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Identification and Validation of ERK5 as a DNA Damage Modulating Drug Target in Glioblastoma
Get PDFYesBrain tumours kill more children and adults under 40 than any other cancer, with approximately half of primary brain tumours being diagnosed as high-grade malignancies known as glioblastomas. Despite de-bulking surgery combined with chemo-/radiotherapy regimens, the mean survival for these patients is only around 15 months, with less than 10% surviving over 5 years. This dismal prognosis highlights the urgent need to develop novel agents to improve the treatment of these tumours. To address this need, we carried out a human kinome siRNA screen to identify potential drug targets that augment the effectiveness of temozolomide (TMZ)-the standard-of-care chemotherapeutic agent used to treat glioblastoma. From this we identified ERK5/MAPK7, which we subsequently validated using a range of siRNA and small molecule inhibitors within a panel of glioma cells. Mechanistically, we find that ERK5 promotes efficient repair of TMZ-induced DNA lesions to confer cell survival and clonogenic capacity. Finally, using several glioblastoma patient cohorts we provide target validation data for ERK5 as a novel drug target, revealing that heightened ERK5 expression at both the mRNA and protein level is associated with increased tumour grade and poorer patient survival. Collectively, these findings provide a foundation to develop clinically effective ERK5 targeting strategies in glioblastomas and establish much-needed enhancement of the therapeutic repertoire used to treat this currently incurable disease
En face OCT imaging for the assessment of glaucoma
Get PDFGlaucoma is a leading cause of irreversible vision loss globally, and demands
early and accurate diagnosis. OCT has become a key investigative technique
in glaucoma, and, although it provides invaluable clinical support, detection of
early glaucoma remains imperfect. Recent OCT developments enabled direct
assessment of retinal nerve fibre bundle (RNFB) reflectance in en face OCT
images. The technique has considerable potential in the assessment of
glaucoma, yet it has limited clinical usability due to an incomplete
understanding of RNFB features in healthy and glaucoma eyes and the lack
of accepted methods to identify reflectance defects. This thesis aimed to better
understand characteristics of RNFB reflectance in en face OCT imaging and
to develop objective methods to extract defects in this domain.
Structural and functional measures of glaucoma changes were collected in
eyes with established glaucoma and age-similar controls. Results showed that
the healthy configuration of RNFB varies across the retina and between
different eyes. We developed a method for automated and objective
examination of reflectivity changes in en face images. This method considers
individual anatomy and varying RNFB configuration, and found more
abnormalities than previous approaches. Measures of en face reflectance and
conventional retinal nerve fibre layer thickness were strongly related. The
agreement between changes of reflectance and visual function was
moderate-to-good, and both testing domains presented concordant abnormalities
in all tested eyes.
Following further minimisation of artefacts in en face images, direct use of
reflectance analysis or its combination with perimetry appear viable and with
significant potential for clinical examination of glaucoma
Integration of Hidden Markov Modelling and Bayesian Networks for fault analysis of complex systems. Development of a hybrid diagnostics methodology based on the integration of hidden Markov modelling and Bayesian networks for fault detection, prediction and isolation of complex automotive systems
No full textThe complexity of engineered systems has increased remarkably to meet
customer needs. In the continuously growing global market, it is essential for
engineered systems to keep their productivities which can be achieved by higher
reliability and availability. Integrated health management based on diagnostics
and prognostics provides significant benefits, which includes increasing system
safety and operational reliability, with a significant impact on the life-cycle costs,
reducing operating costs and increasing revenues. Characteristics of complex
systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity
make diagnostics and prognostics more challenging tasks and decrease the
application of classic reliability methods remarkably – as they cannot address the
dynamic behaviour of these systems.
This research has focused on detecting, predicting and isolating faults in
engineered systems, using operational data with multifarious data characteristics.
Complexities in the data, including non-Gaussianity and high nonlinearity, impose
stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden
Markov modelling (HMM) and Bayesian network (BN) were employed to detect,
predict and isolate faults in a system. The fault detection and prediction were
based on comparing and exploiting pattern similarity in the data via the loglikelihood
values generated through HMM training. To identify the root cause of
the faults, the probability values obtained from updating the BN were used which
were based on the virtual evidence provided by HMM training and log-likelihood
values. To set up a more accurate data-driven model – particularly BN structure
– engineering analyses were employed in a structured way to explore the causal
relationships in the system which is essential for reliability analysis of complex
engineered systems.
The automotive exhaust gas Aftertreatment system is a complex engineered
system consisting of several subsystems working interdependently to meet
emission legislations. The Aftertreatment system is a highly nonlinear, dynamic
and non-stationary system. Consequently, it has multifarious data characteristics,
where these characteristics raise the challenges of diagnostics and prognostics
for this system, compared to some of the references systems, such as the
Tennessee Eastman process or rolling bearings. The feasibility and effectiveness
of the presented framework were discussed in conjunction with the application to
a real-world case study of an exhaust gas Aftertreatment system which provided
good validation of the methodology, proving feasibility to detect, predict, and
isolate unidentified faults in dynamic processes
Accountability in action: how can archaeology make amends?
Get PDFYesThis special issue gathers together a selection of short articles reflecting on the historical construction of inequality and race in the histories of archaeology. The articles also suggest ways in which the discipline might grapple with the—often obvious, sometimes subtle—consequences of that historical process. Solicited via an open call for papers in the summer of 2020 (one made with the aim of speedy publication), the breadth of the topics discussed in the articles reflect how inequality and race have become more prominent research themes within the histories of archaeology in the previous five-to-ten years. At the same time, the pieces show how research can—and should—be connected to attempts to promote social justice and an end to racial discrimination within archaeological practice, the archaeological profession, and the wider worlds with which the discipline interacts. Published at a time when a pandemic has not only swept the world, but also exposed such inequalities further, the special issue represents a positive intervention in what continues to be a contentious issue.The EDH project was funded by the UK’s Arts and Humanities Research Council (AHRC), project number AH/S004580/1, and conducted in compliance with UCL’s ethical guidance, project id 14901/001
Minimisation of energy consumption via optimisation of a simple hybrid system of multi effect distillation and permeate reprocessing reverse osmosis processes for seawater desalination
No full textNoMulti Effect Distillation (MED) and Reverse Osmosis (RO) processes have been extensively used to produce freshwater from seawater resources. amongst many performance indicators, energy consumption of different configuration of hybrid system of MED and RO processes have been analysed in the past. Hybrid MED-RO system is energy intensive and use of fossil fuel can significantly increase the carbon footprint, unless stable renewable energy sources are used. In this work specific energy consumption of a simple hybrid MED-RO system with permeate reprocessing is minimised while optimising a number of operating decision variables using model based optimisation technique. A detailed process model developed earlier by the authors is embedded in the optimisation framework resulting in a constrained Non-linear Programming (NLP) problem. The minimum specific energy consumption achieved in this work is about 18% lower than what is reported in the literature resulting in a significant energy saving and thus carbon footprint.Multi Effect Distillation (MED) and Reverse Osmosis (RO) processes have been extensively used to produce freshwater from seawater resources. amongst many performance indicators, energy consumption of different configuration of hybrid system of MED and RO processes have been analysed in the past. Hybrid MED-RO system is energy intensive and use of fossil fuel can significantly increase the carbon footprint, unless stable renewable energy sources are used. In this work specific energy consumption of a simple hybrid MED-RO system with permeate reprocessing is minimised while optimising a number of operating decision variables using model based optimisation technique. A detailed process model developed earlier by the authors is embedded in the optimisation framework resulting in a constrained Non-linear Programming (NLP) problem. The minimum specific energy consumption achieved in this work is about 18% lower than what is reported in the literature resulting in a significant energy saving and thus carbon footprint
Online Anomaly Detection for Time Series. Towards Incorporating Feature Extraction, Model Uncertainty and Concept Drift Adaptation for Improving Anomaly Detection
Get PDFTime series anomaly detection receives increasing research interest given
the growing number of data-rich application domains. Recent additions
to anomaly detection methods in research literature include deep learning
algorithms. The nature and performance of these algorithms in sequence
analysis enable them to learn hierarchical discriminating features
and time-series temporal nature. However, their performance is affected
by the speed at which the time series arrives, the use of a fixed threshold,
and the assumption of Gaussian distribution on the prediction error
to identify anomalous values. An exact parametric distribution is often
not directly relevant in many applications and it’s often difficult to select
an appropriate threshold that will differentiate anomalies with noise.
Thus, implementations need the Prediction Interval (PI) that quantifies the
level of uncertainty associated with the Deep Neural Network (DNN) point
forecasts, which helps in making a better-informed decision and mitigates
against false anomaly alerts. To achieve this, a new anomaly detection
method is proposed that computes the uncertainty in estimates using quantile
regression and used the quantile interval to identify anomalies. Similarly,
to handle the speed at which the data arrives, an online anomaly detection
method is proposed where a model is trained incrementally to adapt
to the concept drift that improves prediction. This is implemented using a
window-based strategy, in which a time series is broken into sliding windows
of sub-sequences as input to the model. To adapt to concept drift,
the model is updated when changes occur in the new arrival instances.
This is achieved by using anomaly likelihood which is computed using the
Q-function to define the abnormal degree of the current data point based
on the previous data points. Specifically, when concept drift occurs, the
proposed method will mark the current data point as anomalous. However,
when the abnormal behavior continues for a longer period of time,
the abnormal degree of the current data point will be low compared to the
previous data points using the likelihood. As such, the current data point is
added to the previous data to retrain the model which will allow the model
to learn the new characteristics of the data and hence adapt to the concept
changes thereby redefining the abnormal behavior. The proposed method
also incorporates feature extraction to capture structural patterns in the
time series. This is especially significant for multivariate time-series data,
for which there is a need to capture the complex temporal dependencies
that may exist between the variables. In summary, this thesis contributes
to the theory, design, and development of algorithms and models for the
detection of anomalies in both static and evolving time series data.
Several experiments were conducted, and the results obtained indicate the
significance of this research on offline and online anomaly detection in
both static and evolving time-series data. In chapter 3, the newly proposed
method (Deep Quantile Regression Anomaly Detection Method) is evaluated
and compared with six other prediction-based anomaly detection
methods that assume a normal distribution of prediction or reconstruction
error for the identification of anomalies. Results in the first part of
the experiment indicate that DQR-AD obtained relatively better precision
than all other methods which demonstrates the capability of the method
in detecting a higher number of anomalous points with low false positive
rates. Also, the results show that DQR-AD is approximately 2 – 3
times better than the DeepAnT which performs better than all the remaining
methods on all domains in the NAB dataset. In the second part of the
experiment, sMAP dataset is used with 4-dimensional features to demonstrate
the method on multivariate time-series data. Experimental result
shows DQR-AD have 10% better performance than AE on three datasets
(SMAP1, SMAP3, and SMAP5) and equal performance on the remaining
two datasets. In chapter 5, two levels of experiments were conducted
basis of false-positive rate and concept drift adaptation. In the first level
of the experiment, the result shows that online DQR-AD is 18% better
than both DQR-AD and VAE-LSTM on five NAB datasets. Similarly, results
in the second level of the experiment show that the online DQR-AD
method has better performance than five counterpart methods with a relatively
10% margin on six out of the seven NAB datasets. This result
demonstrates how concept drift adaptation strategies adopted in the proposed
online DQR-AD improve the performance of anomaly detection in
time series.Petroleum Technology Development Fund (PTDF
Hypoxia-induced SETX links replication stress with the unfolded protein response
Get PDFYesTumour hypoxia is associated with poor patient prognosis and therapy resistance. A unique transcriptional response is initiated by hypoxia which includes the rapid activation of numerous transcription factors in a background of reduced global transcription. Here, we show that the biological response to hypoxia includes the accumulation of R-loops and the induction of the RNA/DNA helicase SETX. In the absence of hypoxia-induced SETX, R-loop levels increase, DNA damage accumulates, and DNA replication rates decrease. Therefore, suggesting that, SETX plays a role in protecting cells from DNA damage induced during transcription in hypoxia. Importantly, we propose that the mechanism of SETX induction in hypoxia is reliant on the PERK/ATF4 arm of the unfolded protein response. These data not only highlight the unique cellular response to hypoxia, which includes both a replication stress-dependent DNA damage response and an unfolded protein response but uncover a novel link between these two distinct pathways.SR, KBL, PV and MH were supported by a CRUK grant C5255/A23755 (awarded to E.M.H.). N.N. was supported by an MRC studentship (MC_ST_U16007). I. P.F. was supported by CRUK Oxford Centre Prize DPhil Studentship C38302/A12981. N.G. was supported by a Royal Society University Research fellowship. W.-C.C. was funded by CRUK grant 23969 (awarded to F.M.B.). S.F.E.-K. was supported by a Wellcome Trust Investigator Award (103844) and a Lister Institute of Preventative Medicine Fellowship (137661). J.G. was supported by a Jean Shanks Foundation/ Pathological Society of Great Britain and Ireland Clinical PhD Fellowship (JSPS CPhD 2018 01)
Proteomic profiling of matched normal and tumour tongue biopsies from smokers and non-smokers. Oncoproteomic applications for oral tongue squamous cell carcinoma biomarker discovery
No full textDespite considerable development in the therapeutic repertoire for managing cancer-related malignancies, head and neck cancer mortality has not significantly improved. The burden of HNSCC fluctuates across countries and has been associated with exposure to tobacco-derived carcinogens, excessive alcohol consumption or combinations. Due to late detection, patients often present with oral pre-malignant lesions which have progressed to an advanced stage of HNSCC. In this study, the samples were from a male cohort as generally, men are at two to four-fold higher risk than women with over 90% of HNSCCs arising in the upper aerodigestive tract. Therefore, the purpose of this thesis was to identify HNSCC biomarkers in males associated within defined anatomical region (tongue) and causative agents, specific to smoking.
An iTRAQ proteomic approach was used to profile protein changes in matched normal and tumour samples from male non-smoking (n=6) and smoking patients (n=6) with tongue carcinomas revealing identification of potential targets specific to cancer. Samples were subjected to liquid nitrogen cryo-pulverisation and protein determination. Protein extracts from the same category were pooled, trypsin digested and iTRAQ 4-plex labelled. Data was generated by 2D-LC/MS on an Orbitrap Fusion and significantly changed proteins (median ± SD) were subject to bioinformatics appraisal. A total of 3426 proteins were identified and quantified by proteomic analysis. Comparison of non-smoker tumour (NS:T) with smoker tumour (S:T) distinguished 64 proteins that were upregulated and 62 downregulated, S:T vs S:N categorised 349 proteins up- and 395 down-regulated respectively and NS:T vs NS:N identified 469 proteins up- and 431 down-regulated, respectively. Arginase-1 (ARG1), Keratin Type-2 Cytoskeletal 8 (KRT8), Lipocalin-1 (LCN1) and DNA replication licensing factor MCM2 (MCM2) were identified as biologically associated with smoking compared to non-smoking, providing viable targets for verification by immunochemical methods which further supported the proteomic data.
Overall, the project demonstrated the importance of using matched biopsies with good clinicopathological data for experimental design and provided a set of unique targets for a more expanded verification study
Probing cytochrome P450 (CYP) bioactivation with chloromethylindoline bioprecursors derived from the duocarmycin family of compounds
No full textYesThe duocarmycins belong to a class of agent which has great potential for use in cancer therapy. Their exquisite potency means they are too toxic for systemic use, and targeted approaches are required to unlock their clinical potential. In this study, we have explored seco-OH-chloromethylindoline (CI) duocarmycin-based bioprecursors for their potential for cytochrome P450 (CYP)-mediated cancer cell kill. We report on synthetic and biological explorations of racemic seco-CI-MI, where MI is a 5-methoxy indole motif, and dehydroxylated analogues. We show up to a 10-fold bioactivation of de-OH CI-MI and a fluoro bioprecursor analogue in CYP1A1-transfected cells. Using CYP bactosomes, we also demonstrate that CYP1A2 but not CYP1B1 or CYP3A4 has propensity for potentiating these compounds, indicating preference for CYP1A bioactivation
Assessing consumers’ co-production and future participation on value co-creation and business benefit: An F-P-C-B model perspective
No full textYesCo-production and active participation of the consumers are considered to have enhanced the value co-creation activities that would ensure business benefits of a firm. The marketing literature available does not explicitly explain the philosophy that would motivate the consumers to help to increase values for co-creation activities. In this context, attempts have been made to identify the factors that would impact on co-production and consumers’ participation to co-create values. By studying literature and theories such as theory of co-creation, theory of value creation, information processing theory, marketing theory and expectancy value theory, a conceptual model called F-P-C-B (Future Participation (F) - Co-production (P) - Co-creation (C) - Business Benefit (B)) has been developed along with nine hypotheses. The data was from 362 respondents in India and the model was tested using PLS based analysis. The study shows that it is important for the firms to shift from product-oriented activities to customer-related strategies. It is also found that for obtaining more profitability and better business results, customers should be involved in business activities by way of involving in co-design, idea generation, and other relevant activities of the firms. Moreover, the study highlights that knowledge sharing between the customers and the firm authorities ensures better business values