Embedding OWL ontologies with OWL2Vec

In this paper, we present a preliminary study to compute embeddings for OWL 2 ontologies by projecting the ontology axioms into a graph and performing (random) walks over the ontology graph to create a corpus of sentences. This corpus is then given to a neural language model to create concept embeddings. The conducted preliminary evaluation shows promising results

Atlas Data-Challenge 1 on NorduGrid

The first LHC application ever to be executed in a computational Grid environment is the so-called ATLAS Data-Challenge 1, more specifically, the part assigned to the Scandinavian members of the ATLAS Collaboration. Taking advantage of the NorduGrid testbed and tools, physicists from Denmark, Norway and Sweden were able to participate in the overall exercise starting in July 2002 and continuing through the rest of 2002 and the first part of 2003 using solely the NorduGrid environment. This allowed to distribute input data over a wide area, and rely on the NorduGrid resource discovery mechanism to find an optimal cluster for job submission. During the whole Data-Challenge 1, more than 2 TB of input data was processed and more than 2.5 TB of output data was produced by more than 4750 Grid jobs.Comment: Talk from the 2003 Computing in High Energy Physics and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 7 pages, 3 ps figure

High Voltage Powerline Injury Studies

Current pathways and reconstructions of human injury after contact with distribution powerlines are not well understood. The impedance, currents, and modes of tissue destruction are rarely known. Eight anesthetized hogs, weighing 68 to 90 kg, were used in studies with potentials ranging from 2,100 to 14,400 volts. Electrical contact was made between the hindlimbs, from the hindlimb to forelimb, and over other regions of the body. Currents from 4 to 70 amperes rms and impedances ranging from 130 to 477 ohms were measured. Phase angles up to 40° were observed. Copyright © 1981 by The Institute of Electrical and Electronics Engineers, Inc

Enabling Semantic Data Access for Toxicological Risk Assessment

Experimental effort and animal welfare are concerns when exploring the effects a compound has on an organism. Appropriate methods for extrapolating chemical effects can further mitigate these challenges. In this paper we present the efforts to (i) (pre)process and gather data from public and private sources, varying from tabular files to SPARQL endpoints, (ii) integrate the data and represent them as a knowledge graph with richer semantics. This knowledge graph is further applied to facilitate the retrieval of the relevant data for a ecological risk assessment task, extrapolation of effect data, where two prediction techniques are developed

An assertion and alignment correction framework for large scale knowledge bases

Various knowledge bases (KBs) have been constructed via information extraction from encyclopedias, text and tables, as well as alignment of multiple sources. Their usefulness and usability is often limited by quality issues. One common issue is the presence of erroneous assertions and alignments, often caused by lexical or semantic confusion. We study the problem of correcting such assertions and alignments, and present a general correction framework which combines lexical matching, contextaware sub-KB extraction, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated with one set of literal assertions from DBpedia, one set of entity assertions from an enterprise medical KB, and one set of mapping assertions from a music KB constructed by integrating Wikidata, Discogs and MusicBrainz. It has achieved promising results, with a correction rate (i.e., the ratio of the target assertions/alignments that are corrected with right substitutes) of 70.1%, 60.9% and 71.8%, respectively

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. We have applied this knowledge graph to an important task in risk assessment, namely chemical effect prediction. We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance

Expression of cyclin D1a and D1b as predictive factors for treatment response in colorectal cancer.

BACKGROUND: The aim of this study was to investigate the value of the cyclin D1 isoforms D1a and D1b as prognostic factors and their relevance as predictors of response to adjuvant chemotherapy with 5-fluorouracil and levamisole (5-FU/LEV) in colorectal cancer (CRC). METHODS: Protein expression of nuclear cyclin D1a and D1b was assessed by immunohistochemistry in 335 CRC patients treated with surgery alone or with adjuvant therapy using 5-FU/LEV. The prognostic and predictive value of these two molecular markers and clinicopathological factors were evaluated statistically in univariate and multivariate survival analyses. RESULTS: Neither cyclin D1a nor D1b showed any prognostic value in CRC or colon cancer patients. However, high cyclin D1a predicted benefit from adjuvant therapy measured in 5-year relapse-free survival (RFS) and CRC-specific survival (CSS) compared to surgery alone in colon cancer (P=0.012 and P=0.038, respectively) and especially in colon cancer stage III patients (P=0.005 and P=0.019, respectively) in univariate analyses. An interaction between treatment group and cyclin D1a could be shown for RFS (P=0.004) and CSS (P=0.025) in multivariate analysis. CONCLUSION: Our study identifies high cyclin D1a protein expression as a positive predictive factor for the benefit of adjuvant 5-FU/LEV treatment in colon cancer, particularly in stage III colon cancer

Imaginative Representations of Two- and Three-Dimensional Matrices in Children with Nonverbal Learning Disabilities

Children with non-verbal learning disabilities (NLD) are characterized by high verbal and poor non-verbal intelligence, poor cognitive abilities, school difficulties, and—sometimes—depressive symptoms. NLD children lack visuospatial working memory, but it is not clear whether they encounter difficulties in mental imagery tasks. In the present study, NLD adolescents without depressive symptoms, depressed adolescents without NLD symptoms, and a control group were administered a mental imagery task requiring them to imagine to move along the cells of a 2-D (5 × 5) or 3-D (3 × 3 × 3) matrix. Results showed that NLD adolescents had difficulty at performing the imagery task when a 3-D pattern was involved. It is suggested that 3-D mental imagery tasks tap visuospatial processes which are weak in NLD individuals. In addition, their poor cognitive performance cannot be attributed to a depressive state, as the depressed group had a performance similar to that of controls

Invasive Electrical Impedance Tomography for Blood Vessel Detection

We present a novel method for localization of large blood vessels using a bioimpedance based needle positioning system on an array of ten monopolar needle electrodes. The purpose of the study is to develop a portable, low cost tool for rapid vascular access for cooling and controlled reperfusion of cardiac arrest patients. Preliminary results show that localization of blood vessels is feasible with this method, but larger studies are necessary to improve the technology