Pellegrino, A. | bioRxiv Science

bioRxiv2024

Ethanol reduces grapevine water consumption by limiting transpiration

Studies suggest that ethanol (EtOH), triggers plant adaptation to various stresses at low concentrations (10 {micro}M to 10 mM). This study investigates whether EtOH induces drought acclimation in grapevine, as demonstrated previously in Arabidopsis, rice, and wheat. Preliminary results with bare root Gamay cuttings showed that those pre-treated with 10 {micro}M EtOH aqueous solutions lost fewer leaves when deprived of water compared to controls. Subsequently, we ran a potted-cutting experiment with progressive soil water deficit. Plants pre-treated with 250 mM EtOH solutions exhibited slower depletion of the fraction of transpirable soil water (FTSW), compared to controls. While 250 mM EtOH decreased transpiration in early days, these EtOH pre-treated plants maintained higher leaf transpiration than controls after 10 days of soil water depletion. The transpiration response to FTSW was affected by EtOH application. EtOH pre-treatments limited plant leaf expansion without increasing leaf senescence. Interestingly, plants primed with EtOH followed typical hormesis curves. These results suggest that EtOH improves grapevine acclimation to drought, leading to potential water-savings in wine growing regions prone to high water shortages, linked to climate change. These should encourage further testing under various vineyard conditions.

plant biology↗

bioRxiv2023

Disentangling Mixed Classes of Covariability in Large-Scale Neural Data

Recent work has argued that large-scale neural recordings are often well described by low-dimensional latent dynamics identified using dimensionality reduction. However, the view that task-relevant variability is shared across neurons misses other types of structure underlying behavior, including stereotyped neural sequences or slowly evolving latent spaces. To address this, we introduce a new framework that simultaneously accounts for variability that is shared across neurons, trials, or time. To identify and demix these covariability classes, we develop a new unsupervised dimensionality reduction method for neural data tensors called sliceTCA. In three example datasets, including motor cortical dynamics during a classic reaching task and recent multi-region recordings from the International Brain Laboratory, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.

neuroscience↗