Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons

Sodium selenate as a disease-modifying treatment for progressive supranuclear palsy: Protocol for a phase 2, randomised, double-blind, placebo-controlled trial

INTRODUCTION: Progressive supranuclear palsy (PSP) is a neurodegenerative disorder for which there are currently no disease-modifying therapies. The neuropathology of PSP is associated with the accumulation of hyperphosphorylated tau in the brain. We have previously shown that protein phosphatase 2 activity in the brain is upregulated by sodium selenate, which enhances dephosphorylation. Therefore, the objective of this study is to evaluate the efficacy and safety of sodium selenate as a disease-modifying therapy for PSP. METHODS AND ANALYSIS: This will be a multi-site, phase 2b, double-blind, placebo-controlled trial of sodium selenate. 70 patients will be recruited at six Australian academic hospitals and research institutes. Following the confirmation of eligibility at screening, participants will be randomised (1:1) to receive 52 weeks of active treatment (sodium selenate; 15 mg three times a day) or matching placebo. Regular safety and efficacy visits will be completed throughout the study period. The primary study outcome is change in an MRI volume composite (frontal lobe+midbrain-3rd ventricle) over the treatment period. Analysis will be with a general linear model (GLM) with the MRI composite at 52 weeks as the dependent variable, treatment group as an independent variable and baseline MRI composite as a covariate. Secondary outcomes are change in PSP rating scale, clinical global impression of change (clinician) and change in midbrain mean diffusivity. These outcomes will also be analysed with a GLM as above, with the corresponding baseline measure entered as a covariate. Secondary safety and tolerability outcomes are frequency of serious adverse events, frequency of down-titration occurrences and frequency of study discontinuation. Additional, as yet unplanned, exploratory outcomes will include analyses of other imaging, cognitive and biospecimen measures. ETHICS AND DISSEMINATION: The study was approved by the Alfred Health Ethics Committee (594/20). Each participant or their legally authorised representative and their study partner will provide written informed consent at trial commencement. The results of the study will be presented at national and international conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12620001254987).Lucy Vivash, Kelly L Bertram, Charles B Malpas, Cassandra Marotta, Ian H Harding, Scott Kolbe, Joanne Fielding, Meaghan Clough, Simon J G Lewis, Stephen Tisch, Andrew H Evans, John D O, Sullivan, Thomas Kimber, David Darby, Leonid Churilov, Meng Law, Christopher M Hovens, Dennis Velakoulis, Terence J O, Brie

Outstanding challenges in the transferability of ecological models

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.Katherine L. Yates ... Alice R. Jones ... et al

Analysing extinction risk in parrots using decision trees

Comparative analysis techniques have been successfully applied in a number of recent attempts to identify the species traits associated with a current threat of extinction although less often to predict which species may become threatened in the future. Although prediction of risk is obviously a priority, such analyses are undermined by the fact that there may be non-linear and non-additive relationships between the species traits used. A Decision Tree analysis can accommodate with such relationships and here it is used to explore factors affecting extinction risk in parrots. The results firstly verify that simple biological and biogeographical traits can separate threatened from non-threatened species. It is also possible to predict which species are likely to become threatened in the future. The utility of the method is not in testing evolutionary-based hypotheses to explain extinction risk, rather it is a simple and practical method of confirming and/or predicting levels of risk. For well known taxonomic groups it could be used to confirm current IUCN threat categories and identify which species should receive closest attention when the group is next reviewed. For poorly known groups it could be used to predict categories of threat for unclassified species from small groups of classified ones

Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance