Genetics has been an important tool for discovering new aspects of biology across life. In humans, there is growing momentum behind the application of this knowledge to drive innovation in clinical care, most notably through developments in precision medicine. Nowhere has the impact of genetics on clinical practice been more striking than in the field of rare disorders. For most of these conditions, individual disease susceptibility is influenced by DNA sequence variation in a single or a small number of genes. In contrast, most common disorders are multifactorial and are caused by a complex interplay of multiple genetic, environmental and stochastic factors. The longstanding division of human disease genetics into rare and common components has obscured the continuum of human traits and echoes aspects of the century-old debate between the Mendelian and biometric views of human genetics. In this article, we discuss the differences in data and concepts between rare and common disease genetics. Opportunities to unify these two areas are noted and the importance of adopting a holistic perspective that integrates diverse genetic and environmental factors is discussed.

Industrial Data Analytics needs access to huge amounts of data, which is scattered across different IT systems. As part of an integrated reference kit for Industrial Data Analytics, there is a need for a data backend system that provides access to data. This system needs to have solutions for the extraction of data, the management of data and an analysis pipeline for those data. This paper presents an approach for this data backend system.

Low-accruing clinical trials delay translation of research breakthroughs into the clinic, expose participants to risk without providing meaningful clinical insight, increase the cost of therapies, and waste limited resources. By tracking patient accrual, Clinical and Translational Science Awards hubs can identify at-risk studies and provide them the support needed to reach recruitment goals and maintain financial solvency. However, tracking accrual has proved challenging because relevant patient- and protocol-level data often reside in siloed systems. To address this fragmentation, in September 2020 the South Carolina Clinical and Translational Research Institute, with an academic home at the Medical University of South Carolina, implemented a clinical trial management system (CTMS), with its access to patient-level data, and incorporated it into its Research Integrated Network of Systems (RINS), which links study-level data across disparate systems relevant to clinical research. Within the first year of CTMS implementation, 324 protocols were funneled through CTMS/RINS, with more than 2600 participants enrolled. Integrated data from CTMS/RINS have enabled near-real-time assessment of patient accrual and accelerated reimbursement from industry sponsors. For institutions with bioinformatics or programming capacity, the CTMS/RINS integration provides a powerful model for tracking and improving clinical trial efficiency, compliance, and cost-effectiveness.

This article presents the background to and prospects for a new initiative in archaeological field survey and database integration. The Roman Hinterland Project combines data from the Tiber Valley Project, Roman Suburbium Project, and the Pontine Region Project into a single database, which the authors believe to be one of the most complete repositories of data for the hinterland of a major ancient metropolis, covering nearly 2000 years of history. The logic of combining these databases in the context of studying the Roman landscape is explained and illustrated with analyses that show their capacity to contribute to major debates in Roman economy, demography, and the longue durée of the human condition in a globalizing world.

Genebanks play an important role in the conservation of global plant biodiversity. The European Search Catalogue for Plant Genetic Resources (EURISCO) was created as a central entry point to provide information on these collections. However, a major challenge lies in the heterogeneity of scientific plant names. This makes the selection of suitable plant material, e.g. for research or breeding purposes, significantly more difficult. For this reason, the taxonomic backbone of EURISCO has been completely revised. Search terms entered by users are now automatically checked against taxonomic reference repositories, allowing a variety of synonyms to be identified. In addition, a fuzzy search has been implemented, which makes the search function tolerant of erroneous data (e.g. caused by typing errors). Besides improvements of the search interface, more support will be given to EURISCO's data providers. The new developments provide a tool that makes it easier to identify problem cases within the data, such as accepted/non-accepted taxonomic names, and will successively improve the quality of taxonomic information in EURISCO.

In this paper we use formal tools from category theory to develop a foundation for creating and managing models in systems where knowledge is distributed across multiple representations and formats. We define a class of models which incorporate three different representations---computations, logical semantics, and data--as well as model mappings (functors) to establish relationships between them. We prove that our models support model merge operations called colimits and use these to define a methodology for model integration.

The objective of this paper is to accurately determine mobile robots' position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, an inertial measurement unit is exploited to give more reliable attitude information. However, the nonlinear dynamic of these systems and their complexities such as different sources of errors make navigation difficult. Since the dynamic models of navigation systems are nonlinear in practice, in this study, a Cubature Kalman Filter (CKF) has been proposed to estimate and correct the errors of these systems. The information from odometry and a gyroscope are integrated using a CKF. Simulation results are provided to illustrate the superiority and the higher reliability of the proposed approach in comparison with conventional nonlinear filtering algorithms such as an Extended Kalman Filter (EKF).

The need for coordinated regional and global electronic databases to assist prevention, early detection, rapid response, and control of biological invasions is well accepted. The Pacific Basin Information Node (PBIN), a node of the National Biological Information Infrastructure, has been increasingly engaged in the invasive species enterprise since its establishment in 2001. Since this time, PBIN has sought to support frontline efforts at combating invasions, through working with stakeholders in conservation, agriculture, forestry, health, and commerce to support joint information needs. Although initial emphasis has been on Hawaii, cooperative work with other Pacific islands and countries of the Pacific Rim is already underway and planned.

The field of disease ecology – the study of the spread and impact of parasites and pathogens within their host populations and communities – has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard ‘one-host-one-parasite’ paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.

Epidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.

Mixed methods is a youthful but increasingly robust methodological movement characterised by: a growing body of trans-disciplinary literature; prominent research methodologists/authorities; the emergence of mixed method specific journals, research texts, and courses; a growth in popularity amongst research funding bodies. Mixed methods is being utilised and reported within business and management fields, despite the quantitative traditions attached to certain business and management disciplines. This paper has utilised a multistrand conversion mixed model research design to undertake a retrospective content analysis of refereed papers (n = 281) from the 21st Australian and New Zealand Academy of Management (ANZAM) Conference 2007. The aim of the study is to provide a methodological map of the management research reported at the conference, and in particular the use, quality and acceptance level of mixed methods research within business and management fields. Implications for further research are discussed along with a call to the ‘first generation’ of business and management mixed method researchers to instigate mixed methods research training and capacity building within their respective business schools, relevant academies and associated professional forums and publications.

Genebanks are important suppliers of genetic resources to the genomics research community, and access to the resulting information will allow traditional genebank users to better select genetic material for their breeding and scientific programmes. We discuss herein a possible solution to interconnect these data automatically based on semantic web technology.

The information stored in animal feed databases is highly variable, in terms of both provenance and quality; therefore, data pre-processing is essential to ensure reliable results. Yet, pre-processing at best tends to be unsystematic; at worst, it may even be wholly ignored. This paper sought to develop a systematic approach to the various stages involved in pre-processing to improve feed database outputs. The database used contained analytical and nutritional data on roughly 20 000 alfalfa samples. A range of techniques were examined for integrating data from different sources, for detecting duplicates and, particularly, for detecting outliers. Special attention was paid to the comparison of univariate and multivariate solutions. Major issues relating to the heterogeneous nature of data contained in this database were explored, the observed outliers were characterized and ad hoc routines were designed for error control. Finally, a heuristic diagram was designed to systematize the various aspects involved in the detection and management of outliers and errors.