Miracle-dz/UniFAR-FTUs huggingface dataset:
Playing with dolls: Use of simulation technology in the Thompson Rivers University Respiratory Therapy Program

This descriptive case study examines the use of medical simulation technology in the three-year Thompson Rivers University respiratory therapy training program. Qualitative analysis of data gathered from 78 participants through interviews, observations, and discussion groups reveal a wide variety of lowintermediateand high-fidelity technologies used for education and evaluation. Deliberate practice is the predominant learning theory informing the use of simulation for safe and ethical training in competencies that would otherwise pose significant risk to patients. Recommendations include enhancements of the existing technology with psychological and environmental fidelity, and for optimal curriculum placement of high-fidelity simulators at hospital sites for student development of critical thinking and team training. Further research into learning with high-fidelity simulation specifically within the context of a student respiratory therapist as an embedded hospital team member is needed.
Prognostic factors for survival in patients with breast cancer referred to Cancer Research Center in Iran.

BACKGROUND Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. MATERIALS AND METHODS A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. RESULTS The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. CONCLUSIONS Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.
Moving beyond the Dyad in Research on Family Conflict

The paper by Persram et al., entitled “Moving beyond the dyad: broadening our understanding of family conflict,” advances many important insights based on research on family conflict concerning the ways in which families interact, including the implications for youth development. They aptly point out that conflict research has historically focused on dyadic relationships within the family, examining, for example, conflict in only the interparental or parent-child dyad without considering conflicts that involve more than two people. In doing so, they advocate convincingly for the need to redirect research foci toward examining the complexity of conflict, which involves interconnected relationships and bidirectional patterns of influence. Acknowledging the complexity of conflict is crucial for next steps in conflict research. The authors provide a framework for pursuing this important direction by identifying various subsystems within the family that together form a central developmental context for children, delineating ways in which those subsystems interact and influence each other during conflict. Further, the authors provide an overview of some key theoretical models for understanding conflict in families and identify important aspects to consider in future research. They also describe several methodological and analytical approaches to studying family-wide conflict. Broadly, the paper of Persram et al. can guide the forward direction of the field in two important ways. First, it renews calls to broaden investigations into family conflict by moving beyond examining dyadic relationships. This is important because it provides context for understanding what happens at the dyadic level and provides an impetus for taking on the challenging but crucial task of disentangling the influences of multiple members of the family system. Subsystems within families are inextricably linked, and without acknowledging and investigating the mutual influence Published online: August 27, 2019
Understanding the help seeking behaviour of cardiac patients with erectile dysfunction: An exploratory study

10 Introduction 11 Method 12 Results 15 Discussion and implications 29 Conclusion 34 References 35 PART TWO: Understanding the help seeking behaviour of cardiac patients with erectile dysfunction: An exploratory study 41 Abstract 42 Introduction 43 Method 47 Results 49 Discussion 6142 Introduction 43 Method 47 Results 49 Discussion 61
Three Essays on Health Economics.

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THREE ESSAYS ON HEALTH ECONOMICS By Scott Hankins August 2006 Chair: David Figlio Major Department: Economics There are approximately 4 million babies born every year in the United States. My research investigates two distinct issues that may affect both health outcomes of newborns and mothers as well as the costs of their care. The first chapter introduces the issues of hospital competition. In the second chapter, I find that increased levels of hospital competition lead to better outcomes for some premature babies. The third chapter investigates the relationship between competition and prices. I find that increased competition leads to lower prices and this effect is greater for the lower cost, i.e., least complicated patients. The fourth chapter investigates physician decision making in response to medical malpractice lawsuits. I show that doctors respond to lawsuits by increasing the number of caesarean sections performed, a result consistent with defensive medicine.
Glucocorticoid Treatment and Quality of Life in Addison’s disease

Addison’s disease (AD) is rare and result in lack of the adrenal hormones cortisol, aldosterone and adrenal androgens. Despite conventional oral replacement therapy, mortality is increased and Health-Related Quality-of-life (HRQoL) is reduced. Currently, the non-physiological circadian cortisol profile is suspected to be a major cause, although evidence has been lacking. Here, we aimed to develop a better tool for evaluating HRQoL in AD, and to investigate whether a more physiological circadian cortisol profile would result in benefit for AD patients. An AD-specific HRQoL questionnaire (AddiQoL) was developed through a multistep approach. After testing the original AddiQoL in 86 patients in UK, the AddiQoL was translated to five European languages and tested in further 615 AD patients in respective countries. Applying Rasch analysis, a valid and reliable 30 item AddiQoL was produced. In a randomized controlled multicenter trial with cross-over design, we compared the effects of three months treatment with continuous subcutaneous hydrocortisone infusion (CSHI) to the effects of three months treatment with conventional oral hydrocortisone (OHC) in 33 AD patients. The primary endpoint was the effect on ACTH levels. Secondary endpoints were effects on metabolism, HRQoL and sleep. CSHI produced a more physiological circadian cortisol biorhythm than conventional therapy and induced normalization of morning ACTH and cortisol levels, restoration of nighttime cortisol levels and changes in glucocorticoid metabolism resembling healthy individuals. The late night decrease in glucose seen with OHC was counteracted, without decreasing overall insulin sensitivity. CSHI did not significantly affect sleep but might have positive HRQoL effects. The AddiQoL development provided a valid and reliable new tool for HRQoL evaluation in AD. Mimicking the physiological cortisol rhythm with CSHI proved safe and provides a means for further improving replacement therapy in AD.
Selenium Effects on the Trabecular Meshwork

14 CHAPTER 1. 16 INTRODUCTION, HYPOTHESIS, AND AIMS. 16 1.1 General Introduction . 16 1.2 Hypothesis . 20 1.3 Specific Aims. 20 CHAPTER 2. 22 CRITICAL LITERATURE REVIEW. 22 2.1 Selenium. 22 2.1.1 Selenium Physiology . 22 2.1.1.1 Selenium Toxicology . 23 2.1.1.2 Acute Selenium Toxicity. 23 2.1.1.3 Selenium Biochemistry. 24 2.1.2 In vitro and Animal Studies Linking Selenium with Decreased Cancer Prevalence. 27 2.1.2.1 In vitroEeffects of Selenium . 27 2.1.2.2 Selenium and Apoptosis. 27 2.1.2.3 Selenium and Cell Cycle Arrest. 30 2.1.2.4 Selenium and Anti-angiogenesis. 31 2.1.2.5 Selenoproteins and Chemoprevention. 34 2.1.2.6 Animal Studies . 35 2.1.3 The Epidemiology of Selenium and Cancer Prevention. 38 2.1.3.1 Geographical Studies. 38 2.1.3.2 Case-control Studies . 39 2.1.3.3 Supplementation Studies . 45 2.2 The Link Between Selenium and Glaucoma . 50 2.2.1 NPC Trial and Glaucoma. 51 2.2.2 Selenium in Ocular Patients Study. 56 2.3 Glaucoma . 59 2.3.1 Basics of Aqueous Humor Dynamics . 62 2.3.1.1 Aqueous Humor Inflow . 63
FOXG1-Related Disorders: From Clinical Description to Molecular Genetics

Rett syndrome (RTT) is a severe neurodevelopmental disease that affects approximately 1 in 10,000 live female births and is often caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2). Mutations in loci other than MECP2 have also been found in individuals that have been labeled as atypical RTT. Among them, a mutation in the gene forkhead box G1 (FOXG1) has been involved in the molecular aetiology of the congenital variant of RTT. The FOXG1 gene encodes a winged-helix transcriptional repressor essential for the development of the ventral telencephalon in embryonic forebrain. Later, FOXG1 continues to be expressed in neurogenetic zones of the postnatal brain. Although RTT affects quasi-exclusively girls, FOXG1 mutations have also been identified in male patients. As far as we know, about 12 point mutations and 13 cases with FOXG1 molecular abnormalities (including translocation, duplication and large deletion on the chromosome 14q12) have been described in the literature. Affected individuals with FOXG1 mutations have shown dysmorphic features and Rett-like clinical course, including normal perinatal period, postnatal microcephaly, seizures and severe mental retardation. Interestingly, the existing animal models of FOXG1 deficiency showed similar phenotype, suggesting that animal models may be a fascinating model to understand this human disease. Here, we describe the impacts of FOXG1 mutations and their associated phenotypes in human and mouse models.
A Survey of Rate-optimal Power Domain NOMA Schemes for Enabling Technologies of Future Wireless Networks

Non-orthogonal multiple access (NOMA) schemes serve more than one user in the same resource block by multiplexing users in other domains than frequency and time. In this way, NOMA schemes offer several advantages over orthogonal multiple access (OMA) schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA transmission schemes are being increasingly looked at as a promising multiple access scheme for future wireless networks. When the power domain is used to multiplex users, it is referred to as the power domain NOMA (PDNOMA) scheme. In this paper, we survey the integration of the PD-NOMA scheme with other upcoming communication schemes and technologies that satisfy the requirements of 5G and beyond 5G (B5G) networks. In particular, this paper surveys the rate optimization schemes studied in the literature when the PD-NOMA scheme is combined with MIMO and massive MIMO (mMIMO), millimeter wave (mmWave) communications, coordinated multi-point (CoMP) transmission and reception, cooperative communications, cognitive radio (CR), visible light communications (VLC), and unmanned aerial vehicle (UAV) assisted communications. The considered system models, the optimization methods used to maximize the achievable rates, and the main outcomes on the performance of these NOMA-enabled schemes are discussed along with future research directions for these combined schemes.
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment

Emerging resistive random-access memory (ReRAM) has recently been intensively investigated to accelerate the processing of deep neural networks (DNNs). Due to the in-situ computation capability, analog ReRAM crossbars yield significant throughput improvement and energy reduction compared to traditional digital methods. However, the power hungry analog-to-digital converters (ADCs) prevent the practical deployment of ReRAM-based DNN accelerators on end devices with limited chip area and power budget. We observe that due to the limited bit-density of ReRAM cells, DNN weights are bit sliced and correspondingly stored on multiple ReRAM bitlines. The accumulated current on bitlines resulted by weights directly dictates the overhead of ADCs. As such, bitwise weight sparsity rather than the sparsity of the full weight, is desirable for efficient ReRAM deployment. In this work, we propose bit-slice $\ell_{1}$, the first algorithm to induce bit-slice sparsity during the training of dynamic fixed-point DNNs. Experiment results show that our approach achieves 2× sparsity improvement compared to previous algorithms. The resulting sparsity allows the ADC resolution to be reduced to 1-bit of the most significant bit-slice and down to 3-bit for the others bits, which significantly speeds up processing and reduces power and area overhead.
Velocity memory effect without soft particles

We study the behavior of geodesics in the plane-fronted wave background of the three-dimensional (3D) gravity with propagating torsion, which possesses only massive degrees of freedom. We discover the velocity memory effect, in contrast to the current belief that its existence is due to the presence of soft particles.
Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek to develop proactive, adaptive MTD mechanisms.
Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features.

Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices provide an immediate platform for exploring a possible quantum advantage through hybrid quantum--classical machine learning algorithms. One example of such a hybrid algorithm is "quantum kitchen sinks", which builds upon the classical algorithm known as "random kitchen sinks" to leverage a gate model quantum computer for machine learning applications. We propose an alternative algorithm called "adiabatic quantum kitchen sinks", which employs an adiabatic quantum device to transform data features into new features in a non-linear manner, which can then be employed by classical machine learning algorithms. We present the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset. In terms of classification accuracy, our algorithm significantly enhances the performance of a classical linear classifier on the studied binary classification tasks and can potentially be implemented on a current adiabatic quantum device to solve practical problems.
Deterministic algorithms for the Lovász local lemma: Simpler, more general, and more parallel

The Lovász local lemma (LLL) is a keystone principle in probability theory, guaranteeing the existence of configurations which avoid a collection ℬ$$ \mathcal{B} $$ of “bad” events which are mostly independent and have low probability. A seminal algorithm of Moser and Tardos (J. ACM, 2010, 57, 11) (which we call the MT algorithm) gives nearly‐automatic randomized algorithms for most constructions based on the LLL. However, deterministic algorithms have lagged behind. We address three specific shortcomings of the prior deterministic algorithms. First, our algorithm applies to the LLL criterion of Shearer (Combinatorica, 1985, 5, 241–245); this is more powerful than alternate LLL criteria and also leads to cleaner and more legible bounds. Second, we provide parallel algorithms with much greater flexibility. Third, we provide a derandomized version of the MT‐distribution, that is, the distribution of the variables at the termination of the MT algorithm. We show applications to non‐repetitive vertex coloring, independent transversals, strong coloring, and other problems.
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction

Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/\epsilon^{3/2})$ episodes to find an $\epsilon$-approximate stationary point of the nonconcave performance function $J(\boldsymbol{\theta})$ (i.e., $\boldsymbol{\theta}$ such that $\|\nabla J(\boldsymbol{\theta})\|_2^2\leq\epsilon$). This sample complexity improves the existing result $O(1/\epsilon^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/\epsilon^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.
Text Length Adaptation in Sentiment Classification

Can a text classifier generalize well for datasets where the text length is different? For example, when short reviews are sentiment-labeled, can these transfer to predict the sentiment of long reviews (i.e., short to long transfer), or vice versa? While unsupervised transfer learning has been well-studied for cross domain/lingual transfer tasks, Cross Length Transfer (CLT) has not yet been explored. One reason is the assumption that length difference is trivially transferable in classification. We show that it is not, because short/long texts differ in context richness and word intensity. We devise new benchmark datasets from diverse domains and languages, and show that existing models from similar tasks cannot deal with the unique challenge of transferring across text lengths. We introduce a strong baseline model called BaggedCNN that treats long texts as bags containing short texts. We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms. We test our models and find that existing models perform worse than the BaggedCNN baseline, while LeTraNets outperforms all models.
Stopping Criteria for Value and Strategy Iteration on Concurrent Stochastic Reachability Games

We consider concurrent stochastic games played on graphs with reachability and safety objectives. These games can be solved by value iteration as well as strategy iteration, each of them yielding a sequence of under-approximations of the reachability value and a sequence of over-approximation of the safety value, converging to it in the limit. For both approaches, we provide the first (anytime) algorithms with stopping criteria. The stopping criterion for value iteration is based on providing a convergent sequence of over-approximations, which then allows to estimate the distance to the true value. For strategy iteration, we bound the error by complementing the strategy iteration algorithm for reachability by a new strategy iteration algorithm under-approximating the safety-value.
Evaluating Effects of Tuition Fees: Lasso for the Case of Germany

We study the effect of the introduction of university tuition fees on the enrollment behavior of students in Germany. For this, an appropriate Lasso-technique is crucial in order to identify the magnitude and significance of the effect due to potentially many relevant controlling factors and only a short time frame where fees existed. We show that a post-double selection strategy combined with stability selection determines a significant negative impact of fees on student enrollment and identifies relevant variables. This is in contrast to previous empirical studies and a plain linear panel regression which cannot detect any effect of tuition fees in this case. In our study, we explicitly deal with data challenges in the response variable in a transparent way and provide respective robust results. Moreover, we control for spatial cross-effects capturing the heterogeneity in the introduction scheme of fees across federal states ("Bundesl\"ander"), which can set their own educational policy. We also confirm the validity of our Lasso approach in a comprehensive simulation study.
The Role of Cluster Mass in the Multiple Populations of Galactic and Extragalactic Globular Clusters

Studies based on Hubble Space Telescope (HST) photometry in F275W, F336W, and F438W have shown that the incidence and complexity of multiple populations (MPs) in globular clusters (GCs) depend on cluster mass. This result, however, is based on nearby, low-reddening Galactic GCs, for which we can obtain accurate F275W photometry. In this work, we exploit HST photometry in F336W, F438W, and F814W to characterize MPs in 68 Galactic and extragalactic GCs by using the pseudocolor CF336W,F438W,F814W, which is more efficient in terms of observation time than the pseudocolor CF275W,F336W,F438W adopted in previous works. We first analyze the Galactic GCs alone and find that the red giant branch (RGB) color width strongly correlates with [Fe/H]. After removing the dependence from metallicity, we obtain a strong correlation with cluster mass, thus confirming previous findings. We then exploit the RGB width to compare MPs in Galactic and extragalactic GCs. Similarly to Galactic GCs, the RGB width of Magellanic Cloud (MC) GCs correlates with cluster mass after removing the dependence from metallicity. This fact demonstrates that cluster mass is the main factor affecting the properties of MPs. The MC clusters exhibit, on average, narrower RGBs than Galactic GCs with similar mass and metallicity. We suggest that this difference is a signature of stellar mass loss in Galactic GCs. As an alternative, the mass–RGB–width relation would depend on the host galaxy. Finally, we use ground-based photometry to investigate Terzan 7 through the CU,B,I pseudocolor, ground-based analogs of CF336W,F438W,F814W and find that this cluster is consistent with a simple population.