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This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model emerged as the dominant model. It then fit the dominant model and the other models to confirm that the model provides the best fit to the data. The model found to represent the most number of items in the test was the Compensatory Reparameterized Unified Model (C-RUM) and other models compared were the Deterministic-Input, Noisy-And (DINA), Deterministic Input, Noisy-Or-gate (DINO), and Noisy Input, Deterministic-Or-gate (NIDO). The absolute (item-association root mean square error values) and relative (information criteria) model fit indices also indicated that the LCDM and the C-RUM were the best fit to the data. More detailed analyses on the functioning of the C-RUM were conducted and the interpretation of the results was included in the discussion section. The article ends with some suggestions for future research based on the limitations of the study. [ABSTRACT FROM PUBLISHER]

Providing appropriate test accommodations to most English language learners (ELLs) is important to facilitate meaningful inferences about learning. This study compared teacher large-scale test accommodation recommendations to those from a literature- and practitioner-grounded accommodation selection taxonomy. The taxonomy links student-specific needs, strengths, and schooling experiences to large-scale test accommodation recommendations that differentially minimize barriers of access for students with different profiles. A blind panel of experts rated four sets of recommendations for each of 114 ELLs. Results found the taxonomy was a significantly better fit for distinguishing accommodations by student need than teacher recommendations. Further, the fit of teacher recommendations showed no difference when the teacher used a structured data collection procedure to gather profile information about each of their ELLs and when they did not, and teachers’ recommendations were not found to differ significantly from a random set of accommodations. Findings are consistent with previous literature that suggests the task of matching specific accommodations to individual needs, rather than the task of identifying individual needs, is where teachers struggle in recommending appropriate test accommodations. [ABSTRACT FROM PUBLISHER]

MicroRNAs (miRNAs) are 20-22-nucleotide small endogenous non-coding RNAs which regulate gene expression at post-transcriptional level. In the last two decades, identification of almost 2600 miRNAs in human and their potential to be modulated opened a new avenue to target almost all hallmarks of cancer. miRNAs have been classified as tumor suppressors or oncogenes depending on the phenotype they induce, the targets they modulate, and the tissue where they function. miR-200c, an illustrious tumor suppressor, is one of the highly studied miRNAs in terms of development, stemness, proliferation, epithelial-mesenchymal transition (EMT), therapy resistance, and metastasis. In this review, we first focus on the regulation of miR-200c expression and its role in regulating EMT in a ZEB1/E-cadherin axis-dependent and ZEB1/E-cadherin axis-independent manner. We then describe the role of miR-200c in therapy resistance in terms of multidrug resistance, chemoresistance, targeted therapy resistance, and radiotherapy resistance in various cancer types. We highlight the importance of miR-200c at the intersection of EMT and chemoresistance. Furthermore, we show how miR-200c coordinates several important signaling cascades such as TGF-β signaling, PI3K/Akt signaling, Notch signaling, VEGF signaling, and NF-κB signaling. Finally, we discuss miR-200c as a potential prognostic/diagnostic biomarker in several diseases, but mainly focusing on cancer and its potential application in future therapeutics. [ABSTRACT FROM AUTHOR]

There are many methods available for printer identification of questioned documents, however most of them need identical contents of the training and testing documents. There is no effective method yet when the contents of the training and testing documents are different. To overcome this obstacle a method based on synthetic texture analysis is proposed in this paper. The inner texture of printed characters is reconstructed in order to generate an artificial textural image similar to the real texture while independent of document content. Fast Fourier Transform (FFT) and Gray Level Co-occurrence Matrix (GLCM) methods are then used for feature extraction. Experimental results demonstrate that the proposed method can achieve a high recognition rate and provide a new platform for printer identification independent on the content of documents. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is an autoimmune disease, whereas type 2 diabetes (T2D) results from insulin resistance and beta cell dysfunction. Previously, the onset of these two separate diseases was easily distinguished, with children being most at risk for T1D and T2D occurring in overweight adults. However, the dramatic rise in obesity, coupled with the notable increase in T1D, has created a large overlap in these previously discrete patient populations. Delayed diagnosis of T1D can result in severe illness or death, and rapid diagnosis of T1D is critical for the efficacy of emerging therapies. However, attempts to apply next-generation platforms have been unsuccessful for detecting diabetes biomarkers. Here we describe the development of a plasmonic gold chip for near-infrared fluorescence-enhanced (NIR-FE) detection of islet cell-targeting autoantibodies. We demonstrate that this platform has high sensitivity and specificity for the diagnosis of T1D and can be used to discover previously unknown biomarkers of T1D. [ABSTRACT FROM AUTHOR]

This study reports on the use of Analysis of Diagnostic Exam Prompted Teaching (ADEPT) Learning Cycles employing student tutors (peer tutors) as a surrogate for the professor in implementing the system for introductory financial accounting classes. Based on required diagnostic exams prepared by the professor, the peer tutors provided individual feedback to students on their proficiency on selected topics. The peer tutors directed students on corrective steps to resolve deficiencies or provided limited, immediate one-on-one tutoring to resolve them. Students had to complete each diagnostic exam successfully (80% grade) before graded unit exams. The study examined whether applying the ADEPT system improved average test scores and course grades in the introductory financial accounting course. The study reports statistically significant improvement in exam scores and in final course grades using the ADEPT system (with peer tutors). The results included an over fifty percent improvement in the C or better pass rate required for students to proceed to the next accounting course. In addition to the expected positive effects on retention of the enhanced success in the course, the results were achieved with a minimal commitment of faculty time and at a low cost. [ABSTRACT FROM AUTHOR]

The article reports on the influence of endoscopists on the characteristics of the endoscopic examinations. It reports that results of endoscopic procedures of the gastrointestinal tract depend largely on the skill of the endoscopist. It also discusses the characteristics of diagnostic tests in the fractions of true-positive, false-positive, false-negative, and true-negative test results.

More and more systematic reviews of diagnostic test accuracy studies are being published, but they can be methodologically challenging. In this paper, the authors present some of the recent developments in the methodology for conducting systematic reviews of diagnostic test accuracy studies. Restrictive electronic search filters are discouraged, as is the use of summary quality scores. Methods for meta-analysis should take into account the paired nature of the estimates and their dependence on threshold. Authors of these reviews are advised to use the hierarchical summary receiver-operating characteristic or the bivariate model for the data analysis. Challenges that remain are the poor reporting of original diagnostic test accuracy studies and difficulties with the interpretation of the results of diagnostic test accuracy research. [ABSTRACT FROM AUTHOR]