Eta Carinae and the Luminous Blue Variables

We evaluate the place of Eta Carinae amongst the class of luminous blue variables (LBVs) and show that the LBV phenomenon is not restricted to extremely luminous objects like Eta Car, but extends luminosities as low as log(L/Lsun) = 5.4 - corresponding to initial masses ~25 Msun, and final masses as low as ~10-15 Msun. We present a census of S Doradus variability, and discuss basic LBV properties, their mass-loss behaviour, and whether at maximum light they form pseudo-photospheres. We argue that those objects that exhibit giant Eta Car-type eruptions are most likely related to the more common type of S Doradus variability. Alternative atmospheric models as well as sub-photospheric models for the instability are presented, but the true nature of the LBV phenomenon remains as yet elusive. We end with a discussion on the evolutionary status of LBVs - highlighting recent indications that some LBVs may be in a direct pre-supernova state, in contradiction to the standard paradigm for massive star evolution.Comment: 27 pages, 6 figures, Review Chapter in "Eta Carinae and the supernova imposters" (eds R. Humphreys and K. Davidson) new version submitted to Springe

Corticortophin releasing factor 2 receptor agonist treatment significantly slows disease progression in mdx mice

<p>Abstract</p> <p>Background</p> <p>Duchenne muscular dystrophy results from mutation of the dystrophin gene, causing skeletal and cardiac muscle loss of function. The mdx mouse model of Duchenne muscular dystrophy is widely utilized to evaluate the potential of therapeutic regimens to modulate the loss of skeletal muscle function associated with dystrophin mutation. Importantly, progressive loss of diaphragm function is the most consistent striated muscle effect observed in the mdx mouse model, which is the same as in patients suffering from Duchenne muscular dystrophy.</p> <p>Methods</p> <p>Using the mdx mouse model, we have evaluated the effect that corticotrophin releasing factor 2 receptor (CRF2R) agonist treatment has on diaphragm function, morphology and gene expression.</p> <p>Results</p> <p>We have observed that treatment with the potent CRF2R-selective agonist PG-873637 prevents the progressive loss of diaphragm specific force observed during aging of mdx mice. In addition, the combination of PG-873637 with glucocorticoids not only prevents the loss of diaphragm specific force over time, but also results in recovery of specific force. Pathological analysis of CRF2R agonist-treated diaphragm muscle demonstrates that treatment reduces fibrosis, immune cell infiltration, and muscle architectural disruption. Gene expression analysis of CRF2R-treated diaphragm muscle showed multiple gene expression changes including globally decreased immune cell-related gene expression, decreased extracellular matrix gene expression, increased metabolism-related gene expression, and, surprisingly, modulation of circadian rhythm gene expression.</p> <p>Conclusion</p> <p>Together, these data demonstrate that CRF2R activation can prevent the progressive degeneration of diaphragm muscle associated with dystrophin gene mutation.</p

Exploring the fine-grained analysis and automatic detection of irony on Twitter

To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme for irony that allows to identify different irony types. We conduct a series of binary classification experiments for automatic irony recognition using a support vector machine exploiting a varied feature set and a deep learning approach making use of an LSTM network and (pre-trained) word embeddings. Evaluation on a held-out corpus shows that the SVM model outperforms the neural network approach and benefits from combining lexical, semantic and syntactic information sources. A qualitative analysis of the classification output reveals that the classifier performance may be further enhanced by integrating implicit sentiment information and context- and user-based features