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Background: There is extensive evidence that explicit memory, which involves conscious recall of encoded information, can be modulated by emotions; emotions may influence encoding, consolidation or retrieval of information. However, less is known about the modulatory effects of emotions on procedural processes like motor memory, which do not depend upon conscious recall and are instead demonstrated through changes in behaviour. Experiment 1: The goal of the first experiment was to examine the influence of emotions on motor learning. Four groups of subjects completed a motor learning task performing brisk isometric abductions with their thumb. While performing the motor task, the subjects heard emotional sounds varying in arousal and valence: (1) valence negative / arousal low (V-/A-), (2) valence negative / arousal high (V-/A+), (3) valence positive / arousal low (V+/A-), and (4) valence positive / arousal high (V+/A+). Descriptive analysis of the complete data set showed best performances for motor learning in the V-/A- condition, but the differences between the conditions did not reach significance. Results suggest that the interaction between valence and arousal may modulate motor encoding processes. Since limitations of the study cannot be ruled out, future studies with different emotional stimuli have to test the assumption that exposure to low arousing negative stimuli during encoding has a facilitating effect on short term motor memory. Experiment 2: The purpose of the second experiment was to investigate the effects of emotional interference on consolidation of sequential learning. In different sessions, 6 groups of subjects were initially trained on a serial reaction time task (SRTT). To modulate consolidation of the newly learned skill, subjects were exposed, after the training, to 1 of 3 (positive, negative or neutral) different classes of emotional stimuli which consisted of a set of emotional pictures combined with congruent emotional musical pieces or neutral sound. Emotional intervention for each subject group was done in 2 different time intervals (either directly after the training session, or 6 h later). After a 72 h post-training interval, each group was retested on the SRTT. Re-test performance was evaluated in terms of response times and accuracy during performance of the target sequence. Emotional intervention did not influence either response times or accuracy of re-testing SRTT task performance. However, explicit awareness of sequence knowledge was enhanced by arousing negative stimuli applied at 0 h after training. These findings suggest that consolidation of explicit aspects of procedural learning may be more responsive toward emotional interference than are implicit aspects. Consolidation of different domains of skill acquisition may be governed by different mechanisms. Since skill performance did not correlate with explicit awareness we suggest that implicit and explicit modes of SRTT performance are not complementary. Experiment 3: The aim of the third experiment was to analyze if the left hemisphere preferentially controls flexion responses towards positive stimuli, while the right hemisphere is specialized towards extensor responses to negative pictures. To this end, right-handed subjects had to pull or push a joystick subsequent to seeing a positive or a negative stimulus in their left or right hemifield. Flexion responses were faster for positive stimuli, while negative stimuli were associated with faster extensions responses. Overall, performance was fastest when emotional stimuli were presented to the left visual hemifield. This right hemisphere superiority was especially clear for negative stimuli, while reaction times towards positive pictures showed no hemispheric difference. We did not find any interaction between hemifield and response type. Neither was there a triple interaction between valence, hemifield and response type. In our experimental context the interaction between valence and hemifield seems to be stronger than the interaction between valence and motor behaviour. From these results we suppose that under certain conditions a hierarchy scaling of the asymmetry patterns prevails, which might mask any other existing asymmetries.
When More Is Better – Consumption Priming Decreases Responders’ Rejections in the Ultimatum Game
(2017)
During the past decades, economic theories of rational choice have been exposed to outcomes that were severe challenges to their claim of universal validity. For example, traditional theories cannot account for refusals to cooperate if cooperation would result in higher payoffs. A prominent illustration are responders’ rejections of positive but unequal payoffs in the Ultimatum Game. To accommodate this anomaly in a rational framework one needs to assume both a preference for higher payoffs and a preference for equal payoffs. The current set of studies shows that the relative weight of these preference components depends on external conditions and that consumption priming may decrease responders’ rejections of unequal payoffs. Specifically, we demonstrate that increasing the accessibility of consumption-related information accentuates the preference for higher payoffs. Furthermore, consumption priming increased responders’ reaction times for unequal payoffs which suggests an increased conflict between both preference components. While these results may also be integrated into existing social preference models, we try to identify some basic psychological processes underlying economic decision making. Going beyond the Ultimatum Game, we propose that a distinction between comparative and deductive evaluations may provide a more general framework to account for various anomalies in behavioral economics.
Utility is perhaps the most central concept in modern economic theorizing. However, the behaviorist reduction to Revealed Preference not only removed the psychological content of utility but experimental investigations also exposed numerous anomalies in this theory.
This program of research focused on the psychological processes by which utility judgments are generated. For this purpose, the standard assumption of a homogeneous concept is substituted by the Utilitarian Duality Hypothesis.
In particular, judgments concerning categorical utility (uCat) infer an object's category based on its attributes which may subsequently allow the transfer of evaluative information like feelings or attitudes. In contrast, comparative utility (uCom) depends on the distance to a reference value on a specific dimension of comparison. Importantly, dimensions of comparison are manifold and context dependent.
In a series of experiments, we show that the resulting Dual Utility Model is able to explain several known anomalies in a parsimonious fashion. Moreover, we identify central factors determining the relative weight assigned to both utility components.
Finally, we discuss the implications of the Utilitarian Duality for both, the experimental practice in economics as well as the consequences for economic theorizing. In sum, we propose that the Dual Utility Model can serve as an integrative framework for both the rational model and its anomalies.
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.
Assessing protein biomarkers to detect lethal acute traumatic brain injuries in cerebrospinal fluid
(2021)
Diagnosing traumatic brain injury (TBI) from body fluids in cases where there are no obvious external signs of impact would be useful for emergency physicians and forensic pathologists alike. None of the previous attempts has so far succeeded in establishing a single biomarker to reliably detect TBI with regards to the sensitivity: specificity ratio in a post mortem setting. This study investigated a combination of body fluid biomarkers (obtained post mortem), which may be a step towards increasing the accuracy of biochemical TBI detection. In this study, serum and cerebrospinal fluid (CSF) samples from 30 acute lethal TBI cases and 70 controls without a TBI-related cause of death were evaluated for the following eight TBI-related biomarkers: brain-derived neurotrophic factor (BDNF), ferritin, glial fibrillary acidic protein (GFAP), interleukin 6 (IL-6), lactate dehydrogenase, neutrophil gelatinase-associated lipocalin (NGAL), neuron-specific enolase and S100 calcium-binding protein B. Correlations among the individual TBI biomarkers were assessed, and a specificity-accentuated threshold value analysis was conducted for all biomarkers. Based on these values, a decision tree modelling approach was performed to assess the most accurate biomarker combination to detect acute lethal TBIs. The results showed that 92.45% of acute lethal TBIs were able to be diagnosed using a combination of IL-6 and GFAP in CSF. The probability of detecting an acute lethal TBI was moderately increased by GFAP alone and considerably increased by the remaining biomarkers. BDNF and NGAL were almost perfectly correlated (p = 0.002; R\(^2\) = 0.944). This study provides evidence that acute lethal TBIs can be detected to a high degree of statistical accuracy using forensic biochemistry. The high inter-individual correlations of biomarkers may help to estimate the CSF concentration of an unknown biomarker, using extrapolation techniques.
A single, specific, sensitive biochemical biomarker that can reliably diagnose a traumatic brain injury (TBI) has not yet been found, but combining different biomarkers would be the most promising approach in clinical and postmortem settings. In addition, identifying new biomarkers and developing laboratory tests can be time-consuming and economically challenging. As such, it would be efficient to use established clinical diagnostic assays for postmortem biochemistry. In this study, postmortem cerebrospinal fluid samples from 45 lethal TBI cases and 47 controls were analyzed using commercially available blood-validated assays for creatine kinase (CK) activity and its heart-type isoenzyme (CK–MB). TBI cases with a survival time of up to two hours showed an increase in both CK and CK–MB with moderate (CK–MB: AUC = 0.788, p < 0.001) to high (CK: AUC = 0.811, p < 0.001) diagnostic accuracy. This reflected the excessive increase of the brain-type CK isoenzyme (CK–BB) following a TBI. The results provide evidence that CK immunoassays can be used as an adjunct quantitative test aid in diagnosing acute TBI-related fatalities.
Background: The use of assisted reproductive techniques (ART) for treatment of infertility is increasing rapidly worldwide. However, various health effects have been reported including a higher risk of congenital malformations. Therefore, we assessed the risk of anorectal malformations (ARM) after in-vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI).
Methods: Data of the German Network for Congenital Uro-REctal malformations (CURE-Net) were compared to nationwide data of the German IVF register and the Federal Statistical Office (DESTATIS). Odds ratios (95% confidence intervals) were determined to quantify associations using multivariable logistic regression accounting for potential confounding or interaction by plurality of births.
Results: In total, 295 ARM patients born between 1997 and 2011 in Germany, who were recruited through participating pediatric surgeries from all over Germany and the German self-help organisation SoMA, were included. Controls were all German live-births (n = 10,069,986) born between 1997 and 2010. Overall, 30 cases (10%) and 129,982 controls (1%) were born after IVF or ICSI, which translates to an odds ratio (95% confidence interval) of 8.7 (5.9-12.6) between ART and ARM in bivariate analyses. Separate analyses showed a significantly increased risk for ARM after IVF (OR, 10.9; 95% CI, 6.2-19.0; P < 0.0001) as well as after ICSI (OR, 7.5; 95% CI, 4.6-12.2; P < 0.0001). Furthermore, separate analyses of patients with isolated ARM, ARM with associated anomalies and those with a VATER/VACTERL association showed strong associations with ART (ORs 4.9, 11.9 and 7.9, respectively). After stratification for plurality of birth, the corresponding odds ratios (95% confidence intervals) were 7.7 (4.6-12.7) for singletons and 4.9 (2.4-10.1) for multiple births. Conclusions: There is a strongly increased risk for ARM among children born after ART. Elevations of risk were seen after both IVF and ICSI. Further, separate analyses of patients with isolated ARM, ARM with associated anomalies and those with a VATER/VACTERL association showed increased risks in each group. An increased risk of ARM was also seen among both singletons and multiple births.
Fluorescence microscopy is a form of light microscopy that has developed during the 20th century and is nowadays a standard tool in Molecular and Cell biology for studying the structure and function of biological molecules. High-resolution fluorescence microscopy techniques, such as dSTORM (direct Stochastic Optical Reconstruction Microscopy) allow the visualization of cellular structures at the nanometre scale (10−9 m). This has already made it possible to decipher the composition and function of various biopolymers, such as proteins, lipids and nucleic acids, up to the three-dimensional (3D) structure of entire organelles. In practice, however, it has been shown that these imaging methods and their further developments still face great challenges in order to achieve an effective resolution below ∼ 10 nm. This is mainly due to the nature of labelling biomolecules. For the detection of molecular structures, immunostaining is often performed as a standard method. Antibodies to which fluorescent molecules are coupled, recognize and bind specifcally and with high affnity to the molecular section of the target structure, also called epitope or antigen. The fluorescent molecules serve as reporter molecules which are imaged with the use of a fluorescence microscope. However, the size of these labels with a length of about 10-15 nm in the case of immunoglobulin G (IgG) antibodies, cause a detection of the fluorescent molecules shifted to the real position of the studied antigen. In dense regions where epitopes are located close to each other, steric hindrance between antibodies can also occur and leads to an insuffcient label density. Together with the shifted detection of fluorescent molecules, these factors can limit the achievable resolution of a microscopy technique. Expansion microscopy (ExM) is a recently developed technique that achieves a resolution improvement by physical expansion of an investigated object. Therefore, biological samples such as cultured cells, tissue sections, whole organs or isolated organelles are chemically anchored into a swellable polymer. By absorbing water, this so-called superabsorber increases its own volume and pulls the covalently bound biomolecules isotropically apart. Routinely, this method achieves a magnifcation of the sample by about four times its volume. But protocol variants have already been developed that result in higher expansion factors of up to 50-fold. Since the ExM technique includes in the frst instance only the sample treatment for anchoring and magnifcation of the sample, it can be combined with various standard methods of fluorescence microscopy. In theory, the resolution of the used imaging technique improves linearly with the expansion factor of the ExM treated sample. However, an insuffcient label density and the size of the antibodies can here again impair the effective achievable resolution. The combination of ExM with high-resolution fluorescence microscopy methods represents a promising strategy to increase the resolution of light microscopy. In this thesis, I will present several ExM variants I developed which show the combination of ExM with confocal microscopy, SIM (Structured Illumination Microscopy), STED (STimulated Emission Depletion) and dSTORM. I optimized existing ExM protocols and developed different expansion strategies, which allow the combination with the respective imaging technique. Thereby, I gained new structural insights of isolated centrioles from the green algae Chlamydomonas reinhardtii by combining ExM with STED and confocal microscopy. In another project, I combined 3D-SIM imaging with ExM and investigated the molecular structure of the so-called synaptonemal complex. This structure is formed during meiosis in eukaryotic cells and contributes to the exchange of genetic material between homologous chromosomes. Especially in combination with dSTORM, the ExM method showed its high potential to overcome the limitations of modern fluorescence microscopy techniques. In this project, I expanded microtubules in mammalian cells, a polymer of the cytoskeleton as well as isolated centrioles from C. reinhardtii. By labelling after expansion of the samples, I was able to signifcantly reduce the linkage error of the label and achieve an improved label density. In future, these advantages together with the single molecule sensitivity and high resolution obtained by the dSTORM method could pave the way for achieving molecular resolution in fluorescence microscopy
Results are presented of cloning cDNA of procine growth hormone, analysis of its primary structure, and creation of a construction capable of expression of this cDNA in Esqheriahia coti cells. It is shown that in the population of mRNA coding porcine growth hormone, heterogeneity is noted which is manifested not only at the level of the nucleotide sequence, but also is reflected in the amino acid sequence of the mature hormone.