Shiny app to predict agricultural tire dimensions

The main objective of this project, carried out in an industrial context, was to apply a multivariate analysis to variables related to the specifications required for the production of an agricultural tire and the dimensional test results. With the exploratory data analysis, it was possible to identify strong correlations between predictor variables and with the response variables of each test. In this project, the principal component analysis (PCA) serves to eliminate the effects of multicollinearity. The use of regression analysis was intended to predict the behavior of the agricultural tire considering the selected variables of each test. In the case of Test 1, when applying the Stepwise methods to select the variables, the model with the lowest value of Akaike Information Criterion (AIC) was achieved with the technique “Both”. However, the lowest value of AIC for Test 2 was achieved with “Backward”. Regarding the validation of assumptions, both Test 1 and Test 2 were validated. Therefore, all the quantitative variables are important, both in Test 1 and Test 2, because they are a linear combination that determines the principal components. In order to make it easier to compute predictions for future agricultural tires, an application that was developed in Shiny allows the company to know the behavior of the tire before it was produced. Using the application, it is possible to reduce the industrialization time, materials and resources, thus increasing efficiency and profits.This work has been supported by FCT – Fundação para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Patient-Centered Care: An Examination of Provider–Patient Communication Over Time

Objective: To examine the quality of provider communication over time considering the increasing emphasis on patient-centered care (PCC). Patient-centered care has been shown to have a positive impact on health outcomes, care experiences, quality-of-life, as well as decreased costs. Given this emphasis, we expect that provider–patient communication has improved over time. Data Source: We collected primary data by self-report surveys between summer 2017 and fall 2018. Study Design: We use a quantitative retrospective cohort study of a national sample of 353 patients who had an ostomy surgery. Data Extraction Method: We measure provider communication from open-ended self-reports from patients of the number of stated inadequacies in their care. Principal Findings: Results show that the time since patients had their surgery is related to higher quality provider communication. That is, patients who had their surgery further back in time reported higher quality provider communication compared with patients who had their surgery performed more recently. Conclusion: Results suggest that the quality of provider communication has not improved even with an emphasis on PCC.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded by a “Graduate College Foundation Fellowship from the Department of Sociology and the Graduate College at the University of Oklahoma” to Leslie Miller. The funding does not impact the study in any way. Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

The relationship between attitudes, beliefs and physical activity in older adults with knee pain: secondary analysis of a randomised controlled trial

Objective To investigate how attitudes and beliefs about exercise relate to physical activity behavior in older adults with knee pain attributable to osteoarthritis (OA). Methods We conducted secondary data analyses of a randomized controlled trial of exercise interventions (ISRCTN: 93634563). Participants were adults ≥45 years old with knee pain attributable to OA (n = 514). Crude and adjusted cross‐sectional and longitudinal associations between baseline Self‐Efficacy for Exercise (SEE), Positive Outcome Expectations for Exercise (POEE), Negative Outcome Expectations for Exercise scores, and physical activity level, at baseline, 3 months, and 6 months (measured by self‐report using the Physical Activity Scale for the Elderly [PASE]), and important increases in physical activity level (from baseline to 6‐month followup) were investigated using multiple linear and logistic regression. Results Cross‐sectional associations were found between SEE and PASE scores (β = 4.14 [95% confidence interval (95% CI) 0.26, 8.03]) and POEE and PASE scores (β = 16.71 [95% CI 1.87, 31.55]), adjusted for sociodemographic and clinical covariates. Longitudinal associations were found between baseline SEE and PASE scores at 3 months (β = 4.95 [95% CI 1.02, 8.87]) and 6 months β = 3.71 (0.26, 7.16), and baseline POEE and PASE at 3 months (β = 34.55 [95% CI 20.13, 48.97]) and 6 months (β = 25.74 [95% CI 11.99, 39.49]), adjusted for baseline PASE score and intervention arm. However, no significant associations with important increases in physical activity level were found. Conclusion Greater exercise self‐efficacy and more positive exercise outcome expectations were associated with higher current and future physical activity levels. These may be targets for interventions aimed at increasing physical activity

Statistical Power of Model Selection Strategies for Genome-Wide Association Studies

Genome-wide association studies (GWAS) aim to identify genetic variants related to diseases by examining the associations between phenotypes and hundreds of thousands of genotyped markers. Because many genes are potentially involved in common diseases and a large number of markers are analyzed, it is crucial to devise an effective strategy to identify truly associated variants that have individual and/or interactive effects, while controlling false positives at the desired level. Although a number of model selection methods have been proposed in the literature, including marginal search, exhaustive search, and forward search, their relative performance has only been evaluated through limited simulations due to the lack of an analytical approach to calculating the power of these methods. This article develops a novel statistical approach for power calculation, derives accurate formulas for the power of different model selection strategies, and then uses the formulas to evaluate and compare these strategies in genetic model spaces. In contrast to previous studies, our theoretical framework allows for random genotypes, correlations among test statistics, and a false-positive control based on GWAS practice. After the accuracy of our analytical results is validated through simulations, they are utilized to systematically evaluate and compare the performance of these strategies in a wide class of genetic models. For a specific genetic model, our results clearly reveal how different factors, such as effect size, allele frequency, and interaction, jointly affect the statistical power of each strategy. An example is provided for the application of our approach to empirical research. The statistical approach used in our derivations is general and can be employed to address the model selection problems in other random predictor settings. We have developed an R package markerSearchPower to implement our formulas, which can be downloaded from the Comprehensive R Archive Network (CRAN) or http://bioinformatics.med.yale.edu/group/

Relationship Between Attitudes and Beliefs and Physical Activity in Older Adults With Knee Pain: Secondary Analysis of a Randomized Controlled Trial: Attitudes About Physical Activity in Older Adults With Knee Pain

OBJECTIVE:To investigate how attitudes and beliefs about exercise relate to physical activity behavior in older adults with knee pain attributable to osteoarthritis (OA).METHODS:We conducted secondary data analyses of a randomized controlled trial of exercise interventions (ISRCTN: 93634563). Participants were adults ≥45 years old with knee pain attributable to OA (n = 514). Crude and adjusted cross-sectional and longitudinal associations between baseline Self-Efficacy for Exercise (SEE), Positive Outcome Expectations for Exercise (POEE), Negative Outcome Expectations for Exercise scores, and physical activity level, at baseline, 3 months, and 6 months (measured by self-report using the Physical Activity Scale for the Elderly [PASE]), and important increases in physical activity level (from baseline to 6-month followup) were investigated using multiple linear and logistic regression.RESULTS:Cross-sectional associations were found between SEE and PASE scores (β = 4.14 [95% confidence interval (95% CI) 0.26, 8.03]) and POEE and PASE scores (β = 16.71 [95% CI 1.87, 31.55]), adjusted for sociodemographic and clinical covariates. Longitudinal associations were found between baseline SEE and PASE scores at 3 months (β = 4.95 [95% CI 1.02, 8.87]) and 6 months β = 3.71 (0.26, 7.16), and baseline POEE and PASE at 3 months (β = 34.55 [95% CI 20.13, 48.97]) and 6 months (β = 25.74 [95% CI 11.99, 39.49]), adjusted for baseline PASE score and intervention arm. However, no significant associations with important increases in physical activity level were found.CONCLUSION:Greater exercise self-efficacy and more positive exercise outcome expectations were associated with higher current and future physical activity levels. These may be targets for interventions aimed at increasing physical activity

The genomic features that affect the lengths of 5’ untranslated regions in multicellular eukaryotes

<p>Abstract</p> <p>Background</p> <p>The lengths of 5’UTRs of multicellular eukaryotes have been suggested to be subject to stochastic changes, with upstream start codons (uAUGs) as the major constraint to suppress 5’UTR elongation. However, this stochastic model cannot fully explain the variations in 5’UTR length. We hypothesize that the selection pressure on a combination of genomic features is also important for 5’UTR evolution. The ignorance of these features may have limited the explanatory power of the stochastic model. Furthermore, different selective constraints between vertebrates and invertebrates may lead to differences in the determinants of 5’UTR length, which have not been systematically analyzed.</p> <p>Methods</p> <p>Here we use a multiple linear regression model to delineate the correlation between 5’UTR length and the combination of a series of genomic features (G+C content, observed-to-expected (OE) ratios of uAUGs, upstream stop codons (uSTOPs), methylation-related CG/UG dinucleotides, and mRNA-destabilizing UU/UA dinucleotides) in six vertebrates (human, mouse, rat, chicken, African clawed frog, and zebrafish) and four invertebrates (fruit fly, mosquito, sea squirt, and nematode). The relative contributions of each feature to the variation of 5’UTR length were also evaluated.</p> <p>Results</p> <p>We found that 14%~33% of the 5’UTR length variations can be explained by a linear combination of the analyzed genomic features. The most important genomic features are the OE ratios of uSTOPs and G+C content. The surprisingly large weightings of uSTOPs highlight the importance of selection on upstream open reading frames (which include both uAUGs and uSTOPs), rather than on uAUGs <it>per se</it>. Furthermore, G+C content is the most important determinants for most invertebrates, but for vertebrates its effect is second to uSTOPs. We also found that shorter 5’UTRs are affected more by the stochastic process, whereas longer 5’UTRs are affected more by selection pressure on genomic features.</p> <p>Conclusions</p> <p>Our results suggest that upstream open reading frames may be the real target of selection, rather than uAUGs. We also show that the selective constraints on genomic features of 5’UTRs differ between vertebrates and invertebrates, and between longer and shorter 5’UTRs. A more comprehensive model that takes these findings into consideration is needed to better explain 5’UTR length evolution.</p

Empirical Analysis of Factors Affecting Confirmation Bias Levels of Software Engineers

Confirmation bias is defined as the tendency of people to seek evidence that verifies a hypothesis rather than seeking evidence to falsify it. Due to the confirmation bias, defects may be introduced in a software product during requirements analysis, design, implementation and/or testing phases. For instance, testers may exhibit confirmatory behavior in the form of a tendency to make the code run rather than employing a strategic approach to make it fail. As a result, most of the defects that have been introduced in the earlier phases of software development may be overlooked leading to an increase in software defect density. In this paper, we quantify confirmation bias levels in terms of a single derived metric. However, the main focus of this paper is the analysis of factors affecting confirmation bias levels of software engineers. Identification of these factors can guide project managers to circumvent negative effects of confirmation bias, as well as providing guidance for the recruitment and effective allocation of software engineers. In this empirical study, we observed low confirmation bias levels among participants with logical reasoning and hypothesis testing skills