BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning.

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

Automated calculation of spectral-reflectance marker-points to enable analysis of plant colour-signalling to pollinators.

A spectral reflectance curve for a coloured surface can be constructed from a set of radiation reflectance value measurements made across the spectrum at discrete wavelengths. The curve gives an indication of the pattern of light entering the eye of an organism viewing an illuminated object. Marker points represent the positions along a reflectance curve at which sharp transitions in reflectance occur, these being potentially important to visual perception, for instance by insects discriminating between two flowers, each of a different colour. Consequently, methods of marker point analysis have been applied in several studies evaluating flower colours. These studies have sometimes required researchers to place marker points on reflectance curves by eye, or they have used algorithms written as unreleased software. To automate the process systematically and provide open access, we implemented special-purpose software in C++. Below we provide a summary of the approach adopted in our implementation and made available online in a port to TypeScript. The main benefits of our method are summarized as being:•Automation and repeatability.•Standardisation, cross-platform compatibility and Open Access.•Interactive exploration of the effects of parameter variation.

A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation.

The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signalling, which concern how cells determine their states and behaviours. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. Here we introduce MecaGen, an integrative modelling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relies upon a minimal 'cell behaviour ontology' comprising mesenchymal and epithelial cells and their associated behaviours. MecaGen enables the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrate the validity and usefulness of our framework.