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Polyploidization is hypothesized to improve the freezing resistance of plants in cold regions. However, adaptive strategies and key physiological mechanisms involved in the freezing resistant ability of polyploids remain unclear. In Actinidia chinensis (kiwifruits), the tetraploids and hexaploids occupy higher altitude habitats with colder climates than the diploids, providing a study system to investigate mechanisms responsible for differentiation in freezing resistance between cytotypes. We characterized environmental conditions of their natural distribution areas, and measured leaf‐level traits of cold damages and water relations at typical sites of each cytotype along an altitudinal gradient. Polyploids showed lower semi‐lethal temperature (LT50) than ice nucleation temperature (INT), reflecting a tolerance strategy to cope with freezing events in the plateau regions. More negative turgor loss points and larger cell elastic modulus of polyploids could help to alleviate damages from freezing‐induced cell dehydration, thus strengthening their tolerance to lower subzero temperatures (lower LT50). The increased supercooling capacity of polyploids (lower INT) might correlate with less extracellular ice formation due to lower osmotic potential at full turgor, apoplastic water fraction and tissue capacitance. Our study uncovers a greater cold tolerance in polyploid kiwifruits than diploids, and suggests the potential linkage between freezing tolerance and water relations. Taken together, such a divergence in stress resistance may underlie the niche shift of polyploid plants towards harsh environments. [ABSTRACT FROM AUTHOR]

To better understand the dynamics and functional roles of root pressure, we represent a novel and the first complete analytical model for root pressure, which can be applied to complex roots/shoots. The osmotic volume of a single root is equal to that of the vessel lumen in fine roots and adjacent apoplastic spaces. Water uptake occurs via passive osmosis and active solute uptake (Js*, osmol s−1), resulting in the osmolyte concentration Cr (mol·kg−1 of water) at a fixed osmotic volume. Solute loss occurs via two passive processes: radial diffusion of solute Km (Cr− Csoil) from fine roots to soil, where Km is the diffusional constant and Csoil is the soil–solute concentration, and the mass flow of solute and water into the plant from the fine roots. The proposed model predicts the quadratic function of root pressure (Pr), Pr2+bPr+c=0, where b and c are the functions of plant hydraulic resistance, soil water potential, solute flux and gravitational potential. The model demonstrates the root pressure‐mediated distribution of water through the hydraulic architecture of a 6.8‐m‐tall bamboo shoot. This model provides a theoretical basis to test the functional roles of root pressure in shaping the hydraulic architecture and refilling potential xylem embolisms. Summary statement: An analytical solution for root pressure is derived and illustrated by applying it to the measured hydraulic architecture of a 7‐m bamboo shoot. [ABSTRACT FROM AUTHOR]

No gas source, no problem: proximity to pre-existing embolism and segmentation affect embolism spreading in angiosperm xylem by gas diffusion. However, our micro-CT images showed heterogeneity of embolism along the branch sample under given dehydration stages (See Methods S2), and the embolism level did not consistently decrease from the cut end to the distal end (Fig. However, vessels in proximity to air sources (e.g. pre-existing embolism, cut-open vessels) are reported to be more prone to embolism (Brodersen I et al i ., [2]; Guan I et al i ., [6]). Keywords: climate change; drought resistance; embolism; micro-CT; plant hydraulics; vulnerability curves EN climate change drought resistance embolism micro-CT plant hydraulics vulnerability curves 384 391 8 12/22/22 20230115 NES 230115 A reply to Brum et al. (2023) 'Reconciling discrepancies in measurements of vulnerability to... The integrity of the plant water transport system is crucial for plant performance and survival (Rowland I et al i ., [14]). [Extracted from the article]

Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on field surveys by crop protection experts and experienced blueberry growers. Deep learning models could be a more effective approach, but their performance is highly dependent on the volume and quality of labeled data used for training so that the variance in visual symptoms can be incorporated into a model. However, the available dataset for mummy berry disease detection does not contain enough images collected and labeled from a real-field environment essential for making highly accurate models. Complex visual characteristics of lesions due to overlapping and occlusion of plant parts also pose a big challenge to the accurate estimation of disease severity. This may become a bigger issue when spatial variation is introduced by using sampling images derived from different angles and distances. In this paper, we first present the "cut-and-paste" method for synthetically augmenting the available dataset by generating additional annotated training images. Then, a deep learning-based object recognition model Yolov5s-CA was used, which integrates the Coordinated Attention (CA) module on the Yolov5s backbone to effectively discriminate useful features by capturing channel and location information. Finally, the loss function GIoU_loss was replaced by CIoU_loss to improve the bounding box regression and localization performance of the network model. The original Yolov5s and the improved Yolov5s-CA network models were trained on real, synthetic, and combined mixed datasets. The experimental results not only showed that the performance of Yolov5s-CA network model trained on a mixed dataset outperforms the baseline model trained with only real field images, but also demonstrated that the improved model can solve the practical problem of diseased plant part detection in various spatial scales with possible overlapping and occlusion by an overall precision of 96.30%. Therefore, our model is a useful tool for the estimation of mummy berry disease severity in a real field environment. [ABSTRACT FROM AUTHOR]