Interdisciplinary perspectives on the development, integration and application of cognitive ontologies

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data

Automated Annotation of Functional Imaging Experiments via Multi-Label Classification

Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert’s annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k -nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text

The Clinical Assessment and Remote Administration Tablet

Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject’s response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Water clarity is a subjective term and can be measured multiple ways. Different metrics such as light attenuation and Secchi depth vary in effectiveness depending on the research or management application. In this essay, we argue that different questions merit different water clarity metrics. In coastal and inland waters, empirical relationships to estimate light attenuation can yield clarity estimates that either under- or overestimate the underwater light climate for restoration goals, such as potential habitat available for submerged aquatic vegetation. Best practices in reporting water clarity measurements include regionally specific, temporally representative calibrations and communicating the metric that was actually measured. An intentional choice of the water clarity metric best suited to the research or management question yields the most useful results

Intelligent Queries over BIRN Data using the Foundational Model of Anatomy and a Distributed Query-Based Data Integration System

We demonstrate the usefulness of the Foundational Model of Anatomy (FMA) ontology in reconciling different neuroanatomical parcellation schemes in order to facilitate automatic annotation and “intelligent” querying and visualization over a large multisite fMRI study of schizophrenic versus normal controls

Enabling RadLex with the Foundational Model of Anatomy Ontology to Organize and Integrate Neuro-imaging Data

In this study we focused on empowering RadLex with an ontological framework and additional content derived from the Foundational Model of Anatomy Ontology1 thereby providing RadLex the facility to correlate the different standards used in annotating neuroradiological image data. The objective of this work is to promote data sharing, data harmonization and interoperability between disparate neuroradiological labeling systems

Redox-sensitive DNA Binding by Homodimeric Methanosarcina Acetivorans MsvR is Modulated by Cysteine Residues

Background: Methanoarchaea are among the strictest known anaerobes, yet they can survive exposure to oxygen. The mechanisms by which they sense and respond to oxidizing conditions are unknown. MsvR is a transcription regulatory protein unique to the methanoarchaea. Initially identified and characterized in the methanogen Methanothermobacter thermautotrophicus (Mth), MthMsvR displays differential DNA binding under either oxidizing or reducing conditions. Since MthMsvR regulates a potential oxidative stress operon in M. thermautotrophicus, it was hypothesized that the MsvR family of proteins were redox-sensitive transcription regulators. Results: An MsvR homologue from the methanogen Methanosarcina acetivorans, MaMsvR, was overexpressed and purified. The two MsvR proteins bound the same DNA sequence motif found upstream of all known MsvR encoding genes, but unlike MthMsvR, MaMsvR did not bind the promoters of select genes involved in the oxidative stress response. Unlike MthMsvR that bound DNA under both non-reducing and reducing conditions, MaMsvR bound DNA only under reducing conditions. MaMsvR appeared as a dimer in gel filtration chromatography analysis and site-directed mutagenesis suggested that conserved cysteine residues within the V4R domain were involved in conformational rearrangements that impact DNA binding. Conclusions: Results presented herein suggest that homodimeric MaMsvR acts as a transcriptional repressor by binding Ma PmsvR under non-reducing conditions. Changing redox conditions promote conformational changes that abrogate binding to Ma PmsvR which likely leads to de-repression

Automated Collection of Imaging and Phenotypic Data to Centralized and Distributed Data Repositories

Accurate data collection at the ground level is vital to the integrity of neuroimaging research. Similarly important is the ability to connect and curate data in order to make it meaningful and sharable with other investigators. Collecting data, especially with several different modalities, can be time consuming and expensive. These issues have driven the development of automated collection of neuroimaging and clinical assessment data within COINS (Collaborative Informatics and Neuroimaging Suite). COINS is an end-to-end data management system. It provides a comprehensive platform for data collection, management, secure storage, and flexible data retrieval (Bockholt et al., 2010; Scott et al., 2011). It was initially developed for the investigators at the Mind Research Network (MRN), but is now available to neuroimaging institutions worldwide. Self Assessment (SA) is an application embedded in the Assessment Manager (ASMT) tool in COINS. It is an innovative tool that allows participants to fill out assessments via the web-based Participant Portal. It eliminates the need for paper collection and data entry by allowing participants to submit their assessments directly to COINS. Instruments (surveys) are created through ASMT and include many unique question types and associated SA features that can be implemented to help the flow of assessment administration. SA provides an instrument queuing system with an easy-to-use drag and drop interface for research staff to set up participants’ queues. After a queue has been created for the participant, they can access the Participant Portal via the internet to fill out their assessments. This allows them the flexibility to participate from home, a library, on site, etc. The collected data is stored in a PostgresSQL database at MRN. This data is only accessible by users that have explicit permission to access the data through their COINS user accounts and access to MRN network. This allows for high volume data collection and with minimal user access to PHI (protected health information). An added benefit to using COINS is the ability to collect, store and share imaging data and assessment data with no interaction with outside tools or programs. All study data collected (imaging and assessment) is stored and exported with a participant’s unique subject identifier so there is no need to keep extra spreadsheets or databases to link and keep track of the data. Data is easily exported from COINS via the Query Builder and study portal tools, which allow fine grained selection of data to be exported into comma separated value file format for easy import into statistical programs. There is a great need for data collection tools that limit human intervention and error while at the same time providing users with intuitive design. COINS aims to be a leader in database solutions for research studies collecting data from several different modalities

Aquilegia, Vol. 34 No. 2, Summer 2010, Newsletter of the Colorado Native Plant Society

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