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Sex-specific effects of bisphenol A, its substitutes and benzophenone-3 on T helper 1 cell differentiation
No full textBackground: T cells, known to play a pivotal role in the development of a plethora of diseases, may represent as a target of endocrine disrupting chemicals (EDCs). Moreover, there is accumulating evidence that endocrine-immune interactions show some sex-specificity.
Objectives: We aimed to study the influence of various EDCs, namely different bisphenols and benzophenone-3 in single and mixed exposure scenarios on T helper 1 (Th1) cell differentiation in a sex-specific approach.
Methods: Naïve CD4+ T cells were isolated from mouse lymphoid organs or from human peripheral blood of both sexes. A specific cytokine cocktail and T cell receptor stimulants were added to naïve T cell cultures to induce Th1 cell differentiation. Treatment with different concentrations of bisphenol A (BPA; 0,01-100µM), its analogs (BPF, BPS; 0,01-100µM) and benzophenone-3 (BP-3; 0,001-10µM) started 24 hours after T cell differentiation and lasted for another 72 hours. Flow cytometry analyses were applied for detecting changes in cell viability and expression of Th-subset-specific transcriptional factors and cytokines. Data was analyzed with GraphPad Prism after extracting with Flowjo software.
Results: Cell viability was compromised when mouse female T cells were exposed to the highest BPA and BPS concentration (100 µM each) The sub-highest concentration of BPA (10 µM) affected Th1 differentiation in mouse female T cells. For the mixtures, the presence of the 100 µM BPA in combination with all tested BP-3 concentrations showed an inhibitory effect on human Th1 differentiation among both sexes. Moreover, several BPA-BP-3 combinations impaired mouse male Th1 cell differentiation but did not affect mouse female T cells.
Conclusion: Our research indicates that high concentrations of BPA impair the cell viability of mouse Th1 cells. When used in combination, high concentrations of BP-3, with the assistance of BPA, impair the expression of cytokine in Th1, Th17, and Treg cells in female mice, as well as female Treg cells. This phenomenon exhibits sex specificity
Instructional load induces functional connectivity changes linked to task automaticity and mnemonic preference
No full textLearning new rules rapidly and effectively via instructions is ubiquitous in our daily lives, yet the underlying cognitive and neural mechanisms are complex. Using functional magnetic resonance imaging we examined the effects of different instructional load conditions (4 vs. 10 stimulus-response rules) on functional couplings during rule implementation (always 4 rules). Focusing on connections of lateral prefrontal cortex (LPFC) regions, the results emphasized an opposing trend of load-related changes in LPFC-seeded couplings. On the one hand, during the low-load condition LPFC regions were more strongly coupled with cortical areas mostly assigned to networks such as the fronto-parietal network and the dorsal attention network. On the other hand, during the high-load condition, the same LPFC areas were more strongly coupled with default mode network areas. These results suggest differences in automated processing evoked by features of the instruction and an enduring response conflict mediated by lingering episodic long-term memory traces when instructional load exceeds working memory capacity limits. The ventrolateral prefrontal cortex (VLPFC) exhibited hemispherical differences regarding whole-brain coupling and practice-related dynamics. Left VLPFC connections showed a persistent load-related effect independent of practice and were associated with ‘objective’ learning success in overt behavioral performance, consistent with a role in mediating the enduring influence of the initially instructed task rules. Right VLPFC's connections, in turn, were more susceptible to practice-related effects, suggesting a more flexible role possibly related to ongoing rule updating processes throughout rule implementation
Robustness of reinforcement learning based autonomous driving technologies
No full textAutonomous driving technologies offer the potential to substantially improve safety, mobility, and sustainability in the field of transportation. However, the complex and unpredictable nature of real-world driving scenarios demands robust models capable of making safe decisions across diverse and unseen situations. With the rise of machine learning techniques in the recent decade, reinforcement learning is an increasingly used method to tackle the challenging nature of au- tonomous driving tasks. This thesis contributes to the ongoing advancements in reinforcement learning based autonomous driving technologies by offering valuable insights into the design of training environments with a particular focus on safety robustness, which refers to the model’s capability to guarantee safety in diverse scenarios, which may not have been encountered in training. This thesis provides experiments for vehicle-following and obstacle avoidance tasks, which demonstrate that reinforcement learning based models struggle to learn effectively from natural driving data or naturally inspired training environments. In both cases, safety-critical situations are rare, which leads to poor performance in extreme situations, also known as the curse of rarity. To combat this issue, this thesis discusses synthetic training approaches for different reinforcement learning based applications of autonomous driving that increase safety- critical situations in training. Validating the trained models in various scenarios, qualitatively different from the training data, and under extreme conditions, demonstrates their capability to be robust. Furthermore, this thesis suggests different approaches in the design of the objec- tive functions, which should be optimized, and the design of environment observations, from which the model learns, in order to improve safety robustness, for example, by incorporating time-to-collision metrics for vehicle-following and closest-point-of-approach metrics for obstacle avoidance
Phytoremediation Potential of Native Hyperaccumulator Plants Growing on Heavy Metal-Contaminated Soil of Khatunabad Copper Smelter and Refinery, Iran
No full textThe characterization of prospective plants is one of the critical issues in the efficiency and success of the phytoremediation process. Due to adaption and tolerance to different environmental stresses, native plant species have priority in this method. This study examined fifty plants of five species, namely Launaea acanthodes, Artemisia sp., Cousinia congesta, Peganum harmala, and Stipa sp., growing near a smelter and refinery in Iran to identify potential species for phytoextraction and phytostabilization. Therefore, Pb, Ni, Mn, Mo, S, and Cu concentrations in sampled plants and soils were analyzed. Three different pollution indices, namely metal accumulation index (MAI), translocation factor (TF), and bioconcentration factor (BCF) were used for evaluating the metal concentrations in roots and shoots of each plant species. The results indicated that Artemisia sp., with values of 3.21, 1.09, and 1.14 for MAI, BCF, and TF, respectively, is appropriate for phytoextraction in the study area. Plants such as Launaea acanthodes and Cousinia congesta with high BCF and low TF values showed the potential for phytostabilization. Investigating the indices for different elements demonstrated that Launaea acanthodes had a BCF value greater than 1 and a TF value less than 1; therefore, this plant could be used in the phytoremediation of arsenic through the phytostabilization technique. Furthermore, copper has very low bioavailability in these plant species. In addition, these native plant species were highly capable of accumulating sulfur from the soil because the BCF and TF indices for all inspected species were higher than 1; for Launaea acanthodes, the relevant TF value was about 10. The proposed native plant could be applied in practical applications of phytoremediation for soil remediation of contaminated sites around the metal factories and mines in southeastern Iran
Educational Suicide Prevention in Secondary Schools: The Evaluation of the HEYLiFE Prevention Program
No full textIn the past decade, adolescents' mental well-being has significantly deteriorated, and suicide is the second-leading cause of death in numerous nations. Strategies to improve mental health and to prevent suicide in this age group are called for. School-based, educational prevention programs are among the most promising strategies for suicide prevention with adolescents. However, the research on their effectiveness and safety has encountered several challenges. Up to date, there is no shared theoretical model to guide program development and evaluation. Furthermore, the nature of the phenomenon of suicidality (relative rarity, lethality) poses difficulties in designing methodologically sound evaluation studies. Data on the differential effects of programs on different groups of young people are lacking. The Network for Suicide Prevention in Dresden (NeSuD) project was initiated to improve suicide prevention in the city of Dresden (Saxony, Germany). After a literature review on educational suicide prevention, this thesis presents the results of the evaluation of HEYLiFE, an educative suicide prevention program for secondary schools developed during this project.
First, this thesis aimed to contribute to the theoretical foundation of educational suicide prevention. A three-staged Delphi survey was conducted to explore important contents, target outcomes, and methods of effective and safe educational programs. The Delphi survey is a method that allows to assess expert opinions reliably, reducing unfavourable group decision processes. Participating experts suggested that, above reducing suicidal ideation and attempts, educational programs should aim to increase help-seeking intentions and behaviour, enhance the quality of social support between peers, improve mental health literacy and life-skills such as coping with stress, emotional regulation and problem solving. The experts also proposed to embed suicide prevention in educational programs with a larger scope, to facilitate help-seeking among the program participants and to establish suicide prevention measures on multiple levels in schools to enhance the safety of educational programs. The Delphi survey served as a significant base for researchers to develop school-based, educational suicide prevention programs.
Enhancing mental health literacy regarding depression and suicide has been one of the central strategies for educational programs in the last decades. However, the construct of mental health literacy has been discussed critically due to an unsharp definition and unreliable assessments. These problems caused study results to be tautological and confounded. To solve this problem, it is crucial to define and explore what constructs play a role in enabling people to achieve and maintain a good mental health. Although mental health knowledge is considered the core component of mental health literacy, there is a lack of comprehensive reviews on how it interacts with other constructs relevant to prevention. Through a systematic review, we explored the correlation between mental health knowledge, mental health-related stigma and help-seeking. The review showed that mental health knowledge had a medium-sized, negative correlation with personal stigma (Mdn r = -.28) and a medium-sized positive correlation with attitudes towards help-seeking (Mdn r = .29). The correlations to self-stigma (Mdn r = -.18), help-seeking intention (Mdn r = .15) and help-seeking behaviour (Mdn r = .15) were low. Public stigma was not consistently related to mental health knowledge. These findings contributed to a more profound understanding of the construct of mental health knowledge.
HEYLiFE, an educational suicide prevention program for secondary schools, was a key component of the NeSuD project. HEYLiFE’s efficacy, safety, and acceptability were evaluated with a RCT with waiting-control-group with 745 secondary-school-students aged 12 or older. Outcomes were measured immediately after the intervention (short term) and after 6 months (mid term). We used linear mixed models (LMM) for analysing ordinal outcomes and generalized linear mixed models (GLMM) for binary outcomes, controlling for the nested nature of the data. The program led to an improvement of mental health knowledge and favourable attitudes towards suicidality in the short term. In the mid term, the intervention group showed a more favourable development for help-seeking intentions and risk-factors for suicidality (hopelessness, isolation, burdensomeness, sense of entrapment) from baseline to follow-up than the control group. The program had mixed effects on stigma, with a paradoxical increase in stigma (lower prosocial emotional reaction, higher wish for social distance) in the short term but a more favourable development in the intervention group regarding social distance at follow-up. The evaluation study suggested that the program was acceptable and safe. HEYLiFE is a promising intervention for suicide prevention among adolescents and young adults.
Moreover, this dissertation examines differential effects of HEYLiFE for gender, age, and risk status for suicide attempts. In the main evaluation study (N = 745), HEYliFE had less favourable effects for males than for females on stigmatizing emotional responses to a suicidal peer in the short term and on wish for social distance and help-seeking behaviours in the mid term. Participants in the youngest age group (12-13 years) gained more knowledge than the older ones (14-16; 17+ years) in the mid term. However, they seemed to have stigmatizing emotional reactions to suicidal peers in the short term and did not profit as much in the mid term in terms of a reduction of risk factors. In a second study (N = 218, 14-18 years old), three suicide risk clusters were built using cluster analysis based on suicidality, depression, impulsivity/carelessness and emotional avoidance. We assessed the short time effects on suicide knowledge, agency, and help seeking intentions in case of suicidal thoughts. While knowledge improved in all groups, agency, and help seeking intentions only improved in the low and middle risk group. This study confirmed that HEYLiFE is an effective intervention for suicide prevention. Adolescents already suffering from suicidal ideation and behaviour, however, should receive more targeted interventions.
In summary, this thesis contributes to our understanding of school-based, educational suicide prevention and supports the use of interventions such as HEYLiFE to prevent suicidality among adolescents. Future research should explore how these interventions can be tailored for the needs of males, younger adolescents and adolescents at risk.:Dedication iii
Acknowledgements iv
Statement on the Included Publications vi
Summary viii
Contents xi
List of Figures xiv
List of Tables xv
List of Abbreviations xvi
Chapter 1 Introduction 18
1.1 Suicide Among Adolescents: A Global Health Issue 18
1.2 Suicide Prevention: From General Methods to School-Based Practice 20
1.3 Ongoing Challenges for Educational Suicide Prevention 22
Chapter 2 Literature Review 27
2.1 Suicidality: Definition and Terms 27
2.2 Suicide Among Adolescents and Young Adults 29
2.2.1 Trajectories of Suicidality 30
2.2.2 Risk and Protective Factors 31
2.2.3 Demographic Aspects 34
2.3 Psychological Theories of Suicide 37
2.3.1 Hopelessness Theory of Suicide 38
2.3.2 Interpersonal Theory of Suicide and Ideation-to-Action Framework 39
2.3.3 Integrated Motivational-Volitional Model of Suicide 41
2.4 Educational Suicide Prevention 43
2.4.1 Goals and Strategies of Educational Programs 45
2.4.2 Educational Prevention Program Evaluation 52
2.5 Development of the Research Questions 58
2.5.1 Theoretical Framework: Research Questions 1.1 & 1.2 58
2.5.2 Program Development and Evaluation: Research Question 2 59
2.5.3 Differential Results: Research Questions 3.1 & 3.2 59
Chapter 3 Dos and Don'ts in Designing School-Based Awareness Programs for Suicide Prevention 61
Chapter 4 Beyond Knowledge: A Systematic Review on the Correlation of Mental Health Literacy with Stigma and Help Seeking 72
Chapter 5 Addressing Help-seeking, Stigma and Risk Factors for Suicidality in Secondary Schools: Short-term and Mid-term Effects of the HEYLiFE Suicide Prevention Program in a Randomized Controlled Trial 95
Chapter 6 Promoting Protective Factors for Suicidal Behavior in Adolescents at Risk: Differential Efficacy of the HEYLiFE Suicide Prevention Program 122
Chapter 7 Discussion 143
7.1 Theoretical Framework 143
7.1.1 Expert Opinions? It’s a Start 143
7.1.2 School-based Prevention: A Worthwhile Commitment 145
7.1.3 Heading to Shared Theoretical Frameworks for Suicide Prevention 146
7.1.4 From Specific to Upstream Prevention 147
7.1.5 Enhancing Safety 147
7.1.6 Mental Health Knowledge: The Core of Mental Health Literacy 149
7.1.7 Quantifying Correlations: How Strong is the Link between Knowledge, Stigma and Help-Seeking? 150
7.1.8 Unravelling the Correlations: How does Knowledge Contribute to Prevention? 152
7.1.9 The Limits of Knowledge for Suicide Prevention 154
7.2 Program Development and Evaluation 155
7.2.1 Short-lived Effects for Knowledge and Attitudes 156
7.2.2 Protective Effects on the Mid-term 158
7.2.3 Heterogeneous Findings for Stigma 160
7.2.4 Program Safety 162
7.2.5 Program Acceptability 163
7.3 Differential Effects 164
7.3.1 Gender: Masculinity Norms as a Challenge for Prevention 164
7.3.2 Age: Tailoring Prevention to Development 167
7.3.3 Suicide Risk: When Educational Prevention is not Enough 168
7.4 Limitations 170
7.5 Implications for Research and Practice 173
7.5.1 Specific vs. Upstream Prevention 173
7.5.2 Central Mechanisms and Outcomes in Specific Educational Suicide Prevention 174
7.5.3 Extension, Replicability and Generalizability of the Results 175
7.5.4 Further Understanding Differential Effects 176
7.5.5 Towards Multi-Level Suicide Prevention in Schools 177
7.5.6 Lessons Learned During the NeSuD Project 178
7.5.7 Outlook: Recent Developments of the NeSuD Project 180
7.6 Conclusion 181
References 183
Appendix A: Additional Materials Grosselli et al., 2021 236
Appendix B: Additional Materials Grosselli et al., 2024b 246
Appendix C: Additional Materials Grosselli et al., 2024a 251
Erklärung gemäß § 5 der Promotionsordnung 26
Machine Learning Algorithms for Epileptic Seizure Prediction
No full textMit etwa 1 % der Weltbevölkerung ist die Epilepsie eine der häufigsten neurologischen Erkrankungen. Bei einem großen Teil der Patienten können die Anfälle mit Medikamenten nicht ausreichend kontrolliert werden. Die Entwicklung eines Geräts zur Vorhersage von Anfällen birgt das Potenzial, die Angst und Ungewissheit im Zusammenhang mit dem Auftreten von Anfällen zu verringern, das Verständnis für die Krankheit zu verbessern und bei Bedarf die sofortige Verabreichung von Maßnahmen zu erleichtern. In dieser Arbeit werden verschiedene überwachte und unüberwachte Algorithmen des maschinellen Lernens für die Vorhersage epileptischer Anfälle auf der Grundlage des intrakraniellen Elektroenzephalogramms entwickelt und evaluiert. Anhand nicht-kontinuierlichen Benchmark-Daten wird gezeigt, dass vollständig datengetriebene Methoden aus dem Bereich des Deep Learning den Stand der Technik erreichen können. Darüber hinaus zeigen grundlegend unterschiedliche Algorithmen eine ähnliche Leistung sowie Kohärenz in den fehlerhaften Stichproben. Post-hoc-Experimente deuten zudem darauf hin, dass weitere Verbesserungen auf diesem Datensatz begrenzt sein könnten. Analysen von kontinuierlichen Daten zeigen das Vorhandensein einer zyklischen und/oder gerichteten zeitlichen Entwicklung in den Verteilungen der EEG-Daten für alle Patienten. Unüberwachte Methoden zeigen eine klinisch relevante Leistung nur für Patienten, die eine zyklische zeitliche Entwicklung in Kombination mit phasengleichem Auftreten von Anfällen relativ zu dieser Entwicklung aufweisen. Darüber hinaus wird gezeigt, dass die Einbeziehung aktueller Daten in den Trainingssatz zu einer verbesserten Leistung des Klassifikators führt, während die Einbeziehung älterer Daten nur marginale Verbesserungen mit sich bringt. Das deutet darauf hin, dass die Klassifizierung in erster Linie auf aktuellen Daten beruht. Schließlich macht die Studie deutlich, dass die mit Benchmark-Daten erzielte Leistung möglicherweise zu optimistisch ist. Die Definitionen, die zum Labeln dieser Daten verwendet wurden, könnten den klinischen Nutzen des Klassifikators möglicherweise einschränken. Ein klinisch relevantes, vollständig prospektives System zur Vorhersage von Anfällen, das auf dem vorgeschlagenen Klassifikator basiert, wurde getestet und schnitt nicht signifikant besser ab als ein zufälliger Prädiktor. Dies deutet auf die Herausforderungen hin, robuste Vorhersagen in einer realen Anwendung zu erzielen.With approximately 1% of the global population affected, epilepsy is one of the most prevalent neurological diseases. For a substantial portion of patients, medication can not control seizures sufficiently, limiting their treatment options. The development of a predictive device for seizures holds the potential to alleviate the anxiety and uncertainty surrounding seizure occurrences, enhance disease understanding, and facilitate prompt administration of interventions when needed. This thesis designs and evaluates various supervised and unsupervised machine learning algorithms for the prediction of epileptic seizures based on intracranial Electroencephalogram. It demonstrates, that fully data-driven methods from the field of deep learning can achieve state-of-the art performance. Moreover, fundamentally different algorithms exhibit similar performance as well as coherence in the erroneous segments when evaluated on non-continuous benchmark data. Furthermore, post-hoc experiments suggest that further improvement on this dataset may be limited. Analysis of continuous data reveals the presence of cyclic and/or directed temporal evolution in the distributions of EEG data for all patients. Unsupervised methods demonstrate clinically relevant performance only for patients exhibiting cyclic temporal evolution in combination with phase-locked seizure occurrences relative to this evolution. Additionally, the study shows that incorporating recent data into the training set leads to increased classification performance, while the inclusion of older data yields only marginal improvements, suggesting that classification primarily relies on recent data. Lastly, the study highlights that performance achieved on benchmark data may be overly optimistic. The definitions that have been used for labelling this data could potentially limit the clinical utility of the classifier. A clinically relevant, fully prospective seizure prediction system based on the proposed classifier is tested and found to perform not significantly better than a random predictor, indicating the challenges of achieving robust predictions in a real-world scenario
Zwischen Sofia und Berlin: Einige Querverweise in den frühen Schaffensjahren des bulgarischen Komponisten Pancho Vladigerov (1899-1978)
No full textSolving general Twisty Puzzles with Reinforcement Learning and automatic Algorithm Generation
No full textThe Rubik's Cube and hundreds of twisty puzzles that were made afterwards have challenged millions of people around the world for decades. Coming up with new solution strategies for a twisty puzzle can be very difficult and often takes even experienced humans many hours.
Recently, Machine Learning has been used to find new solutions to such puzzles with little to no human knowledge required. But these solutions are difficult for humans to replicate as there is little obvious structure.
We propose a method to automatically generate algorithms for puzzles and use them to augment the action set of reinforcement learning agents. The augmented action set enables learning with an intuitive, dense reward function and the resulting agents learn more interpretable solution strategies while using significantly less compute than previous methods to solve the Rubik's Cube.:1 Introduction and motivation 1
1.1 Related work 3
1.2 Mathematical Foundation 4
1.2.1 Permutations 4
1.2.2 Modeling Twisty Puzzles 4
1.2.3 Group Properties of non-bandaged Twisty Puzzles 6
2 Algorithm generation 7
2.1 Automatic Symmetry Detection 8
2.2 Automatic Piece Detection 11
2.3 Algorithm Generation 14
2.4 Reinforcement Learning 18
3 Experiments and Results 21
3.1 Experiment Description 22
3.1.1 Research Questions 22
3.1.2 Investigated Puzzles 22
3.2 Results and Discussion 27
3.2.1 Training behavior 27
3.2.2 Training Time Scaling with State Space Size 28
3.2.3 Policy Analysis 30
3.2.4 Limitations 39
3.3 Further questions 40
3.4 Conclusion 42
A Compute Hardware & training time 47
B Alternative Symmetry Detection 4
