A5051446557- Mathematical Biology Tumor Growth
- Gene Regulatory Network Analysis
- Artificial Immune Systems Applications
- T-cell and B-cell Immunology
- Evolution and Genetic Dynamics
- Anomaly Detection Techniques and Applications
- Simulation Techniques and Applications
- 3D Printing in Biomedical Research
- Mathematical and Theoretical Epidemiology and Ecology Models
- Traffic and Road Safety
- Autonomous Vehicle Technology and Safety
- Immune Cell Function and Interaction
- Long-Term Effects of COVID-19
- Monoclonal and Polyclonal Antibodies Research
- Human-Automation Interaction and Safety
- Fault Detection and Control Systems
- Explainable Artificial Intelligence (XAI)
- Data Stream Mining Techniques
- Machine Learning and Data Classification
- COVID-19 and Mental Health
- COVID-19 Clinical Research Studies
- COVID-19 epidemiological studies
- Emotion and Mood Recognition
- Single-cell and spatial transcriptomics
- Cellular Mechanics and Interactions
University of Nottingham
2016-2025
University of Oxford
2022
Analysis and Testing Centre
2020
University of Sheffield
2017
Universidade Federal do Rio de Janeiro
2005-2012
Macrophages play a central role in orchestrating immune responses to foreign materials, which are often responsible for the failure of implanted medical devices. Material topography is known influence macrophage attachment and phenotype, providing opportunities rational design "immune-instructive" topographies modulate function thus body biomaterials. However, no generalizable understanding inter-relationship between cell response exists. A high throughput screening approach therefore...
Deep learning has been employed to prognostic and health management of automotive aerospace with promising results. Literature in this area revealed that most contributions regarding deep is largely focused on the model's architecture. However, improvement different aspects learning, such as custom loss function for are scarce. There therefore an opportunity improve upon effectiveness system's prognostics diagnostics without modifying models' To address gap, use two dynamically weighted...
Implanted medical devices often elicit adverse foreign body responses whereby macrophages play a central role. Here, we identify simple polymers that instruct different immunological by modulating macrophage attachment and polarization to pro-inflammatory (M1-like) or anti-inflammatory (M2-like) phenotypes. These immune-instructive were discovered using in vitro high-throughput polymer microarray screening of diverse (meth)acrylate (meth)acrylamide libraries. The bioinstructive function is...
We aimed to explore university students’ perceptions and experiences of SARS-CoV-2 mass asymptomatic testing, social distancing self-isolation, during the COVID-19 pandemic. This qualitative study comprised four rapid online focus groups conducted at a higher education institution in England, high alert (tier 2) national restrictions. Participants were purposively sampled students (n = 25) representing range gender, age, living circumstances (on/off campus), testing/self-isolation...
With the widespread use of machine learning to support decision-making, it is increasingly important verify and understand reasons why a particular output produced. Although post-training feature importance approaches assist this interpretation, there an overall lack consensus regarding how should be quantified, making explanations model predictions unreliable. In addition, many these depend on specific approach employed subset data used when calculating importance. A possible solution...
The World Health Organisation reports distracted driving actions as the main cause of road traffic accidents. Current studies to detect distraction postures focus on analysing spatial features images using Convolutional Neural Networks (CNN). However, approaches addressing both spectral and for are scarce. Our hypothesis is that deep learning can further be exploited consider features, so capture information within image correlations among channels. This paper introduces a novel driver...
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of transport network. This can, for example, support condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated effectiveness deep learning predicting conditions from outdoor images. However, training models to accurately predict using real-world road-facing images...
Fuel poverty is a complex socioenvironmental issue of increasing global significance. In England, fuel assessed via the Low Income Energy Efficiency (LILEE) indicator, yet concerns exist regarding efficacy this metric given its omission households based on Performance Certificate (EPC) ratings, rather than ability occupants to afford energy. To assess potential shortcomings LILEE metric, we perform quantitative analyses and energy security in London, UK. A spatial analysis London exposes...
There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm investigate early-stage cancer interactions with immune system. It does not suffer from some limitations ordinary differential equation models, such lack stochasticity, representation individual behaviours rather than aggregates memory. In this paper we contribution when contrasted stochastic versions ODE models using examples. We seek answers following questions: (1)...
Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for forecasting. According to the highly nonlinear and non-stationary characteristics data, employ Long Short-Term Memory (LSTM) capture long temporal dependencies Convolutional Neural Network (CNN) learn local trend features. However, designing optimal CNN-LSTM network architecture tuning parameters can be challenging would require consistent human supervision. To...
Objective. This study assessed the feasibility, acceptability, and clinical utility of integrating automated, prognostic feedback with Deliberate Practice (DP) for psychological therapists.Method. Nine therapists invited their patients to consent session recordings feedback, which informed DP targeted at personalised skill development. Feasibility, acceptability therapeutic skills were alongside qualitative interviews. Clinical outcomes from 97 pre-intervention 79 intervention-phase...
Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations the whole dataset time-consuming costly. Traditional semi-supervised methods primarily focus on extracting features learning data distributions from to enhance model training. In this paper, we introduce a novel incorporating registration generate pseudo-labels producing more geometrically correct improve Our method was evaluated...
Anti-attachment materials that are sprayable and 3D-printable passively prevent colonization by harmful fungi.
Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, playing a key role the recruitment differentiation of hMSCs. However, engineered biomaterials able simultaneously direct hMSC fate modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform,...
When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output produced. Although feature importance calculation approaches assist interpretation, there lack of consensus regarding how features’ quantified, which makes explanations offered for outcomes mostly unreliable. A possible solution address agreement combine results from multiple quantifiers reduce variance estimates improve quality explanations....
The pandemic coronavirus disease (COVID-19) dramatically spread worldwide. Considering several laboratory parameters and comorbidities may facilitate the assessment of severity. Early recognition progression associated with severe cases COVID-19 is essential for timely patient triaging. Our study investigated characteristics role results in severity cases.The was conducted from early-June to mid-August 2020. Blood samples clinical data were taken 322 patients diagnosed at Qala Hospital,...
To design effective immunomodulatory implants, innate immune cell interactions at the surface of biomaterials need to be controlled and understood. The architectural freedom two-photon polymerization is used produce arrays surface-mounted, geometrically diverse 3D polymer objects. This reveals importance interplay between architecture materials chemistry in determining human macrophage fate vitro. ChemoArchiChip identifies key structure-function relationships rules from machine learning...
Abstract There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, materials manufactured using AM perform well below their traditionally counterparts, particularly creep and fatigue. Research has shown that this difference in performance due complex relationships between process parameters which affect material microstructure consequently mechanical as well. Therefore, it necessary understand impact...
Traditional methods of evaluating the performance journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail accurately model real-world components. Numerical models include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure viscosity can be more accurate but require a large amount computational overhead, making analysis slower costly. To address this limitation,...