nils-herrmann/clean_scirepeval huggingface dataset:
A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health
Venue: Proceedings of the National Academy of Sciences

The parasitic mite Varroa destructor and the deformed wing virus (DWV) are linked in a mutualistic symbiosis. The mite acts as vector of the viral pathogen, whereas the DWV induced immunosuppression in honey bees favors mite feeding and reproduction. This functional interaction underpins a rapidly escalating immunosuppression, which can be primed and/or aggravated by a wealth of stress factors that co trigger colony losses. Our experimental results explain the pivotal role proposed for the Varroa DWV association in the induction of honey bee colony losses. Here we provide a functional framework for studying the dynamics of this multifactorial syndrome and defining effective strategies to reduce its negative impact on the beekeeping industry. Honey bee colony losses are triggered by interacting stress factors consistently associated with high loads of parasites and/or pathogens. A wealth of biotic and abiotic stressors are involved in the induction of this complex multifactorial syndrome, with the parasitic mite Varroa destructor and the associated deformed wing virus (DWV) apparently playing key roles. The mechanistic basis underpinning this association and the evolutionary implications remain largely obscure. Here we narrow this research gap by demonstrating that DWV, vectored by the Varroa mite, adversely affects humoral and cellular immune responses by interfering with NF kB signaling. This immunosuppressive effect of the viral pathogen enhances reproduction of the parasitic mite. Our experimental data uncover an unrecognized mutualistic symbiosis between Varroa and DWV, which perpetuates a loop of reciprocal stimulation with escalating negative effects on honey bee immunity and health. These results largely account for the remarkable importance of this mite virus interaction in the induction of honey bee colony losses. The discovery of this mutualistic association and the elucidation of the underlying regulatory mechanisms sets the stage for a more insightful analysis of how synergistic stress factors contribute to colony collapse, and for the development of new strategies to alleviate this problem.
An elasticity solution for transversely isotropic, functionally graded circular plates

This article presents a new elasticity solution for transversely isotropic, functionally graded circular plates subject to axisymmetric loads. It is assumed that the material properties vary along the thickness of a circular plate according to an exponential form. By extending the displacement function presented by Plevako to the case of transversely isotropic material, we derived the governing equation of the problem studied. The displacement function was assumed as the sum of the Bessel function and polynomial function to obtain the analytical solution of a transversely isotropic, functionally graded circular plate under different boundary conditions. As a numerical example, the influence of the graded variations of the material properties on the displacements and stresses was studied. The results demonstrate that the graded variations have a significant effect on the mechanical behavior of a circular plate.
Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers
Venue: Comput. Hum. Behav.

Traditionally, mobile learning has been more strongly developed within the field of informal education. However, in the past few years there has been a growing interest for the integration of these technologies in the formal education field. Among the key elements needed to successfully achieve this integration process is the acceptance of mobile technologies by the teaching body.In this paper we propose to identify the determining constructs that explain the teachers' intention of using mobile technologies. To this end we have designed a research model based on the Technology Acceptance Model (TAM) expanded with constructs from other theories. The resulting model was subjected to a content validation process performed by a committee of experts.Lastly, we selected nine constructs for the development of the research model and we formulated 13 hypotheses that describe the relationships among them. Further studies on the research model to extend its validity and reliability are suggested. We drafted an extended TAM proposal to assess the acceptance of mobile technologies by primary education teachers.We subjected the model to a content validation process performed by experts.The expert's suggestions were taken into account in order to elaborate a final model.The resulting model opens the door to further research on the topic.
Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression
Venue: Translational psychiatry

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early life trauma in predicting acute response outcomes to antidepressants in a large sample of well characterized patients with major depressive disorder (MDD) The international Study to Predict Optimized Treatment for Depression (iSPOT D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early Life Stress Questionnaire. Impact of early life stressors overall trauma 'load' and specific type of abuse on treatment outcomes measures: response: (50% improvement on the 17 item Hamilton Rating Scale for Depression, HRSD17 or on the 16 item Quick Inventory of Depressive Symptomatology Self Rated, QIDS_SR16) and remission (score 7 on the HRSD17 and 5 on the QIDS_SR16) Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses) Abuse and notably abuse occurring at 7 years of age predicted poorer outcomes after 8 weeks of antidepressants, across the three treatment arms. In addition, the abuses occurring between ages 4 and 7 years differentially predicted the poorest outcome following the treatment with sertraline. Specific types of early life trauma, particularly physical, emotional and sexual abuse, especially when occurring at 7 years of age are important moderators of subsequent response to antidepressant therapy for MDD.
ID Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan.
Venue: Public health

Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs) However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future.  This research focuses on development of ID Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management.  We have developed a robust service oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy logic to categorize syndromes. Standard routines by Center for Disease Control (CDC) i.e. c1 c3 (c1 mild, c2 medium and c3 ultra) and spatial scan statistics are employed for detection of temporal and spatio temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio temporal risk zones.  We analysed performance of surveillance framework using ID data for year 2011 2015. Dynamic syndromic classifier is able to classify chief complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo spatiotemporal displays, i.e. heat maps, and choropleth are shown in respective sections.  The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource constrained regions. It will improve early outbreak detection attributable to natural and man made biological threats, monitor spatio temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation.
PCB current identification based on near field measurements using preconditioning and regularization

Radiated electromagnetic fields from a PCB can be estimated when the source current distribution is known. From a measured near field distribution, the PCB source current distribution can be found. Accuracy depends on the measurement method and its limitations, the radiation model and the choice of the observation area. Many known methods are based on optimization algorithms for inverse problems that vary a set of elementary radiation sources and create a radiation model. However, apart from the time consuming optimization process, such methods find one possible solution for a near field distribution. As this distribution might not reflect the real current distribution, accuracy outside of near field scan area can be low. Furthermore numerical problems can often be observed. Solving the given inverse problem with a system of linear equations and complex near field data it can be very sensitive to noise. Regularization methods and an adjusted preconditioning can increase the accuracy. In this paper, an improved radiation model creation approach based on complex near field data is presented. This approach is based on regularization methods and extended by current estimations from near field data. Preconditioning is done considering some physical properties of the PCB and its possible current paths. Accuracy and stability of the method are investigated in the presence of noisy data.
Localization of the equivalent sources on the PCB surface by using ultra wideband time domain near field measurements
Venue: 2016 International Symposium on Electromagnetic Compatibility EMC EUROPE

In this paper a 2D parametric identification algorithm of stochastic electromagnetic interference (EMI) sources based on near field measurements is presented. The unintentional radiation of electronic devices and connecting cables can be measured by using two point near field planar scanning probes connected to the digital oscilloscope for ultra wideband (UWB) time domain data recording. The characterization of the registered stochastic time domain field distribution was implemented by evaluating the autocorrelation and cross correlation spectra. Due to the cyclostationary property of the stochastic information signals, the ensemble averaging procedure allows evaluating amplitudes and initial phases of clock harmonics, used for the synchronization of electronic device and information transmitting through the cables. The radiating object could be modelled as a set of equivalent electrical dipoles arranged on a grid in the object plane. To determine the equivalent dipole moments, the inverse procedure for the estimated clock harmonic was realized assuming the near field propagation model. The final localization of the equivalent dipoles for the defined clock harmonic was implemented by the application of 2D Matrix Pencil algorithm and Minimum Least Square fitting procedure. The experimental data was processed with proposed parametric identification procedure for a spatial localization of equivalent dipoles and effective sources on the surface of the radiating structure.
Elliptic operator for shape analysis
Venue: ArXiv

Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace Beltrami operator. In this paper, we propose to adapt a classical operator from quantum mechanics to the field of shape analysis where we suggest to integrate a scalar function through a unified elliptical Hamiltonian operator. We study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. Then, we evaluate the resulting spectral basis for different applications such as mesh compression and shape matching. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated by the proposed framework that outperforms existing spectral methods, for example, when applied to shape matching benchmarks.
Application of a global kinetic model on an SCR coated on Filter (SCR F) catalyst for automotive applications

Selective Catalytic Reduction (SCR) catalysts coated on diesel particulate Filter, SCR F, have been introduced for automotive applications in the last years due to the capability of reducing NOx and PM simultaneously below the limits imposed by emission regulations. In this context, the performance of a commercial silicon carbide Cu/zeolite SCR F for controlling NOx emissions in an automotive diesel engine was analyzed both experimentally and numerically for different soot loading levels with the aim to investigate its catalytic properties and to build a simulation model of the aftertreament device capable of predicting NOx conversion efficiency, NH3 storage capacity and soot conversion due to passive regeneration.
Multi Objective Deep Reinforcement Learning
Venue: ArXiv

We propose Deep Optimistic Linear Support Learning (DOL) to solve high dimensional multi objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. To our knowledge, this is the first time that deep reinforcement learning has succeeded in learning multi objective policies. In addition, we provide a testbed with two experiments to be used as a benchmark for deep multi objective reinforcement learning.
Nonlinear eigenvalue problems and contour integrals
Venue: J. Comput. Appl. Math.

In this paper Beyn's algorithm for solving nonlinear eigenvalue problems is given a new interpretation and a variant is designed in which the required information is extracted via the canonical polyadic decomposition of a Hankel tensor. A numerical example shows that the choice of the filter function is very important, particularly with respect to where it is positioned in the complex plane.
Information centric networking for connected vehicles: a survey and future perspectives
Venue: IEEE Communications Magazine

In the connected vehicle ecosystem, a high volume of information rich and safety critical data will be exchanged by roadside units and onboard transceivers to improve the driving and traveling experience. However, poor quality wireless links and the mobility of vehicles highly challenge data delivery. The IP address centric model of the current Internet barely works in such extremely dynamic environments and poorly matches the localized nature of the majority of vehicular communications, which typically target specific road areas (e.g. in the proximity of a hazard or a point of interest) regardless of the identity/address of a single vehicle passing by. Therefore, a paradigm shift is advocated from traditional IP based networking toward the groundbreaking information centric networking. In this article, we scrutinize the applicability of this paradigm in vehicular environments by reviewing its core functionalities and the related work. The analysis shows that, thanks to features like named content retrieval, innate multicast support, and in network data caching, information centric networking is positioned to meet the challenging demands of vehicular networks and their evolution. Interoperability with the standard architectures for vehicular applications along with synergies with emerging computing and networking paradigms are debated as future research perspectives.
Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic

Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang singular bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro simulations. The results show that SAS equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.
The importance of an integrating framework for achieving the Sustainable Development Goals: the example of health and well being
Venue: BMJ Global Health

The 2030 Agenda for Sustainable Development came into force in January 2016 as the central United Nations (UN) platform for achieving 'integrated and indivisible' goals and targets across the three characteristic dimensions of sustainable development: the social, environmental and economic. We argue that, despite the UN adoption of the Sustainable Development Goals (SDGs) a framework for operationalising them in an integrated fashion is lacking. This article puts forth a framework for integrating health and well being across the SDGs as both preconditions and outcomes of sustainable development. We present a rationale for this approach, and identify the challenges and opportunities for implementing and monitoring such a framework through a series of examples. We encourage other sectors to develop similar integrating frameworks for supporting a more coordinated approach for operationalising the 2030 Agenda for Sustainable Development.
Sustainable Consumption of Groceries: the Importance of Believing that One Can Contribute to Sustainable Development

This study investigated relations between consumers' sustainable development self efficacy, attitudes, norms and intentions to purchase sustainable groceries such as ecological and fair trade foods. Demographic variables were also investigated. Attitudes and norms were positively associated with intentions to purchase sustainable products. The importance of different types of attitudes and norms for explaining sustainable consumption depended on the facet of purchasing intentions that was investigated. Self efficacy explained variance in purchasing intentions over and above attitudes, norms and demographic characteristics. Of the self efficacy components, people's perceptions of their indirect impact gained by encouraging others to contribute to sustainable development showed the strongest association with purchasing intentions. This could mean that believing that one can have an impact on other consumers is a strong motivator for buying sustainable products. Implications of these findings for practitioners and environmental policy are discussed.
Quorum sensing signal response systems in Gram negative bacteria
Venue: Nature Reviews Microbiology

Bacteria use quorum sensing to orchestrate gene expression programmes that underlie collective behaviours. Quorum sensing relies on the production, release, detection and group level response to extracellular signalling molecules, which are called autoinducers. Recent work has discovered new autoinducers in Gram negative bacteria, shown how these molecules are recognized by cognate receptors, revealed new regulatory components that are embedded in canonical signalling circuits and identified novel regulatory network designs. In this Review we examine how, together, these features of quorum sensing signal response systems combine to control collective behaviours in Gram negative bacteria and we discuss the implications for host microbial associations and antibacterial therapy.