Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Profiling exploratory browsing behaviour with a semantic data browser.

Semantic Web technologies are increasingly being adopted for aggregating Web data. Tools such as Semantic Data Browsers have been proposed to assist users to access and make sense of the vast semantic space. However, further investigations are needed to understand how users make use of the additional semantic features provided by these new breed of browsers and their effectiveness in supporting exploration of a domain. Measurements of browsing behaviour in a semantic space are also needed. Using the log data from a semantic browser (MusicPinta) for the music domain, this paper takes the first step in profiling browsing behaviour of users in a semantic space and compares the outcome against their task performance. Two exploratory search tasks were designed for the experiment. Movements in terms of users traversing the provided semantic links in the browser were captured and the patterns of clicks between abstract and concrete concepts were analysed

Software Sustainability: The Modern Tower of Babel

The development of sustainable software has been identified as one of the key challenges in the field of computational science and engineering. However, there is currently no agreed definition of the concept. Current definitions range from a composite, non-functional requirement to simply an emergent property. This lack of clarity leads to confusion, and potentially to ineffective and inefficient efforts to develop sustainable software systems. The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability? The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved

Histone H3-wild type diffuse midline gliomas with H3K27me3 loss are a distinct entity with exclusive EGFR or ACVR1 mutation and differential methylation of homeobox genes

Diffuse midline gliomas (DMG) harbouring H3K27M mutation are paediatric tumours with a dismal outcome. Recently, a new subtype of midline gliomas has been described with similar features to DMG, including loss of H3K27 trimethylation, but lacking the canonical H3K27M mutation (H3-WT). Here, we report a cohort of five H3-WT tumours profiled by whole-genome sequencing, RNA sequencing and DNA methylation profiling and combine their analysis with previously published cases. We show that these tumours have recurrent and mutually exclusive mutations in either ACVR1 or EGFR and are characterised by high expression of EZHIP associated to its promoter hypomethylation. Affected patients share a similar poor prognosis as patients with H3K27M DMG. Global molecular analysis of H3-WT and H3K27M DMG reveal distinct transcriptome and methylome profiles including differential methylation of homeobox genes involved in development and cellular differentiation. Patients have distinct clinical features, with a trend demonstrating ACVR1 mutations occurring in H3-WT tumours at an older age. This in-depth exploration of H3-WT tumours further characterises this novel DMG, H3K27-altered sub-group, characterised by a specific immunohistochemistry profile with H3K27me3 loss, wild-type H3K27M and positive EZHIP. It also gives new insights into the possible mechanism and pathway regulation in these tumours, potentially opening new therapeutic avenues for these tumours which have no known effective treatment. This study has been retrospectively registered on clinicaltrial.gov on 8 November 2017 under the registration number NCT03336931 (https://clinicaltrials.gov/ct2/show/NCT03336931)

A novel transcriptional signature identifies T-cell infiltration in high-risk paediatric cancer

Background: Molecular profiling of the tumour immune microenvironment (TIME) has enabled the rational choice of immunotherapies in some adult cancers. In contrast, the TIME of paediatric cancers is relatively unexplored. We speculated that a more refined appreciation of the TIME in childhood cancers, rather than a reliance on commonly used biomarkers such as tumour mutation burden (TMB), neoantigen load and PD-L1 expression, is an essential prerequisite for improved immunotherapies in childhood solid cancers. Methods: We combined immunohistochemistry (IHC) with RNA sequencing and whole-genome sequencing across a diverse spectrum of high-risk paediatric cancers to develop an alternative, expression-based signature associated with CD8+ T-cell infiltration of the TIME. Furthermore, we explored transcriptional features of immune archetypes and T-cell receptor sequencing diversity, assessed the relationship between CD8+ and CD4+ abundance by IHC and deconvolution predictions and assessed the common adult biomarkers such as neoantigen load and TMB. Results: A novel 15-gene immune signature, Immune Paediatric Signature Score (IPASS), was identified. Using this signature, we estimate up to 31% of high-risk cancers harbour infiltrating T-cells. In addition, we showed that PD-L1 protein expression is poorly correlated with PD-L1 RNA expression and TMB and neoantigen load are not predictive of T-cell infiltration in paediatrics. Furthermore, deconvolution algorithms are only weakly correlated with IHC measurements of T-cells. Conclusions: Our data provides new insights into the variable immune-suppressive mechanisms dampening responses in paediatric solid cancers. Effective immune-based interventions in high-risk paediatric cancer will require individualised analysis of the TIME

In vitro and in vivo drug screens of tumor cells identify novel therapies for high-risk child cancer

Biomarkers which better match anticancer drugs with cancer driver genes hold the promise of improved clinical responses and cure rates. We developed a precision medicine platform of rapid high-throughput drug screening (HTS) and patient-derived xenografting (PDX) of primary tumor tissue, and evaluated its potential for treatment identification among 56 consecutively enrolled high-risk pediatric cancer patients, compared with conventional molecular genomics and transcriptomics. Drug hits were seen in the majority of HTS and PDX screens, which identified therapeutic options for 10 patients for whom no targetable molecular lesions could be found. Screens also provided orthogonal proof of drug efficacy suggested by molecular analyses and negative results for some molecular findings. We identified treatment options across the whole testing platform for 70% of patients. Only molecular therapeutic recommendations were provided to treating oncologists and led to a change in therapy in 53% of patients, of whom 29% had clinical benefit. These data indicate that in vitro and in vivo drug screening of tumor cells could increase therapeutic options and improve clinical outcomes for high-risk pediatric cancer patients

What is the Real Problem? Using Corpus Data to Tailor a Community Environment for Dissertation Writing

Training in soft skills is becoming paramount in today’s educational and societal climate, and receives increasing attention in the area of intelligent learning environments for ill-defined domains. We present a study that analyses written feedback given to undergraduate students by tutors at a key stage of dissertation preparation. This allows us to identify key problems students are facing and to understand how these problems are articulated and addressed by tutors. The results of the study are applied to tailor an existing social semantic web environment (AWESOME Dissertation) to address the needs of a particular community for dissertation writing in Computing

Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach.

Sense making is at the heart of cognitively complex and data intensive decision making processes. It is often conducted in collective spaces through exchange of ideas, discussions, analysing situations, and exploring alternatives. This position paper proposes a novel approach to facilitate collective sense making via a collaboration platform which (a) offers multiple views to collaboration (including forums, mind maps, and argumentation structure), and (b) provides intelligent support to understand sense making behaviour by employing user and community modelling techniques. The work is conducted in the framework of the EU funded Dicode project, developing intelligent services for data-intensive collaboration and decision making

I-CAW: intelligent data browser for informal learning on cultural variations in interpersonal communications.

This demonstration will present a system, called I-CAW (Intelligent Content Assembly Workbench), which aggregates content from social spaces into a semantically-enriched data browser to promote informal learning. The work pioneers a new way to interact with social content using nudges (in the form of signposts and prompts) that exploit ontologies and semantically augmented content. The demonstration will offer explanation on the underpinning ontology, how cultural variations are handled, possible application domains and hands-on experience with I-CAW which has been populated with culturally-rich social content for learners wishing to explore variations in interpreting body language for interpersonal communications