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The Japan-UK synthetic biology conference, Spring 2025: strengthening global links to engineer biology
No full textBoth Japan and the UK have recognized the growing importance of synthetic and engineering biology for transforming life science research and transitioning toward a sustainable biobased economy. Such a shift will require extensive international cooperation and collaboration. In this viewpoint, we provide a summary of the recent “Japan-UK Synthetic Biology Conference, Spring 2025” that aimed to facilitate new links between researchers across the broad field of synthetic biology. We cover the core scientific topics discussed, distill some of the emerging trends, and outline the remaining challenges that are hampering progress. We end by highlighting some of the ways in which international collaborations may help address these issues through a combination of sharing expertise, national infrastructures, and aligned funding
Multi-layered defense against oscillations
No full textIncreasing shares of inverter-based resources (IBRs) in power grids are triggering complex dynamic interactions and new stability challenges. A particular challenge for system operators is poorly damped sub-synchronous oscillations (SSO) induced by adverse interaction among IBRs through the network. These oscillations are difficult to foresee, threaten system security and often force grid operators to limit the instantaneous share of IBRs.
The Control and Power research group at Imperial College London are working with the Network Operability team in National Energy System Operator (NESO) in the UK to develop a multi-layered defense strategy to identify and mitigate the risk of poorly damped SSO. Starting from advanced IBR control design which is the genesis of the SSO problem, these layers are: 1) an enhanced IBR connection compliance process capture the risk of SSO more comprehensively, 2) new system strength metrices to identify parts of the grid vulnerable to SSO, 3) characterize operating point dependency of IBRs to detect incipient SSO near real-time and 4) post-event root-cause analysis for targeted and effective mitigation of SSO. Success of this research will enable secure grid operation with high fractions of renewables to facilitate net zero transition
Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties
No full textThe generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties
Meeting report on an integrated research agenda for mosquito-borne arboviruses
No full textThe emergence and re-emergence of mosquito-borne arbovirus (MBV) diseases pose a rapidly expanding global health threat fueled by the convergence of multiple ecologic, economic, and social factors, including climate change, land use, poverty, deficiencies of water storage and sanitation, and limitations of vector control programs. On December 6, 2023, the Wellcome Trust and the University of Minnesota's Center for Infectious Disease Research and Policy held a meeting titled “An integrated approach to mosquito-borne arboviruses: a priority research agenda.” The meeting comprised presentations, panels, and facilitated discussions aimed at describing the state of the field, highlighting recent accomplishments, identifying novel strategies, and defining priority research goals and approaches for addressing MBV disease preparedness and response. This report summarizes meeting discussions in 3 key areas: the changing epidemiology of MBV disease, current and potential transmission- and disease-monitoring strategies, and evolutionary impacts on disease burden and transmission. It concludes with a list of priority strategies for research and investment in MBV disease prevention, preparedness, and control. To prepare for future epidemics of MBV diseases, research and policy will benefit from a multipathogen approach to MBVs. Building on existing knowledge and systems, these efforts must address social and ecological factors and connect with other global health agendas
Porcine respiratory coronavirus as a model for acute respiratory disease: mechanisms of different infection outcomes
No full textPorcine respiratory coronavirus (PRCV) is a naturally occurring pneumotropic coronavirus in the pig, providing a valuable large animal model to study acute respiratory disease. PRCV pathogenesis and the resulting immune response were investigated in pigs, the natural large animal host. We compared 2 strains, ISU-1 and 135, which induced differing levels of pathology in the respiratory tract to elucidate the mechanisms leading to mild or severe disease. The 135 strain induced greater pathology which was associated with higher viral load and stronger spike-specific antibody and T-cell responses. In contrast, the ISU-1 strain triggered mild pathology with a more balanced immune response and greater abundance of T regulatory cells. A higher frequency of putative T follicular helper cells was observed in animals infected with strain 135 at 11 days postinfection. Single-cell RNA-sequencing of bronchoalveolar lavage revealed differential gene expression in B and T cells between animals infected with 135 and ISU-1 at 1 day postinfection. These genes were associated with cell adhesion, migration, and immune regulation. Along with increased IL-6 and IL-12 production, these data indicate that heightened inflammatory responses to the 135 strain may contribute to pronounced pneumonia. Among bronchoalveolar lavage (BAL) immune cell populations, B cells and plasma cells exhibited the most gene expression divergence between pigs infected with different PRCV strains, highlighting their role in maintaining immune homeostasis in the respiratory tract. These findings indicate the potential of the PRCV model for studying coronavirus-induced respiratory disease and identifying mechanisms that determine infection outcomes
Robust impact localisation on composite aerostructures using kernel design and Bayesian-inspired model averaging under environmental and operational uncertainties
No full textImpact localisation on composite aircraft structures remains a significant challenge due to operational and environmental uncertainties, such as variations in temperature, impact mass and energy levels. This study proposes a novel Gaussian process regression (GPR) framework that leverages the order invariance of time difference of arrival (TDOA) inputs to achieve probabilistic impact localisation under such uncertainties. A composite (COMP) kernel function, combining radial basis function and cosine similarity kernels, is designed based on wave propagation dynamics to enhance adaptability to diverse conditions. To jointly predict spatial coordinates, a task covariance kernel is incorporated to support multitask learning, allowing the model to capture correlations between outputs. To further improve robustness, a Bayesian-inspired model averaging strategy is employed to fuse predictions from multiple GPR models, assigning adaptive weights based on both global model fit and local predictive confidence. The proposed framework is experimentally validated on a sensorised composite panel under a wide range of impact conditions, including large-mass drop tower tests and small-mass guided impacts, across varying temperatures and angles. Convolutional neural networks, a widely used deep learning method, are adopted as a baseline for comparison. Results demonstrate that the GPR-based approach achieves higher localisation accuracy and robustness without requiring explicit compensation for environmental or loading variations. The study also highlights the critical role of TDOA preprocessing: sample standardisation outperforms feature standardisation by preserving directional structure and improving GPR model compatibility. These findings underscore the method’s potential for reliable, uncertainty-aware structural health monitoring in complex aerospace environments
Real-world effectiveness of autologous haematopoietic stem cell transplantation for MS in the UK
No full textBackground Autologous haematopoietic stem cell transplantation (AHSCT) is increasingly used as a one-off disease-modifying therapy for aggressive forms of multiple sclerosis (MS). We report real-world effectiveness of AHSCT for MS in the UK.
Methods This retrospective open-label study included patients with (pw)MS treated with AHSCT between 2002 and 2023 in 14 UK centres. Outcomes included relapse-free survival (RFS), MRI activity-free survival (MFS), progression-free survival (PFS) and no evidence of disease activity (NEDA-3). We assessed 6-month confirmed Expanded Disability Status Scale (EDSS) score progression or improvement compared with pre-treatment. Treatment-related mortality (TRM) was defined as death from any cause within 100 days post-autologous graft reinfusion.
Results 364 pwMS were included (median age 40 years; 58% female). Of these, 271 pwMS had adequate neurological follow-up data: 168 (62%) had relapsing-remitting MS (pwRRMS) and 103 (38%) had progressive MS (pwPMS). Median disease duration from symptom onset was 10 years (IQR 6–14), EDSS 6 (IQR 4.0–6.5) and follow-up from AHSCT 46 months. At 2 and 5 years from AHSCT, RFS was 94.6% and 88.6%; MFS 93.1% and 80.1%; PFS 83.5% and 62.4%; NEDA-3 72.3% and 46.2%. pwRRMS had significantly higher rates of PFS (p=0.007) and NEDA-3 (p=0.001) than pwPMS. RRMS was a predictor of EDSS improvement, whose prevalence was 24.2% at 2 years and 20.4% at 5 years. TRM was 1.4% (n=5/364).
Conclusions In this cohort with high EDSS at baseline and including pwPMS, AHSCT led to durable remission of inflammatory activity and stabilisation or improvement of neurological disability, particularly in pwRRMS
Preliminary effectiveness and feasibility of ASHA-led mobile health intervention for diabetes care in Indian primary health care settings
No full textDiabetes management in resource-limited settings faces challenges in screening, guideline-based treatment, and healthcare access. The IMPACT Diabetes study evaluated a community-based, technology-enabled task-shifting intervention for diabetes care in India. A cluster randomized controlled trial was conducted in 16 villages/peri-urban areas across 8 primary health centers (PHCs) in two states in India. Accredited Social Health Activists (ASHAs) screened 1,785 community participants, identifying 418 individuals with diabetes. The intervention group received nine months of CDSS-supported care delivered by ASHAs under physician supervision, while the control group received usual care. The primary outcome was the proportion of participants achieving ≥ 0·5% reduction in glycated haemoglobin (HbA1c) from baseline. Secondary outcomes included healthcare utilization and medication adherence. A significantly higher proportion of intervention participants achieved HbA1c reduction ≥ 0·5% compared to the control group (21.8% vs. 10.3%, p < 0.05). Intervention participants had more frequent physician visits (85·0% vs. 29·8%), higher glucose-lowering medication adherence (63·0% vs. 43·1%, p < 0·05), and better engagement with diabetes management practices. Qualitative findings demonstrated that the intervention was acceptable and feasible for patients, ASHAs, and physicians, empowering ASHAs in chronic disease care. This study demonstrates that task-shifting and digital health tools can improve diabetes outcomes in low-resource settings. Future research should explore long-term sustainability and cost-effectiveness
Emergence of Flucytosine-Resistant Candida tropicalis Clade, the Netherlands
No full textCandida tropicalis is the second most virulent Candida species after C. albicans. Previous studies from the Netherlands and France reported a notable reduction in susceptibility to flucytosine (5-FC) in a substantial proportion of C. tropicalis isolates. We investigated epidemiologic patterns of C. tropicalis isolates in the Netherlands and the genetic mechanisms driving widespread non–wild-type (WT) 5-FC resistance. We conducted antifungal susceptibility testing and used advanced molecular techniques, including short tandem repeat genotyping and whole-genome sequencing paired with single-nucleotide polymorphism analysis, to analyze 250 C. tropicalis isolates collected across the Netherlands during 2012–2022. Our findings revealed the rapid emergence of a 5-FC–resistant, non-WT C. tropicalis clade, accounting for >40% of all C. tropicalis isolates by 2022. Genomic analysis identified a homozygous nonsense mutation in the FCY2 gene, which was exclusive to this non-WT population. Continued surveillance efforts are needed to detect and prevent the spread of drug-resistant Candida species
Explainable adversarial learning framework on physical layer key generation combating malicious reconfigurable intelligent surface
No full textReconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL-SKG, a malicious RIS can poison legitimate channels and crack almost all existing PL-SKGs. In this work, we propose an adversarial learning framework that addresses Man-in-the-middle RIS (MITM-RIS) eavesdropping which can exist between legitimate parties, namely Alice and Bob. First, the theoretical mutual information gap between legitimate pairs and MITM-RIS is deduced. From this, Alice and Bob leverage adversarial learning to learn a common feature space that assures no mutual information overlap with MITM-RIS. Next, to explain the trained legitimate common feature generator, we aid signal processing interpretation of black-box neural networks using a symbolic explainable AI (xAI) representation. These symbolic terms of dominant neurons aid the engineering of feature designs and the validation of the learned common feature space. Simulation results show that our proposed adversarial learning- and symbolic-based PL-SKGs can achieve high key agreement rates between legitimate users, and is further resistant to an MITM-RIS Eve with the full knowledge of legitimate feature generation (NNs or formulas). This therefore paves the way to secure wireless communications with untrusted reflective devices in future 6G