Reframing the Mongols in 1260 : the Armenians, the Mongols and the Magi

The irruption of the Mongols led to profound changes in the political, cultural and confessional climate of the thirteenth-century Near East. While many did not survive the initial onslaught and the years of turmoil that followed, and rulers that opposed the Mongols were largely swept away, the communities and dynasties that remained were forced to seek some sort of accommodation with the new overlords. While subjection to the Mongol yoke was far from desirable, rulers could seek to make the best of the situation, in the hope that the ambitions of the Mongols might come to match their own, or that the Mongols might be persuaded to support their cause. This paper will consider how certain Christian groups in the Near East sought to reconcile themselves to the Mongol presence, and how they sought to place these alien invaders within a more familiar framework. In particular it will examine the visual evidence for this process by looking at a couple of appearances of recognisably Mongol figures within Christian artwork, dating from the time of the second major Mongol invasion of the region, led by the Ilkhan Hülegü, which by 1260 had extended Mongol power into Syria and to the borders of Egypt.PostprintPeer reviewe

Sustainability education, or educating sustainably?

Many universities teach programs in sustainable energy, but should they be incorporating theories and practice of sustainability across many disciplines? The argument is proposed that institutes of higher education should be primary vehicles of change in our transition towards a sustainable future. It is discussed that this can occur at the institutional and curriculum level. Integrating concepts of sustainability within a biomedical discipline area is discussed with strategies exemplified to lift awareness within student groups and for teaching and support staff

Novel Syntheses and Rearrangements of Cyclic Thioethers

Part 1: A novel synthesis is described in which potassium t-butoxide dehalogenation of beta,beta-dihaloalkyl sulphides is used to prepare unsaturated episulphides and limitations of the method are discussed. For example, 31 undergoes monodehydrochlorination to 32, while 50 and 51 fail to react. The intermediacy of sulphonium ions is supported by the conversion of isomeric dihalides, 35 and 40, to the same episulphide, viz 44

Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach

Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. The data are used to train a machine learning based classification algorithm to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases, and the use of such outputs for further studies on pain and mental health. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train three commonly used classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99).Comment: 5 pages, 2 tables, submitted to MEDINFO 2023 conferenc

The New Testament concept of union with Christ

Abstract Not Provided

Temporal trend in the transfer of Sellafield-derived 14C into different size fractions of the carbonate component of NE Irish Sea sediment

From 1994 onwards, 14C discharges from the Sellafield nuclear fuel reprocessing plant have been made largely to the Northeast Irish Sea. They represent the largest contributor to UK and European populations of the collective dose commitment derived from the entire nuclear industry discharges. Consequently, it is important to understand the long-term fate of 14C in the marine environment. Research undertaken in 2000 suggested that the carbonate component of Northeast Irish Sea sediments would increase in 14C activity as mollusc shells, which have become enriched in Sellafield-derived 14C, are broken down by physical processes including wave action and incorporated into intertidal and sub-tidal sediments. The current study, undertaken in 2011, tested this hypothesis. The results demonstrate significant increases in 14C enrichments found in whole mussel shells compared to those measured in 2000. Additionally, in 2000, there was an enrichment above ambient background within only the largest size fraction (>500 μm) of the intertidal inorganic sediment at Nethertown and Flimby (north of Sellafield). In comparison, the present study has demonstrated 14C enrichments above ambient background in most size fractions at sites up to 40 km north of Sellafield, confirming the hypothesis set out more than a decade ago

The influence of multimedia resources in and out of biomedical studies

With respect to the provision of learning support materials, it is generally accepted that enriched learning environments are better than simple didactic sessions and consequently improved resources ultimately lead to better learning, which in turn leads to improved grades. The use of a dedicated multimedia teaching room specifically set up to create a learning environment for biomedical science within a nursing program has been documented and correlated with the student’s final mark and course retention. The significance of this study is that the results and retention for biomedical science studies are further compared to studies in which the resource room would have been of no benefit. Failure rates and course retention rates were not significantly different between students who did not use the facility (n=237) and those who used it only once (n=47), however there were demonstrable differences between the first group and students who accessed the resource on multiple occasions (n=203). However the results of those students using the resource, show that they were significantly disadvantaged in non-biomedical studies where the availability of the resource did not assist them. Within the limitations of the study, the data does support the premise that access to dedicated teaching materials improves learning, which translates to better grades

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.Comment: Accepted at AMIA 2023, New Orlean

Sample Size in Natural Language Processing within Healthcare Research

Sample size calculation is an essential step in most data-based disciplines. Large enough samples ensure representativeness of the population and determine the precision of estimates. This is true for most quantitative studies, including those that employ machine learning methods, such as natural language processing, where free-text is used to generate predictions and classify instances of text. Within the healthcare domain, the lack of sufficient corpora of previously collected data can be a limiting factor when determining sample sizes for new studies. This paper tries to address the issue by making recommendations on sample sizes for text classification tasks in the healthcare domain. Models trained on the MIMIC-III database of critical care records from Beth Israel Deaconess Medical Center were used to classify documents as having or not having Unspecified Essential Hypertension, the most common diagnosis code in the database. Simulations were performed using various classifiers on different sample sizes and class proportions. This was repeated for a comparatively less common diagnosis code within the database of diabetes mellitus without mention of complication. Smaller sample sizes resulted in better results when using a K-nearest neighbours classifier, whereas larger sample sizes provided better results with support vector machines and BERT models. Overall, a sample size larger than 1000 was sufficient to provide decent performance metrics. The simulations conducted within this study provide guidelines that can be used as recommendations for selecting appropriate sample sizes and class proportions, and for predicting expected performance, when building classifiers for textual healthcare data. The methodology used here can be modified for sample size estimates calculations with other datasets.Comment: Submitted to Journal of Biomedical Informatic