"To publish art – to literally make it public – was a political act, one that challenged the art world and the world at large" (Gwen Allen). This critical appraisal on the published journal Next Level reports the result of my research relating to the body of my work from 2005 to 2016. More specifically, I will survey the creative production of the contemporary photography journal Next Level, currently consisting of seven city editions from a volume of twenty-four editions. This acknowledgement is not intended to emphasise the subjectivity of the journal as a limitation, but rather to provide focus to the lens through which I have been looking at my data with important findings about the outcomes of measurable theoretical, critical and artistic approaches. The journal Next Level periodically publishes a number of editions that present the collection of original data about photography art communities through the exploration of various cities around the world. These editions are developed from data collected through on-the-ground research that is central to this evaluation, which is an examination of and response to a large range of data drawn from seven cities, providing new information. This provides a pivot for the work around which my ideas are put across in a meaningful, comparable and communicable way, creating a mapping of each city, always enabling and never limiting. This methodology of gathering data, consisting of governmental cultural reports, museum archives, catalogues, comment books and newsletters, visual artists’ curriculum vitaes (CVs), interviews and rich contextual material, in turn provides primary research for students, photography professionals, photography enthusiasts and future photography historians. By countering the standard framework of research and production, my work is theoretically, critically and artistically traced, not by making things new, but by comprehensively questioning the characteristics that have shaped things in new ways. This framework manifests itself in the preliminary research and creative practice that provided the foundation for the complete scope of the entire space in the journal, which I present alongside this critical appraisal. Through the dissemination of current photographic discourse, I discuss current traditions and new perceptions through various articles and features. These editorial pieces relating to local communities of contemporary art photography look in particular at their cultural outputs in response to the rise of globalisation. Through the roles of artist-as-editor and curator, the journal is an artefact that I have shaped, utilising print production as part of its aesthetic dimension. I have published and distributed between 8,000 and 20,000 copies per edition to 37 countries. The readership of the journal thus has access to viewpoints that are revealing and politically reflective of specific manifestations of power, representation and the unheard stories that are altering various aspects of the conventions of current photographic discourse.

In the past several decades, Next Generation Sequencing (NGS) methods have produced large amounts of genomic data at the exponentially increasing rate. It has also enabled tremendous advancements in the quest to understand the molecular mechanisms underlying human complex traits. Along with the development of the NGS technology, many genetic variation and genotype–phenotype databases and functional annotation tools have been developed to assist scientists to better understand the intricacy of the data. Together, the above findings bring us one step closer towards mechanistic understanding of the complex phenotypes. However, it has rarely been possible to translate such a massive amount of information on mutations and their associations with phenotypes into biological or therapeutic insights, and the mechanisms underlying genotype-phenotype relationships remain partially explained. Meanwhile, increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. Among them, protein- protein interaction (PPI) network studies have attracted perhaps most attention. Our overarching goal of this dissertation is to (i) perform a systematic study to investigate the role of pathogenic human genetic variant in the interactome; (ii) examine how common population-specific SNVs affect PPI network and how they contribute to population phenotypic variance and disease susceptibility; and (iii) develop a novel framework to incorporate the functional effect of mutations for disease module detection. In this dissertation, we first present a systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring effects on the human interactome. Our in-silico analysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction- disrupting mutations in type 2 diabetes mellitus and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. Second, aided with our SNP-IN tool, we performed a systematic edgetic profiling of population specific non-synonymous SNVs and interrogate their role in the human interactome. Our results demonstrated that a considerable amount of normal nsSNVs can cause disruptive impact to the interactome. We also showed that genes enriched with disruptive mutations associated with diverse functions and have implications in various diseases. Further analysis indicates that distinct gene edgetic profiles among major populations can help explain the population phenotypic variance. Finally, network analysis reveals phenotype-associated modules are enriched with disruptive mutations and the difference of the accumulated damage in such modules may suggest population-specific disease susceptibility. Lastly, we propose and develop a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non- synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the specific genetic variation. The current and future in-silico edgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations. Our work shows the feasibility of a large- scale in-silico edgetic study and revealing insights into the orchestrated play of mutations inside a complex PPI network. We also expect for our module detection method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

Next generation networks will provide customers with a service mix placing variable demands for resources on the underlying infrastructure, motivating automated telecommunications services management approaches. This thesis proposes the Autonomic Service Architecture (ASA) for automated service delivery over next generation networks. First, we propose an architectural blueprint for ASA. We describe our view of the next generation network infrastructure, which will be application oriented. We elaborate on the layered design of ASA, the virtualization of resources, and the separation between manual and autonomic functions in the service delivery lifecycle. The autonomic functions are delivered by the interaction between Autonomic Resource Brokers (ARBs). The architecture of an ARB is then detailed, with a description of its different components and the message exchanges needed. Next, we discuss a Peer-to-Peer (P2P) naming and mobility management approach for next generation networks using ASA. This P2P approach will help ensure the scalability, robustness, and flexibility that ASA needs to ensure service delivery over next generation networks. The proposed P2P naming and mobility management infrastructure is then detailed, and its performance is evaluated. Finally, we suggest several autonomic resource management algorithms for ASA. The first algorithm is based on the Transportation Model, commonly used in the Operations Research community for cost minimization in delivering a commodity from sources to destinations, adapted to perform allocation of virtual resources. The second algorithm is based on the Assignment Model, commonly used in the Operations Research community for cost minimization in assigning several jobs to several workers, adapted to perform autonomic assignment of dedicated virtual resources. The third algorithm is based on Inventory Control, commonly used in the Operations Research community to analyze inventory systems, placing and receiving orders when needed for a given product, adapted to predict the demand on virtual resources. The fourth algorithm is based on Reinforcement Learning, commonly used in the Machine Learning community by agents to find a control policy that will maximize the observed rewards over their lifetime, adapted to adjust the prices of virtual resources.

currently DNA and RNA sequencing are performed as standard parts of many scientific experiments. While the majority of the reads produced in these experiments do map to the genome of the organism of interest there are a significant fraction that do not. These reads have often been viewed as uninteresting and thus discarded, sometimes explained as errors created in the sequencing process. However, there may be a real possibility that these reads actually contain genomic sequences belonging to, but not currently in the genome ofthe organism investigated, as well as information about other organisms which live and thrivein the sample material. Considering this, it is of great interest to investigate these reads to see if they contain any usable information. In this project the unmapped reads from SOLiD sequencing of blood and saliva from a twin pair were assembled. The assembled parts were thencompared to different blast databases to investigate if similar genomic regions are reported inother species. We can conclude that indeed a large fraction of the contigs found in this assemblyhave homology to bacterial genes while other contigs share similarity to genomic regions foundin apes and other species closely related to us. All in all the results show that there is more to the unmapped reads than just sequencing errors.

Next generation networks will provide customers with a service mix placing variable demands for resources on the underlying infrastructure, motivating automated telecommunications services management approaches. This thesis proposes the Autonomic Service Architecture (ASA) for automated service delivery over next generation networks. First, we propose an architectural blueprint for ASA. We describe our view of the next generation network infrastructure, which will be application oriented. We elaborate on the layered design of ASA, the virtualization of resources, and the separation between manual and autonomic functions in the service delivery lifecycle. The autonomic functions are delivered by the interaction between Autonomic Resource Brokers (ARBs). The architecture of an ARB is then detailed, with a description of its different components and the message exchanges needed. Next, we discuss a Peer-to-Peer (P2P) naming and mobility management approach for next generation networks using ASA. This P2P approach will help ensure the scalability, robustness, and flexibility that ASA needs to ensure service delivery over next generation networks. The proposed P2P naming and mobility management infrastructure is then detailed, and its performance is evaluated. Finally, we suggest several autonomic resource management algorithms for ASA. The first algorithm is based on the Transportation Model, commonly used in the Operations Research community for cost minimization in delivering a commodity from sources to destinations, adapted to perform allocation of virtual resources. The second algorithm is based on the Assignment Model, commonly used in the Operations Research community for cost minimization in assigning several jobs to several workers, adapted to perform autonomic assignment of dedicated virtual resources. The third algorithm is based on Inventory Control, commonly used in the Operations Research community to analyze inventory systems, placing and receiving orders when needed for a given product, adapted to predict the demand on virtual resources. The fourth algorithm is based on Reinforcement Learning, commonly used in the Machine Learning community by agents to find a control policy that will maximize the observed rewards over their lifetime, adapted to adjust the prices of virtual resources.

Salmonella is one of the most important pathogens of mankind and animals alike, causing several billion pounds worth of damage worldwide each year. We have sequenced, annotated and published 4 genomes of Salmonella of well-defined virulence in farm animals. This provides valuable measures of intraserovar diversity and opportunities to formally link genotypes to phenotypes in target animals. Specifically, we have examined pathway detrition and mutagenesis and linked this to host specificity of the serovars. With the advent of next generation sequencing there has been a boom in genomic sequence submission, and an onslaught of -omics data has ensued. Integrating these different data types is complex and there is little available to visualise this data in the context of its genome. We present GeneBook, a web-based tool that synchronously integrates disparate datasets, displaying a fully annotated genome, enriched with publicly available data and the user's private experiments. It is accessed through a user-friendly interface that allows scientists to interrogate genomic features across multiple, heterogeneous, experiments.

No description available.

Background: Obesity is a metabolic disease caused by environmental and genetic factors. However, the epigenetic mechanisms of obesity are incompletely understood. The aim of our study was to investigate the role of skeletal muscle DNA methylation in combination with transcriptomic changes in obesity. Results: Muscle biopsies were obtained basally from lean (n = 12; BMI = 23.4 +/- 0.7 kg/m(2)) and obese (n = 10; BMI = 32.9 +/- 0.7 kg/m(2)) participants in combination with euglycemic-hyperinsulinemic clamps to assess insulin sensitivity. We performed reduced representation bisulfite sequencing (RRBS) next-generation methylation and microarray analyses on DNA and RNA isolated from vastus lateralis muscle biopsies. There were 13,130 differentially methylated cytosines (DMC; uncorrected P < 0.05) that were altered in the promoter and untranslated (5' and 3'UTR) regions in the obese versus lean analysis. Microarray analysis revealed 99 probes that were significantly (corrected P < 0.05) altered. Of these, 12 genes (encompassing 22 methylation sites) demonstrated a negative relationship between gene expression and DNA methylation. Specifically, sorbin and SH3 domain containing 3 (SORBS3) which codes for the adapter protein vinexin was significantly decreased in gene expression (fold change -1.9) and had nine DMCs that were significantly increased in methylation in obesity (methylation differences ranged from 5.0 to 24.4 %). Moreover, differentially methylated region (DMR) analysis identified a region in the 5' UTR (Chr. 8: 22,423,530-22,423,569) of SORBS3 that was increased in methylation by 11.2 % in the obese group. The negative relationship observed between DNA methylation and gene expression for SORBS3 was validated by a site-specific sequencing approach, pyrosequencing, and qRT-PCR. Additionally, we performed transcription factor binding analysis and identified a number of transcription factors whose binding to the differentially methylated sites or region may contribute to obesity. Conclusions: These results demonstrate that obesity alters the epigenome through DNA methylation and highlights novel transcriptomic changes in SORBS3 in skeletal muscle.

Thesis advisor: Gabor T. Marth / Partitioning an individual's phenotype into genetic and environmental components has been a major goal of genetics since the early 20th century. Formally, the proportion of phenotypic variance attributable to genetic variation in the population is known as heritability. Genome wide association studies have explained a modest percentage of variability of complex traits by genotyping common variants. Currently, there is great interest in what role rare variants play in explaining the missing heritability of complex traits. Advances of next generation sequencing and genomic enrichment technologies over the past several years have made it feasible to re-sequence large numbers of individuals, enabling the discovery of the full spectrum of genetic variation segregating in the human population, including rare variants. The four projects that comprise my dissertation all revolve around the discovery of rare variants from next generation sequencing datasets. In my first project, I analyzed data from the exon sequencing pilot of the 1000 Genomes Project, where I discovered variants from exome capture sequencing experiments in a worldwide sample of nearly 700 individuals. My results show that the allele frequency spectrum of the dataset has an excess of rare variants. My next project demonstrated the applicability of using whole-genome amplified DNA (WGA) in capture sequencing. WGA is a method that amplifies DNA from nanogram starting amounts of template. In two separate capture experiments I compared the concordance of call sets, both at the site and genotype level, of variant calls derived from WGA and genomic DNA. WGA derived calls have excellent concordance metrics, both at the site and genotypic level, suggesting that WGA DNA can be used in lieu of genomic DNA. The results of this study have ramifications for medical sequencing experiments, where DNA stocks are a finite quantity and re-collecting samples maybe too expensive or not possible. My third project kept its focus on capture sequencing, but in a different context. Here, I analyzed sequencing data from Mendelian exome study of non-sensorineural hearing loss (NSHL). A subset of 6 individuals (5 affected, 1 unaffected) from a family of European descent were whole exome sequenced in an attempt to uncover the causative mutation responsible for the loss of hearing phenotype in the family. Previous linkage analysis uncovered a linkage region on chr12, but no mutations in previous candidate genes were found, suggesting a novel mutation segregates in the family. Using a discrete filtering approach with a minor allele frequency cutoff, I uncovered a putative causative non-synonymous mutation in a gene that encodes a transmembrane protein. The variant perfectly segregates with the phenotype in the family and is enriched in frequency in an unrelated cohort of individuals. Finally, for my last project I implemented a variant calling method for family sequencing datasets, named Pgmsnp, which incorporates Mendelian relationships of family members using a Bayesian network inference algorithm. My method has similar detection sensitivities compared to other pedigree aware callers, and increases power of detection for non-founder individuals. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

The earthworm has long been of interest to biologists, most notably Charles Darwin, who was the first to reveal their true role as eco-engineers of the soil. However, to fully understand an animal one needs to combine observational data with the fundamental building blocks of life, DNA. For many years, sequencing a genome was an incredibly costly and time-consuming process. Recent advances in sequencing technology have led to high quality, high throughput data being available at low cost. Although this provides large amounts of sequence data, the bioinformatics knowledge required to assemble and annotate these new data are still in their infancy. This bottleneck is slowly opening up, and with it come the first glimpses into the new and exciting biology of many new species. This thesis provides the first high quality draft genome assembly and annotation of an earthworm, Lumbricus rubellus. The assembly process and resulting data highlight the complexity of assembling a eukaryotic genome using short read data. To improve assembly, a novel approach was created utilising transcripts to scaffold the genome (https://github.com/elswob/SCUBAT). The annotation of the assembly provides the draft of the complete proteome, which is also supported by the first RNA-Seq generated transcriptome. These annotations have enabled detailed analysis of the protein coding genes including comparative analysis with two other annelids (a leech and a polychaete worm) and a symbiont (Verminephrobacter). This analysis identified four key areas which appear to be either highly enhanced or unique to L. rubellus. Three of these may be related to the unique environment from which the sequenced worms originated and add to the mounting evidence for the use of earthworms as bioindicators of soil quality. All data is stored in relational databases and available to search and browse via a website at www.earthworms.org. It is hoped that this genome will provide a springboard for many future investigations into the earthworm and continue research into this wonderful animal.