Miracle-dz/UniFAR-FTUs huggingface dataset:
Using appreciative inquiry to transform student nurses’ image of nursing

Introduction Literature provides adequate evidence of a poor perception of nursing within the profession, resulting in high rates of attrition of student nurses and newly qualified nurses. The nursing profession, in particular nurse educators, has an ethical and professional responsibility to find innovative strategies to promote the positive image of nursing amongst student nurses. Purpose The purpose of the study was to explore the potential of appreciative inquiry (AI) as an intervention teaching strategy to transform student nurses’ image of nursing. Design A quantitative, quasi-experimental, explorative-descriptive design comprising the pretest, appreciative inquiry as intervention, and the post-test was used. Methods Convenience sampling was used to select third and fourth year college and university student nurses in the Gauteng province of South Africa for the pre- and the post-test respectively. Data were collected by means of a questionnaire and analysed by SPSS version 20.0. Findings The pretest results revealed a mix of positive and negative perceptions of the image of nursing amongst student nurses. The negative perceptions of the image of nursing that needed intervention included the working conditions of nurses, and the perception of nursing as a profession that was not respected and appreciated. The post-test results showed a significant and positive change in the student nurses’ perception of the image of nursing as a respected and appreciated profession. Although AI resulted in a negative to positive change in some aspects of student nurses’ image of nursing, the negative perceptions of the working conditions of nurses remained and became more negative. The positive image of gender in nursing was enhanced following the implementation of AI. Conclusion Appreciative inquiry demonstrated potential as a teaching strategy to produce a positive nursing image change and positive orientation towards nursing amongst student nurses.
Robust multi-Bernoulli filtering for visual tracking

To achieve reliable multi-object filtering in vision application, it is of great importance to determine appropriate model parameters. Parameters such as motion and measurement noise covariance can be chosen based on the image frame rate and the property of the designed detector. However, it is not trivial to obtain the average number of false positive measurements or detection probability due to the arbitrary visual scene characteristics from illumination condition or different fields of view. In this paper, we introduce the recently proposed robust multi-Bernoulli filter to deal with unknown clutter rate and detection profile in visual tracking applications. The robust multi-Bernoulli filter treats false positive responses as a special type of target so that the unknown clutter rate is estimated based on the estimated number of clutter targets. Performance evaluation with real videos demonstrates the effectiveness of the robust multi-Bernoulli filter and comparison results with the standard multi-object tracking algorithm show its reliability.
semi-automated quantitative CT method for measuring rotator cuff uscle degeneration in shoulders with primary osteoarthritis

Background: Rotator cuff muscle degeneration is an important parameter to consider when planning shoulder arthroplasty. Hypothesis: We hypothesized that rotator cuff muscle degeneration is correlated with scapulohumeral subluxation in patients planned for anatomical total shoulder arthroplasty (TSA). Materials and methods: We developed a semi-automated quantitative CT method to measure rotator cuff muscle degeneration, and retrospectively analyzed 107 preoperative shoulder CT scans of patients with primary osteoarthritis. On a standardized sagittal-oblique CT slice perpendicular to the scapular axis, two observers measured the cross-sectional areas of residual rotator cuff muscle tissues, normalized by the estimated area of healthy muscles. Muscle degeneration was quantified in a semi-automated manner, and divided into atrophy and fatty infiltration. Scapulohumeral subluxation was determined in 3D as the distance between the humeral head center and the glenoid surface center, projected on the same CT slice, and normalized by the humeral head radius. We tested all potential correlations between muscle degeneration and scapulohumeral subluxation. Results: Muscle degeneration, primarily due to atrophy, predominated in the supraspinatus; it varied from 0.8% to 88.8%. Scapulohumeral subluxation varied from 2.5% to 72.9%, and was mainly in a posterior and postero-superior orientation. There was a significant but weak correlation between the amount of subluxation and both supraspinatus (R = 0.207, P = 0.032) and infraspinatus (R = 0.225, P = 0.020) degeneration. Interand intra-observer reproducibility of muscle degeneration measurements were both excellent (ICCs range = 0.955–0.987 and 0.971–0.988, respectively). Conclusion: This new semi-automated CT method allows to quantitatively and reproducibly measure rotator cuff muscle degeneration in shoulders with primary osteoarthritis. Muscle degeneration is weakly correlated with scapulohumeral subluxation in patients planned for anatomical TSA. Level of evidence: Level IV. Type of study: Diagnostic retrospective study.
Learning, Political Attitudes and the Crisis in Transition Countries

We estimate the impact of the recent economic crisis on support for democracy and a free market economy in 30 post transition countries and five western European countries. Political values are cyclical and reflect a learning process. Support for the market and democracy has decreased between 2006 and 2010 in countries that were hit the hardest and that were the most advanced on the path to liberal reform, and notably new EU members. By contrast, it has increased in the CIS. This last result is driven by the young and unemployed. Although individual exposure to the crisis is associated with lower average support to democracy and markets, it leads these segments of the population, which were most excluded from the political-economic system in place to demand more liberal reforms in countries with corrupt institutions and that lag behind in terms of economic and political reform. We rely on individual level, within-country variation and on the use of a large set of individual controls in order to identify the causal effect of the economic crisis on political attitudes.
Validation of 3-Dimensional Speckle Tracking Imaging to Quantify Regional Myocardial Deformation

Background—Three-dimensional speckle tracking imaging (3D-STI) has been introduced to assess regional left ventricular (LV) myocardial function. This study was designed to validate LV strain measurements by 3D-STI against data obtained by sonomicrometry. Methods and Results—In each of 10 anesthetized sheep, sonomicrometry crystals were implanted on the endocardium and epicardium at the LV basal, mid, and apical anterior and lateral walls. LV 3D-STI data sets were obtained from the apical approach at a frame rate of approximately 30 frames/s. Segmental longitudinal (LS), radial (RS), and circumferential strain (CS) measurements by 3D-STI were compared with those by sonomicrometry at baseline and during pharmacological stress tests (dobutamine and propranolol infusion) and acute myocardial ischemia induced by coronary artery occlusion. Data were available from 136 LS, 108 CS, and 175 RS measurements. Good correlations were observed between strain measurements by 3D-STI and those by sonomicrometry (LS: r=0.89, P<0.001; RS: r=0.84, P<0.001; CS: r=0.90, P<0.001). In each segmental study, significant correlations of the 3 strain components were observed (LS: r=0.65 to 0.68, P<0.001; RS: r=0.59 to 0.70, P<0.001; CS: r=0.71 to 0.78, P<0.001). Conclusions—The newly developed 3D-STI technique can estimate LV regional circumferential, longitudinal, and radial strain components with reasonable correlation to sonomicrometry data. This methodology could be applied clinically to assess alteration of myocardial function by accurately measuring strain in basal, mid, and apical LV segments, even during pharmacological and ischemic interventions. Therefore, 3D-STI appears to be a reliable tool to assess LV regional wall function.
[Translation and validation of the questionnaire "Tinnitus Handicap Questionnaire, 1990].

OBJECTIVE The Tinnitus Handicap Questionnaire (THQ) measures subjective tinnitus handicap in terms of emotional, social, and health impact (factor 1), hearing (factor 2), and perception of tinnitus (factor 3). A French version of the THQ was used with 178 tinnitus sufferers in ENT consultation. Internal validity was confirmed by correlations between (i) semigroups of items (Cronbach's alpha), (ii) item and total scores, and (iii) individual items, for each factor (Pearson's r), and was found to be strong (alpha = .90). All items (except 25 and 26) showed strong total-score correlations (.30 < or = r < or = .74). All factor 1 items intercorrelated strongly (.34 < or = r < or = .70). The factor 2 axis was coherent, with interitem correlations between . 46 and . 74, and its concurrent validation in strong factor 2 items correlations with hearing loss, if any (multiple linear regression: r = .67, p < .0001). Moreover, hearing-impaired (mean = 44.47 +/- 31.13) and normal-hearing (mean = 15.2 +/- 21.10) factor 2 scores were significantly different. CONCLUSION As with the original THQ, factor 3 items were not strongly intercorrelated; moreover, items 25 and 26 failed to correlate with total score, suggesting that factor 3 is to be regarded with great caution.
Increased Glyoxalase-1 Levels in Fkbp5 Knockout Mice Caused by Glyoxalase-1 Gene Duplication

Fkbp5 is genetically linked to stress-related diseases. Fkbp5 knockout mice are available and widely used to explore the role of Fkbp5 in health and disease. We found that these mice carry a gene duplication of glyoxylase-1, which explains why glyoxylase-1 levels are increased in the Fkbp5 knockout mice.
When 90% confidence intervals are 50% certain: on the credibility of credible intervals

Estimated confidence intervals for general knowledge items are usually too narrow. We report five experiments showing that people have much less confidence in these intervals than dictated by the assigned level of confidence. For instance, 90% intervals can be associated with an estimated confidence of 50% or less (and still lower hit rates). Moreover, interval width appears to remain stable over a wide range of instructions (high and low numeric and verbal confidence levels). This leads to a high degree of overconfidence for 90% intervals, but less for 50% intervals or for free choice intervals (without an assigned degree of confidence). To increase interval width one may have to ask exclusion rather than inclusion questions, for instance by soliciting 'improbable' upper and lower values (Experiment 4), or by asking separate 'more than' and 'less than' questions (Experiment 5). We conclude that interval width and degree of confidence have different determinants, and cannot be regarded as equivalent ways of expressing uncertainty. Copyright © 2005 John Wiley & Sons, Ltd. Language: en
A method for the evaluation of hypnotic agents in man. The comparative hypnotic effects of secobarbital, methaqualone and placebo in normal subjects and in psychiatric patients.

Comparative drug trials were performed in 40 normal volunteer medical students and 27 psychiatric patients with insomnia in an attempt to determine the predictive value of hypnotic efficacy data obtained in normal volunteers for patients with insomnia. The same drugs and investigators and similar methods were used in both trials. The comparative effects of randomly administered secobarbital (100 and 200 mg), methaqualone (150 and 300 mg or 300 mg alone) and a placebo on induction and maintenance of sleep and on reawakening were studied by means of subjective evaluations, i.e. , self-responding and interview questionnaires, using a double-blind, cross-over design. Both normal subjects and psychiatric patients with insomnia reported secobarbital (200 mg) effective in rapidly inducing and prolonging sleep and in suppressing disturbing dream recall. In contrast, normal volunteers, but not psychiatric patients, had significant hangover effect with this drug. Neither the normal subjects nor patients with insomnia found the effects of methaqualone (150 or 300 mg) distinguishable from those of the placebo. Briefly, in these two widely different populations, the comparative hypnotic effects of the forms of treatment tested were very similar, but the hangover effects were not. Therefore the experimental data on the efficacy of hypnotic drugs in normal subjects were predictive of results observed in patients with insomnia and imply that normal volunteers might be useful subjects in the early clinical evaluation of new drugs with potential hypnotic activity.
PHD filter for multi-target visual tracking with trajectory recognition

Probability hypothesis density (PHD) filter, as a multi-target recursive Bayes filter, has generated substantial interest in the visual tracking field due to its ability to handle a time-varying number of nonlinear targets. But the target's trajectory cannot be identified within its own framework. To complement the ability of PHD, the auction algorithm is combined to calculate the object trajectories automatically. We present the motion detection, dynamic and measurement equation, as well as visual multi-target tracking algorithm based on Gaussian mixture probability hypothesis density (GM-PHD) in details. Experimental results on a large video surveillance dataset show the proposed multi-target tracking framework improves the tracker and recognizes the tracks when a variable number of targets appear, merge, split and disappear even in cluttered scenes.
Summarizing software concerns

While working on software concerns to perform evolution tasks, developers often encounter a lack of abstraction. They have to work with all the details of large subsets of code that constitute a concern at a low level of abstraction. In this paper we propose a framework to summarize software concerns in order to raise the level of abstraction and to subsequently improve the productivity of software developers. We use a combination of static analysis, information retrieval, and natural language processing techniques to extract and deduct knowledge about different parts of the concern and its interactions with other concerns. Then we produce a description of the concern using natural language generation and summarization techniques.
An Exhaustive Examination of the Replicable Factor Structure of the Maslach Burnout Inventory

Three-factor first order analyses have been used in the most successful of previous attempts to define the factor and subscale structures of the Maslach Burnout Inventory. It is suggested in the present paper that although a three factor structure has been shown to be the most readily replicable, this result does not exhaust the number of possible replicable factors in the Inventory. Using six independent sets of data drawn from previous studies involving a variety of subjects, the present analysis revealed the somewhat doubtful status of the general factor underlying the total score on the 22 items of the Inventory. In all six sets of data, the 22 items of the Inventory also divided between two clearly replicable factors, the larger of which was tentatively identified as the "Core of Burnout. " Responses to the 14 items loading on this factor were subsequently found to divide in turn, into two equally replicable factors. These two factors, together with that differentiated from the "Core of Burnout," were exactly the same as those most frequently identified in previous three factor analyses of the Inventory.
Benchmarking quality for renal cancer surgery: Canadian Kidney Cancer information system (CKCis) perspective.

INTRODUCTION There is a lack of validated quality metrics to evaluate the care of patients receiving surgery for renal cell carcinoma (RCC). To address this, the Kidney Cancer Research Network of Canada defined a list of quality indicators (QI) to assess hospital-level performance. We have case-mix adjusted these QIs to benchmark RCC surgical care at Canadian academic centres. METHODS The Canadian Kidney Cancer information system (CKCis) was used to measure six QIs: laparoscopic approach proportion (LA), partial nephrectomy proportion (PN), partial nephrectomy in patients with chronic kidney disease (CKDPN), positive margin rate (PMR), partial nephrectomy complication rate (PNCx), and warm ischemia time (WIT). To benchmark performance, indirect standardization (observed-to-expected ratio) methodology was employed using multivariate regression models. RESULTS Multivariate models for LA, PN, and CKDPN demonstrated good discrimination and were used for benchmarking. National averages of 74% (70-78%), 73% (70-75%), and 70% (67-74%) for the LA, PN, and CKDPN QIs, respectively, were determined and used to benchmark individual hospital performance. Overall, three (23%), two (15%), and two (15%) hospitals performed below expected for LA, PN, and CKDPN, respectively. Hospital identity was an independent predictor of LA, PN, and CKDPN (p<0.001). CONCLUSIONS Significant variability between CKCis hospitals for three RCC surgical QIs exists. Using the CKCis infrastructure may provide a framework for institution-level audit feedback for quality improvement. Greater CKCis capture rates and further data supporting the construct validity of these QIs are required to extend the use of this dataset to real-world quality initiatives.
Passive Loss Inference in Wireless Sensor Networks Based on Network Coding

The highly stochastic nature of wireless environ- ments makes it desirable to monitor link loss rates in wireless sensor networks. In this paper, we study the loss inference problem in sensor networks with network coding. Unlike tra- ditional transmission protocols, network coding offers reliable communication without using control messages for individual packets. We show, however, that network coding changes the fundamental connection between path and link loss probabilities such that new inference algorithms need to be developed. As end- to-end data are not sufficient to compute link loss rates precisely, we propose inference algorithms based on Bayesian principles to discover the set of highly lossy links in sensor networks. We show that our algorithms achieve high detection and low false-positive rates through extensive simulations. I. INTRODUCTION Recent technological advances have made it feasible to de- ploy large-scale sensor networks using low-cost sensor nodes. However, as the scale of sensor networks becomes larger, two key challenges potentially arise. First, node failures . D ue to their inherent instability and energy constraints, sensor nodes are prone to failures. It would thus be useful to determine which set of nodes or which geographical areas within the network are experiencing high loss rates. Such information is potentially valuable to the design of fault-tolerant protocols or monitoring mechanisms, so that the problem areas may be re-deployed, and critical data may be rerouted to avoid these failure-prone areas suffering high loss rates. Second, bandwidth constraints. One cannot rely on the use of active acknowledgments, which are neither scalable nor bandwidth- efficient, in the design of sensor network protocols. This renders the direct collection of loss rate data impossible in
Analgesic Effects of GpTx-1, PF-04856264 and CNV1014802 in a Mouse Model of NaV1.7-Mediated Pain

Loss-of-function mutations of NaV1.7 lead to congenital insensitivity to pain, a rare condition resulting in individuals who are otherwise normal except for the inability to sense pain, making pharmacological inhibition of NaV1.7 a promising therapeutic strategy for the treatment of pain. We characterized a novel mouse model of NaV1.7-mediated pain based on intraplantar injection of the scorpion toxin OD1, which is suitable for rapid in vivo profiling of NaV1.7 inhibitors. Intraplantar injection of OD1 caused spontaneous pain behaviors, which were reversed by co-injection with NaV1.7 inhibitors and significantly reduced in NaV1.7−/− mice. To validate the use of the model for profiling NaV1.7 inhibitors, we determined the NaV selectivity and tested the efficacy of the reported NaV1.7 inhibitors GpTx-1, PF-04856264 and CNV1014802 (raxatrigine). GpTx-1 selectively inhibited NaV1.7 and was effective when co-administered with OD1, but lacked efficacy when delivered systemically. PF-04856264 state-dependently and selectively inhibited NaV1.7 and significantly reduced OD1-induced spontaneous pain when delivered locally and systemically. CNV1014802 state-dependently, but non-selectively, inhibited NaV channels and was only effective in the OD1 model when delivered systemically. Our novel model of NaV1.7-mediated pain based on intraplantar injection of OD1 is thus suitable for the rapid in vivo characterization of the analgesic efficacy of NaV1.7 inhibitors.
Purine: A bi-graph based deep learning framework

In this paper, we introduce a novel deep learning framework, termed Purine. In Purine, a deep network is expressed as a bipartite graph (bi-graph), which is composed of interconnected operators and data tensors. With the bi-graph abstraction, networks are easily solvable with event-driven task dispatcher. We then demonstrate that different parallelism schemes over GPUs and/or CPUs on single or multiple PCs can be universally implemented by graph composition. This eases researchers from coding for various parallelization schemes, and the same dispatcher can be used for solving variant graphs. Scheduled by the task dispatcher, memory transfers are fully overlapped with other computations, which greatly reduce the communication overhead and help us achieve approximate linear acceleration.
Compressive sensing based image reconstruction for synthetic aperture radar using discrete cosine transform and noiselets

Synthetic Aperture Radar (SAR) has the ability to obtain high resolution 2D imagery from distributed targets, such as landscapes. SAR image resolution is limited by the transmitted signal bandwidth and the antenna length, and also depends on the sampling rate. SAR processing is very computer-intensive since it measures and processes enormous amounts of data to obtain the target image. In this paper, we propose an alternative method using compressed sensing (CS), which is able to overcome the fast sampling and the large-scale data storage limitations. CS is also effective for reconstructing the raw image data with sparsity. For complex raw data obtained by the echo signal, compressed sensing based on ℓ1-norm optimization with discrete cosine transform and noiselet processing is adopted to reconstruct the SAR image. By using fewer SAR echo signal samples with complex data, we demonstrate improved results in the SAR image reconstruction.