Omkar Lele

Augmenting cognitive architectures to support diagrammatic imagination.

Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures--Soar and ACT-R, to name the most prominent--do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive architectures. We discuss the degree to which DRS, our earlier proposal for such an internal representation for diagrams, meets these requirements. In DRS, the diagrams are not raw images, but a composition of objects that can be individuated and thus symbolized, while, unlike traditional symbols, the referent of the symbol is an object that retains its perceptual essence, namely, its spatiality. This duality provides a way to resolve what anti-imagists thought was a contradiction in mental imagery: the compositionality of mental images that seemed to be unique to symbol systems, and their support of a perceptual experience of images and some types of perception on them. We briefly review the use of DRS to augment Soar and ACT-R with a diagrammatic representation component. We identify issues for further research.

Enabling distributed electronic research data collection for a rural Appalachian tobacco cessation study

Tobacco use is increasingly prevalent among vulnerable populations, such as people living in rural Appalachian communities. Owing to limited access to a reliable internet service in such settings, there is no widespread adoption of electronic data capture tools for conducting community-based research. By integrating the REDCap data collection application with a custom synchronization tool, the authors have enabled a workflow in which field research staff located throughout the Ohio Appalachian region can electronically collect and share research data. In addition to allowing the study data to be exchanged in near-real-time among the geographically distributed study staff and centralized study coordinator, the system architecture also ensures that the data are stored securely on encrypted laptops in the field and centrally behind the Ohio State University Medical Center enterprise firewall. The authors believe that this approach can be easily applied to other analogous study designs and settings.

Gene expression profiling of the human natural killer cell response to Fc receptor activation: unique enhancement in the presence of interleukin-12

BackgroundTraditionally, the CD56dimCD16+ subset of Natural Killer (NK) cells has been thought to mediate cellular cytotoxicity with modest cytokine secretion capacity. However, studies have suggested that this subset may exert a more diverse array of immunological functions. There exists a lack of well-developed functional models to describe the behavior of activated NK cells, and the interactions between signaling pathways that facilitate effector functions are not well understood. In the present study, a combination of genome-wide microarray analyses and systems-level bioinformatics approaches were utilized to elucidate the transcriptional landscape of NK cells activated via interactions with antibody-coated targets in the presence of interleukin-12 (IL-12).MethodsWe conducted differential gene expression analysis of CD56dimCD16+ NK cells following FcR stimulation in the presence or absence of IL-12. Next, we functionally characterized gene sets according to patterns of gene expression and validated representative genes using RT-PCR. IPA was utilized for biological pathway analysis, and an enriched network of interacting genes was generated using GeneMANIA. Furthermore, PAJEK and the HITS algorithm were employed to identify important genes in the network according to betweeness centrality, hub, and authority node metrics.ResultsAnalyses revealed that CD56dimCD16+ NK cells co-stimulated via the Fc receptor (FcR) and IL-12R led to the expression of a unique set of genes, including genes encoding cytotoxicity receptors, apoptotic proteins, intracellular signaling molecules, and cytokines that may mediate enhanced cytotoxicity and interactions with other immune cells within inflammatory tissues. Network analyses identified a novel set of connected key players, BATF, IRF4, TBX21, and IFNG, within an integrated network composed of differentially expressed genes in NK cells stimulated by various conditions (immobilized IgG, IL-12, or the combination of IgG and IL-12).ConclusionsThese results are the first to address the global mechanisms by which NK cells mediate their biological functions when encountering antibody-coated targets within inflammatory sites. Moreover, this study has identified a set of high-priority targets for subsequent investigation into strategies to combat cancer by enhancing the anti-tumor activity of CD56dimCD16+ NK cells.

Mapping descriptive models of graph comprehension into requirements for a computational architecture: need for supporting imagery operations

Psychologists have developed many models of graph comprehension, most of them descriptive, some computational. We map the descriptive models into requirements for a cognitive architecture that can be used to build predictive computational models. General symbolic architectures such as Act-R and Soar satisfy the requirements except for those to support mental imagery operations required for many graph comprehension tasks. We show how Soar augmented with DRS, our earlier proposal for diagrammatic representation, satisfies many of the requirements, and can be used for modeling the comprehension and use of a graph requiring imagery operations. We identify the need for better computational models of the perception operations and empirical data on their timing and error rates before predictive computational models can become a reality.

The TOKEn project: knowledge synthesis for in silico science

OBJECTIVE The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual Knowledge Discovery in Databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets. METHODS The authors have conducted a multipart model formulation and validation process, focusing on the development of a methodological and technical approach to using CKDD to support hypothesis discovery for in silico science. The model the authors have developed is known as the Translational Ontology-anchored Knowledge Discovery Engine (TOKEn). This model utilizes a specific CKDD approach known as Constructive Induction to identify and prioritize potential hypotheses related to the meaningful semantic relationships between variables found in large-scale and heterogeneous biomedical data sets. RESULTS The authors have verified and validated TOKEn in the context of a translational research data repository maintained by the NCI-funded Chronic Lymphocytic Leukemia Research Consortium. Such studies have shown that TOKEn is: (1) computationally tractable; and (2) able to generate valid and potentially useful hypotheses concerning relationships between phenotypic and biomolecular variables in that data collection. CONCLUSIONS The TOKEn model represents a potentially useful and systematic approach to knowledge synthesis for in silico discovery science in the context of large-scale and multidimensional research data sets.

Research-IQ: Development and evaluation of an ontology-anchored integrative query tool

Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput.

Enabling Open Science for Health Research: Collaborative Informatics Environment for Learning on Health Outcomes (CIELO)

Background There is an emergent and intensive dialogue in the United States with regard to the accessibility, reproducibility, and rigor of health research. This discussion is also closely aligned with the need to identify sustainable ways to expand the national research enterprise and to generate actionable results that can be applied to improve the nation’s health. The principles and practices of Open Science offer a promising path to address both goals by facilitating (1) increased transparency of data and methods, which promotes research reproducibility and rigor; and (2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge in proximal domains, thereby resulting in greater productivity and a reduction in redundant research investments. Objectives AcademyHealth’s Electronic Data Methods (EDM) Forum implemented a proof-of-concept open science platform for health research called the Collaborative Informatics Environment for Learning on Health Outcomes (CIELO). Methods The EDM Forum conducted a user-centered design process to elucidate important and high-level requirements for creating and sustaining an open science paradigm. Results By implementing CIELO and engaging a variety of potential users in its public beta testing, the EDM Forum has been able to elucidate a broad range of stakeholder needs and requirements related to the use of an open science platform focused on health research in a variety of “real world” settings. Conclusions Our initial design and development experience over the course of the CIELO project has provided the basis for a vigorous dialogue between stakeholder community members regarding the capabilities that will add the greatest value to an open science platform for the health research community. A number of important questions around user incentives, sustainability, and scalability will require further community dialogue and agreement.