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This paper examines situations where two vertically integrated firms consider supplying an input to an independent downstream competitor via privately observed contracts. We identify equilibria where competition in the upstream market emerges—the downstream competitor gets supplied—as well as when the downstream firm does not receive the input and is excluded from the market. The likelihood of the outcome in which the downstream firm does not get supplied depends not only on demand parameters, but also on contractual flexibility and observability. We show that when contracts are unobservable, downstream entry will occur less often. Furthermore, our results suggest that permitting contracts that enable the contracting parties to coordinate their behavior in the downstream market may improve welfare by increasing the likelihood that the downstream firm is supplied.
Reports of major losses in insect biodiversity have stimulated an increasing interest in temporal population changes. Existing datasets are often limited to a small number of study sites, few points in time, a narrow range of land‐use intensities and only some taxonomic groups, or they lack standardised sampling. While new monitoring programs have been initiated, they still cover rather short time periods.
Daskalova et al. 2021 (Insect Conservation and Diversity, 14, 1‐18) argue that temporal trends of insect populations derived from short time series are biased towards extreme trends, while their own analysis of an assembly of shorter‐ and longer‐term time series does not support an overall insect decline. With respect to the results of Seibold et al. 2019 (Nature, 574, 671–674) based on a 10‐year multi‐site time series, they claim that the analysis suffers from not accounting for temporal pseudoreplication.
Here, we explain why the criticism of missing statistical rigour in the analysis of Seibold et al. (2019) is not warranted. Models that include ‘year’ as random effect, as suggested by Daskalova et al. (2021), fail to detect non‐linear trends and assume that consecutive years are independent samples which is questionable for insect time‐series data.
We agree with Daskalova et al. (2021) that the assembly and analysis of larger datasets is urgently needed, but it will take time until such datasets are available. Thus, short‐term datasets are highly valuable, should be extended and analysed continually to provide a more detailed understanding of insect population changes under the influence of global change, and to trigger immediate conservation actions.
Risk measures are commonly used to prepare for a prospective occurrence of an adverse event. If we are concerned with discrete risk phenomena such as counts of natural disasters, counts of infections by a serious disease, or counts of certain economic events, then the required risk forecasts are to be computed for an underlying count process. In practice, however, the discrete nature of count data is sometimes ignored and risk forecasts are calculated based on Gaussian time series models. But even if methods from count time series analysis are used in an adequate manner, the performance of risk forecasting is affected by estimation uncertainty as well as certain discreteness phenomena. To get a thorough overview of the aforementioned issues in risk forecasting of count processes, a comprehensive simulation study was done considering a broad variety of risk measures and count time series models. It becomes clear that Gaussian approximate risk forecasts substantially distort risk assessment and, thus, should be avoided. In order to account for the apparent estimation uncertainty in risk forecasting, we use bootstrap approaches for count time series. The relevance and the application of the proposed approaches are illustrated by real data examples about counts of storm surges and counts of financial transactions.
This study provides a systematic literature review of research (2001–2020) in the field of teaching and learning a foreign language and intercultural learning using immersive technologies. Based on 2507 sources, 54 articles were selected according to a predefined selection criteria. The review is aimed at providing information about which immersive interventions are being used for foreign language learning and teaching and where potential research gaps exist. The papers were analyzed and coded according to the following categories: (1) investigation form and education level, (2) degree of immersion, and technology used, (3) predictors, and (4) criterions. The review identified key research findings relating the use of immersive technologies for learning and teaching a foreign language and intercultural learning at cognitive, affective, and conative levels. The findings revealed research gaps in the area of teachers as a target group, and virtual reality (VR) as a fully immersive intervention form. Furthermore, the studies reviewed rarely examined behavior, and implicit measurements related to inter- and trans-cultural learning and teaching. Inter- and transcultural learning and teaching especially is an underrepresented investigation subject. Finally, concrete suggestions for future research are given. The systematic review contributes to the challenge of interdisciplinary cooperation between pedagogy, foreign language didactics, and Human-Computer Interaction to achieve innovative teaching-learning formats and a successful digital transformation.
Megakaryocytes (MKs) release platelets into the lumen of bone marrow (BM) sinusoids while remaining to reside within the BM. The morphogenetic events of this complex process are still not fully understood. We combined confocal laser scanning microscopy with transmission and serial block-face scanning electron microscopy followed by 3D-reconstruction on mouse BM tissue sections. These analyses revealed that MKs in close vicinity to BM sinusoid (BMS) wall first induce the lateral retraction of CXCL12-abundant reticular (CAR) cells (CAR), followed by basal lamina (BL) degradation enabling direct MK-sinusoidal endothelial cells (SECs) interaction. Subsequently, an endothelial engulfment starts that contains a large MK protrusion. Then, MK protrusions penetrate the SEC, transmigrate into the BMS lumen and form proplatelets that are in direct contact to the SEC surface. Furthermore, such processes are induced on several sites, as observed by 3D reconstructions. Our data demonstrate that MKs in interaction with CAR-cells actively induce BMS wall alterations, including CAR-cell retraction, BL degradation, and SEC engulfment containing a large MK protrusion. This results in SEC penetration enabling the migration of MK protrusion into the BMS lumen where proplatelets that are adherent to the luminal SEC surface are formed and contribute to platelet release into the blood circulation.
Health-related quality of life (HRQL) among migrant populations can be associated with acculturation (i.e., the process of adopting, acquiring and adjusting to a new cultural environment). Since there is a lack of longitudinal studies, we aimed to describe HRQL changes among adults of Turkish descent living in Berlin and Essen, Germany, and their association with acculturation. Participants of a population-based study were recruited in 2012–2013 and reinvited six years later to complete a questionnaire. Acculturation was assessed at baseline using the Frankfurt acculturation scale (integration, assimilation, separation and marginalization). HRQL was assessed at baseline (SF-8) and at follow-up (SF-12) resulting in a physical (PCS) and mental (MCS) sum score. Associations with acculturation and HRQL were analyzed with linear regression models using a time-by-acculturation status interaction term. In the study 330 persons were included (65% women, mean age ± standard deviation 43.3 ± 11.8 years). Over the 6 years, MCS decreased, while PCS remained stable. While cross-sectional analyses showed associations of acculturation status with both MCS and PCS, temporal changes including the time interaction term did not reveal associations of baseline acculturation status with HRQL. When investigating HRQL in acculturation, more longitudinal studies are needed to take changes in both HRQL and acculturation status into account.
Dynamic point cloud compression based on projections, surface reconstruction and video compression
(2021)
In this paper we will present a new dynamic point cloud compression based on different projection types and bit depth, combined with the surface reconstruction algorithm and video compression for obtained geometry and texture maps. Texture maps have been compressed after creating Voronoi diagrams. Used video compression is specific for geometry (FFV1) and texture (H.265/HEVC). Decompressed point clouds are reconstructed using a Poisson surface reconstruction algorithm. Comparison with the original point clouds was performed using point-to-point and point-to-plane measures. Comprehensive experiments show better performance for some projection maps: cylindrical, Miller and Mercator projections.
Effects of Acrophobic Fear and Trait Anxiety on Human Behavior in a Virtual Elevated Plus-Maze
(2021)
The Elevated Plus-Maze (EPM) is a well-established apparatus to measure anxiety in rodents, i.e., animals exhibiting an increased relative time spent in the closed vs. the open arms are considered anxious. To examine whether such anxiety-modulated behaviors are conserved in humans, we re-translated this paradigm to a human setting using virtual reality in a Cave Automatic Virtual Environment (CAVE) system. In two studies, we examined whether the EPM exploration behavior of humans is modulated by their trait anxiety and also assessed the individuals’ levels of acrophobia (fear of height), claustrophobia (fear of confined spaces), sensation seeking, and the reported anxiety when on the maze. First, we constructed an exact virtual copy of the animal EPM adjusted to human proportions. In analogy to animal EPM studies, participants (N = 30) freely explored the EPM for 5 min. In the second study (N = 61), we redesigned the EPM to make it more human-adapted and to differentiate influences of trait anxiety and acrophobia by introducing various floor textures and lower walls of closed arms to the height of standard handrails. In the first experiment, hierarchical regression analyses of exploration behavior revealed the expected association between open arm avoidance and Trait Anxiety, an even stronger association with acrophobic fear. In the second study, results revealed that acrophobia was associated with avoidance of open arms with mesh-floor texture, whereas for trait anxiety, claustrophobia, and sensation seeking, no effect was detected. Also, subjects’ fear rating was moderated by all psychometrics but trait anxiety. In sum, both studies consistently indicate that humans show no general open arm avoidance analogous to rodents and that human EPM behavior is modulated strongest by acrophobic fear, whereas trait anxiety plays a subordinate role. Thus, we conclude that the criteria for cross-species validity are met insufficiently in this case. Despite the exploratory nature, our studies provide in-depth insights into human exploration behavior on the virtual EPM.
The design and evaluation of assisting technologies to support behavior change processes have become an essential topic within the field of human-computer interaction research in general and the field of immersive intervention technologies in particular. The mechanisms and success of behavior change techniques and interventions are broadly investigated in the field of psychology. However, it is not always easy to adapt these psychological findings to the context of immersive technologies. The lack of theoretical foundation also leads to a lack of explanation as to why and how immersive interventions support behavior change processes. The Behavioral Framework for immersive Technologies (BehaveFIT) addresses this lack by 1) presenting an intelligible categorization and condensation of psychological barriers and immersive features, by 2) suggesting a mapping that shows why and how immersive technologies can help to overcome barriers and finally by 3) proposing a generic prediction path that enables a structured, theory-based approach to the development and evaluation of immersive interventions. These three steps explain how BehaveFIT can be used, and include guiding questions for each step. Further, two use cases illustrate the usage of BehaveFIT. Thus, the present paper contributes to guidance for immersive intervention design and evaluation, showing that immersive interventions support behavior change processes and explain and predict 'why' and 'how' immersive interventions can bridge the intention-behavior-gap.
Companies increasingly seek to use gay protagonists in audio-visual commercials to attract a new affluent target group. There is also growing demand for the diversity present in society to be reflected in media formats such as advertising. Studies have shown, however, that heterosexual consumers (especially men), who may be part of the company's loyal consumer base, tend to react negatively to gay-themed advertising campaigns. Searching for an instrument to mitigate this unwanted effect, the present study investigated whether carefully selected background music can shape the perceived gender of gay male advertising protagonists. In a 2 × 2 between-subjects online experiment (musical connotation × gender of the participant), 218 heterosexual participants watched a commercial promoting engagement rings that featured gay male protagonists, scored with feminine- or masculine-connoted background music. As expected, women generally reacted more positively than men to the advertising. Men exposed to the masculine-connoted background music rated the promoted brand more positively, and masculine music also enhanced (at least in the short term) these men's acceptance of gay men in general (low and medium effect sizes) more than was the case for feminine background music. Carefully selected background music affecting the perceived gender of gay male advertising protagonists may prevent negative reactions from heterosexual audiences and, therefore, motivate companies to use gay protagonists in television commercials on a more regular basis.
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
(2021)
Background
Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance.
Results
We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model.
Conclusions
Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.
At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam-scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include the following: (1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; (2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; (3) variability in amphid sensory dendrites among dauers; and (4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments.
Revealing the molecular organization of anatomically precisely defined brain regions is necessary for refined understanding of synaptic plasticity. Although three-dimensional (3D) single-molecule localization microscopy can provide the required resolution, imaging more than a few micrometers deep into tissue remains challenging. To quantify presynaptic active zones (AZ) of entire, large, conditional detonator hippocampal mossy fiber (MF) boutons with diameters as large as 10 mu m, we developed a method for targeted volumetric direct stochastic optical reconstruction microscopy (dSTORM). An optimized protocol for fast repeated axial scanning and efficient sequential labeling of the AZ scaffold Bassoon and membrane bound GFP with Alexa Fluor 647 enabled 3D-dSTORM imaging of 25 mu m thick mouse brain sections and assignment of AZs to specific neuronal substructures. Quantitative data analysis revealed large differences in Bassoon cluster size and density for distinct hippocampal regions with largest clusters in MF boutons. Pauli et al. develop targeted volumetric dSTORM in order to image large hippocampal mossy fiber boutons (MFBs) in brain slices. They can identify synaptic targets of individual MFBs and measured size and density of Bassoon clusters within individual untruncated MFBs at nanoscopic resolution.
Individual-based models are doubly complex: as well as representing complex ecological systems, the software that implements them is complex in itself. Both forms of complexity must be managed to create reliable models. However, the ecological modelling literature to date has focussed almost exclusively on the biological complexity. Here, we discuss methods for containing software complexity.
Strategies for containing complexity include avoiding, subdividing, documenting and reviewing it. Computer science has long-established techniques for all of these strategies. We present some of these techniques and set them in the context of IBM development, giving examples from published models.
Techniques for avoiding software complexity are following best practices for coding style, choosing suitable programming languages and file formats and setting up an automated workflow. Complex software systems can be made more tractable by encapsulating individual subsystems. Good documentation needs to take into account the perspectives of scientists, users and developers. Code reviews are an effective way to check for errors, and can be used together with manual or automated unit and integration tests.
Ecological modellers can learn from computer scientists how to deal with complex software systems. Many techniques are readily available, but must be disseminated among modellers. There is a need for further work to adapt software development techniques to the requirements of academic research groups and individual-based modelling.
Oxidative precipitation is a facile synthesis method to obtain ferromagnetic iron oxide nanoparticles from ferrous salts—with unexplored potential. The concentration of base and oxidant alone strongly affects the particle's structure and thus their magnetic properties despite the same material, magnetite (Fe\(_{3}\)O\(_{4}\)), is obtained when precipitated with potassium hydroxide (KOH) from ferrous sulfate (FeSO\(_{4}\)) and treated with potassium nitrate (KNO\(_{3}\)) at appropriate temperature. Depending on the potassium hydroxide and potassium nitrate concentrations, it is possible to obtain a series of different types of either single crystals or mesocrystals. The time‐dependent mesocrystal evolution can be revealed via electron microscopy and provides insights into the process of oriented attachment, yielding faceted particles, showing a facet‐dependent reactivity. It is found that it is the nitrate and hydroxide concentration that influences the ligand exchange process and thus the crystallization pathways. The presence of sulfate ions contributes to the mesocrystal evolution as well, as sulfate apparently hinders further crystal fusion, as revealed via infrared spectroscopy. Finally, it is found that nitrite, as one possible and ecologically highly relevant reduction product occurring in nature in context with iron, only evolves if the reaction is quantitative.
In the course of a screen designed to produce antibodies (ABs) with affinity to proteins in the honey bee brain we found an interesting AB that detects a highly specific epitope predominantly in the nuclei of Kenyon cells (KCs). The observed staining pattern is unique, and its unfamiliarity indicates a novel previously unseen nuclear structure that does not colocalize with the cytoskeletal protein f-actin. A single rod-like assembly, 3.7-4.1 mu m long, is present in each nucleus of KCs in adult brains of worker bees and drones with the strongest immuno-labelling found in foraging bees. In brains of young queens, the labelling is more sporadic, and the rod-like structure appears to be shorter (similar to 2.1 mu m). No immunostaining is detectable in worker larvae. In pupal stage 5 during a peak of brain development only some occasional staining was identified. Although the cellular function of this unexpected structure has not been determined, the unusual distinctiveness of the revealed pattern suggests an unknown and potentially important protein assembly. One possibility is that this nuclear assembly is part of the KCs plasticity underlying the brain maturation in adult honey bees. Because no labelling with this AB is detectable in brains of the fly Drosophila melanogaster and the ant Camponotus floridanus, we tentatively named this antibody AmBNSab (Apis mellifera Brain Neurons Specific antibody). Here we report our results to make them accessible to a broader community and invite further research to unravel the biological role of this curious nuclear structure in the honey bee central brain.
We consider the Bathnagar–Gross–Krook (BGK) model, an approximation of the Boltzmann equation, describing the time evolution of a single momoatomic rarefied gas and satisfying the same two main properties (conservation properties and entropy inequality). However, in practical applications, one often has to deal with two additional physical issues. First, a gas often does not consist of only one species, but it consists of a mixture of different species. Second, the particles can store energy not only in translational degrees of freedom but also in internal degrees of freedom such as rotations or vibrations (polyatomic molecules). Therefore, here, we will present recent BGK models for gas mixtures for mono- and polyatomic particles and the existing mathematical theory for these models.
We describe a system for the analysis of an important unicellular eukaryotic flagellate in a confining and crowded environment. The parasite Trypanosoma brucei is arguably one of the most versatile microswimmers known. It has unique properties as a single microswimmer and shows remarkable adaptations (not only in motility, but prominently so), to its environment during a complex developmental cycle involving two different hosts. Specific life cycle stages show fascinating collective behaviour, as millions of cells can be forced to move together in extreme confinement. Our goal is to examine such motile behaviour directly in the context of the relevant environments. Therefore, for the first time, we analyse the motility behaviour of trypanosomes directly in a widely used assay, which aims to evaluate the parasites behaviour in collectives, in response to as yet unknown parameters. In a step towards understanding whether, or what type of, swarming behaviour of trypanosomes exists, we customised the assay for quantitative tracking analysis of motile behaviour on the single-cell level. We show that the migration speed of cell groups does not directly depend on single-cell velocity and that the system remains to be simplified further, before hypotheses about collective motility can be advanced.
The macromolecular SMN complex facilitates the formation of Sm-class ribonucleoproteins involved in mRNA processing (UsnRNPs). While biochemical studies have revealed key activities of the SMN complex, its structural investigation is lagging behind. Here we report on the identification and structural determination of the SMN complex from the lower eukaryote Schizosaccharomyces pombe, consisting of SMN, Gemin2, 6, 7, 8 and Sm proteins. The core of the SMN complex is formed by several copies of SMN tethered through its C-terminal alpha-helices arranged with alternating polarity. This creates a central platform onto which Gemin8 binds and recruits Gemins 6 and 7. The N-terminal parts of the SMN molecules extrude via flexible linkers from the core and enable binding of Gemin2 and Sm proteins. Our data identify the SMN complex as a multivalent hub where Sm proteins are collected in its periphery to allow their joining with UsnRNA.