Germanic Mythology in Music: Cultural Memory and Conflict within the Framework of Modern Globalization

The multi-layered enactment of a national past in music has been strongly intertwined with the usage of mythological elements. Having often been compiled as a coherent narrative during the emergence of the European nation-states (like the Finnish Kalevala), the mythological material has often been perceived as a form of historical truth and national justification. This focal role is also apparent in various music genres ranging from folk revival to metal in post-1989 Europe. Within the globalized context, however, local-national interpretations can collide with earlier nationalist appropriations. This complex and sometimes politically conflicting situation becomes particularly evident with groups falling back on symbols and narrations that had previously been employed by Nazi-Germany. While Nazi-Germany had, among others, tried replace the Christmas tradition with elements and songs from Germanic (and other) mythological sources, modern Neo-Nazi music groups often employ central mythological names (like Odin or Tyr) and iconic elements (like Vikings and warriors) in song lyrics and CD cover designs. However, while many covers and lyrics are legally forbidden in Germany, Scandinavian and Baltic groups (like the Faroese Viking metal group Tyr and the Latvian pagan metal band Skyforger) employ similar elements of Norse mythology, which are often combined with traditional material. Discussing selected case studies, this paper highlights central discursive points of colliding historical-national associations and individual interpretations of the mythological elements in musical contexts. How far can the material be disassociated from the earlier historical political usage and instrumentalization? Is this necessary ? And how can the specific global-local conflict points be approached by a theoretical framework ?

Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework

Initialising the ocean internal variability for decadal predictability studies is a new area of research and a variety of ad hoc methods are currently proposed. In this study, we explore how nudging with sea surface temperature (SST) and salinity (SSS) can reconstruct the threedimensional variability of the ocean in a perfect model framework. This approach builds on the hypothesis that oceanic processes themselves will transport the surface information into the ocean interior as seen in ocean-only simulations. Five nudged simulations are designed to reconstruct a 150 years ‘‘target’’ simulation, defined as a portion of a long control simulation. The nudged simulations differ by the variables restored to, SST or SST + SSS, and by the area where the nudging is applied. The strength of the heat flux feedback is diagnosed from observations and the restoring coefficients for SSS use the same time-scale. We observed that this choice prevents spurious convection at high latitudes and near sea-ice border when nudging both SST and SSS. In the tropics, nudging the SST is enough to reconstruct the tropical atmosphere circulation and the associated dynamical and thermodynamical impacts on the underlying ocean. In the tropical Pacific Ocean, the profiles for temperature show a significant correlation from the surface down to 2,000 m, due to dynamical adjustment of the isopycnals. At mid-tohigh latitudes, SSS nudging is required to reconstruct both the temperature and the salinity below the seasonal thermocline. This is particularly true in the North Atlantic where adding SSS nudging enables to reconstruct the deep convection regions of the target. By initiating a previously documented 20-year cycle of the model, the SST + SSS nudging is also able to reproduce most of the AMOC variations, a key source of decadal predictability. Reconstruction at depth does not significantly improve with amount of time spent nudging and the efficiency of the surface nudging rather depends on the period/events considered. The joint SST + SSS nudging applied verywhere is the most efficient approach. It ensures that the right water masses are formed at the right surface density, the subsequent circulation, subduction and deep convection further transporting them at depth. The results of this study underline the potential key role of SSS for decadal predictability and further make the case for sustained largescale observations of this field.

A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy

Due to their outstanding resolution and well-constrained chronologies, Greenland ice-core records provide a master record of past climatic changes throughout the Last Interglacial–Glacial cycle in the North Atlantic region. As part of the INTIMATE (INTegration of Ice-core, MArine and TErrestrial records) project, protocols have been proposed to ensure consistent and robust correlation between different records of past climate. A key element of these protocols has been the formal definition and ordinal numbering of the sequence of Greenland Stadials (GS) and Greenland Interstadials (GI) within the most recent glacial period. The GS and GI periods are the Greenland expressions of the characteristic Dansgaard–Oeschger events that represent cold and warm phases of the North Atlantic region, respectively. We present here a more detailed and extended GS/GI template for the whole of the Last Glacial period. It is based on a synchronization of the NGRIP, GRIP, and GISP2 ice-core records that allows the parallel analysis of all three records on a common time scale. The boundaries of the GS and GI periods are defined based on a combination of stable-oxygen isotope ratios of the ice (δ18O, reflecting mainly local temperature) and calcium ion concentrations (reflecting mainly atmospheric dust loading) measured in the ice. The data not only resolve the well-known sequence of Dansgaard–Oeschger events that were first defined and numbered in the ice-core records more than two decades ago, but also better resolve a number of short-lived climatic oscillations, some defined here for the first time. Using this revised scheme, we propose a consistent approach for discriminating and naming all the significant abrupt climatic events of the Last Glacial period that are represented in the Greenland ice records. The final product constitutes an extended and better resolved Greenland stratotype sequence, against which other proxy records can be compared and correlated. It also provides a more secure basis for investigating the dynamics and fundamental causes of these climatic perturbations.

From Framework to Action: The DESIRE Approach to Combat Desertification

It has become increasingly clear that desertification can only be tackled through a multi-disciplinary approach that not only involves scientists but also stakeholders. In the DESIRE project such an approach was taken. As a first step, a conceptual framework was developed in which the factors and processes that may lead to land degradation and desertification were described. Many of these factors do not work independently, but can reinforce or weaken one another, and to illustrate these relationships sustainable management and policy feedback loops were included. This conceptual framework can be applied globally, but can also be made site-specific to take into account that each study site has a unique combination of bio-physical, socio-economic and political conditions. Once the conceptual framework was defined, a methodological framework was developed in which the methodological steps taken in the DESIRE approach were listed and their logic and sequence were explained. The last step was to develop a concrete working plan to put the project into action, involving stakeholders throughout the process. This series of steps, in full or in part, offers explicit guidance for other organizations or projects that aim to reduce land degradation and desertification.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Ethnography of a Land-Deal. Local Perceptions and Vertical Perspectives of a Large Scale Land Acquisition Project in Northern Sierra Leone

Despite an increased scientific interest in the relatively new phenomenon of large-scale land acquisition (LSLA), data on processes on the local level remain sparse and superficial. However, knowledge about the concrete implementation of LSLA projects and the different impacts they have on the heterogeneous group of project affected people is indispensable for a deepened understanding of the phenomenon. In order to address this research gap, a team of two anthropologists and a human geographer conducted in-depth fieldwork on the LSLA project of Swiss based Addax Bioenergy in Sierra Leone. After the devastating civil war, the Sierra Leonean government created favourable conditions for foreign investors willing to lease large areas of land and to bring “development” to the country. Being one of the numerous investing companies, Addax Bioenergy has leased 57’000 hectares of land to develop a sugarcane plantation and an ethanol factory to produce biofuel for the export to the European market. Based on participatory observation, qualitative interview techniques and a network analysis, the research team aimed a) at identifying the different actors that were necessary for the implementation of this project on a vertical level and b) exploring various impacts of the project in the local context of two villages on a horizontal level. The network analysis reveals a complex pattern of companies, institutions, nongovernmental organisations and prominent personalities acting within a shifting technological and discursive framework linking global scales to a unique local context. Findings from the latter indicate that affected people initially welcomed the project but now remain frustrated since many promises and expectations have not been fulfilled. Although some local people are able to benefit from the project, the loss of natural resources that comes along with the land lease affects livelihoods of vulnerable groups – especially women and land users – considerably. However, this research doesn’t only disclose impacts on local people’s previous lives but also addresses strategies they adopt in the newly created situation that has opened up alternative spaces for renegotiations of power and legitimatisation. Therewith, this explorative study reveals new aspects of LSLA that have not been considered adequately by the investing company nor by the general academic discourse on LSLA.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.