1,590 research outputs found
Archaeological palaeoenvironmental archives: challenges and potential
This Arts and Humanities Research Council (AHRC) sponsored collaborative doctoral project represents one of
the most significant efforts to collate quantitative and qualitative data that can elucidate practices related to
archaeological palaeoenvironmental archiving in England. The research has revealed that archived
palaeoenvironmental remains are valuable resources for archaeological research and can clarify subjects that
include the adoption and importation of exotic species, plant and insect invasion, human health and diet, and
plant and animal husbandry practices. In addition to scientific research, archived palaeoenvironmental remains
can provide evidence-based narratives of human resilience and climate change and offer evidence of the
scientific process, making them ideal resources for public science engagement. These areas of potential have
been realised at an imperative time; given that waterlogged palaeoenvironmental remains at significant sites
such as Star Carr, Must Farm, and Flag Fen, archaeological deposits in towns and cities are at risk of decay due
to climate change-related factors, and unsustainable agricultural practices. Innovative approaches to collecting
and archiving palaeoenvironmental remains and maintaining existing archives will permit the creation of an
accessible and thorough national resource that can service archaeologists and researchers in the related fields
of biology and natural history. Furthermore, a concerted effort to recognise absences in archaeological
archives, matched by an effort to supply these deficiencies, can produce a resource that can contribute to an
enduring geographical and temporal record of England's biodiversity, which can be used in perpetuity in the
face of diminishing archaeological and contemporary natural resources.
To realise these opportunities, particular challenges must be overcome. The most prominent of these include
inconsistent collection policies resulting from pressures associated with shortages in storage capacity and
declining specialist knowledge in museums and repositories combined with variable curation practices. Many of
these challenges can be resolved by developing a dedicated storage facility that can focus on the ongoing
conservation and curation of palaeoenvironmental remains. Combined with an OASIS + module designed to
handle and disseminate data pertaining to palaeoenvironmental archives, remains would be findable,
accessible, and interoperable with biological archives and collections worldwide. Providing a national centre for
curating palaeoenvironmental remains and a dedicated digital repository will require significant funding.
Funding sources could be identified through collaboration with other disciplines. If sufficient funding cannot be
identified, options that would require less financial investment, such as high-level archive audits and the
production of guidance documents, will be able to assist all stakeholders with the improved curation,
management, and promotion of the archived resource
Cybersecurity knowledge graphs
Cybersecurity knowledge graphs, which represent cyber-knowledge with a graph-based data model, provide holistic approaches for processing massive volumes of complex cybersecurity data derived from diverse sources. They can assist security analysts to obtain cyberthreat intelligence, achieve a high level of cyber-situational awareness, discover new cyber-knowledge, visualize networks, data flow, and attack paths, and understand data correlations by aggregating and fusing data. This paper reviews the most prominent graph-based data models used in this domain, along with knowledge organization systems that define concepts and properties utilized in formal cyber-knowledge representation for both background knowledge and specific expert knowledge about an actual system or attack. It is also discussed how cybersecurity knowledge graphs enable machine learning and facilitate automated reasoning over cyber-knowledge
Evaluating Symbolic AI as a Tool to Understand Cell Signalling
The diverse and highly complex nature of modern phosphoproteomics research produces a high volume of data. Chemical phosphoproteomics especially, is amenable to a variety of analytical approaches. In this thesis we evaluate novel Symbolic AI based algorithms as potential tools in the analysis of cell signalling. Initially we developed a first order deductive, logic-based model. This allowed us to identify previously unreported inhibitor-kinase relationships which could offer novel therapeutic targets for further investigation. Following this we made use of the probabilistic reasoning of ProbLog to augment the aforementioned Prolog based model with an intuitively calculated degree of belief. This allowed us to rank previous associations while also further increasing our confidence in already established predictions. Finally we applied our methodology to a Saccharomyces cerevisiae gene perturbation, phosphoproteomics dataset. In this context we were able to confirm the majority of ground truths, i.e. gene deletions as having taken place as intended. For the remaining deletions, again using a purely symbolic based approach we were able to provide predictions on the rewiring of kinase based signalling networks following kinase encoding gene deletions. The explainable, human readable and white-box nature of this approach were highlighted, however its brittleness due to missing, inconsistent or conflicting background knowledge was also examined
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
Processing genome-wide association studies within a repository of heterogeneous genomic datasets
Background
Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions.
Results
To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multisample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals.
Conclusions
As a result of our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inédita leÃda en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de IngenierÃa Informática. Fecha de Lectura: 28-03-202
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Toward relevant answers to queries on incomplete databases
Incomplete and uncertain information is ubiquitous in database management applications. However, the techniques specifically developed to handle incomplete data are
not sufficient. Even the evaluation of SQL queries on databases containing NULL
values remains a challenge after 40 years. There is no consensus on what an answer
to a query on an incomplete database should be, and the existing notions often have
limited applicability.
One of the most prevalent techniques in the literature is based on finding answers
that are certainly true, independently of how missing values are interpreted. However,
this notion has yielded several conflicting formal definitions for certain answers. Based
on the fact that incomplete data can be enriched by some additional knowledge, we
designed a notion able to unify and explain the different definitions for certain answers.
Moreover, the knowledge-preserving certain answers notion is able to provide the first
well-founded definition of certain answers for the relational bag data model and value-inventing queries, addressing some key limitations of previous approaches. However,
it doesn’t provide any guarantee about the relevancy of the answers it captures.
To understand what would be relevant answers to queries on incomplete databases,
we designed and conducted a survey on the everyday usage of NULL values among
database users. One of the findings from this socio-technical study is that even when
users agree on the possible interpretation of NULL values, they may not agree on
what a satisfactory query answer is. Therefore, to be relevant, query evaluation on
incomplete databases must account for users’ tasks and preferences.
We model users’ preferences and tasks with the notion of regret. The regret function
captures the task-dependent loss a user endures when he considers a database as
ground truth instead of another. Thanks to this notion, we designed the first framework
able to provide a score accounting for the risk associated with query answers. It allows
us to define the risk-minimizing answers to queries on incomplete databases. We
show that for some regret functions, regret-minimizing answers coincide with certain
answers. Moreover, as the notion is more agile, it can capture more nuanced answers
and more interpretations of incompleteness.
A different approach to improve the relevancy of an answer is to explain its provenance.
We propose to partition the incompleteness into sources and measure their respective contribution to the risk of answer. As a first milestone, we study several models
to predict the evolution of the risk when we clean a source of incompleteness. We
implemented the framework, and it exhibits promising results on relational databases
and queries with aggregate and grouping operations. Indeed, the model allows us
to infer the risk reduction obtained by cleaning an attribute. Finally, by considering a
game theoretical approach, the model can provide an explanation for answers based
on the contribution of each attributes to the risk
- …