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Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P=1.31 × 10−8), 6q21 (rs9372120, P=9.09 × 10−15), 7q36.1 (rs7781265, P=9.71 × 10−9), 8q24.21 (rs1948915, P=4.20 × 10−11), 9p21.3 (rs2811710, P=1.72 × 10−13), 10p12.1 (rs2790457, P=1.77 × 10−8), 16q23.1 (rs7193541, P=5.00 × 10−12) and 20q13.13 (rs6066835, P=1.36 × 10−13), which localize in or near to JARID2, ATG5, SMARCD3, CCAT1, CDKN2A, WAC, RFWD3 and PREX1. These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development.
Immersive virtual environments provide users with the opportunity to escape from the real world, but scripted dialogues can disrupt the presence within the world the user is trying to escape within. Both Non-Playable Character (NPC) to Player and NPC to NPC dialogue can be non-natural and the reliance on responding with pre-defined dialogue does not always meet the players emotional expectations or provide responses appropriate to the given context or world states. This paper investigates the application of Artificial Intelligence (AI) and Natural Language Processing to generate dynamic human-like responses within a themed virtual world. Each thematic has been analysed against humangenerated responses for the same seed and demonstrates invariance of rating across a range of model sizes, but shows an effect of theme and the size of the corpus used for fine-tuning the context for the game world.