Search

Progress in laboratory automation depends not only on automating the physical aspects of scientific experimentation, but also on the intellectual aspects. We present the conceptual design, implementation, and our user-experience of “Adam,” which uses machine intelligence to autonomously investigate the function of genes in the yeast Saccharomyces cerevisiae. These investigations involve cycles of hypothesis formation, design of experiments to test these hypotheses, physical execution of the experiments using laboratory automation, and the analysis of the results. The physical execution of the experiments involves growing specific yeast strains in specific media and measuring growth curves. Hundreds of such experiments can be executed daily without human intervention. We believe Adam to be the first machine to have autonomously discovered novel scientific knowledge. [Copyright &y& Elsevier]

The body of peer-reviewed papers on enteric methane mitigation strategies in ruminants is rapidly growing and allows for better estimation of the true effect of each strategy though the use of meta-analysis methods. Here we present the development of an online database of measured methane mitigation strategies called MitiGate, currently comprising 412 papers. The database is accessible through an online user-friendly interface that allows data extraction with various levels of aggregation on one hand and data-uploading for submission to the database allowing for future refinement and updates of mitigation estimates as well as providing easy access to relevant data for integration into modelling efforts or policy recommendations. To demonstrate and verify the usefulness of the MitiGate database those studies where methane emissions were expressed per unit of intake (293 papers resulting in 845 treatment comparisons) were used in a meta-analysis. The meta-analysis of the current database estimated the effect size of each of the mitigation strategies as well as the associated variance and measure of heterogeneity. Currently, under-representation of certain strategies, geographic regions and long term studies are the main limitations in providing an accurate quantitative estimation of the mitigation potential of each strategy under varying animal production systems. We have thus implemented the facility for researchers to upload meta-data of their peer reviewed research through a simple input form in the hope that MitiGate will grow into a fully inclusive resource for those wishing to model methane mitigation strategies in ruminants. [ABSTRACT FROM AUTHOR]

Promoting replication of models is unarguably a positive step for agent based modelling, as replication promotes rigorous testing. Model replication remains rare, yet is vital to assessing the repeatability of existing agent based models. Notably, more work is needed to assess cross platform and language replication, which represent potential sources of variability between model results. An existing, complex agent based model was replicated using two widely used platforms (NetLogo and Repast). When results generated by the models were compared, the findings differed not only in magnitude but the trends produced by the data, resulting in different conclusions being drawn from each set of model predictions. The variation between the models is believed to be a result of the complexity of encoding a substantial theoretical model in particular programming languages. This highlights the express need to document replication of existing models in order to fully understand the potential limitations to replication. [ABSTRACT FROM AUTHOR]