Concerns for reduced science content at Radio NZ: The New Zealand Association of Scientists (NZAS) is troubled by news of changes to science content at public broadcaster Radio New Zealand (RNZ)

NZAS Past-President Associate Professor Nicola Gaston says ‘We are particularly concerned about the future for the type of science coverage provided by the Our Changing World programme. This is the only programme delivering quality, in-depth, science stories for, and about, New Zealand. ’Its format allows for in-depth exploration of topics and even allows for scrutiny of scientific thinking, a behind-the-scenes intro into what it is like to work in the world of science. Importantly, it focuses a good deal of the time on New Zealand science and scientists. It aligns well with the Government’s current and laudable push to improve society’s science literacy’, Gaston says. Gaston notes that ‘the very first point of the RNZ charter says it provides ‘programmes which contribute toward intellectual, scientific, cultural ... development’. The Association does understand that there is a need to periodically reinvigorate content. However, from what we have been able to determine, it seems the present plan will see a significant reduction in on-air science content. The need for as many people as possible to understand and value science will only grow with the challenges facing us: climate impacts, ageing yet growing populations, technology, enforced migration, pandemics, environmental stress, biodiversity, and more besides. It appears self-evident to us that RNZ, as a public broadcaster, should play a lead role in delivering this knowledge. Dr Gaston states that ‘by all means, we expect RNZ to question and re-think science programming – but we need to create more New Zealand science content, not less'.o create more New Zealand science content, not less’

Director of GeoNet needs to be heard

The New Zealand Association of Scientists (NZAS) is alarmed at reports that the Acting Civil Defence Minister, Gerry Brownlee, is ‘furious with GeoNet comments’ made by the service’s Director, Dr Ken Gledhill.‘GeoNet is an important service in which all New Zealanders have an interest’, said NZAS President Craig Stevens, ‘and if Dr Gledhill can suggest cost-effective measures to make New Zealand safer, he should be encouraged to say so, and publicly.’ There have been other media reports that have questioned the quality of the response, and these concerns should rightly be taken on board by all involved, including the scientific community. It is of deep concern that the Government response is to vilify voices that seek to encourage us to learn from a post-mortem of events. The magnitude 7.8 Kaikoura earthquake was unique amongst the many events GeoNet has responded to in recent years, because it produced a significant tsunami immediately along the New Zealand coastline. This tsunami potentially threatened population centres, especially the Wellington region, within a short time. The Association calls on the Government to be responsible in its discussions with the science agencies, such as GNS Science, that support critical government services, such as the Ministry of Civil Defence and Emergency Management. To maximise the safety of New Zealanders, the Association recommends that the Government avoid actions that might lead to the repression of scientific advice to the public

Information content-based gene ontology semantic similarity approaches: toward a unified framework theory

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term’s specificity in the GO DAG

Information content-based gene ontology functional similarity measures: which one to use for a given biological data type?

The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration

Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies

Cl, Br, and I

Method of increments (MI) calculations reveal the n-body correlation contributions to binding in solid chlorine, bromine, and iodine. Secondary binding contributions as well as d-correlation energies are estimated and compared between each solid halogen. We illustrate that binding is entirely determined by two-body correlation effects, which account for >80% of the total correlation energy. One-body, three-body, and exchange contributions are repulsive. Using density-fitting (DF) local coupled-cluster singles, doubles, and perturbative triples for incremental calculations, we obtain excellent agreement with the experimental cohesive energies. MI results from DF local second-order Møller-Plesset perturbation (LMP2) yield considerably over-bound cohesive energies. Comparative calculations with density functional theory and periodic LMP2 method are also shown to be less accurate for the solid halogens