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In this poster we would like to present the new architecture and features of EVT 3 (Edition Visualization Technology), an open source Angular-based tool for the visualization of TEI-XML Digital Scholarly Editions. We will use and showcase two digital philology projects as test bed for the new functionalities.

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.

This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.

Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials.

Objective: This study explored the effectiveness of search filters in identifying sex- and gender-specific data in health promotion studies that are indexed in MEDLINE.Methods: Literature searches were conducted to identify studies on patient or consumer attitudes and behaviors toward colorectal cancer screening, nutritional labeling, and influenza vaccination. Publications reporting sex- or gender-specific outcome data constituted the gold standards for this study. The sensitivity and precision of previously published gender-specific filters, as well as individual filter component terms, were calculated and compared with values identified in prior studies.Results: The sensitivity and precision of published sex or gender filters varied across topics. Sensitivity values ranged from 14.3% to 92.5%, while precision varied from 17.9% to 51.4%. These filters were less sensitive and less precise in their identification of relevant studies than has been reported in previous studies. Further, while the MEDLINE Medical Subject Headings (MeSH) term “Sex Factors” achieved the greatest average precision (59.3%) of any individual filter term, the MEDLINE check tag “Female” returned the highest average sensitivity (90.1%), with an average precision of 25.0% across topics.Conclusions: Although search filters can facilitate the identification of research evidence to enable decision making, variability in study abstracting and indexing can limit the generalizability and usability of these filters. This potential for variability should be considered when deciding to incorporate a search filter into any literature search. This research highlights the importance of this awareness when developing strategies for searching the published literature and the potential value of supplementing database searching with other methods of study identification.

This article deals with formulating a suitable title and an appropriate abstract for an original research paper. The "title" and the "abstract" are the "initial impressions" of a research article, and hence they need to be drafted correctly, accurately, carefully, and meticulously. Often both of these are drafted after the full manuscript is ready. Most readers read only the title and the abstract of a research paper and very few will go on to read the full paper. The title and the abstract are the most important parts of a research paper and should be pleasant to read. The "title" should be descriptive, direct, accurate, appropriate, interesting, concise, precise, unique, and should not be misleading. The "abstract" needs to be simple, specific, clear, unbiased, honest, concise, precise, stand-alone, complete, scholarly, (preferably) structured, and should not be misrepresentative. The abstract should be consistent with the main text of the paper, especially after a revision is made to the paper and should include the key message prominently. It is very important to include the most important words and terms (the "keywords") in the title and the abstract for appropriate indexing purpose and for retrieval from the search engines and scientific databases. Such keywords should be listed after the abstract. One must adhere to the instructions laid down by the target journal with regard to the style and number of words permitted for the title and the abstract.

Aim Electronic formats (ePROs) of paper-based patient-reported outcomes (PROs) should be validated before they can be reliably used. This review aimed to examine studies investigating measurement equivalence between ePROs and their paper originals to identify methodologies used and to determine the extent of such validation. Methods Three databases (OvidSP, Web of Science and PubMed) were searched using a set of keywords. Results were examined for compliance with inclusion criteria. Articles or abstracts that directly compared screen-based electronic versions of PROs with their validated paper-based originals, with regard to their measurement equivalence, were included. Publications were excluded if the only instruments reported were stand-alone visual analogue scales or interactive voice response formats. Papers published before 2007 were excluded, as a previous meta-analysis examined papers published before this time. Results Fifty-five studies investigating 79 instruments met the inclusion criteria. 53 % of the 79 instruments studied were condition specific. Several instruments, such as the SF-36, were reported in more than one publication. The most frequently reported formats for ePROs were Web-based versions. In 78 % of the publications, there was evidence of equivalence or comparability between the two formats as judged by study authors. Of the 30 publications that provided preference data, 87 % found that overall participants preferred the electronic format. Conclusions When examining equivalence between paper and electronic versions of PROs, formats are usually judged by authors to be equivalent. Participants prefer electronic formats. This literature review gives encouragement to the further widespread development and use of ePROs.