Comparison of optimal designs of steel portal frames including topological asymmetry considering rolled, fabricated and tapered sections

A structural design optimisation has been carried out to allow for asymmetry and fully tapered portal frames. The additional weight of an asymmetric structural shape was found to be on average 5–13% with additional photovoltaic (PV) loading having a negligible effect on the optimum design. It was also shown that fabricated and tapered frames achieved an average percentage weight reduction of 9% and 11%, respectively, as compared to comparable hot-rolled steel frames. When the deflection limits recommended by the Steel Construction Institute were used, frames were shown to be deflection controlled with industrial limits yielding up to 40% saving.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.