Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data

In the realm of safeguarding digital systems, the ability to detect anomalies in log sequences is paramount, with applications spanning cybersecurity, network surveillance, and financial transaction monitoring. This thesis presents AdvSVDD, a sophisticated deep learning model designed for sequence anomaly detection. Built upon the foundation of Deep Support Vector Data Description (Deep SVDD), AdvSVDD stands out by incorporating Adversarial Reweighted Learning (ARL) to enhance its performance, particularly when confronted with limited training data. By leveraging the Deep SVDD technique to map normal log sequences into a hypersphere and harnessing the amplification effects of Adversarial Reweighted Learning, AdvSVDD demonstrates remarkable efficacy in anomaly detection. Empirical evaluations on the BlueGene/L (BG/L) and Thunderbird supercomputer datasets showcase AdvSVDD’s superiority over conventional machine learning and deep learning approaches, including the foundational Deep SVDD framework. Performance metrics such as Precision, Recall, F1-Score, ROC AUC, and PR AUC attest to its proficiency. Furthermore, the study emphasizes AdvSVDD’s effectiveness under constrained training data and offers valuable insights into the role of adversarial component has in the enhancement of anomaly detection

Phenomic and Genetic Controls of the Drought Stress Response in Sorghum

Drought, one of the most common abiotic stressors, is a result of the precipitation and temperature fluctuations influenced by climate change. As consistent weather patterns are crucial for the maintenance of crop yield, drought threatens food security through its impact on plant growth and development. It is essential to ensure the quality, availability, and affordability of grain-based products in the face of climate change due to expectations of population growth. Therefore, shedding light on the mechanisms associated with drought tolerance is integral to maintaining agricultural production under water-limited conditions. My dissertation work aimed to uncover the morphological, physiological, and genetic controls of drought resistance in Sorghum, a C4 grain crop grown for food, feed, and biofuel. In Chapter 3, two Sorghum bicolor accessions that differ in their pre-flowering responses to drought were evaluated following long-term drought exposure across juvenile and adult vegetative stages. Findings from this work emphasized accession-specific responses to drought, indicating that morphological/histological and physiological strategies both play roles in promoting hydraulic safety in response to drought, and these mechanisms may be mutually exclusive. Chapter 4 expanded upon the findings of Chapter 3 by uncovering the evolutionary origins of the morphological and physiological responses associated with drought exposure. Using quantitative trait loci (QTL) mapping in a Sorghum recombinant inbred line (RIL) population, eight QTL unique for drought exposure were detected. S. bicolor alleles controlled reductions in height and enhanced aboveground biomass, emphasizing the impact of grain Sorghum varieties (i.e. TX7000) on drought-responsive phenotypes. These biological impacts may be influenced by the candidate genes with these QTL, specifically those involved in reproductive processes. These gene products facilitate grain production and may promote early flowering, a common drought escape mechanism that influences the transition into reproduction before stress becomes too severe. Physiologically, S. bicolor alleles increased leaf temperature while Sorghum propinquum alleles increased relative water content; these species-specific strategies reflect their variable belowground growth and impact of domestication on drought-responsive phenotypes. The QTL detected for relative water content and leaf temperature contained genes involved in auxin and abscisic acid (ABA) synthesis and signaling. In addition to playing roles in root development and water uptake, phytohormones can also affect aboveground responses, such as growth and stomatal closure. Therefore, our findings highlight the contribution of plant hormones to root-to-shoot communication and water uptake and loss through both above- and belowground strategies. The relationship between above- and belowground responses and hormone signaling was explored further in Chapter 5. Using the same Sorghum RIL population, five QTL for belowground responses to drought exposure were identified. Three of these QTL co-localized on chromosome four and with a root biomass QTL detected in this same population evaluated under salinity stress, suggesting shared genetic control of belowground traits under osmotic stress. Further, these traits were all controlled by S. bicolor alleles. This control demonstrated that root system architecture is reorganized under osmotic stress by the domesticated parent to favor vertical growth while also increasing root biomass, suggesting a main goal of enhanced water uptake in the osmotic stress response. Candidate genes within these QTL were associated with root development and hormone synthesis/recognition, contributing additional support to the allelic effects described in this work, as well as to the role of water acquisition described in Chapter 4. Genes within the two remaining QTL detected in the drought population were also involved in plant hormone responses, specifically abscisic acid (ABA). Genes encoding pentatricopeptide repeat (PPR)-containing proteins and Late Embryogenesis Abundant- like (LEA) proteins were identified in these regions. PPR’s have established roles in ABA signaling in Arabidopsis and were also shown to be up-regulated in response to heat and drought stress in Sorghum. Further, LEA proteins are induced upon ABA and osmotic stress exposure, and function as molecular chaperones. Altogether, these findings further highlight the contribution of phytohormones in drought resistance, particularly through intricate signal cascades that influence plant functioning under drought, at the morphological, physiological, and molecular levels

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Studies on the role of integrins in corticotroph tumorigenesis

Introduction: Integrins are heterodimeric transmembrane proteins composed of alpha and beta subunits that mediate cell-cell and cell-extracellular matrix (ECM) interactions. Several integrins are overexpressed in human cancers and their ECM recognition motif, arginine-glycine-aspartate (RGD), is being utilized for tumor imaging and targeting. Aim: To explore the expression and function of RGD-binding integrins in corticotroph tumors. Methods: We determined the expression of RGD-binding integrins by qPCR in 18 corticotroph tumors and compared transcript levels with gonadotroph tumors (n=16) and normal pituitaries (n=2). To study the role of integrins, we established their expression profile in murine corticotroph tumor AtT-20 cells by RT-PCR and investigated the effect of their inhibition with RNA interference on human POMC promoter activity and cell viability (WST-1 colorimetric assay). We used fluorescence microscopy to assess RGD peptide binding in these cells. Results: Corticotroph tumours express αv (ITGAV), β1 (ITGB1), β5 (ITGB5), β8 (ITGB8), and α8 (ITGA8). Integrins αv, β1, β5 are overexpressed in corticotroph compared to gonadotroph tumors, where expression was almost undetectable (P<0.0001) and human normal pituitary (P<0.001). The expression of β8 was higher in corticotroph only compared to gonadotroph tumors (P=0.04), but not to the normal pituitary. We found that AtT-20 cells express all these four integrins. Knocking down each αv, β1, and β5, decreased human POMC promoter activity compared to scramble control (% suppression 63±22, 54±23, and 69±28 respectively; P<0.05), while β8 had little effect. Knocking down αv and β1 had a small but significant effect on AtT-20 cell viability (% suppression 15.92±1.6 and 27.4±1.4 respectively; P<0.05). Using immunofluorescence, we observed that an RGD peptide conjugated with the near-infrared fluorophore Cy5.5 could bind to and label AtT20 cells, with no deleterious effects on AtT-20 cell viability (WST-1 assay) and function (determined by POMC promoter activity). Conclusions: This study shows that corticotroph tumors express the genes encoding the α and β subunits of the RGD-binding integrins αvβ1, αvβ5, and αvβ8. We have preliminary evidence that these integrins may regulate POMC promoter activity. RGD peptide conjugates potential as corticotroph tumor imaging agents

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Deep Learning for Embedding and Integrating Multimodal Biomedical Data

Biomedical data is being generated in extremely high throughput and high dimension by technologies in areas ranging from single-cell genomics, proteomics, and transcriptomics (cytometry, single-cell RNA and ATAC sequencing) to neuroscience and cognition (fMRI and PET) to pharmaceuticals (drug perturbations and interactions). These new and emerging technologies and the datasets they create give an unprecedented view into the workings of their respective biological entities. However, there is a large gap between the information contained in these datasets and the insights that current machine learning methods can extract from them. This is especially the case when multiple technologies can measure the same underlying biological entity or system. By separately analyzing the same system but from different views gathered by different data modalities, patterns are left unobserved if they only emerge from the multi-dimensional joint representation of all of the modalities together. Through an interdisciplinary approach that emphasizes active collaboration with data domain experts, my research has developed models for data integration, extracting important insights through the joint analysis of varied data sources. In this thesis, I discuss models that address this task of multi-modal data integration, especially generative adversarial networks (GANs) and autoencoders (AEs). My research has been focused on using both of these models in a generative way for concrete problems in cutting-edge scientific applications rather than the exclusive focus on the generation of high-resolution natural images. The research in this thesis is united around ideas of building models that can extract new knowledge from scientific data inaccessible to currently existing methods

Human-iPSC-Derived Cardiac Stromal Cells Enhance Maturation in 3D Cardiac Microtissues and Reveal Non-cardiomyocyte Contributions to Heart Disease

Cardiomyocytes (CMs) from human induced pluripotent stem cells (hiPSCs) are functionally immature, but this is improved by incorporation into engineered tissues or forced contraction. Here, we showed that tri-cellular combinations of hiPSC-derived CMs, cardiac fibroblasts (CFs), and cardiac endothelial cells also enhance maturation in easily constructed, scaffold-free, three-dimensional microtissues (MTs). hiPSC-CMs in MTs with CFs showed improved sarcomeric structures with T-tubules, enhanced contractility, and mitochondrial respiration and were electrophysiologically more mature than MTs without CFs. Interactions mediating maturation included coupling between hiPSC-CMs and CFs through connexin 43 (CX43) gap junctions and increased intracellular cyclic AMP (cAMP). Scaled production of thousands of hiPSC-MTs was highly reproducible across lines and differentiated cell batches. MTs containing healthy-control hiPSC-CMs but hiPSC-CFs from patients with arrhythmogenic cardiomyopathy strikingly recapitulated features of the disease. Our MT model is thus a simple and versatile platform for modeling multicellular cardiac diseases that will facilitate industry and academic engagement in high-throughput molecular screening