Text mining for the biocuration workflow

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community

THE ROLE OF INTERDEPENDENCE IN THE MICRO-FOUNDATIONS OF ORGANIZATION DESIGN: TASK, GOAL, AND KNOWLEDGE INTERDEPENDENCE

Interdependence is a core concept in organization design, yet one that has remained consistently understudied. Current notions of interdependence remain rooted in seminal works, produced at a time when managers’ near-perfect understanding of the task at hand drove the organization design process. In this context, task interdependence was rightly assumed to be exogenously determined by characteristics of the work and the technology. We no longer live in that world, yet our view of interdependence has remained exceedingly task-centric and our treatment of interdependence overly deterministic. As organizations face increasingly unpredictable workstreams and workers co-design the organization alongside managers, our field requires a more comprehensive toolbox that incorporates aspects of agent-based interdependence. In this paper, we synthesize research in organization design, organizational behavior, and other related literatures to examine three types of interdependence that characterize organizations’ workflows: task, goal, and knowledge interdependence. We offer clear definitions for each construct, analyze how each arises endogenously in the design process, explore their interrelations, and pose questions to guide future research

An Analysis of the Abstracts Presented at the Annual Meetings of the Society for Neuroscience from 2001 to 2006

Annual meeting abstracts published by scientific societies often contain rich arrays of information that can be computationally mined and distilled to elucidate the state and dynamics of the subject field. We extracted and processed abstract data from the Society for Neuroscience (SFN) annual meeting abstracts during the period 2001–2006 in order to gain an objective view of contemporary neuroscience. An important first step in the process was the application of data cleaning and disambiguation methods to construct a unified database, since the data were too noisy to be of full utility in the raw form initially available. Using natural language processing, text mining, and other data analysis techniques, we then examined the demographics and structure of the scientific collaboration network, the dynamics of the field over time, major research trends, and the structure of the sources of research funding. Some interesting findings include a high geographical concentration of neuroscience research in the north eastern United States, a surprisingly large transient population (66% of the authors appear in only one out of the six studied years), the central role played by the study of neurodegenerative disorders in the neuroscience community, and an apparent growth of behavioral/systems neuroscience with a corresponding shrinkage of cellular/molecular neuroscience over the six year period. The results from this work will prove useful for scientists, policy makers, and funding agencies seeking to gain a complete and unbiased picture of the community structure and body of knowledge encapsulated by a specific scientific domain

Photometric and Spectroscopic Properties of Type Ia Supernova 2018oh with Early Excess Emission from the Kepler 2 Observations

Supernova (SN) 2018oh (ASASSN-18bt) is the first spectroscopically confirmed Type Ia supernova (SN Ia) observed in the Kepler field. The Kepler data revealed an excess emission in its early light curve, allowing us to place interesting constraints on its progenitor system. Here we present extensive optical, ultraviolet, and near-infrared photometry, as well as dense sampling of optical spectra, for this object. SN 2018oh is relatively normal in its photometric evolution, with a rise time of 18.3 ± 0.3 days and Δm 15(B) = 0.96 ± 0.03 mag, but it seems to have bluer B − V colors. We construct the "UVOIR" bolometric light curve having a peak luminosity of 1.49 × 1043 erg s−1, from which we derive a nickel mass as 0.55 ± 0.04 M ⊙ by fitting radiation diffusion models powered by centrally located 56Ni. Note that the moment when nickel-powered luminosity starts to emerge is +3.85 days after the first light in the Kepler data, suggesting other origins of the early-time emission, e.g., mixing of 56Ni to outer layers of the ejecta or interaction between the ejecta and nearby circumstellar material or a nondegenerate companion star. The spectral evolution of SN 2018oh is similar to that of a normal SN Ia but is characterized by prominent and persistent carbon absorption features. The C ii features can be detected from the early phases to about 3 weeks after the maximum light, representing the latest detection of carbon ever recorded in an SN Ia. This indicates that a considerable amount of unburned carbon exists in the ejecta of SN 2018oh and may mix into deeper layers

Knowledge synthesis with maps of neural connectivity

This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets

Two-Phase Decision Fusion Based On User Preference ∗

In order to prevent information overload on the Web, people sometimes rely on critics ’ evaluations to narrow down their search for favorite objects. However, due to the subjectivity of humans ’ perception, the set of favorite objects varies from one person to the other. This is because each object can be reviewed using different criteria, and different people have different reliance on the same set of criteria and/or critics. Therefore, the task of fusing the decisions of different critics considering the users ’ preferences is challenging. To address this challenge, we introduce a new concept, termed two-phase decision fusion, where both critics and criteria evaluations are considered in order to personalize a search. In each phase, we utilize various decision fusion operators and investigate their interplay. We conducted several empirical experiments within the context of two real-world application domains. Our experimental results indicate that the behaviors of the various decision fusion methods stay consistent across the two different application domains. 1