5,924 research outputs found

    The Metabolic Impact of Two Different Parenteral Nutrition Lipid Emulsions in Children after Hematopoietic Stem Cell Transplantation: A Lipidomics Investigation

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    This research was funded by the "Salud Investiga Modalidad Joven 2010" award from the Junta de Andalucia, Spain and CIBERobn.Hematopoietic stem cell transplantation (HSCT) involves the infusion of either bone marrow or blood cells preceded by toxic chemotherapy. However, there is little knowledge about the clinical benefits of parenteral nutrition (PN) in patients receiving high-dose chemotherapy during HSCT. We investigated the lipidomic profile of plasma and the targeted fatty acid profiles of plasma and erythrocytes in children after HSCT using PN with either a fish oil-based lipid emulsion or a classic soybean oil emulsion. An untargeted liquid chromatography high-resolution mass spectrometry platform connected with a novel in silico annotation algorithm was utilized to determine the most relevant chemical subclasses affected. In addition, we explored the interrelation between the lipidomics profile in plasma, the targeted fatty acid profile in plasma and erythrocytes, several biomarkers of inflammation, and antioxidant defense using an innovative data integration analysis based on Latent Components. We observed that the fish oil-based lipid emulsion had an impact in several lipid subclasses, mainly glycerophosphocholines (PC), glycerophosphoserines (PS), glycerophosphoethanolamines (PE), oxidized PE (O-PE), 1-alkyl,2-acyl PS, lysophosphatidylethanolamines (LPE), oxidized PS (O-PS) and dicarboxylic acids. In contrast, the classic soybean oil emulsion did not. Several connections across the different blocks of data were found and aid in interpreting the impact of the lipid emulsions on metabolic health.Junta de Andalucia European CommissionCIBERob

    From algorithms to connectivity and back: finding a giant component in random k-SAT

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    We take an algorithmic approach to studying the solution space geometry of relatively sparse random and bounded degree kk-CNFs for large kk. In the course of doing so, we establish that with high probability, a random kk-CNF Φ\Phi with nn variables and clause density α=m/n2k/6\alpha = m/n \lesssim 2^{k/6} has a giant component of solutions that are connected in a graph where solutions are adjacent if they have Hamming distance Ok(logn)O_k(\log n) and that a similar result holds for bounded degree kk-CNFs at similar densities. We are also able to deduce looseness results for random and bounded degree kk-CNFs in a similar regime. Although our main motivation was understanding the geometry of the solution space, our methods have algorithmic implications. Towards that end, we construct an idealized block dynamics that samples solutions from a random kk-CNF Φ\Phi with density α=m/n2k/52\alpha = m/n \lesssim 2^{k/52}. We show this Markov chain can with high probability be implemented in polynomial time and by leveraging spectral independence, we also observe that it mixes relatively fast, giving a polynomial time algorithm to with high probability sample a uniformly random solution to a random kk-CNF. Our work suggests that the natural route to pinning down when a giant component exists is to develop sharper algorithms for sampling solutions in random kk-CNFs.Comment: 41 pages, 1 figur

    Surrogate modelling strategies for the prediction of near-field blast impulse

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    The detonation of a high explosive results in the rapid release of energy as the explosive charge undergoes a rapid change in state and is converted into a high pressure, high temperature gas. As the gas expands, the surrounding air is displaced, resulting in a high pressure shock discontinuity -- a shock wave. As this shock wave propagates away from the charge, it can cause severe damage to any structure that it impacts on. Structural blast engineers are tasked with designing infrastructure in a way that it is robust enough to withstand extreme loading, whilst dealing with several constraints such as time, cost and space. Due to the variability in initiation conditions (such as charge shape, charge location, chemical composition of charge and localised point of detonation), and the subsequent variability in loading produced, it becomes impractical to perform numerical simulations or experiments for all possible scenarios, though an understanding of the loading is required to accurately model structural response. Predictive models are therefore required that can predict the blast load parameters of interest (impulse) given certain input parameters that are fast to run and accurate -- predictive models such as these are known as surrogate models. The blast protection community, when tasked with assessing the viability and safety of structures (the structural response), need an accurate picture of what exactly the loading is. This loading information is composed of both a magnitude and location of a load across a structure, and therefore any predictive approach must predict both these constituent parts of the loading. However, obtaining this loading information, especially within a blast engineering context when the distance between a charge and target is small, is expensive and physical or numerical experiments are costly in both time and money. Current predictive approaches are severely limited in this regard, in that they do not provide sufficient accurate information, nor are they flexible to handle more than the most simple scenarios. This thesis proposes strategies for surrogate model development in a blast protection engineering context, that allow the rapid evaluation of structural load, given input conditions for a range of scenarios. Furthermore, this thesis demonstrates three applications of strategies that increase the utility of data and knowledge already obtained, that address the fundamental issue of data being expensive to obtain. To achieve this end three approaches are presented: firstly, data transformation procedures, that reduce the dimensionality of the data enabling the use of simpler surrogate models; secondly, the use of directly including known physics into the objective function when model training as a regularisation procedure; and finally, implementing transfer learning by embedding learned knowledge into the architecture of a neural network. These three applications provide statistically significant improvements to model performance and training efficiency, and provide justification to their use in surrogate modelling strategies generally within blast protection engineering. The results of this thesis should be used to guide surrogate model development for the prediction of peak specific impulse in the near-field for spherical and cylindrical charges. It presents frameworks for creating surrogate models and demonstrates how prior knowledge can be used to improve the performance of surrogate models, or the efficiency when training surrogate models in a new domain, and thereby drastically reducing the need for new data to be obtained. It is shown extensively that machine learning methods can reliably be used in surrogate model development. The findings presented within this thesis have the potential to be implemented into load prediction software which would be of great utility to the blast protection community and insurance industry

    Group field theory cosmology

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    This thesis explores the cosmological sector of group field theory (GFT) which is a proposal for a theory of quantum gravity. We provide an overview of GFT and introduce the framework necessary to study cosmology in the context of GFT. We discuss two approaches to GFT cosmology in detail. Firstly, we study the canonical approach where one implements the canonical quantisation program for a GFT coupled to one or more massless scalar fields. The main result is that we are able to derive equations for the volume which take the same functional form as the Friedmann equations from classical cosmology in certain limits. Secondly, we employ a certain method of studying quantum systems with constraints in the context of GFT cosmology. We accomplish this by identifying a subset of one-body operators of the GFT with the classical observables. The resulting dynamics allow us to identify one of the operators with the extrinsic curvature of classical cosmology

    Network based analysis to identify master regulators in prostate carcinogenesis

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    Prostate cancer (PCa) is the second most common tumor diagnosed in man, for which robust prognostic markers and novel targets for therapy are lacking. Major challenges in PCa therapeutical management arise from the marked intra and inter-tumors heterogeneity, hampering the discernment of molecular subtypes that can be used to guide treatment decisions. For this reason, virtually all patients undergoing standard of care androgen deprivation therapy for locally advanced or metastatic cancer, will eventually progress into the more aggressive and currently incurable form of PCa, referred to as castration resistant prostate cancer (CRPC). By exploiting the richness of information stored in gene-gene interactions, I tested the hypothesis that a gene regulatory network derived from transcriptomic profiles of PCa orthografts can reveal transcriptional regulators to be subsequently adopted as robust biomarkers or as target for novel therapies. Among the 1308 regulons identified from the preclinical models, Cox regression analysis coherently associated JMJD6 regulon activity with disease-free survival in three clinical cohorts, outperforming three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). Given its potential role in a number of cancers, in-depth investigations of JMJD6 mediated function in PCa is warranted to test if it has a driver role in tumor progression. Encouraged by the predictive abilities of the gene regulatory network inferred from transcriptomics data, I explored the possibility of integrating the regulons structure with data from the proteomes of the same preclinical orthografts studied by RNA sequencing. This approach leverages the complementarity between gene and protein expression, to increase the robustness of the statistical analysis. Similar to gene-gene co-expression profiles, protein-protein co-expression data can provide a distinct representation of the molecular alterations underlying a biological phenotype. By implementing a pipeline to integrate modules derived from transcriptomic based regulons and proteinprotein interactions respectively from matched RNA-seq and quantitative proteomic data, I obtained 516 joint modules entailing a median of four protein complexes (range 1-41) per individual transcription factor regulon, providing new insight into its regulatory mechanisms. In the final step of the analysis, a permutation-based enrichment of the genes/proteins integrative modules implicated MID1 (an E3 ubiquitin ligase belonging to the family of tripartite motif containing protein) to be a driver transcriptional regulator in CRPC. In fact, MID1 module was the only candidate for which gene-gene and proteinprotein interactions were supported (p-value < 0.05) by both differentially expressed genes and proteins obtained from the CRPC vs PC contrast. Finally, I wished to test the usefulness of a network based investigation as a tool to identify predictors of treatment response. To this end, I obtained transcriptomics data from an in vivo subcutaneous xenograft treatment experiment (namely mychophenolic acid or abiraterone/ARN-509 as stand alone treatment or in combination) and determined which regulons were inferred to be active in the tumours following treatment. The androgen receptor positive human LNCaP C4-2b prostate cancer cells were injected into mice. The effects of treatment were assessed by collecting serial tumor sizes and by performing RNAseq at the designed endpoint of the study. Noteworthy, the gene graph enrichment analysis provided novel hypothesisbehind the anti- proliferative effect of mychophenolic acid (MPA), suggesting the SET proto-oncogene to be a target for MPA mediated suppression of proliferation. Of note, standard gene-set enrichment analysis, without input on specific gene-gene interactions, was not effective in prioritising the SET protooncogene, demonstrating the usefulness of the network based investigation. Collectively, data presented in this thesis provides an alternative perspective for the analysis of multi-omics profiles from PCa and highlights the importance of gene-gene and protein protein interactions in prostate cancer growth and progression

    Development of small, engineered heart tissues and their acute response to implantation

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    Introduction: Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are a great source of human cardiac cells and can be combined with biomaterials to form engineered heart tissues (EHTs). Implantation of EHTs into the myocardium after a myocardial infarction (MI) is a promising strategy to regenerate the scar area. However, the ongoing challenge is the lack of electrical and mechanical coupling of the graft to the host tissue. Moreover, to replace the number of lost cells after an MI, current EHTs are large (cm-scale) and consist of >107 cells, resulting in extremely high costs. Therefore, the aim of this study was to develop a cost-efficient platform based on the implantation of small EHTs (<50,000 hiPSC-CMs) grafted into the rabbit myocardium in vitro, and subsequently to assess the electrophysiological adaptations of the hiPSC-CMs to the surrounding myocardium using of fluorescent indicators. Methods: The EHTs (mm-scale) consisted of commercially available hiPSC-CMs (45.000 cells) seeded on top of a recombinant collagen-like peptide hydrogel (22 kPa; 6 mm diameter; 350 μm thick). Excitation-contraction coupling (ECC) properties of the EHTs were assessed in vitro. Adult male rabbits were euthanized and, subsequently, the heart was excised and placed on a Langendorff rig. EHTs were stained a fluorescent calcium indicator prior to implantation to track their activity. Calcium traces of the EHT and the ECG of the rabbit heart were compared during analysis. Results: Incorporation of fibronectin into the hydrogel improved cell adhesion and resulted in viable EHTs for at least 7 days. EHTs were not able to follow high pacing frequencies comparable to the intrinsic rate of the rabbit heart, most likely because of slower calcium handling. HiPSC-CMs seeded on the hydrogel showed an improved (“twitch”-like) contraction profile in comparison to cells seeded on rigid matrixes that showed a multi-phasic time-course. Once implanted, EHTs were viable for at least 90 min and calcium transients could be recorded for this time, however, there were no signs of electrical coupling during the experiment. Conclusion: With this work, a novel ex vivo platform has been developed to investigate functionality of implanted cells within the myocardium in the acute phase post-implantation. This platform can be used to investigate many factors that might affect the integration and survival of implanted cells during the acute phase in a cost-effective way and is therefore a good intermediate step between in vitro and in vivo experiments

    Novel applications of structured light in the field of atom optics. Imagining optical magnetometry with images

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    Complex vectorial light fields offer unprecedented information capacity and flexibility to design light potentials with correlated or multiplexed intensity, phase and polarisation structures. In recent years many different new techniques and technological devices have been developed with the goal of allowing for the efficient generation of these fields with maximum flexibility and reliability. During my studies I employed some representatives of these newer techniques, as the light fields involved in the experiments were generated with setups containing equipment such as Digital Micromirror Devices (DMDs), Spatial Light Modulators (SLMs) and Q-plates, all of which can be regarded as being on the forefront of the development in the world of structured optics. The main beneficiaries of this "expansion" in the generation of light fields are topics traditionally linked to optics such as microscopy, imaging and spectroscopy. However, another field that could benefit from these newer applications is atom optics. In fact, the interaction between atoms and light is vectorial in nature, as it is manifest in the electric dipole coupling which is the principal avenue of interaction between them. Main consequence of this kind of interaction are the appearance of non-linear behaviours, even for light parameters associated to a semiclassical description, e.g. coherent light from a laser source. The principal investigation during my Ph.D. can be regarded as one of the tentative efforts to introduce the innovation of structured light and atom optics, since I have focused my efforts around the experimental study of the mutual interaction of a cloud of cold rubidium atoms with a vectorial light field, carrying Orbital Angular Momentum (OAM) in the presence of a magnetic field. The main goal of the study was to describe and demonstrate how 3D magnetic field alignment can be inferred from single absorption images of an atomic cloud. The atomic cloud was prepared in a particular state of density, temperature and population distribution with the employ of a Magneto Optical Trap (MOT) first, and Spontaneous Force Optical Trap (SpOT) second, which are widely utilized techniques in the world of atom optics. Then a vector vortex beam was used to interrogate the magnetic spin states population of the atoms cloud. In fact due to the relative position between the local light polarisation, which varies in the beam, and the magnetic field direction, fixed for the whole atomic sample, the absorption of the light would be affected. By varying the magnetic field inclination or azimuthal angles, the absorption pattern would vary as well confirming the previous model developed by former PhD students. In the future it is planned to address some of the limitations that are intrinsic to the selected method, the Q-plate, of generating the vector vortex light by switching to one of the other above mentioned SLM or DMD setups to obtain a wider selection of polarisation patterns to stimulate the atoms. Another venue of development is the translation of the whole system at room temperature, with the prospect of achieving a faster rate of repetition for the experiment at the expense of some control over the atomic medium. In addition to the atomic magnetometry experiment in Glasgow, during my PhD I have been collaborating on other projects with various other physics group both within the same University of Glasgow and in the wider optics fields worldwide. The most relevant of these has been the European Training Network (ETN) called Collective Effects and Optomechanics in Ultra-cold Matter (ColOpt), which is the main funder of my PhD position

    The Resilience and Sustainability of Suranga Irrigation in the Western Ghats of India

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    This study focused on a little known traditional water management system, known as suranga, historically used by marginalised agricultural communities in the remote foothills of the Western Ghats in India to evaluate the resilience and sustainability of the suranga system. A hill irrigation analytical framework was used to provide a pragmatic epistemology. The research methodology was interdisciplinary, incorporating mixed methods taken from both the physical and social sciences to answer five research questions about suranga linked to their history, distribution, design principles, operational characteristics, governance, and organisation. Results suggest that suranga originate from the early 20th century. A field survey, supported by in-depth interviews of suranga users (n=173), found 700 suranga mainly distributed in fourteen villages in the Dakshin Kannada and Kasaragod districts. Data from previous studies, including this study, suggest there are a minimum of ~3000 suranga in the region as a whole. Suranga were defined as a groundwater collection gallery filtration tunnel system sourced from perched aquifers. Key strengths of the system were found to be the basic design principles, flexible excavation approaches, adaptability, clear use boundaries, relatively low construction and maintenance costs, self-regulated discharge, private ownership and management, and ease of access. Weaknesses of the system were a laborious and risky excavation process, limited water yield, non-collaboration, the absence of governance, and low earnings for suranga workers. Suranga were also found to be vulnerable to pollution, forest cover loss, and the impacts of climate change. However, suranga have contributed to a resilient and sustainable community in the past when the population, water demands, and the size of the irrigated area were low, and farm choices were limited. Currently, the suranga system may soon be unable to meet increased water demands because of population increase, intensification and reorientation of agriculture, alternative borewell technology and improved socioeconomic conditions. However, Suranga do retain some humanitarian relevance to farmers in the study area having improved the quality of life for many low-income families, but new emerging endogenous and exogenous pressures may make them vulnerable to changes in the future that cause the collapse of the system unless further adaptation occurs

    A computational study of cyclic peptides with vibrational circular dichroism

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    Cyclic peptides are a class of molecules that has shown antimicrobial potential. These are complex compounds to investigate with their large conformational space and multiple chiral centers. A technique that can be used to investigate both conformational preferences and absolute configuration (AC) is vibrational circular dichroism (VCD). To extract information from the experimental VCD spectra a comparison with calculated spectra is often needed and this is the focus of this thesis: the calculation of VCD spectra. The VCD spectra are very sensitive to small structural changes, and to accurately calculate the spectra, all important conformers need to be identified. The first part of this thesis has been to establish a reliable computational protocol using meta-dynamics to sample the conformational space and ab initio methods to calculate the spectra for cyclic peptides. Using our protocol, we have investigated if VCD alone can determine the AC of cyclic tetra- and hexapeptides. We show that it is possible to determine the AC of the cyclic peptides with two chiral centers while for the peptides with three and four chiral centers, VCD is at best able to reduce the number of possible ACs and further investigation with other techniques is needed. Further, we investigated four cyclic hexapeptides with antimicrobial potential. These peptides, in contrast to the ones used for validating the protocol, consist of several amino acids with long and positively charged side chains. For these peptides, a molecular dynamics based approach provided VCD spectra in better agreement with experiment than our protocol. Reasons for this may be the lack of atomistic detail in the solvent model used during the conformational search and insufficient description of dispersion interactions during the meta-dynamics simulation
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