Interrogating green social prescribing in South Wales; A multi-stakeholder qualitative exploration

As an umbrella term, social prescribing offers varied routes into society which promise to support, enhance, and empower individual citizens to take control of their own health and wellbeing. Globally healthcare systems are struggling to cope with the increasing demands of an ageing population and the NHS (UK) is no exception. Social prescribing is heralded as a means to relieve the burden on primary care and provide support for the 20% of patients whose needs are non-medical. As such an increasing array of schemes are available, spanning five sub-sets: creative or nature-based referrals, welfare services, exercise referrals, education programmes or befriending support. Green social prescription offers significant potential to promote wellbeing and improve health outcomes. However limited research has explored this emergent sub-set

Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions

A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly

Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field

Parasite Abundance‐Occupancy Relationships Across Biogeographic Regions: Joint Effects of Niche Breadth, Host Availability and Climate

Aim: Changing biodiversity and environmental conditions may allow multi-host pathogens to spread among host species and affect prevalence. There are several widely acknowledged theories about mechanisms that may influence variation in pathogen prevalence, including the controversially debated dilution effect and abundance-occupancy relationship hypotheses. Here, we explore such abundance-occupancy relationships for unique lineages of three vector-borne avian blood parasite genera (the avian malaria parasite Plasmodium and the related haemosporidian parasites Parahaemoproteus and Leucocytozoon) across biogeographical regions.Location: Nearctic-Neotropical and Palearctic-Afrotropical regions.Methods: We compiled a cross-continental dataset of 17,116 bird individuals surveyed from 46 bird assemblages across the Nearctic-Neotropical and Palearctic-Afrotropical regions and explored relationships between local parasite lineage prevalence and host assemblage metrics in a Bayesian random regression framework.Results: Most lineages from these three genera infected ≥ 5 host species and exhibited clear phylogenetic or functional host specificity. Lineage prevalence from all three genera increased with host range, but also with higher degrees of specialisation to phylogenetically or functionally related host species. Local avian community features were also found to be important drivers of prevalence. For example, bird species richness was positively correlated with lineage prevalence for Plasmodium and Leucocytozoon, whereas higher relative abundances of the main host species were associated with lower prevalence for Plasmodium and Parahaemoproteus but higher prevalence for Leucocytozoon.Conclusions: Our results broadly support several of the leading hypotheses about mechanisms that influence pathogen prevalence, including the niche breadth hypothesis in that higher avian host species diversity and broader host range amplify prevalence through increasing ecological opportunities and the trade-off hypotheses in that specialisation among subsets of available host species may increase prevalence. Furthermore, the three studied avian haemosporidian genera exhibited different abundance-occupancy relationships across the major global climate gradients and in relation to host availability, emphasising that these relationships do not strictly follow common rules for vector-borne parasites with different life histories

From attributes to natural language: A survey and foresight on text-based person re-identification

Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodalanalysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptions. Despite the wide range of applicable areas such as security surveillance, video retrieval, person tracking, and social media analytics, there is a notable absence of comprehensive reviews dedicated to summarizing the text-based person Re-ID from a technical perspective. To address this gap, we propose to introduce a taxonomy spanning Evaluation, Strategy, Architecture, and Optimization dimensions, providing a comprehensive survey of the text-based person Re-ID task. We start by laying the groundwork for text-based person Re-ID, elucidating fundamental concepts related to attribute/natural language-based identification. Then a thorough examination of existing benchmark datasets and metrics is presented. Subsequently, we further delve into prevalent feature extraction strategies employed in text-based person Re-ID research, followed by a concise summary of common network architectures within the domain. Prevalent loss functions utilized for model optimization and modality alignment in text-based person Re-ID are also scrutinized. To conclude, we offer a concise summary of our findings, pinpointing challenges in text-based person Re-ID. In response to these challenges, we outline potential avenues for future open-set text-based person Re-ID and present a baseline architecture for text-based pedestrian image generation guided re-identification (TBPGR)

Developing a prototype for federated analysis to enhance privacy and enable trustworthy access to COVID-19 research data

The use of federated networks can reduce the risk of disclosure for sensitive datasets by removing the requirement to physically transfer data. Federated networks support federated analytics, a type of privacy-enhancing technology, enabling trustworthy data analysis without the movement of source data. To set out the methodology used by the International COVID-19 Data Alliance (ICODA) and its partners, the Secure Anonymised Information Linkage (SAIL) Databank and Aridhia Informatics in piloting a federated network infrastructure and consequently testing federated analytics using test data provided from an ICODA project, the International Perinatal Outcome in the Pandemic (iPOP) Study. To share the challenges and benefits of using a federated network infrastructure to enable trustworthy analysis of health-related data from multiple countries and sources. This project successfully developed a federated network between the SAIL Databank and the ICODA Workbench and piloted the use of federated analysis using aggregate-level model outputs as test data from the iPOP Study, a one-year, multi-country COVID-19 research project. This integration is a first step in implementing the necessary technical, governance and user experiences for future research studies to build upon, including those using individual-level datasets from multiple data nodes. Creating federated networks requires extensive investment from a data governance, technology, training, resources, timing and funding perspective. For future initiatives, the establishment of a federated network should be built into medium to long term plans to provide researchers with a secure and robust data analysis platform to perform joint multi-site collaboration. Federated networks can unlock the enormous potential of national and international health datasets through enabling collaborative research that addresses critical public health challenges, whilst maintaining privacy and trustworthiness by preventing direct access to the source data

Predator–Prey Movement Interactions: Jaguars and Peccaries in the Spotlight

Understanding how landscape structure influences predator–prey dynamics is critical for conservation. This study analyzed jaguar‐peccary interactions, revealing uncommon close distances and prevalent 3–5 km ranges, especially away from grasslands. Low peccary densities increased interactions. Findings inform conservation strategies, highlighting landscape structure and prey density roles in maintaining Pantanal's balance