Disentangling genetic and environmental risk factors for individual diseases from multiplex comorbidity networks

Most disorders are caused by a combination of multiple genetic and/or environmental factors. If two diseases are caused by the same molecular mechanism, they tend to co-occur in patients. Here we provide a quantitative method to disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with disease co-occurrence data obtained from almost two million patients. From this data we construct a multiplex network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity of the patients or the involvement of a certain molecular mechanism. From the similarity of phenotypic and mechanism-based networks for each disorder we derive measure that allows us to quantify the relative importance of various molecular mechanisms for a given disease. We find that most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. Almost never we find that more than one type of mechanisms is involved in the pathogenesis of diseases

Emotionalization in the Media Coverage of Honey Bee Colony Losses

Emotionalization is increasingly used in the daily news. However, communication scholars have only just begun to explore how journalists use emotionalization in coverage of scientific and environmental topics. This study contributes to filling this research gap by investigating emotionalization in reporting on honey bee colony losses. The aim of the study is to analyze the amount of emotionalization that took place, as well as to observe changes over time. Emotionalization is assessed in two ways; by analyzing to what extent journalists (1) explicitly mentioned discrete emotions in news stories (joy, hope, fear, anger, etc.) and/or (2) used rhetorical devices to evoke emotions (affective vocabulary, metaphors, colloquial language, superlatives, etc.). Results from a quantitative content analysis of four Austrian newspapers in 2010/2011, 2013/2014, and 2017/2018 show that the coverage is highly emotionalized across all three time periods studied. Emotionalization occurs far more often by using rhetorical devices than by explicitly mentioning positive or negative emotions. Interestingly, the incorporation of emotional elements and scientific expertise in the news items do not exclude one another. Hence, there seems to be no strict dichotomy between rational/objective and emotional reporting

Holzschädlinge und Holzschutz im Terminologievergleich Deutsch-Tschechisch

Die vorliegende Diplomarbeit beschäftigt sich mit den Fachsprachen im Bereich der Holzschädlinge und des Holzschutzes. Das Ziel dieser Diplomarbeit war einerseits die fachspezifische Terminologie im Deutschen und im Tschechischen aus der verfügbaren Fachliteratur zu erfassen und andererseits sie mit der Gegensprache zu vergleichen. Die Diplomarbeit dient sowohl als einführende Fachlektüre als auch Glossar für Übersetzer, wenn sie bei einer Übersetzung mir der Fachterminologie aus diesen Bereichen konfrontiert werden. Die Diplomarbeit gliedert sich in zwei Teile, wobei im ersten Teil zunächst die Anatomie und Chemie des Holzes dargestellt wird. Des weiterem werden hier die wichtigsten pflanzlichen und tierischen Vertreter der Holzschädlinge präsentiert. Schließlich wird der Holzschutz, d.h. vorbeugender, baulicher, chemischer Holzschutz sowie Bekämpfungsmethoden kurz erläutert. Der zweite Teil der Diplomarbeit bildet das Glossar. Im Glossar werden die einzelnen Fachtermini gegenübergestellt bzw. miteinander verglichen und durch Definitionen und einem Kontext, in dem der Terminus verwendet wird, ergänzt

Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty

Applying a machine learning model for decision-making in the real world requires to distinguish what the model knows from what it does not. A critical factor in assessing the knowledge of a model is to quantify its predictive uncertainty. Predictive uncertainty is commonly measured by the entropy of the Bayesian model average (BMA) predictive distribution. Yet, the properness of this current measure of predictive uncertainty was recently questioned. We provide new insights regarding those limitations. Our analyses show that the current measure erroneously assumes that the BMA predictive distribution is equivalent to the predictive distribution of the true model that generated the dataset. Consequently, we introduce a theoretically grounded measure to overcome these limitations. We experimentally verify the benefits of our introduced measure of predictive uncertainty. We find that our introduced measure behaves more reasonably in controlled synthetic tasks. Moreover, our evaluations on ImageNet demonstrate that our introduced measure is advantageous in real-world applications utilizing predictive uncertainty.Comment: M3L & InfoCog Workshops NeurIPS 2

Quantification of Uncertainty with Adversarial Models

Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a divergence function and the posterior. Current methods such as Deep Ensembles or MC dropout underperform at estimating the epistemic uncertainty, since they primarily consider the posterior when sampling models. We suggest Quantification of Uncertainty with Adversarial Models (QUAM) to better estimate the epistemic uncertainty. QUAM identifies regions where the whole product under the integral is large, not just the posterior. Consequently, QUAM has lower approximation error of the epistemic uncertainty compared to previous methods. Models for which the product is large correspond to adversarial models (not adversarial examples!). Adversarial models have both a high posterior as well as a high divergence between their predictions and that of a reference model. Our experiments show that QUAM excels in capturing epistemic uncertainty for deep learning models and outperforms previous methods on challenging tasks in the vision domain

Kräuter für Nutz- und Heimtiere: Ratgeber für die Anwendung ausgewählter Heil- und Gewürzpflanzen

Dieser Ratgeber führt wissenschaftliche Erkenntnisse und traditionelles Hausmittelwissen zusammen, beleuchtet alles Wissenswerte zu über 50 Heilpflanzen und gibt konkrete Anwendungsbeispiele. Ziel ist es, altbewährte Pflanzenanwendungen wieder mehr in die moderne Tierhaltung einzubinden. Der anwenderorientierte Aufbau des Buches ermöglicht es dem Leser, Kenntnisse über die verschiedenen Zubereitungen und Anwendungen von Heilpflanzen zu erwerben und diese in der Praxis einzusetzen. Zubereitung, Aufbewahrung und Anwendung von Kräutern, sowie deren Wirkung und Einsatz bei einzelnen Tierarten werden ausführlich dargestellt. „Die Aufgabe heutiger Wissenschaft ist weniger die Suche nach neuen wirksamen Pflanzen, vielmehr die Überprüfung und Absicherung dieses althergebrachten Wissensschatzes im Lichte moderner Erkenntnisse. Das Autorenteam setzt sich aus jungen engagierten Wissenschaftlern und Tierärzten zusammen. Ihnen ist es ein großes Anliegen, dass die Erkenntnisse der Kräuterheilkunde möglichst vielen Tierhaltern – insbesondere ihren Tieren – von Nutzen sein werden. Es bleibt der Wunsch: die vielen praktischen Anleitungen mögen einen starken Impuls zur Wiederbelebung der Kräuterheilkunde bei Tieren geben.“ Dr. Gerhard Plakol

Identification of Basophils as a Major Source of Hepatocyte Growth Factor in Chronic Myeloid Leukemia: A Novel Mechanism of BCR-ABL1-Independent Disease Progression

AbstractChronic myeloid leukemia (CML) is a hematopoietic neoplasm characterized by the Philadelphia chromosome and the related BCR-ABL1 oncoprotein. Acceleration of CML is usually accompanied by basophilia. Several proangiogenic molecules have been implicated in disease acceleration, including the hepatocyte growth factor (HGF). However, little is known so far about the cellular distribution and function of HGF in CML. We here report that HGF is expressed abundantly in purified CML basophils and in the basophil-committed CML line KU812, whereas all other cell types examined expressed only trace amounts of HGF or no HGF. Interleukin 3, a major regulator of human basophils, was found to promote HGF expression in CML basophils. By contrast, BCR-ABL1 failed to induce HGF synthesis in CML cells, and imatinib failed to inhibit expression of HGF in these cells. Recombinant HGF as well as basophil-derived HGF induced endothelial cell migration in a scratch wound assay, and these effects of HGF were reverted by an anti-HGF antibody as well as by pharmacologic c-Met inhibitors. In addition, anti-HGF and c-Met inhibitors were found to suppress the spontaneous growth of KU812 cells, suggesting autocrine growth regulation. Together, HGF is a BCR-ABL1-independent angiogenic and autocrine growth regulator in CML. Basophils are a unique source of HGF in these patients and may play a more active role in disease-associated angiogenesis and disease progression than has so far been assumed. Our data also suggest that HGF and c-Met are potential therapeutic targets in CML

When local poverty is more important than your income: Mental health in minorities in inner cities

Volkswagen Foundation and the German Federal Ministry for Education and Research . Grant Number: BMBF 01 EL080