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Sonstige beteiligte Institutionen
- IZKF Nachwuchsgruppe Geweberegeneration für muskuloskelettale Erkrankungen (5)
- Bernhard-Heine-Centrum für Bewegungsforschung (4)
- Fraunhofer-Institut für Silicatforschung ISC (3)
- The Italian Federation of Parks and Nature Reserves (3)
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- ALPARC - The Alpine Network of Protected Areas (2)
- Eurac research (2)
- Helmholtz Institute for RNA-based Infection Research (HIRI) (2)
- Krankenhaushygiene und Antimicrobial Stewardship (Universitätsklinikum) (2)
- Salzburg Institute for Regional Planning and Housing (2)
Objectives
The pathogenesis of fibromyalgia syndrome (FMS) is unclear. Transcranial ultrasonography revealed anechoic alteration of midbrain raphe in depression and anxiety disorders, suggesting affection of the central serotonergic system. Here, we assessed midbrain raphe echogenicity in FMS.
Methods
Sixty-six patients underwent transcranial sonography, of whom 53 were patients with FMS (27 women, 26 men), 13 patients with major depression and physical pain (all women), and 14 healthy controls (11 women, 3 men). Raphe echogenicity was graded visually as normal or hypoechogenic, and quantified by digitized image analysis, each by investigators blinded to the clinical diagnosis.
Results
Quantitative midbrain raphe echogenicity was lower in patients with FMS compared to healthy controls (p<0.05), but not different from that of patients with depression and accompanying physical pain. Pain and FMS symptom burden did not correlate with midbrain raphe echogenicity as well as the presence and severity of depressive symptoms.
Conclusion
We found reduced echogenicity of the midbrain raphe area in patients with FMS and in patients with depression and physical pain, independent of the presence or severity of pain, FMS, and depressive symptoms. Further exploration of this sonographic finding is necessary before this objective technique may enter diagnostic algorithms in FMS and depression.
The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument.
In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed.
To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere.
In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area.
These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions.
Breaking inversion symmetry in crystalline solids enables the formation of spin-polarized electronic states by spin-orbit coupling without the need for magnetism. A variety of interesting physical phenomena related to this effect have been intensively investigated in recent years, including the Rashba effect, topological insulators and Weyl semimetals. In this work, the interplay of inversion symmetry breaking and spin-orbit coupling and, in particular their general influence on the character of electronic states, i.e., on the spin and orbital degrees of freedom, is investigated experimentally. Two different types of suitable model systems are studied: two-dimensional surface states for which the Rashba effect arises from the inherently broken inversion symmetry at the surface, and a Weyl semimetal, for which inversion symmetry is broken in the three-dimensional crystal structure. Angle-resolved photoelectron spectroscopy provides momentum-resolved access to the spin polarization and the orbital composition of electronic states by means of photoelectron spin detection and dichroism with polarized light. The experimental results shown in this work are also complemented and supported by ab-initio density functional theory calculations and simple model considerations.
Altogether, it is shown that the breaking of inversion symmetry has a decisive influence on the Bloch wave function, namely, the formation of an orbital angular momentum. This mechanism is, in turn, of fundamental importance both for the physics of the surface Rashba effect and the topology of the Weyl semimetal TaAs.
In der vorliegenden Studie wurden QST, QSART, Hautbiopsien und Fragebögen
genutzt, um die Beteiligung kleiner Nervenfasern bei verschiedenen Formen der
Immunneuropathien zu untersuchen. Wir konnten hierbei eine signifikante
Beeinträchtigung der thermischen Reizleitung bei CIDP- und MADSAM-Patient/-innen
nachweisen sowie eine signifikant reduzierte Schweißproduktion am distalen
Unterschenkel bei MADSAM-Patient/-innen. Diese Ergebnisse belegen in allen drei
Untergruppen der immunvermittelten Neuropathien eine Beteiligung kleiner auch
unmyelinisierter Nervenfasertypen. MADSAM- und CIDP-Patient/-innen wiesen in der
QST ein ähnliches Schädigungsmuster auf. Dagegen unterschieden sie sich signifikant
in der QSART. Diese Ergebnisse können als weiterer Hinweis auf unterschiedliche
zugrundeliegende Pathomechanismen verstanden werden. MMN-Patient/-innen wiesen
insgesamt die geringste Small-Fiber-Beteiligung in den quantitativen Testungen auf.
Auch lagen bei MMN-Patient/-innen durchschnittlich die geringsten Schmerz-Scores und
autonomen Symptome vor. Es zeigten sich wenig signifikante Unterschiede zwischen
seropositiven und seronegativen Neuropathie-Patient/-innen. Diese jedoch bestätigten
unsere Hypothese einer etwas geringeren Small-Fiber-Beteiligung bei seropositiven
Patient/-innen. Bei der Vielzahl an unterschiedlichen Pathomechanismen innerhalb der
immunvermittelten Neuropathien erscheinen weitere Subklassifizierungen für eine
optimale Diagnosestellung und Therapie unabdingbar. Diese Arbeit konnte mit den oben
genannten Untersuchungen einen weiteren Beitrag zur Identifikation von klinischen und
quantitativen Unterschieden innerhalb dieser großen Erkrankungsgruppe leisten.
Künftige, größere Studien dieser Art können möglicherweise hier nur als Tendenzen
gesehene Erkenntnisse belegen und sollten durch zusätzliche Informationen wie
Korrelation zu Krankheitsdauer, Therapie, Laborchemie und elektrophysiologischen
Untersuchen weitere interessante Erkenntnisse liefern.
Introduction/Aims
Schwann cell clusters have been described at the murine dermis-epidermis border. We quantified dermal Schwann cells in the skin of patients with small-fiber neuropathy (SFN) compared with healthy controls to correlate with the clinical phenotype.
Methods
Skin punch biopsies from the lower legs of 28 patients with SFN (11 men, 17 women; median age, 54 [range, 19-73] years) and 9 healthy controls (five men, four women, median age, 34 [range, 25-69] years) were immunoreacted for S100 calcium-binding protein B as a Schwann cell marker, protein-gene product 9.5 as a pan-neuronal marker, and CD207 as a Langerhans cell marker. Intraepidermal nerve fiber density (IENFD) and subepidermal Schwann cell counts were determined.
Results
Skin samples of patients with SFN showed lower IENFD (P < .05), fewer Schwann cells per millimeter (P < .01), and fewer Schwann cell clusters per millimeter (P < .05) than controls. When comparing SFN patients with reduced (n = 13; median age, 53 [range, 19-73] years) and normal distal (n = 15, median age, 54 [range, 43-68] years) IENFD, the number of solitary Schwann cells per millimeter (p < .01) and subepidermal nerve fibers associated with Schwann cell branches (P < .05) were lower in patients with reduced IENFD. All three parameters correlated positively with distal IENFD (P < .05 to P < .01), whereas no correlation was found between Schwann cell counts and clinical pain characteristics.
Discussion
Our data raise questions about the mechanisms underlying the interdependence of dermal Schwann cells and skin innervation in SFN. The temporal course and functional impact of Schwann cell presence and kinetics need further investigation.
The importance of proactive and timely prediction of critical events is steadily increasing, whether in the manufacturing industry or in private life. In the past, machines in the manufacturing industry were often maintained based on a regular schedule or threshold violations, which is no longer competitive as it causes unnecessary costs and downtime. In contrast, the predictions of critical events in everyday life are often much more concealed and hardly noticeable to the private individual, unless the critical event occurs. For instance, our electricity provider has to ensure that we, as end users, are always supplied with sufficient electricity, or our favorite streaming service has to guarantee that we can watch our favorite series without interruptions. For this purpose, they have to constantly analyze what the current situation is, how it will develop in the near future, and how they have to react in order to cope with future conditions without causing power outages or video stalling.
In order to analyze the performance of a system, monitoring mechanisms are often integrated to observe characteristics that describe the workload and the state of the system and its environment. Reactive systems typically employ thresholds, utility functions, or models to determine the current state of the system. However, such reactive systems cannot proactively estimate future events, but only as they occur. In the case of critical events, reactive determination of the current system state is futile, whereas a proactive system could have predicted this event in advance and enabled timely countermeasures. To achieve proactivity, the system requires estimates of future system states. Given the gap between design time and runtime, it is typically not possible to use expert knowledge to a priori model all situations a system might encounter at runtime. Therefore, prediction methods must be integrated into the system. Depending on the available monitoring data and the complexity of the prediction task, either time series forecasting in combination with thresholding or more sophisticated machine and deep learning models have to be trained.
Although numerous forecasting methods have been proposed in the literature, these methods have their advantages and disadvantages depending on the characteristics of the time series under consideration. Therefore, expert knowledge is required to decide which forecasting method to choose. However, since the time series observed at runtime cannot be known at design time, such expert knowledge cannot be implemented in the system. In addition to selecting an appropriate forecasting method, several time series preprocessing steps are required to achieve satisfactory forecasting accuracy. In the literature, this preprocessing is often done manually, which is not practical for autonomous computing systems, such as Self-Aware Computing Systems. Several approaches have also been presented in the literature for predicting critical events based on multivariate monitoring data using machine and deep learning. However, these approaches are typically highly domain-specific, such as financial failures, bearing failures, or product failures. Therefore, they require in-depth expert knowledge. For this reason, these approaches cannot be fully automated and are not transferable to other use cases. Thus, the literature lacks generalizable end-to-end workflows for modeling, detecting, and predicting failures that require only little expert knowledge.
To overcome these shortcomings, this thesis presents a system model for meta-self-aware prediction of critical events based on the LRA-M loop of Self-Aware Computing Systems. Building upon this system model, this thesis provides six further contributions to critical event prediction. While the first two contributions address critical event prediction based on univariate data via time series forecasting, the three subsequent contributions address critical event prediction for multivariate monitoring data using machine and deep learning algorithms. Finally, the last contribution addresses the update procedure of the system model. Specifically, the seven main contributions of this thesis can be summarized as follows:
First, we present a system model for meta self-aware prediction of critical events. To handle both univariate and multivariate monitoring data, it offers univariate time series forecasting for use cases where a single observed variable is representative of the state of the system, and machine learning algorithms combined with various preprocessing techniques for use cases where a large number of variables are observed to characterize the system’s state. However, the two different modeling alternatives are not disjoint, as univariate time series forecasts can also be included to estimate future monitoring data as additional input to the machine learning models. Finally, a feedback loop is incorporated to monitor the achieved prediction quality and trigger model updates.
We propose a novel hybrid time series forecasting method for univariate, seasonal time series, called Telescope. To this end, Telescope automatically preprocesses the time series, performs a kind of divide-and-conquer technique to split the time series into multiple components, and derives additional categorical information. It then forecasts the components and categorical information separately using a specific state-of-the-art method for each component. Finally, Telescope recombines the individual predictions. As Telescope performs both preprocessing and forecasting automatically, it represents a complete end-to-end approach to univariate seasonal time series forecasting. Experimental results show that Telescope achieves enhanced forecast accuracy, more reliable forecasts, and a substantial speedup. Furthermore, we apply Telescope to the scenario of predicting critical events for virtual machine auto-scaling. Here, results show that Telescope considerably reduces the average response time and significantly reduces the number of service level objective violations.
For the automatic selection of a suitable forecasting method, we introduce two frameworks for recommending forecasting methods. The first framework extracts various time series characteristics to learn the relationship between them and forecast accuracy. In contrast, the other framework divides the historical observations into internal training and validation parts to estimate the most appropriate forecasting method. Moreover, this framework also includes time series preprocessing steps. Comparisons between the proposed forecasting method recommendation frameworks and the individual state-of-the-art forecasting methods and the state-of-the-art forecasting method recommendation approach show that the proposed frameworks considerably improve the forecast accuracy.
With regard to multivariate monitoring data, we first present an end-to-end workflow to detect critical events in technical systems in the form of anomalous machine states. The end-to-end design includes raw data processing, phase segmentation, data resampling, feature extraction, and machine tool anomaly detection. In addition, the workflow does not rely on profound domain knowledge or specific monitoring variables, but merely assumes standard machine monitoring data. We evaluate the end-to-end workflow using data from a real CNC machine. The results indicate that conventional frequency analysis does not detect the critical machine conditions well, while our workflow detects the critical events very well with an F1-score of almost 91%.
To predict critical events rather than merely detecting them, we compare different modeling alternatives for critical event prediction in the use case of time-to-failure prediction of hard disk drives. Given that failure records are typically significantly less frequent than instances representing the normal state, we employ different oversampling strategies. Next, we compare the prediction quality of binary class modeling with downscaled multi-class modeling. Furthermore, we integrate univariate time series forecasting into the feature generation process to estimate future monitoring data. Finally, we model the time-to-failure using not only classification models but also regression models. The results suggest that multi-class modeling provides the overall best prediction quality with respect to practical requirements. In addition, we prove that forecasting the features of the prediction model significantly improves the critical event prediction quality.
We propose an end-to-end workflow for predicting critical events of industrial machines. Again, this approach does not rely on expert knowledge except for the definition of monitoring data, and therefore represents a generalizable workflow for predicting critical events of industrial machines. The workflow includes feature extraction, feature handling, target class mapping, and model learning with integrated hyperparameter tuning via a grid-search technique. Drawing on the result of the previous contribution, the workflow models the time-to-failure prediction in terms of multiple classes, where we compare different labeling strategies for multi-class classification. The evaluation using real-world production data of an industrial press demonstrates that the workflow is capable of predicting six different time-to-failure windows with a macro F1-score of 90%. When scaling the time-to-failure classes down to a binary prediction of critical events, the F1-score increases to above 98%.
Finally, we present four update triggers to assess when critical event prediction models should be re-trained during on-line application. Such re-training is required, for instance, due to concept drift. The update triggers introduced in this thesis take into account the elapsed time since the last update, the prediction quality achieved on the current test data, and the prediction quality achieved on the preceding test data. We compare the different update strategies with each other and with the static baseline model. The results demonstrate the necessity of model updates during on-line application and suggest that the update triggers that consider both the prediction quality of the current and preceding test data achieve the best trade-off between prediction quality and number of updates required.
We are convinced that the contributions of this thesis constitute significant impulses for the academic research community as well as for practitioners. First of all, to the best of our knowledge, we are the first to propose a fully automated, end-to-end, hybrid, component-based forecasting method for seasonal time series that also includes time series preprocessing. Due to the combination of reliably high forecast accuracy and reliably low time-to-result, it offers many new opportunities in applications requiring accurate forecasts within a fixed time period in order to take timely countermeasures. In addition, the promising results of the forecasting method recommendation systems provide new opportunities to enhance forecasting performance for all types of time series, not just seasonal ones. Furthermore, we are the first to expose the deficiencies of the prior state-of-the-art forecasting method recommendation system.
Concerning the contributions to critical event prediction based on multivariate monitoring data, we have already collaborated closely with industrial partners, which supports the practical relevance of the contributions of this thesis. The automated end-to-end design of the proposed workflows that do not demand profound domain or expert knowledge represents a milestone in bridging the gap between academic theory and industrial application. Finally, the workflow for predicting critical events in industrial machines is currently being operationalized in a real production system, underscoring the practical impact of this thesis.
The Coronavirus disease 2019 (COVID-19) has not only had negative effects on employees' health, but also on their prospects to gain and maintain employment. Using a longitudinal research design with two measurement points, we investigated the ramifications of various psychological and organizational resources on employees' careers during the COVID-19 pandemic. Specifically, in a sample of German employees (N = 305), we investigated the role of psychological capital (PsyCap) for four career-related outcomes: career satisfaction, career engagement, coping with changes in career due to COVID-19, and career-related COVID-19 worries. We also employed leader–member exchange (LMX) as a moderator and career adaptability as a mediating variable in these relationships. Results from path analyses revealed a positive association between PsyCap and career satisfaction and career coping. Furthermore, PsyCap was indirectly related to career engagement through career adaptability. However, moderation analysis showed no moderating role of LMX on the link between PsyCap and career adaptability. Our study contributes to the systematic research concerning the role of psychological and organizational resources for employees' careers and well-being, especially for crisis contexts.
In der vorliegenden experimentellen Arbeit wurde in in-vitro-Modellen der Zusammenhang zwischen Expression der Tumorantigene MAGE-A (melanoma-associated antigenes) und der Wirksamkeit von Chemotherapeutika untersucht. Die MAGE-Antigene MAGE-A1 bis MAGE-A12 (ohne A7) kommen in diversen malignen Tumoren vor; neben Melanomen auch in Tumoren der Lunge, Brust, Prostata, Ovarien, Harnblase, des Gastrointestinal-Trakts und des Kopf-Hals-Bereichs. Bereits vielfach wurden Zusammenhänge zwischen MAGE-A-Tumorantigen-Expression und einer erhöhten Tumorinvasivität, Zellproliferation, Metastasierungsrate und kürzerem Überleben hergestellt. In dieser Arbeit gelang nun der erstmalige Nachweis, dass MAGE-A-Tumorantigene die chemotherapeutische Wirksamkeit beeinflussen. Zunächst gelang der Nachweis, dass die Expression von MAGE-A11 mit geringer Cisplatin-Wirksamkeit korreliert. Eine im Anschluss generierte MAGE-A11 überexprimierende Zelllinie zeigte ein durchschnittlich um 9 % schlechteres Ansprechen auf Cisplatin als die Kontrollzelllinie.
Government funding of research beyond biomedicine: challenges and opportunities for neuroethology
(2022)
Curiosity-driven research is fundamental for neuroethology and depends crucially on governmental funding. Here, we highlight similarities and differences in funding of curiosity-driven research across countries by comparing two major funding agencies—the National Science Foundation (NSF) in the United States and the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG). We interviewed representatives from each of the two agencies, focusing on general funding trends, levels of young investigator support, career-life balance, and international collaborations. While our analysis revealed a negative trend in NSF funding of biological research, including curiosity-driven research, German researchers in these areas have benefited from a robust positive trend in DFG funding. The main reason for the decrease in curiosity-driven research in the US is that the NSF has only partially been able to compensate for the funding gap resulting from the National Institutes of Health restricting their support to biomedical research using select model organisms. Notwithstanding some differences in funding programs, particularly those relevant for scientists in the postdoctoral phase, both the NSF and DFG clearly support curiosity-driven research.
Despite available diagnostic tests and recent advances, diagnosis of pulmonary invasive aspergillosis (IPA) remains challenging. We performed a longitudinal case-control pilot study to identify host-specific, novel, and immune-relevant molecular candidates indicating IPA in patients post allogeneic stem cell transplantation (alloSCT). Supported by differential gene expression analysis of six relevant in vitro studies, we conducted RNA sequencing of three alloSCT patients categorized as probable IPA cases and their matched controls without Aspergillus infection (66 samples in total). We additionally performed immunoassay analysis for all patient samples to gain a multi-omics perspective. Profiling analysis suggested LGALS2, MMP1, IL-8, and caspase-3 as potential host molecular candidates indicating IPA in investigated alloSCT patients. MMP1, IL-8, and caspase-3 were evaluated further in alloSCT patients for their potential to differentiate possible IPA cases and patients suffering from COVID-19-associated pulmonary aspergillosis (CAPA) and appropriate control patients. Possible IPA cases showed differences in IL-8 and caspase-3 serum levels compared with matched controls. Furthermore, we observed significant differences in IL-8 and caspase-3 levels among CAPA patients compared with control patients. With our conceptual work, we demonstrate the potential value of considering the human immune response during Aspergillus infection to identify immune-relevant molecular candidates indicating IPA in alloSCT patients. These human host candidates together with already established fungal biomarkers might improve the accuracy of IPA diagnostic tools.