Integrating natural language processing and pragmatic argumentation theories for argumentation support

Natural language processing (NLP) research and design that aims to model and detect opposition in text for the purpose of opinion classification, sentiment analysis, and meeting tracking, generally excludes the interactional, pragmatic aspects of online text. We propose that a promising direction for NLP is to incorporate the insights of pragmatic, dialectical theories of argumentation to more fully exploit the potential of NLP to offer sound, robust systems for various kinds of argumentation support

Accessing and browsing 3D anatomical images with a navigational ontology.

The problem that our research addresses is the lack of a comprehensive, universally useful system for navigating 3D images ofanatomical structures. In this paper we discuss the organization of anatomical information in a navigational ontology, a knowledge representation formalism that supports intelligent browsing of 3D anatomical images. For the purposes ofthis project, 'intelligent' means that the computer system behaves as if it had accurate knowledge of human anatomy consistent with that of a trained anatomist (though not necessarily as complete). To give a simple example, if the user asks to see the component structures of the urinary system, the system will return to the user either a list of structures and/or a model of them, just as an anatomy instructor might do. The Vesalius Anatomy Browser provides an interface for navigating 3D anatomical images in which anatomical images are linked to a hierarchical representation of conceptual information that corresponds directly to the images displayed on the screen. The association of the concepts with images makes possible simultaneous visual exploration of anatomical information via word and image

Difícil de usar: La interfase humana como elemento de diseño, desarrollo e investigación

La interfase humana es el punto de contacto entre el ser humano y los objetos que utiliza diariamente. A medida que un objeto cotidiano específico multiplica sus prestaciones, complejiza proporcionalmente esa interfase. Esta charla pretende abrir un camino de experimentación y estudio en el desarrollo de interfases gráficas aplicadas tanto a objetos corpóreos como gráficos

Evolutionary Inference in Transposable Elements

Transposable elements (TEs) are a large component of many eukaryotic genomes, and the evolution of TEs is closely connected to that of their hosts. Accurate inference of TE evolutionary relationships is essential to understanding the biology and evolution of TE families and the role they play in genome evolution. Additionally, the great quantity of TEs makes them a useful model system for understanding genomic processes such as mutation and recombination, and their utility as a research system also depends on accurate evolutionary inference. In this dissertation, I describe novel computational methods for evolutionary inference in TEs, applying them primarily to the Alu family of primate retroelements. A major task in TE evolutionary study is the classification of elements of a family into subfamilies. I developed the AnTE algorithm, a Bayesian approach to subfamily classification that, in contrast to previous deterministic methods, allows for probabilistic subfamily classification, an important advance due to the high uncertainty involved. I use AnTE to provide a more complete picture of the evolutionary history of Alu elements than provided by previous analyses, especially regarding the role of gene conversion. This work suggests that current Alu subfamily classification found in widely-used databases such as RepeatMasker and RepBase provides a misleading account of Alu evolutionary relationships. Building on the AnTE research, I developed a Bayesian phylogenetics approach to the detection and characterization of gene conversion events among TEs in a genome. I use this approach to identify a burst of interlocus gene conversion among Alu elements in the gorilla genome, occurring at much higher rates than on any other branch of the Great Ape phylogeny. Abnormally high Alu gene conversion rates in gorilla appear to be driven by binding to Alu by PRDM9, a rapidly-evolving protein that targets DNA sequence motifs for double-strand breaks in meiosis. These findings indicate one evolutionary pathway for rapid gene conversion in a TE family, and the conversion events identified provide a rich dataset for understanding the dynamics of gene conversion in primates

Population Substructure and Control Selection in Genome-Wide Association Studies

Determination of the relevance of both demanding classical epidemiologic criteria for control selection and robust handling of population stratification (PS) represents a major challenge in the design and analysis of genome-wide association studies (GWAS). Empirical data from two GWAS in European Americans of the Cancer Genetic Markers of Susceptibility (CGEMS) project were used to evaluate the impact of PS in studies with different control selection strategies. In each of the two original case-control studies nested in corresponding prospective cohorts, a minor confounding effect due to PS (inflation factor λ of 1.025 and 1.005) was observed. In contrast, when the control groups were exchanged to mimic a cost-effective but theoretically less desirable control selection strategy, the confounding effects were larger (λ of 1.090 and 1.062). A panel of 12,898 autosomal SNPs common to both the Illumina and Affymetrix commercial platforms and with low local background linkage disequilibrium (pair-wise r2<0.004) was selected to infer population substructure with principal component analysis. A novel permutation procedure was developed for the correction of PS that identified a smaller set of principal components and achieved a better control of type I error (to λ of 1.032 and 1.006, respectively) than currently used methods. The overlap between sets of SNPs in the bottom 5% of p-values based on the new test and the test without PS correction was about 80%, with the majority of discordant SNPs having both ranks close to the threshold. Thus, for the CGEMS GWAS of prostate and breast cancer conducted in European Americans, PS does not appear to be a major problem in well-designed studies. A study using suboptimal controls can have acceptable type I error when an effective strategy for the correction of PS is employed

Enhancing collaboration and community for the discipline of organizing

The overall purpose of this workshop is to strengthen the existing collaboration and community among instructors and schools using The Discipline of Organizing (Glushko 2015), to promote further innovation in digital publishing, and to enhance iSchool teaching practices through experimentation with new models of collaborative courses. Information about participation, planning materials, presentations, and follow-up artifacts for the workshop are at disciplineoforganizing.org.

A Flexible Bayesian Model for Studying Gene–Environment Interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant

Modeling and analysis of disease and risk factors through learning Bayesian networks from observational data

This paper focuses on identification of the relationships between a disease and its potential risk factors using Bayesian networks in an epidemiologic study, with the emphasis on integrating medical domain knowledge and statistical data analysis. An integrated approach is developed to identify the risk factors associated with patients' occupational histories and is demonstrated using real-world data. This approach includes several steps. First, raw data are preprocessed into a format that is acceptable to the learning algorithms of Bayesian networks. Some important considerations are discussed to address the uniqueness of the data and the challenges of the learning. Second, a Bayesian network is learned from the preprocessed data set by integrating medical domain knowledge and generic learning algorithms. Third, the relationships revealed by the Bayesian network are used for risk factor analysis, including identification of a group of people who share certain common characteristics and have a relatively high probability of developing the disease, and prediction of a person's risk of developing the disease given information on his/her occupational history. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58076/1/893_ftp.pd

A regression model for risk difference estimation in population-based case-control studies clarifies gender differences in lung cancer risk of smokers and never smokers

BACKGROUND: Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case-control studies. METHODS: Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case-control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002-2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables. RESULTS: In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons. CONCLUSIONS: In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case-control studies

An invitation to grieve: reconsidering critical incident responses by support teams in the school setting

This paper proposes that consideration could be given to an invitational intervention rather than an expectational intervention when support personnel respond to a critical incident in schools. Intuitively many practitioners know that it is necessary for guidance/counselling personnel to intervene in schools in and following times of trauma. Most educational authorities in Australia have mandated the formulation of a critical incident intervention plan. This paper defines the term critical incident and then outlines current intervention processes, discussing the efficacy of debriefing interventions. Recent literature suggests that even though it is accepted that a planned intervention is necessary, there is scant evidence as to the effectiveness of debriefing interventions in stemming later symptoms of post traumatic stress disorder. The authors of this paper advocate for an expressive therapy intervention that is invitational rather than expectational, arguing that not all people respond to trauma in the same way and to expect that they will need to recall and retell what has happened is most likely a dangerous assumption. A model of invitation using Howard Gardner’s (1983) multiple intelligences is proposed so that students are invited to grieve and understand emotionally what is happening to them following a critical incident