A Study on Reflective Reciprocal Peer Coaching for Pre-service Teachers: Change in Reflectivity

Reflective practice is considered as an effective way for professional development in order to gain awareness of one’s own teaching as well as to compete with the changing needs of the students. Especially in pre-service period, when pre-service teachers work cooperatively with their peers in a reciprocal fashion towards reflectivity, it has a potential to promote advancement in reflective practices and help them focus on the underlying meaning behind their actions. Based on these ideas, this study aimed at engaging pre-service teachers in a reflective reciprocal peer coaching experience and investigating whether such experience caused any changes in their reflectivity. For this purpose, 12 pre-service teachers in a Turkish ELT context participated in the study and a reflective reciprocal peer coaching program was implemented with a training aspect. In a mixed method study design, change in participants’ reflectivity was measured with a profile of reflective thinking attributes scale quantitatively and data were supported qualitatively with reflective diaries, video recordings of post-conference sessions and focus-group interviews. Results of quantitative and qualitative analyses put forward that the pre-service teachers advanced in their reflectivity throughout the reflective reciprocal peer coaching practice and benefited much from this experience before they embark into professional life. This study provides valuable implications to use reflection embedded in a peer coaching program and offers suggestions for teacher educators

Bland-Altman Plots for Evaluating Agreement Between Solid Tumor Measurements

Rationale and Objectives. Solid tumor measurements are regularly used in clinical trials of anticancer therapeutic agents and in clinical practice managing patients\u27 care. Consequently studies evaluating the reproducibility of solid tumor measurements are important as lack of reproducibility may directly affect patient management. The authors propose utilizing a modified Bland-Altman plot with a difference metric that lends itself naturally to this situation and facilitates interpretation. Materials and Methods. The modification to the Bland-Altman plot involves replacing the difference plotted on the vertical axis with the relative percent change (RC) between the two measurements. This quantity is the same one used in assessing tumor response to therapeutic agents and is very familiar to radiologists and clinicians working with cancer patients.The distribution of the RC is explored and revised equations for the limits of agreement (LoA) are presented. These methods are applied to positron emission tomography (PET) data studying two radiotracers. Results. The RC can be calculated separately for each lesion measured or at the patient level by summing over lesions within patient. In both cases, the distribution of the RC is highly skewed and is approximated by a negative shifted lognormal distribution. The standard equations for the 95% LoA assume the differences are approximately normally distributed and are not appropriate for the RC. Conclusions. The modified Bland-Altman plot with correctly calculated LoA can aid in evaluating agreement between solid tumor measurements

Assessing noninferiority in a three-arm trial using the Bayesian Approach

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm clinical trial is usually recommended for non-inferiority trials by the Food and Drug Administration (FDA). The three-arm trial consists of a placebo, a reference, and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with comparing this reference to an experimental treatment. In this paper, we consider the analysis of noninferiority trials using Bayesian methods which incorporate both parametric as well as semi-parametric models. The resulting testing approach is both flexible and robust. The benefit of the proposed Bayesian methods is assessed via simulation, based on a study examining Home Based Blood Pressure Interventions

Inferential Methods to Assess the Difference in the Area Under the Curve From Nested Binary Regression Models

The area under the curve (AUC) is the most common statistical approach to evaluate the discriminatory power of a set of factors in a binary regression model. A nested model framework is used to ascertain whether the AUC increases when new factors enter the model. Two statistical tests are proposed for the difference in the AUC parameters from these nested models. The asymptotic null distributions for the two test statistics are derived from the scenarios: (A) the difference in the AUC parameters is zero and the new factors are not associated with the binary outcome, (B) the difference in the AUC parameters is less than a strictly positive value. A confidence interval for the difference in AUC parameters is developed. Simulations are generated to determine the finite sample operating characteristics of the tests and a pancreatic cancer data example is used to illustrate this approach

Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach

Virtual screening (VS) is widely used during computational drug discovery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein interactions (CPIs) from known CPI network data using several methods, including machine learning and data mining. Although CGBVS facilitates highly efficient and accurate CPI prediction, it has poor performance for prediction of new compounds for which CPIs are unknown. The pairwise kernel method (PKM) is a state-of-the-art CGBVS method and shows high accuracy for prediction of new compounds. In this study, on the basis of link mining, we improved the PKM by combining link indicator kernel (LIK) and chemical similarity and evaluated the accuracy of these methods. The proposed method obtained an average area under the precision-recall curve (AUPR) value of 0.562, which was higher than that achieved by the conventional Gaussian interaction profile (GIP) method (0.425), and the calculation time was only increased by a few percent

Structure-based inhibitors of amyloid beta core suggest a common interface with tau.

Alzheimer's disease (AD) pathology is characterized by plaques of amyloid beta (Aβ) and neurofibrillary tangles of tau. Aβ aggregation is thought to occur at early stages of the disease, and ultimately gives way to the formation of tau tangles which track with cognitive decline in humans. Here, we report the crystal structure of an Aβ core segment determined by MicroED and in it, note characteristics of both fibrillar and oligomeric structure. Using this structure, we designed peptide-based inhibitors that reduce Aβ aggregation and toxicity of already-aggregated species. Unexpectedly, we also found that these inhibitors reduce the efficiency of Aβ-mediated tau aggregation, and moreover reduce aggregation and self-seeding of tau fibrils. The ability of these inhibitors to interfere with both Aβ and tau seeds suggests these fibrils share a common epitope, and supports the hypothesis that cross-seeding is one mechanism by which amyloid is linked to tau aggregation and could promote cognitive decline