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Against a background of increasing violence against non-natives, we estimate the effect of hate crime on refugees’ mental health in Germany. For this purpose, we combine two datasets: administrative records on xenophobic crime against refugee shelters by the Federal Criminal Office and the IAB-BAMF-SOEP Survey of Refugees. We apply a regression discontinuity in time design to estimate the effect of interest. Our results indicate that hate crime has a substantial negative effect on several mental health indicators, including the Mental Component Summary score and the Patient Health Questionnaire-4 score. The effects are stronger for refugees with closer geographic proximity to the focal hate crime and refugees with low country-specific human capital. While the estimated effect is only transitory, we argue that negative mental health shocks during the critical period after arrival have important long-term consequences. Keywords: Mental health, hate crime, migration, refugees, human capital.
This article is a discussion of Plin. Ep. 7.29 and Ep. 8.6, in which he presents his reaction to seeing the grave monument of Marcus Antonius Pallas, the freedman and minister of the Emperor Claudius, beside the Via Tiburtina. The monument records a senatorial vote of thanks to Pallas, and Pliny expresses intense indignation at the Senate’s subservience and at the power and influence wielded by a freedman. This article compares Pliny’s letters with Tacitus’ account of the senatorial vote of thanks to Pallas at Ann. 12.52–3 and explores the differences between the ways in which the two authors encourage readers to relate to past events. It is noted that the Pallas letters are unusual amongst Pliny’s let- ters for their treatment of material unconnected with the life and career of Pliny and his friends, and argued that in Ep. 7.29 Pliny uses language and attitudes drawn from satire to evoke the past. Ep. 8.6 is read as an idiosyncratic piece of historical enquiry, consider- ing Pliny’s use of citation and his anonymization of historical individuals. Both letters are considered in the context of the surrounding letters, and a hypothesis is offered regarding the identity of their addressee Montanus, considering evidence from Tacitus’ Histories and Annals. Discussion of Tac. Ann. 12.52–3 focusses on the use of irony. Pliny’s evocation of enargeia (‘vividness’) is compared with that of Tacitus. The article concludes with comparison of the historical accounts offered by Pliny and Tacitus through reflection on Juvenal, Satire 1.
The Central Andes region in South America is characterized by a complex and heterogeneous deformation system. Recorded seismic activity and mapped neotectonic structures indicate that most of the intraplate deformation is located along the margins of the orogen, in the transitions to the foreland and the forearc. Furthermore, the actively deforming provinces of the foreland exhibit distinct deformation styles that vary along strike, as well as characteristic distributions of seismicity with depth. The style of deformation transitions from thin-skinned in the north to thick-skinned in the south, and the thickness of the seismogenic layer increases to the south. Based on geological/geophysical observations and numerical modelling, the most commonly invoked causes for the observed heterogeneity are the variations in sediment thickness and composition, the presence of inherited structures, and changes in the dip of the subducting Nazca plate. However, there are still no comprehensive investigations on the relationship between the lithospheric composition of the Central Andes, its rheological state and the observed deformation processes. The central aim of this dissertation is therefore to explore the link between the nature of the lithosphere in the region and the location of active deformation. The study of the lithospheric composition by means of independent-data integration establishes a strong base to assess the thermal and rheological state of the Central Andes and its adjacent lowlands, which alternatively provide new foundations to understand the complex deformation of the region. In this line, the general workflow of the dissertation consists in the construction of a 3D data-derived and gravity-constrained density model of the Central Andean lithosphere, followed by the simulation of the steady-state conductive thermal field and the calculation of strength distribution. Additionally, the dynamic response of the orogen-foreland system to intraplate compression is evaluated by means of 3D geodynamic modelling.
The results of the modelling approach suggest that the inherited heterogeneous composition of the lithosphere controls the present-day thermal and rheological state of the Central Andes, which in turn influence the location and depth of active deformation processes. Most of the seismic activity and neo--tectonic structures are spatially correlated to regions of modelled high strength gradients, in the transition from the felsic, hot and weak orogenic lithosphere to the more mafic, cooler and stronger lithosphere beneath the forearc and the foreland. Moreover, the results of the dynamic simulation show a strong localization of deviatoric strain rate second invariants in the same region suggesting that shortening is accommodated at the transition zones between weak and strong domains. The vertical distribution of seismic activity appears to be influenced by the rheological state of the lithosphere as well. The depth at which the frequency distribution of hypocenters starts to decrease in the different morphotectonic units correlates with the position of the modelled brittle-ductile transitions; accordingly, a fraction of the seismic activity is located within the ductile part of the crust. An exhaustive analysis shows that practically all the seismicity in the region is restricted above the 600°C isotherm, in coincidence with the upper temperature limit for brittle behavior of olivine. Therefore, the occurrence of earthquakes below the modelled brittle-ductile could be explained by the presence of strong residual mafic rocks from past tectonic events. Another potential cause of deep earthquakes is the existence of inherited shear zones in which brittle behavior is favored through a decrease in the friction coefficient. This hypothesis is particularly suitable for the broken foreland provinces of the Santa Barbara System and the Pampean Ranges, where geological studies indicate successive reactivation of structures through time. Particularly in the Santa Barbara System, the results indicate that both mafic rocks and a reduction in friction are required to account for the observed deep seismic events.
While the last few decades have seen impressive improvements in several areas in Natural Language Processing, asking a computer to make sense of the discourse of utterances in a text remains challenging. There are several different theories that aim to describe and analyse the coherent structure that a well-written text inhibits. These theories have varying degrees of applicability and feasibility for practical use. Presumably the most data-driven of these theories is the paradigm that comes with the Penn Discourse TreeBank, a corpus annotated for discourse relations containing over 1 million words. Any language other than English however, can be considered a low-resource language when it comes to discourse processing.
This dissertation is about shallow discourse parsing (discourse parsing following the paradigm of the Penn Discourse TreeBank) for German. The limited availability of annotated data for German means the potential of modern, deep-learning based methods relying on such data is also limited. This dissertation explores to what extent machine-learning and more recent deep-learning based methods can be combined with traditional, linguistic feature engineering to improve performance for the discourse parsing task. A pivotal role is played by connective lexicons that exhaustively list the discourse connectives of a particular language along with some of their core properties.
To facilitate training and evaluation of the methods proposed in this dissertation, an existing corpus (the Potsdam Commentary Corpus) has been extended and additional data has been annotated from scratch. The approach to end-to-end shallow discourse parsing for German adopts a pipeline architecture and either presents the first results or improves over state-of-the-art for German for the individual sub-tasks of the discourse parsing task, which are, in processing order, connective identification, argument extraction and sense classification. The end-to-end shallow discourse parser for German that has been developed for the purpose of this dissertation is open-source and available online.
In the course of writing this dissertation, work has been carried out on several connective lexicons in different languages. Due to their central role and demonstrated usefulness for the methods proposed in this dissertation, strategies are discussed for creating or further developing such lexicons for a particular language, as well as suggestions on how to further increase their usefulness for shallow discourse parsing.
Ionizing radiation is used in cancer radiation therapy to effectively damage the DNA of tumors leading to cell death and reduction of the tumor tissue. The main damage is due to generation of highly reactive secondary species such as low-energy electrons (LEE) with the most probable energy around 10 eV through ionization of water molecules in the cells. A simulation of the dose distribution in the patient is required to optimize the irradiation modality in cancer radiation therapy, which must be based on the fundamental physical processes of high-energy radiation with the tissue. In the present work the accurate quantification of DNA radiation damage in the form of absolute cross sections for LEE-induced DNA strand breaks (SBs) between 5 and 20 eV is done by using the DNA origami technique. This method is based on the analysis of well-defined DNA target sequences attached to DNA origami triangles with atomic force microscopy (AFM) on the single molecule level. The present work focuses on poly-adenine sequences (5'-d(A4), 5'-d(A8), 5'-d(A12), 5'-d(A16), and 5'- d(A20)) irradiated with 5.0, 7.0, 8.4, and 10 eV electrons. Independent of the DNA length, the strand break cross section shows a maximum around 7.0 eV electron energy for all investigated oligonucleotides confirming that strand breakage occurs through the initial formation of negative ion resonances. Additionally, DNA double strand breaks from a DNA hairpin 5'-d(CAC)4T(Bt-dT)T2(GTG)4 are examined for the first time and are compared with those of DNA single strands 5'-d(CAC)4 and 5'- d(GTG)4. The irradiation is made in the most likely energy range of 5 to 20 eV with an anionic resonance maximum around 10 eV independently of the DNA sequence. There is a clear difference between σSSB and σDSB of DNA single and double strands, where the strand break for ssDNA are always higher in all electron energies compared to dsDNA by the factor 3. A further part of this work deals with the characterization and analysis of new types of radiosensitizers used in chemoradiotherapy, which selectively increases the DNA damage upon radiation. Fluorinated DNA sequences with 2'-fluoro-2'-deoxycytidine (dFC) show an increased sensitivity at 7 and 10 eV compared to the unmodified DNA sequences by an enhancement factor between 2.1 and 2.5. In addition, light-induced oxidative damage of 5'-d(GTG)4 and 5'-d((CAC)4T(Bt-dT)T2(GTG)4) modified DNA origami triangles by singlet oxygen 1O2 generated from three photoexcited DNA groove binders [ANT994], [ANT1083] and [Cr(ddpd)2][BF4]3 illuminated in different experiments with UV-Vis light at 430, 435 and 530 nm wavelength is demonstrated. The singlet oxygen induced generation of DNA damage could be detected in both aqueous and dry environments for [ANT1083] and [Cr(ddpd)2][BF4]3.
Emotions are a complex concept and they are present in our everyday life. Persons on the autism spectrum are said to have difficulties in social interactions, showing deficits in emotion recognition in comparison to neurotypically developed persons. But social-emotional skills are believed to be positively augmented by training. A new adaptive social cognition training tool “E.V.A.” is introduced which teaches emotion recognition from face, voice and body language. One cross-sectional and one longitudinal study with adult neurotypical and autistic participants were conducted. The aim of the cross-sectional study was to characterize the two groups and see if differences in their social-emotional skills exist. The longitudinal study, on the other hand, aimed for detecting possible training effects following training with the new training tool. In addition, in both studies usability assessments were conducted to investigate the perceived usability of the new tool for neurotypical as well as autistic participants. Differences were found between autistic and neurotypical participants in their social-emotional and emotion recognition abilities. Training effects for neurotypical participants in an emotion recognition task were found after two weeks of home training. Similar perceived usability was found for the neurotypical and autistic participants. The current findings suggest that persons with ASC do not have a general deficit in emotion recognition, but are in need for more time to correctly recognize emotions. In addition, findings suggest that training emotion recognition abilities is possible. Further studies are needed to verify if the training effects found for neurotypical participants also manifest in a larger ASC sample.
Media artists have been struggling for financial survival ever since media art came into being. The non-material value of the artwork, a provocative attitude towards the traditional arts world and originally anti-capitalist mindset of the movement makes it particularly difficult to provide a constructive solution. However, a cultural entrepreneurial approach can be used to build a framework in order to find a balance between culture and business while ensuring that the cultural mission remains the top priority.
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
We developed an orbital tuned age model for the composite Chew Bahir sediment core, obtained from the Chew Bahir basin (CHB), southern Ethiopia. To account for the effects of sedimentation rate changes on the spectral expression of the orbital cycles we developed a new method: the Multi-band Wavelet Age modeling technique (MUBAWA). By using a Continuous Wavelet Transformation, we were able to track frequency shifts that resulted from changing sedimentation rates and thus calculated tuned age model encompassing the last 620 kyrs. The results show a good agreement with the directly dated age model that is available from the dating of volcanic ashes. Then we used the XRF data from CHB and developed a new and robust humid-arid index of east African climate during the last 620 kyrs. To disentangle the relationship of the selected elements we performed a principal component analysis (PCA). In a following step we applied a continuous wavelet transformation on the PC1, using the directly dated age model. The resulting wavelet power spectrum, unlike a normal power spectrum, displays the occurrence of cycles/frequencies in time. The results highlight that the precession cycles are most dominantly expressed under the 400 kyrs eccentricity maximum whereas weakly expressed during eccentricity minimum. This suggests that insolation is a key driver of the climatic variability observed at CHB throughout the last 620 kyrs. In addition, the prevalence of half-precession and obliquity signals was documented. The latter is attributed to the inter-tropical insolation gradient and not interpreted as an imprint of high latitudes forcing on climatic changes in the tropics. In addition, a windowed analysis of variability was used to detect changes in variance over time and showed that strong climate variability occurred especially along the transition from a dominant insolation-controlled humid climate background state towards a predominantly dry and less-insolation controlled climate. The last chapter dealt with non-linear aspects of climate changes represented by the sediments of the CHB. We use recurrence quantification analysis to detect non-linear changes within the potassium concentration of Chew Bahir sediment cores during the last 620 kyrs. The concentration of potassium in the sediments of the lake is subject to geochemical processes related to the evaporation rate of the lake water at the time of deposition. Based on recurrence analysis, two types of variabilities could be distinguished. Type 1 represents slow variations within the precession period bandwidth of 20 kyrs and a tendency towards extreme climatic events whereas type 2 represents fast, highly variable climatic transitions between wet and dry climate states. While type 1 variability is linked to eccentricity maxima, type 2 variability occurs during the 400 kyrs eccentricity minimum. The climate history presented here shows that during high eccentricity a strongly insolation-driven climate system prevailed, whereas during low eccentricity the climate was more strongly affected by short-term variability changes. The short-term environmental changes, reflected in the increased variability might have influenced the evolution, technological advances and expansion of early modern humans who lived in this region. In the Olorgesaille Basin the temporal changes in the occurrence of stone tools, which bracket the transition from Acheulean to Middle Stone Age (MSA) technologies at between 499–320 kyrs, could potentially correlate to the marked transition from a rather stable climate with less variability to a climate with increased variability in the CHB. We conclude that populations of early anatomically modern humans are more likely to have experienced climatic stress during episodes of low eccentricity, associated with dry and high variability climate conditions, which may have led to technological innovation, such as the transition from the Acheulean to the Middle Stone Age.
Foresight in networks
(2021)
The goal of this dissertation is to contribute to the corporate foresight research field by investigating capabilities, practices, and challenges particularly in the context of interorganizational settings and networked organizations informed by the theoretical perspectives of the relational view and dynamic capabilities.
Firms are facing an increasingly complex environment and highly complex product and service landscapes that often require multiple organizations to collaborate for innovation and offerings. Public-private partnerships that are targeted at supporting this have been introduced by policy-makers in the recent past. One example for such a partnership is the European Institute of Innovation and Technology (EIT) with multiple Knowledge and Innovation Communities (KICs). The EIT has been initiated by the European Commission in 2008 with the ambition of addressing grand societal challenges, driving innovativeness of European companies, and supporting systemic change. The resulting network organizations are managed similarly to corporations with managers, boards, and firm-like governance structures. EIT Digital as one of the EIT KICs are a central case of this work.
Research in this dissertation was based on the expectation that corporate foresight activities will increasingly be embedded in such interorganizational settings and a) can draw on such settings for the benefit of themselves and b) may contribute to shared visions, trust building and planning in these network organizations. In this dissertation the EIT Digital (formerly EIT ICT Labs) is a central case, supplemented with insights from three additional cases. I draw on the rich theoretical understanding of the resource-based view, dynamic capabilities, and particularly the relational view to further the discussion in the field of corporate foresight—defined as foresight in organizations in contrast to foresight with a macro-economical perspective—towards a relational understanding. Further, I use and revisit Rohrbeck’s Maturity Model for the Future Orientation of Firms as conceptual frame for corporate foresight in interorganizational settings. The analyses—available as four individual publications complemented by on additional chapter—are designed as exploratory case studies based on multiple data sources including an interview series with 49 persons, two surveys (N=54, n=20), three supplementary interviews, access to key documents and presentations, and observation through participation in meetings and activities of the EIT Digital. This research setting allowed me to contribute to corporate foresight research and practice by 1) integrating relational constructs primarily drawn from the relational view and dynamic capabilities research into the corporate foresight research stream, 2) exploring and understanding capabilities that are required for corporate foresight in interorganizational and networked organizations, 3) discussing and extending the Maturity Model for network organizations, and 4) to support individual organizations to tie their foresight systems effectively to networked foresight systems.
There is a general consensus that diverse ecological communities are better equipped to adapt to changes in their environment, but our understanding of the mechanisms by which they do so remains incomplete. Accurately predicting how the global biodiversity crisis affects the functioning of ecosystems, and the services they provide, requires extensive knowledge about these mechanisms.
Mathematical models of food webs have been successful in uncovering many aspects of the link between diversity and ecosystem functioning in small food web modules, containing at most two adaptive trophic levels. Meaningful extrapolation of this understanding to the functioning of natural food webs remains difficult, due to the presence of complex interactions that are not always accurately captured by bitrophic descriptions of food webs. In this dissertation, we expand this approach to tritrophic food web models by including the third trophic level. Using a functional trait approach, coexistence of all species is ensured using fitness-balancing trade-offs. For example, the defense-growth trade-off implies that species may be defended against predation, but this defense comes at the cost of a lower maximal growth rate. In these food webs, the functional diversity on a given trophic level can be varied by modifying the trait differences between the species on that level.
In the first project, we find that functional diversity promotes high biomass on the top level, which, in turn, leads to a reduction in the temporal variability due to compensatory dynamical patterns governed by the top level. Next, these results are generalized by investigating the average behavior of tritrophic food webs, for wide intervals of all parameters describing species interactions in the food web. We find that the diversity on the top level is most important for determining the biomass and temporal variability of all other trophic levels, and show how biomass is only transferred efficiently to the top level when diversity is high everywhere in the food web. In the third project, we compare the response of a simple food chain against a nutrient pulse perturbation, to that of a food web with diversity on every trophic level. By joint consideration of the resistance, resilience, and elasticity, we uncover that the response is efficiently buffered when biomass on the top level is high, which is facilitated by functional diversity on every trophic level in the food web. Finally, in the fourth project, we show that even in a simple consumer-resource model without any diversity, top-down control on the intermediate level frequently causes the phase difference between the intermediate and basal level to deviate from the quarter-cycle lag rule. By adding a top predator, we show that these deviations become even more likely, and anti-phase cycles are often observed.
The combined results of these projects show how the properties of the top trophic level, including its functional diversity, have a decisive influence on the functioning of tritrophic food webs from a mechanistic perspective. Because top species are often among the most vulnerable to extinction, our results emphasize the importance of their conservation in ecosystem management and restoration strategies.
In her writings on ancient myth, the British author Natalie Haynes moves women to the centre of attention. Her two latest books, A Thousand Ships and Pandora’s Jar – a fiction novel and a non-fiction one – approach this topic from two different perspectives. This interview takes stock of Haynes’ motives and methodology as well as of the challenges she faces in the process of writing.
Interview with Alana Jelinek
(2021)
Alana Jelinek is an art historian and artist — “an artist making art, and also writing about art”, in her words — , a former European Research Council artist in residence at the Museum of Anthropology and Archaeology at the University of Cambridge, and currently teaching in the School of Creative Arts at the University of Hertfordshire. Her art has revolved mostly around the issues of post- and neocolonialism and their connections with neoliberalism — a more implicit topic in her works from the 1990s on the “tourist gaze” developed into an interest in museums, collecting and ethnography throughout the past two decades. In this interview, she talks to thersites about the role of classical heritage and ancient art in her own work.
This article focuses on the feminist reception of Zenobia of Palmyra in Great Britain during the long nineteenth century and the early twentieth century. A special focus lies on her reception by the British suffragettes who belonged to the Women’s Social and Political Union. Even though Zenobia’s story did not end happily, the warrior queen’s example served to inspire these early feminists. Several products of historical culture – such as books, pieces of art, newspaper articles and theatre plays – provide insight into the reception of her as an historical figure, which is dominated by the image of a strong and courageous woman. The article will shed light on how exactly Zenobia’s example was instrumentalised throughout the first feminist movement in Britain.
Spring Issue
(2021)
The suitability of a newly developed cell-based functional assay was tested for the detection of the activity of a range of neurotoxins and neuroactive pharmaceuticals which act by stimulation or inhibition of calcium-dependent neurotransmitter release. In this functional assay, a reporter enzyme is released concomitantly with the neurotransmitter from neurosecretory vesicles. The current study showed that the release of a luciferase from a differentiated human neuroblastoma-based reporter cell line (SIMA-hPOMC1-26-GLuc cells) can be stimulated by a carbachol-mediated activation of the Gq-coupled muscarinic-acetylcholine receptor and by the Ca2+-channel forming spider toxin α-latrotoxin. Carbachol-stimulated luciferase release was completely inhibited by the muscarinic acetylcholine receptor antagonist atropine and α-latrotoxin-mediated release by the Ca2+-chelator EGTA, demonstrating the specificity of luciferase-release stimulation. SIMA-hPOMC1-26-GLuc cells express mainly L- and N-type and to a lesser extent T-type VGCC on the mRNA and protein level. In accordance with the expression profile a depolarization-stimulated luciferase release by a high K+-buffer was effectively and dose-dependently inhibited by L-type VGCC inhibitors and to a lesser extent by N-type and T-type inhibitors. P/Q- and R-type inhibitors did not affect the K+-stimulated luciferase release. In summary, the newly established cell-based assay may represent a versatile tool to analyze the biological efficiency of a range of neurotoxins and neuroactive pharmaceuticals which mediate their activity by the modulation of calcium-dependent neurotransmitter release.