Overthickening of sedimentary sequences by igneous intrusions

This study was part of a collaboration between the University of Aberdeen, Siccar Point Energy Ltd and Integrated Geochemical Interpretation (IGI). Siccar Point Energy Ltd are thanked for giving the author permission to publish. Karolina Harvie and Kevin Ward from Petrosys are thanks for their support during the mapping process of the project. The lead author’s PhD is funded by JX Nippon Exploration and Production (U.K.) Limited as part of the Volcanic Margin Research Consortium Phase 2. PGS are thanked for allowing the author access to the MegaSurveyPlus and PGS/TGS FSB 2011-12 MultiClient GeoStreamer data and for granting permission to publish this work. Seismic interpretation was carried out using Schlumberger Petrel software. Well log analysis was carried out using Schlumberger Techlog software. Dave Ellis and Victoria Pease are thanked for the comments which greatly improved the revisions of this paper. Well data was obtained from the UK Oil and Gas Authority (OGA) Common Data Access (CDA).Peer reviewedPostprin

Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability

VPRS-based regional decision fusion of CNN and MRF classifications for very fine resolution remotely sensed images

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNN), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterise the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery, and partition this uncertainty into positive regions (correct classifications) and non-positive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a Multi-Layer Perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as Semantic Labelling datasets. The MRF-CNN consistently outperformed the benchmark MLP, SVM, MLP-MRF and CNN and the baseline methods. This research provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification

Understanding Substance Use and the Wider Support Needs of Scotland’s Prison Population

Introduction: The Scottish Government has committed to undertaking a comprehensive, national assessment of the health and social care needs of Scotland’s prison population. The last prison health needs assessment was conducted in 2007 and a great deal has changed in the policy and service delivery landscape since then. This needs assessment is one of four commissioned studies1 and focuses on an assessment of the needs of the prison population relating to alcohol, drugs, and tobacco use. Background Substance use has long been a concern for the health and wellbeing of people living in Scotland’s prisons and remains one of the most prominent challenges to Scotland’s prison system. People who live in prison are disproportionately more likely to use alcohol, drugs, and tobacco than those individuals who do not enter prison (Toomey et al., 2022). Crime and substance use are known to be closely associated2. Problemetic substance use often contributes to the factors involved in why someone is in prison and often continues (or for some begins) whilst living in prison (Carnie and Boderick, 2019). Many people living in prison have substance use needs that pre-date their imprisonment and can stem from multiple factors, such as experiencing trauma and social and economic inequality (Devries et al., 2014; Najavits, 2015). Within custody, people experience limited access to family and community supports, bullying, and feelings of hopelessness which risks perpetuating and escalating substance use. The prevalence of substance use within prisons is a serious threat to the health of people living there and to general public health. It threatens the safety of prison officers and healthcare staff and creates challenges in terms of maintaining good order and discipline (O’Hagan, 2017). With the recent rates in drug-related deaths continuing to soar across Scotland, there is increasing pressure to take more urgent action to address substances and the harms that they present (NRS, 2021). The exact picture of substance use in Scottish prisons is unclear. There is a lack of available data, which will be highlighted later in this report. Estimating the prevalence of substance use in Scottish prisons is therefore highly challenging, particularly in relation to drug use (Toomey et al., 2022). What is apparent though is that levels of drug deaths across Scotland are very high. In 2019, in reponse to the high levels of drug deaths in Scotland, the Drug Deaths Taskforce [DDTF] was established.3 This group is made up of volunteer members who have been proactive about pushing for the implementation of the MAT [Medical Assisted Treatment] Standards.4 1 The other three studies are: (1) physical and general health, including major clinical and long-term conditions, infectious disease, non-communicable disease, sexual health; (2) mental health; and (3) social care and support needs. 2 The Scottish Parliament - Healthcare in Prisons 3 Drug Deaths Task Force 4 MAT Standards, introduced by the Scottish Government in 2021, are an evidence-based set of standards to enable the consistent delivery of safe, accessible, high-quality drug treatment across Scotland. 2 Current substance use data often relies on self-reporting5 or upon incident reports which do not present the full picture of substance use. In the most recent Scottish Prison Service [SPS] Prisoner Survey (Carnie and Boderick, 2019), 41% self reported that they engaged in problematic drug use prior to imprisonment; 45% had been under the influence of drugs, and 40% reported being drunk at the time of their offence. This is indicative of a high level of need. People living in prison experience substantially poorer health than the general population, in part because of the high prevalence of smoking amongst those living in prison (Spaulding, 2018). High rates of smoking in prisons had been consistently reported in the SPS Prisoner Survey prior to the introduction of a smoke free prison environment (2018). The 2013 survey reported that 74% of people living in prison smoked which contrasts with a prevalence rate of around 20% in Scotland as a whole. Whilst the prison system continues to be the host for many of those who are found to have committed substance related crimes, there have been increasing conversations about whether this is in fact the right place for them (Scottish Parliament, 2022). It has long been acknowledged that the opportunities and provisions for rehabilitation within a prison setting are limited (ibid.)6. So too there is a real sense that the revolving door of prisons can exacerbate and encourage substance use: ‘We do not rehabilitate prisoners well, we do not prepare them for release well and we do not support them on release well, because our system is chock-a-block with people who should not be in it.’ (Professor Fergus McNeill, evidence provided to Scottish Parliament, 2022, pg. 11) Alcohol and Drugs Each prison in Scotland has developed its own policies and procedures to manage drug and alcohol use. In part this can be traced back to the NHS takeover of addictions work in Scottish prisons from Phoenix Futures (2011). In the absence of universal guidance, each NHS Board that had a prison in its catchment area decided what approach to take (NHS, 2016). One requirement, however, was that services should be available on an equitable basis to community-based services. Whilst there are different approaches across prisons, there are a number of policies and documents that offer guidance to all prisons. One of these is the National Naloxone Programme which has been active in Scottish Prisons since 2011. This has provided all those leaving prison with Naloxone in an attempt to address opioid related overdoses upon release (Bird et al. 2014). As data collection for this study progressed, some respondents informed us that naloxone provision had continued to be developed. For example, intranasal naloxone is now offered, making it a needle-less product. Online service, training and learning opportunities have been expanded, and there has also been a move to educate and organise peer naloxone 5 Self-reporting results in limited findings as, particularly in relation to substance use, it is uncommon for people to be completely honest in their self-disclosures and so results in data consisting of what people are willing to self-disclose. 6 It should be acknowledged here that rehabilitation opportunities do vary across prisons and prisoner categories. 3 distributors and trainers, something which has enjoyed much success (DDTF, 2021). Across Scotland, the SPS’ Management of Offender at Risk Due to Any Substance [MORS] policy was introduced in December 2014. This guidance instructs prison staff on how to respond if they identify someone as being at risk from a substance and how healthcare staff should engage with the incident. Rights, Respect and Recovery, Scotland’s strategy for reducing drug and alcohol related harms and deaths, was published in 2018 (Scottish Government, 2018). The strategy provided a specific focus on prisons as one of the key organisations that should be involved in delivering on national substance use goals. In January 2021, the strategic approach was further enhanced through the announcement by the First Minister of a new ‘National Mission’ to reduce drug-related deaths and harms, supported by an additional £50 million funding per year (for the next five years). 7 In response to the Covid-19 pandemic, the Scottish Government allocated £1.9 million to support people to switch to Buvidal as an OST treatment option (MacNeill, 2021). Buvidal is a longer-acting form of OST that means people can switch from a daily medication regime to only needing to take their presciption on a weekly or monthly basis. Intial small-scale feedback on Buvidal has highlighted its potential to support people to make positive changes to their lives and demonstrated it may improve outcomes for prison leavers, such avoiding relapses in the community or helping them look employment (MacNeill, 2020). Increasing the number of people being prescribed Buvidal in Scotland’s prisons may also go some way towards alleviating the current burden placed on prison operations and healthcare by the daily administration of methadone. Tobacco SPS and partners have successfully delivered smoke free environments since November 2018. This change was introduced as part of a wider Scottish Government focus on changing smoking habits for future generations. In the lead up to, and in the aftermath of the introduction of a smoke free policy, smoking in prison has transformed from an under-researched and poorly understood policy area, to one which is underpinned by a rich literature base which engenders ongoing policy and practice conversations. In January 2022 the final report for the Tobacco in Prisons Study [TiPS] was published (Hunt et al., 2022). The study documents the impact of smoke free prisons in Scotland. It indicates that smoke free prisons policy have quickly become the ‘new normal’. Second hand smoking has been reduced by 90% and e-cigarette use has became commonplace. TiPS was the first study internationally to explore this topic and did so extensively. As such it has not been appropriate nor useful for this needs assessment to 7 National mission - Alcohol and drugs 4 replicate or duplicate evidence gathering with regard to current policy and programmes around Tobacco. Therefore, the team has focused on alcohol and drug use as a priority for the data collection for this project whilst considering the place of tobacco use alongside other substance use. Study Aim and Objectives The aim of this needs assessment study was to help the Scottish Government and its partners better understand what the healthcare needs of people with substance use problems living in Scotland’s prisons are. The specific objectives of the needs assessment were to: 1. Conduct a rapid review of the research literature from the UK and (if there is a strong rationale for it) comparable jurisdictions on the nature and extent of substance use needs and support within prison populations. 2. Synthesise available national and local-level data and research to report on the epidemiology of substance use experienced by Scotland’s prison population, including newer trends such as New Psychoactive Substances [NPS] usage, compared to others in the criminal justice system (e.g. people serving community sentences) and the general population. 3. Map current models of substance use care/interventions within Scotland’s prisons, how they interface with other healthcare interventions within prisons, and how they interface with community care models and services, including assessing aspects of treatment continuity, finding examples of best practice, and throughcare pathways during transition from custody to the community. 4. Assess the scope for the improved collection of routine data that can be made available to analysts, managers, and service providers for continued monitoring and analysis of support needs relating to substance use. 5. Offer insights for future data linkage and data collection priorities. 6. Include the perspectives of people with lived experience of prison and substance use to incorporate their views and insights. Methodology Study methods The core elements of the study focused on qualitative approaches (comprising of: (1) semi-structured interviews with a broad range of professional stakeholder groups; (2) a short-life working group with a diverse range of professional stakeholders from key partners in SPS, NHS, and the Third Sector; and (3) interviews with those who have lived and/or living experience). These approaches were supplemented with a rapid literature review, a review of existing (published) data, and a mapping exercise (see Table 1 below). Although the original study design included a desk-based review and synthesis of all available (published) datasets, and that this would be expected to be seen within a Health Needs Assessment report, it is not included in the usual way in this report. 5 From our early review of available Scottish health datasets, it became evident that published healthcare data regarding substance use for Scotland’s prisons was deficient and would not provide meaningful, real-time insights. We have included (see Table 2 in Chapter 5) an overview of the available datasets (including comments upon their individual strengths and limitations), but have focused our approach on a qualitative high-level strategic review of how healthcare data is gathered and used in order to identify the areas where substance use data collection, analysis and linkaging needs to improve (see Chapter 5). The context of the Covid-19 pandemic necessitated a flexible approach, with all working group sessions and semi-structured interviews conducted remotely or on the phone. Full details of study methods and our approach to analysis is provided in Appendix A. Recruitment, sampling, and activity completed A summary of study methods, recruitment, sampling and activity completed is presented in the table below. Fieldwork activities took place between October 2021 and February 2022

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification

An object-based convolutional neural network (OCNN) for urban land use classification

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images

A major genetic locus in <i>Trypanosoma brucei</i> is a determinant of host pathology

The progression and variation of pathology during infections can be due to components from both host or pathogen, and/or the interaction between them. The influence of host genetic variation on disease pathology during infections with trypanosomes has been well studied in recent years, but the role of parasite genetic variation has not been extensively studied. We have shown that there is parasite strain-specific variation in the level of splenomegaly and hepatomegaly in infected mice and used a forward genetic approach to identify the parasite loci that determine this variation. This approach allowed us to dissect and identify the parasite loci that determine the complex phenotypes induced by infection. Using the available trypanosome genetic map, a major quantitative trait locus (QTL) was identified on T. brucei chromosome 3 (LOD = 7.2) that accounted for approximately two thirds of the variance observed in each of two correlated phenotypes, splenomegaly and hepatomegaly, in the infected mice (named &lt;i&gt;TbOrg1&lt;/i&gt;). In addition, a second locus was identified that contributed to splenomegaly, hepatomegaly and reticulocytosis (&lt;i&gt;TbOrg2&lt;/i&gt;). This is the first use of quantitative trait locus mapping in a diploid protozoan and shows that there are trypanosome genes that directly contribute to the progression of pathology during infections and, therefore, that parasite genetic variation can be a critical factor in disease outcome. The identification of parasite loci is a first step towards identifying the genes that are responsible for these important traits and shows the power of genetic analysis as a tool for dissecting complex quantitative phenotypic traits

Opportunities for machine learning and artificial intelligence in national mapping agencies:enhancing ordnance survey workflow

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows

A randomised controlled trial of a physical activity and nutrition program targeting middle-aged adults at risk of metabolic syndrome in a disadvantaged rural community

Background: Approximately 70% of Australian adults aged over 50 are overweight or obese, with the prevalence significantly higher in regional/remote areas compared to cities. This study aims to determine if a low-cost, accessible lifestyle program targeting insufficiently active adults aged 50-69 y can be successfully implemented in a rural location, and whether its implementation will contribute to the reduction/prevention of metabolic syndrome, or other risk factors for type 2 diabetes, and cardiovascular disease.Methods/Design: This 6-month randomised controlled trial will consist of a nutrition, physical activity, and healthy weight intervention for 50–69 year-olds from a disadvantaged rural community. Five hundred participants with central obesity and at risk of metabolic syndrome will be recruited from Albany and surrounding areas in Western Australia (within a 50 kilometre radius of the town). They will be randomly assigned to either the intervention (n = 250) or wait-listed control group (n = 250). The theoretical concepts in the study utilise the Self-Determination Theory, complemented by Motivational Interviewing. The intervention will include a custom-designed booklet and interactive website that provides information, and encourages physical activity and nutrition goal setting, and healthy weight management. The booklet and website will be supplemented by an exercise chart, calendar, newsletters, resistance bands, accelerometers, and phone and email contact from program staff. Data will be collected at baseline and post-intervention.Discussion: This study aims to contribute to the prevention of metabolic syndrome and inter- related chronic illnesses: type 2 diabetes mellitus, cardiovascular disease, and some cancers; which are associated with overweight/obesity, physical inactivity, and poor diet. This large rural community-based trial will provide guidelines for recruitment, program development, implementation, and evaluation, and has the potential to translate findings into practice by expanding the program to other regional areas in Australia. Trial registration: Australian and New Zealand Clinical Trials Registry [ACTRN12614000512628, registration date 14th May 2014]