The P3 platform: an approach and software system for developing diagrammatic model-based methods in design research

Many issues in design and design management have been explored by building models which capture the relationships between different aspects of the problem at hand. These models require computer support to construct and analyse. However, appropriate modelling tools can be time-consuming to develop in a research environment. Reflecting upon five design research projects, this paper proposes that such projects can be facilitated by recognising the iterative and tightly-coupled nature of research and tool development, and by attempting to minimise the effort of solution prototyping within this process. Our approach is enabled by a software platform which can be rapidly configured to implement many conceivable modelling approaches. This configurability is complemented by an emerging library of modelling and analysis approaches tailored to explore design process systems. The platform-based approach enables any mix of modelling concepts to be easily created. We propose it could thus help researchers to explore a wide range of questions without being constrained to existing conventions for modelling – or for model integration

The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

Convergent evolution of modularity in metabolic networks through different community structures

Background: It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of themodularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results: In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions: We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organismﾒs metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations

Explain3D: Explaining Disagreements in Disjoint Datasets

Data plays an important role in applications, analytic processes, and many aspects of human activity. As data grows in size and complexity, we are met with an imperative need for tools that promote understanding and explanations over data-related operations. Data management research on explanations has focused on the assumption that data resides in a single dataset, under one common schema. But the reality of today's data is that it is frequently un-integrated, coming from different sources with different schemas. When different datasets provide different answers to semantically similar questions, understanding the reasons for the discrepancies is challenging and cannot be handled by the existing single-dataset solutions. In this paper, we propose Explain3D, a framework for explaining the disagreements across disjoint datasets (3D). Explain3D focuses on identifying the reasons for the differences in the results of two semantically similar queries operating on two datasets with potentially different schemas. Our framework leverages the queries to perform a semantic mapping across the relevant parts of their provenance; discrepancies in this mapping point to causes of the queries' differences. Exploiting the queries gives Explain3D an edge over traditional schema matching and record linkage techniques, which are query-agnostic. Our work makes the following contributions: (1) We formalize the problem of deriving optimal explanations for the differences of the results of semantically similar queries over disjoint datasets. (2) We design a 3-stage framework for solving the optimal explanation problem. (3) We develop a smart-partitioning optimizer that improves the efficiency of the framework by orders of magnitude. (4)~We experiment with real-world and synthetic data to demonstrate that Explain3D can derive precise explanations efficiently

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Realising context-oriented information filtering.

The notion of information overload is an increasing factor in modern information service environments where information is ‘pushed’ to the user. As increasing volumes of information are presented to computing users in the form of email, web sites, instant messaging and news feeds, there is a growing need to filter and prioritise the importance of this information. ‘Information management’ needs to be undertaken in a manner that not only prioritises what information we do need, but to also dispose of information that is sent, which is of no (or little) use to us.The development of a model to aid information filtering in a context-aware way is developed as an objective for this thesis. A key concern in the conceptualisation of a single concept is understanding the context under which that concept exists (or can exist). An example of a concept is a concrete object, for instance a book. This contextual understanding should provide us with clear conceptual identification of a concept including implicit situational information and detail of surrounding concepts.Existing solutions to filtering information suffer from their own unique flaws: textbased filtering suffers from problems of inaccuracy; ontology-based solutions suffer from scalability challenges; taxonomies suffer from problems with collaboration. A major objective of this thesis is to explore the use of an evolving community maintained knowledge-base (that of Wikipedia) in order to populate the context model from prioritise concepts that are semantically relevant to the user’s interest space. Wikipedia can be classified as a weak knowledge-base due to its simple TBox schema and implicit predicates, therefore, part of this objective is to validate the claim that a weak knowledge-base is fit for this purpose. The proposed and developed solution, therefore, provides the benefits of high recall filtering with low fallout and a dependancy on a scalable and collaborative knowledge-base.A simple web feed aggregator has been built using the Java programming language that we call DAVe’s Rss Organisation System (DAVROS-2) as a testbed environment to demonstrate specific tests used within this investigation. The motivation behind the experiments is to demonstrate that the combination of the concept framework instantiated through Wikipedia can provide a framework to aid in concept comparison, and therefore be used in news filtering scenario as an example of information overload. In order to evaluate the effectiveness of the method well understood measures of information retrieval are used. This thesis demonstrates that the utilisation of the developed contextual concept expansion framework (instantiated using Wikipedia) improved the quality of concept filtering over a baseline based on string matching. This has been demonstrated through the analysis of recall and fallout measures