Miracle-dz/UniFAR-FTUs huggingface dataset:
Nurse teacherhood and the categories influencing it

Abstract Nurse teacherhood is composed of many factors. It includes nurse teachers’ tasks and different multidimensional roles and also nurse teachers’ individual experiences of being a nurse teacher. Nurse teachers’ experiences of nurse teacherhood depend on the meanings they give to the different factors. In other words, there are not only one but also several interpretations of what nurse teacherhood is. These interpretations affect what is important for each nurse teacher in their own nurse teacherhood and how satisfied they are with their profession. This paper is part of a larger study examining how Finnish nurse teachers experience their nurse teacherhood in polytechnics. The purpose of this paper was to describe nurse teacherhood during the years 2003 and 2004, when polytechnics and thus also nurse education were actively being developed in Finland. Nurse teachers (N = 34) were interviewed and the data were analysed using the grounded theory method. The categories describing nurse teacherhood were: process of change in the organisation, operating culture of the health care working community, professional self-esteem, focus of competence, relationship with students, the future in one’s profession and requirements for staying in the profession. The core category was commitment to nurse teacherhood. Nurse teachers’ individual experiences of the significance of the categories describing teacherhood were reflected by their level of commitment to teacherhood. Nurse teachers, whose commitment to nurse teacherhood was weak, were especially unsatisfied with changes related to their teacherhood. Nurse teachers’ conceptions of their future as nurse teachers were significant in terms of the direction in which they were developing their own teacherhood. Some nurse teachers who were weakly committed to nurse teacherhood considered it unlikely that they would continue in the profession in the future.
Safety and Tolerability of Acamprosate in Alcohol-dependent Individuals With Bipolar Disorder: An Open-label Pilot Study

BackgroundPersons with bipolar disorders have high rates of comorbid substance use disorders, especially alcohol dependence. Currently approved treatments for alcohol dependence have not been studied systematically in this comorbid population. ObjectivesWe report results of an open-label pilot study of add-on acamprosate in individuals with cooccurring bipolar disorder and alcohol dependence receiving a stable dose of mood-stabilizing medication(s). MethodsNine men and women who met DSM-IV criteria for alcohol dependence and bipolar disorder I or II were randomized to receive either 1998 mg/d of acamprosate plus mood-stabilizing medication or mood-stabilizing medication alone. Alcohol consumption and craving and mood symptoms were evaluated weekly for 8 weeks using standardized instruments. Blood chemistries were assessed at baseline and study conclusion. ResultsAt study end point, acamprosate-treated subjects reported a significantly greater reduction in drinks per week relative to baseline as compared with the nontreatment group. Mood scores did not differ between groups at any study time points. Acamprosate was well tolerated, with no higher incidence of adverse events in acamprosate-treated subjects relative to controls. No serious adverse events were reported. ConclusionsAcamprosate seems to be safe and well tolerated in individuals with bipolar disorder and comorbid alcohol dependence, and may have efficacy in reducing alcohol consumption in this population. A larger double-blind, placebo-controlled study of acamprosate in bipolar individuals is warranted.
Edinburgh Research Explorer Opening the Gate

ZIF-8 is a zeolitic imidazole-based metal-organic framework with large cavities interconnected by narrow windows. Because the small size of the windows, it allows in principle for molecular sieving of gases such as H 2 and CH 4 . However, the unexpected adsorption of large molecules on ZIF-8 suggests the existence of structural flexibility. ZIF-8 flexibility is explored in this work combining different experimental techniques with molecular simulation. We show that the ZIF-8 structure is modified by gas adsorption uptake in the same way as it does at very high pressure (i
Total Variation Structured Total Least Squares Method for Image Restoration

In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.
Implementation Factors Related to Student Outcomes on the Pilot of the Coping Power Program in a City School Board

Coping Power is a standardized psycho-educational program designed to teach youth at risk for antisocial behavior self-management skills such as goal setting, anger management, communication and problem-solving [1]. In the 2012-2013 school year, the Hamilton-Wentworth District School Board (HWDSB) in Ontario, Canada, piloted the Coping Power program at four elementary schools and two vocational secondary schools. Participants completed the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997) pre-and post-intervention. This questionnaire measured five behavioral domains targeted by Coping Power: emotional disturbance, hyperactivity, conduct problems, peer problems, and prosocial behavior. To assess the implementation process, staff interviews were conducted based on the Consolidated Framework for Implementation Research (CFIR), a model that systematically evaluates factors that affect the success of program implementation [2]. Thepresent study examined the relationship between student outcomes in the Coping Power program and staff’s perception of program implementation factors. Results indicated that reported difficulty with implementation was significantly related to student outcome scores for five key behavioral domains.
Dynamic Pseudo‐Observations: A Robust Approach to Dynamic Prediction in Competing Risks

In this article, we propose a new approach to the problem of dynamic prediction of survival data in the presence of competing risks as an extension of the landmark model for ordinary survival data. The key feature of our method is the introduction of dynamic pseudo‐observations constructed from the prediction probabilities at different landmark prediction times. They specifically address the issue of estimating covariate effects directly on the cumulative incidence scale in competing risks. A flexible generalized linear model based on these dynamic pseudo‐observations and a generalized estimation equations approach to estimate the baseline and covariate effects will result in the desired dynamic predictions and robust standard errors. Our approach has a number of attractive features. It focuses directly on the prediction probabilities of interest, avoiding in this way complex modeling of cause‐specific hazards or subdistribution hazards. As a result, it is robust against departures from these omnibus models. From a computational point of view an advantage of our approach is that it can be fitted with existing statistical software and that a variety of link functions and regression models can be considered, once the dynamic pseudo‐observations have been estimated. We illustrate our approach on a real data set of chronic myeloid leukemia patients after bone marrow transplantation.
Testing for the Multivariate Stochastic Order among Ordered Experimental Groups with Application to Dose–Response Studies

The comparison of two or more ordered experimental groups based on multivariate data is common in a variety of applications such as toxicology, clinical trials and drug development, to name just a few. In this article, we develop a nonparametric methodology for analyzing such data. In particular we propose a global K sample nonparametric test for order among vector valued outcomes. The testing procedure can also be used in a post‐hoc fashion to answer questions about the ordering of subgroups and/or single outcomes within any subset of experimental groups. Such a methodology does not currently exist. In contrast with standard methodology such as multivariate analysis of variance (MANOVA), and its nonparametric analogues, we do not assume that the groups differ only by a location parameter or that the components of the response vector have the same marginal distributions between and across groups, that is, we allow for the shape of the distribution to change across groups. We emphasize that our test compares the outcome distributions, not just their mean tendencies, and explicitly incorporates and exploits the order constraints. Consequently it is more powerful than the existing unordered tests. The methodology is illustrated using genotoxicity data where the effect of hydrogen peroxide exposure on damage to DNA is evaluated using a comet assay.
On Maximal Gains Guaranteeing a Small-Gain Condition

In the analysis of large-scale interconnected systems, small-gain conditions may be used to derive stability properties of the overall system. This paper investigates the question of how large several gains may be chosen while still satisfying a small-gain condition, if a subset of the gains is fixed. In particular, the cases of summation of linear gains and maximization of gains lead to computable solutions. As an application, we consider the interconnection of a large-scale system with a communication network to be designed. To this end a small-gain theorem in the context of hybrid systems for the interconnection of input-to-state stable and globally pre-stable (pre-GS) subsystems is derived. The pre-GS subsystems model data channels. The construction methods then give gain bounds for the data channels guaranteeing stability of the overall system.
High-Accuracy Series Solution for Two-Dimensional Convection in a Horizontal Concentric Cylinder

High order series solutions of two-dimensional buoyancy-induced flow in the horizontal annulus between concentric cylinders are computed. A new efficient algorithm based on computation sequences and a detailed analysis of the shape of the solution is used to compute the solutions up to 30th order. The quotient-difference (QD) algorithm is applied to locate the singularities of the series solution, which gives accurate estimation of the radius of convergence. The condition number and the stability of the solution are also studied. All these resources obtained from the series solution make it a good reference to justify the results from more commonly used purely numerical methods, particularly for delicate solutions.
Dimensionality Reduction of High-Dimensional Data with a NonLinear Principal Component Aligned Generative Topographic Mapping

Most high-dimensional real-life data exhibit some dependencies such that data points do not populate the whole data space but lie approximately on a lower-dimensional manifold. A major problem in many data mining applications is the detection of such a manifold and the expression of the given data in terms of a moderate number of latent variables. We present a method which is derived from the generative topographic mapping (GTM) and can be seen as a nonlinear generalization of the principal component analysis (PCA). It can detect certain nonlinearities in the data but does not suffer from the curse of dimensionality with respect to the latent space dimension as the original GTM and thus allows for higher embedding dimensions. We provide experiments that show that our approach leads to an improved data reconstruction compared to the purely linear PCA and that it can furthermore be used for classification.
Linear mixed function‐on‐function regression models

We develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration. In simulation studies, the approach outperforms in terms of the fitting error and the MSE of estimating the “regression coefficients. ”
UGT2B17 status and risk of prostate cancer: A meta-analysis

Objective: Recent studies on the association between Uridine diphosphoglucuronosyl tranferases (UGT) 2B17 status and risk of prostate cancer (PCa) showed inconclusive results. To clarify this possible association, we conducted a meta-analysis of published studies. Methods: We searched published literature from PubMed, Embase, Google Scholar and China National Knowledge Infrastructure (CNKI). According to our inclusion criteria, studies that observed the association between UGT2B17 status and PCa risk were included. The principal outcome measure was the adjusted odds ratio (OR) with 95% confidence interval (CI) for the risk of PCa associated with UGT2B17 status. Results: A total of 6 studies with 7029 subjects (3839 cases and 3190 controls) were eligible for inclusion in the meta-analysis. Overall, there was a significant association between UGT2B17 status and increased risk of prostate cancer (OR = 1.74, 95% CI 1.14 - 2.64, P 0.001). Similar results were found in the subgroup analyses by ethnicity and types of controls. Conclusion: This meta-analysis demonstrates that UGT2B17 status is associated with prostate cancer susceptibility, and it contributes to increased risk of prostate cancer.
Set‐based tests for genetic association in longitudinal studies

Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within‐subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene–time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large‐scale genome‐wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi‐Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene‐based tests using the average value of repeated measurements as the outcome.
Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification

In the context of group testing screening, McMahan, Tebbs, and Bilder (2012, Biometrics 68, 287–296) proposed a two‐stage procedure in a heterogenous population in the presence of misclassification. In earlier work published in Biometrics, Kim, Hudgens, Dreyfuss, Westreich, and Pilcher (2007, Biometrics 63, 1152–1162) also proposed group testing algorithms in a homogeneous population with misclassification. In both cases, the authors evaluated performance of the algorithms based on the expected number of tests per person, with the optimal design being defined by minimizing this quantity. The purpose of this article is to show that although the expected number of tests per person is an appropriate evaluation criteria for group testing when there is no misclassification, it may be problematic when there is misclassification. Specifically, a valid criterion needs to take into account the amount of correct classification and not just the number of tests. We propose, a more suitable objective function that accounts for not only the expected number of tests, but also the expected number of correct classifications. We then show how using this objective function that accounts for correct classification is important for design when considering group testing under misclassification. We also present novel analytical results which characterize the optimal Dorfman (1943) design under the misclassification.
An Iteration Free Backward Semi-Lagrangian Scheme for Guiding Center Problems

In this paper, we develop an iteration free backward semi-Lagrangian method for nonlinear guiding center models. We apply the fourth-order central difference scheme for the Poisson equation and employ the local cubic interpolation for the spatial discretization. A key problem in the time discretization is to find the characteristic curve arriving at each grid point which is the solution of a system of highly nonlinear ODEs with a self-consistency imposed by the Poisson equation. The proposed method is based on the error correction method recently developed by the authors. For the error correction method, we introduce a modified Euler's polygon and solve the induced asymptotically linear differential equation with the midpoint quadrature rule to get the error correction term. We prove that the proposed iteration free method has convergence order at least $3$ in space and $2$ in time in the sense of the $L_{2}$-norm. In particular, it is shown that the proposed method has a good performance in computational.
Web-based Supplementary Materials for “Bayesian predictive modeling for genomic based personalized treatment selection”

The consensus clustering (CC) approach involves computing pairwise similarities by the proportion of times two patients cluster in the same group upon multiple runs of an existing clustering method (e.g., K-means). The so-called consensus matrix stores all the pairwise similarities. This approach can use any existing clustering algorithm (Monti et al., 2003). The negative matrix factorization (NMF) algorithm factorizes a nonnegative matrix (transformation of X l×n) into two nonnegative matrices W l×p and Hp×n. The rank p of the factorization represents the number of latent clusters, which is often less than min(l, n); each row in matrix H represents one metagene. Similar to the CC approach, NMF often conducts multiple runs to generate the consensus matrix. In general, the similarity matrix, S, is in essence a consensus matrix that can be generated by any clustering method. Because clustering approaches are intrinsically data-driven, other clustering methods may be used with our framework, taking into account the data at hand.
Asymptotic Behavior and Numerical Simulations for an Infection Load-Structured Epidemiological Model: Application to the Transmission of Prion Pathologies

This article studies an infection load-structured SI model with exponential growth of the infection, that incorporates a potential external source of contamination. We perform the analysis of the time asymptotic behavior of the solution by exhibiting epidemiological thresholds, such as the basic reproduction number, that ensure extinction or persistence of the disease in the contagion process. Moreover, a numerical scheme adapted to the model is developed and analyzed. This scheme is then used to illustrate the model with numerical simulations, applying this last to the transmission of prion pathologies.
Spurious Resonance in SemiDiscrete Methods for the Korteweg-de Vries Equation

A multiple scales analysis of semidiscrete methods for the Korteweg--de Vries equation is conducted. Methods that approximate the spatial derivatives by finite differences with arbitrary order accuracy and the limiting method, the Fourier pseudospectral method, are considered. The analysis reveals that a resonance effect can occur in the semidiscrete solution but not in the solution of the continuous equation. It is shown for the Fourier pseudospectral discretization that resonance can only be caused by aliased modes. The spurious semidiscrete solutions are investigated in numerical experiments and we suggest methods for avoiding spurious resonance.
Assessment of DPOAE test‐retest difference curves via hierarchical Gaussian processes

Distortion product otoacoustic emissions (DPOAE) testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. The central goal of this study is to assess whether significant changes in the DPOAE frequency/emissions curve (DP‐gram) occur in pediatric patients in a test‐retest scenario. This is accomplished through the construction of normal reference charts, or credible regions, that DP‐gram differences lie in, as well as contour probabilities that measure how abnormal (or in a certain sense rare) a test‐retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject‐specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together.
The Maximum Principle for Global Solutions of Stochastic Stackelberg Differential Games

For stochastic Stackelberg differential games played by a leader and a follower, there are several solution concepts in terms of the players' information sets. In this paper we derive the maximum principle for the leader's global Stackelberg solution under the adapted closed-loop memoryless information structure, where the term global signifies the leader's domination over the entire game duration. As special cases, we study linear quadratic Stackelberg games under both adapted open-loop and adapted closed-loop memoryless information structures, as well as the resulting Riccati equations.
Psychological response to cancer diagnosis and disease outcome in patients with breast cancer and lymphoma

The relationship between psychological response (PRD) to cancer diagnosis measured three months post‐diagnosis and disease outcome up to five years later was studied prospectively in a consecutive series of 107 women with early (T0 – 2, N0 – 1, M0) breast cancer and a consecutive series of 61 men and women with Hodgkin's and Non‐Hodgkin's lymphoma. It was only possible to assign 138 patients (88 breast cancer and 50 lymphoma) to single psychological response categories. In those who could be so categorised a significant trend of worsening prognosis as response varied from ‘fighting spirit’ or ‘denial’ through ‘anxious preoccupation’ to ‘stoic acceptance’ or ‘hopelessness/helplessness’, was demonstrated for both overall survival (χ2 = 5.49; p = 0.02) and recurrence‐free survival (χ2 = 6.09; p = 0.015). Male lymphoma patients who had died by five years tended to have higher scores on the suppression of anger scales of the Courtauld Emotional Control Scale and the‘Powerful Others Scale’ of the Mental Health Locus of Control Scale; they more often reported avoiding seeking information or‘keeping busy’ as a way of avoiding thinking about their diagnosis. The relative values of psychological test scores and semi‐structured interview data in the study are discussed.