA were significantly lower during- and post-lockdown compared to pre-lockdown in those individuals who had been highly active pre-lockdown. In contrast, for moderately active individuals pre-lockdown, vigorous and moderate intensity PA was significantly higher during-lockdown compared to pre-lockdown, and these increased levels of vigorous PA were maintained post-lockdown. Participants experienced daily hassles due to inner concerns, time pressures, family, and financial concerns to the same extent during- and post-lockdown. Those daily hassles had a small negative (Standardized β = -0.11;
< 0.05) predictive effect on post-lockdown PA. It appears that to understand the effect of COVID-19 restrictions on PA, the activity status of individuals pre-lockdown needs to be taken into account. The daily hassles appeared to play a role in post-lockdown PA behavior, but future research should investigate why these results occurred.
Background
Super‐resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.
Purpose
To evaluate MRI super‐resolution using quantitative and ...qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring.
Study Type
Retrospective.
Population
In all, 176 MRI studies of subjects at varying stages of osteoarthritis.
Field Strength/Sequence
Original‐resolution 3D double‐echo steady‐state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super‐resolution and tricubic interpolation (TCI) at 3T.
Assessment
A quantitative comparison of femoral cartilage morphometry was performed for the original‐resolution DESS, the super‐resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans.
Statistical Tests
Mann–Whitney U‐tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super‐resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super‐resolution and TCI images, with the original‐resolution as a reference.
Results
DC for the original‐resolution (90.2 ± 1.7%) and super‐resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super‐resolution with the original‐resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through‐plane quantitative blur factor for super‐resolution images, was significantly (P < 0.001) better than TCI. Super‐resolution osteophyte detection sensitivity of 80% (76–82%), specificity of 93% (92–94%), and DOR of 32 (22–46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69–76%), specificity of 90% (89–91%), and DOR of 17 (13–22).
Data Conclusion
Super‐resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2020;51:768–779.
This study examined the relationships among cognitive acceptance, behavioral commitment, psychological need satisfaction, autonomous extrinsic motivation (EM) for physical activity (PA), and PA ...behavior. Participants (N = 456, Mage = 40.7 years) completed online measures of these variables, and data were analyzed using structural equation modeling. Results indicated a direct pathway from behavioral commitment to autonomous EM, plus indirect effects via autonomy, competence, and relatedness. There was no direct pathway from cognitive acceptance to autonomous EM, but there were indirect effects via competence and autonomy satisfaction. There was a direct pathway from cognitive acceptance to self-reported PA plus indirect effects via autonomous EM. There was no direct pathway from behavioral commitment to self-reported PA, but there were indirect effects via autonomous EM. Cognitive acceptance and behavioral commitment potentially support the development of autonomous EM for PA. Future research using longitudinal and intervention-based research designs is required to determine the causal relationships among these variables.
Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as ...training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.
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