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From data to decisions: towards a biodiversity monitoring standards framework
Get PDFAchieving the goals of the Kunming–Montreal Global Biodiversity Framework (GBF) requires monitoring systems that can transform heterogeneous observations into consistent, decision-relevant knowledge. Yet current biodiversity data are fragmented, uneven in quality, and seldom comparable across space or time. Existing standards such as Darwin Core, Findable, Accessible, Interoperable, and Reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles provide important foundations, but they do not connect the full chain from field observation to policy reporting. We introduce the Biodiversity Monitoring Standards Framework (BMSF)—a unifying architecture that links ethical principles, standardized data collection, accredited analytical workflows, and transparent reporting into a single auditable “chain of evidence.” The framework’s novelty lies in its tiered and federated design, enabling national agencies, Indigenous knowledge holders, local communities, and private-sector actors to operate under shared principles while maintaining data sovereignty. By integrating Essential Variables, accredited analytical methods, and open-source implementation pathways, the BMSF allows locally generated data to be aggregated into credible, comparable indicators aligned with GBF targets. Concrete application, such as a national forest-connectivity assessment, demonstrates how the BMSF improves reproducibility, transparency, and policy relevance relative to existing approaches. Implemented generally, this framework would convert fragmented monitoring efforts into a coordinated, scalable system capable of tracking and guiding collective progress toward halting and reversing biodiversity loss
A Refined Graph Container Lemma and Applications to the Hard‐Core Model on Bipartite Expanders
Get PDFWe establish a refined version of a graph container lemma due to Galvin and discuss several applications related to the hard‐core model on bipartite expander graphs. Given a graph G and λ > 0 , the hard‐core model on G at activity λ is the probability distribution μ G , λ on independent sets in G given by μ G , λ ( I ) ∝ λ | I | . As one of our main applications, we show that the hard‐core model at activity λ on the hypercube Q d exhibits a ‘structured phase’ for λ = Ω ( log 2 d / d 1 / 2 ) in the following sense: in a typical sample from μ Q d , λ , most vertices are contained in one side of the bipartition of Q d . This improves upon a result of Galvin, which establishes the same for λ = Ω ( log d / d 1 / 3 ) . As another application, we establish a fully polynomial‐time approximation scheme (FPTAS) for the hard‐core model on a d ‐regular bipartite α ‐expander, with α > 0 fixed, when λ = Ω ( log 2 d / d 1 / 2 ) . This improves upon the bound λ = Ω ( log d / d 1 / 4 ) due to the first author, Perkins and Potukuchi. We discuss similar improvements to results of Galvin‐Tetali, Balogh‐Garcia‐Li and Kronenberg‐Spinka
A model-independent measurement of the CKM angle γ in the decays B ± → [ K + K − π + π − ] D h ± and B ± → [ π + π − π + π − ] D h ± ( h = K, π )
Get PDFA model-independent determination of the CKM angle γ is presented, using the B± → [K+K−π+π−]Dh± and B± → [π+π−π+π−]Dh± decays, with h = K, π. This measurement is the first phase-space binned study of these decay modes, and uses a sample of proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1. The phase-space bins are optimised for sensitivity to γ, and in each bin external inputs from the BESIII experiment are used to constrain the charm strong-phase parameters. The result of this binned analysis is γ=53.9−8.9+9.5°, where the uncertainty includes both statistical and systematic contributions. Furthermore, when combining with existing phase-space integrated measurements of the same decay modes, a value of γ=52.6−6.4+8.5° is obtained, which is one of the most precise determinations of γ to date
A novel deep learning model for enhanced segmentation of internal mammary artery, aorta and their perivascular regions
Get PDFBackground: A prior study demonstrated that the novel radiotranscriptomic signature, C19RS, holds prognostic significance for clinical outcomes (Figure 1a). The automated segmentation of vascular structures, including the internal mammary artery (IMA), the aorta, and their surrounding perivascular areas from contrast-enhanced CT angiography (CCTA), would facilitate the high-throughput extraction of radiomic profiles. Purpose: Our goal is to create an innovative deep learning (DL) model for the IMA and aorta that facilitates the automated calculation of C19RS in extensive cohort analyses. Methods: The model utilises a distinct architecture that combines a CNN (squeeze-and-excitation block) and a transformer (Swin block) to improve segmentation by alternating between these blocks, which helps in capturing discriminative features (Figure 1b). The model was built using the CCTA (n = 227) dataset from the OxHVF study conducted in the UK, applying standardised preprocessing techniques such as resampling, clipping, and intensity normalisation. An iterative refinement process occurred three times (n = 140), resulting in a robust model (see Figure 1c). An external validation cohort (n = 751) from an international site in the United States was utilised, with all segmentations subjected to manual expert review for quality assessment. Lastly, a publicly available dataset (ASOCA) was also validated externally (n = 318). Results: The model achieved a mean Dice similarity score (DSC) of 0.7876±0.0176 for IMA/peri-IMA segmentation and 0.9207±0.0057 for aorta/periaortic region segmentation (See figure). After refinement, it achieved a DSC of 0.947 for IMA/peri-IMA segmentation (Figure 1c,d). In the external cohort, 679 out of 751 cases (90.4%) were considered clinically acceptable for both regions; the remaining cases were excluded because the CCTAs' narrow field of view did not capture the IMA/aorta. In the ASOCA cohort, the model consistently performed at 0.961 ± 0.039. These results underscore the model’s generalizability and scalability for large-scale clinical applications. Conclusion: This study presents a powerful and clinically flexible DL model designed for the automatic segmentation of vascular structures, specifically the IMA, aorta, and their surrounding perivascular space. Its use in radiotranscriptomic biomarker analysis presents an exciting opportunity for non-invasive prediction of patient outcomes, making it a significant resource for cardiovascular research and clinical applications
Supporting data for the paper "Neural networks for learning macroscopic chemotactic sensitivity from microscopic models"
No full textThe attached files contain the supporting computer codes and data for the paper: "Neural networks for learning macroscopic chemotactic sensitivity from microscopic models" by Radek Erba
A qualitative study assessing the acceptability of a multi-agent AI Chatbot for providing HIV and mental health support among men who have sex with men and transgender women in KwaZulu-Natal, South Africa
Get PDFBackground: Transgender women (TGW) and men who have sex with men (MSM) are disproportionately affected by human immunodeficiency virus (HIV) and mental health challenges. Mental well-being influences uptake and adherence to HIV prevention and treatment. However, gaps in mental health service delivery present challenges for scalability in public health systems. Artificial intelligence (AI)-driven chatbots may offer a novel, scalable solution to expand access to mental health support. Methods: This qualitative study was conducted at the Aurum POP INN clinic in Pietermaritzburg, KwaZulu-Natal. A multi-agent AI chatbot, designed to simulate supportive counselling based on the Inuka model, was piloted with TGW and MSM. Ten participants engaged in in-depth interviews after interacting with the chatbot. An additional 34 participants experienced both chatbot and in-person counselling through a randomised crossover design and then participated in four focus group discussions. The Unified Theory of Acceptance and Use of Technology and the Acceptability of Healthcare Interventions Framework guided the analysis. Results: The chatbot was generally acceptable, with participants valuing its privacy, convenience and human-like interaction. Acceptability was enhanced by associations with modernity and anonymity. Trust, usability and accessibility improved engagement. Key barriers included slow response times, limited rapport and repetitive messaging. Conclusions: AI chatbots offer a promising, scalable approach to supporting mental health among key populations in HIV care
Methods for analysing care pathways: ontology, representation, and process perspectives on health data
Get PDFEvery interaction with the healthcare system leaves some kind of trace in the form of data, which forms a valuable resource for research. Modern healthcare is about more than individual interventions and diagnoses: equally important is the combination, ordering and timing of events, and the way that the patient moves through the system: the patient pathway. This is the source of a great many research questions, which are difficult to answer: patients cannot be neatly divided into groups, and “compliance” with a particular standard is hard to measure when there are many decision points and unseen variables. I first examine the extent to which contextual factors affect treatment decisions, and demonstrate that pathway data is shaped by human practices, processes and biases. I then consider the logic behind which data is included in process analysis, and propose an approach that uses ontological knowledge to infer relationships between diagnoses and procedures. I then propose embedding-based dynamic time warping (E-DTW), an algorithm for describing the similarity between two patients’ pathways. This algorithm is designed with practical characteristics in mind: it incorporates information on both the semantic similarity of the events in a pathway and their temporal patterns, it uses knowledge from standard and publicly available ontologies, and its embeddings can be re-used for different tasks. Finally, I extend the E-DTW method to measure the semantic similarity between a patient’s pathway and a pathway as laid down in guidelines; in the process, I describe a set of steps for assessing the gap between a guideline and given dataset, and a notation for encoding pathway guidelines in computable form. Structured data is, by virtue of the way it is recorded and encoded, rich in semantics that can be exploited to create useful insights. Pathways are inherent complex, and purely logical or statistical attempts to analyse them have drawbacks. This thesis combines the use of modern and flexible machine learning concepts with grounding in ontological knowledge, describing a set of methods that allow the benefits of health data to be realised whilst also ensuring that analysis is relevant, reliable, and reproducible
Looking to the past to conserve present-day diversity
Get PDFThis thesis analyses, or develops tools for, the investigation of macroecological patterns of change across varied temporal and spatial scales. I have sought to develop new and innovative methods for the gathering and analysis of a diverse range of ecological data, alongside previously unimplemented strategies within the field of macroecology. In the first study I developed and then tested the Historical Occurrence Georeferencing System (HOGS). This exploits historical distributional data in printed literature to generate previously untapped distributional occurrence information. HOGS was able to detect and classify large volumes of map-marker occurrence data in a fraction of the time taken for previous manual methods. It provides a rapidly deployable method for the generation of historical data, vital to accurate definition of baseline species conditions. The second study determined the degree and factorial underpinnings of avian community shift within southern Africa by utilising community weighted mean temperature and precipitation metrics. I found that avian species responded strongly to these climatic changes and the multi-metric approach allowed for a more in-depth analysis than traditionally temperature-only strategies. Indices to measure the rate of change for avian communities employed alongside derivation of important predictive factors is highly useful when designing management strategies going forward. The third study was conducted in partnership with Kew. I created a voronoi tessellation scheme for the assignment of areas of control in the analysis of socioeconomic and wellbeing indicators surrounding Madagascan protected areas. This allowed for a more nuanced understanding of fine scale heterogeneity in poverty indicators that can inform the successful delivery of conservation and sustainable development interventions. In the final study I developed a modular machine learning pipeline for the automated analysis of mollusc field images. It combines object detection and classification techniques to detect molluscs and drill holes, identify species, and enable biologically meaningful measurements. The system demonstrates strong performance across tasks, achieving high accuracy in mollusc detection, robust species classification across a range of dataset thresholds and reliable localisation and classification of drill holes. Together, these studies add new methodologies for modern and historical macroecological assessment, which will be crucial in a world experiencing accelerating change
Schools using nature for education and wellbeing: Burford School
Get PDFThis practice-led case study is part of a research project looking at Nature-based programmes in secondary schools for mental health and wellbeing. Four case studies were produced in 2025 with secondary school teachers who use nature-based programmes. Burford School is a large state mixed secondary school in a rural location at the edge of Burford in Oxfordshire. The school has large grounds with plenty of green space, mature trees and hedgerows, a small woodland, a copse, ponds, an orchard and allotment space. The school has built on experiences of the impact of green space on particular students over the last five years to build a school-wide and routine use of green space for education and wellbeing