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Investigating temperature-dependent differences in human coronavirus OC43 replication and Type I interferon signalling in human cells
Full text linkHuman coronaviruses (HCoVs) are widespread respiratory pathogens that generally cause mild upper respiratory tract (URT) infections, unlike epidemic coronaviruses such as SARS-CoV, MERS-CoV and SARS-CoV-2, which more often replicate in the lower respiratory tract (LRT) and cause severe disease. A physiological temperature gradient exists in the airway, with 33 °C in the URT and 37 °C in the LRT. I tested whether HCoV-OC43 confinement to the URT reflects temperature-dependent regulation of type I interferon. I combined multi-cycle growth assays across human cell infection models, transcriptomics in primary nasal epithelial cells, stimulation of pattern-recognition receptors, IFNα pretreatment and JAK/STAT inhibition, and targeted knockouts. Across multiple cell types, HCoV-OC43 replicated more efficiently at 33 °C than at 37 °C. Replication was sensitive to IFNα at both temperatures, yet induction of interferon-stimulated genes was reduced at 33 °C, resulting in a weaker antiviral state. Cytokine profiling following stimuli revealed that secretion of cytokines was generally stronger at 33 °C. PRR stimulation revealed temperature sensitivity of RIG-I signalling, with stronger ISG induction and MAPK or STAT phosphorylation at 37 °C and attenuated responses at 33 °C. In contrast, MDA5/TLR3 pathways and direct IFNAR signalling were relatively stable across temperatures. In A549 knockouts, loss of RIG-I, MDA5, or MAVS increased replication, however, only MAVS deficiency attenuated the temperature-dependent replication difference, indicating that MAVS is a major contributor to antiviral restriction at 37 °C while additional temperature-sensitive mechanisms remain operative. These data support a dual mechanism for HCoV-OC43 upper-airway tropism, where cooler temperatures promote replication and attenuate MAVS-linked antiviral programs. While MAVS emerges as a critical adaptor in this process, further research is needed to define its interplay with downstream effectors and to assess how such temperature-sensitive innate responses shape coronavirus tissue tropism more broadly
Brain state dependent repetitive transcranial magnetic stimulation improves motor learning outcomes
Full text linkObjective Motor learning is key to successful neuro-rehabilitation. Combinations of Brain-Computer Interfaces (BCIs) and repetitive transcranial magnetic stimulation (rTMS) have been proposed for neurorehabilitation following conditions such as stroke. However, rTMS is typically delivered via a fixed protocol without taking into consideration the current brain states of participants. We propose a new BCI-based rTMS delivery protocol for supporting motor learning. Specifically, we propose BCI-based brain state dependent delivery of rTMS, in which a BCI system measures the event-related desynchronisation (\ERD; a neural marker of motor learning in the alpha band, selected because it is a robust, well-established real-time EEG correlate of motor activity and cortical excitability) in order to determine when to deliver rTMS.
Approach We compare our proposed rTMS delivery protocol with two state of the art comparable protocols: delivery of rTMS prior to the BCI-based motor learning and delivery of rTMS at fixed times throughout the experiment, as well as a control condition in which no rTMS was used. Each protocol is tested with a different group (n=8) of participants (n=32 total participants).
Main Results Our results reveal a significant effect of changing the rTMS delivery protocol (p=0.005) and that our proposed rTMS delivery protocol delivers better motor learning outcomes than other state of the art rTMS delivery protocols (e.g. BCI group vs. fixed times group: p=0.003, BCI group vs. no rTMS group: p=0.03). Inspection of ERD dynamics from each of our participant groups demonstrates that our BCI-based rTMS paradigm keeps corticospinal excitability relatively stable throughout the learning period, keeping the brain in a more optimal learning state for longer.
Significance These findings suggest potential applications for adaptive rTMS–BCI systems in clinical neurorehabilitation, sports skill learning, and neuroprosthetic control
Quantifying the effects of offshore infrastructure on shelf sediment blue carbon dynamics
Full text linkThe effect of offshore infrastructure on shelf sediment blue carbon dynamics, is a key knowledge gap for blue carbon science, and shelf systems’ ability to mitigate climate change. This thesis addresses this research gap, by quantifying sediment type, organic carbon stocks, accumulation rate, source and vulnerability in sediments surrounding two decommissioned oil and gas platforms (North West Hutton and Miller) in the North Sea. Overall, organic carbon stocks and sediment type varied distinctly between sites, with higher carbon stocks closer to Miller, but no relationship with distance at North West Hutton. Accumulation rates were refined to account for heavy metal contamination, which provides a new methodological framework of correcting for this issue in future blue carbon assessments. Using a novel binary mixing model application and hydrocarbon analysis, both sites presented organic carbon enrichment from anthropogenic sources, namely hydrocarbons, within close proximity (50 m) to the decommissioned sites. This highlights that sources of organic carbon other than marine may be present within shelf sediment stocks. A key recommendation from these studies is to take these measurements throughout the lifecycle of infrastructure; from pre and post construction, during operation, and pre and post decommissioning to better assess the varying impact of infrastructure. Finally, this thesis presents a methodological exploration to determine organic carbon mineralisation rates from acute disturbance events in sediments using oxygen consumption rates as a proxy. This provides a baseline for addressing the key question of carbon vulnerability from increasing infrastructure induced disturbance in a warming climate
From Aspirational to Transformed: Configurational Recipes for Analytics-Driven Operational Excellence
Full text linkAnalytics technologies are vital for improving operational performance, yet the configurations that maximise their value remain unclear. Drawing on complexity theory and contingency theory, this study examines how supply chain governance, organisational capability, and environmental conditions interact with different levels of analytics capability. Using secondary data from 205 Chinese high-tech firms over two years, we combine content analysis with fuzzy-set qualitative comparative analysis to uncover configurations that enhance performance. Results show that firms must align governance, capabilities, and environmental strategies with their analytics maturity, aspirational, experienced, or transformed. The study advances understanding of analytics implementation and offers practical guidance
Discursive Constructions of Masculinity in Campaigns Addressing Gender Violence in London
No full textPublic service announcements (PSAs) increasingly position men as key agents in preventing gender-based violence (GBV), yet their ideological foundations remain underexplored. This study critically analyses two London-based PSAs—Have a Word and Maaate— through a discourse analytic lens to examine how they construct responsibility and masculinity. We introduce the concept of responsibilised masculinity to describe a hybrid formation that blends progressive gender ideals with neoliberal self-regulation. While the campaigns promote male intervention, they represent a broader trend that risks individualising responsibility and obscuring the structural conditions that sustain GBV. We argue that these PSAs discipline masculinities through moral governance, reinforcing dominant power relations while appearing transformative. The paper calls for prevention efforts that move beyond responsibilisation toward systemic, relational, and abolitionist approaches to gender justice
Double Machine Learning for Static Panel Models with Fixed Effects
Full text linkRecent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for panel data in which these algorithms are used to approximate high-dimensional and nonlinear nuisance functions of the covariates. Our new procedures are extensions of the well-known correlated random effects, within-group and first-difference estimators from linear to nonlinear panel models, specifically, Robinson (1988)’s partially linear regression model with fixed effects and unspecified nonlinear confounding. Our simulation study assesses the performance of these procedures using different machine learning algorithms. We use our procedures to re-estimate the impact of minimum wage on voting behaviour in the UK. From our results, we recommend the use of first-differencing because it imposes the fewest constraints on the distribution of the fixed effects, and an ensemble learning strategy to ensure optimum estimator accuracy
Social processing of dynamic naturalistic social interactions
Full text linkResearch suggests that static depictions of social interactions preferentially capture our attention compared to non-interactions. Research also suggests that motion captures attention. To date, therefore, it is unknown whether dynamic social interactions preferentially capture attention relative to non-interactions, over and above motion cues. The present study captured 81 participants’ eye-gaze when viewing 4-second video clips of social-interactions compared to motion-matched non-interactions. We hypothesised that participants would spend more time looking at the two agents in the videos relative to the background when viewing social interactions compared to non-interactions. Results confirmed our hypothesis and demonstrated that this effect was stronger for individuals with greater empathy and lower autistic traits. These results add to the growing body of research investigating the processing of social interactions in complex, naturalistic stimuli and demonstrate that social interactions do preferentially capture attention, even when motion cues are present
UAV-Empowered Integrated Sensing and Communication for 6G
Full text linkUncrewed aerial vehicle (UAV)-aided integrated sensing and communication (ISAC) is envisioned to play an increasing important role in sixth-generation (6G) mobile communication systems, where the UAVs can serve as an aerial base station to extend the network coverage and provide improved sensing and communication services for mobile users. Compared to terrestrial base stations, UAVs exhibit distinct attributes (i.e., line-of-sight links, controllable mobility, restricted endurance, etc.) introduces both opportunities and challenges to improve the ISAC performance. To shed light on future research trends, this paper provides a comprehensive survey for UAV-aided ISAC. We firstly introduce the hierarchical network architecture, unique performance advantages, and typical applications of UAV-aided ISAC. Then, we present the key techniques including ISAC frame design, waveform design, UAV deployment optimization, operational mode, and resource management to reveal how to achieve UAV-aided ISAC. Next, a case study is provided to demonstrate the performance advantages of UAV-aided ISAC. Finally, some existing design challenges and open issues are elaborated to provide the guidance for future works
Deep learning approaches for ECG-based detection and diagnosis of coronary artery disease
Full text linkCoronary artery disease (CAD) is among the most prevalent and life-threatening cardiovascular conditions worldwide. Early detection is essential for improving patient outcomes and improving the efficiency of healthcare systems. Electrocardiography (ECG) is widely used for assessing cardiac function, yet manual interpretation of ECG signals can be inconsistent and prone to error. Developing reliable automated methods is therefore of great importance for enabling earlier and more consistent CAD detection. Although recent advances in deep learning have achieved strong performance in ECG analysis, many existing methods remain limited in practice. Current state-of-the-art models are often computationally complex and thus unsuitable for deployment on resource-constrained platforms. To address the limited attention given to CAD in current ECG research, various deep learning models were developed in this thesis. First, a one-dimensional convolutional neural network (1D-CNN) was proposed to detect CAD directly from raw ECG signals without manual feature extraction. The model achieved 97.3% accuracy and demonstrated strong generalisability when using 250-point ECG segments. Then, a feature engineering approach was applied to select high-quality signal segments using sample entropy and normalisation techniques, further improving both accuracy and robustness. Next, a lightweight neural network architecture (CADNet) was developed, outperforming existing lightweight models by offering lower complexity and smaller size without compromising accuracy. The fourth study focused on 12-lead ECG, introducing a depthwise, squeeze-and-excitation-based model that captured both lead-specific and inter-lead patterns, and achieved efficient deployment on an STM32 microcontroller. Finally, an attention-driven model was proposed for the detection of multiple cardiovascular diseases from a single ECG recording, demonstrating high diagnostic capability. Our qualitative evaluations in this study demonstrate that lightweight deep learning models can provide reliable ECG-based CAD diagnosis while remaining suitable for real-time deployment. The proposed approaches are robust to ECG variability and show consistent performance across different experimental and diagnostic scenarios, supporting their practical use in resource-constrained environments. Overall, the results highlight the relevance of lightweight deep learning architectures in enabling ECG-based diagnostic methods to be used as potential pre-screening tools
On the Robustness of AFDM and OTFS Against Passive Eavesdroppers
Full text linkWe investigate the robustness of affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) waveforms against passive eavesdroppers performing bruteforce demodulation to intercepted signals, under the assumption that eavesdroppers have no knowledge of chirp parameters (in AFDM) or the delay–Doppler grid configuration (in OTFS), such that they must search exhaustively over possible demodulation matrices. Analytical results show that the brute-force complexity scales as O(√N) for OTFS and O(N²) for AFDM, where N is the number of subcarriers, indicating that AFDM has superior resilience over OTFS. Simulation results demonstrate that AFDM is capable of preventing reliable demodulation at the eavesdropper side with a bit error rate (BER) of 0.5, while OTFS allows partial signal recovery under equivalent conditions