Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them. This has resulted in the development of models which treat conversation as a sequenceto-sequence generation task (i.e., given a sequence of utterances generate the response sequence). This is not only an overly simplistic view of conversation but it is also emphatically different from the way humans converse by heavily relying on their background knowledge about the topic (as opposed to simply relying on the previous sequence of utterances). For example, it is common for humans to (involuntarily) produce utterances which are copied or suitably modified from background articles they have read about the topic. To facilitate the development of such natural conversation models which mimic the human process of conversing, we create a new dataset containing movie chats wherein each response is explicitly generated by copying and/or modifying sentences from unstructured background knowledge such as plots, comments and reviews about the movie. We establish baseline results on this dataset (90K utterances from 9K conversations) using three different models: (i) pure generation based models which ignore the background knowledge (ii) generation based models which learn to copy information from the background knowledge when required and (iii) span prediction based models which predict the appropriate response span in the background knowledge.

IL-13 is an important mediator of inflammation and remodeling. We hypothesized that adenosine accumulation, alterations in adenosine receptors, and adenosine–IL-13 autoinduction are critical events in IL-13–induced pathologies. To test this, we characterized the effects of IL-13 overexpression on the levels of adenosine, adenosine deaminase (ADA) activity, and adenosine receptors in the murine lung. We also determined whether adenosine induced IL-13 in lungs from ADA-null mice. IL-13 induced an inflammatory and remodeling response that caused respiratory failure and death. During this response, IL-13 caused a progressive increase in adenosine accumulation, inhibited ADA activity and mRNA accumulation, and augmented the expression of the A1, A2B, and A3 but not the A2A adenosine receptors. ADA enzyme therapy diminished the IL-13–induced increase in adenosine, inhibited IL-13–induced inflammation, chemokine elaboration, fibrosis, and alveolar destruction, and prolonged the survival of IL-13–transgenic animals. In addition, IL-13 was strongly induced by adenosine in ADA-null mice. These findings demonstrate that adenosine and adenosine signaling contribute to and influence the severity of IL-13–induced tissue responses. They also demonstrate that IL-13 and adenosine stimulate one another in an amplification pathway that may contribute to the nature, severity, progression, and/or chronicity of IL-13 and/or Th2-mediated disorders

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Adenosine is a signaling nucleoside that is elevated in the lungs of asthmatics. We have engineered a mouse model that has elevated levels of adenosine as a result of the partial expression of the enzyme that metabolizes adenosine, adenosine deaminase (ADA). Mice with lowered levels of ADA enzymatic activity were generated by the ectopic expression of an ADA minigene in the gastrointestinal tract of otherwise ADA-deficient mice. These mice developed progressive lung inflammation and damage and died at 4–5 mo of age from respiratory distress. Associated with this phenotype was a progressive increase in lung adenosine levels. Examination of airway physiology at 6 wk of age revealed alterations in airway hyperresponsiveness. This was reversed following the lowering of adenosine levels using ADA enzyme therapy and also through the use of the adenosine receptor antagonist theophylline, implicating both the nucleoside and its receptors in airway physiological alterations. All four adenosine receptors were expressed in the lungs of both control and partially ADA-deficient mice. However, transcript levels for the A1, A2B, and A3 adenosine receptors were significantly elevated in partially ADA-deficient lungs. There was a significant increase in alveolar macrophages, and monocyte chemoattractant protein-3 was found to be elevated in the bronchial epithelium of these mice, which may have important implications in the regulation of pulmonary inflammation and airway hyperresponsiveness. Collectively, these findings suggest that elevations in adenosine can directly impact lung inflammation and physiology.

Rationale: Microarray technology is widely employed for studying the molecular mechanisms underlying complex diseases. However, analyses of individual diseases or models of diseases frequently yield extensive lists of differentially expressed genes with uncertain relationships to disease pathogenesis. Objectives: To compare gene expression changes in a heterogeneous set of lung disease models in order to identify common gene expression changes seen in diverse forms of lung pathology, as well as relatively small subsets of genes likely to be involved in specific pathophysiological processes. Methods: We profiled lung gene expression in 12 mouse models of infection, allergy, and lung injury. A linear model was used to estimate transcript expression changes for each model, and hierarchical clustering was used to compare expression patterns between models. Selected expression changes were verified by quantitative polymerase chain reaction. Measurements and Main Results: A total of 24 transcripts, including many involved in inflammation and immune activation, were differentially expressed in a substantial majority (9 or more) of the models. Expression patterns distinguished three groups of models: (1) bacterial infection (n 5 5), with changes in 89 transcripts, including many related to nuclear factor-kB signaling, cytokines, chemokines, and their receptors; (2) bleomycin-induced diseases (n 5 2), with changes in 53 transcripts, including many related to matrix remodeling and Wnt signaling; and (3) T helper cell type 2 (allergic) inflammation (n 5 5), with changes in 26 transcripts, including many encoding epithelial secreted molecules, ion channels, and transporters. Conclusions: This multimodel dataset highlights novel genes likely involved in various pathophysiological processes and will be a valuable resource for the investigation of molecular mechanisms underlying lung disease pathogenesis.