Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

<i>TP53</i> copy number expansion is associated with the evolution of increased body size and an enhanced DNA damage response in elephants

A major constraint on the evolution of large body sizes in animals is an increased risk of developing cancer. There is no correlation, however, between body size and cancer risk. This lack of correlation is often referred to as ’Peto’s Paradox’. Here, we show that the elephant genome encodes 20 copies of the tumor suppressor gene TP53 and that the increase in TP53 copy number occurred coincident with the evolution of large body sizes, the evolution of extreme sensitivity to genotoxic stress, and a hyperactive TP53 signaling pathway in the elephant (Proboscidean) lineage. Furthermore, we show that several of the TP53 retrogenes (TP53RTGs) are transcribed and likely translated. While TP53RTGs do not appear to directly function as transcription factors, they do contribute to the enhanced sensitivity of elephant cells to DNA damage and the induction of apoptosis by regulating activity of the TP53 signaling pathway. These results suggest that an increase in the copy number of TP53 may have played a direct role in the evolution of very large body sizes and the resolution of Peto’s paradox in Proboscideans

Uncovering Unique Concept Vectors through Latent Space Decomposition

Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach that is more interpretable than feature attribution estimates such as pixel saliency. However, defining the concepts for the interpretability analysis biases the explanations by the user's expectations on the concepts. To address this, we propose a novel post-hoc unsupervised method that automatically uncovers the concepts learned by deep models during training. By decomposing the latent space of a layer in singular vectors and refining them by unsupervised clustering, we uncover concept vectors aligned with directions of high variance that are relevant to the model prediction, and that point to semantically distinct concepts. Our extensive experiments reveal that the majority of our concepts are readily understandable to humans, exhibit coherency, and bear relevance to the task at hand. Moreover, we showcase the practical utility of our method in dataset exploration, where our concept vectors successfully identify outlier training samples affected by various confounding factors. This novel exploration technique has remarkable versatility to data types and model architectures and it will facilitate the identification of biases and the discovery of sources of error within training data

Characterizing mechanisms of regulatory specificity in the nuclear receptors and general transcriptional cofactors

Gene regulation, at its most basic level, is controlled by transcription factors (TFs) binding to genomic regulatory elements and recruiting regulatory cofactors (CoFs). Therefore, to understand specificity in gene regulation, we must address how TF-DNA binding translates into target gene specification in the genome and how TF-CoF interactions are regulated within the cell. Towards this goal, we describe a comprehensive study of the DNA binding specificity of the type II nuclear receptor (NR) family of TFs, and introduce a novel high-throughput technique for assaying the many TF-CoF complexes functioning in a cell. The type II nuclear receptors function as heterodimeric TFs with the retinoid X receptor (RXR) to regulate diverse biological processes. DNA-binding specificity has been proposed as a primary mechanism for NR gene regulatory specificity. We use protein-binding microarrays (PBMs) to comprehensively analyze the DNA binding of 12 NR:RXRα heterodimers. We find more promiscuous NR-DNA binding than has been reported, challenging the view that NR binding specificity is defined by half-site spacing. We show that NRs bind DNA using two distinct modes, explaining widespread NR binding to half-sites in vivo. Finally, we show that the current models of NR specificity better reflect binding-site activity rather than binding-site affinity. Our rich dataset and revised NR binding models provide a framework for understanding NR regulatory specificity and will facilitate more accurate analyses of genomic datasets. Central to gene regulation is the recruitment of CoFs (e.g., co-activators and co-repressors) to DNA by site-specific TFs. There are currently no high-throughput approaches to identify and characterize the many TF-cofactor complexes simultaneously operating in a cell. To this end, we have developed the CoRec (Cofactor Recruitment) approach to monitor cofactor recruitment by potentially hundreds of TFs from nuclear lysates. We have used CoRec to examine CoF recruitment in resting and LPS-activated human macrophages, as well as resting and T cell receptor-stimulated human T cells. We demonstrate CoF recruitment to both known and novel regulatory elements and compare regulatory strategies between these two cell types. We anticipate CoRec will be a powerful tool to study the assembly and regulation of nuclear TF-cofactor complexes in a cellular context

The Gcn4 transcription factor reduces protein synthesis capacity and extends yeast lifespan

In Saccharomyces cerevisiae, deletion of large ribosomal subunit protein-encoding genes increases the replicative lifespan in a Gcn4-dependent manner. However, how Gcn4, a key transcriptional activator of amino acid biosynthesis genes, increases lifespan, is unknown. Here we show that Gcn4 acts as a repressor of protein synthesis. By analyzing the messenger RNA and protein abundance, ribosome occupancy and protein synthesis rate in various yeast strains, we demonstrate that Gcn4 is sufficient to reduce protein synthesis and increase yeast lifespan. Chromatin immunoprecipitation reveals Gcn4 binding not only at genes that are activated, but also at genes, some encoding ribosomal proteins, that are repressed upon Gcn4 overexpression. The promoters of repressed genes contain Rap1 binding motifs. Our data suggest that Gcn4 is a central regulator of protein synthesis under multiple perturbations, including ribosomal protein gene deletions, calorie restriction, and rapamycin treatment, and provide an explanation for its role in longevity and stress response

NRF2 promotes urothelial cell response to bacterial infection by regulating reactive oxygen species and RAB27B expression

Uropathogenic Escherichia coli (UPEC) cause urinary tract infections (UTIs) by invading urothelial cells. In response, the host mounts an inflammatory response to expel bacteria. Here, we show that the NF-E2-related factor 2 (NRF2) pathway is activated in response to UPEC-triggered reactive oxygen species (ROS) production. We demonstrate the molecular sequence of events wherein NRF2 activation in urothelial cells reduces ROS production, inflammation, and cell death, promotes UPEC expulsion, and reduces the bacterial load. In contrast, loss of NRF2 leads to increased ROS production, bacterial burden, and inflammation, both in vitro and in vivo. NRF2 promotes UPEC expulsion by regulating transcription of the RAB-GTPase RAB27B. Finally, dimethyl fumarate, a US Food and Administration-approved NRF2 inducer, reduces the inflammatory response, increases RAB27B expression, and lowers bacterial burden in urothelial cells and in a mouse UTI model. Our findings elucidate mechanisms underlying the host response to UPEC and provide a potential strategy to combat UTIs

Doctor of Philosophy

dissertationBalancing the trade off between the spatial and temporal quality of interactive computer graphics imagery is one of the fundamental design challenges in the construction of rendering systems. Inexpensive interactive rendering hardware may deliver a high level of temporal performance if the level of spatial image quality is sufficiently constrained. In these cases, the spatial fidelity level is an independent parameter of the system and temporal performance is a dependent variable. The spatial quality parameter is selected for the system by the designer based on the anticipated graphics workload. Interactive ray tracing is one example; the algorithm is often selected due to its ability to deliver a high level of spatial fidelity, and the relatively lower level of temporal performance isreadily accepted. This dissertation proposes an algorithm to perform fine-grained adjustments to the trade off between the spatial quality of images produced by an interactive renderer, and the temporal performance or quality of the rendered image sequence. The approach first determines the minimum amount of sampling work necessary to achieve a certain fidelity level, and then allows the surplus capacity to be directed towards spatial or temporal fidelity improvement. The algorithm consists of an efficient parallel spatial and temporal adaptive rendering mechanism and a control optimization problem which adjusts the sampling rate based on a characterization of the rendered imagery and constraints on the capacity of the rendering system

Analysis of the epigenetic landscape in murine macrophages

Macrophages are cells of the innate immune system and play essential roles in the regulation of inflammatory responses in all parts of the body. Furthermore, macrophages are also involved in different tissue–specific functions and maintenance of the tissue homeostasis. These functions are controlled by the epigenetic landscape, consisting of promoters and enhancers that together regulate gene expression. Enhancers are stretches of regulatory genomic sequences in the non–coding regions of the genome that can be bound by lineage– determining transcription factors. These enhancers can loop in three–dimensional space to be in close proximity to promoters and contribute to the regulation of gene expression. Previous studies suggest that there are about 1 million enhancers in the mammalian genome, of which only about 30,000 – 40,000 are selected in each specific cell type. This dissertation studies the regulation of the epigenetic landscape of murine macrophages by utilizing different tissue macrophages, different complex and simple stimuli, as well as natural genetic variation as a mutagenesis screen. The overarching research question of this dissertation is to understand how the enhancer landscape in macrophages gets selected and regulated in order to control gene expression. In more detail, the main questions answered in this dissertation are: What are the epigenetic mechanisms that are responsible for tissue–specific functions? How do complex stimuli change the epigenetic landscape of macrophages in comparison to simple stimuli? How does natural genetic variation influence the epigenetic landscape and gene expression in murine macrophages? In Chapter 1 (Gosselin, D., Link, V. M., Romanoski, C. E. et al. (2014) appeared in Cell) we investigate the influence of the tissue environment on the epigenetic landscape in mouse macrophages. We compare macrophages residing in the brain (microglia) with macrophages from the peritoneal cavity by measuring mRNA expression, as well as enhancer activation (H3K4me2, H3K27ac, and PU.1). We find highly expressed genes unique to one population of macrophages, which correlates well with the activity signature at enhancers in the corresponding cells. By analyzing the enhancer landscape, we find that the macrophage lineage–determining transcription factor PU.1 plays a key role in establishing the enhancer repertoire, creating a common, macrophage–specific enhancer landscape. Furthermore, expression of tissue–specific transcription factors in collaboration with PU.1 drives a subset of tissue–specific enhancers regulating the differences in gene expression between different tissue–specific macrophage populations. In Chapter 2 (Eichenfield, D. Z., Troutman, D. T., Link, V. M. et al. (2016) appeared in eLife) we investigate the effect of complex stimuli onto the epigenetic landscape in macrophages on the example of wounds. Stimulation of macrophages with homogenated tissue to mimic a wound environment shows a unique pattern of gene expression, which is different from gene expression patterns found after single stimuli (e.g. LPS, IL–4 etc.). To gain insight into the regulation of the enhancer landscape after complex stimuli, we compare the epigenome after single stimuli and tissue homogenate and find substantial differences in enhancer selection and activation. We find that the complex damage signal promotes co–localization of several signal–dependent transcription factors to enhancers not observed under the single stimuli. Therefore, more complex polarizations of cells lead to new combinations of signal–dependent transcription factors and an epigenetic landscape different than observed with single stimuli. In Chapter 3 (Link et al. (2018b) appeared in bioRxiv) MARGE (Mutation Analysis for Regulatory Genomic Elements) is presented, a new method to analyze the effect of natural genetic variation on transcription factor binding and open chromatin. MARGE provides a suite of software tools that integrates genome–wide genetic variation data (including insertions and deletions) with epigenetic data. It provides software to create custom genomes based on a reference genome and variation data, to shift coordinates between different custom genomes, as well as do downstream ChIP–seq analysis. The main algorithm in MARGE analyzes if mutations in transcription factor binding motifs are significantly affecting transcription factor binding or open chromatin. MARGE provides a pairwise comparison, in which the significance of each motif is calculated with a student’s t–test. It compares the transcription factor binding distribution of each mutated motif in individual one with the distribution in individual two. For a more general approach that allows comparisons of many individuals MARGE implements a linear mixed model, modeling transcription factor binding with fixed effects motif existence and random effects locus and genotype. The development of this software allows in depth analysis of genetic variation data in combination with epigenetic data. In Chapter 4 (Link et al. (2018a) under review in Cell) we analyze the effect of natural genetic variation in five diverse strains of mice on the epigenetic landscape. We choose three well–known laboratory inbred mouse strains, as well as two very diverse wild–derived inbred mouse strains. We investigate the enhancer landscape, open chromatin and binding of the most important macrophage lineage–determining transcription factors. We observe substantial strain–specific differences in gene expression of which the majority can be explained by cis–regulatory elements. Application of MARGE onto the transcription factor binding data reveals roles of about 100 transcription factors in establishing the enhancer repertoire in macrophages. Unexpectedly, we find that a substantial fraction of strain– specific DNA binding of transcription factors cannot be explained by local mutations. Investigation of this phenomenon in more detail shows highly interconnected clusters of transcription factors that reside within topologically associating domains. These interconnected clusters are highly correlated with activation of enhancers and gene expression of the nearest gene, uncovering a new layer of transcriptional regulation. In Chapter 5, I briefly discuss additional contributions to the field of macrophage biology I made during my Ph.D. Namely, I was involved in two additional projects. In the first project (Pirzgalska et al. (2017) appeared in Nature Medicine) we identify sympathetic neuron–associated macrophages (SAM) that import and degrade norepinephrine via expression of solute carrier family 6 member 2 (Slc6a2) and monoamine oxidase A (MAOa). We demonstrate that SAM–mediated clearance of extracellular norepinephrine contributes to obesity and we show the relevance of this finding in humans, as we found that SAMs are also present in human tissues. The second project (Oishi et al. (2017) appeared in Cell Metabolism) studies the role of nuclear receptors (LXR and SREBP) in induction of anti–inflammatory fatty acids. We find that right after stimulation of TLR4 (during the induction phase) NF–kB dependent genes are upregulated, whereas LXR dependent genes are repressed. This leads to activation of SREBP1, which drives the expression of enzymes involved in mono–unsaturated and omega–3 polyunsaturated fatty acid biosynthesis. The fatty acids produced by these enzymes repress inflammatory genes under the control of NF–kB and the inflammatory signal gets resolved. In summary, my studies used a combination of experimental and computational approaches to investigate the effect of tissue–environment and factors, complex stimuli and natural genetic variation on the epigenetic landscape in macrophages. These studies broadened our understanding of the regulation of gene expression by the epigenetic landscape substantially. We showed that there is a core set of lineage–determining transcription factors in macrophages, which require diverse signal–dependent transcription factors to establish the enhancer landscape. Not only did we show that transcription factors regulated by the local environment play essential roles in establishing and maintaining tissue–specific functions of macrophages, but also that more complex stimuli can re–direct and combine signal–dependent transcription factors to establish new enhancers, not observed under the single stimuli. Using natural genetic variation as a mutagenesis screen allowed us to estimate the involvement of about 100 transcription factors in shaping the enhancer landscape, as well as to uncover a new layer of transcription regulation due to highly interconnected clusters of concordantly bound transcription factors