2,557 research outputs found
Beauty for the Present: Mill, Arnold, Ruskin and Aesthetic Education
No full textThe present thesis examines the idea of aesthetic education of three eminent Victorians: John Stuart Mill, Matthew Arnold and John Ruskin. By focusing on the essence of what they meant with ‘the cultivation of the beautiful’ and, more importantly, the way their ideas of beauty informed their criticism of society, my study aims to contribute to our understanding of the idea of aesthetic education in the Victorian context and, further, to participate in a recent debate about the nature of beauty and aesthetic education.
Chapter One focuses on John Stuart Mill’s concept of ‘feeling’ in a series of essays. I will demonstrate how Mill’s idea of ‘aesthetic education’ was an ‘education of feelings,’ and moreover, how this idea was integrated into his literary criticism, his later critique of democratisation, his description of an ideal liberal society and even his own style of writing. Chapter Two contains a comparative study of Matthew Arnold and Friedrich Schiller. Through a rereading of Arnold, I will argue that his idea of aesthetic education is essentially Schillerian and that their resemblance consists primarily in their stress on the importance of aesthetic unity for modern life, which was becoming increasingly fragmentary and multitudinous. Chapter Three examines John Ruskin’s idea of aesthetic education and concentrates particularly on the cultivation of perception. Perception, as I shall show, was pivotal in Ruskin’s idea of aesthetic education. Just as what happened in Mill and Arnold, the emphasis on the education of seeing continued from his early writings well into his art and social criticisms. It not only differentiated him from his fellow art critics; the conviction that people should perceive with a pure heart also enabled him to link observation of artistic details with moral criticism of contemporary society and, thereby, to turn the cultivation of the beautiful into a moral-aesthetic experience
Adapting authoritarianism: institutions and co-optation in Egypt and Syria
No full textThis PhD thesis compares Egypt and Syria’s authoritarian political systems. While the tendency in social science political research treats Egypt and Syria as similarly authoritarian, this research emphasizes differences between the two systems with special reference to institutions and co-optation. Rather than reducibly understanding Egypt and Syria as sharing similar histories, institutional arrangements, or ascribing to the oft-repeated convention that “Syria is Egypt but 10 years behind,” this thesis focuses on how events and individual histories shaped each states current institutional strengthens and weaknesses. Specifically, it explains the how varying institutional politicization or de-politicization affects each state’s capabilities for co-opting elite and non-elite individuals.
Beginning with a theoretical framework that considers the limited utility of democratization and transition theoretical approaches, the work underscores the persistence and durability of authoritarianism. Chapter two details the politicized institutional divergence between Egypt and Syria that began in the 1970s. Chapter three and four examines how institutional politicization or de-politicization affects elite and non-elite individual co-optation in Egypt and Syria. Chapter five discusses the study’s general conclusions and theoretical implications.
This thesis’s argument is that Egypt and Syria co-opt elites and non-elites differently because of the varying degrees of institutional politicization in each governance system. Rather than view one country as more politically developed than the other, this work argues that Syria’s political institutions are more politicized than their Egyptian counterparts. Syria’s political arena is, thus, described as politicized-patrimonialism. Syria’s politicized-patrimonial arena produces uneven co-optation of elites and non-elites as they are diffused through competing institutions. Conversely, the Egyptian political arena remains highly personalized as weak institutions and individuals are manipulated and molded according to the president’s ruling clique. This is referred to as personalized-patrimonialism. As a consequence, Egypt’s political establishment demonstrates more flexibility in ad hoc altering and adapting its arena depending on the emergence of crises.
This study’s theoretical implications suggest that, contrary to modernization and democratization theory’s adage that institutions lead to a political development, politicized institutions within a patrimonial order actually hinder regime adaptation because consensus is harder to achieve and maintain. It is within this context that Egypt’s de-politicized institutional framework advantages its top political elite. In this reading of Egyptian and Syrian politics, Egypt’s personalized political arena is more adaptable than Syria’s. These conclusions do not indicate that political reform is a process underway in either state
Effect of changes in testing parameters on the cost-effectiveness of two pooled test methods to classify infection status of animals in a herd
No full textMonte Carlo simulation was used to determine optimal fecal pool sizes for identification of all Mycobacterium avium subsp. paratuberculosis (MAP)-infected cows in a dairy herd. Two pooling protocols were compared: a halving protocol involving a single retest of negative pools followed by halving of positive pools and a simple protocol involving single retest of negative pools but no halving of positive pools. For both protocols, all component samples in positive pools were then tested individually. In the simulations, the distributions of number of tests required to classify all individuals in an infected herd were generated for various combinations of prevalence (0.01, 0.05 and 0.1), herd size (300, 1000 and 3000), pool size (5, 10, 20 and 50) and test sensitivity (0.5–0.9). Test specificity was fixed at 1.0 because fecal culture for MAP yields no or rare false-positive results. Optimal performance was determined primarily on the basis of a comparison of the distributions of numbers of tests needed to detect MAP-infected cows using the Mann–Whitney U test statistic. Optimal pool size was independent of both herd size and test characteristics, regardless of protocol. When sensitivity was the same for each pool size, pool sizes of 20 and 10 performed best for both protocols for prevalences of 0.01 and 0.1, respectively, while for prevalences of 0.05, pool sizes of 10 and 20 were optimal for the simple and halving protocols, respectively. When sensitivity decreased with increasing pool size, the results changed for prevalences of 0.05 and 0.1 with pool sizes of 50 being optimal especially at a prevalence of 0.1. Overall, the halving protocol was more cost effective than the simple protocol especially at higher prevalences. For detection of MAP using fecal culture, we recommend use of the halving protocol and pool sizes of 10 or 20 when the prevalence is suspected to range from 0.01 to 0.1 and there is no expected loss of sensitivity with increasing pool size. If loss in sensitivity is expected and the prevalence is thought to be between 0.05 and 0.1, the halving protocol and a pool size of 50 is recommended. Our findings are broadly applicable to other infectious diseases under comparable testing conditions.ID: S0167587710000085; M3: Article; Accession Number: S0167587710000085; Author: Locksley L. McV. Messam (a, b); Author: Joshua M. O’Brien (c); Author: Sharon K. Hietala (d); Author: Ian A. Gardner (e, ⁎); Affiliation: St. Georges University, Department of Public Health and Preventive Medicine, School of Medicine, P.O. Box 7, True Blue, St. Georges, Grenada, West Indies; Affiliation: St. Georges University, Office of the Dean, School of Veterinary Medicine, P.O. Box 7, True Blue, St. George's, Grenada, West Indies; Affiliation: Center for Animal Disease Modeling and Surveillance, University of California, Davis, CA 95616, USA; Affiliation: California Animal Health and Food Safety Laboratory System, Davis, CA 95616, USA; Affiliation: Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA 95616, USA; Keyword: Cost-effectiveness; Keyword: Pooled testing; Keyword: Mycobacterium avium subsp. paratuberculosis; Keyword: Retesting; Number of Pages: 11; Language: English
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Discovering Molecular Patterns with Therapeutic Implications in Large-Cohort Heterogeneous Cross-Cancer Data
No full textRecent advances in high-throughput genomic technologies and high-performance computing have propelled the science of computational genomics into a new era and launched the field of precision medicine. Computational genomics is now an integral part of biomedical research and genomic testing is routinely performed in clinical settings. In the field of cancer informatics, the integration of genomics has led to invaluable insights and discoveries. We study cancers in order to better understand tumorigenesis and disease progression. This understanding can, in turn, inform and guide therapeutic decisions and suggest directions for drug development and repositioning. The ultimate goal of cancer precision medicine is to sequence and analyze every patients tumor in order to provide the most effective and least toxic treatment.Various experimental platforms are available for collection of different perspectives or views of the cell state, which help us characterize and understand molecular signals driving the cell phenotype. We collectively refer to these views as ’omics’ data. While vast amounts of ’omics’ data are being collected from tumor samples at an accelerating rate, few resources exist to aid biologists and clinicians in identifying trends in these data, finding connections within and between cancer subtypes, and matching patients to previously studied patient groups to infer therapeutic implications. In our analysis we also utilize bioinformatics methods that manipulate, transform, and integrated these views to derive new views of the cell. In my doctoral thesis, I present my work developing new tools and methods to aid the scientific community in understand- ing and interpreting cancer biology (Chapter 2). I also present my work applying such methods to contribute to cancer subtype-specific analyses as part of various projects and collaborations during my doctoral work (Chapter 3).Finally, I describe my work and contributions to the field of personalized medicine in pediatric cancer. While similar in some ways to adult cancers, pediatric cancers differ dramatically from their adult counterparts on a molecular level. For ex- ample, pediatric tumors generally have fewer genomic alterations than adult tumors. Further,childhood cancers are rarer than adult cancers and thus more difficult to study due to a lack of sufficiently large patient cohorts. While some clinics now regularly sequence pediatric tumors for bioinformatic analysis, the sequencing of patient genomes in the clinic is only beginning to impact patient care. Most computational methods for detecting differentially expressed genes are designed for analyzing patient cohorts in research settings and are thus unsuitable for interpreting RNA sequencing data from a single patient. Further, analyzing individuals genomic data leads to actionable treatment options in only fifteen percent of all childhood cancer cases. This is because pediatric cancers are often not driven by non-hereditary genomic changes, and any genomic aberrations that do exist may not be targetable by existing drugs. More sophisticated informatics tools and methods are needed in the field of personalized medicine. To this end, I describe my work developing methods for single-patient analyses in pediatric cancer (Chapter 4). While my methods were developed for pediatric cancers, they may also be used to analyze adult tumors
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Transcriptional Signatures of the Tumor and the Tumor Microenvironment Predict Cancer Patient Outcomes.
No full textPredicting the most effective cancer therapy for patients is a challenging yet very important task. In my doctoral thesis, I describe new insights gained from using transcriptional signatures from gene expression data of tumors and the tumor microenvironment. Out of different multi-omics data types, gene expression is found to be the most useful in predicting cancer drug sensitivity in a data set of cancer cell lines. Gene expression data can also be used to predict the presence of cancer driver events, genetic abnormalities responsible for tumor growth and progression. I describe the detection of rare genomic driver events found by association with known driver events using transcriptional signatures.Cancers are traditionally classified into types and subtypes by the organ- and cell-of-origin. However, more and more cancer subtypes are now being defined on a molecular basis using for example gene expression or mutation data. I perform a meta-analysis of molecular subtype classifiers for 26 different cancer cohorts that demonstrates which aspects of the input samples and input data are important to build an accurate molecular subtype classifier. In advanced prostate cancer, I use transcriptional signatures to reliably classify samples into subtypes. The gene expression data, in combination with histological review, is able to define the most at-risk patients in this cohort. However, I show that the classification of cancers into distinct subtypes is not applicable to all samples in this cohort, because they exist on a continuous spectrum between the subtypes - a finding that I was able to recapitulate in a study of lung cancer samples. I define and describe this continuum between subtypes of advanced prostate cancer using gene expression data.The tumor microenvironment, the normal cells that mix with cancer cells to form a tumor, plays an important role in cancer progression and treatment response. I built a landscape of about 10,000 cancer samples from immune cell infiltration estimated by deconvolution of gene expression profiles. However, immune cells are not the only cell types in the tumor microenvironment. I present a comprehensive deconvolution analysis of tumor patient samples using a cell type library defined from single-cell RNA sequencing data. These cell type estimates enable the detection of a pan-cancer high-risk sample group that is not detected by traditional gene expression analysis
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Sample-Specific Cancer Pathway Prediction From Genomic, Transcriptomic and Phosphoproteomic Data
No full textCancer phenotypes such as invasion, evasion of programmed cell death, and rapid growth, arise from the complex interactions of genes, proteins and extra-cellular environments. Understanding how selective alterations in the genome convert healthy cells to cancer is a critical step in developing new targeted and combination therapies. Current technology allows for detailed measurement of both genomic state as well as phenotype, through measurement of gene expression, chromatin state and protein activation. I present a method, Tied Diffusion through Interacting Events (TieDIE), that uses a “heat diffusion” model of information transfer to find pathways linking key genomic alterations to phenotypic effects, using high-throughput data collected from cohorts of cancer patients. Applying this method to four large data sets developed by The Cancer Genome Atlas (TCGA), I found key genes and interactions linking mutations related to histone modification and protein kinase signaling to gene-expression signatures of growth and proliferation. In a TCGA study of thyroid carcinoma, TieDIE found key proteins that modulate oncogenic signaling from mutant BRAF and RAS proteins to downstream MEK and ERK pathways, including the kinase suppressor of ras 1 scaffold protein.TieDIE was next applied to a study of prostate cancer that included detailed measurements of the phosphpoproteome. With collaborators at UCLA, we used tissue from lethal, metastatic castration-resistant prostate cancer (CRPC) patients obtained from rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using TieDIE, I integrated transcriptional, genomic and phosphoproteomics datasets to reveal a “map” of activated kinases and signaling pathways in CRPC. In contrast to single-dataset analyses, I show this integrative approach provides a more comprehensive and detailed look at metastatic signaling, and is generally useful in combining diverse datasets with only partially overlapping samples. Patient-specific network models were created by intersecting each sample’s data and protein activity predictions with the CRPC signaling “map.” These models reveal a hierarchy of the top kinase targets for each patient analyzed, and the corresponding therapeutic intervention, allowing for the construction of feasible strategies for patients with high activities in multiple kinases
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COMPUTATIONAL EVALUATION AND DERIVATION OF BIOLOGICAL NETWORKS IN CANCER AND STEM CELLS
No full textBiological network models have become standard tools for genome-wide analysis of both cancer disease processes and healthy differentiation from stem cells. In this thesis, I ad- dress a method for evaluating network models in terms of their ability to predict held out expression data given information about other genes in the same network. I apply this test to several extensions of our pathway database to demonstrate the transcrip- tional modeling utility of reverse phase protein array data, natural language processed literature, and transcription factor target predictions in stem cells and differentiated tissue. I then explore the addition of one new type of network data; co-localization in DNA domains. However, preexisting functional data is not the only source of networks. In the final part of the thesis, I elucidate a method to integrate prior biological knowl- edge with time series observations to infer causal relationships between phosphorylation events on proteins
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Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms
No full textTo develop more effective therapies to treat human diseases, a better method of finding the biological targets and modes of action of new compounds is needed. Target predictions have traditionally been made by comparing a new compound's molecular structure to that of known compounds. In many cases this method does not accurately predict a chemical's function since "small chemical changes in an active molecule can render it either nearly or completely inactive or increase its activity dramatically" (Eckert and Bajorath, 2007). Further, prediction by structural comparison has limited application; it can only be used on chemicals with established structures and only identifies new compounds that are structurally similar to known compounds.A majority of existing drugs have been discovered by identifying the active ingredient of traditional medicines. More recent techniques of drug discovery screen a library of compounds for effectiveness in treating a single disease. However, this method requires re-screening the library when searching for treatments for other diseases; a critical barrier to expediting and scaling drug discovery. Screening efficiency is particularly important since advances in robotic chemical synthesis and the search for natural products from the oceans are rapidly increasing the size of drug candidate libraries.In contrast to current approaches which screen compounds for treatments for single disease; my research focused on creating screening methods that deliver a library of chemical fingerprints which can be used to find potential drug candidates for a multiplicity of diseases.My work produced three screening methods that generate fingerprints useful for predicting a compound's mode of action: cytological profiling, D- Map, and BioSpace. All of these showed positive results towards solving the screening bottleneck. Finally, combining these approaches to integrate these various fingerprints could increase prediction accuracy of screening methods
Sleep Apnoea Syndrome Correlated with Progression of Normal Tension Glaucoma
No full textElla Claire Berry, Henry Marshall, Mark Hassall, Antonia Kolovos, Joshua Schmidt, Sean Mullany, Stewart Lake, Anna Galanopoulos, John Landers, Paul Healey, Stuart L Graham, Alex W Hewitt, Stuart MacGregor, Robert Casson, Owen Siggs, Jamie E Crai
Dressing up: Menswear in the Age of Social Media (2022): By Joshua Bluteau
No full text"What does men's fashion say about contemporary masculinity? How do these notions operate in an increasingly digitized world? To answer these questions, author Joshua M. Bluteau combines theoretical analysis with vibrant narrative, exploring men's fashion in the online world of social media as well as the offline worlds of retail, production, and the catwalk. Is it time to reassess notions of masculinity? How do we construct ourselves in the online world, and what are the dangers of doing so? From the ateliers of London to the digital landscape of Instagram, Dressing Up re-examines the ways men dress, and the ways men post.
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