Exploring associations between perceived HCV status and injecting risk behaviors among recent initiates to injecting drug use in Glasgow

The aim of this study was to explore the influence of testing for hepatitis C virus (HCV) and perceived HCV status on injecting risk behavior. A cross-sectional, community-wide survey was undertaken at multiple sites throughout Greater Glasgow during 2001-2002. Four hundred ninety-seven injecting drug users (IDUs) consented to participate and were interviewed using a structured questionnaire to ascertain HCV test history and injecting risk behavior. The average age of participants was 27 years and the majority of the sample were male (70.4%). Participants had been injecting for an average duration of 2.5 years. Logistic regression analysis revealed no significant associations between having been tested and injecting risk behavior. After adjustment for potential confounding variables, HCV-negatives were significantly less likely to borrow needles/syringes and spoons or filters as compared with unawares and were significantly less likely to borrow spoons or filters as compared with HCV-positives. Due to the cross-sectional design of the study, it is uncertain whether this reduction in risk behavior could be attributed to perception of HCV status. Further research is recommended to consolidate the evidence for this relationship

Isolation and characterisation of polymorphic microsatellite loci for studies of the big blue octopus, Octopus cyanea

The big blue octopus, Octopus cyanea, occurs on coral reefs throughout the Indo-Pacific region from East Africa to the Hawaiian Islands, wherein it is of great ecological and socio-economic importance. However, many components of its intraspecific biodiversity, such as population structure, are unresolved due to a lack of informative genetic markers. To address this issue, which may compromise conservation and sustainability efforts, the development and characterisation of the first species-specific microsatellite loci for O. cyanea are described here. The eight loci were characterised by the genotyping of 40 adults from Madagascar, which revealed an average of 13.5 alleles per locus (range 9?18). The observed and expected heterozygosity per locus ranged from 0.432 to 0.949 and from 0.481 to 0.989, respectively. No evidence of linkage disequilibrium was detected between pairs of loci. Genotype proportions at six loci conformed to Hardy?Weinberg equilibrium expectations, with two loci exhibiting significant heterozygote deficits. These loci are applicable to multiple areas of eco-evolutionary research and, thus, represent a valuable resource for future studies of O. cyaneapublishersversionPeer reviewe

Optimizing the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large scale research for psychiatry and mental health. Research using so-called “real world data” – such as electronic medical/health records – can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real world data sources is complex because the clinically important ‘signal’ is often contained in both structured and unstructured (narrative or “free-text”) data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real world) data for psychiatric research

Clinical prompt learning with frozen language models

When the first transformer-based language models were published in the late 2010s, pretraining with general text and then fine-tuning the model on a task-specific dataset often achieved the state-of-the-art performance. However, more recent work suggests that for some tasks, directly prompting the pretrained model matches or surpasses fine-tuning in performance with few or no model parameter updates required. The use of prompts with language models for natural language processing (NLP) tasks is known as prompt learning. We investigated the viability of prompt learning on clinically meaningful decision tasks and directly compared this with more traditional fine-tuning methods. Results show that prompt learning methods were able to match or surpass the performance of traditional fine-tuning with up to 1000 times fewer trainable parameters, less training time, less training data, and lower computation resource requirements. We argue that these characteristics make prompt learning a very desirable alternative to traditional fine-tuning for clinical tasks, where the computational resources of public health providers are limited, and where data can often not be made available or not be used for fine-tuning due to patient privacy concerns. The complementary code to reproduce the experiments presented in this work can be found at https://github.com/NtaylorOX/Public_Clinical_Prompt

Bliain le baisteach - sonifying a year with rain

Presented at the 7th International Conference on Auditory Display (ICAD), Espoo, Finland, July 29-August 1, 2001.In this paper the development of software for the creation of Bliain Le Baisteach is described. Over 77,000 datapoints were received from the Irish meteorological service. A neural network was designed and trained with 1,000 traditional Irish melodies. The data was then partitioned according to the four geographical provinces of Ireland and made to stimulate the network, generating the different parts of a score for the Irish Chamber Orchestra

On social class, anno 2014

This article responds to the critical reception of the arguments made about social class in Savage et al. (2013). It emphasises the need to disentangle different strands of debate so as not to conflate four separate issues: (a) the value of the seven class model proposed; (b) the potential of the large web survey – the Great British Class Survey (GBCS) for future research; (c) the value of Bourdieusian perspectives for re-energising class analysis; and (d) the academic and public reception to the GBCS itself. We argue that, in order to do justice to the full potential of the GBCS, we need a concept of class which does not reduce it to a technical measure of a single variable and which recognises how multiple axes of inequality can crystallise as social classes. Whilst recognising the limitations of what we are able to claim on the basis of the GBCS, we argue that the seven classes defined in Savage et al. (2013) have sociological resonance in pointing to the need to move away from a focus on class boundaries at the middle reaches of the class structure towards an analysis of the power of elite formation

Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks

The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods. We present an investigation into the suitability of different PEFT methods to clinical decision-making tasks, across a range of model sizes, including extremely small models with as few as $25$ million parameters. Our analysis shows that the performance of most PEFT approaches varies significantly from one task to another, with the exception of LoRA, which maintains relatively high performance across all model sizes and tasks, typically approaching or matching full fine-tuned performance. The effectiveness of PEFT methods in the clinical domain is evident, particularly for specialised models which can operate on low-cost, in-house computing infrastructure. The advantages of these models, in terms of speed and reduced training costs, dramatically outweighs any performance gain from large foundation LLMs. Furthermore, we highlight how domain-specific pre-training interacts with PEFT methods and model size, and discuss how these factors interplay to provide the best efficiency-performance trade-off. Full code available at: tbd