Knowledge Questions from Knowledge Graphs

We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach

Discovery of Dual-Action Membrane-Anchored Modulators of Incretin Receptors

The glucose-dependent insulinotropic polypeptide (GIP) and the glucagon-like peptide-1 (GLP-1) receptors are considered complementary therapeutic targets for type 2 diabetes. Using recombinant membrane-tethered ligand (MTL) technology, the present study focused on defining optimized modulators of these receptors, as well as exploring how local anchoring influences soluble peptide function.Serial substitution of residue 7 in membrane-tethered GIP (tGIP) led to a wide range of activities at the GIP receptor, with [G(7)]tGIP showing enhanced efficacy compared to the wild type construct. In contrast, introduction of G(7) into the related ligands, tGLP-1 and tethered exendin-4 (tEXE4), did not affect signaling at the cognate GLP-1 receptor. Both soluble and tethered GIP and GLP-1 were selective activators of their respective receptors. Although soluble EXE4 is highly selective for the GLP-1 receptor, unexpectedly, tethered EXE4 was found to be a potent activator of both the GLP-1 and GIP receptors. Diverging from the pharmacological properties of soluble and tethered GIP, the newly identified GIP-R agonists, (i.e. [G(7)]tGIP and tEXE4) failed to trigger cognate receptor endocytosis. In an attempt to recapitulate the dual agonism observed with tEXE4, we conjugated soluble EXE4 to a lipid moiety. Not only did this soluble peptide activate both the GLP-1 and GIP receptors but, when added to receptor expressing cells, the activity persists despite serial washes.These findings suggest that conversion of a recombinant MTL to a soluble membrane anchored equivalent offers a means to prolong ligand function, as well as to design agonists that can simultaneously act on more than one therapeutic target

A method for the isolation of human gastric mucous epithelial cells for primary cell culture: A comparison of biopsy vs surgical tissue

We have developed a method for the isolation and growth of normal human gastric mucous epithelial cells using biopsies or surgically resected tissues as the source of the cells. The attachment and growth of cells were dependent upon: (1) cell planting density, ∼50,000 cells/cm 2 ; (2) extracellular matrix (fibronectin); and (3) and the use of a porous filter. In all experiments we found better cells attachment and growth of human gastric mucous cells isolated from surgical specimens compared with those gastric mucous cells isolated from gastric biopsies. The initial cell viability (as measured by Trypan-blue) was the same in both populations of gastric mucous epithelial cells isolated from either gastric biopsies or surgical specimens. After 4–5 days in culture one could detect various amounts of mucin in all the cells using either periodic acid Schiff (PAS) staining or a specific anti-mucin antibody. A similar pattern of much straining was also found in primary cultures of guinea pig gastric mucous epithelial cells. Immunohistochemical staining for chief cells (anti-pepsinogen) or parietal cells (anti-H + /K + ATPasc) in the gastric mucous cuboidal-like epithelial cells with tight junctions, desmosomes,short microvilli, a filamentous terminal web, mucous granules, and basal lamina-like structure. We could not detect the presence of fibroblasts during the 7–9 days that the primary cells were in culture. This cell culture method will prove useful in the isolation of normal human gastric mucous epithelial cells for in vitro studies of gastric mucosal injury and repair.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43235/1/11022_2004_Article_BF00127904.pd

Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains

In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models. ReStA is a variant of the popular representational similarity analysis (RSA) in cognitive neuroscience. While RSA can be used to compare representations in models, model components, and human brains, ReStA compares instances of the same model, while systematically varying single model parameter. Using ReStA, we study four recent and successful neural language models, and evaluate how sensitive their internal representations are to the amount of prior context. Using RSA, we perform a systematic study of how similar the representational spaces in the first and second (or higher) layers of these models are to each other and to patterns of activation in the human brain. Our results reveal surprisingly strong differences between language models, and give insights into where the deep linguistic processing, that integrates information over multiple sentences, is happening in these models. The combination of ReStA and RSA on models and brains allows us to start addressing the important question of what kind of linguistic processes we can hope to observe in fMRI brain imaging data. In particular, our results suggest that the data on story reading from Wehbe et al./ (2014) contains a signal of shallow linguistic processing, but show no evidence on the more interesting deep linguistic processing.<br/