Unnoticed Fragments of Dante’s \u27Monarchia\u27 with the Commentary Attributed to Cola di Rienzo

This note draws attention to and briefly describes fragments in the Bodleian Library, Oxford, which preserve a previously unknown copy of Dante’s Monarchia with the commentary on that text attributed to Cola di Rienzo. The fragments survive in fifteenth-century bindings from Erfurt but seem to have been written in Central Europe around the middle of the fourteenth century by a combination of Central European and Italian scribes. In their layout, decoration, text, corrections and annotations the fragments provide significant new evidence for the circulation both of the Monarchia and of the commentary. They are also important for the possibility that they originated in the milieu of mid-fourteenth-century Bohemia where Rienzo’s commentary is believed to have been composed

Analysis of Cancer Omics Data In A Semantic Web Framework

Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancel cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Lobic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent

Optimized placement of parasitic vibration energy harvesters for autonomous structural health monitoring

Energy harvesting, based on sources including vibration and thermal gradients, has been exploited in recent years to power telemetry, small devices, or to charge batteries or capacitors. Generating the higher levels of power which have thus far been required to run sensor systems such as those needed for structural health monitoring has been more challenging. In addition, harvesters such as those required to capture vibration often require additional elements (e.g. cantilevers) to be added to the structure and harvest over a relatively narrow band of frequencies. In aerospace applications, where weight is at a premium and vibrations occur over a broader range of frequencies, this is non-ideal. With the advent of new, lower power monitoring systems, the potential for energy harvesting to be utilized is significantly increased. This article optimizes the placement of a set of parasitic piezoelectric patches to harvest over the broad band of frequencies found in an aircraft wing and validates the results experimentally. Results are compared with the requirements of a low-power structural health monitoring system, with a closing of the gap between the energy generated and that required being demonstrated

Improved acoustic emission source location during fatigue and impact events in metallic and composite structures

In order to overcome the difficulties in applying traditional Time-Of Arrival (TOA) techniques for locating Acoustic Emission (AE) events in complex structures and materials, a technique termed “delta-t mapping” was developed. This paper presents a significant improvement on this, in which the difficulties in identifying the precise arrival time of an AE signal are addressed by incorporating the Akaike Information Criteria (AIC). The performance of the TOA, the delta-t mapping and the AIC delta-t mapping techniques is assessed by locating artificial AE sources, fatigue damage and impact events in aluminium and composite materials respectively. For all investigations conducted the improved AIC delta-t technique shows a reduction in average Euclidean source location error irrespective of material or source type. For locating H-N sources on a complex aluminium specimen the average source location error (Euclidean) is 32.6, (TOA), 5.8 (delta-t) and 3mm (AIC delta-t). For locating fatigue damage on the same specimen the average error is 20.2, (TOA), 4.2 (delta-t) and 3.4mm (AIC delta-t). For locating H-N sources on a composite panel the average error is 19.3, (TOA), 18.9 (delta-t) and 4.2mm (AIC delta-t). Finally the AIC delta-t mapping technique had the lowest average error (3.3mm) when locating impact events when compared with the delta-t (18.9mm) and TOA (124.7mm) techniques. Overall the AIC delta-t mapping technique is the only technique which demonstrates consistently the lowest average source location error (greatest average error 4.2mm) when compared with the delta-t (greatest average error 18.9mm) and TOA (greatest average error 124.7mm) techniques. These results demonstrate that the AIC delta-t mapping technique is a viable option for AE source location, increasing the accuracy and likelihood of damage detection, irrespective of material, geometry and source type

A semantic web framework to integrate cancer omics data with biological knowledge

BACKGROUND: The RDF triple provides a simple linguistic means of describing limitless types of information. Triples can be flexibly combined into a unified data source we call a semantic model. Semantic models open new possibilities for the integration of variegated biological data. We use Semantic Web technology to explicate high throughput clinical data in the context of fundamental biological knowledge. We have extended Corvus, a data warehouse which provides a uniform interface to various forms of Omics data, by providing a SPARQL endpoint. With the querying and reasoning tools made possible by the Semantic Web, we were able to explore quantitative semantic models retrieved from Corvus in the light of systematic biological knowledge. RESULTS: For this paper, we merged semantic models containing genomic, transcriptomic and epigenomic data from melanoma samples with two semantic models of functional data - one containing Gene Ontology (GO) data, the other, regulatory networks constructed from transcription factor binding information. These two semantic models were created in an ad hoc manner but support a common interface for integration with the quantitative semantic models. Such combined semantic models allow us to pose significant translational medicine questions. Here, we study the interplay between a cell's molecular state and its response to anti-cancer therapy by exploring the resistance of cancer cells to Decitabine, a demethylating agent. CONCLUSIONS: We were able to generate a testable hypothesis to explain how Decitabine fights cancer - namely, that it targets apoptosis-related gene promoters predominantly in Decitabine-sensitive cell lines, thus conveying its cytotoxic effect by activating the apoptosis pathway. Our research provides a framework whereby similar hypotheses can be developed easily

Semantic Web-Based Integration of Cancer Pathways and Allele Frequency Data

We demonstrate the use of Semantic Web technology to integrate the ALFRED allele frequency database and the Starpath pathway resource. The linking of population-specific genotype data with cancer-related pathway data is potentially useful given the growing interest in personalized medicine and the exploitation of pathway knowledge for cancer drug discovery. We model our data using the Web Ontology Language (OWL), drawing upon ideas from existing standard formats BioPAX for pathway data and PML for allele frequency data. We store our data within an Oracle database, using Oracle Semantic Technologies. We then query the data using Oracle’s rule-based inference engine and SPARQL-like RDF query language. The ability to perform queries across the domains of population genetics and pathways offers the potential to answer a number of cancer-related research questions. Among the possibilities is the ability to identify genetic variants which are associated with cancer pathways and whose frequency varies significantly between ethnic groups. This sort of information could be useful for designing clinical studies and for providing background data in personalized medicine. It could also assist with the interpretation of genetic analysis results such as those from genome-wide association studies

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis

BACKGROUND: To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. RESULTS: We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. CONCLUSION: PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s)

A Layered Digital Library for Cataloguing and Research: Practical Experiences with Medieval Manuscripts, from TEI to Linked Data

In this paper we report our experiences developing and applying a set of digital infrastructure elements which, in combination, realise a layered digital library (Page et al 2017) for the investigation of manuscript provenance. We describe several related technical contributions: encoding of manuscript catalogue and local authority records as TEI; using Github for version control, issue tracking, and collaboration; automated production of catalogue user interfaces derived from the TEI; an XML processing workflow identifying, extracting, and processing TEI elements for reuse in research; mapping workflow output into a CIDOC-CRM RDF export; reconciliation of RDF entities with external authorities enabling the creation and use of Linked Data bridging multiple datasets. We contextualise the co-evolution of these components and exemplify their use in studies of the provenance of medieval manuscripts. We reflect on the flexibility and extensibility provided by our layered approach, and the independent benefits for catalogers and scholars

Acoustic emission source location in complex structures using full automatic delta T mapping technique

An easy to use, fast to apply, cost-effective, and very accurate non-destructive testing (NDT) technique for damage localisation in complex structures is key for the uptake of structural health monitoring systems (SHM). Acoustic emission (AE) is a viable technique that can be used for SHM and one of the most attractive features is the ability to locate AE sources. The time of arrival (TOA) technique is traditionally used to locate AE sources, and relies on the assumption of constant wave speed within the material and uninterrupted propagation path between the source and the sensor. In complex structural geometries and complex materials such as composites, this assumption is no longer valid. Delta T mapping was developed in Cardiff in order to overcome these limitations; this technique uses artificial sources on an area of interest to create training maps. These are used to locate subsequent AE sources. However operator expertise is required to select the best data from the training maps and to choose the correct parameter to locate the sources, which can be a time consuming process. This paper presents a new and improved fully automatic delta T mapping technique where a clustering algorithm is used to automatically identify and select the highly correlated events at each grid point whilst the “Minimum Difference” approach is used to determine the source location. This removes the requirement for operator expertise, saving time and preventing human errors. A thorough assessment is conducted to evaluate the performance and the robustness of the new technique. In the initial test, the results showed excellent reduction in running time as well as improved accuracy of locating AE sources, as a result of the automatic selection of the training data. Furthermore, because the process is performed automatically, this is now a very simple and reliable technique due to the prevention of the potential source of error related to manual manipulation