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Ring-Closure Mechanisms Mediated by Laccase to Synthesize Phenothiazines, Phenoxazines, and Phenazines
The green and environmentally friendly synthesis of highly valuable organic substances is one possibility for the utilization of laccases (EC 1.10.3.2). As reactants for the herein described syntheses, different o-substituted arylamines or arylthiols and 2,5-dihydroxybenzoic acid and its derivatives were used. In this way, the formation of phenothiazines, phenoxazines, and phenazines was achieved in aqueous solution mediated by the laccase of Pycnoporus cinnabarinus in the presence of oxygen. Two types of phenothiazines (3-hydroxy- and 3-oxo-phenothiazines) formed in one reaction assay were described for the first time. The cyclization reactions yielded C–N, C–S, or C–O bonds. The syntheses were investigated with regard to the substitution pattern of the reaction partners. Differences in C–S and C–N bond formations without cyclization are discussed.
Biobank Oversight and Sanctions Under the General Data Protection Regulation
This contribution offers an insight into the function and problems of the oversight and sanctions mechanisms outlined in the General Data Protection Regulation as they relate to the biobanking context. These mechanisms might be considered as meta-mechanisms—mechanisms relating to, but not consisting of, substantive legal principles—functioning in tandem to ensure biobank compliance with data protection principles. Each of the mechanisms outlines, on paper at least, comprehensive and impressive compliance architecture—both expanding on their capacity in relation to Directive 95/46. Accordingly, each mechanism looks likely to have a significant and lasting impact on biobanks and biobanking. Despite this comprehensiveness, however, the mechanisms are not immune from critique. Problems appear regarding the standard of protection provided for research subject rights, regarding the disproportionate impact on legitimate interests tied up with the biobanking process—particularly genomic research interests—and regarding their practical implementability in biobanking.
Combining Textual Features for the Detection of Hateful and Offensive Language
The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms.
Leveraging Literals for Knowledge Graph Embeddings
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entity recognition, entity linking, question answering. However, there is a huge computational and storage cost associated with these KG-based applications. Therefore, there arises the necessity of transforming the high dimensional KGs into low dimensional vector spaces, i.e., learning representations for the KGs. Since a KG represents facts in the form of interrelations between entities and also using attributes of entities, the semantics present in both forms should be preserved while transforming the KG into a vector space. Hence, the main focus of this thesis is to deal with the multimodality and multilinguality of literals when utilizing them for the representation learning of KGs. The other task is to extract benchmark datasets with a high level of difficulty for tasks such as link prediction and triple classification. These datasets could be used for evaluating both kind of KG Embeddings, those using literals and those which do not include literals.
Sperm Micromotors for Cargo Delivery through Flowing Blood
Micromotors are recognized as promising candidates for untethered micromanipulation and targeted cargo delivery in complex biological environments. However, their feasibility in the circulatory system has been limited due to the low thrust force exhibited by many of the reported synthetic micromotors, which is not sufficient to overcome the high flow and complex composition of blood. Here we present a hybrid sperm micromotor that can actively swim against flowing blood (continuous and pulsatile) and perform the function of heparin cargo delivery. In this biohybrid system, the sperm flagellum provides a high propulsion force while the synthetic microstructure serves for magnetic guidance and cargo transport. Moreover, single sperm micromotors can assemble into a train-like carrier after magnetization, allowing the transport of multiple sperm or medical cargoes to the area of interest, serving as potential anticoagulant agents to treat blood clots or other diseases in the circulatory system.
An OER Recommender System Supporting Accessibility Requirements
Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
Basic material and technology investigations for material bonded hybrids by continuous hybrid profile fabrication
The development of multi-material hybrids by injection molding has been studied very intensively at the IPF in the past. For that, a material bonding between the different substrates was achieved by using a newly developed two-step curing powder coating material as latent reactive adhesive. The aim of the project “Hybrid Pultrusion” was to perform a novel approach for the fabrication of material bonded metal-plastic joints (profiles) in a modified pultrusion process. Therefore, powder pre-coated steel coil is combined with a glass-fiber reinforced epoxy resin matrix. For initial basic studies, the impregnated fiber material has been applied on the pre-coated steel sheets using the Resin Transfer Molding process (RTM-process). It was proved via lap shear tests, that this procedure resulted in very high adhesive strengths up to 35 MPa resulting from the formation of a covalent matrix-steel bonding as well. In addition, the failure mechanism was subsequently studied. Furthermore, by adapting the successful material combination to the pultrusion process it was demonstrated that material bonded hybrids can be achieved even under these continuous processing conditions.
Contextual Language Models for Knowledge Graph Completion
Knowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the _ne-tuning of the GPT-2 model for triple classi_cation strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed.
Check square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet.
Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned
An increasing number of archival institutions aim to provide public access to historical documents. Ontologies have been designed, developed and utilised to model the archival description of historical documents and to enable interoperability between different information sources. However, due to the heterogeneous nature of archives and archival systems, current ontologies for the representation of archival content do not always cover all existing structural organisation forms equallywell. After briefly contextualising the heterogeneity in the hierarchical structure of German archives, this paper describes and evaluates differences between two archival ontologies, ArDO and RiC-O, and their approaches to modelling hierarchy levels and archive dynamics.