Contributors: Neupane, Sankalpa
... Patients with Type 1 Diabetes still suffer from recurrent and troublesome hypoglycaemia despite improvements in glucose monitoring, insulin delivery and structured education. The key objectives of my thesis were to explore ways to identify patients at greater risk to develop hypoglycaemia and to investigate novel ways to prevent hypoglycaemic episodes by detecting this early and with ease. This might ultimately translate into reduction in overall risk of hypoglycaemia occurrence in those with Type 1 Diabetes. I investigated potential factors that could predict persistent presence of severe hypoglycaemia in the HypoCOMPaSS study, a multi-centre UK study examining clinical strategies to reduce burden of hypoglycaemia. Looking at a subset of these participants who underwent detailed hypoglycaemic clamp studies, I found no obvious parameters predicting persistent risk of severe hypoglycaemia. I then examined whether individual genetic factors might contribute to risk of persistent severe hypoglycaemia, exploring the association between polymorphisms in Angiotensin Converting Enzyme ACE gene and severe hypoglycaemia in 77 participants in HypoCOMPaSS study. Interestingly, I found that the homozygous DD ACE gene polymorphism was associated with a significantly increased risk of severe hypoglycaemia. Considering then practical approaches to trying to minimise hypoglycaemia risk, I examined the efficacy and safety of a novel implantable Continuous Glucose Monitoring Senseonics CGM System in 10 subjects with Type 1 Diabetes from the Cambridge cohort of the pivotal European PRECISE 1 study. In keeping with the global data, I found in Cambridge participants that this novel system was safe and its efficacy was comparable to commercially available Continuous Glucose Monitoring. This device was useful in detecting hypoglycaemia early with high device satisfaction among the users. I also explored alternative non-invasive methods to measure blood glucose and detect hypoglycaemia easily using breath sample in Type 1 Diabetes participants during experimental hypoglycaemia. The concentration of Isoprene exhaled in breath was significantly raised during hypoglycaemia. Finally, I looked at the stability of ‘diluted insulin aspart (NovoRapid®)’ in ambient temperature and CSII over 30 days. Both neat and diluted insulin aspart were stable beyond 30 days and could potentially be used by patients with T1D requiring very low insulin doses to avoid hypoglycaemia.
Biomineralization plasticity and environmental heterogeneity predict geographical resilience patterns of foundation species to future change.
Contributors: Telesca, Luca, Peck, Lloyd S, Sanders, Trystan, Thyrring, Jakob, Sejr, Mikael K, Harper, Elizabeth M
... Although geographic patterns of species’ sensitivity to environmental changes are defined by interacting multiple stressors, little is known about compensatory processes shaping regional differences in organismal vulnerability. Here, we examine large-scale spatial variations in biomineralisation under heterogeneous environmental gradients of temperature, salinity, and food availability across a 30° latitudinal range (3,334 km), to test whether plasticity in calcareous shell production and composition, from juveniles to large adults, mediates geographic patterns of resilience to climate change in critical foundation species, the mussels Mytilus edulis and M. trossulus. We find shell calcification decreased towards high latitude, with mussels producing thinner shells with a higher organic content in polar than temperate regions. Salinity was the best predictor of within-region differences in mussel shell deposition, mineral and organic composition. In polar, subpolar, and Baltic low-salinity environments, mussels produced thin shells with a thicker external organic layer (periostracum), and an increased proportion of calcite (prismatic layer, as opposed to aragonite) and organic matrix, providing potentially higher resistance against dissolution in more corrosive waters. Conversely, in temperate, higher-salinity regimes, thicker, more calcified shells with a higher aragonite (nacreous layer) proportion were deposited, which suggests enhanced protection under increased predation pressure. Interacting effects of salinity and food availability on mussel shell composition predict the deposition of a thicker periostracum and organic-enriched prismatic layer under forecasted future environmental conditions, suggesting a capacity for increased protection of high-latitude populations from ocean acidification. These findings support biomineralisation plasticity as a potentially advantageous compensatory mechanisms conferring Mytilus species a protective capacity for quantitative and qualitative trade-offs in shell deposition as a response to regional alterations of abiotic and biotic conditions in future environments. Our work illustrates that compensatory mechanisms, driving plastic responses to the spatial structure of multiple stressors, can define geographic patterns of unanticipated species resilience to global environmental change.
Contributors: Doherty, Gary, Petruzzelli, Michele, Beddowes, Emma, Ahmad, Syed, Caldas, Carlos, Gilbertson, Richard
... The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel, predictive biomarkers that can be either simple or complex. Expected final online publication date for the Annual Review of Biochemistry Volume 88 is June 20, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Contributors: Copoiu, Liviu, Torres, Pedro HM, Asher, David Benjamin, Blundell, Thomas, Malhotra, Sony
... Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their three-dimensional structures are reported, has led to difficulties in characterising the protein-carbohydrate interfaces. In order to better understand protein-carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 three-dimensional X-ray crystal structures of protein-carbohydrate complexes. In ProCarbDB the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein-carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.
Contributors: Lila, Eardi
... In this thesis, we introduce a comprehensive framework for the analysis of statistical samples that are functional data with non-trivial geometry. Geometry can interplay with functional data in different forms. The most general setting considered here is that of functional data supported on random non-linear smooth manifolds. This is a situation often encountered in neuroimaging, where modern imaging modalities are now able to produce structural brain representations coupled with functional information. Practitioners have commonly approached the analysis of such data with a two step approach. In the first step the manifolds are registered to a template and in the second step the functional information is analyzed on the template ignoring the registration step. The separation of the two steps precludes studies aimed at understanding how geometric variations relate to functional variations. On the other hand, functional data analysis has mostly developed tools for simplified settings, such as one-dimensional functional samples, limiting their applicability to real data. We formulate a model which is able to jointly represent geometric and functional variations. In this setting, modeling functional information requires the formulation of models able to incorporate structural information on the geometry of the underlying domains, with the aim of mitigating the curse of dimensionality. This is achieved by adopting regularized models involving differential operator penalties. Modeling random smooth manifolds requires the formulation of models constrained to produce `sensible' shapes, e.g. not self-intersecting. This is achieved by means of diffeomorphic flows. The proposed models have been applied to real data to perform studies able to relate structural changes to functional changes, and specifically, to study associations between brain shape and cerebral cortex thickness. We can also deal with more complex functional samples, themselves constrained to lie in a non-linear subspace. This is for instance the case of covariance operators, describing brain connectivity, which are symmetric and positive semi-definite operators. Thanks to the proposed models, we are able to model connectivity as an `object' and study its variations in time or across individuals. We also consider further extensions of this framework to the inverse problems setting, which is the setting where each sample is a latent object, and only indirect measurements are available.
Contributors: Ding, Sihua
... This PhD dissertation is a study of how social networks and clubs form in different contexts. Chapter 1 investigates the incentives of individuals to make introductions (the act of creating a link for two neighbours) in a social network. The chapter assumes that players are endowed with different ability levels and have a network among them. Given an ability endowment and a network, players undergo a matching process where one can only be matched with one of his neighbours or stay alone, and one always prefers a more capable matching partner to a less capable one to staying alone. A strict ability ranking would yield a unique stable matching for all network structures. Our research question is: If a player can create a link for a pair of his neighbours, when would he want to do so? Two results are derived to address this question. First, the matching of a player would be unchanged if he makes an introduction for two neighbours, at least one of whom is less capable than him. Second, an introduction could benefit the introducer when both neighbours involved are more capable than him, and there exists an even-length alternating path from one of the neighbours to him. The chapter also examines the stability of networks based on no profitable introductions and characterizes Pareto efficient networks. Chapter 2 studies a general model of investment in relationships. Existing research on network formation proceeds under strong assumptions on how a link between two agents can be produced: typically link investments are assumed to be unweighted and links are formed either reciprocally or unilaterally. This chapter proposes a more general approach by allowing weighted link investment and employing a constant elasticity of substitution (CES) link formation function. This formulation has two advantages other than permitting a more flexible sponsorship of links. First, it nests the two commonly employed bilateral and unilateral link formation assumptions as special cases and thus enables robustness checks on existing works. Second, it introduces a variation in link investment substitutability and hence enables the analysis of how different link formation technologies affect network formation. We illustrate this approach through two applications: a game of pure network formation and a game of network formation with assorted activities. Chapter 3, which is co-authored with Prof Sanjeev Goyal and Dr Marcin Dziubinski, explores club joining activities of individuals and member admission activities of clubs. We assume that links between clubs are formed when they share common members. The productivity of a club is determined by its number of members and how connected it is to other clubs. Individuals wish to join clubs with high productivity and clubs admit members with the aim to raise productivity. We study the efficient and the stable club membership structures and find that both efficiency and stability implies the segregation of individuals (and clubs) into two groups with very different levels of club joining (and member admission) activeness and welfare. Our results provide a simple explanation for the phenomena of the “power elite” and interlocking board of directors.
Crustal seismic velocity responds to a magmatic intrusion and seasonal loading in Iceland’s Northern Volcanic Zone
Contributors: Donaldson, Clare, Winder, Tom, Caudron, Corentin, White, Robert S
... Seismic noise interferometry is an exciting technique for studying volcanoes, providing a continuous measurement of seismic velocity changes (dv/v), which are sensitive to magmatic processes that affect the surrounding crust. However, understanding the exact mechanisms causing changes in dv/v is often difficult. We present dv/v measurements over ten years in central Iceland, measured using single-station cross-component correlation functions from 51 instruments across a range of frequency bands. We observe a linear correlation between changes in dv/v and volumetric strain at stations in regions of both compression and dilatation associated with the 2014 Bárðarbunga-Holuhraun dike intrusion. Furthermore, a clear seasonal cycle in dv/v is modeled as resulting from elastic and poro-elastic responses to changing snow thickness, atmospheric pressure and groundwater level. This study comprehensively explains variations in dv/v arising from diverse crustal stresses and highlights the importance of deformation modeling when interpreting dv/v, with implications for volcano and environmental monitoring worldwide.
Contributors: Kaiser, Marcus, Jack, Robert, Zimmer, Johannes
... We consider hydrodynamic scaling limits for a class of reversible interacting particle systems, which includes the symmetric simple exclusion process and certain zero-range processes. We study a (non-quadratic) microscopic action functional for these systems. We analyse the behaviour of this functional in the hydrodynamic limit and we establish conditions under which it converges to the (quadratic) action functional of Macroscopic Fluctuation Theory. We discuss the implications of these results for rigorous analysis of hydrodynamic limits.
Contributors: Zmigrod, Leor
... The collective ideologies of the 20th and 21st century have illustrated the horrifying scale of human atrocities that can be committed in the name of ideological groups and causes. While philosophers and historians have developed rich accounts of the societal factors shaping the forces behind participation in collective ideologies, there has been remarkably little rigorous scientific investigation into the cognitive and neural factors that can increase an individual’s susceptibility to ideological dogmatism and extremism. The aim of the current doctoral research was therefore to examine what psychological traits make some individuals more vulnerable to ideological thinking than others. Theory-driven and data-driven approaches were employed to map out the cognitive underpinnings of ideological thinking. A series of large online studies encompassing over 1,500 participants revealed that ideological rigidity may be rooted in cognitive rigidity, such that the rigidity with which individuals process and evaluate neutral stimuli predicts the rigidity and extremity of their ideological beliefs. This relationship was corroborated across multiple ideological domains, including nationalism, religion, political partisanship, dogmatism, and extremist attitudes, uncovering a tight link between low-level perceptual processes and high-level ideological attitudes. Furthermore, a data-driven approach using Bayesian analyses was adopted to study the cognitive and personality signatures of political conservatism, nationalism, religiosity, and dogmatism. This exposed that psychological dispositions can predict ideological attitudes substantially better than traditional demographic variables, challenging the dominant perspective in the social sciences that socioeconomic indicators are the most powerful predictors of how citizens vote and what they believe. This research program therefore suggests that ideological attitudes are amenable to careful cognitive and computational analysis. The findings signify that individual differences in our cognitive dispositions may underpin the intensity of our ideological adherence – and so a rigorous scientific study of the ideological mind may illuminate pertinent societal questions facing modern democracies.
Contributors: Hung, Johnathan Man Chiu
... The $\Lambda$CDM model for the Universe is highly successful in explaining cosmological observations to date, and its parameters tightly constrained by Cosmic Microwave Background (CMB) experiments such as Planck. Higher-order statistics, like the three-point correlation function or bispectrum in Fourier space, will be indispensable for furthering our understanding of the Universe. While these methodologies have been developed over the years and applied to CMB analyses, similar work on large-scale structure is still in its infancy. Additionally, information from future galaxy surveys such as LSST and Euclid will soon exceed that available from the CMB, demonstrating a pressing need for such tools. The theoretical modelling of non-linear gravitational interactions is difficult beyond the perturbative regime, necessitating large, expensive $N$-body dark matter simulations to understand the small-scale dynamics. Additionally, the direct numerical computation of the matter bispectrum is intractable due to the multiplicity of triangular configurations. In this Thesis, we make breakthroughs in both of these problems. First, we present the newly rewritten MODAL-LSS formalism that enables efficient and optimal estimation of the full bispectrum for any matter density field to unprecedented accuracy, as well as demonstrating rapid convergence which makes it ideal for the analysis of large datasets. This has allowed us to benchmark fast dark matter codes (e.g. particle-mesh or L-PICOLA) against GADGET-3 using the bispectrum, showing quantitatively how the mismatch at large $k$ can be improved with a simple boosting technique in the power spectrum. We have also estimated the non-Gaussian contribution to the dark matter bispectrum covariance, which cannot be computed analytically in the non-linear regime. This will be vital for the extraction of cosmological parameters from data in the future. In preparation for the analysis of future galaxy datasets we have also investigated the non-trivial problem of linking the underlying dark matter density field to the observed galaxy distribution. As an important milestone we have investigated the effects of the halo profile, the Halo Occupation Distribution (HOD) model, and multivariate assembly bias models of the halo occupation and concentration on the power spectrum and full bispectrum of a subhalo catalogue derived from the ROCKSTAR halo finder. These fast, phenomenological methods allow us to pave the way for the efficient generation of mock galaxy catalogues.