Infectious Ideas: Modelling the Diffusion of Ideas Across Social Networks

Will the practice of collecting wild honey while wearing no clothes become a widespread practice in Zimbabwe? Or will beekeeping take over as the main way that people acquire honey? Both practices impact on forest resources; how can the foresters influence the uptake of these ideas? This paper describes an exploratory modelling study investigating how social network patterns affect the way ideas spread around communities. It concludes that increasing the density of social networks increases the spread of successful ideas whilst speeding the loss of ideas with no competitive advantage. Some different kinds of competitive advantage are explored in the context of forest management and rural extension

To reduce fire risk and meet climate targets, over 300 scientists call for stronger land clearing laws

Australia’s high rates of forest loss and weakening land clearing laws are increasing bushfire risk, and undermining our ability to meet national targets aimed at curbing climate change. This dire situation is why we are among the more than 300 scientists and practitioners who have signed a declaration calling for governments to restore, or better strengthen regulations to protect native vegetation

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases

Are there three main subgroups within the patellofemoral pain population? A detailed characterisation study of 127 patients to help develop targeted Intervention (TIPPs)

• Background Current multimodal approaches for the management of non-specific patellofemoral pain are not optimal, however, targeted intervention for subgroups could improve patient outcomes. This study explores whether subgrouping of non-specific patellofemoral pain patients, using a series of low cost simple clinical tests, is possible. • Method The exclusivity and clinical importance of potential subgroups was assessed by applying à priori test thresholds (1 SD) from seven clinical tests in a sample of adult patients with non-specific patellofemoral pain. Hierarchical clustering and latent profile analysis, were used to gain additional insights into subgroups using data from the same clinical tests. • Results One hundred and thirty participants were recruited, 127 had complete data: 84 (66%) female, mean age 26 years (SD 5.7) and mean BMI 25.4 (SD 5.83), median (IQR) time between onset of pain and assessment was 24 (7-60) months. Potential subgroups defined by the à priori test thresholds were not mutually exclusive and patients frequently fell into multiple subgroups. Using hierarchical clustering and latent profile analysis three subgroups were identified using 6 of the 7 clinical tests. These subgroups were given the following nomenclature: (i) ‘strong’, (ii) ‘weak and tighter’, and (iii) ‘weak and pronated foot’. • Conclusions We conclude that three subgroups of patellofemoral patients may exist based on the results of six clinical tests which are feasible to perform in routine clinical practice. Further research is needed to validate these findings in other datasets and, if supported by external validation, to see if targeted interventions for these subgroups improve patient outcomes

Preregistration of secondary data analysis:A template and tutorial

Multivariate analysis of psychological dat

Glutathione pathway gene variation and risk of autism spectrum disorders

Despite evidence that autism is highly heritable with estimates of 15 or more genes involved, few studies have directly examined associations of multiple gene interactions. Since inability to effectively combat oxidative stress has been suggested as a mechanism of autism, we examined genetic variation 42 genes (308 single-nucleotide polymorphisms (SNPs)) related to glutathione, the most important antioxidant in the brain, for both marginal association and multi-gene interaction among 318 case–parent trios from The Autism Genetic Resource Exchange. Models of multi-SNP interactions were estimated using the trio Logic Regression method. A three-SNP joint effect was observed for genotype combinations of SNPs in glutaredoxin, glutaredoxin 3 (GLRX3), and cystathione gamma lyase (CTH); OR = 3.78, 95% CI: 2.36, 6.04. Marginal associations were observed for four genes including two involved in the three-way interaction: CTH, alcohol dehydrogenase 5, gamma-glutamylcysteine synthetase, catalytic subunit and GLRX3. These results suggest that variation in genes involved in counterbalancing oxidative stress may contribute to autism, though replication is necessary

Divergent metabolism between Trypanosoma congolense and Trypanosoma brucei results in differential sensitivity to metabolic inhibition

Animal African Trypanosomiasis (AAT) is a debilitating livestock disease prevalent across sub-Saharan Africa, a main cause of which is the protozoan parasite Trypanosoma congolense. In comparison to the well-studied T. brucei, there is a major paucity of knowledge regarding the biology of T. congolense. Here, we use a combination of omics technologies and novel genetic tools to characterise core metabolism in T. congolense mammalian-infective bloodstream-form parasites, and test whether metabolic differences compared to T. brucei impact upon sensitivity to metabolic inhibition. Like the bloodstream stage of T. brucei, glycolysis plays a major part in T. congolense energy metabolism. However, the rate of glucose uptake is significantly lower in bloodstream stage T. congolense, with cells remaining viable when cultured in concentrations as low as 2 mM. Instead of pyruvate, the primary glycolytic endpoints are succinate, malate and acetate. Transcriptomics analysis showed higher levels of transcripts associated with the mitochondrial pyruvate dehydrogenase complex, acetate generation, and the glycosomal succinate shunt in T. congolense, compared to T. brucei. Stable-isotope labelling of glucose enabled the comparison of carbon usage between T. brucei and T. congolense, highlighting differences in nucleotide and saturated fatty acid metabolism. To validate the metabolic similarities and differences, both species were treated with metabolic inhibitors, confirming that electron transport chain activity is not essential in T. congolense. However, the parasite exhibits increased sensitivity to inhibition of mitochondrial pyruvate import, compared to T. brucei. Strikingly, T. congolense exhibited significant resistance to inhibitors of fatty acid synthesis, including a 780-fold higher EC50 for the lipase and fatty acid synthase inhibitor Orlistat, compared to T. brucei. These data highlight that bloodstream form T. congolense diverges from T. brucei in key areas of metabolism, with several features that are intermediate between bloodstream- and insect-stage T. brucei. These results have implications for drug development, mechanisms of drug resistance and host-pathogen interactions

Modelling creativity: identifying key components through a corpus-based approach

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research