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#plantphysiology

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New Insights into Flowering Regulation

An international team uncovered how carbon and nitrogen signals regulate flowering in Arabidopsis thaliana. The study reveals that these pathways converge on FLOWERING LOCUS C to fine-tune flowering time, offering insights to develop resilient, resource-efficient crops for sustainable agriculture.

globalplantcouncil.org/new-ins #PlantScience #Science #Plants #PlantSci #Flowers #arabidopsis #agriculture #PlantPhysiology

Fast electrical signals mapped in plants with new technology

What happens inside the carnivorous plant Venus Flytrap when it catches an insect? New technology has led to discoveries about the electrical signalling that causes the trap to snap shut. Bioelectronic technology enables advanced research into how plants react to their surroundings, and to stress.

globalplantcouncil.org/fast-el via @liu.international @Columbia #PlantPhysiology #PlantScience #Plants #science

6-JUL-2023
The solstice switch: Warming’s effects on autumn leaf senescence depend on timing

eurekalert.org/news-releases/9

EurekAlert!The solstice switch: Warming’s effects on autumn leaf senescence depend on timingHow temperate and boreal trees’ leaves respond to climate change remains uncertain. Now, a new study of northern forests reports that while early-season climate warming – that occurring before the summer solstice – tends to be associated with earlier autumn leaf senescence, late-season warming (after the summer solstice) has the opposite impact, delaying onset of leaf senescence in fall. “Improved models of plant development and growth under climate change will need to incorporate the reversal of warming effects after the summer solstice,” write Constantin Zohner and colleagues, authors of the study. Climate change has resulted in changes to the growing seasons of plants. For example, research shows that the start of the growing season for boreal and temperate trees – when leaves emerge during the spring and trees begin to photosynthesize – occurs, on average, two weeks earlier than it did during the 19th and 20th centuries. Similarly, the end of season (EOS) – when autumn leaves die and fall – is being delayed. Not only do these shifts affect tree performance, but they can also lead to changes in ecosystem structure and functioning, impacting global biogeochemical cycles. A longer growing season could mean greater carbon sequestration in forests, for example. The timing of EOS largely depends on dynamic environmental and biological interactions, which aren’t well understood. Using a combination of satellite, ground, carbon flux, and experimental data, Zohner and colleagues evaluated how leaf senescence relates to various environmental cues, including day length, temperature, and early-season photosynthesis, across northern forests. Across 84% of the study area, Zohner et al. found that warming had opposing effects on leaf senescence depending on when the warming occurred. According to the findings, increased temperatures and leaf growth before the summer solstice was correlated with earlier onset of leaf senescence by a rate of roughly 2 days per degree Celsius (C) of warming. Warmer temperatures following the solstice delayed autumn leaf senescence by ~2.5 days per C. “The solstice switch in trees’ physiological responsiveness to temperature calibrates their seasonal rhythms and mediates how they react to warm or cool temperatures, now and in the future,” say the authors. For reporters interested in trends, a 2020 Science study (http://www.sciencemag.org/doi/10.1126/science.abd8911) reporting results of a large-scale analysis of European trees reported that climate warming was causing tree leaves to fall earlier in autumn. The results were built on growing evidence that plant growth is limited by the ability of tree tissues to use and store carbon.

Discovery of novel gene to aid breeding of climate resilient crops

Researchers have revealed for the first time how a key gene in plants allows them to use their energy more efficiently, enabling them to grow more roots and capture more water and nutrients.

globalplantcouncil.org/discove via Penn State, University of Nottingham, WUR #plantscience #plantsci #science #plants #plantphysiology #research #roots

Image credit: University of Nottingham

Hey #PlantScience people. Can you suggest some #books to get back into the subject?

I completed two years of an undergrad degree in PlantSci at the start of the 90s, so I'm comfortable with #biochemistry at the level of the Calvin-Benson cycle, redox reactions, cytochromes and all that jazz.

Other stuff I'm interested in: #SoilScience, #PlantPathology, #PlantPhysiology, general #Microbiology and #PlantGenetics (transgenic toms were all the rage where I studied).

Rec me some reading! #BookRec

Non destructive stomatal measurements. Nice progress on good use cases of #ML

#datascience #PlantPhysiology

plantmethods.biomedcentral.com

BioMed CentralRapid non-destructive method to phenotype stomatal traits - Plant MethodsBackground Stomata are tiny pores on the leaf surface that are central to gas exchange. Stomatal number, size and aperture are key determinants of plant transpiration and photosynthesis, and variation in these traits can affect plant growth and productivity. Current methods to screen for stomatal phenotypes are tedious and not high throughput. This impedes research on stomatal biology and hinders efforts to develop resilient crops with optimised stomatal patterning. We have developed a rapid non-destructive method to phenotype stomatal traits in three crop species: wheat, rice and tomato. Results The method consists of two steps. The first is the non-destructive capture of images of the leaf surface from plants in their growing environment using a handheld microscope; a process that only takes a few seconds compared to minutes for other methods. The second is to analyse stomatal features using a machine learning model that automatically detects, counts and measures stomatal number, size and aperture. The accuracy of the machine learning model in detecting stomata ranged from 88 to 99%, depending on the species, with a high correlation between measures of number, size and aperture using the machine learning models and by measuring them manually. The rapid method was applied to quickly identify contrasting stomatal phenotypes. Conclusions We developed a method that combines rapid non-destructive imaging of leaf surfaces with automated image analysis. The method provides accurate data on stomatal features while significantly reducing time for data acquisition and analysis. It can be readily used to phenotype stomata in large populations in the field and in controlled environments.