Neuroactive compounds are crucial tools in drug discovery and neuroscience. However, discovering mechanistically novel drugs has proven challenging. Behavioral screens in larval zebrafish have helped researchers discover compounds with novel mechanisms.

In Chapter 2, we introduce and evaluate an open platform for behavioral screening, SauronX. This instrument records movement behaviors in multiwell plates, capturing high-resolution video data at high framerate (100 Hz) under complex photic and acoustic stimuli. To test, we trained machine learning models to resolve phenotypes caused by compounds with diverse mechanisms in fully randomized screens. First, we benchmarked the system with 14 quality--control (QC) compounds and found that all 14 could be distinguished from each other and from vehicle controls. We then extended to a set of reference phenotypic readouts from 648 neuroactive compounds.

The hardware and software system has been used in studies by several research groups, so far limited to direct collaborations. In this work, we have sought to document the platform fully, providing 3D diagrams, component information, and source code. We have also deposited 7 years of phenotypic data for 3.2 million animals and 34,000 compounds. The data are curated, structured, tied to extensive metadata, and available under a permissive Creative Commons (CC-BY) license.

Ground-motion prediction equations (GMPEs) relate a ground-motion parameter (e.g. peak ground acceleration) to a set of explanatory variables describing the source, wave propagation path and site conditions. In the past five decades many hundreds of GMPEs for the prediction of PGA and linear elastic response spectral ordinates have been published. An accompanying paper discusses the pre-selection of GMPEs undertaken within the framework of the Global Earthquake Model (GEM) Global GMPEs project, coordinated by the Pacific Earthquake Engineering Research Center (PEER). Here, we discuss the following step undertaken to reduce the long list of pre-selected models down to a more manageable number for global hazard assessments. The procedure followed, consisting of an examination of themulti-dimensional (e.g. magnitude, distance and structural period) predicted ground-motion space in various ways and published quantitative tests of the GMPEs against observational data not used for their derivation, is discussed and illustrated for subduction zones.