Disentangling visual and olfactory signals in mushroom-mimicking Dracula orchids using realistic three-dimensional printed flowers.

Flowers use olfactory and visual signals to communicate with pollinators. Disentangling the relative contributions and potential synergies between signals remains a challenge. Understanding the perceptual biases exploited by floral mimicry illuminates the evolution of these signals. Here, we disentangle the olfactory and visual components of Dracula lafleurii, which mimics mushrooms in size, shape, color and scent, and is pollinated by mushroom-associated flies. To decouple signals, we used three-dimensional printing to produce realistic artificial flower molds that were color matched and cast using scent-free surgical silicone, to which we could add scent. We used GC-MS to measure scents in co-occurring mushrooms, and related orchids, and used these scents in field experiments. By combining silicone flower parts with real floral organs, we created chimeras that identified the mushroom-like labellum as a source of volatile attraction. In addition, we showed remarkable overlap in the volatile chemistry between D. lafleurii and co-occurring mushrooms. The characters defining the genus Dracula - a mushroom-like, 'gilled' labellum and a showy, patterned calyx - enhance pollinator attraction by exploiting the visual and chemosensory perceptual biases of drosophilid flies. Our techniques for the manipulation of complex traits in a nonmodel system not conducive to gene silencing or selective breeding are useful for other systems.

Infraslow dynamic patterns in human cortical networks track a spectrum of external to internal attention.

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left-right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.