kingroup is an open source java program implementing a maximum likelihood approach to pedigree relationships reconstruction and kin group assignment. kingroup implements a new method (currently being performance tested) for reconstructing groups of kin that share a common relationship by estimating an overall likelihood for alternative partitions. A number of features found in kinship (Goodnight & Queller 1999) have also been implemented to make them available outside the Classic Macintosh OS platform for the first time.

Robotic weed control has seen increased research of late with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for croplands, ignoring the weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust classification of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the complex rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust classification methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification accuracy of 95.1% and 95.7%, respectively. We also demonstrate real time performance of the ResNet-50 architecture, with an average inference time of 53.4 ms per image. These strong results bode well for future field implementation of robotic weed control methods in the Australian rangelands.

Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.

It is shown that the Poet-Temkin model of electron-hydrogen scattering could be solved to any required accuracy using the J-matrix method. The convergence in the basis size is achieved to an accuracy of better than 2% with the inclusion of 37 basis L2 functions. Previously observed pseudoresonances in the J-matrix calculation naturally disappear with an increase in basis size. No averaging technique is necessary to smooth the convergent J-matrix results.

A quantitative structure-activity relationship (QSAR) model is typically developed to predict the biochemical activity of untested compounds from the compounds' molecular structures. "The gold standard" of model validation is the blindfold prediction when the model's predictive power is assessed from how well the model predicts the activity values of compounds that were not considered in any way during the model development/calibration. However, during the development of a QSAR model, it is necessary to obtain some indication of the model's predictive power. This is often done by some form of cross-validation (CV). In this study, the concepts of the predictive power and fitting ability of a multiple linear regression (MLR) QSAR model were examined in the CV context allowing for the presence of outliers. Commonly used predictive power and fitting ability statistics were assessed via Monte Carlo cross-validation when applied to percent human intestinal absorption, blood-brain partition coefficient, and toxicity values of saxitoxin QSAR data sets, as well as three known benchmark data sets with known outlier contamination. It was found that (1) a robust version of MLR should always be preferred over the ordinary-least-squares MLR, regardless of the degree of outlier contamination and that (2) the model's predictive power should only be assessed via robust statistics. The Matlab and java source code used in this study is freely available from the QSAR-BENCH section of www.dmitrykonovalov.org for academic use. The Web site also contains the java-based QSAR-BENCH program, which could be run online via java's Web Start technology (supporting Windows, Mac OSX, Linux/Unix) to reproduce most of the reported results or apply the reported procedures to other data sets.

Computer code written in java is available upon request from the first author.

Previously reported maximum-likelihood pairwise relatedness (r) estimator of Thompson and Milligan (M) was extended to allow for negative r estimates under the regression interpretation of r. This was achieved by establishing the equivalency of the likelihoods used in the kinship program and the likelihoods of Thompson. The new maximum-likelihood (ML) estimator was evaluated by Monte Carlo simulations. It was found that the new ML estimator became unbiased significantly faster compared to the original M estimator when the amount of genotype information was increased. The effects of allele frequency estimation errors on the new and existing relatedness estimators were also considered.

Accurate modelling of electron transport in plasmas, plasma-liquid and plasma-tissue interactions requires (i) the existence of accurate and complete sets of cross-sections, and (ii) an accurate treatment of electron transport in these gaseous and soft-condensed phases. In this study we present progress towards the provision of self-consistent electron-biomolecule cross-section sets representative of tissue, including water and THF, by comparison of calculated transport coefficients with those measured using a pulsed-Townsend swarm experiment. Water-argon mixtures are used to assess the self-consistency of the electron-water vapour cross-section set proposed in de Urquijo et al (2014 J. Chem. Phys. 141 014308). Modelling of electron transport in liquids and soft-condensed matter is considered through appropriate generalisations of Boltzmann's equation to account for spatialtemporal correlations and screening of the electron potential. The ab initio formalism is applied to electron transport in atomic liquids and compared with available experimental swarm data for these noble liquids. Issues on the applicability of the ab initio formalism for krypton are discussed and addressed through consideration of the background energy of the electron in liquid krypton. The presence of self-trapping (into bubble/cluster states/solvation) in some liquids requires a reformulation of the governing Boltzmann equation to account for the combined localised-delocalised nature of the resulting electron transport. A generalised Boltzmann equation is presented which is highlighted to produce dispersive transport observed in some liquid systems.