RELEASE-AI:Protecting Sensitive Data Across The AI Lifecycle Disclosure Risks and Mitigations in Trusted Research Environments

Trusted Research Environments (TREs) provide secure access to personal and sensitive data, such as Electronic Healthcare Records (EHRs), for approved research. The increasing use of Artificial Intelligence (AI) and Machine Learning (ML) in health research introduces new challenges for managing privacy risks of individuals’ data. We present a comprehensive framework that embeds mitigation strategies throughout the entire AI project lifecycle, structured across six project phases: design, governance, development, evaluation, disclosure control, and release.This framework aims to empower all stakeholders - researchers, project teams, output checkers, and TRE staff - with clear, phase-specific recommendations on which measures and checks are necessary before model release to help identify potential disclosure risks to data. It promotes early identification of risks with corresponding mitigations and ensures responsibilities are clearly assigned to relevant actors at each stage, from initial planning through to deployment and monitoring. Mitigation strategies include good AI/ML practices, both in terms of code and documentation, privacy-enhancing techniques during training and evaluation, restricting model access via secure query systems, licencing agreements, and adversarial attack testing using tools like SACRO-ML. This process highlights the need to train everyone involved appropriately with relevant role-specific material.A novel tiering system for disclosure control is proposed, categorising AI projects based on the likelihood of attack and associated sensitive data leakage risks. By integrating a lifecycle-focused risk management process with a scalable disclosure control tiering system, this approach enables innovative AI research while maintaining rigorous data protection standards and public trust

Numerical simulation of three-dimensional focused waves by new deep water HLGN model

Rogue waves pose a significant threat to the safety of ships and offshore structures, making it crucial to understand their physical mechanisms, such as spatial-temporal focusing, which can lead to their formation. This study investigates three-dimensional focused waves using the newly developed deep-water high-level Green-Naghdi (HLGN) model. Through numerical simulations, we evaluate the selection of the involved wave numbers within the HLGN model and present the algorithm for the three-dimensional implementation. Validation of the model is conducted through numerical reproduction of the three-dimensional focused waves considered in other’s laboratory measurements. The simulated wave profiles and velocity fields are compared with experimental data, demonstrating strong agreement. Discussion is provided about the robustness and accuracy of the HLGN model in simulating three-dimensional focused waves under deep-water conditions.</p

Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition

Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission to centralized processing unit with substantial computational resources after collecting it from edge devices. Neuromorphic computing is an emerging field that seeks to design specialized hardware for computing systems inspired by the structure, function, and dynamics of the human brain, offering significant advantages in latency and power consumption. This paper explores a novel neuromorphic implementation approach for gesture recognition by extracting spatiotemporal spiking information from surface electromyography (sEMG) data in an event-driven manner. At the same time, the network was designed by implementing a simple-structured and hardware-friendly Physical Reservoir Computing (PRC) framework called Rotating Neuron Reservoir (RNR) within the domain of Spiking neural network (SNN). The spiking RNR (sRNR) is promising to pipeline an innovative solution to compact embedded wearable systems, enabling low-latency, real-time processing directly at the sensor level. The proposed system was validated by an open-access large-scale sEMG database and achieved an average classification accuracy of 74.6% and 80.3% using a classical machine learning classifier and a delta learning rule algorithm respectively. While the delta learning rule could be fully spiking and implementable on neuromorphic chips, the proposed gesture recognition system demonstrates the potential for near-sensor low-latency processing.</p

Genetic associations of neuropathic pain and sensory profile in a deeply phenotyped neuropathy cohort

We aimed to investigate the genetic associations of neuropathic pain in a deeply phenotyped cohort. Participants with neuropathic pain were cases and compared with those exposed to injury or disease but without neuropathic pain as control subjects. Diabetic polyneuropathy was the most common aetiology of neuropathic pain. A standardised quantitative sensory testing protocol was used to categorize participants based on sensory profile. We performed genome-wide association study, and in a subset of participants, we undertook whole-exome sequencing targeting analyses of 45 known pain-related genes. In the genome-wide association study of diabetic neuropathy (N 5 1541), a top significant association was found at the KCNT2 locus linked with pain intensity (rs114159097, P 5 3.55 3 10 28). Gene-based analysis revealed significant associations between LHX8 and TCF7L2 and neuropathic pain. Polygenic risk score for depression was associated with neuropathic pain in all participants. Polygenic risk score for C-reactive protein showed a positive association, while that for fasting insulin showed a negative association with neuropathic pain, in individuals with diabetic polyneuropathy. Gene burden analysis of candidate pain genes supported significant associations between rare variants in SCN9A and OPRM1 and neuropathic pain. Comparison of individuals with the “irritable” nociceptor profile to those with a “nonirritable” nociceptor profile identified a significantly associated variant (rs72669682, P 5 4.39 3 10 28) within the ANK2 gene. Our study on a deeply phenotyped cohort with neuropathic pain has confirmed genetic associations with the known pain-related genes KCNT2, OPRM1, and SCN9A and identified novel associations with LHX8 and ANK2, genes not previously linked to pain and sensory profiles, respectively.</p

Data analytics driving net zero tracker for renewable energy

This research aims to assess the impact of renewable energy policies, investments, and emissions reductions toward achieving net-zero targets by 2050. We analysed key metrics using the Net Zero Tracker (NZT), including renewable energy capacity, policy strength, financial investment, and carbon emissions across multiple regions and industries. Our methodology involved data collection from 2020 to 2050, utilising predictive modeling to project trends in renewable energy adoption and emissions reduction. Key findings show that renewable energy capacity is expected to surpass 1000 GW by 2050, with an exponential increase around 2045. Policy Strength Index (PSI) will grow by 20 %, from 50 in 2020 to 60 in 2050, while investments in renewable energy will rise from $10 billion to$25 billion over the same period. Emissions are projected to steadily decrease to zero by 2050, which aligns with net-zero goals. The margin of error in the projections is ±5 %, considering potential policy implementation and technology development variations. These results underscore the critical role of enhanced policies, sustained investments, and international cooperation in accelerating the global transition to renewable energy. The research offers valuable insights for policymakers and stakeholders to guide future strategies for achieving a sustainable energy future.</p

The self-memory system:Exploring developmental links between self and memory across early to late childhood.

This study tests whether developments in self-knowledge and autobiographical event memory across early to late childhood are related. Self-descriptions and autobiographical memory reports were collected from 379 3- to 11-year-old predominantly white Scottish children, Mage=90.3 months, SD=31.1, 54% female. Episodic memory was measured in an enactment task involving recall and source monitoring of self-performed and witnessed actions. The volume and complexity of self-knowledge and autobiographical memory reports increased with age, as did source monitoring ability and recall bias for own actions. Regression analyses and structural equation modelling confirmed a predictive relationship between these developments. These results inform our theoretical understanding of the development of the self-memory system in childhood, which may contribute to the gradual offset of childhood amnesia

Identifying Nigerian literature to inform culturally relevant social work education:A scoping review

This study is the first to provide evidence of an extensive Nigerian social work literature. It is significant as it unsettles and refutes the prevailing narrative of an absence of African, specifically Nigerian, social work literature to inform the development of a culturally relevant curriculum. A scoping review was conducted to identify Nigerian social work literature that yielded 308 papers. Health emerged as the most established area of research (n = 70), followed by ageing (n = 44) and child welfare (n = 42). The findings have global relevance for social work educators seeking to decolonise the knowledge underpinning social work education and practice.</p

Nunalleq Digital Museum:multi-vocal narration of a Yup’ik past

Digital technology facilitates remote access to archaeological collections and offers an accessible platform for knowledge sharing and innovative storytelling. Here, the authors present a newly developed online museum resource co-curated by archaeologists and the descendant community in Quinhagak, Alaska

A genome-annotated bacterial collection of the plant food system microbiota

This study reports draft genomes of 30 bacteria representative of the plant food system microbiota and isolated from different sources in Italy and France. Individual genomes were reconstructed using PacBIO DNA sequencing: taxonomic classification and distribution of genes involved in microbe-environment interactions are reported to facilitate strains characterisation and utilisation