Dominant mutations in p97/VCP (valosin-containing protein) cause a rare multisystem degenerative disease with varied phenotypes that include inclusion body myopathy, Paget's disease of bone, frontotemporal dementia, and amyotrophic lateral sclerosis. p97 disease mutants have altered N-domain conformations, elevated ATPase activity, and altered cofactor association. We have now discovered a previously unidentified disease-relevant functional property of p97 by identifying how the cofactors p37 and p47 regulate p97 ATPase activity. We define p37 as, to our knowledge, the first known p97-activating cofactor, which enhances the catalytic efficiency (k cat /K m ) of p97 by 11-fold. Whereas both p37 and p47 decrease the K m of ATP in p97, p37 increases the k cat of p97. In contrast, regulation by p47 is biphasic, with decreased k cat at low levels but increased k cat at higher levels. By deleting a region of p47 that lacks homology to p37 (amino acids 69-92), we changed p47 from an inhibitory cofactor to an activating cofactor, similar to p37. Our data suggest that cofactors regulate p97 ATPase activity by binding to the N domain. Induced conformation changes affect ADP/ATP binding at the D1 domain, which in turn controls ATPase cycling. Most importantly, we found that the D2 domain of disease mutants failed to be activated by p37 or p47. Our results show that cofactors play a critical role in controlling p97 ATPase activity, and suggest that lack of cofactorregulated communication may contribute to p97-associated disease pathogenesis.AAA ATPase | p97/VCP | MSP1 | p47 | steady-state kinetics

Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-theart performance.