Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and rule-based algorithm to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open source resource

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.</p

Matthew: Effect or Fable?

In a market context, a status effect occurs when actors are accorded differential recognition for their efforts depending on their location in a status ordering, holding constant the quality of these efforts. In practice, because it is very difficult to measure quality, this ceteris paribus proviso often precludes convincing empirical assessments of the magnitude of status effects. We address this problem by examining the impact of a major status-conferring prize that shifts actors' positions in a prestige ordering. Specifically, using a precisely constructed matched sample, we estimate the effect of a scientist becoming a Howard Hughes Medical Institute (HHMI) Investigator on citations to articles the scientist published before the prize was awarded. We do find evidence of a postappointment citation boost, but the effect is small and limited to a short window of time. Consistent with theories of status, however, the effect of the prize is significantly larger when there is uncertainty about article quality, and when prize winners are of (relatively) low status at the time of election to the HHMI Investigator Program.National Science Foundation (U.S.) (SciSIP Program [Award SBE-0738142]

Value co-destruction in tourism and hospitality: a systematic literature review and future research agenda

This study systematically reviews, synthesises and integrates the extant literature on value co-destruction in the field of tourism and hospitality. The results indicate that research in this field is still in its infancy, suffers from a contextual imbalance and employs mainly qualitative methods. Several gaps are identified, and four areas for future work are proposed: further theorisation, application of the topic and scale development, fostering a broader focus on cross-cultural studies and a need for studies in different hospitality and tourism settings; greater use of on-site data collection and engaging in mixed-methods analysis; and greater consideration of service-provider and multiple-actor perspectives

Creation and Application of Various Tools for the Reconstruction, Curation, and Analysis of Genome-Scale Models of Metabolism

Systems biology uses mathematics tools, modeling, and analysis for holistic understanding and design of biological systems, allowing the investigation of metabolism and the generation of actionable hypotheses based on model analyses. Detailed here are several systems biology tools for model reconstruction, curation, analysis, and application through synthetic biology. The first, OptFill, is a holistic (whole model) and conservative (minimizing change) tool to aid in genome-scale model (GSM) reconstructions by filling metabolic gaps caused by lack of system knowledge. This is accomplished through Mixed Integer Linear Programming (MILP), one step of which may also be independently used as an additional curation tool. OptFill is applied to a GSM reconstruction of the melanized fungus Exophiala dermatitidis, which underwent various analyses investigating pigmentogenesis and similarity to human melanogenesis. Analysis suggest that carotenoids serve a currently unknown function in E. dermatitidis and that E. dermatitidis could serve as a model of human melanocytes for biomedical applications. Next, a new approach to dynamic Flux Balance Analysis (dFBA) is detailed, the Optimization- and Runge-Kutta- based Approach (ORKA). The ORKA is applied to the model plant Arabidopsis thaliana to show its ability to recreate in vivo observations. The analyzed model is more detailed than previous models, encompassing a larger time scale, modeling more tissues, and with higher accuracy. Finally, a pair of tools, the Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) tools, is introduced which can aid in the design and modeling of synthetic biology applications hypothesized using systems biology. These tools bring a computational approach to synthetic biology, and are applied to Arabidopsis thaliana to design thousands of potential two-input genetic circuits which satisfy 27 different input and logic gate combinations. EuGeneCiM is further used to model a repressilator circuit. Efforts are ongoing to disseminate these tools to maximize their impact on the field of systems biology. Future research will include further investigation of E. dermatitidis through modeling and expanding my expertise to kinetic models of metabolism. Advisor: Rajib Sah

Recommendations in Academic Social Media: the shaping of scholarly communication through algorithmic mediation

Scholarly communication is increasingly being mediated by Academic Social Media (ASM) platforms, which combine the functions of a scientifi c repository with social media features such as personal profi les, followers and comments. In ASM, algorithmic mediation is responsible for fi ltering the content and distributing it in personalised individual feeds and recommendations according to inferred relevance to users. However, if communication among researchers is intertwined with these platforms, in what ways may the recommendation algorithms in ASM shape scholarly communication? Scientifi c literature has been investigating how content is mediated in data-driven environments ranging from social media platforms to specifi c apps, whereas algorithmic mediation in scientifi c environments remains neglected. This thesis starts from the premise that ASM platforms are sociocultural artefacts embedded in a mutually shaping relationship with research practices and economic, political and social arrangements. Therefore, implications of algorithmic mediation can be studied through the artefact itself, peoples’ practices and the social/political/ economic arrangements that aff ect and are aff ected by such interactions. Most studies on ASM focus on one of these elements at a time, either examining design elements or the users’ behaviour on and perceptions about such platforms. In this thesis, a multifaceted approach is taken to analyse the artefact as well as the practices and arrangements traversed by algorithmic mediation. Chapter 1 reviews the literature about ASM platforms, and explains the history of algorithmic recommendations, starting from the fi rst Information Retrieval systems to current Recommender Systems, highlighting the use of diff erent data sources and techniques. The chapter also presents the mediation framework and how it applies to ASM platforms, before outlining the thesis. The rest of the thesis is divided in two parts. Part I focuses on how recommender systems in ASM shape what users can see and how users interact with and through the platform. Part II investigates how, in turn, researchers make sense of their online interactions within ASM. The end of Chapter 1 shows the methodological choices for each following chapter. Part I presents a case study of one of the most popular ASM platforms in which a walkthrough method was conducted in four steps (interface analysis, web code inspection, patent analysis and company inquiry using the General Data Protection Regulation (GDPR)). In Chapter 2 it is shown that almost all the content in ASM platforms are algorithmically mediated through mechanisms of profi ling, information selection and commodifi cation. It is also discussed how the company avoids explaining the workings of recommender systems and the mutually shaping characteristic of ASM platforms. Chapter 3 explores the distortions and biases that ASM platforms can uphold. Results show how profi ling, datafi cation and prioritization have the potential to foster homogeneity bias, discrimination, the Matthew eff ect of cumulative advantage in science and other distortions. Part II consists of two empirical studies involving participants from diff erent countries in interviews (n=11) and a research game (n=13). Chapter 4 presents the interviews combined with the show and tell technique. The results show the participant’s perceptions on ASM aff ordances, that revolve around six main themes: (1) getting access to relevant content; (2) reaching out to other scholars; (3) algorithmic impact on exposure to content; (4) to see and to be seen; (5) blurred boundaries of potential ethical or legal infringements, and (6) the more I give, the more I get. We argue that algorithmic mediation not only constructs a narration of the self, but also a narration of the relevant other in ASM platforms, confi guring an image of the relevant other that is both participatory and productive. Chapter 5 presents the design process of a research game and the results of the empirical sessions, where participants were observed while playing the game. There are two outcomes for the study. First, the human values researchers relate to algorithmic features in ASM, the most prominent being stimulation, universalism and self-direction. Second, the role of the researcher’s approach (collaborative, competitive or ambivalent) in academic tasks, showing the consequential choices people make regarding algo- rithmic features and the motivations behind those choices. The results led to four archetypal profi les: (1) the collaborative reader; (2) the competitive writer; (3) the collaborative disseminator; and (4) the ambivalent evaluator. The fi nal chapter summarises the ways in which ASM platforms forges people’s perceptions and the strategies people employ to use the systems in benefi t of their careers, answering each research question. Chapter 6 discusses the implications of algorithmic mediation for scholarly communication and science in general. The dissertation ends with refl ections on human agency in data-driven environments, the role of algorithmic inferences in science and the challenge of reconciling individual user’s needs with broader goals of the scientifi c community. By doing so, the contribution of this thesis is twofold, (1) providing in-depth knowledge about the ASM artefact, and (2) unfolding diff erent aspects of the human perspective in dealing with algorithmic mediation in ASM. Both perspectives are discussed in light of social arrangements that are mutually shaped by artefact and practices.A comunicação acadêmica é cada vez mais mediada por plataformas de Mídia Social Acadêmica (MSA), que combinam as funções de um repositório científi co com recursos de mídia social, como perfi s pessoais, seguidores e comentários. Nas MSA, a mediação algorítmica é responsável por fi ltrar o conteúdo e distribuí-lo em feeds e recomendações individuais personalizados de acordo com a relevância inferida para os usuários. No entanto, se a comunicação entre pesquisadores está entrelaçada com essas plataformas, de que forma os algoritmos de recomendação nas MSA podem moldar a comunicação acadêmica? A literatura científi ca vem investigando como o conteúdo é mediado em ambientes orientados por dados, desde plataformas de mídia social até aplicativos específi cos, enquanto a mediação algorítmica em ambientes científi cos permanece negligenciada. Esta tese parte da premissa de que as plataformas de MSA são artefatos socioculturais inseridos em uma relação mutuamente modeladora com práticas de pesquisa e arranjos econômicos, políticos e sociais. Portanto, as implicações da mediação algorítmica podem ser estudadas através do próprio artefato, das práticas humanas e dos arranjos sociais/políticos/ econômicos que afetam e são afetados por tais interações. A maioria dos estudos sobre MSA se concentra em um desses elementos de cada vez, seja examinando elementos de design ou o comportamento e percepções dos usuários sobre essas plataformas. Nesta tese, uma abordagem multifacetada é feita para analisar o artefato, bem como as práticas e arranjos atravessados pela mediação algorítmica. O Capítulo 1 revisa a literatura sobre plataformas de MSA e explica a história das recomendações algorítmicas, desde os primeiros sistemas de Recuperação de Informação até os atuais Sistemas de Recomendação, destacando o uso de diferentes fontes de dados e técnicas. O capítulo também apresenta o quadro teórico (mediation framework) e como ele se aplica às plataformas MSA, antes de delinear a estrutura da tese. O restante da tese está dividido em duas partes. A Parte I se concentra em como os sistemas de recomendação nas MSA moldam o que os usuários podem ver e como os usuários interagem com e na plataforma. A Parte II, por sua vez, investiga como os pesquisadores dão sentido às suas interações online dentro das MSA. O fi nal do Capítulo 1 mostra as opções metodológicas para cada capítulo seguinte. A Parte I apresenta um estudo de caso de uma das plataformas de MSA mais populares em que o walkthrough method foi realizado em quatro etapas (análise de interface, inspeção de código web, análise de patente e consulta à empresa usando o General Data Protection Regulation (GDPR)). No Capítulo 2 é mostrado que quase todo o conteúdo das plataformas ASM é mediado por algoritmos por meio de mecanismos de perfi - lamento, seleção de informações e mercantilização. Também é discutido como a empresa evita explicar o funcionamento dos sistemas de recomendação e a característica de modelagem mútua das plataformas de MSA. O Capítulo 3 explora as distorções e vieses que as plataformas de MSA podem sustentar. Os resultados mostram como o perfi lamento, a datifi cação e a priorização de conteúdo têm o potencial de promover viés de homogeneidade, discriminação o efeito Mateus de vantagem cumulativa na ciência e outras distorções. A Parte II consiste em dois estudos empíricos envolvendo participantes de diferentes países em entrevistas (n=11) e um jogo de pesquisa (n=13). O capítulo 4 apresenta as entrevistas combinadas com a técnica show and tell. Os resultados mostram as percepções dos participantes sobre as aff ordances das MSA, que giram em torno de seis temas principais: (1) ter acesso a conteúdos relevantes; (2) acesso a outros pesquisadores; (3) impacto algorítmico na exposição ao conteúdo; (4) ver e ser visto; (5) limites difusos de potenciais infrações éticas ou legais e (6) quanto mais eu dou, mais eu recebo. Argumentamos que a mediação algorítmica não apenas constrói uma narração do eu, mas também uma narração do outro nas plataformas de MSA, confi gurando uma imagem do outro ao mesmo tempo participativa e produtiva. O capítulo 5 apresenta o processo de design de um jogo de pesquisa e os resultados das sessões empíricas, onde os participantes foram observados enquanto jogavam o jogo. Há dois resultados para o estudo. Primeiro, quais valores humanos os pesquisadores relacionam com recursos algorítmicos nas MSA, sendo os mais proeminentes o estímulo, o universalismo e o autodirecionamento. Em segundo lugar, o papel da abordagem do pesquisador (colaborativa, competitiva ou ambivalente) em tarefas acadêmicas, mostrando as escolhas consequentes que as pessoas fazem em relação aos recursos algorítmicos e as motivações por trás dessas escolhas. Os resultados levaram a quatro perfi s arquetípicos: (1) o leitor colaborativo; (2) o escritor competitivo; (3) o divulgador colaborativo; e (4) o avaliador ambivalente. O capítulo fi nal (Capítulo 6) resume as maneiras pelas quais as plataformas de MSA forjam as percepções das pessoas e as estratégias que as pessoas empregam para usar os sistemas em benefício de suas carreiras, respondendo a cada questão de pesquisa. O capítulo discute ainda as implicações da mediação algorítmica para a comunicação acadêmica e a ciência em geral. A dissertação termina com refl exões sobre a agência humana em ambientes orientados por dados, o papel das inferências algorítmicas na ciência e o desafi o de conciliar as necessidades individuais do usuário com os objetivos mais amplos da comunidade científi ca. Ao fazê-lo, a contribuição desta tese é dupla, (1) fornecendo conhecimento aprofundado sobre o artefato plataformas de MSA, e (2) desdobrando diferentes aspectos da perspectiva humana ao lidar com mediação algorítmica em ASM. Ambas as perspectivas são discutidas à luz de arranjos sociais que são mutuamente moldados por artefatos e práticas

Transcriptional profiling of Aspergillus niger

The industrially important fungus Aspergillus niger feeds naturally on decomposing plant material, of which a significant proportion is lipid. Examination of the A. niger genome sequence suggested that all proteins required for metabolic conversion of lipids are present, including 63 predicted lipases. In contrast to polysaccharide-degrading enzyme networks, not much is known about the signaling and regulatory processes that control lipase expression and activity in fungi. This project was aimed to gain better understanding of lipid degradation mechanisms and how this process is regulated in A. niger, primarily via assessment of its gene transcription levels. Minimizing biological and technical variation is crucial for experiments in which transcription levels are determined, such as microarray and quantitative real-time PCR experiments. However, A. niger is difficult to cultivate in a reproducible way due to its filamentous growth. In addition, the complex processing steps of transcriptomics technologies add non-experimental variation to the biological variation. To reduce this data noise, robust protocols based on a batch-fermentation setup were developed. Variation in this setup was surveyed by examining the fungal transcriptional response towards a pulse of D-xylose. The sources of non-experimental variation were described by variance components analysis. Two-thirds of total variation stems from differences in routine handling of fermentations, but in absolute terms this variation is low. As D-xylose is an inducer of the xylanolytic system, the high reproducibility of cultures for the first time allowed a detailed description of the global fungal transcriptional response towards D-xylose using microarrays. The transcriptional response towards three plant derived oils was examined in another study. Both olive oil and a wheat-gluten extracted oil induce the transcription of genes involved in lipid metabolism and peroxisome assembly, albeit with different expression profiles. The third oil, a plant membrane lipid, did not trigger a transcriptional response. Microarray data are related to the physiology of the fungus. To better understand the general principles that underlie gene regulation and gene transcription, microarray data from cultures grown under mildly and strongly perturbed conditions were analyzed for co-expression of genes. Despite the diverse culturing conditions, co-expressed gene modules could be identified. Some of these modules can be related to biological functions. For some modules, conserved promoter elements were identified, which suggests that genes in these modules are regulated on a transcriptional level. The work described in this thesis shows that (i) high-quality -omics data for A. niger can be generated; that (ii) analysis and interpretation of these data enhances our understanding of the xylanolytic and lipid metabolic regulons; and (iii) that these data give insight into the regulatory mechanisms of this fungus. <br/