This paper explores the challenges of resolving conflicting feelings around talking with a child about their terminal prognosis. When children are left out of such conversations it is usually done with good intent, with a parent wishing to protect their child from anxiety or loss of hope. There is however growing evidence that sensitive, timely, age appropriate information from those with whom children have a good relationship is helpful both for the child and their family. There is no evidence that involving children in sensitive and timely discussions creates significant problems, rather that withholding information may lead to confusion, frustration, distress and anger. The authors discuss ways in which families can be supported to have these significant conversations with their children.
A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.
Purpose – The purpose of this paper is to describe a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. Design/methodology/approach – A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked open data standards provided the knowledge representation technique for the data set underlying the network. Findings – The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real-world scenario. The data set from which the network is based is accessible via an application programming interface and, thus, shareable with the knowledge management community for reuse and mash-ups. Originality/value – This paper presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying data set, making the data readable across platforms and linkable with external data sets. This approach has the potential to make social networks dynamic and open to integration with external data sources.
The physical effort required to seek out and extract a resource is an important consideration for a foraging animal. A second consideration is the variability or risk associated with resource delivery. An intriguing observation from ethological studies is that animals shift their preference from stable to variable food sources under conditions of increased physical effort or falling energetic reserves. Although theoretical models for this effect exist, no exploration into its biological basis has been pursued. Recent advances in understanding the neural basis of effort- and risk-guided decision making suggest that opportunities exist for determining how effort influences risk preference. In this review, we describe the intersection between the neural systems involved in effort- and risk-guided decision making and outline two mechanisms by which effort-induced changes in dopamine release may increase the preference for variable rewards.
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