4,829 research outputs found

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Exploring new avenues for the meta-analysis method in personality and social psychology research

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    This dissertation addresses theoretical validity and bias in meta-analytic research in personality and social psychology research. The conceptual starting point of the dissertation is research on ego depletion (Baumeister et al., 1998). In this line of research, hundreds of studies documented an experimental effect that probably does not exist, as was later revealed by extensive replication work (Hagger et al., 2010, 2016). This debacle has presumably been caused by dysfunctional structures and procedures in psychological science, such as widespread publication bias (Carter & McCullough, 2014). Unfortunately, these dysfunctionalities were (and in some cases still are) also prevalent in other areas of psychological research beside ego depletion (Ferguson & Brannick, 2012; Open Science Collaboration, 2015). Because extensive replication research is too costly to be repeated for all past work, it has been a contentious question what to do with research data that has been generated during an era of questionable research practices: should this research be abandoned or can some of it be salvaged? In four research papers, this dissertation project attempts to address these questions. In part I of the dissertation project, two papers highlight and analyze challenges when summarizing past research in social psychology and personality research. Paper 1 (Friese et al., 2017) attempted to find summary evidence for the effectiveness of self-control training, a research field related to ego depletion, but came to a sobering conclusion: The summary effect was small, likely inflated by publication bias, and could not be attributed beyond doubt to a theoretical mechanism. Paper 2 (Friese & Frankenbach, 2020) reported on a simulation study that showed how multiple sources of bias (publication bias, p-hacking) can interact with contextual factors and each other to create significant meta-analytic evidence from very small or even zero true effects. Part II of the dissertation project is an attempt to advance social-psychological and personality theory with meta-scientific work despite an unknowable risk of bias in the literature. In part II, two papers (Frankenbach et al., 2020, 2022) make use of one key idea: Re-using existing raw research data to test novel theoretical ideas in secondary (meta-)analyses. Results revealed that this idea helps towards both goals of the dissertation project, that is, advancing theory while reducing risk-of-bias. The general discussion analyses promises and limitations of such secondary data analyses in more detail and attempts to situate the idea more broadly in the psychological research toolkit by contrasting integrative versus innovative research. Further discussion covers how conceptual and technological innovations may facilitate more secondary data analyses in the future, and how such advances may pave the way for a slower, more incremental, but truly valid and cumulative psychological science.Die vorliegende Dissertation behandelt theoretischen Validität und Verzerrung (Bias) von meta-analytischer Forschung in der Persönlichkeits- und Sozialpsychologie. Der konzeptuelle Ausgangspunkt der Dissertation ist die Forschung zu „Ego Depletion“ (Baumeister et al., 1998). In dieser Forschungslinie haben hunderte von Studien einen Effekt belegt, der, wie sich später durch umfangreiche Replikationsarbeiten (Hagger et al., 2010, 2016) herausstellte, vermutlich nicht existiert. Dieses Debakel wurde mutmaßlich mitverursacht durch dysfunktionale Strukturen und Prozesse in der psychologischen Forschung, insbesondere Publikationsbias („publication bias“). Unglücklicherweise lagen (und liegen) diese Dysfunktionalitäten neben Ego Depletion auch in anderen psychologischen Forschungsbereichen vor (Ferguson & Brannick, 2012; Open Science Collaboration, 2015). Da aus Kostengründen nicht alle Forschungsarbeiten der Vergangenheit repliziert werden können, ergibt sich eine kritische Frage: Wie soll mit psychologischer Forschung umgegangen werden, die unter mutmaßlich verzerrenden Bedingungen generiert wurde? Sollte diese Forschung ad acta gelegt werden oder können Teile davon weiterverwendet werden? Das vorliegende Dissertationsprojekt versucht im Rahmen von vier Forschungsbeiträgen sich diesen Fragen anzunähern. Im ersten Teil der Dissertation beleuchten und analysieren zwei Forschungsbeiträge Probleme und Herausforderungen, die sich bei der Zusammenfassung von bestehender Forschung der Sozial- und Persönlichkeitspsychologie ergeben. Der erste Beitrag (Friese et al., 2017) versucht in einer Meta-Analyse Evidenz für die Wirksamkeit von Selbstkontrolltrainings zu finden, aber kommt zu einem ernüchternden Ergebnis: Die Gesamteffekte sind klein, mutmaßlich durch Publikationsbias fälschlich überhöht und können überdies nicht zweifelsfrei einem theoretischen Kausalmechanismus zugeordnet werden. Der zweite Beitrag (Friese & Frankenbach, 2020) umfasst eine Simulationsstudie, die aufzeigt, wie verschiedene Formen von Bias (Publikationsbias und sog. „p-hacking“) miteinander und mit Kontextfaktoren interagieren können, wodurch signifikante, meta-analytische Effekte aus sehr kleinen wahren Effekten oder sogar Nulleffekten entstehen können. Der zweite Teil der Dissertation versucht, trotz eines unbestimmbaren Bias-Risikos, Fortschritte in der sozial- und persönlichkeitspsychologischen Theorie zu erzielen. Zu diesem Zweck wird in zwei Forschungsbeiträgen (Frankenbach et al., 2020, 2022) auf eine Schlüssel-Idee zurückgegriffen: Die Testung von neuen theoretischen Hypothesen unter Wiederverwendung von existierenden Forschungsdaten in Sekundärdatenanalysen. Die Ergebnisse zeigen, dass dieser Ansatz tatsächlich dazu beitragen kann, theoretische Fortschritte mit vermindertem Verzerrungsrisiko zu machen. Die anschließende, übergreifende Diskussion behandelt Möglichkeiten und Limitationen solcher Sekundärdatenanalysen und versucht, den Ansatz in einer Gegenüberstellung von integrativer und innovativer Forschung übergreifender in die psychologische Forschungsmethodik einzuordnen. Im Weiteren wird diskutiert, wie konzeptuelle und technologische Entwicklungen in der Zukunft Sekundärdatenanalysen erleichtern könnten und wie solche Fortschritte den Weg ebnen könnten für eine langsamere, inkrementelle, aber wahrhaft valide und kumulative psychologische Wissenschaft.German Research Foundation (DFG): "Die Rolle mentaler Anstrengung bei Ego Depletion

    Assessing Atmospheric Pollution and Its Impacts on the Human Health

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    This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

    Specificity of the innate immune responses to different classes of non-tuberculous mycobacteria

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    Mycobacterium avium is the most common nontuberculous mycobacterium (NTM) species causing infectious disease. Here, we characterized a M. avium infection model in zebrafish larvae, and compared it to M. marinum infection, a model of tuberculosis. M. avium bacteria are efficiently phagocytosed and frequently induce granuloma-like structures in zebrafish larvae. Although macrophages can respond to both mycobacterial infections, their migration speed is faster in infections caused by M. marinum. Tlr2 is conservatively involved in most aspects of the defense against both mycobacterial infections. However, Tlr2 has a function in the migration speed of macrophages and neutrophils to infection sites with M. marinum that is not observed with M. avium. Using RNAseq analysis, we found a distinct transcriptome response in cytokine-cytokine receptor interaction for M. avium and M. marinum infection. In addition, we found differences in gene expression in metabolic pathways, phagosome formation, matrix remodeling, and apoptosis in response to these mycobacterial infections. In conclusion, we characterized a new M. avium infection model in zebrafish that can be further used in studying pathological mechanisms for NTM-caused diseases

    Short-term forecast techniques for energy management systems in microgrid applications

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    A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency
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