Latent variable regression and applications to planetary seismic instrumentation

The work presented in this thesis is framed by the concept of latent variables, a modern data analytics approach. A latent variable represents an extracted component from a dataset which is not directly measured. The concept is first applied to combat the problem of ill-posed regression through the promising method of partial least squares (PLS). In this context the latent variables within a data matrix are extracted through an iterative algorithm based on cross-covariance as an optimisation criterion. This work first extends the PLS algorithm, using adaptive and recursive techniques, for online, non-stationary data applications. The standard PLS algorithm is further generalised for complex-, quaternion- and tensor-valued data. In doing so it is shown that the multidimensional algebras facilitate physically meaningful representations, demonstrated through smart-grid frequency estimation and image-classification tasks. The second part of the thesis uses this knowledge to inform a performance analysis of the MEMS microseismometer implemented for the InSight mission to Mars. This is given in terms of the sensor's intrinsic self-noise, the estimation of which is achieved from experimental data with a colocated instrument. The standard coherence and proposed delta noise estimators are analysed with respect to practical issues. The implementation of algorithms for the alignment, calibration and post-processing of the data then enabled a definitive self-noise estimate, validated from data acquired in ultra-quiet, deep-space environment. A method for the decorrelation of the microseismometer's output from its thermal response is proposed. To do so a novel sensor fusion approach based on the Kalman filter is developed for a full-band transfer-function correction, in contrast to the traditional ill-posed frequency division method. This algorithm was applied to experimental data which determined the thermal model coefficients while validating the sensor's performance at tidal frequencies 1E-5Hz and in extreme environments at -65C. This thesis, therefore, provides a definitive view of the latent variables perspective. This is achieved through the general algorithms developed for regression with multidimensional data and the bespoke application to seismic instrumentation.Open Acces

Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing

Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the standard covariance and the three complementary covariance matrices. Although such matrices have a tremendous amount of structure and their decomposition is a powerful tool in a variety of applications, the non-commutative nature of the quaternion product has been prohibitive to the development of quaternion uncorrelating transforms. To this end, we introduce novel techniques for a simultaneous decomposition of the covariance and complementary covariance matrices in the quaternion domain, whereby the quaternion version of the Takagi factorisation is explored to diagonalise symmetric quaternion-valued matrices. This gives new insights into the quaternion uncorrelating transform (QUT) and forms a basis for the proposed quaternion approximate uncorrelating transform (QAUT) which simultaneously diagonalises all four covariance matrices associated with improper quaternion signals. The effectiveness of the proposed uncorrelating transforms is validated by simulations on both synthetic and real-world quaternion-valued signals.Comment: 41 pages, single column, 10 figure

Clostridium difficile: a healthcare associated infection of unknown significance in adults in sub-Saharan Africa

Background: Clostridium difficile infection (CDI) causes a high burden of disease in high-resource healthcare systems, with significant morbidity, mortality and financial implications. CDI is a healthcare-associated infection for which the primary risk factor is antibiotic usage and it is the leading cause of bacterial diarrhoea in HIV infected patients in USA. Little is known about the disease burden of CDI in sub-Saharan Africa, where HIV and healthcare associated infection have a higher prevalence and antibiotic usage is less restricted. Aim: To review published literature on CDI in sub-Saharan Africa, highlighting areas for future research. Methods: English language publications since 1995 were identified from online databases (PubMed, Medline, Google Scholar, SCOPUS) and personal collections of articles, using combinations of keywords to include C. difficile, Africa and HIV. Results: Ten relevant studies were identified. There is considerable variation in methodology to assess for carriage of toxigenic C. difficile and its associations. Eight studies report carriage of toxigenic C. difficile. Three (of four) studies found an association with antibiotic usage. One (of four) studies showed an association with HIV infection. One study showed no association with degree of immunosuppression in HIV. Two (of three) studies showed an association between carriage of toxigenic C. difficile and diarrhoeal illness. Conclusion: Whilst the carriage of toxigenic C. difficile is well described in sub-Saharan Africa, the impact of CDI in the Region remains poorly l understood and warrants high quality research

The cosmic history of hot gas cooling and radio active galactic nucleus activity in massive early-type galaxies

We study the X-ray properties of 393 optically selected early-type galaxies (ETGs) over the redshift range of z≈ 0.0–1.2 in the Chandra Deep Fields (CDFs). To measure the average X-ray properties of the ETG population, we use X-ray stacking analyses with a subset of 158 passive ETGs (148 of which were individually undetected in X-ray). This ETG subset was constructed to span the redshift ranges of z= 0.1–1.2 in the ≈4 Ms CDF-South and ≈2 Ms CDF-North and z= 0.1–0.6 in the ≈250 ks Extended-CDF-South where the contribution from individually undetected active galactic nuclei (AGN) is expected to be negligible in our stacking. We find that 55 of the ETGs are detected individually in X-ray, and 12 of these galaxies have properties consistent with being passive hot-gas-dominated systems (i.e. systems not dominated by an X-ray bright AGN). On the basis of our analyses, we find little evolution in the mean 0.5–2 keV to B-band luminosity ratio (LX/LB∝ [1 +z]1.2) since z≈ 1.2, implying that some heating mechanism prevents the gas from cooling in these systems. We consider that feedback from radio-mode AGN activity could be responsible for heating the gas. We select radio AGN in the ETG population using their far-infrared/radio flux ratio. Our radio observations allow us to constrain the duty cycle history of radio AGN activity in our ETG sample. We estimate that if scaling relations between radio and mechanical power hold out to z≈ 1.2 for the ETG population being studied here, the average mechanical power from AGN activity is a factor of ≈1.4–2.6 times larger than the average radiative cooling power from hot gas over the redshift range z≈ 0–1.2. The excess of inferred AGN mechanical power from these ETGs is consistent with that found in the local Universe for similar types of galaxies

Expression and cellular localization of hepcidin mRNA and protein in normal rat brain.

BACKGROUND: Hepcidin is a peptide hormone belonging to the defensin family of cationic antimicrobial molecules that has an essential role in systemic iron homeostasis. The peptide is synthesised by hepatocytes and transported in the circulation to target tissues where it regulates the iron export function of the ferrous iron permease, ferroportin. In the brain hepcidin protein has been identified using immuno-histochemistry and mRNA by real-time PCR but not by in situ hybridisation raising the question of whether there is measurable transcription of the hepcidin gene in the central nervous system. Alternatively hepcidin could be transported as a hormone to the brain via the circulation. RESULTS: By RT-PCR hepcidin mRNA was present at low level throughout normal rat brain while in situ hybridisation to detect low-abundant mRNA revealed that transcripts were restricted to endothelium of blood vessels and choroid plexus. In contrast, hepcidin protein analysed by immuno-histochemistry was highly expressed in blood vessels, in endothelium and in pericytes. Hepcidin was also present in glial cells and in the olfactory bulb, sub-ventricular zone and dentate gyrus, areas where neurogenesis and synaptic plasticity are maintained throughout adult life. The hepcidin species identified by Western blotting in sub-ventricular zone, cortex and hippocampus migrated as a ~2.8 kDa band, identical in size to hepcidin present in normal rat serum suggesting that hepcidin in brain was the full-length biologically active 25 amino acid peptide. Hepcidin co-localised with ferroportin in ependymal cells of the sub-ventricular zone and in the corpus callosum consistent with a regulatory role in iron metabolism at these sites. CONCLUSIONS: Hepcidin protein was widely expressed in brain parenchyma while levels of hepcidin gene transcription appeared to be below the limits of detection of the in situ hybridisation probes. This disparity suggests that not all hepcidin in the brain is transcribed in situ and may originate in part outside the brain. The properties of hepcidin as a cationic peptide hormone are reflected in the finding of hepcidin in the walls of blood vessels and in pericytes and glia, cells that may be involved in transporting the peptide into brain interstitium

Cost-effectiveness of therapist-assisted internet-delivered psychological therapies for PTSD differing in trauma focus in England: an economic evaluation based on the STOP-PTSD trial

Background: Although there are effective psychological treatments for post-traumatic stress disorder (PTSD), they remain inaccessible for many people. Digitally enabled therapy is a way to overcome this problem; however, there is little evidence on which forms of these therapies are most cost effective in PTSD. We aimed to assess the cost-effectiveness of the STOP-PTSD trial, which evaluated two therapist-assisted, internet-delivered cognitive behavioural therapies: cognitive therapy for PTSD (iCT-PTSD) and a programme focusing on stress management (iStress-PTSD). Methods: In this health economic evaluation, we used data from the STOP-PTSD trial (n=217), a single-blind, randomised controlled trial, to compare iCT-PTSD and iStress-PTSD in terms of resource use and health outcomes. In the trial, participants (aged ≥18 years) who met DSM-5 criteria for PTSD were recruited from primary care therapy services in South East England. The interventions were delivered online with therapist support for the first 12 weeks, and three telephone calls over the next 3 months. Participants completed questionnaires on symptoms, wellbeing, quality of life, and resource use at baseline, 13 weeks, 26 weeks, and 39 weeks after randomisation. We used a cost-effectiveness analysis to assess cost per quality-adjusted life year (QALY) at 39 weeks post-randomisation, from the perspective of the English National Health Service (NHS) and personal social services and on the basis of intention-to-treat for complete cases. Treatment modules and the platform design were developed with extensive input from service users: service users also advised on the trial protocol and methods, including the health economic measures. This is a pre-planned analysis of the STOP-PTSD trial; the trial was registered prospectively on the ISRCTN Registry (ISRCTN16806208). Findings: NHS costs were similar across treatment groups, but clinical outcomes were superior for iCT-PTSD compared with iStress-PTSD. The incremental cost-effectiveness ratio for NHS costs and personal social services was estimated as £1921 per QALY. iCT-PTSD had an estimated 91·6% chance of being cost effective at the £20 000 per QALY threshold. From the societal perspective, iCT-PTSD was cost saving compared with iStress-PTSD. Interpretation: iCT-PTSD is a cost-effective form of therapist-assisted, internet-delivered psychological therapy relative to iStress-PTSD, and it could be considered for clinical implementation

GraphCast: Learning skillful medium-range global weather forecasting

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.Comment: Main text: 21 pages, 8 figures, 1 table. Appendix: 15 pages, 5 figures, 2 table