The Syntax of Non-syntactic Dependencies

In this dissertation I explore the nature of interpretive dependencies in human language. In particular I investigate the limits of syntactically mediated interpretive dependencies as well as non-syntactic ones. Broadly speaking I investigate the limits of grammatical dependencies and note that current theory cannot possibly handle certain dependencies. That certain dependencies evade grammatical explanation requires a rethinking of the representations of those dependencies. The results of this investigation concern the primacy and the purview of the syntax component of the grammar. In short, the purview of syntactic relations is limited to c-command and if a c-command relation holds between two related elements, a syntactic relation must hold between them, either directly or indirectly. When c-command does not hold between the related elements, a syntactic dependency is not possible and the dependency must hold at a subsequent level of representation. To show this, I explore interpretive dependencies that I argue only superficially resemble standard, syntactically-mediated relations (such as Wh-gap dependencies). I show that these dependencies are not amenable to analysis as syntactically-mediated relations. These include Coordinated-Wh Questions like those explored in Gracanin-Yuksek 2007, Right Node Raising constructions like those explored in Postal 1974, and Across-the-board constructions like those explored in Williams 1978. Each of these involves an interpretive dependency that I claim cannot be derived syntactically. The above constructions evade explanation via traditional syntactic tools as well as semantic and pragmatic means of analysis. If the above constructions involve dependencies that cannot be construed as syntactically-, semantically-, or pragmatically-mediated, it must be the case that these otherwise normal dependencies are captured via other means, whatever that may be

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data