7,151 research outputs found
A Molecular Approach to the Diagnosis, Assessment, Monitoring and Treatment of Pulmonary Non-Tuberculous Mycobacterial Disease
Introduction: Non-Tuberculous Mycobacteria (NTM) can cause disease of the lungs and sinuses, lymph nodes, joints and central nervous system as well as disseminated infections in immunocompromised individuals. Efforts to tackle infections in NTM are hampered by a lack of reliable biomarkers for diagnosis, assessment of disease activity, and prognostication.
Aims: The broad aims of this thesis are:
1. to develop molecular assays capable of quantifying the 6 most common pathogenic mycobacteria (M. abscessus, M. avium, M. intracellulare, M. malmoense, M. kansasii, M. xenopi) and calculate comparative sensitivities and specificities for each assay.
2. to assess patients’ clinical course over 12 – 18 months by performing the developed molecular assays against DNA extracted from sputum from patients with NTM infection.
3. to assess dynamic bacterial changes of the lung microbiome in patients on treatment for NTM disease and those who are treatment na ve.
Methods: DNA was extracted from a total of 410 sputum samples obtained from 38 patients who were either:
• commencing treatment for either M. abscessus or Mycobacterium avium complex.
• considered colonised with M. abscessus or Mycobacterium avium complex (i.e. cultured NTM but were not deemed to have infection as they did not meet ATS or BTS criteria for disease).
• Diagnosed with cystic fibrosis (CF) or non-CF bronchiectasis but had never cultured NTM.
For the development of quantitative molecular assays, NTM hsp65 gene sequences were aligned and interrogated for areas of variability. These variable regions enabled the creation of species specific probes. In vitro sensitivity and specificity for each probe was determined by testing each probe against a panel of plasmids containing hsp65
gene inserts from different NTM species. Quantification accuracy was determined by using each assay against a mock community containing serial dilutions of target DNA.
Each sample was tested with the probes targeting: M. abscessus, M. avium and M. intracellulare producing a longitudinal assessment of NTM copy number during each patient’s clinical course.
In addition, a total of 64 samples from 16 patients underwent 16S rRNA gene sequencing to characterise longitudinal changes in the microbiome of both NTM disease and controls.
Results: In vitro sensitivity for the custom assays were 100% and specificity ranged from 91.6% to 100%. In terms of quantification accuracy, there was no significant difference between the measured results of each assay and the expected values when performed in singleplex. The assays were able to accurately determine NTM copy number to a theoretical limit of 10 copies/μl.
When used against samples derived from human sputum and using culture results as a gold standard, the sensitivity of the assay for M. abscessus was found to be 0.87 and 0.86 for MAC. The specificity of the assay for M. abscessus was 0.95 and 0.62 for MAC. The negative predictive value of the assay for M. abscessus was 0.98 and 0.95 for MAC. This resulted in an AUC of 0.92 for M. abscessus and 0.74 for MAC.
Longitudinal analysis of the lung microbiome using 16SrRNA gene sequencing showed that bacterial burden initially decreases after initiation of antibiotic therapy but begins to return to normal levels over several months of antibiotic therapy. This effect is mirrored by changes in alpha diversity. The decrease in bacterial burden and loss of alpha diversity was found to be secondary to significant changes in specific genera such as Veillonella and Streptococcus. The abundance of other Proteobacteria such as Pseudomonas remain relatively constant.
Conclusion: The molecular assay has shown high in vitro sensitivity and specificity for the detection and accurate quantification of the 6 most commonly pathogenic NTM species. The assays successfully identified NTM DNA from human sputum samples.
A notable association between NTM copy number and the cessation of one or more antibiotics existed (i.e. when one antibiotic was stopped because of patient intolerance, NTM copy number increased, often having been unrecordable prior to this). The qPCR assays developed in this thesis provide an affordable, real time and rapid measurement of NTM burden allowing clinicians to act on problematic results sooner than currently possible.
There was no significant difference between the microbiome in bronchiectasis and cystic fibrosis nor was there a significant difference between the microbiome in patients requiring treatment for NTM and those who did not. Patients receiving treatment experienced an initial decrease in bacterial burden over the first weeks of treatment followed by a gradual increase towards baseline over the next weeks to months. This change was mirrored in measures of alpha diversity. Changes in abundance and diversity were accounted for by decreases in specific bacteria whilst the abundance of other bacteria increased, occupying the microbial niche created. These bacteria (for example Pseudomonas spp) are often associated with morbidity.Open Acces
Statistical Learning for Gene Expression Biomarker Detection in Neurodegenerative Diseases
In this work, statistical learning approaches are used to detect biomarkers for neurodegenerative diseases (NDs). NDs are becoming increasingly prevalent as populations age, making understanding of disease and identification of biomarkers progressively important for facilitating early diagnosis and the screening of individuals for clinical trials. Advancements in gene expression profiling has enabled the exploration of disease biomarkers at an unprecedented scale. The work presented here demonstrates the value of gene expression data in understanding the underlying processes and detection of biomarkers of NDs. The value of novel approaches to previously collected -omics data is shown and it is demonstrated that new therapeutic targets can be identified. Additionally, the importance of meta-analysis to improve power of multiple small studies is demonstrated. The value of blood transcriptomics data is shown in applications to researching NDs to understand underlying processes using network analysis and a novel hub detection method. Finally, after demonstrating the value of blood gene expression data for investigating NDs, a combination of feature selection and classification algorithms were used to identify novel accurate biomarker signatures for the diagnosis and prognosis of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Additionally, the use of feature pools based on previous knowledge of disease and the viability of neural networks in dimensionality reduction and biomarker detection is demonstrated and discussed. In summary, gene expression data is shown to be valuable for the investigation of ND and novel gene biomarker signatures for the diagnosis and prognosis of PD and AD
Optimizing transcriptomics to study the evolutionary effect of FOXP2
The field of genomics was established with the sequencing of the human genome, a pivotal achievement that has allowed us to address various questions in biology from a unique perspective. One question in particular, that of the evolution of human speech, has gripped philosophers, evolutionary biologists, and now genomicists. However, little is known of the genetic basis that allowed humans to evolve the ability to speak. Of the few genes implicated in human speech, one of the most studied is FOXP2, which encodes for the transcription factor Forkhead box protein P2 (FOXP2). FOXP2 is essential for proper speech development and two mutations in the human lineage are believed to have contributed to the evolution of human speech. To address the effect of FOXP2 and investigate its evolutionary contribution to human speech, one can utilize the power of genomics, more specifically gene expression analysis via ribonucleic acid sequencing (RNA-seq).
To this end, I first contributed in developing mcSCRB-seq, a highly sensitive, powerful, and efficient single cell RNA-seq (scRNA-seq) protocol. Previously having emerged as a central method for studying cellular heterogeneity and identifying cellular processes, scRNA-seq was a powerful genomic tool but lacked the sensitivity and cost-efficiency of more established protocols. By systematically evaluating each step of the process, I helped find that the addition of polyethylene glycol increased sensitivity by enhancing the cDNA synthesis reaction. This, along with other optimizations resulted in developing a sensitive and flexible protocol that is cost-efficient and ideal in many research settings.
A primary motivation driving the extensive optimizations surrounding single cell transcriptomics has been the generation of cellular atlases, which aim to identify and characterize all of the cells in an organism. As such efforts are carried out in a variety of research groups using a number of different RNA-seq protocols, I contributed in an effort to benchmark and standardize scRNA-seq methods. This not only identified methods which may be ideal for the purpose of cell atlas creation, but also highlighted optimizations that could be integrated into existing protocols.
Using mcSCRB-seq as a foundation as well as the findings from the scRNA-seq benchmarking, I helped develop prime-seq, a sensitive, robust, and most importantly, affordable bulk RNA-seq protocol. Bulk RNA-seq was frequently overlooked during the efforts to optimize and establish single-cell techniques, even though the method is still extensively used in analyzing gene expression. Introducing early barcoding and reducing library generation costs kept prime-seq cost-efficient, but basing it off of single-cell methods ensured that it would be a sensitive and powerful technique. I helped verify this by benchmarking it against TruSeq generated data and then helped test the robustness by generating prime-seq libraries from over seventeen species. These optimizations resulted in a final protocol that is well suited for investigating gene expression in comprehensive and high-throughput studies.
Finally, I utilized prime-seq in order to develop a comprehensive gene expression atlas to study the function of FOXP2 and its role in speech evolution. I used previously generated mouse models: a knockout model containing one non-functional Foxp2 allele and a humanized model, which has a variant Foxp2 allele with two human-specific mutations. To study the effect globally across the mouse, I helped harvest eighteen tissues which were previously identified to express FOXP2. By then comparing the mouse models to wild-type mice, I helped highlight the importance of FOXP2 within lung development and the importance of the human variant allele in the brain.
Both mcSCRB-seq and prime-seq have already been used and published in numerous studies to address a variety of biological and biomedical questions. Additionally, my work on FOXP2 not only provides a thorough expression atlas, but also provides a detailed and cost-efficient plan for undertaking a similar study on other genes of interest. Lastly, the studies on FOXP2 done within this work, lay the foundation for future studies investigating the role of FOXP2 in modulating learning behavior, and thereby affecting human speech
SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS
In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures
The Neural Mechanisms of Value Construction
Research in decision neuroscience has characterized how the brain makes decisions by assessing the expected utility of each option in an abstract value space that affords the ability to compare dissimilar options. Experiments at multiple levels of analysis in multiple species have localized the ventromedial prefrontal cortex (vmPFC) and nearby orbitofrontal cortex (OFC) as the main nexus where this abstract value space is represented. However, much less is known about how this value code is constructed by the brain in the first place. By using a combination of behavioral modeling and cutting edge tools to analyze functional magnetic resonance imaging (fMRI) data, the work of this thesis proposes that the brain decomposes stimuli into their constituent attributes and integrates across them to construct value. These stimulus features embody appetitive or aversive properties that are either learned from experience or evaluated online by comparing them to previously experienced stimuli with similar features. Stimulus features are processed by cortical areas specialized for the perception of a particular stimulus type and then integrated into a value signal in vmPFC/OFC.
The project presented in Chapter 2 examines how food items are evaluated by their constituent attributes, namely their nutrient makeup. A linear attribute integration model succinctly captures how subjective values can be computed from a weighted combination of the constituent nutritive attributes of the food. Multivariate analysis methods revealed that these nutrient attributes are represented in the lateral OFC, while food value is encoded both in medial and lateral OFC. Connectivity between lateral and medial OFC allows this nutrient attribute information to be integrated into a value representation in medial OFC.
In Chapter 3, I show that this value construction process can operate over higher-level abstractions when the context requires bundles of items to be valued, rather than isolated items. When valuing bundles of items, the constituent items themselves become the features, and their values are integrated with a subadditive function to construct the value of the bundle. Multiple subregions of PFC including but not limited to vmPFC compute the value of a bundle with the same value code used to evaluate individual items, suggesting that these general value regions contextually adapt within this hierarchy. When valuing bundles and single items in interleaved trials, the value code rapidly switches between levels in this hierarchy by normalizing to the distribution of values in the current context rather than representing all options on an absolute scale.
Although the attribute integration model of value construction characterizes human behavior on simple decision-making tasks, it is unclear how it can scale up to environments of real-world complexity. Taking inspiration from modern advances in artificial intelligence, and deep reinforcement learning in particular, in Chapter 4 I outline how connectionist models generalize the attribute integration model to naturalistic tasks by decomposing sensory input into a high dimensional set of nonlinear features that are encoded with hierarchical and distributed processing. Participants freely played Atari video games during fMRI scanning, and a deep reinforcement learning algorithm trained on the games was used as an end-to-end model for how humans evaluate actions in these high-dimensional tasks. The features represented in the intermediate layers of the artificial neural network were found to also be encoded in a distributed fashion throughout the cortex, specifically in the dorsal visual stream and posterior parietal cortex. These features emerge from nonlinear transformations of the sensory input that connect perception to action and reward. In contrast to the stimulus attributes used to evaluate the stimuli presented in the preceding chapters, these features become highly complex and inscrutable as they are driven by the statistical properties of high-dimensional data. However, they do not solely reflect a set of features that can be identified by applying common dimensionality reduction techniques to the input, as task-irrelevant sensory features are stripped away and task-relevant high-level features are magnified.</p
Evolutionary Dynamic Multi-Objective Optimisation : A survey
Peer reviewedPostprin
Regularized interior point methods for convex programming
Interior point methods (IPMs) constitute one of the most important classes of optimization methods, due to their unparalleled robustness, as well as their generality. It is well known that a very large class of convex optimization problems can be solved by means of IPMs, in a polynomial number of iterations. As a result, IPMs are being used to solve problems arising in a plethora of fields, ranging from physics, engineering, and mathematics, to the social sciences, to name just a few. Nevertheless, there remain certain numerical issues that have not yet been addressed. More specifically, the main drawback of IPMs is that the linear algebra task involved is inherently ill-conditioned. At every iteration of the method, one has to solve a (possibly large-scale) linear system of equations (also known as the Newton system), the conditioning of which deteriorates as the IPM converges to an optimal solution. If these linear systems are of very large dimension, prohibiting the use of direct factorization, then iterative schemes may have to be employed. Such schemes are significantly affected by the inherent ill-conditioning within IPMs.
One common approach for improving the aforementioned numerical issues, is to employ regularized IPM variants. Such methods tend to be more robust and numerically stable in practice. Over the last two decades, the theory behind regularization has been significantly advanced. In particular, it is well known that regularized IPM variants can be interpreted as hybrid approaches combining IPMs with the proximal point method. However, it remained unknown whether regularized IPMs retain the polynomial complexity of their non-regularized counterparts. Furthermore, the very important issue of tuning the regularization parameters appropriately, which is also crucial in augmented Lagrangian methods, was not addressed.
In this thesis, we focus on addressing the previous open questions, as well as on creating robust implementations that solve various convex optimization problems. We discuss in detail the effect of regularization, and derive two different regularization strategies; one based on the proximal method of multipliers, and another one based on a Bregman proximal point method. The latter tends to be more efficient, while the former is more robust and has better convergence guarantees. In addition, we discuss the use of iterative linear algebra within the presented algorithms, by proposing some general purpose preconditioning strategies (used to accelerate the iterative schemes) that take advantage of the regularized nature of the systems being solved.
In Chapter 2 we present a dynamic non-diagonal regularization for IPMs. The non-diagonal aspect of this regularization is implicit, since all the off-diagonal elements of the regularization matrices are cancelled out by those elements present in the Newton system, which do not contribute important information in the computation of the Newton direction. Such a regularization, which can be interpreted as the application of a Bregman proximal point method, has multiple goals. The obvious one is to improve the spectral properties of the Newton system solved at each IPM iteration. On the other hand, the regularization matrices introduce sparsity to the aforementioned linear system, allowing for more efficient factorizations. We propose a rule for tuning the regularization dynamically based on the properties of the problem, such that sufficiently large eigenvalues of the non-regularized system are perturbed insignificantly. This alleviates the need of finding specific regularization values through experimentation, which is the most common approach in the literature. We provide perturbation bounds for the eigenvalues of the non-regularized system matrix, and then discuss the spectral properties of the regularized matrix. Finally, we demonstrate the efficiency of the method applied to solve standard small- and medium-scale linear and convex quadratic programming test problems.
In Chapter 3 we combine an IPM with the proximal method of multipliers (PMM). The resulting algorithm (IP-PMM) is interpreted as a primal-dual regularized IPM, suitable for solving linearly constrained convex quadratic programming problems. We apply few iterations of the interior point method to each sub-problem of the proximal method of multipliers. Once a satisfactory solution of the PMM sub-problem is found, we update the PMM parameters, form a new IPM neighbourhood, and repeat this process. Given this framework, we prove polynomial complexity of the algorithm, under standard assumptions. To our knowledge, this is the first polynomial complexity result for a primal-dual regularized IPM. The algorithm is guided by the use of a single penalty parameter; that of the logarithmic barrier. In other words, we show that IP-PMM inherits the polynomial complexity of IPMs, as well as the strong convexity of the PMM sub-problems. The updates of the penalty parameter are controlled by IPM, and hence are well-tuned, and do not depend on the problem solved. Furthermore, we study the behavior of the method when it is applied to an infeasible problem, and identify a necessary condition for infeasibility. The latter is used to construct an infeasibility detection mechanism. Subsequently, we provide a robust implementation of the presented algorithm and test it over a set of small to large scale linear and convex quadratic programming problems, demonstrating the benefits of using regularization in IPMs as well as the reliability of the approach.
In Chapter 4 we extend IP-PMM to the case of linear semi-definite programming (SDP) problems. In particular, we prove polynomial complexity of the algorithm, under mild assumptions, and without requiring exact computations for the Newton directions. We furthermore provide a necessary condition for lack of strong duality, which can be used as a basis for constructing detection mechanisms for identifying pathological cases within IP-PMM.
In Chapter 5 we present general-purpose preconditioners for regularized Newton systems arising within regularized interior point methods. We discuss positive definite preconditioners, suitable for iterative schemes like the conjugate gradient (CG), or the minimal residual (MINRES) method. We study the spectral properties of the preconditioned systems, and discuss the use of each presented approach, depending on the properties of the problem under consideration. All preconditioning strategies are numerically tested on various medium- to large-scale problems coming from standard test sets, as well as problems arising from partial differential equation (PDE) optimization.
In Chapter 6 we apply specialized regularized IPM variants to problems arising from portfolio optimization, machine learning, image processing, and statistics. Such problems are usually solved by specialized first-order approaches. The efficiency of the proposed regularized IPM variants is confirmed by comparing them against problem-specific state--of--the--art first-order alternatives given in the literature.
Finally, in Chapter 7 we present some conclusions as well as open questions, and possible future research directions
Biologically-inspired Neural Networks for Shape and Color Representation
The goal of human-level performance in artificial vision systems is yet to be achieved. With this goal, a reasonable choice is to simulate this biological system with computational models that mimic its visual processing. A complication with this approach is that the human brain, and similarly its visual system, are not fully understood. On the bright side, with remarkable findings in the field of visual neuroscience, many questions about visual processing in the primate brain have been answered in the past few decades. Nonetheless, a lag in incorporating these new discoveries into biologically-inspired systems is evident. The present work introduces novel biologically-inspired models that employ new findings of shape and color processing into analytically-defined neural networks. In contrast to most current methods that attempt to learn all aspects of behavior from data, here we propose to bootstrap such learning by building upon existing knowledge rather than learning from scratch. Put simply, the processing networks are defined analytically using current neural understanding and learned where such knowledge is not available. This is thus a hybrid strategy that hopefully combines the best of both worlds. Experiments on the artificial neurons in the proposed networks demonstrate that these neurons mimic the studied behavior of biological cells, suggesting a path forward for incorporating analytically-defined artificial neural networks into computer vision systems
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