A Knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry

Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic

Comparing the Accuracy of Automatic Scoring Solutions for a Text Comprehension Diagramming Intervention

Students typically have great difficulty monitoring their comprehension of textual materials. Completing diagrams about causal relations in expository texts has been a successful intervention to enhance the accuracy of students’ reading comprehension judgments (ie, monitoring accuracy), although there is still room for improvement. Such judgments play a role in crucial self-regulated learning decisions that students make such as allocating time and effort, selecting content for restudy, and/or consulting additional sources. The automated scoring of students’ diagram content can provide a basis for strengthening the diagramming intervention with individual and simultaneous feedback to a high number of students. Leveraging an existing human-coded (correct and incorrect) dataset of 6000+ diagram answers (completed in Dutch by 700+ secondary students), we compared different automatic scoring solutions in terms of classification accuracy. Four computational linguistic models for Dutch were identified and tested in combination with four popular machine learning classification algorithms. The best solution reached 81% accuracy (ie, four out of five answers matched the human coding). Depending on the accuracy required for different applications, these results could be used for fully-or semiautomated scorings of students’ answers to generative activities used in reading comprehension interventions

Retrieving Relevant EU Drone Legislation with Citation Analysis

Can the retrieval of relevant unmanned aircraft systems (UAS) legislation be automated? In this article, references from and to EU legislation are used to create an overview that is subsequently compared to legislation considered relevant by subject-matter experts. The overlap between the results of the citation analysis and the expert overview is promising. Additionally, an approach was proposed and tested where, first, a relatively large number of laws were identified and, second, the laws that were considered relevant were selected. The findings reveal that this approach was successful at retrieving the majority of relevant laws. The results are relevant to researchers, policymakers, practitioners, and laypeople searching for relevant EU legislation on UAS

Hands-on federated analysis of semantic data using the Personal Health Train

The Semantic Web was built for interoperability; for combining and sharing data. The reality is unfortunately that not all data can be shared as-is. Healthcare data is an obvious example due to its privacy-sensitive nature, but other organisations and individuals in general are becoming more aware of the sensitivity and practical problems of sharing data. Additionally the amount of data is increasing exponentially and we need help analysing and unlocking the potential of these data, which will allow for a lot of knowledge and insights to be discovered. The combination of semantic data with Federated Analysis (FA) as described in the Personal Health Train manifesto, will enable machine actionability and re-use of data; the main goal of the FAIR principles. FA techniques (e.g. federated learning, multiparty computation) are rapidly becoming more and more proficient in solving this problem by expanding the ways we can share insights and models without having to share sensitive data. FA is showing a way towards secure and ethical big data analytics, where sensitive data does not need to travel, but allows models to learn from data sets without compromising on privacy and security. Now you know the why, let’s explain the how: In this 4 hour crash course, we will present an open source federated analysis architecture and a real world usecase. This practical application of the Personal Health Train concept will show how federated data analysis can benefit patients, clinicians and researchers. And hopefully also you

Caffeine improves anticipatory processes in task switching

We studied the effects of moderate amounts of caffeine on task switching and task maintenance using mixed-task (AABB) blocks, in which participants alternated predictably between two tasks, and single-task (AAAA, BBBB) blocks. Switch costs refer to longer reaction times (RT) on task switch trials (e.g. AB) compared to task-repeat trials (e.g. BB); mixing costs refer to longer RTs in task-repeat trials compared to single-task trials. In a double-blind, within-subjects experiment, two caffeine doses (3 and 5 mg/kg body weight) and a placebo were administered to 18 coffee drinkers. Both caffeine doses reduced switch costs compared to placebo. Event-related brain potentials revealed a negative deflection developing within the preparatory interval, which was larger for switch than for repeat trials. Caffeine increased this switch-related difference. These results suggest that coffee consumption improves task-switching performance by enhancing anticipatory processing such as task set updating, presumably through the neurochemical effects of caffeine on the dopamine system. (c) 2006 Elsevier B.V. All rights reserved

vantage6/vantage6: 3.8.1

The main vantage6 repository: code for the central server, nodes, CLI and Python Clien