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Risikokommunikation spielt eine zentrale Rolle in Public-Health-Notlagen: Sie muss informierte Entscheidungen ermöglichen, schützendes bzw. lebenserhaltendes Verhalten fördern und das Vertrauen in öffentliche Institutionen bewahren. Zudem müssen Unsicherheiten über wissenschaftliche Erkenntnisse transparent benannt werden, irrationale Ängste und Gerüchte entkräftet werden. Risikokommunikation sollte die Bevölkerung partizipativ einbeziehen. Ihre Risikowahrnehmung und -kompetenz müssen kontinuierlich erfasst werden. In der aktuellen Pandemie der Coronavirus-Krankheit 2019 (COVID-19) ergeben sich spezifische Herausforderungen für die Risikokommunikation.
Der Wissensstand zu vielen wichtigen Aspekten, die COVID-19 betreffen, war und ist oftmals unsicher oder vorläufig, z. B. zu Übertragung, Symptomen, Langzeitfolgen und Immunität. Die Kommunikation ist durch wissenschaftliche Sprache sowie eine Vielzahl von Kennzahlen und Statistiken geprägt, was die Verständlichkeit erschweren kann. Neben offiziellen Mitteilungen und Einschätzungen von Expertinnen und Experten wird über COVID-19 in großem Umfang in sozialen Medien kommuniziert, dabei werden auch Fehlinformationen und Spekulationen verbreitet; diese „Infodemie“ erschwert die Risikokommunikation.
Nationale wie internationale Forschungsprojekte sollen helfen, die Risikokommunikation zu COVID-19 zielgruppenspezifischer und effektiver zu machen. Dazu gehören u. a. explorative Studien zum Umgang mit COVID-19-bezogenen Informationen, das COVID-19 Snapshot Monitoring (COSMO), ein regelmäßig durchgeführtes Onlinesurvey zu Risikowahrnehmung und Schutzverhalten sowie eine interdisziplinäre qualitative Studie, die die Konzeption, Umsetzung und Wirksamkeit von Risikokommunikationsstrategien vergleichend in 4 Ländern untersucht.
Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of similar to 35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Deliberative and paternalistic interaction styles for conversational agents in digital health
(2021)
Background:
Recent years have witnessed a constant increase in the number of people with chronic conditions requiring ongoing medical support in their everyday lives. However, global health systems are not adequately equipped for this extraordinarily time-consuming and cost-intensive development. Here, conversational agents (CAs) can offer easily scalable and ubiquitous support. Moreover, different aspects of CAs have not yet been sufficiently investigated to fully exploit their potential. One such trait is the interaction style between patients and CAs. In human-to-human settings, the interaction style is an imperative part of the interaction between patients and physicians. Patient-physician interaction is recognized as a critical success factor for patient satisfaction, treatment adherence, and subsequent treatment outcomes. However, so far, it remains effectively unknown how different interaction styles can be implemented into CA interactions and whether these styles are recognizable by users.
Objective:
The objective of this study was to develop an approach to reproducibly induce 2 specific interaction styles into CA-patient dialogs and subsequently test and validate them in a chronic health care context.
Methods:
On the basis of the Roter Interaction Analysis System and iterative evaluations by scientific experts and medical health care professionals, we identified 10 communication components that characterize the 2 developed interaction styles: deliberative and paternalistic interaction styles. These communication components were used to develop 2 CA variations, each representing one of the 2 interaction styles. We assessed them in a web-based between-subject experiment. The participants were asked to put themselves in the position of a patient with chronic obstructive pulmonary disease. These participants were randomly assigned to interact with one of the 2 CAs and subsequently asked to identify the respective interaction style. Chi-square test was used to assess the correct identification of the CA-patient interaction style.
Results:
A total of 88 individuals (42/88, 48% female; mean age 31.5 years, SD 10.1 years) fulfilled the inclusion criteria and participated in the web-based experiment. The participants in both the paternalistic and deliberative conditions correctly identified the underlying interaction styles of the CAs in more than 80% of the assessments (X-1(,8)8(2)=38.2; P<.001; phi coefficient r(phi)=0.68). The validation of the procedure was hence successful.
Conclusions:
We developed an approach that is tailored for a medical context to induce a paternalistic and deliberative interaction style into a written interaction between a patient and a CA. We successfully tested and validated the procedure in a web-based experiment involving 88 participants. Future research should implement and test this approach among actual patients with chronic diseases and compare the results in different medical conditions. This approach can further be used as a starting point to develop dynamic CAs that adapt their interaction styles to their users.
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.
The correct orientation of seismic sensors is critical for studies such as full moment tensor inversion, receiver function analysis, and shear-wave splitting. Therefore, the orientation of horizontal components needs to be checked and verified systematically. This study relies on two different waveform-based approaches, to assess the sensor orientations of the broadband network of the Kandilli Observatory and Earthquake Research Institute (KOERI). The network is an important backbone for seismological research in the Eastern Mediterranean Region and provides a comprehensive seismic data set for the North Anatolian fault. In recent years, this region became a worldwide field laboratory for continental transform faults. A systematic survey of the sensor orientations of the entire network, as presented here, facilitates related seismic studies. We apply two independent orientation tests, based on the polarization of P waves and Rayleigh waves to 123 broadband seismic stations, covering a period of 15 yr (2004-2018). For 114 stations, we obtain stable results with both methods. Approximately, 80% of the results agree with each other within 10 degrees. Both methods indicate that about 40% of the stations are misoriented by more than 10 degrees. Among these, 20 stations are misoriented by more than 20 degrees. We observe temporal changes of sensor orientation that coincide with maintenance work or instrument replacement. We provide time-dependent sensor misorientation correction values for the KOERI network in the supplemental material.
The first detections of black hole-neutron star mergers (GW200105 and GW200115) by the LIGO-Virgo-Kagra Collaboration mark a significant scientific breakthrough. The physical interpretation of pre- and postmerger signals requires careful cross-examination between observational and theoretical modelling results. Here we present the first set of black hole-neutron star simulations that were obtained with the numerical-relativity code BAM. Our initial data are constructed using the public LORENE spectral library, which employs an excision of the black hole interior. BAM, in contrast, uses the moving-puncture gauge for the evolution. Therefore, we need to "stuff" the black hole interior with smooth initial data to evolve the binary system in time. This procedure introduces constraint violations such that the constraint damping properties of the evolution system are essential to increase the accuracy of the simulation and in particular to reduce spurious center-of-mass drifts. Within BAM we evolve the Z4c equations and we compare our gravitational-wave results with those of the SXS collaboration and results obtained with the SACRA code. While we find generally good agreement with the reference solutions and phase differences less than or similar to 0.5 rad at the moment of merger, the absence of a clean convergence order in our simulations does not allow for a proper error quantification. We finally present a set of different initial conditions to explore how the merger of black hole neutron star systems depends on the involved masses, spins, and equations of state.
Water bodies are a highly abundant feature of Arctic permafrost ecosystems and strongly influence their hydrology, ecology and biogeochemical cycling. While very high resolution satellite images enable detailed mapping of these water bodies, the increasing availability and abundance of this imagery calls for fast, reliable and automatized monitoring. This technical work presents a largely automated and scalable workflow that removes image noise, detects water bodies, removes potential misclassifications from infrastructural features, derives lake shoreline geometries and retrieves their movement rate and direction on the basis of ortho-ready very high resolution satellite imagery from Arctic permafrost lowlands. We applied this workflow to typical Arctic lake areas on the Alaska North Slope and achieved a successful and fast detection of water bodies. We derived representative values for shoreline movement rates ranging from 0.40-0.56 m yr(-1) for lake sizes of 0.10 ha-23.04 ha. The approach also gives an insight into seasonal water level changes. Based on an extensive quantification of error sources, we discuss how the results of the automated workflow can be further enhanced by incorporating additional information on weather conditions and image metadata and by improving the input database. The workflow is suitable for the seasonal to annual monitoring of lake changes on a sub-meter scale in the study areas in northern Alaska and can readily be scaled for application across larger regions within certain accuracy limitations.
This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.
The leniency rule revisited
(2021)
The experimental literature on antitrust enforcement provides robust evidence that communication plays an important role for the formation and stability of cartels. We extend these studies through a design that distinguishes between innocuous communication and communication about a cartel, sanctioning only the latter. To this aim, we introduce a participant in the role of the competition authority, who is properly incentivized to judge the communication content and price setting behavior of the firms. Using this novel design, we revisit the question whether a leniency rule successfully destabilizes cartels. In contrast to existing experimental studies, we find that a leniency rule does not affect cartelization. We discuss potential explanations for this contrasting result.