6297 research outputs found
Sort by
The Use of Trace Element Analysis to Understand Burial Environments from Nineteenth Century Newchapel, Staffordshire
Full text linkIn recent years, increasing numbers of studies have focused on the health and wellbeing of populations living during the English Industrial Revolution (1760-1860 C.E.). During this period pollution increased, and standards of living decreased for many as a result of social and income inequalities. Trace element analysis of human remains from contexts dating to this period could potentially shed further light on the effect of roles within industry on the body. Initially, this research aimed to establish how occupation affected long-term health of individuals (n=7) buried in the Newchapel (Staffordshire) cemetery, but the research questions were extended to consider trace element adsorption from soil and coffin furniture during diagenesis of the human remains. Chemical analysis did not reveal any information that could be used to explore occupational health in the nineteenth century as the trace element composition of human remains was incredibly variable throughout the site, and even within the same set of remains which, in some cases, could potentially be related to the presence of corroding metals from the coffin furniture. This research has ultimately furthered our understanding of the complex interactions that occur between the skeletal material, the soil, and the burial environment – this allows us to better establish which trace metals were incorporated into the skeletal material during life, contributing to our understanding of the local area in the late nineteenth century
Real-Time Sign Language to Speech Recognition System for BSL
No full textThis paper presents a groundbreaking real- time system that transforms British Sign Language (BSL) gestures into audible speech using long short-term memory (LSTM), employing innovative machine learning techniques. The contribution, rooted in a blend of quantitative and qualitative research methods, showcases significant advances in technologies that assist BSL users. By integrating TensorFlow and MediaPipe, we designed a sophisticated multi-layer LSTM neural network that effectively handles real-time translation. Our approach encompassed several stages: gathering data, intensive preprocessing, meticulous training of the model, and thorough testing. The results from these tests confirmed the model's high accuracy and robust performance across various environments, marking a notable achievement in usability and technological application in real-life scenarios. Additionally, feedback from users indicated a more than 90% success rate in real-time gesture recognition, emphasizing the system’s practical utility. This research not only bridges a vital communication gap for those who rely on BSL but also paves the way for future innovations, including integration into mobile and web platforms, thus expanding access and usability. The insights gained here highlight the profound impact machine learning can have on enhancing communication tools for the speech-impaired, resonating with ongoing trends in computational linguistics and interactive technologie
Patient Centred Care in Vasculitis: Patient Education
No full textA presentation on the research into patients informational needs in Anca Associated Vasculiti
Spaceflight causes strain-dependent gene expression changes in the kidneys of mice
Full text linkIncidence of kidney stones in astronauts is a major risk factor associated with long-term missions, caused by increased blood calcium levels due to bone demineralisation triggered by microgravity and space radiation. Transcriptomic changes have been observed in tissues during spaceflight, including the kidney. We analysed kidney transcriptome patterns in two different strains of mice flown on the International Space Station, C57BL/6J and BALB/c. Here we show a link between spaceflight and transcriptome patterns associated with dysregulation of lipid and extracellular matrix metabolism and altered transforming growth factor-beta signalling. A stronger response was seen in C57BL/6J mice than BALB/c. Genetic differences in hyaluronan metabolism between strains may confer protection against extracellular matrix remodelling through the downregulation of epithelial-mesenchymal transition. We intend for our findings to contribute to the development of new countermeasures against kidney disease in astronauts and people here on Earth
Type 2 Diabetes and Cardiovascular Conditions Prediction in Individuals with Metabolic Syndrome-Associated Lipoprotein Lipase Gene (LPL), Single Nucleotide Polymorphisms (SNPs)
Full text linkObjective: Metabolic syndrome (MetS) is predictive of increased risk of type 2 diabetes (T2D) and cardiovascular conditions (CVC). Lipoprotein lipase gene (LPL) single nucleotide polymorphisms (SNPs) may be of importance to the eventual diagnosis of T2D and CVC. This study aimed to predict the diagnosis of T2D and CVC amongst individuals with LPL SNPs rs268, rs11542065, rs116403115, rs118204057, rs118204061, rs144466625, and rs547644955.
Methods: This is a retrospective study using the UK Biobank data. Variables associated with MetS, T2D and CVC were selected from the data set. The total number of subjects in the cohort was 12,872 (mean age 56 years + 8.1, 90.0% were of British ethnicity, and 53.9% were females). Logistic regression was used to assess whether the T2D and CVC can be predicted based on the presence of LPL SNPs and some of the clinical measures.
Results: Prediction models using clinical parameters showed good area under the curve (AUC) for prediction of T2D and CVC diagnosis (in receiver operating characteristic (ROC) analysis, area under the curve (AUC)=.959 for T2D, AUC=.772 for CVC). The addition of Polygenic Risk Scores (PRS/s) showed an improvement for diagnosis of both (AUC=.961 and .790 for TD and CVC, respectively). Further addition of SNPs showed more increase in AUC (AUC=.965 and .837 for T2D and CVC, respectively).
The additive effect of the PRSs and LPL SNPs was more pronounced in the CVC than in the T2D model. The variant that had major significance for both T2D and CVC diagnoses was rs547644955 (AUC 1.0 and .910, respectively). The SNPs rs116403115 and rs118204057 both had an AUC of 1.0 for T2D diagnosis.
Conclusion: The prediction of T2D and CVC diagnoses with the use of clinically available factors may be enhanced with the addition of PRSs and SNPs, including LPL SNPs, which may have implications for stratified or personalised approaches for disease prevention or treatment
Theoretical sensitivity and reflexivity in grounded theory
Full text linkBackground Grounded theory (GT) has become one of the foremost tools in qualitative nursing research. There are different approaches to GT but a feature common to all of
them is theoretical sensitivity, which facilitates GT’s iterative process. However, differences between the approaches in how to apply theoretical sensitivity and how much influence existing knowledge should play have contributed to tribalism.
Aim To critically evaluate the role of theoretical sensitivity and reflexivity in GT and the involvement they can have, as well as explore what steps researchers can take to improve their insight.
Discussion Theoretical sensitivity enables researchers to steer their studies to answer their research questions, gain insight into their study’s findings and develop theory grounded in the data. However, reflection is required for researchers to understand their effect on the theories
that emerge, prevent them from applying preconceived ideas and allow for the unfettered emergence of theory.
Conclusion Researchers who do not demonstrate insight into their own philosophical positions and influences risk being accused of bias; this may result in the perceived value of their theoretical outcomes being reduced. Applying a reflexive process may mitigate this, enabling them to understand and refine their methodological processes and produce high-quality GT research.
Implications for practice All researchers should consider using reflexivity when conducting research. Understanding influences and positionality in qualitative methodologies allows for transparency and improves the rigour of their outcomes
Banana Leaf Disease Detection and Classification Using CNN Model
No full textOne of the most extensively farmed tropical fruits is the banana. It produces 16% of global fruit, second only to citrus. Leaves diseases affect banana crop production which results in poor economic condition of banana farmers. This research focuses on the detection and classification of banana plant diseases using deep learning and transfer learning models. The primary objective is to develop a reliable tool for farmers which enables early and accurate banana plant disease detection. Researchers employed multiple convolutional neural network models, such as VGG19, LeNet, ResNet101V2, MobileNetV2, and InceptionV3, to classify six different banana plant diseases, namely Insect Pests, Cordana, Pestalotiopsis, Sigatoka, Bract Mosaic, and Moko. The data has undergone appropriate data processing steps, and each model was fine-tuned for optimized results. VGG19 model has delineated the best performance, achieving an accuracy of 97%. This study highlights the potential of transfer learning models used in plant disease detection tasks and provides a scalable solution for managing crop health
Data sources:
Access Repository Dashboard
Do you manage STORE - Staffordshire Online Repository? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!