Jackfruit: Composition, structure, and progressive collapsibility in the largest fruit on the Earth for impact resistance

Comparative pericarp biomechanics and germination physiology of Raphanus raphanistrum and Raphanus pugioniformis indehiscent fruits

BACKGROUND

The biomechanical, morphological and ecophysiological properties of plant seed/fruit structures are adaptations that support survival in unpredictable environments. High phenotypic variability of noxious and invasive weed species such as Raphanus raphanistrum (wild radish) allow diversification into new environmental niches. Dry indehiscent fruits (thick and lignified pericarp [fruit coat] enclosing seeds) have evolved many times independently.

METHODS

A multiscale biomechanics and imaging (microscopy, X-ray, finite element stress simulation, puncture force analysis) approach was used to comparatively investigate the indehiscent fruits of R. raphanistrum (global weed), R. pugioniformis (endemic weed) and R. sativus (cultivated radish).

RESULTS

The hard pericarp of Raphanus species (Brassicaceae) imposes mechanical dormancy by preventing full phase-II water uptake of the enclosed seeds. The apparently unilocular fruits of Raphanus species develop from two fused valves, pericarp rupture to permit germination is confined to the midvalve regions, and each midvalve region contains a predetermined breaking zone that is biomechanically defined by the internal shape of the seed chambers. Direct biomechanical analysis revealed great variability in within-fruit and between-fruits pericarp resistances.

CONCLUSIONS

Variability in pericarp-imposed dormancy provides a bet-hedging strategy to affect soil seed bank persistence and prolong the germinability period.

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.