Systematic Analysis of the Factors Contributing to the Variation and Change of the Microbiome

abstract: Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.Dissertation/ThesisDoctoral Dissertation Biology 201

A Transdisciplinary Emergent Approach for Systems and Interventions (EASI)

In modeling human behavior and social structures several factors can emerge over time this can be attributed to the availability of new data, increased complexity, changes to the organizational structure, interventions, introduction of innovative technology or services and due to improved knowledge and treatments. We hypothesize a new class of emergent decision support systems that continually evolve to account for this Causal Drift . In this work, we illustrate the application of the Emergent Approach to Systems and Intervention (EASI™) methodology with the example of Community Intervention Activity Model (CIAM) to reduce the rate of diabetic hospitalization at the local/ county level. A key contribution of this work is the design of an efficient theoretically informed emergent data collection system. A second key contribution of this work is that it offers practitioners a methodology to systematically determine data that needs to be collected and then model the collected data. Thus EASI™ methodology supports the efficient capture of data that has utility in decision making. To ensure applicability of this work publicly available Behavioral Risk Factor Surveillance System (BRFSS) and Social Vulnerability Index (SVI) data sets have been utilized. The EASI™ method has four significant advantages: a) the prediction is based on theoretically informed causal structure; this allows it to be used as a basis for evaluation of interventions as opposed to deep learning and other machine-based structure learning methods which are susceptible to spurious associations, b) existing data is utilized to evaluate clinical relevance of predictions, c) leveraging high dimensional synthetic observational health data to model health objectives, and d) provides guidance on transformation of system from the emergent basis to the new emergent system as new knowledge is gained. The dissertation proposes, implements, and evaluates the EASI™ methodology as applied to a CAIM for the reduction in diabetic hospitalizations

Using attention methods to predict judicial outcomes

Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants

Naturalism and Process Ontology for Rhetorical Theory and Methodology: Reconsidering the Ideological Tautology

Rhetorical Theory and Criticism primarily features modes of close reading that reconstructs the meaning of a text by constructing meaning through contingent textual moments within a theoretical perspective, typically ideological criticism. The dominant mode of ideological critique projects ideology as an anterior and universal cause; this projection strips individual and group agency from within various systems by totalizing them under one system. I strive to answer how we can preserve descriptive acuity while opening and exploiting contingent gaps to make scholarship more efficacious for social justice. Chapter one explores the inevitability of infinite regress in response to problems of vagueness endemic to the philosophical enterprise. Chapter two explores Bergson’s Retrospective Illusion: strict modes of ontological necessity in a transcendental reasoning pattern produce tautological ontologies in which an effect becomes projected backwards as universal but, ultimately, illusory cause. Chapter three maps out Bergson’s solution to the “Retrospective Illusion” and names it the “Prospective Illusion.” In short, chains of sufficient reasoning are projected out towards tendencies in becoming such that universals are always in construction and never fully actual. Ontologies founded upon spatial necessity are replaced by a process ontology closely attuned to scientific process that folds space and time topologically into tendential becoming. Chapter four applies both illusions to rhetorical theory in its ideological and new materialist modes to argue for the usefulness of both models in breaking rhetorical theory out of its tacit methodological reliance upon reconstructive close reading and by re-evaluating some of rhetorical theory’s ontological assumptions. The project concludes with prospective directions in methodology

Linguistic Variation from Cognitive Variability: The Case of English \u27Have\u27

In this dissertation, I seek to construct a model of meaning variation built upon variability in linguistic structure, conceptual structure, and cognitive makeup, and in doing so, exemplify an approach to studying meaning that is both linguistically principled and neuropsychologically grounded. As my test case, I make use of the English lexical item ‘have\u27 by proposing a novel analysis of its meaning based on its well-described variability in English and its embed- ding into crosslinguistically consistent patterns of variation and change.I support this analysis by investigating its real-time comprehension patterns through behavioral, electropsychophysiological, and hemodynamic brain data, thereby incorporating dimensions of domain-general cognitive variability as crucial determinants of linguistic variability. Per my account, ‘have\u27 retrieves a generalized relational meaning which can give rise to a conceptually constrained range of readings, depending on the degree of causality perceived from either linguistic or contextual cues. Results show that comprehenders can make use of both for ‘have\u27-sentences, though they vary in the degree to which they rely on each.At the very broadest level, the findings support a model in which the semantic distribution of ‘have\u27 is inherently principled due to a unified conceptual structure. This underlying conceptual structure and relevant context cooperate in guiding comprehension by modulating the salience of potential readings, as comprehension unfolds; though, this ability to use relevant context–context-sensitivity–is variable but systematic across comprehenders. These linguistic and cognitive factors together form the core of normal language processing and, with a gradient conceptual framework, the minimal infrastructure for meaning variation and change

Linguistic variation from cognitive variability: the case of English \u27have\u27

In this dissertation, I seek to construct a model of meaning variation built upon variability in linguistic structure, conceptual structure, and cognitive makeup, and in doing so, exemplify an approach to studying meaning that is both linguistically principled and neuropsychologically grounded. As my test case, I make use of the English lexical item \u27have\u27 by proposing a novel analysis of its meaning based on its well-described variability in English and its embedding into crosslinguistically consistent patterns of variation and change. I support this analysis by investigating its real-time comprehension patterns through behavioral, electropsychophysiological, and hemodynamic brain data, thereby incorporating dimensions of domain-general cognitive variability as crucial determinants of linguistic variability. Per my account, \u27have\u27 retrieves a generalized relational meaning which can give rise to a conceptually constrained range of readings, depending on the degree of causality perceived from either linguistic or contextual cues. Results show that comprehenders can make use of both for \u27have\u27-sentences, though they vary in the degree to which they rely on each. At the very broadest level, the findings support a model in which the semantic distribution of \u27have\u27 is inherently principled due to a unified conceptual structure. This underlying conceptual structure and relevant context cooperate in guiding comprehension by modulating the salience of potential readings, as comprehension unfolds; though, this ability to use relevant context--context-sensitivity--is variable but systematic across comprehenders. These linguistic and cognitive factors together form the core of normal language processing and, with a gradient conceptual framework, the minimal infrastructure for meaning variation and change

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Computational Stylistics in Poetry, Prose, and Drama

The contributions in this edited volume approach poetry, narrative, and drama from the perspective of Computational Stylistics. They exemplify methods of computational textual analysis and explore the possibility of computational generation of literary texts. The volume presents a range of computational and Natural Language Processing applications to literary studies, such as motif detection, network analysis, machine learning, and deep learning

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

Music Encoding Conference Proceedings 2021, 19–22 July, 2021 University of Alicante (Spain): Onsite & Online

Este documento incluye los artículos y pósters presentados en el Music Encoding Conference 2021 realizado en Alicante entre el 19 y el 22 de julio de 2022.Funded by project Multiscore, MCIN/AEI/10.13039/50110001103