Miracle-dz/UniFAR-FTUs huggingface dataset:
Amylomaltase of Pyrobaculum aerophilum IM2 Produces Thermoreversible Starch Gels

ABSTRACT Amylomaltases are 4-α-glucanotransferases (EC 2.4.1.25) of glycoside hydrolase family 77 that transfer α-1,4-linked glucans to another acceptor, which can be the 4-OH group of an α-1,4-linked glucan or glucose. The amylomaltase-encoding gene (PAE1209) from the hyperthermophilic archaeon Pyrobaculum aerophilum IM2 was cloned and expressed in Escherichia coli, and the gene product (PyAMase) was characterized. PyAMase displays optimal activity at pH 6.7 and 95°C and is the most thermostable amylomaltase described to date. The thermostability of PyAMase was reduced in the presence of 2 mM dithiothreitol, which agreed with the identification of two possible cysteine disulfide bridges in a three-dimensional model of PyAMase. The kinetics for the disproportionation of malto-oligosaccharides, inhibition by acarbose, and binding mode of the substrates in the active site were determined. Acting on gelatinized food-grade potato starch, PyAMase produced a thermoreversible starch product with gelatin-like properties. This thermoreversible gel has potential applications in the food industry. This is the first report on an archaeal amylomaltase.
Occupational and Mental Health Consequences of Women's Experiences of Gender Discrimination and Negative Workplace Acts in the Legal Profession

Women have been entering the legal profession increasingly over the last three decades, yet continue to leave the profession at significantly greater rates than men. Previous research has documented the influence of workplace discrimination in influencing women's entry and retention in the legal profession, but less research attention has been directed toward the occupational and mental health consequences for these women. The present study examined whether negative acts in the workplace, gender discrimination, and work-life conflict were associated with negative occupational and mental health consequences for women lawyers. Questionnaires containing measures of negative acts in the workplace, gendered elements of the work environment, work-family conflict, job satisfaction, burnout, and mental health symptoms were mailed to a stratified random sample of Canadian women lawyers, resulting in a sample of 277 women lawyers from Alberta, Ontario, New Brunswick, and Nova Scotia. Findings supported our hypothesis that negative workplace experiences, including gender discrimination, would predict lower levels of job satisfaction and greater levels of burnout in women lawyers. In terms of mental health outcomes, work-life conflict emerged as the strongest predictor of perceived stress and anxiety, while negative workplace acts significantly predicted depression. The findings of this study highlight the importance of addressing negative acts and gender discrimination in the legal profession in order to increase women's job satisfaction and to safeguard their health and well-being.
Summarizing and correcting the GC content bias in high-throughput sequencing

GC content bias describes the dependence between fragment count (read coverage) and GC content found in Illumina sequencing data. This bias can dominate the signal of interest for analyses that focus on measuring fragment abundance within a genome, such as copy number estimation (DNA-seq). The bias is not consistent between samples; and there is no consensus as to the best methods to remove it in a single sample. We analyze regularities in the GC bias patterns, and find a compact description for this unimodal curve family. It is the GC content of the full DNA fragment, not only the sequenced read, that most influences fragment count. This GC effect is unimodal: both GC-rich fragments and AT-rich fragments are underrepresented in the sequencing results. This empirical evidence strengthens the hypothesis that PCR is the most important cause of the GC bias. We propose a model that produces predictions at the base pair level, allowing strand-specific GC-effect correction regardless of the downstream smoothing or binning. These GC modeling considerations can inform other high-throughput sequencing analyses such as ChIP-seq and RNA-seq.
Non-parametric lane estimation in urban environments

Lane estimation of the ego vehicle plays a key role in navigating a car through unknown areas. In fact, solving this problem is a prerequisite for any vehicle driving autonomously in previously unmapped areas. Most of the proposed methods for lane detection are tuned for freeways and rural environments. In urban scenarios, however, they are unable to reliably detect the ego lane in many situations. Often, these methods simply work on the principle of fitting a parametric model to lane markers. Since a large variety of lane shapes are found in urban environments, it is obvious that these models are too restrictive. Moreover, the complex structure of intersection-like situations further hampers the success of the aforementioned methods. Therefore we propose a non-parametric lane model which can handle a wide range of different features such as grass verge, free space, lane markers etc. The ego lane estimation is formulated as a shortest path problem. A directed acyclic graph is constructed from the feature pool rendering it efficiently solvable. The proposed approach is easily extendable as it is able to cope with pixel-wise low level features as well as highlevel ones jointly. We demonstrate the potential of our method in urban and rural areas and present experimental findings on difficult real world data sets.
Output-feedback control for uncertain nonlinear systems with unmeasured states dependent growth

This paper is devoted to the problem of global stabilization by output-feedback for a class of nonlinear systems with uncertain control coefficients, stable zero-dynamics and linearly unmeasured states dependent growth. By first introducing two kinds of appropriate state transformations, the original system is converted into the new system with deterministic virtual control coefficients and the separated zero-dynamics. Then, a suitable observer based on high-gain K-filters is constructed for the new system, and the backstepping design approach is successfully proposed to the output-feedback controller. It is shown that the global asymptotic stability of the closed-loop system can be guaranteed by the appropriate choice of the design parameters.
Adipose tissue in obesity and obstructive sleep apnoea

A European Respiratory Society research seminar on “Metabolic alterations in obstructive sleep apnoea (OSA)” was jointly organised in October 2009 together with two EU COST actions (Cardiovascular risk in the obstructive sleep apnoea syndrome, action B26, and Adipose tissue and the metabolic syndrome, action BM0602) in order to discuss the interactions between obesity and OSA. Such interactions can be particularly significant in the pathogenesis of metabolic abnormalities and in increased cardiovascular risk in OSA patients. However, studying the respective role of OSA and obesity is difficult in patients, making it necessary to refer to animal models or in vitro systems. Since most OSA patients are obese, their management requires a multidisciplinary approach. This review summarises some aspects of the pathophysiology and treatment of obesity, and the possible effects of sleep loss on metabolism. OSA-associated metabolic dysfunction (insulin resistance, liver dysfunction and atherogenic dyslipidaemia) is discussed from the perspective of both obesity and OSA in adults and children. Finally, the effects of treatment for obesity or OSA, or both, on cardio-metabolic variables are summarised. Further interdisciplinary research is needed in order to develop new comprehensive treatment approaches aimed at reducing sleep disordered breathing, obesity and cardiovascular risk.
Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty

Abstract. An update is provided of Northern Hemisphere (NH) spring (March, April) snow cover extent (SCE) over the 1922–2010 period incorporating the new climate data record (CDR) version of the NOAA weekly SCE dataset, with annual 95% confidence intervals estimated from regression analysis and intercomparison of multiple datasets. The uncertainty analysis indicates a 95% confidence interval in NH spring SCE of ±5–10% over the pre-satellite period and ±3–5% over the satellite era. The multi-dataset analysis shows larger uncertainties monitoring spring SCE over Eurasia (EUR) than North America (NA) due to the more complex regional character of the snow cover variability and larger between-dataset variability over northern Europe and north-central Russia. Trend analysis of the updated SCE series provides evidence that NH spring snow cover extent has undergone significant reductions over the past ~90 yr and that the rate of decrease has accelerated over the past 40 yr. The rate of decrease in March and April NH SCE over the 1970–2010 period is ~0.8 million km2 per decade corresponding to a 7% and 11% decrease in NH March and April SCE respectively from pre-1970 values. In March, most of the change is being driven by Eurasia (NA trends are not significant) but both continents exhibit significant SCE reductions in April. The observed trends in SCE are being mainly driven by warmer air temperatures, with NH mid-latitude air temperatures explaining ~50% of the variance in NH spring snow cover over the 89-yr period analyzed. However, there is also evidence that changes in atmospheric circulation around 1980 involving the North Atlantic Oscillation and Scandinavian pattern have contributed to reductions in March SCE over Eurasia.
The vertical structure of ocean heat transport

One of the most important contributions the ocean makes to Earth's climate is through its poleward heat transport: about 1.5 PW or more than 30% of that accomplished by the ocean‐atmosphere system (Trenberth and Caron, 2001). Recently, concern has arisen over whether global warming could affect this heat transport (Watson et al., 2001), for example, reducing high latitude convection and triggering a collapse of the deep overturning circulation (Rahmstorf, 1995). While the consequences of abrupt changes in oceanic circulation should be of concern, we argue that the attention devoted to deep circulations is disproportionate to their role in heat transport. For this purpose, we introduce a heat function which identifies the contribution to the heat transport by different components of the oceanic circulation. A new view of the ocean emerges in which a shallow surface intensified circulation dominates the poleward heat transport.
Predicting customer churn by integrating the effect of the customer contact network

Customer retention is one of the most important tasks for many enterprises. In order to retain customers, customer churn prediction is necessary. This paper investigates the effects of network attributes on the accuracy of predicting customer churn. The contributions include the following: (1) a relatively complete set of network attributes are provided and incorporated into prediction models that are built using machine learning algorithms; (2) the effects of network attributes on prediction accuracy are measured while including the determinants proven to be effective by other researchers, and several models are constructed and compared to assess model effectiveness. This study uses a customer data set of a Chinese mobile telecommunication. The results show that the network attributes can greatly improve the prediction accuracy.
Image Segmentation using Commute Times

This paper exploits the properties of the commute time to develop a graphspectral method for image segmentation. Our starting point is the lazy random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. We explore the application of the commute time for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix.
Effects of flare definitions on the statistics of derived flare distributions

The statistical examination of solar flares is crucial to revealing their global characteristics and behaviour. Such examinations can tackle large-scale science questions or give context to detailed single-event studies. However, they are often performed using standard but basic flare detection algorithms relying on arbitrary thresholds. This arbitrariness may lead to important scientific conclusions being drawn from results caused by subjective choices in algorithms rather than the true nature of the Sun. In this paper, we explore the effect of the arbitrary thresholds used in the GOES (Geostationary Operational Environmental Satellite) event list and LYRA (Large Yield RAdiometer) Flare Finder algorithms. We find that there is a small but significant relationship between the power law exponent of the GOES flare peak flux frequency distribution and the flare start thresholds of the algorithms. We also find that the power law exponents of these distributions are not stable, but appear to steepen with increasing peak flux. This implies that the observed flare size distribution may not be a power law at all. We show that depending on the true value of the exponent of the flare size distribution, this deviation from a power law may be due to flares missed by the flare detection algorithms. However, it is not possible determine the true exponent from GOES/XRS observations. Additionally we find that the PROBA2/LYRA flare size distributions are artificially steep and clearly non-power law. We show that this is consistent with an insufficient degradation correction. This means that PROBA2/LYRA should not be used for flare statistics or energetics unless degradation is adequately accounted for. However, it can be used to study variations over shorter timescales and for space weather monitoring.
The Open method of co-ordination and the analysis of mutual learning processes of the European employment strategy: methodological and theoretical considerations

The purpose of this paper is solely to address two interlinked methodological and theoretical questions concerning the Open Method of Coordination (OMC), using the European Employment Strategy as a case: First, what is the most appropriate approach to learning in the analyses of the processes of the European Employment Strategy (EES)? Second, how is mutual learning processes diffused among the Member States? In answering these two questions the paper draws on a social constructivist approach to learning thereby contributing to the debate about learning in the political science literature. At the same time, based on this concept of learning, it is concluded that the learning effects of the EES are probably somewhat larger than what is normally suggested, but that successful diffusion still depends on a variety of contextual factors.
Object-oriented and Multi-scale Image Analysis in Semantic Networks Introduction: the Necessity for Integration of Remote Sensing and Gis

Important semantic information necessary to interpret an image is mostly not represented in single pixels but in meaningful image objects and their mutual relations. Delphi2 Creative Technologies has developed a new technology and software for object-oriented and multi-scale image analysis. The procedure is based on the so called 'Fractal Net Evolution' approach which is a efficient method to describe complex semantics within largely self constructing and dynamic networks. It combines insights in the fractal structure of the world and of semantics with object orientation. The procedure first extracts image objects which afterwards are classified by means of fuzzy-logic. Basic strategy is to build up a hierarchical network of image objects which allows to represent the image information in different resolutions simultaneously. By operating on the relations between networked objects it is possible to classify local context information. Beyond the pure spectral information this often essential information can be used together with form and texture features of image objects to improve classification significantly. A basic part is a new patented technique for object segmentation. It extracts image object-primitives in arbitrary resolution-fine or coarse structures-and high quality. This technique has especially been adapted to find image objects in textured data, such as SAR images, high-resolution satellite imagery, airborn data or medical images. Additionally it is used to construct a hierarchical network of image objects, where each object 'knows' its context, its neighborhood and its subobjects. The knowledge base for analysis comes in form of a hierarchical network of classes. It is threefold and distinguishes between inheritance of features, semantic grouping of classes and collection of classes into structure groups for the purpose of knowledge based object extraction. Features are evaluated by means of next-neighbour and membership functions. The procedure allows the description and analysis of complex semantic tasks even on textured data. It works on an arbitrary number of channels and allows the treatment of arbitrary data types simultaneously, e.g. different resolution, GIS-layer, alivation etc. It includes a new method for object oriented texture analysis and object oriented form analysis, both based on the analysis of subobjects. Finally it can be used to extract and export image objects for GIS applications in vectorized form. For different fields of applications, especially bridging remote sensing and GIS, the method proofed its far-reaching potential.
Synchronous cortical gamma‐band activity in task‐relevant cognition

Widespread synchronous oscillatory activity, particularly in the gamma (‘40 Hz’) band, has been postulated to exist in the brain as a mechanism underlying binding. A new method of examining phase synchronicity across multiple electrode sites in specific EEG frequency bands as a function of time was employed, in a conventional cognitive ERP paradigm in 40 normal subjects. A significant late post-stimulus gamma synchronicity response occurred for task-relevant stimuli, whereas for task-irrelevant stimuli no such response was evident. However, an early response was seen for both task-relevant and irrelevant stimuli. This is the first empirical demonstration that widespread synchronous high frequency oscillations occur in humans in relation to cognition.
Should next generation wireless mesh networks consider dynamic channel access?

The increasing demand for large and low cost wireless coverage, ranging from campus to city wide areas, has motivated a high interest in multi-hop communications with IEEE 802.11s as the most significant and successful standard for wireless mesh networks. Although IEEE 802.11s introduces new interworking, routing and wireless frame forwarding at the link layer, the multi channel architecture receives less attention. In this paper we provide insights into the IEEE 802.11s standard and explain some channel assignment (CA) protocols which can be considered for wireless mesh networks to improve their performance by limiting the negative interference effects.
Playing a First-person Shooter Video Game Induces Neuroplastic Change

Playing a first-person shooter (FPS) video game alters the neural processes that support spatial selective attention. Our experiment establishes a causal relationship between playing an FPS game and neuroplastic change. Twenty-five participants completed an attentional visual field task while we measured ERPs before and after playing an FPS video game for a cumulative total of 10 hr. Early visual ERPs sensitive to bottom–up attentional processes were little affected by video game playing for only 10 hr. However, participants who played the FPS video game and also showed the greatest improvement on the attentional visual field task displayed increased amplitudes in the later visual ERPs. These potentials are thought to index top–down enhancement of spatial selective attention via increased inhibition of distractors. Individual variations in learning were observed, and these differences show that not all video game players benefit equally, either behaviorally or in terms of neural change.
Effect of dietary fish oil on the sensitivity of hepatic lipid metabolism to regulation by insulin.

The contribution of dietary fat content and type to changes in the sensitivity of hepatic lipid metabolism to insulin was studied in primary hepatocyte cultures from donor rats maintained on a low-fat diet (LF), or on diets enriched in olive oil (OO) or fish oil (FO). The higher rate of fatty acid oxidation in hepatocytes from the FO-fed group was resistant to the inhibitory effects of insulin observed in hepatocytes from the other groups. Insulin stimulation of fatty acid incorporation into triglyceride (TG) was also less pronounced in hepatocytes from the FO-fed group than in those from the OO-fed group but there was no difference in the stimulatory effect of insulin on fatty acid incorporation into phospholipid (PL) in these two groups. In the case of fatty acid incorporation into both PL and TG, hepatocytes from the LF group were refractory to stimulation by insulin. At each concentration of insulin, hepatocytes from the FO-fed group secreted less very low density lipoprotein (VLDL) TG than those from the other groups. However, the absolute suppression of VLDL TG secretion by insulin was similar irrespective of the diet of the donor animals.We conclude that chronic consumption of a particular type of dietary fat does not affect the insulin sensitivity of the major pathways of hepatic lipid metabolism in a consistent manner.
Efficiently Answering Top-k Typicality Queries on Large Databases

Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognition science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. To answer questions like "Who are the top-k most typical NBA players? ", the measure of simple typicality is developed. To answer questions like "Who are the top-k most typical guards distinguishing guards from other players? ", the notion of discriminative typicality is proposed. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations. (1) The randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers. (2) The direct local typicality approximation using VP-trees provides an approximation quality guarantee. (3) A VP-tree can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree data structure. An extensive performance study using two real data sets and a series of synthetic data sets clearly show that top-k typicality queries are meaningful and our methods are practical.