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Modelling the structural relationships between COVID-19 knowledge, attitudes and behaviours in Jordanian undergraduates
No full textBackground: Regulatory restrictions and mandates typically offer short-term behaviour guidance, whereas interventions to improve knowledge and attitudes could result in more sustainable behavioural changes. Health authorities implemented awareness campaigns to enhance public knowledge and attitudes regarding COVID-19. This study explored the interplay between knowledge, attitudes and behaviours related to COVID-19 among university undergraduate students in Jordan, aiming to inform public health initiatives and educational programmes. Methods: A cross-sectional survey targeting undergraduate students enrolled at Yarmouk University in Jordan was conducted between January and May 2021. Participants consented to complete an anonymised validated self-administered questionnaire to evaluate their understanding of COVID-19 symptoms, treatment and transmission and attitudes and behaviours towards preventive measures. Data were analysed using descriptive and inferential statistics and structural equation modelling to investigate the associations between knowledge, attitudes and behaviours. Results: A total of 1375 undergraduate students participated in the survey. Knowledge of COVID-19 was low among most participants, with only 1.3% demonstrating high knowledge. Conversely, 58.5% exhibited good behaviour, and 31.4% reported full compliance with recommended behaviours. Significant differences were found in knowledge, attitudes and behaviours across different faculty clusters, with health faculties showing superior knowledge and more positive attitudes. Female participants (66.3%) were more likely to engage in positive behaviours than males (p-value = 0.02). Structural equation model (SEM) analysis showed that knowledge significantly influenced attitudes, which affected behaviours, confirming the model’s validity. Conclusions: The study highlights the critical role of knowledge and attitudes in shaping COVID-19-related behaviours among university students. Significant variations in knowledge and attitudes across different academic disciplines highlight the need for tailored educational interventions. The analysis supports the theoretical model linking knowledge, attitudes and behaviours, emphasising the importance of improving knowledge and attitudes to drive behaviour change. The findings suggest that comprehensive health education programmes targeting cognitive and affective aspects are essential for effective public health responses during pandemics
Embodied tactile perception of soft objects properties
No full textTo enable robots to perform human-like dexterous manipulation, it is essential to understand how mechanical compliance, multi-modal sensing, and purpose-ful interaction jointly shape tactile perception. In this study, we use a dedicated modular e-Skin with interchangeable mechanical compliance and multi-modal sensing to systematically investigate how sensing embodiment and interaction strategies influence robotic perception of objects. Leveraging a curated set of soft wave objects with controlled viscoelastic and surface properties, we explore
a rich set of palpation primitivesthat vary indentation depth, frequency, and directionality. In addition, we propose the latent filter, an unsupervised, action-conditioned deep state-space model of the sophisticated interaction dynamics and infer causal mechanical properties into a structured latent space. This provides in-depth interpretable representation of how embodiment and interaction determine and influence perception. Our investigation demonstrates that multi-modal sensing outperforms uni-modal sensing, emphasizing complex interaction between the environment and the mechanical properties of e-Ski
Mechanisms driving altitude- and latitude-dependent air quality variations from high-altitude NOx emissions
No full textThe environmental impact of nitrogen oxide (NOx) emissions varies with emission altitude and latitude. NOx emissions from subsonic aviation (9-12 km) contribute to net global ozone formation, whereas those from supersonic aircraft (above 14 km) lead to net global ozone depletion. However, the effects of NOx emission altitude on surface air quality remain understudied. We evaluate how NOx emissions at different altitudes (8-22 km) and latitudes influence near-surface concentrations of two known air pollutants: ozone and fine particulate matter (PM2.5). Using the global chemical transport model GEOS-Chem, we find that NOx emissions of 1 Tg N yr-1 at 8-10 km (30-60°N) increase surface ozone (population-weighted) by 0.52 ppb and surface PM2.5 by 35 ng m-3, whereas emissions at 20-22 km reduce surface ozone by 1.73 ppb and increase surface PM2.5 by 310 ng m-3; this is nine times the PM2.5 increase per unit NOx from lower-altitude emissions. These effects stem from altitude-dependent mechanisms: at lower altitudes typical of subsonic aviation, NOx emissions increase upper tropospheric ozone which leads to enhanced surface ozone and nitrate aerosol. However, when emitted at higher altitudes NOx instead depletes ozone, permitting more ultraviolet light to reach the troposphere which boosts OH production and accelerates production of sulfate aerosol while destroying near-surface ozone. Our findings suggest that NOx emissions from high-altitude sources including supersonic aircraft may not only contribute to stratospheric ozone depletion but also cause larger changes (albeit of mixed sign) in surface air quality than subsonic aviation per unit of NOx emitted
Amoeba-inspired nano-robots for trace low-molecular-weight emerging contaminant removal from water
No full textTrace low-molecular-weight emerging contaminants (LMWECs) in drinking water sources pose chronic health risks but remain challenging to remove using conventional treatment processes. Here, we describe an amoeba-inspired nano-robot (NRm, where m refers to the molar ratio of Fe:Si), engineered with flexible polymer chains and iron (hydr)oxide nanodomains, for the simultaneous capture and catalytic degradation of 20 representative LMWECs at initial concentrations from 100 ng/L to 1 mg/L in a real surface water. Under optimized operational conditions, NR10 achieved over twice the removal efficiencies compared to conventional water treatment chemicals involving FeCl3 and polyacrylamide (PAM). The nano-robot autonomously extended polymer “pseudopodia” to bind LMWECs into flocs via hydrophobic association, and used H2O2 both as a “propulsion fuel” and as a source of •OH radicals via Fenton-like reactions to accelerate degradation of captured LMWECs. This multi-function mechanism enabled efficient capture and degradation of LMWECs, while reducing toxicity (from “acute” of raw water to “nontoxic” of the treated water) and improving sludge dewaterability. After use, 91% of NR10 could be recovered from flocs, and the recovered nano-robots maintained high LMWEC REs with only ∼2% reduction for each recovery-reuse cycle. NR10 offers a deployable, infrastructure-compatible solution to the growing problem of LMWECs in drinking water
SharkTrack: an accurate, generalisable software for streamlining shark and ray underwater video analysis
No full textElasmobranchs (sharks and rays) represent a critical component of marine ecosystems. However, they are experiencing global population declines, making effective monitoring essential for their management. Underwater stationary videos, such as those from Baited
Remote Underwater Video Stations (BRUVS), are vital for understanding elasmobranch spatial ecology and abundance. However, processing these videos requires time-consuming manual analysis, which can delay conservation efforts. To address this challenge, we
developed SharkTrack, a semi-automatic underwater video analysis software. SharkTrack uses Object Detection and Multi-Object Tracking models to automatically detect and track elasmobranchs, providing an annotation pipeline to manually classify elasmobranch species and compute species-specific MaxN (ssMaxN), the standard metric of relative abundance. When tested on BRUVS footage from locations unseen by the model during training, SharkTrack computed ssMaxN with 89% accuracy over 207 hours of footage. The semi automatic SharkTrack pipeline required only two minutes of human labour per hour of video, an estimated 95% reduction in manual analysis time compared to traditional methods
3D texture analysis of the corpus callosum in T1-weighted MR images of children with a traumatic brain injury
No full textInvestigating traumatic brain injuries (TBI) in the developing brain is a challenging task. The superposition of an injury to the normal development trajectory can lead to brain impairments which are not obvious at diagnosis. T1-weighted MRI, acquired routinely post-injury, has the potential to better inform diagnosis, but is limited by qualitative assessment by radiologists. Using T1-weighted volume images, we investigated the use of three-dimensional texture analysis (TA) on regions of the corpus callosum (CC) in children with TBI and typically developing controls (TDCs) in conjunction with analysis of diffusion weighted image (DWI)-derived metrics. Nineteen TDCs and 37 participants with TBI were included in the study. T1 textural metrics were extracted from the splenium, genu and body of the CC and assessed for differences between the groups. Textural skewness was found to be significantly higher in children with TBI than TDCs in the body of the CC (t-test: p 0.6). Non-significant reductions in ADC were found between TBI and TDC groups in the body and the splenium of the CC. Interestingly, no differences were found between TDCs and the TBI sample using FA. The results suggest that TA can potentially be used to assess white matter integrity after paediatric TBI
Imperial College London Grantham Institute for Climate Change and the Environment - Inside IPBES12: From Deliberation to Direction on Business and Biodiversity
No full textA group of Imperial College delegates visited a rainy Manchester last week (2-8 February) to attend the 12th Plenary session of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Read Dr Caroline Howe’s explainer on IPBES to catch-up! In this blog, the delegation shares their summaries and reflections
Compression with distributional and sequential constraints
No full textClassical data compression focuses on the most accurate reproduction of data. However, modern applications, particularly in artificial intelligence, increasingly impose new statistical or behavioral constraints. This thesis extends compression theory and practice to these new settings. Our main tool is channel simulation—a generalization of data compression for remotely generating samples from a specified distribution.
We first establish a framework for universal channel simulation, analogous to universal source coding for when the target distribution is unknown, and provide a provably near-optimal algorithm. We then develop a computationally efficient algorithm for simulating the Gaussian channel, a ubiquitous model for which no such practical method existed. In distributed learning, we formalize remote reinforcement learning under communication constraints and propose a general, highly efficient solution employing channel simulation. Furthermore, we uncover a fundamental connection between speculative decoding—a method to accelerate large language models—and channel simulation, yielding new algorithms and speed-up guarantees. Finally, we introduce a goal-oriented compression framework for agents whose actions and communications are concurrent, for which we derive performance bounds and design practical coding schemes. The contributions in this thesis highlight the fundamental nature of channel simulation as a novel and effective tool to address a variety of the challenges in federated learning, reinforcement learning, realistic image compression, and large language model inference
Using in vitro and ex vivo models of lung injury as a platform to identify and study mechanisms driving lung repair
No full textCurrent treatments for respiratory diseases, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF) and asthma are not curative, highlighting an unmet need for novel therapies. Our limited understanding of signalling pathways involved in lung repair, and lack of advanced models replicating the complexity of the human lung poses a challenge. Developmental signalling pathways are reactivated during lung repair; modulating these pathways offers potential for regenerative therapeutics. This PhD tested the hypothesis that pathways essential for lung development play critical roles in alveolar repair and/or regeneration.
WNT, retinoic acid (RA), and Sonic Hedgehog (SHH) pathways were screened using in vitro wound healing assays to investigate their effects on epithelial and endothelial cell migration during repair. Based on these assays, SHH was selected for further investigation. SHH is critical for lung development, particularly branching morphogenesis and fibroblast expansion in the adult lung, however, its role in repair remains poorly understood. I demonstrated that SHH modulates fibroblasts, SHH treatment increased migration in human lung fibroblasts (HLFs) while co-culture of HLF with alveolar epithelial cells (A549) increased migration of both cell types. Using the ex vivo Acid injury and repair (AIR) model, SHH increased vimentin+ fibroblasts in acid-injured regions of AIR-PCLS and was associated with myofibroblast (a-SMA) and lipofibroblasts (ADRP) expression. The human model (hAIR) was established as part of this thesis, SHH treatment similarly increased ADRP+ cells in hAIR-PCLS.
To explore whether SHH signalling contributes to disease pathology, I analysed a cohort of emphysematous tissue samples and observed upregulation of SHH and downregulation of HHIP, suggesting changes in pathway activation. These findings were supported by COPD Cell Atlas data, showing consistent changes in SHH pathway activity across multiple cell populations. Collectively, these findings support a role for SHH in lung repair and highlights its potential as a regenerative therapeutic target
The role of APOE and ABCA7 in lipid metabolism for late-onset Alzheimer's disease
No full textAccording to genome-wide association studies, a large number of Alzheimer’s disease (AD)-related variants are involved in lipid metabolism, implying the correlation of lipid dysregulation and AD pathogenesis. Variants in genes that encode lipid transporters, including the APOEε4 allele and loss-of-function single nucleotide polymorphisms in ABCA7, are strongly associated with an increased risk of AD, especially late-onset AD. Although lipidomics has begun to uncover lipid pathways affected by these genetic variants, systemic and brain cell-specific lipid alterations caused by their expression remain unknown. The aim of this study was to determine the effects of APOE4 and loss-of-function mutation of ABCA7 on lipid metabolism at the tissue and cellular levels. In this study, I conducted untargeted lipidomic profiling using ultrahigh-performance liquid chromatography coupled with ion-mobility quadrupole time-offlight
mass spectrometry. Analyses were performed on brain, plasma, and peripheral tissues from humanised APOE knock-in mice overexpressing human islet amyloid polypeptide (hIAPP) and homozygous ABCA7 knock-out mice. To investigate lipid changes at the cellular level, I also profiled iPSC-derived microglia expressing different APOE genotypes, and ABCA7-/- iPSC-derived microglia. In the brains of both 6-month-old APOE4/4 hIAPP and ABCA7-/-
mice, a downregulation of polyunsaturated fatty acid-containing glycerophospholipids, including plasmalogens, was observed. These glycerophospholipids include docosahexaenoic acid and arachidonic acid (ARA), which are essential for cognitive function. While peripheral apoE
is usually separated from apoE in the brain, I also detected elevated levels of ARA-derived pro-inflammatory eicosanoids in the liver of 6-month APOE4/4 mice, which implied peripheral APOE4 expression may also contribute to AD pathogenesis. At the cellular level, changes in sphingolipid metabolism, particularly involving ceramides, were consistently observed in both APOE4/4 and ABCA7-/- iPSC-derived microglia compared to controls. Understanding the shared alterations in lipid pathways caused by these AD risk factors can reveal the targetable mechanisms for disease progression and provide new avenues of treatment