Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis

Importance Burnout is prevalent in physicians and can have a negative influence on performance, career continuation, and patient care. Existing evidence does not allow clear recommendations for the management of burnout in physicians. Objective To evaluate the effectiveness of interventions to reduce burnout in physicians and whether different types of interventions (physician-directed or organization-directed interventions), physician characteristics (length of experience), and health care setting characteristics (primary or secondary care) were associated with improved effects. Data Sources MEDLINE, Embase, PsycINFO, CINAHL, and Cochrane Register of Controlled Trials were searched from inception to May 31, 2016. The reference lists of eligible studies and other relevant systematic reviews were hand searched. Study Selection Randomized clinical trials and controlled before-after studies of interventions targeting burnout in physicians. Data Extraction and Synthesis Two independent reviewers extracted data and assessed the risk of bias. The main meta-analysis was followed by a number of prespecified subgroup and sensitivity analyses. All analyses were performed using random-effects models and heterogeneity was quantified. Main Outcomes and Measures The core outcome was burnout scores focused on emotional exhaustion, reported as standardized mean differences and their 95% confidence intervals. Results Twenty independent comparisons from 19 studies were included in the meta-analysis (n = 1550 physicians; mean [SD] age, 40.3 [9.5] years; 49% male). Interventions were associated with small significant reductions in burnout (standardized mean difference [SMD] = −0.29; 95% CI, −0.42 to −0.16; equal to a drop of 3 points on the emotional exhaustion domain of the Maslach Burnout Inventory above change in the controls). Subgroup analyses suggested significantly improved effects for organization-directed interventions (SMD = −0.45; 95% CI, −0.62 to −0.28) compared with physician-directed interventions (SMD = −0.18; 95% CI, −0.32 to −0.03). Interventions delivered in experienced physicians and in primary care were associated with higher effects compared with interventions delivered in inexperienced physicians and in secondary care, but these differences were not significant. The results were not influenced by the risk of bias ratings. Conclusions and Relevance Evidence from this meta-analysis suggests that recent intervention programs for burnout in physicians were associated with small benefits that may be boosted by adoption of organization-directed approaches. This finding provides support for the view that burnout is a problem of the whole health care organization, rather than individuals.
Internationally distributed frozen oyster meat causing multiple outbreaks of norovirus infection in Australia.

BACKGROUND Between November 2003 and January 2004, outbreaks of norovirus in 3 Australian jurisdictions involving 83 cases of illness were associated with imported oyster meat. METHODS Cohort studies were conducted in 2 jurisdictions to identify relative risks of illness for the consumption of oysters. A case series was conducted in the third jurisdiction. RESULTS The cohort studies conducted in the first 2 jurisdictions identified relative risks of illness of 17 (95% confidence interval, 5-51) and 35 (95% confidence interval, 5-243), respectively, for the consumption of oysters. Multiple strains of norovirus were detected in fecal specimens from 8 of 14 patients and in 1 of the 3 batches of implicated oyster meat using seminested reverse-transcriptase polymerase chain reaction methods. Traceback investigations revealed that all oyster meat was harvested from the same estuary system in Japan within the same month. CONCLUSIONS These outbreaks demonstrate the potential of foodborne disease to spread internationally and the need for national and international collaboration to investigate such outbreaks. Foodborne illness related to norovirus is underestimated because of underreporting of human cases and challenges in laboratory detection of viruses in foods, both of which can delay public health action.
INVERSE PHOTON TRANSPORT : SPACE SCIENCE APPLICATIONS

This paper is focused on the application of inverse problem methodology for solving some problems that have emerged in space science. The inverse model is an implicit technique: a constrained non-linear optimization problem, in which the forward problem is iteratively solved for successive approximations of the unknown parameters. Iteration proceeds until an objective-function, representing the least-square fit of model results and experimental data added to a regularization term, converges to a specified small value. 1. FORMULATION OF THE INVERSE PROBLEM A technique for property reconstruction from measurements can be described as a generalized least squares approximation. The standard least squares solution can be unstable in the presence of noise. In order to have a robust inverse model, assuring that parameter variation is bounded to become the final solution physically acceptable, some a priori information must be added to the quadratic difference term. In general, this additional information associated to the inverse solution means smoothness. Denoting by p = [ p1, p2, . , pNp ]T the unknown vector to be determined by the inverse analysis, the inverse problem can be formulated as a nonlinear optimization problem, min J(p) , lq ≤ pq ≤ uq , q = 1, . , Np , (1) where the lower and upper bounds lq and uq are chosen in order to allow the inversion to lie within some known physical limits, and the objective function is given by
Scaling abstraction refinement via pruning

Many static analyses do not scale as they are made more precise. For example, increasing the amount of context sensitivity in a k-limited pointer analysis causes the number of contexts to grow exponentially with k. Iterative refinement techniques can mitigate this growth by starting with a coarse abstraction and only refining parts of the abstraction that are deemed relevant with respect to a given client. In this paper, we introduce a new technique called pruning that uses client feedback in a different way. The basic idea is to use coarse abstractions to prune away parts of the program analysis deemed irrelevant for proving a client query, and then using finer abstractions on the sliced program analysis. For a k-limited pointer analysis, this approach amounts to adaptively refining and pruning a set of prefix patterns representing the contexts relevant for the client. By pruning, we are able to scale up to much more expensive abstractions than before. We also prove that the pruned analysis is both sound and complete, that is, it yields the same results as an analysis that uses a more expensive abstraction directly without pruning.
Extracellular Matrix Proteins and Tumor Angiogenesis

Tumor development is a complex process that relies on interaction and communication between a number of cellular compartments. Much of the mass of a solid tumor is comprised of the stroma which is richly invested with extracellular matrix. Within this matrix are a host of matricellular proteins that regulate the expression and function of a myriad of proteins that regulate tumorigenic processes. One of the processes that is vital to tumor growth and progression is angiogenesis, or the formation of new blood vessels from preexisting vasculature. Within the extracellular matrix are structural proteins, a host of proteases, and resident pro- and antiangiogenic factors that control tumor angiogenesis in a tightly regulated fashion. This paper discusses the role that the extracellular matrix and ECM proteins play in the regulation of tumor angiogenesis.
Prediction of Malignant Middle Cerebral Artery Infarction by Diffusion-Weighted Imaging

Background and Purpose This study was designed to analyze whether early diffusion-weighted imaging (DWI) provides reliable quantitative information for the prediction of stroke patients at risk of malignant brain infarct. Methods We selected 28 patients with a middle cerebral artery (MCA) infarct and proven MCA or carotid T occlusion on DWI and MRI angiography performed within 14 hours after onset (mean 6.5±3.5 hours, median 5.2 hours). Of these, 10 patients developed malignant MCA infarct, whereas 18 did not. For the 2 groups, we compared the National Institutes of Health Stroke Scale (NIHSS) score at admission, site of arterial occlusion, standardized visual analysis of DWI abnormalities, quantitative volume measurement of DWI abnormalities (volumeDWI), and apparent diffusion coefficient values. Univariate and multivariate discriminant analysis was used to determine the most accurate predictors of malignant MCA infarct. Results Univariate analysis showed that an admission NIHSS score >20, total versus partial MCA infarct, and volumeDWI >145 cm3 were highly significant predictors of malignant infarct. The best predictor was volumeDWI >145 cm3, which achieved 100% sensitivity and 94% specificity. Prediction was further improved by bivariate models combining volumeDWI and apparent diffusion coefficient measurements, which reached 100% sensitivity and specificity in this series of patients. Conclusions Quantitative measurement of infarct volume on DWI is an accurate method for the prediction of malignant MCA infarct in patients with persistent arterial occlusion imaged within 14 hours of onset. This may be of importance for early management of severe stroke patients.
Instructional Simulations for Teaching High School Computer Science Concepts: A Technology Acceptance Perspective

The Experiential-based Learning of Computer Science (ELCS) was employed as a pedagogical framework for instructional simulation design supporting high school students to learn abstract concepts of computer science. ELCS aims to provide teachers and high school learners with flexible-to-adopt, ease-of-use, from-concrete-to-abstract, and from-observation-to-experimentation digital materials on computer science concepts and to fulfill the High School Computer Curricula of Taiwan 2010. Various types of digital media, such as graphics, pictures, animations, simulations, instructional games, etc., were employed to implement four types of learning objects, including examples of concrete experiences, contexts for reflective observation, conceptual models for abstract conceptualization and contexts for active experimentation. A quality evaluation of content expert review revealed that the design of ELCS instructional simulations were appropriate for learning computer science concepts as measured from the perspective of technology acceptance. Further efforts were suggested to examine the effectiveness of types of instructional simulations for the target learners.
Convolutive Blind Source Separation for mixed audio signals using lifting schemes

This paper describes an efficient level decomposition for convolutive Blind Source Separation (BSS) for mixed audio signals using lifting wavelets. The convolutive mixed audio signals consist of source signals and same amount of delay or echo of the same source signal. To achieve BSS for convolutive mixtures, wavelet based Independent Component Analysis (ICA) is considered. First mixture is convolutive mix of Speech and Music. The second mixture is convolutive mix of Speech and speech. The mixed signals were decomposed by lifting wavelet from level 4 to 7. ICA is applied on each level signals then the separated signal is reconstructed using inverse lifting wavelet. The experimental results show that mixtures 2 produce better results than the mixture 1. As the decomposition level increases the Signal to Noise Ratio (SNR) also increases. The best SNR obtained for mixture 1 is 20.8261 dB in 7th level. The best SNR obtained for mixtures 2 is 21.2019 dB in the 7th level.
Keyword Based Searching on Uncertain Graph Data by Enhanced Approach

: As in various search mechanism keyword search is used which provides a simple but user friendly interface to retrieve information from complicated data structure. Since many datasets are represented by trees and graph, but in real life application this graph are not certain. It is subjected to uncertainties due to incompleteness of data. Because of its uncertainty, it is difficult task to search keyword on uncertain graph, also it provides unwanted result. To overcome from this drawback, this paper used new techniques. This technique provides effective result for searching keyword on graph. Uncertain graph is used in PPI network, modeling Road network, RDF data and social network etc. This technique takes less processing time as compared to previous research. Approximate mining algorithms can be used to form sub graph from uncertain graph data based on scores at the level of keywords, data elements, element sets, and sub graphs that connect these elements. To retrieve the efficient keyword from sub graph keyword matching algorithm can be used for uncertain graph data. The objective of propose technique is to reduce the high cost of processing keyword search queries on uncertain graph data and improve the performance of keyword search, without compromising its result quality. Also o reduce processing time for keyword search in uncertain graph data.
Feeling, story and time: an unruly life lived through David Bowie

Abstract Drawing upon feeling, story and time, in this article I will make sense of my unruly life through the way David Bowie’s musical and film work has impacted upon my sense of self and belonging in the world. I will remember and recall major life events and stinging memories through his songs and performances. David Bowie has provided me with what I have elsewhere defined as the star metronome, providing me with the psychological, existential and phenomenological rhythms out of which (my) bare life emerges, blossoms and sometimes withers – its beat not linear or singular but irregular and amplified. I make sense of these wayward life stories through recall to the senses, and sensorial memory – remembering Bowie through touch, texture, sight and sound. I suggest that fan identifications such as this emerge through the interplay of three elements: affect and emotion, narratives of the self, and remembering and forgetting. Feeling, story and time produce the conditions for deeply meaningful star and fan relations to emerge.
Locus Characterization and Gene Expression of Bovine FNDC5: Is the Myokine Irisin Relevant in Cattle?

The transmembrane protein FNDC5 was recently characterized as precursor of an exercise induced myokine named irisin. Previous studies found a relationship between circulating irisin levels and muscle mass in humans. Consequently, we tested the hypothesis whether FNDC5/irisin is involved in the modulation of body composition in cattle. Since information on the bovine FNDC5 locus was scarce, we characterized the gene experimentally as prerequisite for these investigations. We provide here a revised and extended gene model for bovine FNDC5. Although similarly organized like the human and murine loci, a higher variability was observed at transcript level in the bovine locus. FNDC5 mRNA was abundant in bovine skeletal muscle and was detected at lower levels in adipose tissue and liver. There were no expression differences between two groups of bulls highly different in muscularity and adiposity. Full-length FNDC5 protein (25 kDa) was present in bovine skeletal muscle independent of muscularity. Neither FNDC5 nor its putatively secreted peptide irisin were found in circulation of bulls. In contrast, we demonstrated that FNDC5 (25 kDa) and irisin (12 kDa) were present in murine skeletal muscle and that irisin was circulating in murine serum. This indicates fundamental differences in the regulation of FNDC5 and irisin between rodents and cattle.
Model and Range Image Features for Free-form Object Recognition

Recognition of free-form objects in range data is hampered by the diiculty of extracting robust and reliable features. This paper develops two approaches for detection of local features for recognition from both polyhedral mesh approximations to free-form geometry and from range data. Several experiments with models of varying complexity are proposed and speciic recommendations are given for appropriate weighting of feature detectors based on thèquality' of the polyhedral approximation.
A nearest-neighbor approach to relevance feedback in content based image retrieval

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. The main difficulties in exploiting relevance information are i) the gap between user perception of similarity and the similarity computed in the feature space used for the representation of image content, and ii) the availability of few training data (users typically label a few dozen of images). At present, SVM are extensively used to learn from relevance feedback due to their capability of effectively tackling the above difficulties. However, the performances of SVM depend on the tuning of a number of parameters. In this paper a different approach based on the nearest neighbor paradigm is proposed. Each image is ranked according to a relevance score depending on nearest-neighbor distances. This approach is proposed both in low-level feature spaces, and in "dissimilarity spaces", where image are represented in terms of their dissimilarities from the set of relevant images. Reported results show that the proposed approach allows recalling a higher percentage of images with respect to SVM-based techniques.
Chinese query expansion based on topic-relevant terms

In this paper we present a Chinese query expansion model based on topic-relevant terms which were acquired from the Google search engine automatically. In contrast to earlier methods, our queries are expanded by adding those terms that are most relevant to the concept of the query, rather than selecting terms that are relevant to the query terms. Firstly, we use automatically extracted short terms from document sets to build indexes and use the short terms in both the query and documents to do initial retrieval. Next, we acquire the topic-relevant terms of the short terms from the Internet and the top 30 initial retrieval documents. Finally, we use the topic-relevant terms to do query expansion. The experiments show that our query expansion model is more effective than the standard Rocchio expansion.
Visible Distortion Predictors Based on Visual Attention in Color Images

An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.
Factors associated with oxidative stress in women with breast cancer.

OBJECTIVE To assess the association between physiological, physical, lifestyle and nutritional variables and oxidative stress biomarkers in women with breast cancer. METHODS This cross-sectional study was conducted on 55 women newly diagnosed with breast cancer. The extent of oxidative stress was analyzed by the measurement of plasma lipid hydroperoxides (LH), thiobarbituric acid reactive substances (TBARS), protein carbonyl, whole blood reduced glutathione (GSH) and serum antioxidant capacity (AC). Diet data were obtained from food frequency questionnaire. Linear regression was used to determine the association between the variables studied and oxidative stress biomarkers. The protein carbonyl data was not included in the linear regression analyses since the data did not show a normal distribution, even after logarithmic and other transformations. RESULTS After adjusting for energy intake, the intake of chicken and high-fat dairy products was associated with increased levels of LH, while vitamin E intake was associated with decreased LH levels (R² = 23.8%). Intake of oils was associated with increased levels of TBARS (R² = 6.82%). Positive axillary lymph node status was associated with decreased levels of GSH (R² = 9.31%). Increasing age was directly associated with levels of AC, while animal fat, dairy product, and sweet food intakes were associated with low levels of AC (R² = 41.42%). CONCLUSION Intake of chicken, vitamin E, dairy products (particularly high-fat dairy products), oils, animal fat, and sweet foods, along with axillary lymph node status and age, may be important determinants of oxidative stress in women with breast cancer.
Atmospheric pressure plasma of dielectric barrier discharges

Abstract The dielectric barrier discharge (DBD) has a number of industrial applications and has been a subject of research for many years. Many studies have been carried out to understand the underlying DBD physics. Despite the fact that much progress has been made, some important issues are still far from being clear. In this work, we summarize the basics of DBD physics and introduce innovative concepts of discharge behavior that were discovered recently.
Ontobroker : How to make the WWW Intelligent

The World Wide Web can be viewed as the largest knowledge base that has ever existed. However, its support in query answering and automated inference is very limited. We propose formalized ontologies as means to enrich web documents for representing semantic information to overcome this bottleneck. Ontologies enable informed search as well as the derivation of new knowledge that is not represented in the WWW. The paper describes a software tool called Ontobroker that provides the necessary support in realizing this idea. Basically it provides formalisms and tools for formulating queries, for defining ontologies, and for annotating HTML documents with ontological information.
A highly accurate software scanned cell projection algorithm and its parallelization

We develop a highly accurate software scanned cell projection algorithm which is applicable of any classification system. This algorithm could handle both convex and non-convex meshes, and provide maximum flexibilities in applicable types of cells. Compared with previous algorithms using 3D commodity graphics hardware, it introduces no the volume decomposition and rendering artifacts in the resulting images. Furthermore, its parallel version is investigated to overcome the bottleneck of the serial one in terms of time and memory consuming for visualizing large scale unstructured data. Finally, high resolution images generated by the parallel algorithm are provided, and the scalability of the algorithm is demonstrated on a PC Cluster with modest parallel resources.
An empirical investigation of entity-based and object-oriented data modeling: a development life cycle approach

This paper examines end-user performance with conceptual and logical data models in the context of the database development life cycle. Both entity-based and object-oriented modeling methods were examined in a within-subjects study using 19 graduate students as subjects. The first method employed the extended entityrelationship (EER) model and relational data model (RDM), while the second method employed the objectoriented diagram (OOD) and object-oriented text (OOT) models. The models were assessed on the accuracy of modeling entities/classes and attributes, association relationships, and generalization relationships. Conceptual models (EER and OOD) were more effective than logical models (RDM and OOT) for representing all types of constructs. Further, the OOD model was superior to the EER model for representing entities/classes and attributes, while the OOT model was superior to the RDM for representing generalization relationships. Finally, mapping from conceptual to logical design proved to be more effective using the OOD-OOT method than the EER-RDM method.