Appalling behaviour

'Appalling Behaviour' is a critically acclaimed contemporary Australian monologue, written by AWGIE Award winning playwright, Stephen House. This production, directed and creatively adapted by Shane Pike, was presented at the Brisbane Powerhouse in February 2016, as part of Queensland's LGBTIQ festival, Melt. This adaptation of the work experimented with notions of gender, taking the original script and manipulating character and scene to investigate expressions of identity beyond the traditional notions of binary gender-norms. To this end, the sole character (and actor) was (re)presented as a homeless bi-sexual queen with the aim of inferring that gender un/ab-normative characters can exist not only as disruptors/comments on/agitators of traditional expectations of performed gender (both onstage and off), but can also exist as accepted characters in and of themselves. Put simply: can a bi-sexual queen just be an actor/character in a play, or do all gender extra-normative characters inherently exist as political, social and cultural challengers? If so, why is this the case and should we be aiming for this kind of character to be an accepted part of the performative fabric, seamless and fitting within any onstage situation and play (why can't Willy Loman, King Lear or Nora be gender non-normative), or should such (re)presentations always exist as 'different'? Is it time for individual expressions of gender to just 'be' and be accepted as givens, or are we not quite there yet?

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. Prostate 75:1467–1474, 2015. © 2015 Wiley Periodicals, Inc.