Miracle-dz/UniFAR-FTUs huggingface dataset:
Medication adherence and the risk of severe asthma exacerbations: a systematic review

The benefits of drug therapy for asthma have been well established, but adherence to treatment is poor, and this might be associated with an increased risk of asthma exacerbations. The aim of this study was to review the literature on the association between adherence to asthma controller treatment and risk of severe asthma exacerbations in children and adults. A systematic literature search was performed in PubMed, Embase and Web of Science, from inception until January 2014. Studies were included if data on the association between medication adherence and severe asthma exacerbations were presented. Quality was assessed using a modified version of the Newcastle–Ottawa Scale. The search yielded 2319 unique publications, of which 23 met the inclusion criteria and underwent data extraction and quality scoring. High levels of heterogeneity across studies with regard to adherence and exacerbation measurements, designs and analysis precluded a formal meta-analysis. Although effect measures varied widely, good adherence was associated with fewer severe asthma exacerbations in high-quality studies. Good adherence tended to be associated with lower risk of severe asthma exacerbations. Future studies should use standardised methodology to assess adherence and exacerbations, and should consider inhaler competence. High-quality studies indicated that better adherence is associated with fewer severe asthma exacerbations http://ow.ly/BU6ro
The role of immigration policies for immigrants’ selection and economic success

This study aims at exploring whether host-country immigration policies related to the selection of immigrants with regard to human capital and other characteristics relevant for the labour market are effective and result in these immigrants’ more favourable economic integration. The focus in on immigration policies in two groups of countries. We compare liberal regimes (Ireland and the UK) which policies aimed at attracting highly-skilled immigrants to meet these countries’ economic needs in highly-skilled jobs with those of Southern European countries (Italy, Spain and Greece), which pursued more lax and unselective policies, trying to attract labour force for low-skilled jobs in their countries’ economies. Economic immigrants are expected to have favourable employment entry chances in each group of countries, not least due to the fact that the supply of immigrants apparently met the labour demand in host countries’ economies. We also expect that more selective policies attracting better-qualified immigrants in Ireland and the UK would lead to these immigrants’ better chances of higher-quality employment.
A Robust Optimal Control for Robot Manipulators

A mixed Optimal/robust control is proposed is this paper for the tracking of rigid robotic systems under parametric uncertainties and external perturbations. The design objective is that under a prescribed disturbance norm level, an optimal control system is to be designed as well as a robust control to overcome the effect of uncertainties. The optimal control is based on the solution of a nonlinear Ricatti equation, which by virtue of the skew symmetry property of manipulators and an adequate choice of state variables becomes an algebraic equation easy to solve. We then investigate the design of the robust control of the uncertain system by a continuous state feedback function. It will be shown that our approach globally asymptotically stabilizes the uncertain dynamical system. We illustrate our approach by applying our control approach to a 2-DOF manipulator. Key-Words: Robot Manipulators, optimal control, robust control, uncertainties.
From trace-based inter-model validation to bidirectional model synchronization with reconciliation

Model to model transformation is a process of generating a target model from the source input model. However, after applying the transformation, both source and target models may evolve independently. In these cases, bidirectional transformation (Bx) is required to propagate changes across models to resolve the inter-model inconsistency. In this paper, we propose an interactive Bx approach via inter-model validation which enables developers synchronizing models with reconciliation. The proposed Bx system is implemented using the Epsilon Validation Language (EVL). A trace meta-model is designed to provide change propagation leads to bidirectional correspondence between models. The approach is formalized based on the synchronization concepts achieving 13 different states for each bidirectional relation. Our approach is compared to some other well-known Bx approaches considering six significant synchronization features. The results illustrate the appropriateness of the proposed approach.
Design, analysis and validation of an observer-based delay compensation structure for a Network Control System

A pressing issue of Network Control Systems is the time-varying delay which alters the system's performance. The current study designs and analysis a control structure based on a communication disturbance observer for delay compensation for second order linear processes. The proposed networked control system is tested through simulations and experiments, using a new network model recently proposed by the authors, for a scenario involving random delay variation and packets loss during network transmissions.
Unraveling Microbial Interactions in Food Fermentations: from Classical to Genomics Approaches

Fermentation, the microbial degradation of organic compounds without net oxidation, is an important process in the global carbon cycle and is also exploited worldwide for the production and preservation of food. It is one of the oldest food-processing technologies known, with some records dating back to 6,000 b.c. (50). The link between food and microbiology was laid by Pasteur, who found that yeasts were responsible for alcoholic fermentation (106). Since that discovery, scientific and industrial interests in food microbiology started to grow and continue to increase today. The number of food products that rely on fermentation in one or more steps of their production is tremendous. They form an important constituent of the daily diet and rank among the most innovative product categories in the food industry. Most of the important microorganisms applied in the production of fermented foods have been studied for decades, yielding a wealth of information on their physiology and genetics in relation to product functionalities, such as the development of flavor, taste, and texture. The recent emergence of genomics has opened new avenues for the systematic analysis of microbial metabolism and the responses of microorganisms to their environment. Additionally, genomics has boosted research on important food microbes (22, 90, 93). Much of this research focuses on the performance of a single strain, including its interactions with the food matrix. However, food fermentations are typically carried out by mixed cultures consisting of multiple strains or species. Population dynamics play a crucial role in the performance of mixed-culture fermentations, and for many years, studies on mixed-culture food fermentations have focused on analyzing population dynamics using classical and molecular methods. Many of these studies are mainly descriptive, and relatively little is known about the mechanisms governing population dynamics in general and the molecular interactions that occur between the consortium members in particular. The availability of genome sequences for several species that are of industrial importance as well as technological advances in functional genomics enable new approaches to study food microbiology beyond the single species level and allow an integral analysis of the interactions and metabolic activity in mixed cultures. Here we review the current knowledge on important food fermentation processes, focusing on the bacterial interactions. In addition, we illustrate how genomics approaches may contribute to the elucidation of the interaction networks between microbes, including interactions with the food environment. This information may find application in the industry through rational optimization and increased control over mixed-culture fermentations.
A Gridding Method for Sequential Analysis Problems

This paper introduces a numerical method for nding optimal or approximately optimal decision rules and corresponding expected losses in Bayesian sequential decision problems. The method, based on the classical backward induction method, constructs a grid approximation to the expected loss at each decision time, viewed as a function of certain statistics of the posterior distribution of the parameter of interest. In contrast with existing techniques, this method has a computation time which is linear in the number of stages in the sequential problem. It can also be applied to problems with insuÆcient statistics for the parameters of interest. Furthermore, it is well-suited to be implemented using parallel processors.
An Extensive Review of Significant Researches on Epileptic Seizure Detection and Prediction using Electroencephalographic Signals

-Epileptic seizure is a brain disorder caused by large number of small electrical discharges of nerve cells. Electroencephalographic (EEG) signals are the most commonly used source for Epileptic seizure prediction. A variety of temporal changes in perception and behavior may be caused by Epileptic seizures. In the human EEG, they are depicted by multiple ictal patterns, where epileptic seizures typically become perceptible as characteristic, usually rhythmic signals, often concurring with or even preceding the earliest observable changes in behavior. Earliest possible detection of observable onset of ictal patterns in the EEG can, thus, serve to initiate more-detailed diagnostic procedures during seizures and to distinguish epileptic seizures from other stipulations with seizure-like symptoms. Lately, warning and intervention systems triggered by the detection of ictal EEG patterns have fascinated escalating interest. As the workload implicated in the detection and prediction of seizures by human experts is quite formidable, a number of attempts have been made to devise automatic seizure detection and prediction systems. So far, none of these have found extensive application. There have been an increased number of researches carried out with the purpose of devising an automatic seizure detection and prediction system. ADVANCES IN BIOMEDICAL RESEARCH ISSN: 1790-5125 330 ISBN: 978-960-474-164-9 In this paper, we present an extensive review of the significant researches associated with the detection and prediction of Epileptic Seizures using EEG signals. In addition, we have presented an in depth description of Electroencephalogram, Epileptic Seizure and its types along with Epilepsy.
A Blind Feedforward Frequency Offset Estimator for OFDM Systems in Multipath Channels

In this paper we consider the problem of blind feedforward carrier-frequency offset (CFO) estimation for orthogonal frequency division multiplexing (OFDM) systems in multipath channels. Specifically, by exploiting the least-squares approach a closed-form blind CFO estimator is derived. The proposed algorithm does not require the knowledge of the channel impulse response and/or the insertion of virtual subcarriers. Computer simulation results show that the derived CFO estimator can outperform previously proposed blind CFO synchronization algorithms
The Missing Medians: Exclusion of Ordinal Data from Meta-Analyses

Background Meta-analyses are considered the gold standard of evidence-based health care, and are used to guide clinical decisions and health policy. A major limitation of current meta-analysis techniques is their inability to pool ordinal data. Our objectives were to determine the extent of this problem in the context of neurological rating scales and to provide a solution. Methods Using an existing database of clinical trials of oral neuroprotective therapies, we identified the 6 most commonly used clinical rating scales and recorded how data from these scales were reported and analysed. We then identified systematic reviews of studies that used these scales (via the Cochrane database) and recorded the meta-analytic techniques used. Finally, we identified a statistical technique for calculating a common language effect size measure for ordinal data. Results We identified 103 studies, with 128 instances of the 6 clinical scales being reported. The majority– 80%–reported means alone for central tendency, with only 13% reporting medians. In analysis, 40% of studies used parametric statistics alone, 34% of studies employed non-parametric analysis, and 26% did not include or specify analysis. Of the 60 systematic reviews identified that included meta-analysis, 88% used mean difference and 22% employed difference in proportions; none included rank-based analysis. We propose the use of a rank-based generalised odds ratio (WMW GenOR) as an assumption-free effect size measure that is easy to compute and can be readily combined in meta-analysis. Conclusion There is wide scope for improvement in the reporting and analysis of ordinal data in the literature. We hope that adoption of the WMW GenOR will have the dual effect of improving the reporting of data in individual studies while also increasing the inclusivity (and therefore validity) of meta-analyses.
Emergence of ontological relations from visual data with Self-Organizing Maps

In this paper we examine how Self-Organizing Maps (SOMs) can be used in detecting and describing emergent ontological relations between semantic objects and object classes in a visual database. The ontological relations we have studied include co-existence, taxonomies of visual and semantic similarity and spatial relationships. The used database contains 2618 images, each of which belongs to one or more of ten predefined semantic classes.
Cyclostationary Detection in Spectrum Pooling System of Undefined Secondary Users

Spectrum sensing continuing to emerge as an essential topic for the cognitive networks where two kinds of users primary and secondary will share the band. This paper proposes a method for real-time detection of secondary users at the base stations. Cognitive Radios may hide themselves in between the primary users and Rental secondary users based on spectrum pooling system to avoid being charged for using spectrum. To deal with such scenario, a cyclostationary Fast Fourier Transform FFT Accumulation Method (FAM) has been used to develop a new scheme for channels users recognition. Channels users are tracked according to the changes in their signal parameters for instance modulation techniques. The Matlab simulation runs three signals transmitted on spectrum pooling system channels. Obtained spectral correlation density functions show successful Differentiation between signals. Keywords— Cognitive Radio; Sensing; Cyclostationary Detection; Undefined.
Data Outsourcing in Cloud Environments: A Privacy Preserving Approach

With the increasing cost of maintaining IT centers, organizations are looking into outsourcing thier storage and computational needs to a cloud server. However, such outsourcing has also raised the more serious issue of data privacy. In this paper, we summarize our work in privacy-preserving data outsourcing. In particular, we discuss the issue of employing vertical fragmentation to a relation so that the fragment that is assigned to the cloud server contains maximum data without violating privacy. Here, privacy is expressed in terms of a set of confidentiality constraints. We represent the confidentiality constraints as a graph where the nodes are the attributes and links represent paired confidentiality. We then apply the graph coloring problem with two colors for the a cyclic portion of the graph. We use some heuristic to eliminate the cycles, and complete the coloring of all nodes. We are currently extending the work to multiple relations and constraints with multiple attributes in a constraint (i.e., triplet, quadruplet, etc.) instead of just pairs.
The Internalisation of External Costs in Transport: From the Polluter Pays to the Cheapest Cost Avoider Principle

The European Commission mandated the consulting firm CE Delft to develop a framework for the internalisation of external costs and to devise a number of potential internalisation scenarios for further analysis. The results of their preliminary research are published in a CE discussion paper, and the Commission is now formulating draft legislative proposals which will be subjected to a consultation of stakeholders. The present report offers a critical assessment of the above mentioned CE study, providea a theoretical examination of the relative strengths and weaknesses of the polluter pays principle and the cheapest cost avoider principle in relation to the problems arising in the road transport sector, demonstrates how the cheapest cost avoider principle can be applied in practice by providing examples of external costs in road transport, and puts forward recommendations for the appropriate principles that should guide the Commission's further activities in this area.
Development of the diencephalon in the rat. I. Autoradiographic study of the time of origin and settling patterns of neurons of the hypothalamus

Groups of pregnant rats were injected with two successive daily doses of 3H‐thymidine from gestational day 13 (E13+14) until the day before birth (E21+22). This double labelling procedure was combined with an injection schedule of a single day delay between groups (E13+14; E14+15; E1516.). The two injections assured the comprehensive labelling of practically all neurons of a given structure prior to the onset of their differentiation (comprehensive labelling), whereas the progressive daily delay in injections made it possible to estimate the proportion of neurons formed in various regions of the hypothalamus on a single day. Hypothalamic areas or nuclei were assigned into four classes on the basis of their cytogenetic isochronicity. Structures composed of the earliest arising (class 1) neurons constitute a lateral tier that includes the lateral preoptic and lateral hypothalamic areas, and the lateral mammillary nucleus. Structures composed of early arising (class 2) neurons form a heterogeneous collection of nuclear systems, including the Para ventricular, intern clear and supraoptic magnocellular neurons, and several intermediate tier nuclei of the anterior and posterior hypothalamus. The late arising (class 3) and latest arising (class 4) nuclei constitute a periventricular system anteriorly and a more extensive region posteriorly. The latter two nuclear systems may constitute the hypophysiotropic area of the hypothalamus. The nuclei of the mammillary system, which are produced sequentially, are distinguished from other hypothalamic structures by their more rapid generation time. Internuclear labelling gradients were used to infer the neuroepithelial site of origin and setting pattern of neurons. common sites of origin were indicated for the following structures: the magnocellular neurohypophysial neurons; the neurons of the dorsomedial and ventromedial nuclei; and the neurons of the tuberomammillary and arcuate nuclei. The sites of origin of these groups of nuclei were related to specialized ventricular linings in the mature hypothalamus.
On Multicategory Truncated-Hinge-Loss Support Vector Machines

Abstract. With its elegant margin theory and accurate classification performance, the Support Vector Machine (SVM) has been widely applied in both machine learning and statistics. Despite its success and popularity, it still has some drawbacks in certain situations. In particular, the SVM classifier can be very sensitive to outliers in the training sample. Moreover, the number of support vectors (SVs) can be very large in many applications. To solve these problems, [WL06] proposed a new SVM variant, the robust truncatedhinge-loss SVM (RSVM), which uses a truncated hinge loss. In this paper, we apply the operation of truncation on the multicategory hinge loss proposed by [LLW04]. We show that the proposed robust multicategory truncated-hingeloss SVM (RMSVM) is more robust to outliers and deliver more accurate classifiers using a smaller set of SVs than the original multicategory SVM (MSVM) proposed by [LLW04].
LOCAL ADAPTATION IN THE LINUM MARGINALE—MELAMPSORA LINI HOST‐PATHOGEN INTERACTION

Abstract The potential for local adaptation between pathogens and their hosts has generated strong theoretical and empirical interest with evidence both for and against local adaptation reported for a range of systems. We use the Linum marginale—Melampsora lini plant‐pathogen system and a hierarchical spatial structure to investigate patterns of local adaptation within a metapopulation characterised by epidemic dynamics and frequent extinction of pathogen populations. Based on large sample sizes and comprehensive cross‐inoculation trials, our analyses demonstrate strong local adaptation by Melampsora to its host populations, with this effect being greatest at regional scales, as predicted from the broader spatial scales at which M. lini disperses relative to L. marginale. However, there was no consistent trend for more distant pathogen populations to perform more poorly. Our results further show how the coevolutionary interaction between hosts and pathogens can be influenced by local structure such that resistant hosts select for generally virulent pathogens, while susceptible hosts select for more avirulent pathogens. Empirically, local adaptation has generally been tested in two contrasting ways: (1) pathogen performance on sympatric versus allopatric hosts; and (2) sympatric versus allopatric pathogens on a given host population. In situations where no host population is more resistant or susceptible than others when averaged across pathogen populations (and likewise, no pathogen population is more virulent or avirulent than others), results from these tests should generally be congruent. We argue that this is unlikely to be the case in the metapopulation situations that predominate in natural host‐pathogen interactions, thus requiring tests that control simultaneously for variation in plant and pathogen populations.
Molecular adaptations of neuromuscular disease‐associated proteins in response to eccentric exercise in human skeletal muscle

The molecular events by which eccentric muscle contractions induce muscle damage and remodelling remain largely unknown. We assessed whether eccentric exercise modulates the expression of proteinases (calpains 1, 2 and 3, proteasome, cathepsin B+L), muscle structural proteins (α‐sarcoglycan and desmin), and the expression of the heat shock proteins Hsp27 and αB‐crystallin. Vastus lateralis muscle biopsies from twelve healthy male volunteers were obtained before, immediately after, and 1 and 14 days after a 30 min downhill treadmill running exercise. Eccentric exercise induced muscle damage as evidenced by the analysis of muscle pain and weakness, creatine kinase serum activity, myoglobinaemia and ultrastructural analysis of muscle biopsies. The calpain 3 mRNA level was decreased immediately after exercise whereas calpain 2 mRNA level was increased at day 1. Both mRNA levels returned to control values by day 14. By contrast, cathepsin B+L and proteasome enzyme activities were increased at day 14. The α‐sarcoglycan protein level was decreased immediately after exercise and at day 1, whereas the desmin level peaked at day 14. αB‐crystallin and Hsp27 protein levels were increased at days 1 and 14. Our results suggest that the differential expression of calpain 2 and 3 mRNA levels may be important in the process of exercise‐induced muscle damage, whereas expression of α‐sarcoglycan, desmin, αB‐crystallin and Hsp27 may be essentially involved in the subsequent remodelling of myofibrillar structure. This remodelling response may limit the extent of muscle damage upon a subsequent mechanical stress.