VictorJuiz/thesis huggingface dataset:
Molecular engineering with endohedral fullerenes: towards solid-state molecular qubits
Keywords: nanomaterials
quantum nanoscience
fullerenes
quantum computing

Information processors that harness quantum mechanics may be able to outperform their classical counterparts at certain tasks. Quantum information processing (QIP) can utilize the quantum mechanical phenomenon of entanglement to implement quantum algorithms. Endohedral fullerenes, where atoms, ions or clusters are trapped in a carbon cage, are a class of nanomaterials that show great promise as the basis for a solid-state QIP architecture. Some endohedral fullerenes are spin–active, and offer the potential to encode information in their spin-states. This thesis addresses the challenges of how to engineer the components of a scalable QIP architecture based on endohedral fullerenes. It focuses on the synthesis and characterization of molecules which may, in the future, permit the demonstration of entanglement; the optical read-out of quantum states; and the creation of quasi-one-dimensional molecular arrays.
Due to its long spin decoherence time, N@C60 is the selected as the basic molecular unit for ‘coupled’ fullerene pairs, molecular systems for which it may be possible to demonstrate entanglement. To this end, isolated fullerene pairs, in the form of spin-bearing fullerene dimers, are created. This begins with the processing of N@C60 at the macroscale and leads towards the synthesis of 15N@C60-15N@C60 dimers at the microscale. High throughput processing is introduced as the most efficient technique to obtain high purity N@C60 on a reasonable timescale. A scheme to produce symmetric and asymmetric fullerene dimers is also demonstrated. EPR spectroscopy of the dimers in the solid-state confirms derivatization, whilst permitting the modelling of spin–spin interactions for 'coupled' fullerene pairs. This suggests that the optimum inter–spin separation for which to observe spin–spin coupling in powders is circa 3 nm.
Motivated by the properties of the trivalent erbium ion for the optical detection of quantum states, optically–active erbium–doped fullerenes are also investigated. These erbium metallofullerenes are synthesized and isolated as individual isomers. They are characterized by low temperature photoluminescence spectroscopy, emitting in the infra- red at a wavelength of 1.5 μm. The luminescence is markedly different where a C2 cluster is trapped alongside the erbium ions in the fullerene cage. Er2C2@C82 (isomer I) exhibits emission linewidths that are comparable to those observed for Er3+ in crystals. Finally, the discovery of a novel praseodymium-doped fullerene is reported. The balance of evidence favours the structure being assigned as Pr2@C72. This novel endohedral fullerene forms quasi-one-dimensional arrays in carbon nanotubes, which is a useful proof-of-principle of how a scaled fullerene-based architecture may be achieved.
The evolutionary dynamics of neutral networks: lessons from RNA
Keywords: adaptive
map
RNA folding
point mutation
phenotype
simulation
genotype
landscape
fitness
epistasis

The evolutionary options of a population are strongly influenced by the avail- ability of adaptive mutants. In this thesis, I use the concept of neutral networks to show that neutral drift can actually increase the accessibility of adaptive mu- tants, and therefore facilitate adaptive evolutionary change. Neutral networks are groups of unique genotypes which all code for the same phenotype, and are connected by simple point mutations. I calculate the size and shape of the networks in a small but exhaustively enumerated space of RNA genotypes by mapping the sequences to RNA secondary structure phenotypes. The qual- itative results are similar to those seen in many other genotype–phenotype map models, despite some significant methodological differences. I show that the boundary of each network has single point–mutation connections to many more phenotypes than the average individual genotype within that network. This means that paths involving a series of neutral point–mutation steps across a network can allow evolution to adaptive phenotypes which would otherwise be extremely unlikely to arise spontaneously. This can be likened to walking along a flat ridge in an adaptive landscape, rather than traversing or jumping across a lower fitness valley. Within this model, when a genotype is made up of just 10 bases, the mean neutral path length is 1.88 point mutations. Furthermore, the map includes some networks that are so convoluted that the path through the network is longer than the direct route between two sequences. A minimum length adaptive walk across the genotype space usually takes as many neutral steps as adaptive ones on its way to the optimum phenotype. Finally I show that the shape of a network can have a very important affect on the number of generations it takes a population to drift across it, and that the more routes between two sequences, the fewer generations required for a population to find an advantageous sequence. My conclusion is that, within the RNA map at least, the size, shape and connectivity of neutral networks all have a profound effect on the way that sequences change and populations evolve, and by not considering them, we risk missing an important evolutionary mechanism.
Deep brain surgery for pain
Keywords: Blood Pressure
Thalamus
Amputation
Brachial Plexus Injury
Local Field Potentials
Deep Brain Stimulation
Single Photon Emission Tomography
Opioids
Pain
Periaqueductal Grey
Heart Rate Variability

Deep brain stimulation (DBS) is a neurosurgical intervention now established for the treatment of movement disorders. For the treatment of chronic pain refractory to medical therapies, several prospective case series have been reported, but few centres worldwide have published findings from patients treated during the last decade using current standards of technology. This thesis seeks to survey the current clinical status of DBS for pain, investigate its mechanisms and their interactions with autonomic function, its clinical limitations and ablative alternatives.
Presented first is a review of the current status of analgesic DBS including contemporary clinical studies. The historical background, scientific rationale, patient selection and assessment methods, surgical techniques and results are described. The clinical outcomes of DBS of the sensory thalamus and periventricular / periaqueductal grey (PAVG) matter in two centres are presented including results from several pain and quality of life measures.
A series of translational investigations in human subjects receiving DBS for pain elucidating mechanisms of analgesic DBS and its effects upon autonomic function are then presented. Single photon emission tomography comparing PAVG, VP thalamus and dual target stimulation is described. Somatosensory and local field potential (LFP) recordings suggesting PAVG somatotopy are shown. ABPM results demonstrating changes with PAVG DBS are given and Portapres studies into heart rate variability changes with ventral PAVG DBS are detailed. Investigations using naloxone are then shown to hypothesise separate dorsal opioidergic and ventral parasympathetic analgesic streams in the PAVG. Finally, cingulotomy in lung cancer to relieve pain and dyspnoea results are discussed in the context of altering pain and autonomic function by functional neurosurgery. Pain and autonomic interactions and mechanisms in deep brain surgery for pain are then discussed alongside its limitations with proposals made for optimising treatment and improving outcomes.
Intracellular delivery of radioimmunoconjugates that target the cancer testis antigen, NY-ESO-1 
Keywords: protein transfection reagent
NY-ESO-1
cell penetrating peptide
radioimmunoconjugates
Cancer Testis Antigen

Cancer testis antigens (CTA) represent attractive targets for targeted radiotherapy and imaging as their expression is restricted to cancer and germ cells. NY-ESO-1, a member of the CTA family, is highly immunogenic and expressed in multiple tumor types including carcinoma of bladder, liver lung. The aim of this study was to develop radioimmunoconjugates (RIC) to target NY-ESO-1 protein in cancer cells. Anti-NY-ESO-1 antibodies were modified by addition of DTPA for 111In-labelling or, in the presence of Iodogen, were 123I-labelled. Delivery of radiolabeled immunoconjugates across the cell membrane was achieved using a protein transfection (PT) reagent (SAINT-PhD) and by chemical linkage with the cell-penetrating and nuclear-localizing peptide, TAT (YGRKKRRQRRR). Cellular internalization, distribution and efflux of 111In-DTPA-anti-NY-ESO-1-TAT-PT and 123I-anti-NY-ESO-1-TAT-PT were investigated in cell fractionation and retention assays. It was shown that protein transfection reagent has promoted the cellular uptake of RICs into SK-MEL-37 and both of 111In-DTPA-anti-NY-ESO-1-TAT-PT and 123I-anti-NY-ESO-1-TAT-PT was retained longer in SK-MEL-37 cells in comparison to their isotope control RIC. In clonogenic assays, 111In-DTPA-anti-NY-ESO-1-TAT-PT significantly reduced surviving fraction of SK-MEL-37 cells. Cytotoxicity was inversely proportional to specific activity and the concentration of cells exposed to 111In-DTPA-anti-NY-ESO-1-TAT-PT. siRNA knock down of NY-ESO-1 resulted in partial reversal of 111In-DTPA-anti-NY-ESO-1-TAT-PT associated cytotoxicity. These promising results obtained from the in vitro study has brought the probe further into in vivo study. In preliminary biodistribution studies in SK-MEL-37 xenograft-bearing mice, tumour:muscle ratio for 111In-DTPA-anti-NY-ESO-1-TAT-PT was statistically significant compared to the control RIC 48 h post injection. This clearly indicated that the probe can be delivered into tumour in in vivo model and the successful uptake of radioactivity increased the chance of causing cytotoxicity to tumour cells through DNA damage. All of these findings have suggested that intracellular cancer associated antigen NY-ESO-1 can be reached by protein transfection reagent and cell penetrating peptide and initiates DNA damage through radio-isotope mediated cytotoxicity. Therefore, it represents a novel approach to the treatment of CTA-expressing cancers.
The applications of artificial intelligence techniques in carcinogen chemistry
Keywords: cancer
neural networks
support vector machines

Computer-based drug design is a vital area of pharmaceutical chemistry; Quantitative Structure-Activity Relationships (QSARs), determined computationally from experimental observations, are crucial in identifying candidate drugs by early screening, saving time on synthesis and in vivo testing.
This thesis investigates the viability and the practicalities of using Mass Spectra-based pseudo-molecular descriptors, in comparison with other molecular descriptor systems, to predict the carcinogenicity, mutagenicity and the Cltransport inhibiting ability of a variety of molecules, and in the first case, of chemotherapeutic drugs particularly. It does so by identifying a number of QSARs which link the physical properties of chemicals with their concomitant activities in a reliable and mathematical manner.
First, this thesis confirms that carcinogenicity and mutagenicity are indeed predictable using a variety of Artificial Intelligence techniques, both supervised and unsupervised, information germane to pharmaceutical research groups interested in the preliminary screening of candidate anti-cancer drugs.
Secondly, it demonstrates that Mass Spectral intensities possess great descriptive fidelity and shows that reducing the burden of dimensionality is not only important, but imperative; selecting this smaller set of orthogonal descriptors is best achieved using Principal Component Analysis as opposed to the selection of a set of the most frequent fragments, or the use of every peak up to a number determined by the boundaries of supervised learning.
Thirdly, it introduces a novel system of backpropagation and demonstrates that it is more efficient than its principal competitor at monitoring a series of connection weights when applied to this area of research, which requires complex relationships.
Finally, it promulgates some preliminary conclusions about which AI techniques are applicable to certain problem-scenarios, how these techniques might be applied, and the likelihood that that application will result in the identification of series of reliable QSARs.
Society and its outsiders in the novels of Jakob Wassermann
Keywords: Jewish Studies
German society
sainthood
outsider
Wassermann
Weimar Republic
homosexuality
child

This thesis looks at a number of Jakob Wassermann’s novels and the ways in which society is depicted in them. Seen as a whole, Wassermann’s oeuvre can therefore be understood as an attempt to portray (mostly) German society at different historical stages. The periods in question are Biedermeier Germany, the Wilhelmine era, the years of the Great War and finally the Weimar Republic, the depiction of all of which reveal Wassermann as a fierce critic of his time. In addition to this interest in society, this thesis will examine Wassermann’s concern with various outsider figures which complement his portrayals of society. The outsider figures Wassermann seems to be mostly interested in are the Jew, the woman, the child and the homosexual man. However, Wassermann is not just interested in these outsiders on their own but also draws extensive parallels between the various forms of exclusion they experience in a society dominated by the Gentile man or, as in the case of the child, by the adult. These parallels have proven to be revelatory and have led to new insights into Wassermann’s works. The dynamic of the outsider vs. society is, however, in many ways no longer applicable to those novels written during and after the Great War. Instead Wassermann now combines his interest in the figure of the outsider with an interest in the depiction of character. At the same time character becomes a mirror not only for the society Wassermann portrays in his writing but also for the society he lived in. This makes for an altogether more complex but also more intriguing structure of his later writing. This thesis will examine how all these different elements when combined offer new ways of looking at Wassermann’s writing.
Parallel algorithms for free and associative-commutative unification
Keywords: parallel processing
complexity
unification
algorithms
automated deduction

A survey of algorithms for free unification is given, followed by an overview of the computability and complexity of unification problems. Second-order unification is known to be undecidable, and a proof is given that the first-order problem is also undecidable under an arbitrary set of axioms. A new systolic algorithm is introduced for term minimisation or term compaction. This is a general-purpose tool for systems using structure sharing. Apart from time and space savings, its use allows subterms to be tested for equality in constant time. The use of compact terms greatly simplifies free term matching and gives rise to a linear-time algorithm with lower processing overheads than the Paterson-Wegman unification algorithm. A sublinear-time solution to the same problem is also given, assuming preloaded data. No existing algorithm for free unification has a sublinear-time implementation and this is related to the notion of a sparse P-complete problem. The complexity of restricted associative-commutative term matching is analysed. Contrary to an earlier conjecture the problem is NP-complete if variables occur at most twice but their number is unrestricted. Parallel methods are suggested as efficient solutions for the | tractable | linear and 1-variable versions of the problem. Results presented here should be useful in the implementation of fast symbolic ma- nipulation systems.
Computer vision for the analysis of cellular activity
Keywords: fluorescence microscopy
temporal models
Computer vision

In the field of cell biology, there is an increasing use of time-lapse data to understand cellular function. Using automated microscopes, large numbers of images can be acquired, delivering videos of cell samples over time. Analysing the images manually is extremely time consuming as there are typically thousands of individual images in any given sequence. Additionally, decisions made by those analysing the images, e.g. labelling a mitotic phase (one of a set of distinct sequential stages of cell division) can be subjective, especially around transition boundaries between phases, leading to inconsistencies in the annotation. There is therefore a need for tools which facilitate automated high-throughput analysis.
In this thesis we develop systems to automatically detect, track and analyse sub-cellular structures in image sequences to address biological research needs in three areas: (i) Mitotic phase labelling, (ii) Mitotic defect detection, and (iii) Cell volume estimation.
We begin by presenting a system for automated segmentation and mitotic phase labelling using temporal models. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather than over single frames, thereby capturing the distinctive behaviour over the phases. We compare and contrast three different temporal models: Dynamic Time Warping, Hidden Markov Models, and Semi Markov Models. A new loss function is proposed for the Semi Markov model to make it more robust to inconsistencies in data annotation near transition boundaries.
We then present an approach for detecting subtle chromosome segregation errors in mitosis in embryonic stem cells, targeting two cases: misaligned chromosomes in a metaphase cell, and lagging chromosomes between anaphase cells. We additionally explore an unsupervised approach to detect unusual mitotic occurrences and test its applicability to detecting misaligned metaphase chromosomes.
Finally, we describe a fully automated method, suited to high-throughput analysis, for estimating the volume of spherical mitotic cells based on a learned membrane classifier and a circular Hough transform. We also describe how it is being used further in biological research.
The effect of inflammation on the central  5-HT system in mood disorders
Keywords: 5-HT
cytokines
inflammation
depression

Inflammation appears to play a major role in the pathogenesis of Major Depression, and understanding the relationship between the immune system and mood disorders is a growing area of research. Patients undergoing cytokine therapy frequently develop depressive-like signs which cease upon termination of treatment. In addition, depressed patients often present with high levels of circulating pro-inflammatory cytokines. There are also significant behavioural correlates between inflammation-associated sickness behaviours and Major Depression, suggesting there is a bidirectional relationship between the immune system and central systems regulating behaviour. Both sickness behaviours in animals and cytokine-induced depression in humans can be reversed with antidepressants targeting the serotonergic (5-HT) system. Against this background, this thesis sought to investigate the role of inflammation in the development of certain specific characteristics of depressive-like states in animal models. Specifically, a single systemic endotoxin challenge (LPS) was used to model acute sickness behaviour and to study the behaviour and molecular effects of this challenge on the 5-HT system. These studies indicated that a single systemic challenge changed the function of the 5-HT system, as well as increasing expression levels of a number of 5-HT-related genes, and markers of inflammation in the CNS. A rodent chronic stress paradigm was employed to investigate whether these changes also occur in animals displaying depressive-like signs. Chronically stressed mice were shown to display significant features of rodent depression; decreased sucrose preference and increased forced swim immobility. These changes were also accompanied by increased expression of 5-HT-related genes and inflammatory markers within the CNS. Importantly, many of the molecular changes observed in LPS-induced sickness behaviour were conserved in the model of stress-induced Major Depression. In particular, increased TNF expression within the CNS was highlighted as a feature in both models. Novel anti-inflammatory agents were used to study the role of TNF in the development of sickness behaviours. Anti-TNF therapy, specifically the fusion protein etanercept, ameliorated sickness behaviour induced by LPS. Together, these data demonstrate that inflammation is capable of significantly changing the 5-HT system, and that stress per se is capable of inducing an inflammatory state within the CNS, which support the thesis that sickness and Major Depression are closely related at a molecular level.
Adiposity and diabetes in China: the China Kadoorie Biobank study of 500,000 men and women
Keywords: OBESITY; DIABETES; ADIPOSITY

Despite the rapid increase in both adiposity and diabetes in China, substantial uncertainty remains about the relationship between these two conditions in the population. Using data from the China Kadoorie Biobank study of 0.5 million adults recruited during 2004-8 from 10 diverse areas of China, this thesis examines the associations of different adiposity measures (overall adiposity: BMI and percentage body fat; central adiposity: waist circumference, waist-hip ratio, and waist-height ratio) with type 2 diabetes prevalence and incidence. To assess the quality of diagnosis, a separate event-verification study was conducted in ~1,000 reported diabetes cases.
Overall at baseline, the mean age of the analysed participants was 52 years, 41% were men, 32% had a BMI≥25 kg/m2 (4% ≥30 kg/m2) and 5.2% had self-reported or screen-detected diabetes. Both cross-sectional and prospective analyses of well-characterised diabetic cases (26,622 prevalent and 2,910 incident cases) showed that adiposity is strongly positively associated with diabetes (p<0.0001), throughout all or most of the distribution of each adiposity measure. Per 1 SD higher adiposity measure, measures of central adiposity were associated with ~90% increased risk, compared with ~80-85% increased risk for general adiposity measures. Among measures of central adiposity, waist-hip ratio was the most strongly associated with diabetes prevalence, whereas waist circumference was the most predictive of diabetes incidence. Although measures of central adiposity were the most strongly associated with diabetes risk, there was still a strong positive association with measures of general adiposity after adjusting for central adiposity (p<0.0001), and the combination of both types of measure improved risk prediction. Given waist circumference, hip circumference was inversely associated with both diabetes prevalence and incidence (p<0.0001). For many of the above associations, there was possible effect modification by age and sex. These findings will provide important and reliable evidence to quantify the level of diabetic risk associated with adiposity, hence to inform clinical interventional strategies and future public health programmes.