EFFECT OF THE LOCATION OF THE FOOT IMPACT POINT ON BALL VELOCITY IN A SOCCER PENALTY KICK

The aim of this study was to identify the impact point on the foot that maximizes ball velocity in a soccer instep penalty kick. One male player performed 23 maximum-effort penalty kicks using a wide range of impact points along the length of his foot. The kicks were recorded by a video camera at 100 Hz and a biomechanical analysis was conducted to obtain measures of impact point, ball projection velocity, and kinematics of the kicking leg. We found that ball velocity was insensitive to the location of the impact point (at least for positions between the ankle joint and the base of the toes). This result suggests that players should consider other factors (such as shot accuracy, shot reliability, and foot comfort) when selecting the impact point

HarriGT:A Tool for Linking News to Science

Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society. Linking these articles is challenging because the language used in the two domains is very different, and the gathering of online resources to align the two is a substantial information retrieval endeavour. We present HarriGT, a semi-automated tool for building corpora of news articles linked to the scientific papers that they discuss. Our aim is to facilitate future development of information-retrieval tools for newspaper/scientific work citation linking. HarriGT retrieves newspaper articles from an archive containing 17 years of UK web content. It also integrates with 3 large external citation networks, leveraging named entity extraction, and document classification to surface relevant examples of scientific literature to the user. We also provide a tuned candidate ranking algorithm to highlight potential links between scientific papers and newspaper articles to the user, in order of likelihood. HarriGT is provided as an open source tool (http://harrigt.xyz).preprintPeer reviewe

Natural language processing methods for detecting and measuring the impact of scientific work beyond academia

Scientific research has a profoundly important impact on our society and the environment. However, the multifaceted nature of this impact makes it particularly difficult to measure and, as shown in this thesis, it cannot be measured using traditional academic impact metrics that focus on counting citations and publications. Furthermore, existing societal and environmental impact metrics are only applicable to one scientific discipline or geography or are expensive processes run irregularly by government agencies. This thesis investigates natural language processing methods for identifying and measuring societal and environmental scientific impact and how such impact is reported in the news. A novel regression task and model are presented for identifying and quantifying this impact based on text extracted from scientific papers and news articles that discuss them. This is enabled by developing methods for linking and comparing news articles with academic papers that they discuss, whilst accounting for the structural and linguistic differences between the two types of document. Text encoding strategies for representation and comparison of long documents are also a focus of the thesis. A new cross-domain, co-reference resolution task between news articles and scientific papers is introduced so that co-referring entities may be used as anchors for aligning the two types of documents. Through comparisons of news article excerpts and sentences from corresponding scientific papers, it is shown that scientific discourse structure and argumentation in scientific papers is a likely predictor of which information will be presented prominently in news articles. This work introduces several novel natural language task settings for which no pre existing data sets exist. This has necessitated the production of new human-annotated datasets which were built using bespoke annotation tools that use semi-supervised learning to accelerate the labelling process and minimise the cognitive load of the task on the annotator. The thesis also makes use of low resource approaches including few-shot and multi-task learning to facilitate the development of accurate models with small data-sets. The resulting annotated data-sets, annotation tools and guidelines along with state-of-the art machine learning models are all made available as open assets. This thesis contributes new ways to measure societal and environmental impact of scientific work and help scientists and funding bodies understand how work is being used by others, justify the spending of public funding and inform better public engagement

Measuring scientific impact beyond academia:An assessment of existing impact metrics and proposed improvements

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature

CDˆ2CR:Co-reference resolution across documents and domains

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD$^2$CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD$^2$CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.Comment: 9 pages, 5 figures, accepted at EACL 202

Cardiac Non-myocyte Cells Show Enhanced Pharmacological Function Suggestive of Contractile Maturity in Stem Cell Derived Cardiomyocyte Microtissues

The immature phenotype of stem cell derived cardiomyocytes is a significant barrier to their use in translational medicine and pre-clinical in vitro drug toxicity and pharmacological analysis. Here we have assessed the contribution of non-myocyte cells on the contractile function of co-cultured human embryonic stem cell derived cardiomyocytes (hESC-CMs) in spheroid microtissue format. Microtissues were formed using a scaffold free 96-well cell suspension method from hESC-CM cultured alone (CM microtissues) or in combination with human primary cardiac microvascular endothelial cells and cardiac fibroblasts (CMEF microtissues). Contractility was characterized with fluorescence and video-based edge detection. CMEF microtissues displayed greater Ca(2+ )transient amplitudes, enhanced spontaneous contraction rate and remarkably enhanced contractile function in response to both positive and negative inotropic drugs, suggesting a more mature contractile phenotype than CM microtissues. In addition, for several drugs the enhanced contractile response was not apparent when endothelial cell or fibroblasts from a non-cardiac tissue were used as the ancillary cells. Further evidence of maturity for CMEF microtissues was shown with increased expression of genes that encode proteins critical in cardiac Ca(2+ )handling (S100A1), sarcomere assembly (telethonin/TCAP) and β-adrenergic receptor signalling. Our data shows that compared with single cell-type cardiomyocyte in vitro models, CMEF microtissues are superior at predicting the inotropic effects of drugs, demonstrating the critical contribution of cardiac non-myocyte cells in mediating functional cardiotoxicity