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Measures of comorbid cardiometabolic burden and cardiovascular disease risk in people with MRI-confirmed steatotic liver disease: a prospective cohort study
No full textBackground: Steatotic liver disease (SLD) is commonly associated with higher burden of cardiometabolic risk factors (CMRFs). This study aimed to examine the associations between CMRF count, patterns and risk of cardiovascular disease.
Methods: We included 10121 UK Biobank participants (39% women) with MRI-confirmed liver steatosis. Latent class analysis was used to derive CMRF patterns based on 5 CMRFs (obesity, diabetes, hypertension, high triglycerides and low HDL). Cox models were used to estimate associations between CMRF count and patterns with incidence and mortality of cardiovascular disease (CVD), and all-cause mortality.
Results: Approximately 95% of SLD participants had ≥ 2 CMRFs. During a median follow-up of 4.9 years, 268 CVD events and 212 deaths were recorded. Higher CMRF count was independently associated with elevated risk of CVD (HR per each additional CMRF: 1.23 (1.08, 1.40)), CVD mortality (1.47 (1.07, 2.02)), and all-cause mortality (1.25 (1.08, 1.44)). Three distinct CMRF patterns were identified, reflecting varying levels of CMRF burden and demographic characteristics. While certain patterns with high CMRF burden were associated with increased CVD risk, the associations were substantially attenuated after adjusting for CMRF count.
Conclusions: CMRF burden is a key determinant of cardiovascular risk in people with SLD, but data-driven CMRF patterns do not improve risk prediction beyond simple counts. CMRF count remains a practical measure of cardiometabolic burden
An online singing-based breathing and wellbeing programme (ENO Breathe) for people with long COVID breathlessness: results from 1413 participants
No full textBackground
Long COVID breathlessness is a common, complex and frequently debilitating problem, for which limited evidence-based interventions exist. A previous randomised controlled trial found that participation in an online six-week breathing and wellbeing
programme (ENO Breathe), using singing techniques, was associated with improvements in health-related quality of life (HRQoL) and breathlessness. This study
aimed to assess this intervention’s impact outside a trial setting.
Methods
We compared baseline and post-intervention data to assess the impact of the programme on HRQoL (RAND SF-36) Mental and Physical Health Composite (MHC,
PHC) scores (primary outcome), breathlessness (Dyspnoea-12, Visual Analogue Score (VAS) for breathlessness at rest, walking, stairs, running), anxiety (GAD-7), and respiratory symptoms (CAT).
Findings 1413 people started the programme, mean(SD) age 49(11.9)years, BMI 28(7.2)kg/m2, 1150(80%) female, 1165(82%) white ethnicity, symptom duration 415 [IQR 246-601] days, following assessment in 51 UK-based NHS long COVID clinics. 1188 participants
provided follow-up data. Completing ENO Breathe was associated with improvements (median difference [IQR], or mean difference (95% CI)) in RAND-36 MHC 2.98 [-1.53 to 8.42], PHC 1.69 [-1.32 to 5.01], Dyspnoea-12 -4.29 (-4.64 to -3.94), VAS breathlessness walking -5 [-18 to 6]; stairs -10 [-25 to 3]; and running -3 [-19 to 0],
GAD-7 -1 [-4 to 1]), CAT -2.50 (-2.81 to -2.19)), all p<0.0001. VAS breathlessness was unchanged 0 [-10 to 13]; p=0.24)). Response to the ENO Breathe intervention did not differ by age, gender, ethnicity or pre-existing asthma. There were no reported significant adverse events.
Interpretation
The ENO-Breathe programme can improve health-related quality of life, breathlessness, anxiety and respiratory symptoms in people with long COVID and
breathlessness
Validation of the measure of case-discussion complexity (MeDic) and the metric for the observation of decision-making (MODe) for streamlining workflow and evaluating decision-making in US tumor boards
No full textBackground. Clinical guidelines for cancer care recognize multidisciplinary tumour boards
(MTBs) as a gold standard for providing quality of care for cancer patients. However, the
requirements and workflow for MTBs varies across countries, influencing their roles in decision
making, care efficiency and medical education. This study compares validity and performance of
MDT evaluation tools, Measure of case-Discussion Complexity (MeDiC) and Metric for the
Observation of Decision-Making (MODe) in the UK vs US.
Methods. MeDiC, developed to assess case complexity, and MODe, designed to evaluate decision making quality, were applied to 555 cases and 104 MTB meetings, from urological and
gynecological tumor boards at an academic cancer center between December 2021 and June 2024.
MeDiC was used to assess clinical complexity through pathology, patient, and treatment factors,
while MODe evaluated decision-making quality based on patient information and team
contributions. All assessors underwent formal training. Data analysis included reliability testing
using intraclass correlation coefficients (ICCs), Kappa, Cronbach's alpha, and bootstrapping was
used to estimate confidence intervals for correlation estimates.
Results. MeDiC demonstrated good reliability (ICC=0.849) in the US MDT and identified key
factors contributing to complexity, such as malignancy and significant comorbidities. The tool also
revealed that case discussion duration was not significantly associated with complexity levels.
MODe similarly showed moderate reliability (ICC=0.628), with high correlations between
decision-making quality and contributions from core specialties such as medical oncologists.
Conclusions. The validity of MeDiC and MODe in US were similar to the tools performance in
the UK settings. However, case complexity metrics demonstrated significant selection of more
complex cases for US MDT presentation compared to UK MDTs
Synergy mediates long-range correlations in the visual cortex near criticality
No full textLong-range correlations are a key signature of systems operating near criticality, indicating spatially-extended interactions across large distances. These extended dependencies underlie other emergent properties of critical dynamics, such as high susceptibility and multi-scale coordination. In the brain, along with other signatures of criticality, long-range correlations have
been observed across various spatial scales, suggesting that the brain may operate near a critical point to optimise information processing and adaptability. However, the mechanisms underlying these long-range correlations remain poorly understood. Here, we investigate the role of synergistic interactions in mediating long-range correlations in the visual cortex of awake mice. We leverage recent advances in mesoscale two-photon calcium imaging to analyse the activity of thousands of neurons across a wide field of view, allowing us to confirm the presence of long-range correlations at the level of neuronal populations.
By applying the Partial Information Decomposition (PID) framework, we decompose the correlations into synergistic and redundant information interactions. Our results reveal that the increase in long-range correlations during visual stimulation is accompanied by a significant increase in synergistic rather than redundant interactions among neurons. Furthermore, we
analyse a combined network formed by the union of synergistic and redundant interaction networks, and find that both types of interactions complement each other to facilitate efficient information processing across long distances. This complementarity is further enhanced during the visual stimulation. These findings provide new insights into the computational mechanisms that
give rise to long-range correlations in neural systems and highlight the importance of considering different types of information interactions in understanding correlations in the brain
Development and application of numerical methods for large-scale cilia simulation
No full textCilia are slender, active filaments whose coordinated beating plays a vital role in organism locomotion and fluid transport. Their collective dynamics often form metachronal waves, whose origin of formation and influence on ciliary function are still not fully understood. Capturing these behaviours requires resolving hydrodynamic interactions among thousands of filaments, which is computationally challenging: traditional methods quickly become prohibitively expensive and often numerically intractable. Minimal models, such as the squirmer model or rotor framework, are computationally efficient but neglect important mechanical details such as waveform adaptation and hydrodynamic dissipation. This leaves a gap between coarse conceptual models and fully filament-resolved simulations. This thesis addresses this gap through the development and implementation of the Fast Force-Coupling Method (FFCM), a versatile, GPU-accelerated solver for efficiently computing low-Reynolds number hydrodynamics. On top of this, a filament oscillator framework based on the Lagrangian Mechanics of Active Systems was developed, in which each cilium is represented by a reduced set of phase and shape variables that evolve according to prescribed beat sequences and hydrodynamic coupling. This framework enables the simulation of coordination dynamics among thousands of cilia. Large-scale simulations reveal bistability between symplectic and diaplectic metachronal waves, with stability strongly influenced by filament stiffness. It is shown that the symplectic wave, which is naturally observed in Volvox carteri, persists at larger swimmer sizes as we successfully solve for the coordination of 4291 cilia, a scale comparable to that of a real Volvox carteri, on a single consumer-grade GPU. Further investigations inspired by Volvox demonstrate that beat-plane tilt induces a rotational velocity proportional to the tilt angle. Comparative studies show that propulsion is strongly influenced by beat geometry through hydrodynamic asymmetries in the stroke cycle, consistent with classical wall- and orientation-mediated mechanisms, with a lowered recovery stroke enhancing forward transport.Open Acces
Climate Shifts, Corporate Drifts: How Climate Change Exposure Reshapes Multinational Offshoring
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This study examines the contextual conditions that influence how climate change exposure (CCE) affects the extent of firms’ offshoring activities. Using data from U.S.-based multinational corporations (MNCs), we show that CCE significantly influences the degree of firms’ offshoring. Additional analyses indicate that regulatory and opportunity exposures, rather than physical exposure, are the primary drivers of strategic and proactive, rather than defensive, offshoring. We also find that CCE exerts its strongest influence on downstream activities. The interaction between climate change and offshoring decisions plays a critical role in determining whether MNCs relocate activities to advanced, low-carbon economies rather than to emerging, developing, or high-carbon economies. Our study extends international business research by elucidating how climate change exposure reshapes multinational firms’ downstream and upstream activities. Taken together, the findings provide actionable insights for policymakers, corporate strategists, and scholars and underscore the importance of integrating climate resilience with low-carbon objectives in global operations
A multi-strategy optimizer for energy minimization of multi-UAV-assisted MEC systems
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Internet of Things (IoT) task offloading involves conflicting objectives of energy consumption and delay. We formulate a bi-objective optimization model for a multi-UAV-assisted mobile edge computing (MEC) system, jointly optimizing resource allocation, task offloading decisions, and UAV deployment to minimize both energy consumption and delay. As the number of offloaded tasks increases, finding feasible solutions becomes more challenging. To address this, we develop an Information Feedback Evolutionary Algorithm (IFEA) that leverages feedback driven guidance to enhance the diversity and convergence of the Pareto front (PF). Simulation results show that IFEA achieves better trade offs than the other four multi-objective algorithms
Community detection in attributed networks based on deep attention autoencoder with block diagonal subspace constraint
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Community detection in attributed networks has become a hotspot in contemporary complex network research. It integrates topological structures and attribute features to uncover latent community structures, providing considerable value in practical applications like recommendation systems, social network analysis, and bioinformatics. Although neural network-based community detection methods have achieved decent performance, these methods demonstrate weak learning capabilities for spatial structural features and neglect to consider the clustering distribution in the embedding space. To address this issue, this paper proposes a subspace plugin strategy that utilizes subspace constraints to guide representation vectors to learn the clustering distribution in the embedding space, making it more appropriate for clustering tasks. Additionally, to overcome the challenges of insufficient capture of network spatial features and inadequate extraction of attribute information in subspace clustering for attributed network community detection, an attribute-topology fusion strategy and a subspace autoencoder strategy are devised. These strategies enable the representation vectors to capture network features better and solve the difficulty of extracting attribute information. Experimental results on real and synthetic networks demonstrated that DAEAS has higher accuracy than several state-of-the-art community detection algorithms
A factor integrating transcription and repression of surface antigen genes in African trypanosomes
No full textAntigenic variation in Trypanosoma brucei (T. brucei) requires monoallelic expression of one variant surface glycoprotein (VSG) from one of the subtelomeric bloodstream form (BSF) expression sites (BESs). This transcription is unusually mediated by RNA polymerase I (RNA Pol I) and occurs in a specialized nuclear body, the expression site body (ESB). While factors promoting active BES transcription and silencing inactive BESs are known, how these opposing activities are integrated remains unknown. Here, we identify ESBX (Tb927.3.1660) as a BSF-specific ESB protein necessary for this coordination. We show that ESBX RNAi knockdown prevents RNA Pol I localizing to the ESB and reduces active BES transcription, while also derepressing inactive BESs with low processivity transcription. Conversely, ESBX overexpression weakly activates inactive BESs in a distinct manner from ESBX knockdown, leading to processive transcription, without disrupting the active BES or forming supernumerary ESBs. ESBX knockdown causes a similar transcriptomic defect to ESB1 and VEX2 knockdown combined, establishing ESBX as a key factor linking transcriptional activation of the active BES with inactive BES silencing through the VSG exclusion (VEX) phenomenon. This allows us to suggest models for understanding the establishment and maintenance of monoallelic expression critical for parasite immune evasion
Integral modelling for water security beyond the water cycle
No full textWater pollution is a critical constraint to water security, impacting natural environment and reducing the resilience of infrastructure. Conventional approaches, often focused on modelling the hydrological cycle, struggle to capture the wider interactions between natural and engineered
systems in which water is embedded. However, water systems are closely linked with transport, food and energy, creating feedback that remains poorly understood. In this Perspective, we propose that advancing water security requires integral modelling frameworks that move beyond water-cycle-only approaches. Such frameworks provide a modular, graph-based representation capable of linking physical systems with human behaviour and decisions. We illustrate this conceptually through the Water Systems Integration Modelling framework, showing how its modular
development can extend modelling beyond the water cycle. As an example, we outline how tyre wear pollution can be conceptualised through pathways that connect water and food systems. We conclude by highlighting three priorities for future work: developing interdisciplinary processes for cross-sectoral integral modelling, evaluating systemic portfolios of interventions, and extending applications to sectors such as energy, all of which will shape the next generation of integral modelling for water security