An Economic Assessment of the Costs and Benefits of Natura 2000 Sites in Scotland

In accordance with the European Commission "Habitats Directive" (Directive 92/43/EEC) and the "Birds Directive" (Directive 79/409/EEC), Scotland must contribute to the development of a UK network of protected areas that represent the most important wildlife sites in the European Union, known as the Natura 2000 (N2K) network. This network is made up of Special Protection Areas (SPAs) classified under the Birds Directive and of Special Areas of Conservation (SACs) under the Habitats Directive. In Scotland, by 31/12/02, 355 N2K sites had been identified, comprising 223 candidate SACs (cSACs) and 132 SPAs, accounting for 9.3% of Scotland's land surface. As 55 sites are both cSACs and SPAs, there are actually 300 separate individual N2K sites

Sit-to stand ground reaction force changes after hip resurfacing or total hip replacement: a pilot study

Two groups of osteoarthritis patients had their ground reaction forces measured during a sit-to-stand task at three months post-operation. One group had a 32mm femoral head fitted during a total hip replacement procedure and the other group had a hip resurfacing procedure. Three validated orthopaedic score questionnaires and an activity questionnaire were completed prior to surgery and at three months post-operation. This pilot study showed that there were no significant differences in the ground reaction forces in the operated and non-operated limb between the groups although both groups exhibited significantly higher ground reaction forces on the non-operated limb compared to the operated one. None of the orthopaedic scores showed any significant differences between the groups, despite the resurfacing group reporting higher levels of sporting activity at three months postoperation

Clinically insignificant association between anterior knee pain and patellofemoral lesions which are found incidentally.

Patellofemoral chondral lesions are frequently identified incidentally during the arthroscopic treatment of other knee pathologies. A role has been described for arthroscopic debridement of such lesions when symptoms are known to originate from pathology of the patellofemoral joint. However, it remains unclear how to manage lesions which are found incidentally whilst tackling other pathologies. The purpose of this study was to establish the strength of association between anterior knee pain and patellofemoral lesions identified incidentally in a typical arthroscopic population. A consecutive series of patients undergoing arthroscopy for a range of standard indications formed the basis of this cross section study. We excluded those with patellofemoral conditions in order to identify patellofemoral lesions which were solely incidental. Pre-operative assessments were performed on 64 patients, where anterior knee pain was sought by three methods: an annotated photographic knee pain map (PKPM), patient indication with one finger and by palpated tenderness. A single surgeon, who was blinded to previous recordings, performed standard arthroscopies and recorded patellofemoral lesions. Statistical correlations were performed to identify the association magnitude. Associations were identified between incidental patellofemoral lesions and tenderness palpated on the medial patella (P=0.007, χ2=0.32) and the quadriceps tendon (P=0.029, χ2=0.26), but these associations were at best fair, which could be interpreted as clinically insignificant. In which case incidental patellofemoral lesions are not necessarily associated with anterior knee pain, we suggest that they could be left alone. This recommendation is only applicable to patellofemoral lesions which are found incidentally whilst addressing other pathology

Can patients really make an informed choice? An evaluation of the availability of online information about consultant surgeons in the United Kingdom.

Objectives - The National Health Service (NHS) 'Choose and Book' online scheme, which allows patients to select the location and time of hospital appointments, has now been extended to include the option for patients to select a specific consultant to carry out any necessary treatment. The aim of this study was to determine whether there is sufficient online information about consultants or consultant-led teams for patients to make an informed choice regarding a specific consultant. Design - A web-based analysis of the availability of information. Setting - North of England. Participants - Two hundred websites of orthopaedic surgeons. Main outcome measures - The websites were analysed using a bespoke template that took into account recommendations of the 2010 UK Government white paper. Each website was scored in relation to the availability of specific content relating to each surgeon. Results - The majority of websites detailed authorship information (73.2%), level of professional qualification (98.5%) and area of general (73.7%) and specialist (93.3%) interest. However, approximately 50% of websites provided no information in relation to update cycle, involvement in teaching or research and patient satisfaction. Only five (2.6%) of the websites presented death rates, and none indicated morbidity rates. Conclusions - For patients to be able to make informed choices about their healthcare, surgeons need to ensure that sufficient information is available online, according to the identified limitations of the websites investigated in this study

Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition

When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is error-prone and can impact target model accuracy. To identify the extent of such impact, we perform and briefly present a differential analysis against three DNNs widely used for image recognition (MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which revealed numerous model crashes and output label discrepancies of up to 72%. To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models. Our technique consists of four stages of analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters, and 4) graph representation. In addition, we propose various strategies towards fault repair of the faults detected. We implement our technique on top of the Apache TVM deep learning compiler, and we test it by conducting a preliminary fault localization analysis for the conversion of InceptionV3 from TF to TFLite. Our approach detected a fault in a common DNN converter tool, which introduced precision errors in weights, reducing model accuracy. After our fault localization, we repaired the issue, reducing our conversion error to zero

DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as the computational environment. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood. In this paper we present a differential testing framework, DeltaNN, that allows us to assess the impact of different computational environment parameters on the performance of image recognition models during deployment, post training. DeltaNN generates different implementations of a given image recognition model for variations in environment parameters, namely, deep learning frameworks, compiler optimizations and hardware devices and analyzes differences in model performance as a result. Using DeltaNN, we conduct an empirical study of robustness analysis of three popular image recognition models using the ImageNet dataset. We report the impact in terms of misclassifications and inference time differences across different settings. In total, we observed up to 72% output label differences across deep learning frameworks, and up to 81% unexpected performance degradation in terms of inference time, when applying compiler optimizations

Validation of an electrogoniometry system as a measure of knee kinematics during activities of daily living

Purpose: The increasing use of electrogoniometry (ELG) in clinical research requires the validation of different instrumentation. The purpose of this investigation was to examine the concurrent validity of an ELG system during activities of daily living. Methods: Ten asymptomatic participants gave informed consent to participate. A Biometrics SG150 electrogoniometer was directly compared to a 12 camera three dimensional motion analysis system during walking, stair ascent, stair descent, sit to stand, and stand to sit activities for the measurement of the right knee angle. Analysis of validity was undertaken by linear regression. Standard error of estimate (SEE), standardised SEE (SSEE), and Pearson’s correlation coefficient r were computed for paired trials between systems for each functional activity. Results: The 95% confidence interval of SEE was reasonable between systems across walking (LCI = 2.43 °; UCI = 2.91 °), stair ascent (LCI = 2.09 °; UCI = 2.42 °), stair descent (LCI = 1.79 °; UCI = 2.10 °), sit to stand (LCI = 1.22 °; UCI = 1.41 °), and stand to sit (LCI = 1.17 °; UCI = 1.34 °). Pearson’s correlation coefficient r across walking (LCI = 0.983; UCI = 0.990), stair ascent (LCI = 0.995; UCI = 0.997), stair descent (LCI = 0.995; UCI = 0.997), sit to stand (LCI = 0.998; UCI = 0.999), and stand to sit (LCI = 0.996; UCI = 0.997) was indicative of a strong linear relationship between systems. Conclusion: ELG is a valid method of measuring the knee angle during activities representative of daily living. The range is within that suggested to be acceptable for the clinical evaluation of patients with musculoskeletal conditions

Individual, social and environmental factors influencing dietary behaviour in shift workers with type 2 diabetes working in UK healthcare: A cross-sectional survey

BACKGROUND: The present study aimed to understand the individual, social and environmental factors influencing dietary behaviour in shift workers with type 2 diabetes (T2D) working in UK healthcare settings. METHODS: A cross-sectional study was conducted using data collected from an anonymous online survey. Participant agreement was measured using five-point Likert scale (strongly disagree to strongly agree) against 38 belief statements informed by the Theoretical Domains Framework (TDF) of behaviour change. RESULTS: From the complete responses (n = 119), 65% worked shifts without nights, 27% worked mixed shift rota including nights and 8% worked only night shifts. The statements ranked with the highest agreements were in the TDF domains: Environment Context/Resources (ECR) - mainly identified as a barrier to healthy eating, Behaviour Regulation (BR) and intention (IN) - identified as enablers to healthy eating. For the belief statement 'the available options for purchasing food are too expensive' (ECR), 80% of night workers and 75% non-night workers agreed/strongly agreed. Taking their own food to work to prevent making unhealthy food choices (BR) had agreement/strong agreement in 73% of non-night and 70% night workers; 74% non-night workers and 80% of night workers agreed/strongly agreed with the statement 'I would like to eat healthily at work' (IN). Mixed shift workers agreed that following dietary advice was easier when working a non-night compared to a night shift (p = 0.002). CONCLUSIONS: Access and affordability of food were identified as important determinants of dietary behaviour during shifts. The findings support interventions targeting the food environment for shift workers with T2D

Exploration of the individual, social and environmental factors influencing dietary behaviour in shift workers with type 2 diabetes working in UK healthcare - the Shift-Diabetes Study: a qualitative study using the Theoretical Domains Framework

AIM: To identify factors influencing dietary behaviour in shift workers with type 2 diabetes (T2D) working in UK healthcare settings. METHODS: Semi-structured qualitative interviews based on the Theoretical Domains Framework (TDF) were conducted with a convenience sample (n=15) of shift-workers (32 - 59 years) diagnosed with T2D who worked night shifts as part of a mixed shift schedule. The TDF was applied to analyse transcripts using a combined deductive framework and inductive thematic analysis approach. Identified influences were mapped to the behaviour change technique taxonomy to identify potential strategies to change dietary behaviour in this context. RESULTS: Key barriers to healthy dietary behaviours were access and cost of food available during night work (TDF domain: Environment Context and Resources). Factors identified as both enablers and barriers included: availability of staff facilities and time to take a break, (Environment Context and Resources), the physical impact of night work (Beliefs About Consequences), eating in response to stress or tiredness (Emotion), advance planning of meals/food and taking own food to work (Behavioural Regulation). Potential techniques to address these influences and improve dietary behaviour in this context include: meal planning templates, self-monitoring, and biofeedback, and increasing accessibility and availability of healthier food choices during night shifts. CONCLUSIONS: The dietary behaviour of shift workers with T2D is influenced by interacting individual, socio-cultural and environmental factors. Intervention should focus on environmental restructuring and strategies that enable monitoring and meal planning

Exploration of the individual, social and environmental factors influencing dietary behaviour in shift workers with type 2 diabetes working in UK healthcare—The Shift-Diabetes Study:A qualitative study using the theoretical domains framework

Aim: To identify factors influencing dietary behaviour in shift workers with type 2 diabetes (T2D) working in UK healthcare settings. Methods: Semi-structured qualitative interviews based on the theoretical domains framework (TDF) were conducted with a convenience sample (n = 15) of shift workers (32–59 years) diagnosed with T2D who worked night shifts as part of a mixed shift schedule. The TDF was applied to analyse transcripts using a combined deductive framework and inductive thematic analysis approach. Identified influences were mapped to the behaviour change technique taxonomy to identify potential strategies to change dietary behaviour in this context. Results: Key barriers to healthy dietary behaviours were access and cost of food available during night work (TDF domain: Environment Context and Resources). Factors identified as both enablers and barriers included: availability of staff facilities and time to take a break, (Environment Context and Resources), the physical impact of night work (Beliefs About Consequences), eating in response to stress or tiredness (Emotion), advance planning of meals/food and taking own food to work (Behavioural Regulation). Potential techniques to address these influences and improve dietary behaviour in this context include: meal planning templates, self-monitoring and biofeedback, and increasing accessibility and availability of healthier food choices during night shifts. Conclusions: The dietary behaviour of shift workers with T2D is influenced by interacting individual, socio-cultural and environmental factors. Intervention should focus on environmental restructuring and strategies that enable monitoring and meal planning.</p