Between the genotype and the phenotype lies the microbiome: symbiosis and the making of 'postgenomic' knowledge.

Emphatic claims of a "microbiome revolution" aside, the study of the gut microbiota and its role in organismal development and evolution is a central feature of so-called postgenomics; namely, a conceptual and/or practical turn in contemporary life sciences, which departs from genetic determinism and reductionism to explore holism, emergentism and complexity in biological knowledge-production. This paper analyses the making of postgenomic knowledge about developmental symbiosis in Drosophila melanogaster by a specific group of microbiome scientists. Drawing from both practical philosophy of science and Science and Technology Studies, the paper documents epistemological questions of artefactuality and representativeness of model organisms as they emerge in the day-to-day labour producing and being produced by the "microbiome revolution." Specifically, the paper builds on all the written and editorial exchanges involved in the troubled publication of a research paper studying the symbiotic role of the microbiota in the flies' development. These written materials permit us to delimit the network of justifications, evidence, standards of knowledge-production, trust in the tools and research designs that make up the conditions of possibility of a postgenomic fact. More than reframing the organism as a radically novel multiplicity of reactive genomes, we conclude, doing postgenomic research on the microbiota and symbiosis means producing a story that deviates from the scripts embedded into the sociotechnical experimental systems of post-Human Genome Project life sciences.

Does dishonesty really invite third-party punishment? Results of a more stringent test.

Many experiments have demonstrated that people are willing to incur cost to punish norm violators even when they are not directly harmed by the violation. Such altruistic third-party punishment is often considered an evolutionary underpinning of large-scale human cooperation. However, some scholars argue that previously demonstrated altruistic third-party punishment against fairness-norm violations may be an experimental artefact. For example, envy-driven retaliatory behaviour (i.e. spite) towards better-off unfair game players may be misidentified as altruistic punishment. Indeed, a recent experiment demonstrated that participants ceased to inflict third-party punishment against an unfair player once a series of key methodological problems were systematically controlled for. Noticing that a previous finding regarding apparently altruistic third-party punishment against honesty-norm violations may have been subject to methodological issues, we used a different and what we consider to be a more sound design to evaluate these findings. Third-party punishment against dishonest players withstood this more stringent test.

The Pixel Anomaly Detection Tool: a user-friendly GUI for classifying detector frames using machine-learning approaches.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.