Public health engagement: detection of suspicious skin lesions, screening and referral behaviour of UK based chiropractors.

BACKGROUND: UK morbidity and mortality rates from skin cancer are increasing despite existing preventative strategies involving education and early detection. Manual therapists are ideally placed to support these goals as they see greater quantities of exposed patient skin more often than most other healthcare professionals. The purpose of this study therefore was to ascertain the ability of manual therapists to detect, screen and refer suspicious skin lesions. METHOD: A web-based questionnaire and quiz was used in a sample of UK chiropractic student clinicians and registered chiropractors to gather data during 2011 concerning skin screening and referral behaviors for suspicious skin lesions. RESULTS: A total of 120 questionnaires were included. Eighty one percent of participants agreed that screening for suspicious skin lesions was part of their clinical role, with nearly all (94%) assessing their patients for lesions during examination. Over 90% of the participants reported regularly having the opportunity for skin examination; with nearly all (98%) agreeing they would refer patients with suspicious skin lesions to a medical practitioner. A third of respondents had referred a total of 80 suspicious lesions within the last 12 months with 67% warranting further investigation. CONCLUSIONS: Nearly all respondents agreed that screening patients for suspicious skin lesions was part of their clinical role, with a significant number already referring patients with lesions

Deep learning for image-based cassava disease detection

Open Access Journal; Published online: 27 Oct 2017Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection

The social relations of a health walk group: an ethnographic study

It is already well established that regular walks are conducive to health and wellbeing. This paper considers the production of social relations of regular, organized weekly group walks for older people. It is based on an ethnographic study of a Walking for Health group in a rural area of the United Kingdom. Different types of social relations are identified arising from the walk experience. The social relations generated are seen to be shaped by organizational factors that are constitutive of the walks, the resulting culture having implications for the sustainability of the experience. Since there appears to be no single uniting theory linking group walk experiences to the production of social relations at this time, the findings are considered against therapeutic landscape, therapeutic mobility and social capital theorizing. Finally, implications for the continuance of walking schemes for older people and for further research are considered

A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis

Convolutional neural network (CNN) models have the potential to improve plant disease phenotyping where the standard approach is visual diagnostics requiring specialized training. In scenarios where a CNN is deployed on mobile devices, models are presented with new challenges due to lighting and orientation. It is essential for model assessment to be conducted in real world conditions if such models are to be reliably integrated with computer vision products for plant disease phenotyping. We train a CNN object detection model to identify foliar symptoms of diseases in cassava (Manihot esculenta Crantz). We then deploy the model in a mobile app and test its performance on mobile images and video of 720 diseased leaflets in an agricultural field in Tanzania. Within each disease category we test two levels of severity of symptoms-mild and pronounced, to assess the model performance for early detection of symptoms. In both severities we see a decrease in performance for real world images and video as measured with the F-1 score. The F-1 score dropped by 32% for pronounced symptoms in real world images (the closest data to the training data) due to a decrease in model recall. If the potential of mobile CNN models are to be realized our data suggest it is crucial to consider tuning recall in order to achieve the desired performance in real world settings. In addition, the varied performance related to different input data (image or video) is an important consideration for design in real world applications

ASEAN's unchanged melody? The theory and practice of 'non-interference' in Southeast Asia

This is an Author's Accepted Manuscript of an article published as Jones, Lee. (2010). ASEAN's unchanged melody? The theory and practice of 'non-interference' in Southeast Asia. Pacific Review 23:4, 479-502 as published in PACIFIC REVIEW 2010 © Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/09512748.2010.49599

Interactive Responses of a Thalamic Neuron to Formalin Induced Lasting Pain in Behaving Mice

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice