The First Part of ‘Text Analysis’ is ‘Text’: Applying Digital Methods to an Under-Documented Language

Digital Humanities Seminar, University of Kansas, Institute for Digital Research in the Humanities & Hall Center for the Humanities, September 29, 2014: http://idrh.ku.edu Matt Menzenski is in the Department of Slavic Languages and Literatures.The digitization and curation of large bodies of text has inspired and encouraged new methods of research into language and literature, but only into those languages for which such corpora have been established. What sort of strategies are available to a researcher wishing to apply these research methods to a language which is not yet represented in a digitized collection? Is construction of a text corpus a feasible task for a researcher more interested in human languages than in programming languages? This talk provides a case study of the creation of a small text corpus for Tohono O’odham (an endangered language of the Southwest), the use of that corpus to investigate questions about the way that verbs are used in narratives, and more broadly, the sometimes unexpected ways in which the development of a text corpus can influence the research process

Introduction to Text Analysis with Python and the Natural Language Toolkit

<p>The Python programming language is famously 'human-readable', which makes using it straightforward, even for humanists with no prior programming experience. This three-hour workshop covers the basics of text analysis in Python, using the Natural Language Toolkit (NLTK), a collection of linguistic tools which make Python a very powerful tool for working with language data. Topics covered include getting up and running with Python and the NLTK as well as fundamental text processing tasks such as tokenizing a text, counting word frequencies, finding collocations, finding specific words, and constructing simple plots.</p

Aspectual Morphemes as Verb Classifiers in Slavic and Non-Slavic Languages

<p>This paper was presented at the Slavic Linguistics Society Annual Meeting in Seattle, Washington on September 19 2014.</p> <p> </p> <p>Abstract:</p> <p>Janda et al. (2013) propose an analysis of Russian aspectual prefixes as verb classifiers, arguing that the prefix which forms the 'natural perfective' from a given verb serves to classify that verb according to its semantic characteristics. This analysis contrasts with the traditional analysis of Russian aspect, described by Tixonov (1998) and others, in which natural perfectives are formed via the addition of 'empty' prefixes which contribute no semantic content of their own. In Tohono O’odham (formerly known as Papago, an Uto-Aztecan language spoken in present-day Arizona and Mexico), as in Russian, there is a broad two-way distinction between two aspects, perfective and imperfective (Saxton 1982:232). In a fashion similar to the traditional aspectological assumption that there are lexically empty perfectivizing prefixes in Russian, traditional analyses of O’odham aspect posit a process of lexically empty perfectivization. In O’odham, the perfective is usually considered to be formed from the imperfective by truncation of the final consonant, in large part due to a lack of clear and separable semantic content in that segment (Mason 1950; Hale 1965; Saxton 1982; Zepeda 1983; Hill & Zepeda 1992; Kosa 2008). In this paper, I argue instead that O’odham imperfective verbs are formed from perfective verbs by suffixation, and that the suffixes involved are not ‘empty’, but serve a verb-classifying function similar to that of aspectual prefixes in Russian. Imperfectivizing suffixes have been proposed in the literature, by Dolores (1913) and Stonham (1994), but this hypothesis has not yet been systematically investigated. This paper represents a first attempt to do so. Data for this study are drawn from a nearly 5,000-verb database created from a two-volume dictionary of Tohono O’odham usage (Mathiot 1973a; 1973b). A preliminary analysis of these verbs supports a correlation between the final consonant of the imperfective and the verb’s lexical semantics, demonstrating that like those of Russian, O’odham aspectual morphemes fulfill the criteria for a verb classifier system as described by McGregor (2002), and providing further evidence that research on Slavic aspect can inform typological studies of verbal aspect cross-linguistically.</p

Masking and Triggered Unmasking of Targeting Ligands on Liposomal Chemotherapy Selectively Suppress Tumor Growth <i>in Vivo</i>

We investigated the feasibility and efficacy of a drug delivery strategy to vascularized cancer that combines targeting selectivity with high uptake by targeted cells and high bioexposure of cells to delivered chemotherapeutics. Targeted lipid vesicles composed of pH responsive membranes were designed to reversibly form phase-separated lipid domains, which are utilized to tune the vesicle’s apparent functionality and permeability. During circulation, vesicles mask functional ligands and stably retain their contents. Upon extravasation in the tumor interstitium, ligand-labeled lipids become unmasked and segregated within lipid domains triggering targeting to cancer cells followed by internalization. In the acidic endosome, vesicles burst release the encapsulated therapeutics through leaky boundaries around the phase-separated lipid domains. The pH tunable vesicles contain doxorubicin and are labeled with an anti-HER2 peptide. <i>In vitro,</i> anti-HER2 pH tunable vesicles release doxorubicin in a pH dependent manner, and exhibit 233% increase in binding to HER2-overexpressing BT474 breast cancer cells with lowering pH from 7.4 to 6.5 followed by significant (50%) internalization. In subcutaneous BT474 xenografts in nude mice, targeted pH tunable vesicles decrease tumor volumes by 159% relative to nontargeted vesicles, and they also exhibit better tumor control by 11% relative to targeted vesicles without an unmasking property. These results suggest the potential of pH tunable vesicles to ultimately control tumor growth at relatively lower administered doses

Sticky Patches on Lipid Nanoparticles Enable the Selective Targeting and Killing of Untargetable Cancer Cells

Effective targeting by uniformly functionalized nanoparticles is limited to cancer cells expressing at least two copies of targeted receptors per nanoparticle footprint (approximately ≥2 × 10⁵ receptor copies per cell); such a receptor density supports the required multivalent interaction between the neighboring receptors and the ligands from a single nanoparticle. To enable selective targeting below this receptor density, ligands on the surface of lipid vesicles were displayed in clusters that were designed to form at the acidic pH of the tumor interstitium. Vesicles with clustered HER2-targeting peptides within such sticky patches (sticky vesicles) were compared to uniformly functionalized vesicles. On HER2-negative breast cancer cells MDA-MB-231 and MCF7 {expressing (8.3 ± 0.8) × 10⁴ and (5.4 ± 0.9) × 10⁴ HER2 copies per cell, respectively}, only the sticky vesicles exhibited detectable specific targeting (<i>K</i>_D ≈ 49–69 nM); dissociation (0.005–0.009 min^–1) and endocytosis rates (0.024–0.026 min^–1) were independent of HER2 expression for these cells. MDA-MB-231 and MCF7 were killed only by sticky vesicles encapsulating doxorubicin (32–40% viability) or α-particle emitter ²²⁵Ac (39–58% viability) and were not affected by uniformly functionalized vesicles (>80% viability). Toxicities on cardiomyocytes and normal breast cells (expressing HER2 at considerably lower but not insignificant levels) were not observed, suggesting the potential of tunable clustered ligand display for the selective killing of cancer cells with low receptor densities