Knowledge Author: Facilitating user-driven, Domain content development to support clinical information extraction

Background: Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician's domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. Results: Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author's schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system's performance. The first study evaluated Knowledge Author's flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76%) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author's output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author's domain content produced high recall for concepts (targeted findings: 86%) and varied recall for modifiers (certainty: 91% sidedness: 80%, neurovascular anatomy: 46%). Conclusion: Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts

Collaborative research and development (R&D) for climate technology transfer and uptake in developing countries: Towards a needs driven approach

While international cooperation to facilitate the transfer and uptake of climate technologies in developing countries is an ongoing part of climate policy conversations, international collaborative R&D has received comparatively little attention. Collaborative R&D, however, could be a potentially important contributor to facilitating the transfer and uptake of climate technologies in developing countries. But the complexities of international collaborative R&D options and their distributional consequences have been given little attention to date. This paper develops a systematic approach to informing future empirical research and policy analysis on this topic. Building on insights from relevant literature and analysis of empirical data based on a sample of existing international climate technology R&D initiatives, three contributions are made. First, the paper analyses the coverage of existing collaborative R&D efforts in relation to climate technologies, highlighting some important concerns, such as a lack of coverage of lower-income countries or adaptation technologies. Second, it provides a starting point for further systematic research and policy thinking via the development of a taxonomic approach for analysing collaborative designs. Finally, it matches characteristics of R&D collaborations against developing countries’ climate technology needs to provide policymakers with guidance on how to Configure R&D collaborations to meet these needs

Marginalization of end-use technologies in energy innovation for climate protection

Mitigating climate change requires directed innovation efforts to develop and deploy energy technologies. Innovation activities are directed towards the outcome of climate protection by public institutions, policies and resources that in turn shape market behaviour. We analyse diverse indicators of activity throughout the innovation system to assess these efforts. We find efficient end-use technologies contribute large potential emission reductions and provide higher social returns on investment than energy-supply technologies. Yet public institutions, policies and financial resources pervasively privilege energy-supply technologies. Directed innovation efforts are strikingly misaligned with the needs of an emissions-constrained world. Significantly greater effort is needed to develop the full potential of efficient end-use technologies

Building professional discourse in emerging markets: Language, context and the challenge of sensemaking

Using ethnographic evidence from the former Soviet republics, this article examines a relatively new and mainly unobserved in the International Business (IB) literature phenomenon of communication disengagement that manifests itself in many emerging markets. We link it to the deficiencies of the local professional business discourse rooted in language limitations reflecting lack of experience with the market economy. This hampers cognitive coherence between foreign and local business entities, adding to the liability of foreignness as certain instances of professional experience fail to find adequate linguistic expression, and complicates cross-cultural adjustments causing multi-national companies (MNCs) financial losses. We contribute to the IB literature by examining cross-border semantic sensemaking through a retrospectively constructed observational study. We argue that a relative inadequacy of the national professional idiom is likely to remain a feature of business environment in post-communist economies for some time and therefore should be factored into business strategies of MNCs. Consequently, we recommend including discursive hazards in the risk evaluation of international projects

Trisomy of a Down Syndrome Critical Region Globally Amplifies Transcription via HMGN1 Overexpression

Down syndrome (DS, trisomy 21) is associated with developmental abnormalities and increased leukemia risk. To reconcile chromatin alterations with transcriptome changes, we performed paired exogenous spike-in normalized RNA and chromatin immunoprecipitation sequencing in DS models. Absolute normalization unmasks global amplification of gene expression associated with trisomy 21. Overexpression of the nucleosome binding protein HMGN1 (encoded on chr21q22) recapitulates transcriptional changes seen with triplication of a Down syndrome critical region on distal chromosome 21, and HMGN1 is necessary for B cell phenotypes in DS models. Absolute exogenous-normalized chromatin immunoprecipitation sequencing (ChIP-Rx) also reveals a global increase in histone H3K27 acetylation caused by HMGN1. Transcriptional amplification downstream of HMGN1 is enriched for stage-specific programs of B cells and B cell acute lymphoblastic leukemia, dependent on the developmental cellular context. These data offer a mechanistic explanation for DS transcriptional patterns and suggest that further study of HMGN1 and RNA amplification in diverse DS phenotypes is warranted. How trisomy 21 contributes to Down syndrome phenotypes, including increased leukemia risk, is not well understood. Mowery et al. use per-cell normalization approaches to reveal global transcriptional amplification in Down syndrome models. HMGN1 overexpression is sufficient to induce these alterations and promotes lineage-associated transcriptional programs, signaling, and B cell progenitor phenotypes

Alliances and the innovation performance of corporate and public research spin-off firms

We explore the innovation performance benefits of alliances for spin-off firms, in particular spin-offs either from other firms or from public research organizations. During the early years of the emerging combinatorial chemistry industry, the industry on which our empirical analysis focuses, spin-offs engaged in alliances with large and established partners, partners of similar type and size, and with public research organizations, often for different reasons. We seek to understand to what extent alliances of spin-offs with other firms (either large- or small- and medium-sized firms) affected their innovation performance and also how this performance may have been affected by their corporate or public research background. We find evidence that in general alliances of spin-offs with other firms, in particular alliances with large firms, increased their innovation performance. Corporate spin-offs that formed alliances with other firms outperformed public research spin-offs with such alliances. This suggests that, in terms of their innovation performance, corporate spin-offs that engaged in alliances with other firms seemed to have benefitted from their prior corporate background. Interestingly, it turns out that the negative impact of alliances on the innovation performance of public research spin-offs was largely affected by their alliances with small- and medium-sized firms

Proximity Dimensions and Scientific Collaboration among Academic Institutions in Europe: The Closer, the Better?

The main objective of this paper is to examine the effect of various proximity dimensions (geographical, cognitive, institutional, organizational, social and economic) on academic scientific collaborations (SC). The data to capture SC consists of a set of co-authored articles published between 2006 and 2010 by universities located in EU-15, indexed by the Science Citation Index (SCI Expanded) of the ISI Web of Science database. We link this data to institution-level information provided by the EUMIDA dataset. Our final sample consists of 240,495 co-authored articles from 690 European universities that featured in both datasets. Additionally, we also retrieved data on regional R&D funding from Eurostat. Based on the gravital equation, we estimate several econometrics models using aggregated data from all disciplines as well as separated data for Chemistry & Chemical Engineering, Life Sciences and Physics & Astronomy. Our results provide evidence on the substantial role of geographical, cognitive, institutional, social and economic distance in shaping scientific collaboration, while the effect of organizational proximity seems to be weaker. Some differences on the relevance of these factors arise at discipline level