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Access to mechanical thrombectomy for stroke in the UK: the unacceptable postcode lottery
No full textTask-related differences in network connectivity and dynamics in people with severe opioid use disorder compared with healthy controls
No full textOne approach to addressing the immense unmet need for treatments of severe opioid use disorder (sOUD) is to understand more about associated changes in the brain’s reward circuitry. It has been shown that during reward anticipation in the Monetary Incentive Delay (MID) task, people with severe substance use disorder (SUD) show blunted responses in reward neural circuitry compared with healthy controls (HC). Conversely, drug-related cues result in heightened responses in the same neural reward circuitry in those with SUD compared with HC. However, it is unclear how such dysfunctional reward processing is related to neural correlates of other processes commonly dysregulated in addiction, such as attention and cognition. The aim of this work was to evaluate whether people with sOUD show different relationships between reward networks to networks that regulate cognition, attention, sensory processes, and more. Then, we evaluated whether there is a spatial relationship between differences in brain function and atlases of μ-opioid receptor (MOR) and dopamine D2 receptor (DRD2) availability. We collected fMRI data while people with sOUD receiving methadone (MD; n = 25) and HC (n = 22) completed the MID and cue reactivity tasks. We evaluated differences in functional connectivity (FC) and measures of brain state dynamics. Partial least squared (PLS) analysis computed the spatial relationship between FC metrics to MOR and D2DR availability. We found that MD participants generally exhibited weaker miFC compared to HC in both tasks except when comparing the difference in miFC during anticipation of monetary reward or drug related stimuli vs neutral stimuli. Contrasts between rewarding or drug-related to neutral stimuli showed MD participants had stronger miFC between reward/anti-reward networks to regions in the control network and default mode Network (DMN) in both tasks. Analysis of brain state dynamics showed the DMN was more prevalent in MD participants during the MID task. PLS analysis showed spatial autocorrelation between MOR and D2DR availability and connectivity metrics during the MID task. These findings reveal distinct patterns of neural network interactions in individuals with sOUD, characterized by generally reduced FC but enhanced connections between reward-related networks and cognitive control regions in response to either monetary or drug-related cues vs neutral cues. We observed spatial correspondence between receptor availability and altered connectivity and dynamics in MD vs HC. These results provide new insights into the neural basis of reward processing dysfunction in sOUD and may inform the development of targeted neuromodulation therapeutic approaches. Clinical trial registration: This study is not a clinical trial and therefore was not registered as a clinical trial. The study design and planned analytical approach for the primary analyses was pre-registered [1]. This paper consists of secondary analyses which were not primary considerations when designing the research study that collected the data
Motion compensated spin echo cardiac diffusion tensor imaging in multiple cardiac phases using an ultrahigh gradient strength scanner
No full textBackground
Cardiac diffusion tensor imaging (cDTI) has traditionally relied on inefficient stimulated echo techniques to robustly assess microstructural changes over the cardiac cycle. Ultrahigh gradient strength systems (>80mT/m) allow shorter motion compensated diffusion encoding. This study compares the ability of high and ultrahigh strength gradient systems to provide systolic and diastolic motion compensated spin echo (MCSE) cDTI.
Methods
Second order MCSE sequences were developed for a research-only Siemens 3T Connectom (300mT/m maximum gradient amplitude per axis) and breath hold cDTI was acquired at peak systole and end diastole. Acquisitions used the maximum achievable gradient strength (GUH, 116mT/m) and also limited to typical high gradient strengths (GH, 66mT/m based on 80mT/m maximum allowable), giving TE=48ms and 58ms respectively. Data were acquired at 2.8x2.8x8mm3, b=500s/mm2 (8 averages) and b=150s/mm2 (2 averages) in 6 encoding directions.
Results
22 healthy subjects were recruited. 20/21 and 21/22 systolic acquisitions at GUH and GH respectively met the >50% criteria of the circumferential myocardium showing the expected transmural variation in helix angle. For GUH and GH (16/20) 80% and (16/22) 73% of diastolic acquisitions were successful respectively. SNR was increased using GUH compared to GH (median [IQR]: 112.9 [3.8] vs. 9.6 [2.9], p=0.0002 diastole, 15.6 [5.9] vs. 12.5 [6.7], p=0.006 systole). Using GUH fractional anisotropy was lower in systole (0.349 [0.040] vs. 0.373 [0.019], p=0.002) and GUH transmural helix angle gradient (HAG) was steeper in diastole (-0.70 [0.17] vs. -0.55 [0.12] ˚/%, p=0.04). At both GUH and GH, sheetlet angle (|E2A|) was higher in systole than in diastole (30.7 [7.3] vs. 21.3 [6.7]˚ p=10-4 and 32.6 [10.9] vs. 26.0 [7.4]˚, p=0.03 respectively). Differences in HAG between phases were only apparent with GH (-0.88 [0.23] vs. -0.55 [0.15], p=10-4) and differences in the mean diffusivity only with GUH (1.64 [0.11] vs. 1.52 [0.24] x10-3mm2/s, p=0.002).
Conclusion
Ultrahigh strength gradient systems deliver higher SNR for MCSE and more robust imaging in diastole. While further work is required to further improve the reliability in diastole, at ultrahigh gradient strengths, cDTI using MCSE can identify dynamic changes in the cardiac microstructure. These findings will lead to more widespread use of multiphase MCSE in cDTI clinical research
Reasoning about hidden hybrid assumptions in assured temporal missions
No full textTemporal mission specifications, together with efficient controller synthesis techniques, enable the vision of effective and assured adaptation of robotic behaviour to new missions. However, the assurances provided by controller synthesis can be misleading. Temporal mission specifications adopt a discrete view of the world —through propositional variables or discrete events— while the synthesised controllers ultimately rely on lower-level robotic implementation layers, including control feedback loops and hardware, that interact with continuous physical phenomena.
In this paper, we present a specification framework that allows capturing explicitly the hybrid assumptions linking discrete and continuous domains. We also formalize a soundness relation between discrete mission goals, hybrid assumptions, and continuous mission goals which provides a rigorous foundation for end-to-end reasoning about missions. Making hybrid assumption explicit supports pre-deployment validation and runtime monitoring but also mitigation of and recovery from hybrid assumption violations. We apply this specification framework to four case studies from the literature making explicit hidden hybrid assumptions to achieve mission soundness, discuss their validity, and the related adaptation strategies that they can inspire
A satellite based machine learning approach for estimating high resolution daily average air temperature in a megacity in Brazil
No full textSpatiotemporally resolved ambient temperature data are essential for environmental epidemiology, especially in urban areas where temperature can vary sharply over short distances, influencing population exposure. Additionally, heat distribution often reflects built environment patterns and may correlate with existing social and environmental disparities. Continuous temporal records at high spatial resolution are, however, often lacking, especially in low- and middle-income countries. We developed a generalizable tree-based machine learning approach to estimate daily mean temperatures at 500 x 500 metres resolution using São Paulo, a megacity in Brazil, as a case study, to demonstrate its utility in highly urbanized settings
with a heterogeneous urban fabric and unevenly distributed temperature monitoring stations. We trained a Random Forest model using open-access remote sensing data, along with derived products, and temperature measurements from 43 ground stations. To prevent overfitting and select relevant features, we
employed a forward feature selection algorithm with target-oriented (spatial) cross-validation. Hyperparameter tuning was performed using grid search approach. The model was validated through ten-
fold station-based cross-validation and an external hold-out dataset. The model demonstrated strong performance (RMSERF = 0.80; R²RF = 0.95), with slightly reduced accuracy in rural areas (R²rural = 0.91; R²urban = 0.95). Compared to traditional multilinear approaches (RMSEMLR = 1.02; R²MLR = 0.92), the Random Forest model outperformed, likely due to its ability to better capture microclimates and complex relationships between data sources. This 500 x 500 metres daily temperature dataset is the first of its kind in South America, with the São Paulo pipeline and data freely accessible. The approach is adaptable to other regions with appropriate retraining and validation, enabling high-resolution exposure assessments
Operational anomaly detection for DC mini-grids through dynamic admittance estimation
No full textThis work combines model based and data driven approaches to detect and localize sensor anomalies in grid-side measurement units of an interconnected DC mini-grid system. It focuses on utilizing the inherent system admittance relation (Y matrix) from the measured voltages and currents using a pseudoinverse based least squares technique. This method was developed based on a
systematic pattern recognition in the relationship between various anomalies and their influence on the Y matrix variables. The deviations between the estimated and nominal values over time are used to detect their occurrence and determine the location of the affected sensor. The results are implemented through MATLAB SIMULINK under multiple classes of sensor offset anomalies which demonstrate accurate anomaly detection under well-informed conditions regardless of the network topolog
Pensions in an ageing society – strains and pains of becoming older together
No full textI focus on three issues central to the economic impacts of changing demographics brought about by trends in life expectancy and in fertility. First, how might people adjust to longer lives when the scope of the state to devote funds to pensions and to health care will come under strain? Second, how great might the strain on the public finances be and how might it be alleviated? Third, how might individuals handle the effects of uncertainty when the ability of the state to offer insurance against risks might be reduced? I focus on the issues for the UK – a country with a set of challenges which is in many ways typical of rich countries. I argue that attempts to offset demographic change and the strains it brings by consistently high
immigration or by trying to boost fertility rates of the domestic population are likely to either have undesirable effects in the long run or be ineffective over almost any horizon. Instead, a combination of market forces,, and policies which take account of long run fiscal sustainability, will allow ageing populations to adjust
Hallucination-resistant security planning with a large language model
No full textLarge language models (LLMs) are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucinate remain significant challenges. In this paper, we address these challenges by introducing a principled framework for using an LLM as decision support in security management. Our framework integrates the LLM in an iterative loop where it generates candidate actions that are checked for consistency with system constraints and lookahead predictions. When consistency is low, we abstain from the generated actions and instead collect external feedback, e.g., by evaluating actions in a digital twin. This feedback is then used to refine the candidate actions through in-context learning (ICL). We prove that this design allows to control the hallucination risk by tuning the consistency threshold. Moreover, we establish a bound on the regret of ICL under certain assumptions. To evaluate our framework, we apply it to an incident response use case where the goal is to generate a response and recovery plan based on system logs. Experiments on four public datasets show that our framework reduces recovery times by up to 30% compared to frontier LLMs
Impacts of lignin nanoparticles as antibacterial agents on the composition of biofilms in water treatment
No full textThe development of green, high-efficiency biocides is critical for the effective control of microbial biofilms in water treatment processes. In this study, lignin nanoparticles (NPs), with an average size of around 30 nm, were synthesized via a ‘green’ process and applied as biocides. A gravity-driven ultrafiltration membrane system was employed to investigate their effects on biofilm composition. The results showed that when the dosage of lignin NPs was only 1 mg C/L, the abundances of bacteria and fungi in the membrane biofilms were reduced by >98%, and the total protein and polysaccharide contents in the extracellular polymeric substance (EPS) decreased significantly, demonstrating remarkable advantages. The core antimicrobial mechanism involves two synergistic effects: (1) Hydrogen bonds are formed between the hydrophilic carboxyl groups on lignin nanoparticles (NPs) and EPS-components of microbial cell walls, directly inhibiting microbial viability and proliferation while reducing EPS secretion; (2) Lignin NPs are oxidized to aromatic derivatives, with benzene rings converted into hydroxyl‑enriched phenolic structures, and the concurrent elevation of carbonyl (C=O) groups in extracellular proteins enhances hydrophobicity. The oxidized lignin NPs then amplify antimicrobial efficacy via hydrophobic associations with these modified aromatic compounds and proteins. This work provides a novel approach and new insights into the rational design of green biocides and the regulation of membrane biofilms
Belief network assessment of fire management in east African savannas under socioeconomic and climate change
No full textFire regimes across East Africa's savanna conservation landscapes increasingly reflect interconnected ecological and biocultural breakdown, reinforcing systemic vulnerabilities. Yet colonially inherited fire suppression and exclusionary tenure arrangements continue to overlook the ecological value of pyrodiversity and the stewardship roles of Indigenous and local actors. This study presents a novel probabilistic systems model for evaluating seven predictive, exploratory, and normative fire management approaches across best-, intermediate-, and worst-case socioeconomic–climate futures. The SAV Belief Network (SAV BN) advances BN modelling by explicitly incorporating system complexity and future uncertainty through systemic feedbacks, bidirectional interactions, and high node complexity, supporting rigorous scenario analysis in data-limited contexts. Grounded in empirical data from the Tsavo Conservation Area, the model reflects relational epistemologies that emphasise human–nature interdependencies and place-based knowledge. No approach proved capable of simultaneously achieving wildfire mitigation, ecological integrity, and livelihood resilience. Most reduced wildfire risk and, under best-case trajectories, improved livelihoods; however, even highly normative approaches only slowed, rather than halted or reversed, ecological degradation. Fire suppression and carbon-oriented strategies focused on above-ground biomass accounting intensified ecological decline, particularly under inequitable futures, while locally conceptualised bottom-up strategies failed to confront entrenched colonial legacies and reproduced exclusionary power structures and degradation narratives. These findings highlight the need to reimagine fire regimes as products of multi-scalar, path-dependent dynamics shaped by institutional erosion, political–economic preferences, and contested land claims. Addressing this complexity requires moving beyond ‘integrated’ or ‘community-based’ framings towards historical institutional and environmental justice approaches that centre representation and equity