Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps

Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.Comment: Published at EMNLP 201

Automatic Structured Text Summarization with Concept Maps

Efficiently exploring a collection of text documents in order to answer a complex question is a challenge that many people face. As abundant information on almost any topic is electronically available nowadays, supporting tools are needed to ensure that people can profit from the information's availability rather than suffer from the information overload. Structured summaries can help in this situation: They can be used to provide a concise overview of the contents of a document collection, they can reveal interesting relationships and they can be used as a navigation structure to further explore the documents. A concept map, which is a graph representing concepts and their relationships, is a specific form of a structured summary that offers these benefits. However, despite its appealing properties, only a limited amount of research has studied how concept maps can be automatically created to summarize documents. Automating that task is challenging and requires a variety of text processing techniques including information extraction, coreference resolution and summarization. The goal of this thesis is to better understand these challenges and to develop computational models that can address them. As a first contribution, this thesis lays the necessary ground for comparable research on computational models for concept map--based summarization. We propose a precise definition of the task together with suitable evaluation protocols and carry out experimental comparisons of previously proposed methods. As a result, we point out limitations of existing methods and gaps that have to be closed to successfully create summary concept maps. Towards that end, we also release a new benchmark corpus for the task that has been created with a novel, scalable crowdsourcing strategy. Furthermore, we propose new techniques for several subtasks of creating summary concept maps. First, we introduce the usage of predicate-argument analysis for the extraction of concept and relation mentions, which greatly simplifies the development of extraction methods. Second, we demonstrate that a predicate-argument analysis tool can be ported from English to German with low effort, indicating that the extraction technique can also be applied to other languages. We further propose to group concept mentions using pairwise classifications and set partitioning, which significantly improves the quality of the created summary concept maps. We show similar improvements for a new supervised importance estimation model and an optimal subgraph selection procedure. By combining these techniques in a pipeline, we establish a new state-of-the-art for the summarization task. Additionally, we study the use of neural networks to model the summarization problem as a single end-to-end task. While such approaches are not yet competitive with pipeline-based approaches, we report several experiments that illustrate the challenges - mostly related to training data - that currently limit the performance of this technique. We conclude the thesis by presenting a prototype system that demonstrates the use of automatically generated summary concept maps in practice and by pointing out promising directions for future research on the topic of this thesis

Self-Induced Docking Site of a Deeply Embedded Peripheral Membrane Protein

AbstractAs a first step toward understanding the principles of the targeting of C2 domains to membranes, we have carried out a molecular dynamics simulation of the C2 domain of cytosolic phospholipase A2 (cPLA2-C2) in a 1-palmitoyl-2-oleoyl-phosphatidylcholine bilayer at constant pressure and temperature (NPT, 300K and 1atm). Using the high-resolution crystal structure of cPLA2-C2 as a starting point, we embedded two copies of the C2 domain into a preequilibrated membrane at the depth and orientation previously defined by electron paramagnetic resonance (EPR). Noting that in the membrane-bound state the three calcium binding loops are complexed to two calcium ions, we initially restrained the calcium ions at the membrane depth determined by EPR. But the depth and orientation of the domains remained within EPR experimental errors when the restraints were later removed. We find that the thermally disordered, chemically heterogeneous interfacial zones of phosphatidylcholine bilayers allow local lipid remodeling to produce a nearly perfect match to the shape and polarity of the C2 domain, thereby enabling the C2 domain to assemble and optimize its own lipid docking site. The result is a cuplike docking site with a hydrophobic bottom and hydrophilic rim. Contrary to expectations, we did not find direct interactions between the protein-bound calcium ions and lipid headgroups, which were sterically excluded from the calcium binding cleft. Rather, the lipid phosphate groups provided outer-sphere calcium coordination through intervening water molecules. These results show that the combined use of high-resolution protein structures, EPR measurements, and molecular dynamics simulations provides a general approach for analyzing the molecular interactions between membrane-docked proteins and lipid bilayers

NEOTωIST: A relatively Inexpensive Kinetic Impactor Demonstration Mission Concept

Mission concept: NEOTωIST stands for Near-Earth Object Transfer of angular momentum (ω∙I) Spin Test, and is a concept for a kinetic impactor demonstration mission, which aims to change the spin rate of an asteroid by impacting it off-center (Drube et al. 2016, Engel et al. 2016). The change would be measured by means of lightcurve measurements with Earth-based telescopes. In contrast to most other kinetic impactor demonstration mission concepts, NEOTωIST does not require a reconnaissance spacecraft to rendezvous with the target asteroid for orbit change and impact-effect measurements, and is therefore a relatively inexpensive alternative. The NEOTωIST mission would determine the efficiency of momentum transfer (the β-factor) during an impact, and help mature the technology required for a kinetic impactor mission, both of which are important precursor measures for a future space mission to deflect an asteroid by collisional means in an emergency impact hazard situation

Lignite planning, structural change and coal phase-out in Germany

Lignite planning in the Rhineland, central German and Lusatian coalfields is a core spatial development planning task at the federal state and regional levels. The Lignite Planning Information Group and Initiative (Informations- und Initiativkreis Braunkohlenplanung) was founded in 1994 at the ARL - Academy for Territorial Development in the Leibniz Association to provide a platform for expert discussion. Starting from experiences with the Rhineland and the structural upheavals in the new federal states in the early 1990s, it has since continuously addressed new technical and legal requirements involving resettlement, water balance issues, environmental assessments, the energy transition and the common good. Against the backdrop of rapid change and geopolitical events, the combination of structural change and the politically initiated phase-out of lignite-based power generation in a time frame between 'ideally 2030' and no later than the end of 2038 constitutes a challenge that will have to be met by the active players from the perspective of both federal state and regional planning and of regional development. This position paper takes stock of the situation across federal states and across coalfields and describes the required actions for lignite planning as a basis for reaching conclusions about a process with far-reaching national consequences. The various aspects of this process are subject to constant change and call for proactive strategies to exploit opportunities, tap potential, and effectively identify and avoid negative developments

Calpeptin is a potent cathepsin inhibitor and drug candidate for SARS-CoV-2 infections

Several drug screening campaigns identified Calpeptin as a drug candidate against SARS-CoV-2. Initially reported to target the viral main protease (Mpro), its moderate activity in Mpro inhibition assays hints at a second target. Indeed, we show that Calpeptin is an extremely potent cysteine cathepsin inhibitor, a finding additionally supported by X-ray crystallography. Cell infection assays proved Calpeptin’s efficacy against SARS-CoV-2. Treatment of SARS-CoV-2-infected Golden Syrian hamsters with sulfonated Calpeptin at a dose of 1 mg/kg body weight reduces the viral load in the trachea. Despite a higher risk of side effects, an intrinsic advantage in targeting host proteins is their mutational stability in contrast to highly mutable viral targets. Here we show that the inhibition of cathepsins, a protein family of the host organism, by calpeptin is a promising approach for the treatment of SARS-CoV-2 and potentially other viral infections

Massive X-ray screening reveals two allosteric drug binding sites of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous health problems and economical challenges for mankind. To date, no effective drug is available to directly treat the disease and prevent virus spreading. In a search for a drug against COVID-19, we have performed a massive X-ray crystallographic screen of repurposing drug libraries containing 5953 individual compounds against the SARS-CoV-2 main protease (Mpro), which is a potent drug target as it is essential for the virus replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds binding to Mpro. In subsequent cell-based viral reduction assays, one peptidomimetic and five non-peptidic compounds showed antiviral activity at non-toxic concentrations. Interestingly, two compounds bind outside the active site to the native dimer interface in close proximity to the S1 binding pocket. Another compound binds in a cleft between the catalytic and dimerization domain of Mpro. Neither binding site is related to the enzymatic active site and both represent attractive targets for drug development against SARS-CoV-2. This X-ray screening approach thus has the potential to help deliver an approved drug on an accelerated time-scale for this and future pandemics

X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput X-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (M^(pro)), which is essential for viral replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to M^(pro). In subsequent cell-based viral reduction assays, one peptidomimetic and six non-peptidic compounds showed antiviral activity at non-toxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2

Audiovisuelle Medien in E-Learning-Szenarien. Formen der Implementierung audiovisueller Medien in E-Learning-Szenarien in der Hochschule – Forschungsstand und Ausblick

Die Anwendung audiovisueller Medien im Internet findet dank der Verbreitung von Breitbandtechnologien und Web-2.0-Diensten verstärkten Einsatz. Durch die Implementierung audiovisueller Medien in die Hochschullehre werden die didaktischen Möglichkeiten moderner Lehr-Lernszenarien erweitert. Ziel dieser Arbeit ist es, die unterschiedlichen Formen audiovisueller Medien, die in E-Learning-Szenarien in der Hochschullehre Anwendung finden, systematisch darzustellen. Unter Verwendung leitfadengestützter Experteninterviews wurden elf Vertreter aus Hochschulen und angrenzenden Bereichen befragt, die Forschungs- und Entwicklungsarbeit im Bereich der Produktion und Implementierung von audiovisuellen Medien in E-Learning-Szenarien in Hochschulen leisten. Aus den Ergebnissen der Expertenbefragung, konnten fünf Formen audiovisueller Medien, die in E-Learning-Szenarien Anwendung finden, herausgearbeitet werden. Im Rahmen der Ergebnisdarstellung wurden ihre didaktische Anwendung beschrieben und Potenziale und Grenzen diskutiert. (DIPF/ Orig.