87value/wos11967 huggingface dataset:
Background: Here we integrate verified signals from previous genetic association studies with gene expression and pathway analysis for discovery of new candidate genes and signaling networks, relevant for rheumatoid arthritis (RA). Method: RNA-sequencing-(RNA-seq)-based expression analysis of 377 genes from previously verified RA-associated loci was performed in blood cells from 5 newly diagnosed, non-treated patients with RA, 7 patients with treated RA and 12 healthy controls. Differentially expressed genes sharing a similar expression pattern in treated and untreated RA sub-groups were selected for pathway analysis. A set of connector genes derived from pathway analysis was tested for differential expression in the initial discovery cohort and validated in blood cells from 73 patients with RA and in 35 healthy controls. Results: There were 11 qualifying genes selected for pathway analysis and these were grouped into two evidence-based functional networks, containing 29 and 27 additional connector molecules. The expression of genes, corresponding to connector molecules was then tested in the initial RNA-seq data. Differences in the expression of ERBB2, TP53 and THOP1 were similar in both treated and non-treated patients with RA and an additional nine genes were differentially expressed in at least one group of patients compared to healthy controls. The ERBB2, TP53. THOP1 expression profile was successfully replicated in RNA-seq data from peripheral blood mononuclear cells from healthy controls and non-treated patients with RA, in an independent collection of samples. Conclusion: Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA.
With respect to automotive safety, the driver plays a crucial role. Stress level, tiredness, and distraction of the driver are therefore of high interest. In this paper, a driver state detection system based on cellular neural networks (CNNs) to monitor the driver's stress level is presented. We propose to include a capacitive-based wireless hand detection (position and touch) sensor for a steering wheel utilizing ink-jet printed sensor mats as an input sensor in order to improve the performance. A driving simulator platform providing a realistic virtual traffic environment is utilized to conduct a study with 22 participants for the evaluation of the proposed system. Each participant is driving in two different scenarios, each representing one of the two no-stress/stress driver states. A threefold cross validation is applied to evaluate our concept. The subject dependence is considered carefully by separating the training and testing data. Furthermore, the CNN approach is benchmarked against other state-of-the-art machine learning techniques. The results show a significant improvement combining sensor inputs from different driver inherent domains, giving a total related detection accuracy of 92%. Besides that, this paper shows that in case of including the capacitive hand detection sensor, the accuracy increases by 10%. These findings indicate that adding a subject-independent sensor, such as the proposed capacitive hand detection sensor, can significantly improve the detection performance.
High levels of worry are a common phenomenon in undergraduate populations, with 28 to 40% of undergraduates in introductory psychology courses classified as high worriers. Worry is characterized by a pervasive neglect of the present moment and persistent thoughts about the future. Various methods of increasing present moment focus have been devised. The purpose of this study was to compare the effects of two brief present moment awareness training paradigms-Mindfulness Meditation and Present Moment Joy-in high worriers. Although both paradigms seek to increase present moment awareness, the processes by which each does so are different and potentially contradictory. Prior studies have not compared these two paradigms, and the field has yet to examine Present Moment Joy as an independent technique. Ninety-seven high worriers completed two, 20-minute training sessions across 2 days of either Mindfulness Meditation or Present Moment Joy. Participants rated levels of state mindfulness (curiosity and decentering), affect, and anxiety before and after each session. Both conditions increased curiosity and decentering, but Mindfulness Meditation led to greater decentering changes on day 2 than did Present Moment Joy. Both conditions decreased anxiety and negative affect. Only Present Moment Joy increased positive affect. Results suggest that, although Mindfulness Meditation and Present Moment Joy operate via different mechanisms, both increase present moment awareness and reduce anxiety and negative affect; however, when one's goal is also to increase positive affect, Present Moment Joy may be the more effective intervention.
Background. Preoperative disinfection with povidone-iodine results in a significant reduction of the risk for postoperative endophthalmitis and secondary irreversible vision loss in intraocular surgeries and intravitreal injections. Nevertheless, this important measure is often omitted if so-called iodine allergy is suspected. We analyze the physiological and allergological basis for the construct of iodine allergy. Methods. This article is based on a selective literature review using the search term allergy in combination with iodine, povidone, indocyanine green, or seafood. Results. Iodine is a chemical element and an essential component of the human body. Scientific proof for the existence of an antibody-mediated allergic reaction (type I reaction) and in particular an immunoglobulin (Ig) E-mediated anaphylaxis against iodine is lacking. Chemical irritations and contact allergies (type IV reaction) induced by iodine-containing disinfectants are not antibody-mediated and do not cause anaphylaxis (type I reaction). The uncommon antibody-mediated allergies against iodine-containing disinfectants, fluorescent dyes, radiocontrast media, or seafood are not directed against the contained iodine itself but against other components of the respective formulation. Thus, allergic cross-reactivities between these different substance groups are not to be expected. Conclusion. So-called iodine allergy is a medical myth lacking a scientific basis and should not result in increased patient risks due to omitted preoperative disinfection.
In the framework of storm water management, Domestic Rainwater Harvesting (DRWH) systems are recently recognized as source control solutions according to LID principles. In order to assess the impact of these systems in storm water runoff control, a simple methodological approach is proposed. The hydrologic-hydraulic modelling is undertaken using EPA SWMM; the DRWH is implemented in the model by using a storage unit linked to the building water supply system and to the drainage network. The proposed methodology has been implemented for a residential urban block located in Genoa (Italy). Continuous simulations are performed by using the high-resolution rainfall data series for the do nothing and DRWH scenarios. The latter includes the installation of a DRWH system for each building of the urban block. Referring to the test site, the peak and volume reduction rate evaluated for the 2125 rainfall events are respectively equal to 33 and 26 percent, on average (with maximum values of 65 percent for peak and 51 percent for volume). In general, the adopted methodology indicates that the hydrologic performance of the storm water drainage network equipped with DRWH systems is noticeable even for the design storm event (T = 10 years) and the rainfall depth seems to affect the hydrologic performance at least when the total depth exceeds 20 mm. 2017 Elsevier Ltd. All rights reserved.
In cryptography one needs large families of binary sequences with strong pseudorandom properties. In the last decades many families of this type have been constructed. However, in many applications it is not enough if our family of good sequences is large, it is more important to know that it has a rich, complex structure, and the sequences in the family are independent, and they are far apart. Thus various measures have been introduced and applied for studying pseudorandomness of families of binary sequences: family complexity, collision, distance minimum, avalanche effect and cross-correlation measure. In this paper a survey of all these definitions and results will be presented. (C) 2015 Elsevier B.V. All rights reserved.
Envelope tracking (ET) and envelope elimination and restoration (EER) are techniques that have gained in importance in the last decade in order to obtain highly efficient radio frequency power amplifier that transmits signals with high peak-to-average power ratio. In this study, a multilevel multiphase buck converter is presented as a solution for the envelope amplifier used in ET and EER. The presented multiphase buck converter generates multilevel voltage using node duty cycles and nonlinear control. In this way, the multilevel is implemented using only one simple power stage. However, the complexity of the multilevel converter implementation has been shifted from complicated power topologies to complicated digital control. Detailed discussion regarding the influence of the design parameters (switching frequency, output filter, and time resolution of the digital control) on the performance of the proposed envelope amplifier is presented. The design of the output filter is conducted fulfilling the constraints of the envelope slew rate and minimum driver pulse that can be reproduced. In the cases when these two constraints cannot be fulfilled, they may be relieved by the modified control that is presented and experimentally validated. Finally, in order to validate the concept, a prototype has been designed and integrated with a nonlinear class F amplifier. Efficiency measurements showed that by employing EER, it is possible to save up to 15% of power losses, comparing to the case when it is supplied by a constant voltage. Additionally, adjacent channel power ratio has been measured. The obtained results showed the value higher than 30 dB for signals up to 5 MHz of bandwidth, without using a predistortion technique.
Recently, we are in the epoque of sophisticated Ambient Intelligence (AmI) applications, mainly based on utilisation of wide sensor networks together with advanced AmI approaches. If we concentrate on environmental (that is, open space or outdoor) applications, there is just a few of them, although basically focused on environmental monitoring, that enable more functionalities than just collecting environmentally related data and passing them to further elaboration. In our paper we intend to go further on with the ideas how environmentally oriented wireless sensor networks used in large-scale throughout the open natural environment could be enhanced using some recent AmI approaches in order to be beneficial in preventing primarily environmentally related problems, if not even disasters. We focus our attention on the area of water management with accent on two critical water related problems: floods management and drought-stricken periods. We suggest a concept of an approach how to solve a particular water management problem in Czech Republic using large-scale ambient intelligence approach enhancing thus wireless sensor network focused on environmental monitoring.
An edaphic-landscape restoration was performed in two anthracite coal mine spoil heaps located in NW Spain as a demonstration and pilot program for the restoration of coal mine spoils. Terrestrial arthropods were used to monitor the process as an alternative to the use of secondary succession of plants or physico-chemical indicators of soils. This study analyzes the usefulness of the terrestrial arthropods at different taxonomic levels (orders of Arthropoda, families of Coleoptera, and species of Carabidae) as restoration indicators in the initial restoration stages. The terrestrial arthropods respond rapidly to restoration, and the results may be explained by short-term increase in abiotic factors resulting from the applied techniques, mainly the animal origin of the organic matter added and the biodegradable coconut geotextile. Carabidae is of relatively low value as a short-term restoration indicator at a specific level. Coleoptera provided the best information for interpreting the ecological results over the short term and had the best relationship with the cost of identification. Likewise, the results showed that the edaphic-landscape restoration leads to an abnormal arthropod assemblage in the short term because of an excessive abundance of isopods and polydesmids. No clear convergence appeared toward any of the terrestrial arthropod communities present in the grassland, scrubland, or woodland semi-natural areas, which were used as reference end point. The restoration indicator value produced by the higher taxonomic levels of the studied terrestrial arthropods may be useful and cost-effective for assessing short-term changes caused by environmental restorations performed to recover ecosystems affected by mining activities. Copyright (C) 2014 John Wiley & Sons, Ltd.
Clinicians make a variety of assessments about their clients, from judging personality traits to making diagnoses, and a variety of methods are available to do so, ranging from observations to structured interviews. A large body of work demonstrates that from a brief glimpse of another's nonverbal behavior, a variety of traits and inner states can be accurately perceived. Additionally, from these thin slices of behavior, even future outcomes can be predicted with some accuracy. Certain clinical disorders such as Parkinson's disease and facial paralysis disrupt nonverbal behavior and may impair clinicians' ability to make accurate judgments. In certain contexts, personality disorders, anxiety, depression, and suicide attempts and outcomes can be detected from others' nonverbal behavior. Additionally, thin slices can predict psychological adjustment to divorce, bereavement, sexual abuse, and wellbeing throughout life. Thus, for certain traits and disorders, judgments from a thin slice could provide a complementary tool for the clinician's toolbox.
This paper seeks to investigate the impact of foreign direct investments (FDIs) on industrial pollution ( and BOD emissions) on a large sample of highly heterogeneous countries. By using panel data on manufacturing FDIs from France, Germany, Sweden, and the United Kingdom between 1995 and 2008, and by developing an empirical model with first and second order interaction terms, we investigate the existence and the conditionality of the most controversial FDI-induced effects on industrial emissions, i.e., Pollution Haven, Factor Endowments and Pollution Halo hypotheses. The paper has three main findings: (1) the central hypotheses linking pollution to FDI are found to act simultaneously, with opposing effects; (2) FDIs are associated with pollution reduction, i.e., predominating pollution halo induced effect, in countries with low to average capital-to-labour ratio but not too lax environmental regulation; (3) FDIs are found to increase pollution, i.e., prevailing pollution haven and/or factor endowments induced effects, in countries with average capital endowments and lax environmental regulations, as well as in all the capital abundant countries, though with a smaller magnitude in countries having strict environmental regulations and/or a high-skilled labour force. Some specific and interesting findings are discussed regarding different FDI-origin countries and FDI-host country groups.
Research into leadership emergence typically focuses on the attributes of the emergent leader. By considering also the attributes of perceivers and the passage of time, we develop a more complete theory of leadership emergence in short-lived groups. Using expectation states theory as an overarching theoretical framework, and integrating it with the surface- and deep-level diversity literature and with theories of self-serving biases, we examine the predictors of leadership emergence in short timeframes. We conduct a field study in a military assessment boot camp ( a pilot study, n = 60; and a main study, n = 89). We use cross-sectional and longitudinal exponential random graph models to analyze data on participants' abilities and on their perceptions of who, in their respective groups, were leaders. We find that the criteria by which people perceive leadership in others change over time, from easily noticeable attributes to covert leadership-relevant attributes, and that people also rely on leadership-relevant attributes that they possess at high levels to inform their perceptions of leadership in others. The integration of expectation states theory, attribute salience over time and theories of self-serving bias is needed for a full understanding of leadership emergence in groups, because perceivers' own abilities are instrumental in shaping their perceptions of emergent leadership over time. Theoretical and practical implications are discussed.
We introduce a heuristic called pseudocontingencies (PCs) as an alternative account of various stereotyping phenomena. PCs give rise to the expectation that attributes are correlated based solely on asymmetries in attribute base rates. Attributes that are encountered frequently and attributes that are encountered rarely are perceived to be correlated with each other. Such differences in information densities are typical of many stereotyped targets, including the self vs. others, the in-group vs. out-groups and majority vs. minority groups. Evidence is reviewed for PCs underlying illusory correlations, confirmation biases, gender stereotypes, Simpson's paradox, and in procedures used for implicit stereotype measurement. PCs are shown to predict specific patterns of self-enhancement and self-depreciation, the effects of intergroup contact on in-group biases and the readiness to infer stereotypes from aggregated big-data. Although PCs can lead to seriously flawed stereotypic expectations, they afford an efficient and possibly adaptive inference strategy.
Communion and agency are fundamental dimensions of social perception and judgment. Previous research revealed a primacy of communion in social information processing. The present research investigates whether there is a similar asymmetry in the density of communion and agency. We test whether communal content is more densely clustered in memory than agentic content, that is, more similar to other communal content than agentic content is similar to other agentic content. Three multidimensional scaling studies address this question and suggest an interaction with valence: While negative communal content is more densely clustered than negative agentic content, we find no differences in density between positive communal and positive agentic content. In addition to enhancing our understanding of the fundamental dimensions and their structural representation, this research might open a new perspective on old questions regarding person perception and implicit personality theories.
Background: Vernal keratoconjunctivitis (VKC) is a chronic and often severe bilateral conjunctivitis. VKC etiology still remains unclear although endocrine, genetic, neurogenic and environmental factors have been implicated. Vitamin D is a fat-soluble prohormone whose main function is the regulation of calcium and phosphate metabolism. The aim of this study was to evaluate serum vitamin D in children affected by VKC compared to the healthy children and investigate the relationship between its levels and disease severity. Methods: A total of 110 children, 47 affected by VKC, aged between 5 and 12 years were enrolled at the Department of Pediatrics, Division of Allergy and Immunology, Sapienza University of Rome. Used as controls were 63 healthy children with negative skin prick test (SPT), without allergic, ocular and systemic disease. Serum samples were obtained in April from all the children included in the study. Vitamin D dosage was repeated in October in 20 patients after therapy and in 20 controls. A conjunctival scraping was performed in all children affected by VKC. Results: Children affected by VKC had lower vitamin D levels compared to healthy controls and we found an increase in vitamin D levels after therapy with cyclosporine eye drops 1% although this increase was lower than that of healthy controls. Moreover we found significant correlations between vitamin D level and the severity of the disease. Conclusions: The study shows that children affected by VKC have lower vitamin D levels when compared to healthy controls and highlights a significant correlation between its levels and disease severity.
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called matching dependencies (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language LogiQL-an extended form of Datalog supported by the LogicBlox platform-for all activities related to data processing, and the specification and enforcement of MDs. (C) 2017 Elsevier Inc. All rights reserved.
Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis (bagFDA) performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Na < ve Bayes adjustments. A bagFDA model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting (xgbTree) performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an xgbTree model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.