Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of methicillin-resistant Staphylococcus aureus (MRSA) carrying the mecC gene in wild small mammals in Spain.

OBJECTIVES To determine the rate of Staphylococcus aureus faecal carriage in 101 wild small mammals in Spain and to characterize the isolates obtained. METHODS Faecal samples were seeded on mannitol salt agar and ORSAB plates. The presence of the resistance genes mecA, mecC and blaZ and the new blaZ allotype associated with staphylococcal cassette chromosome mec (SCCmec) XI (blaZ-SCCmecXI) was studied by PCR. S. aureus isolates were characterized by spa typing, agr typing and multilocus sequence typing. The presence of immune evasion cluster (IEC) genes and virulence genes was analysed by PCR. RESULTS S. aureus was detected in 13/101 studied faecal samples and one isolate per positive sample was further studied. Two S. aureus isolates were methicillin-resistant S. aureus (MRSA) (recovered from wood mice, Apodemus sylvaticus) and 11 were methicillin-susceptible S. aureus (MSSA). Both MRSA isolates harboured the mecC gene and the novel blaZ-SCCmecXI, were typed as spa-t1535/agrIII/ST1945(CC130)/SCCmecXI (where ST stands for sequence type and CC stands for clonal complex), carried the exfoliative toxin etd2 gene and were IEC type E. Eight different spa types were identified among the 11 MSSA isolates (five new) and six different sequence types were identified (two new). All MSSA strains were susceptible to the antibiotics tested except one blaZ-positive penicillin-resistant isolate (spa-t120/agrII/ST15). MSSA isolates were ascribed to the CCs (number of strains) CC5 (1), CC1956 (4) and singleton (6). Nine of 11 MSSA isolates carried the cna virulence gene. Only one MSSA isolate carried IEC genes (type C). CONCLUSIONS This is the first report of MRSA carrying mecC in faecal samples of wild small mammals in Spain. These resistant isolates carried genes of the IEC system, unusual in S. aureus from animals. Wild small mammals could be a reservoir of the mecC gene with important implications for public health.
Mammographic Mass Segmentation Using Variational Method

Mass segmentation is a very important step in most computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign or malignant. Although a lot of algorithms have been proposed for mass segmentation in mammography, accurate segmentation is still a challenge. In this article, a global optimal active contour model was applied to mammography masses segmentation. Variational method was employed to solve it. At last, a lot of experiments were carried out to demonstrate the effeteness of the proposed hybrid method.
Effect of Vitamins A, C, and E Supplementation in the Treatment of Metabolic Syndrome in Albino Rats

Obesity and metabolic syndrome increase the risk of cardiovascular morbidity and mortality. Oxidative stress seems to be involved in the path physiology of cardiovascular complications of metabolic syndrome. In this study we investigated the effects of vitamins A, C, and E in the management of metabolic syndrome traits condition in albino rats fed with high salt diet. The rats were placed on 8% NaCl diet for 5 weeks and then supplemented with these vitamins for additional 4 weeks in the presence of salt diet. Supplementation with vitamins significantly (P < 0.01 ) decreased blood pressure of the rats as compared with the control. Supplementation also significantly (P < 0.05) reduced serum total cholesterol, triglyceride, low-density lipoprotein cholesterol, and very-low-density lipoprotein cholesterol and increased high-density lipoprotein cholesterol, and total antioxidant status as compared with untreated group. The percentage protection of the supplemented groups against atherogenesis indicated 55.50 ± 3.75%. Percentage weight gain indicated significant positive correlation with triglyceride, insulin resistance, and malondialdehyde while total antioxidant status and nitric oxide showed significant negative correlation. Salt diet significantly (P < 0.05) induced features of metabolic syndrome. The result, therefore, indicated strong relationship between obesity and metabolic syndrome and underscores the role of these vitamins in the management of metabolic syndrome.
The construct and criterion validity for being “in-the-zone” leading to optimal academic and job performance

Flow is a state of complete focus and immersion with the activity at hand. It is commonly referred to as “being in the zone. ” Flow has been a key component of the recent positive psychology movement and the downstream effects of flow include greater happiness, more creativity, and reduced defensiveness. An overview of the research on flow and more specifically, an overview of the research on flow at work is provided. Previous research, however, has failed to illustrate why flow produces positive outcomes. This paper outlines the origins of the flow experience using anthropological evidence consistent with I-D Compensation Theory. Next, the results of two studies are presented to demonstrate that optimal experience (i.e., flow) leads to optimal performance. Study one surveyed undergraduate students and clarified the construct of flow, established discriminant validity from engagement, and showed flow as a significant predictor of academic performance and citizenship behavior. Study two surveyed an adult working population and established the criterion validity of flow to job performance and organizational citizenship behavior. More specifically, higher levels of flow in dockworkers and drivers lead to fewer costly accidents and greater productivity over and above engagement alone. Also, flow mediated the relationship between engagement and organizational citizenship behavior. Theoretical and practical implications for flow in academics and in the workplace are discussed. INDEX WORDS: flow, engagement, academic performance, job performance THE CONSTRUCT AND CRITERION VALIDITY FOR BEING “IN-THE-ZONE” LEADING TO OPTIMAL ACADEMIC AND JOB PERFORMANCE
Entering Postgraduate Study: A Qualitative Study of a Neglected Transition

In this paper we demonstrate that whilst attention has been given to other educational transitions, the postgraduate experience has been largely ignored. We suggest this may be due to assumptions of expertise in that group. Here we consider literature together with data from a one year, multi-methods study into postgraduate transition. The literature and data suggest that postgraduate students lead complex lives and require targeted support to enable their study. The participants here did not position themselves as expert, confident learners but instead suffered a range of doubts surrounding their study skills and ability to manage the different aspects of their lives. University practices did not always support their struggles. We use communities of practice theory to understand the participatory transition trajectories. We end by acknowledging that this research represents a small percentage of postgraduate students in the particular context of the UK and call for further research in different contexts to develop our understanding of postgraduate transition.
Comparing hard and overlapping clusterings

Similarity measures for comparing clusterings is an important component, e.g., of evaluating clustering algorithms, for consensus clustering, and for clustering stability assessment. These measures have been studied for over 40 years in the domain of exclusive hard clusterings (exhaustive and mutually exclusive object sets). In the past years, the literature has proposed measures to handle more general clusterings (e.g., fuzzy/probabilistic clusterings). This paper provides an overview of these new measures and discusses their drawbacks. We ultimately develop a corrected-for-chance measure (13AGRI) capable of comparing exclusive hard, fuzzy/probabilistic, non-exclusive hard, and possibilistic clusterings. We prove that 13AGRI and the adjusted Rand index (ARI, by Hubert and Arabie) are equivalent in the exclusive hard domain. The reported experiments show that only 13AGRI could provide both a fine-grained evaluation across clusterings with different numbers of clusters and a constant evaluation between random clusterings, showing all the four desirable properties considered here. We identified a high correlation between 13AGRI applied to fuzzy clusterings and ARI applied to hard exclusive clusterings over 14 real data sets from the UCI repository, which corroborates the validity of 13AGRI fuzzy clustering evaluation. 13AGRI also showed good results as a clustering stability statistic for solutions produced by the expectation maximization algorithm for Gaussian mixture. Implementation and supplementary figures can be found at http://sn.im/25a9h8u.
Frontal EEG asymmetry as predictor of physiological responses to aversive memories.

Evidence suggests that asymmetry in frontal electrical activity predicts responses to aversive experiences, such that higher left-sided activity might dampen responses to trauma reminders. We measured frontal asymmetry at rest and during viewing of a trauma film, and assessed startle responses to film-reminder images. To explore potential moderators, we compared two films (Study 1; N = 64) and modulated reappraisal (Study 2; N = 72). As expected, left frontal activation during film viewing predicted dampened responses in individuals who viewed a staged road accident. However, this effect tended to be reversed when a genocide documentary was used. In Study 2, all participants viewed the genocide film. Left frontal activity at rest again predicted higher startle responses, while reappraisal did not moderate the effects. Thus, the type of trauma film plays a crucial role in the effects of frontal asymmetry, which warrants further critical investigation.
Prediction of convergence dynamics of design performance using differential recurrent neural networks

Computational fluid dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have been seen using feedforward neural networks to approximate CFD models. However, feedforward neural network models cannot capture well the dynamics of the differential equations. Thus, training data from a large number of different designs are needed to train feedforward neural network models to achieve reliable generalization. In this work, a technique using differential recurrent neural networks has been proposed to predict the performance of candidate designs before the CFD simulation is fully converged. Compared to existing methods based on feedforward neural networks, this approach does not need a large number of previous designs. Case studies show that the proposed method is very promising.
A Structural Model for Sovereign Credit Risk

This study examines the risk inherent to sovereign default on external debts denominated in foreign currency. I develop a contingent claims theory of a sovereign, which optimally chooses its level of foreign debt and default policy. In the model, renegotiation upon default plays an important role in determining country creditworthiness. In crises episodes, the sovereign initiates a process of debt restructuring with the lender. Both parties bargain over a reduction of the debt service, which determines the level of economic sanctions. With a recovery rate being endogenous to the model, I show that the generated credit spreads are higher than when renegotiation is not accounted for and thus more consistent to the data. This prediction stands in stark contrast to the existing theoretical literature. On the empirical side, the analysis explores the relation between economic fundamentals
Finding a Needle in a Haystack: Identification of EGFP Tagged Neurons during Calcium Imaging by Means of Two-Photon Spectral Separation

The combination of two-photon in vivo imaging and genetic labeling of specific cell types in the mouse brain is a powerful method to refine our understanding of brain circuitry and to dissect the contribution of specific neural classes to cortical function. The synthetic calcium indicators are the best fluorescent reporters for cellular activity that are presently available but their spectral proprieties are often overlapped with those of the fluorescent proteins used for genetic labeling. Such is the case of Oregon Green BAPTA1 and EGFP, the most widely used fluorophores for targeted two-photon imaging. The emission spectra of these molecules are virtually identical, precluding their separation by narrow band filters at the detector side. However, even if their one photon excitation spectra are very similar, their two-photon excitation spectra differ significantly: here we show how it is possible to exploit this difference to separate the relative contributions of EGFP and Oregon Green to the total fluorescence signal. This approach addresses two different issues: the unbiased detection of cells expressing EGFP in a cortical volume injected with Oregon Green, and the computation of the Ca2+ insensitive fluorescence background. The latter data is essential for the quantitative comparison of the relative changes in Ca2+ concentration between different cells, containing variable concentrations of EGFP. This strategy can be easily extended to any couple of fluorophores provided that have a different two-photon excitation spectra.
Intentional Binding Is Driven by the Mere Presence of an Action and Not by Motor Prediction

Intentional binding refers to the fact that when a voluntary action produces a sensory outcome, action and outcome are perceived as being closer together in time. This phenomenon is often attributed, at least partially, to predictive motor mechanisms. However, previous studies failed to unequivocally attribute intentional binding to these mechanisms, since the contrasts that have been used to demonstrate intentional binding covered not only one but two processes: temporal control and motor identity prediction. In the present study we aimed to isolate the respective role of each of these processes in the emergence of intentional binding of action-effects. The results show that motor identity prediction does not modulate intentional binding of action-effects. Our findings cast doubts on the assumption that intentional binding of action effects is linked to internal forward predictive process.
Bayesian Function Learning Using MCMC Methods

The paper deals with the problem of reconstructing a continuous 1D function from discrete noisy samples. The measurements may also be indirect in the sense that the samples may be the output of a linear operator applied to the function. Bayesian estimation provides a unified treatment of this class of problems. We show that a rigorous Bayesian solution can be efficiently implemented by resorting to a Markov chain Monte Carlo (MCMC) simulation scheme. In particular, we discuss how the structure of the problem can be exploited in order to improve the computational and convergence performances. The effectiveness of the proposed scheme is demonstrated on two classical benchmark problems as well as on the analysis of IVGTT (IntraVenous glucose tolerance test) data, a complex identification-deconvolution problem concerning the estimation of the insulin secretion rate following the administration of an intravenous glucose injection.
Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association.

SUMMARY In 1995 the American College of Sports Medicine and the Centers for Disease Control and Prevention published national guidelines on Physical Activity and Public Health. The Committee on Exercise and Cardiac Rehabilitation of the American Heart Association endorsed and supported these recommendations. The purpose of the present report is to update and clarify the 1995 recommendations on the types and amounts of physical activity needed by healthy adults to improve and maintain health. Development of this document was by an expert panel of scientists, including physicians, epidemiologists, exercise scientists, and public health specialists. This panel reviewed advances in pertinent physiologic, epidemiologic, and clinical scientific data, including primary research articles and reviews published since the original recommendation was issued in 1995. Issues considered by the panel included new scientific evidence relating physical activity to health, physical activity recommendations by various organizations in the interim, and communications issues. Key points related to updating the physical activity recommendation were outlined and writing groups were formed. A draft manuscript was prepared and circulated for review to the expert panel as well as to outside experts. Comments were integrated into the final recommendation. PRIMARY RECOMMENDATION To promote and maintain health, all healthy adults aged 18 to 65 yr need moderate-intensity aerobic (endurance) physical activity for a minimum of 30 min on five days each week or vigorous-intensity aerobic physical activity for a minimum of 20 min on three days each week. [I (A)] Combinations of moderate- and vigorous-intensity activity can be performed to meet this recommendation. [IIa (B)] For example, a person can meet the recommendation by walking briskly for 30 min twice during the week and then jogging for 20 min on two other days. Moderate-intensity aerobic activity, which is generally equivalent to a brisk walk and noticeably accelerates the heart rate, can be accumulated toward the 30-min minimum by performing bouts each lasting 10 or more minutes. [I (B)] Vigorous-intensity activity is exemplified by jogging, and causes rapid breathing and a substantial increase in heart rate. In addition, every adult should perform activities that maintain or increase muscular strength and endurance a minimum of two days each week. [IIa (A)] Because of the dose-response relation between physical activity and health, persons who wish to further improve their personal fitness, reduce their risk for chronic diseases and disabilities or prevent unhealthy weight gain may benefit by exceeding the minimum recommended amounts of physical activity. [I (A)]
An Online Approach to Structure from Motion Paper

In this paper, we present an online FastSLAM-based algorithm for extracting three dimensional structure from the motion of a single monocular camera. Nearly all published structure from motion (SFM) algorithms are offline, that is, they require the entire data set for processing. Our algorithm exploits the notion of conditional independence in Rao-Blackwellized particles, allowing us to decouple the complexity of the algorithm from the size of the data set. By doing so, we are able to utilize a particle filter to perform structure from motion as an online algorithm. At the heart of our algorithm is an online least squares technique to estimate the locations of all the landmarks as each new image comes in. Using this algorithm, we successfully extract structure from images taken by a camera constrained to linear motion.
Self-Tuning Semi-Supervised Spectral Clustering

Spectral clustering (SC), as an unsupervised learning algorithm, has been used successfully in the field of computer vision for data clustering. In some applications, however, background prior knowledge can be easily obtained, such as pairwise constraints. Therefore, semi-supervised learning is getting increasing attention in recent years. In this paper, a new algorithm named self-tuning semi-supervised spectral clustering (STS3C) is proposed. We incorporate two types of instance-level constraints-must-link and cannot-link into SC and use self-tuning parameter to solve the scaling parameter selection problem in SC. Experimental results over four datasets from UCI machine learning repository show that STS3C performs better than semi-supervised spectral clustering with fixed scaling parameter, and also avoids the time-consuming procedure of parameter selection.
Analysis of Binary Relations and Hierarchies of Enzymes in the Metabolic Pathways

In conjunction with a new database system that efficiently organizes the metabolic pathway data from various organisms, we are developing computational methodologies using binary relations and hierarchies of enzymes. Biological knowledge integrated in the system includes genes, gene products, chemical compounds, enzyme reactions and metabolic pathway diagrams. By automatically mapping the enzymes of a specific organism on the pathway diagrams, it becomes possible to visualize the characteristic features of the organismspecific metabolic pathways. With the aid of the computational methodology implemented in the system, it becomes again possible to analyze and investigate the pathways in terms of their function and evolution. In this paper, we describe the outline of the system and present new biological features of metabolic pathways revealed by the system.
Reduction Of Query Optimizer Plan Diagrams

Modern database systems use a query optimizer to identify the most efficient strategy, called “plan”, to execute declarative SQL queries. Optimization is a mandatory exercise since the difference between the cost of the best plan and a random choice could be in orders of magnitude. The role of query optimizers is especially critical for the decision-support queries featured in data warehousing and data mining applications. For a query on a given database and system configuration, the optimizer’s plan choice is primarily a function of the selectivities of the base relations participating in the query. A pictorial enumeration of the execution plan choices of a database query optimizer over this relational selectivity space is called a “plan diagram”. It has been shown recently that these diagrams are often remarkably complex and dense, with a large number of plans covering the space. An interesting research problem that immediately arises is whether complex plan diagrams can be reduced to a significantly smaller number of plans, without materially compromising the query processing quality. The motivation is that reduced plan diagrams provide several benefits, including quantifying the redundancy in the plan search space, enhancing the applicability of parametric query optimization, identifying error-resistant and least-expected-cost plans, and minimizing the overhead of multi-plan approaches. In this thesis, we investigate the plan diagram reduction issue from theoretical, statistical and empirical perspectives. Our analysis shows that optimal plan diagram reduction, w.r. t. minimizing the number of plans in the reduced diagram, is an NP-hard problem in general, and remains so even for a storage-constrained variation. We then present CostGreedy, a greedy reduction algorithm that has tight and optimal performance guarantees, and whose complexity scales linearly with the number of plans in the diagram. Next, we construct an extremely fast
JPEG-2000 Workload Prediction for Adaptive System on Chip Entropy Coders Architecture

Multimedia applications are quickly becoming the most common workload for embedded systems and portable devices. Video, sound, image applications including digital TV, Web access through wireless transmission are among the various possible tasks to be handled by next generation portable devices. In contrast with traditional workloads mostly static in nature, multimedia workloads exhibit high variability depending on the data processed which when coupled with real time processing requirements make difficult to dimension hardware resources when designing systems on chip. In this paper, we propose the use of a neural network for workload forecasting in order to adapt hardware resources during JPEG-2000 based image compression. The major performance bottleneck in JPEG-2000 being the entropy coder our aim is to adapt in real time the number of concurrent entropy coders through workload forecasting.