An analysis of New South Wales electronic vote counting

We re-examine the 2012 local government elections in New South Wales, Australia. The count was conducted electronically using a randomised form of the Single Transferable Vote (STV). It was already well known that randomness does make a difference to outcomes in some seats. We describe how the process could be amended to include a demonstration that the randomness was chosen fairly. Second, and more significantly, we found an error in the official counting software, which caused a mistake in the count in the council of Griffith, where candidate Rina Mercuri narrowly missed out on a seat. We believe the software error incorrectly decreased Mercuri's winning probability to about 10%---according to our count she should have won with 91% probability. The NSW Electoral Commission (NSWEC) corrected their code when we pointed out the error, and made their own announcement. We have since investigated the 2016 local government election (held after correcting the error above) and found two new errors. We notified the NSWEC about these errors a few days after they posted the results

Random errors are not necessarily politically neutral

Errors are inevitable in the implementation of any complex process. Here we examine the effect of random errors on Single Transferable Vote (STV) elections, a common approach to deciding multi-seat elections. It is usually expected that random errors should have nearly equal effects on all candidates, and thus be fair. We find to the contrary that random errors can introduce systematic bias into election results. This is because, even if the errors are random, votes for different candidates occur in different patterns that are affected differently by random errors. In the STV context, the most important effect of random errors is to invalidate the ballot. This removes far more votes for those candidates whose supporters tend to list a lot of preferences, because their ballots are much more likely to be invalidated by random error. Different validity rules for different voting styles mean that errors are much more likely to penalise some types of votes than others. For close elections this systematic bias can change the result of the election

Auditing Ranked Voting Elections with Dirichlet-Tree Models: First Steps

Ranked voting systems, such as instant-runo voting (IRV) and single transferable vote (STV), are used in many places around the world. They are more complex than plurality and scoring rules, pre- senting a challenge for auditing their outcomes: there is no known risk- limiting audit (RLA) method for STV other than a full hand count. We present a new approach to auditing ranked systems that uses a sta- tistical model, a Dirichlet-tree, that can cope with high-dimensional pa- rameters in a computationally e cient manner. We demonstrate this ap- proach with a ballot-polling Bayesian audit for IRV elections. Although the technique is not known to be risk-limiting, we suggest some strategies that might allow it to be calibrated to limit risk

Towards Evidence-Based Implementation of Pharmacogenomics in Southern Africa: Comorbidities and Polypharmacy Profiles across Diseases

Pharmacogenomics may improve patient care by guiding drug selection and dosing; however, this requires prior knowledge of the pharmacogenomics of drugs commonly used in a specific setting. The aim of this study was to identify a preliminary set of pharmacogenetic variants important in Southern Africa. We describe comorbidities in 3997 patients from Malawi, South Africa, and Zimbabwe. These patient cohorts were included in pharmacogenomic studies of anticoagulation, dyslipidemia, hypertension, HIV and breast cancer. The 20 topmost prescribed drugs in this population were identified. Using the literature, a list of pharmacogenes vital in the response to the top 20 drugs was constructed leading to drug–gene pairs potentially informative in translation of pharmacogenomics. The most reported morbidity was hypertension (58.4%), making antihypertensives the most prescribed drugs, particularly amlodipine. Dyslipidemia occurred in 31.5% of the participants, and statins were the most frequently prescribed as cholesterol-lowering drugs. HIV was reported in 20.3% of the study participants, with lamivudine/stavudine/efavirenz being the most prescribed antiretroviral combination. Based on these data, pharmacogenes of immediate interest in Southern African populations include ABCB1, CYP2B6, CYP2C9, CYP2C19, CYP2D6 CYP3A4, CYP3A5, SLC22A1, SLCO1B1 and UGT1A1. Variants in these genes are a good starting point for pharmacogenomic translation programs in Southern Africa

Invasive species differ in key functional traits from native and non‐invasive alien plant species

QUESTIONS : Invasive species establish either by possessing traits, or trait trade‐offs similar to native species, suggesting pre‐adaptation to local conditions; or by having a different suite of traits and trait trade‐offs, which allow them to occupy unfilled niches. The trait differences between invasives and non‐invasives can inform on which traits confer invasibility. Here, we ask: (a) are invasive species functionally different or similar to native species? (b) which traits of invasives differ from traits of non‐invasive aliens and thus confer invasibility? and (c) do results from the sub‐Antarctic region, where this study was conducted, differ from findings from other regions? LOCATION : Sub‐Antarctic Marion Island. METHODS : We measured 13 traits of all terrestrial native, invasive and non‐invasive alien plant species. Using principal components analysis and phylogenetic generalized least‐squares models, we tested for differences in traits between invasive (widespread alien species) and native species. Bivariate trait relationships between invasive and native species were compared using standardized major axis regressions to test for differences in trait trade‐offs between the two groups. Second, using the same methods, we compared the traits of invasive species to non‐invasive aliens (alien species that have not spread). RESULTS : Between invasive and native species, most traits differed, suggesting that the success of invasive species is mediated by being functionally different to native species. Additionally, most bivariate trait relationships differed either in terms of their y‐intercept or their position on the axes, highlighting that plants are positioned differently along a spectrum of shared trait trade‐offs. Compared to non‐invasive aliens, invasive species had lower plant height, smaller leaf area, lower frost tolerance, and higher specific leaf area, suggesting that these traits are associated with invasiveness. The findings for the sub‐Antarctic corresponded to those of other regions, except lower plant height which provides a competitive advantage to invaders in the windy sub‐Antarctic context. CONCLUSION : Our findings support the expectation that trait complexes of invasive species are predominantly different to those of coexisting native species, and that high resource acquisition and low defence investment are characteristic of invasive plant species.Supplementary information : Raw data, tables and figures of results from trait comparisons between native, invasive and non‐invasive alien species of Marion Island Appendix S1. A list of all vascular plants surveyed on Marion Island Appendix S2. Residence time of alien vascular plants species on Marion Island Appendix S3. Map of Marion Island and sampling localities Appendix S4. Terrestrial habitats of Marion Island Appendix S5. Sampling design Appendix S6. Sampling data Appendix S7. Trait data Appendix S8. Literature sources Appendix S9. Trait data from literature sources Appendix S10. Descriptions of traits used Appendix S11. Trait processing Appendix S12. Multivariate analysis (principal component analysis) Appendix S13. Phylogenetic tree of all study species Appendix S14. Univariate analysis (phylogenetic generalized least‐squares models) Appendix S15. Trait data of vascular plant species common in the coastal areas of Marion Island Appendix S16. Bivariate trait analysis (standardized major axis) Appendix S17. Ordination of invasive and non‐invasive vascular plant species Appendix S18. Trait differences between native and invasive species common in the coastal areas of Marion Island Appendix S19. Results of standardized major axis regression analysis for vascular plant species on Marion IslandThe South African National Research Foundationhttp://wileyonlinelibrary.com/journal/jvs2020-09-01hj2019Plant Production and Soil Scienc