Towards high-throughput 3D insect capture for species discovery and diagnostics

Digitisation of natural history collections not only preserves precious information about biological diversity, it also enables us to share, analyse, annotate and compare specimens to gain new insights. High-resolution, full-colour 3D capture of biological specimens yields color and geometry information complementary to other techniques (e.g., 2D capture, electron scanning and micro computed tomography). However 3D colour capture of small specimens is slow for reasons including specimen handling, the narrow depth of field of high magnification optics, and the large number of images required to resolve complex shapes of specimens. In this paper, we outline techniques to accelerate 3D image capture, including using a desktop robotic arm to automate the insect handling process; using a calibrated pan-tilt rig to avoid attaching calibration targets to specimens; using light field cameras to capture images at an extended depth of field in one shot; and using 3D Web and mixed reality tools to facilitate the annotation, distribution and visualisation of 3D digital models.Comment: 2 pages, 1 figure, for BigDig workshop at 2017 eScience conferenc

HBIM application in historic timber structures: A systematic review

Despite the recent significant increase on the use of Building Information Modelling (BIM) in the cultural heritage field, its application on heritage timber structures aiming at their conservation and assessment has not yet been fully established. Comparing with other construction materials, timber presents singular features that must be addressed in order to carry out a proper condition assessment. For this reason, this review summarizes existing works on historical timber structures using Historical BIM (HBIM), focusing not only on various geometric surveying and 3D modelling methods, but also on nongeometric information included in the model which are especially related with conservation, testing, and monitoring. In addition, this work illustrates the effectiveness increase given by a structural analysis, as to assess structural heath, after being implemented within a HBIM-based framework. To that aim, a global framework is proposed where the development and implementation level of different analysis stages are described.This work has been supported by Xunta de Galicia through grant GRC-ED431C 2020/01. This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 769255. The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the Innovation and Networks Executive Agency (INEA) nor the European Commission are responsible for any use that may be made of the information contained therein

Computational Toxinology

Venoms are complex mixtures of biological macromolecules and other compounds that are used for predatory and defensive purposes by hundreds of thousands of known species worldwide. Throughout human history, venoms and venom components have been used to treat a vast array of illnesses, causing them to be of great clinical, economic, and academic interest to the drug discovery and toxinology communities. In spite of major computational advances that facilitate data-driven drug discovery, most therapeutic venom effects are still discovered via tedious trial-and-error, or simply by accident. In this dissertation, I describe a body of work that aims to establish a new subdiscipline of translational bioinformatics, which I name “computational toxinology”. To accomplish this goal, I present three integrated components that span a wide range of informatics techniques: (1) VenomKB, (2) VenomSeq, and (3) VenomKB’s Semantic API. To provide a platform for structuring, representing, retrieving, and integrating venom data relevant to drug discovery, VenomKB provides a database-backed web application and knowledge base for computational toxinology. VenomKB is structured according to a fully-featured ontology of venoms, and provides data aggregated from many popular web re- sources. VenomSeq is a biotechnology workflow that is designed to generate new high-throughput sequencing data for incorporation into VenomKB. Specifically, we expose human cells to controlled doses of crude venoms, conduct RNA-Sequencing, and build profiles of differential gene expression, which we then compare to publicly-available differential expression data for known dis- eases and drugs with known effects, and use those comparisons to hypothesize ways that the venoms could act in a therapeutic manner, as well. These data are then integrated into VenomKB, where they can be effectively retrieved and evaluated using existing data and known therapeutic associations. VenomKB’s Semantic API further develops this functionality by providing an intelligent, powerful, and user-friendly interface for querying the complex underlying data in VenomKB in a way that reflects the intuitive, human-understandable mean- ing of those data. The Semantic API is designed to cater to the needs of advanced users as well as laypersons and bench scientists without previous expertise in computational biology and semantic data analysis. In each chapter of the dissertation, I describe how we evaluated these 3 components through various approaches. We demonstrate the utility of VenomKB and the Semantic API by testing a number of practical use-cases for each, designed to highlight their ability to rediscover existing knowledge as well as suggesting potential areas for future exploration. We use statistics and data science techniques to evaluate VenomSeq on 25 diverse species of venomous animals, and propose biologically feasible explanations for significant findings. In evaluating the Semantic API, I show how observations on VenomSeq data can be interpreted and placed into the context of past research by members of the larger toxinology community. Computational toxinology is a toolbox designed to be used by multiple stakeholders (toxinologists, computational biologists, and systems pharmacologists, among others) to improve the return rate of clinically-significant findings from manual experimentation. It aims to achieve this goal by enabling access to data, providing means for easy validation of results, and suggesting specific hypotheses that are preliminarily supported by rigorous inferential statistics. All components of the research I describe are open-access and publicly available, to improve reproducibility and encourage widespread adoptio

KnetMiner:A comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org

A knowledge-based system for automated discovery of ecological interactions in flower-visiting data.

Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2017Studies on the community ecology of flower-visiting insects, which can be inferred to pollinate flowers, are important in agriculture and nature conservation. Many scientific observations of flower-visiting insects are associated with digitized records of insect specimens preserved in natural history collections. Specimen annotations include heterogeneous and incomplete, in situ field documentation of ecologically significant relationships between individual organisms (i.e. insects and plants), which are nevertheless potentially valuable. A wealth of unrepresented biodiversity and ecological knowledge can be unlocked from such detailed data by augmenting the data with expert knowledge encoded in knowledge models. An analysis of the knowledge representation requirements of flower-visiting community ecologists is presented, as well as an implementation and evaluation of a prototype knowledge-based system for automated semantic enrichment, semantic mediation and interpretation of flower-visiting data. A novel component of the system is a semantic architecture which incorporates knowledge models validated by experts. The system combines ontologies and a Bayesian network to enrich, integrate and interpret flower- visiting data, specifically to discover ecological interactions in the data. The system’s effectiveness, to acquire and represent expert knowledge and simulate the inferencing ability of expert flower-visiting ecologists, is evaluated and discussed. The knowledge-based system will allow a novice ecologist to use standardised semantics to construct interaction networks automatically and objectively. This could be useful, inter alia, when comparing interaction networks for different periods of time at the same place or different places at the same time. While the system architecture encompasses three levels of biological organization, data provenance can be traced back to occurrences of individual organisms preserved as evidence in natural history collections. The potential impact of the semantic architecture could be significant in the field of biodiversity and ecosystem informatics because ecological interactions are important in applied ecological studies, e.g. in freshwater biomonitoring or animal migration

An educational ontology-based M-learning system

Smart devices applications can assist young children in improving their learning capabilities and comprehension skills. However most of learning applications are built without taking into consideration the effective needs and background of Arab users. They are somehow incompatible with their local environment and culture. We propose in this paper a mobile-based educational system that displays illustrations automatically to characterize the content of children' stories on animal domain, and we use some intelligent technique to answer users' questions using an educational ontology. The generation of illustrations passes through different phases which include text processing, extraction of word-to-word relationships, building and accessing an ontology, and using Internet search engines for retrieval of complimentary information. The system can be used also by instructors to teach the children in a nonconventional manner. They can customize semantically the structure of the questions and determine how the answers will be displayed. In order to customize questions and answers, we have defined a semantic infrastructure to define the logic of terms, and the workflow of answers. The aim of our system is to improve the children educational skills to grasp vocabulary and grammar using multimedia with a portable smart device which includes observation, comprehension, realization, and deduction. Children will then be able to continue learning outside the limited time of their schools and from any location.Scopu

Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article

Scientific innovation depends on finding, integrating, and re-using the products of previous research. Here we explore how recent developments in Web technology, particularly those related to the publication of data and metadata, might assist that process by providing semantic enhancements to journal articles within the mainstream process of scholarly journal publishing. We exemplify this by describing semantic enhancements we have made to a recent biomedical research article taken from PLoS Neglected Tropical Diseases, providing enrichment to its content and increased access to datasets within it. These semantic enhancements include provision of live DOIs and hyperlinks; semantic markup of textual terms, with links to relevant third-party information resources; interactive figures; a re-orderable reference list; a document summary containing a study summary, a tag cloud, and a citation analysis; and two novel types of semantic enrichment: the first, a Supporting Claims Tooltip to permit “Citations in Context”, and the second, Tag Trees that bring together semantically related terms. In addition, we have published downloadable spreadsheets containing data from within tables and figures, have enriched these with provenance information, and have demonstrated various types of data fusion (mashups) with results from other research articles and with Google Maps. We have also published machine-readable RDF metadata both about the article and about the references it cites, for which we developed a Citation Typing Ontology, CiTO (http://purl.org/net/cito/). The enhanced article, which is available at http://dx.doi.org/10.1371/journal.pntd.0000228.x001, presents a compelling existence proof of the possibilities of semantic publication. We hope the showcase of examples and ideas it contains, described in this paper, will excite the imaginations of researchers and publishers, stimulating them to explore the possibilities of semantic publishing for their own research articles, and thereby break down present barriers to the discovery and re-use of information within traditional modes of scholarly communication

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

32. Forum Bauinformatik 2021

Das Forum Bauinformatik ist eine jährlich stattfindende Tagung und ein wichtiger Bestandteil der Bauinformatik im deutschsprachigen Raum. Insbesondere Nachwuchswissenschaftlerinnen und -wissenschaftlern bietet es die Möglichkeit, ihre Forschungsarbeiten zu präsentieren, Problemstellungen fachspezifisch zu diskutieren und sich über den neuesten Stand der Forschung zu informieren. Es bietet sich ausgezeichnete Gelegenheit, in die wissenschaftliche Gemeinschaft im Bereich der Bauinformatik einzusteigen und Kontakte mit anderen Forschenden zu knüpfen