Preference of Silicon Accumulation on the Shaded Foliage of Tree Crowns and its Implications for Juniperus chinensis L

Abstract

The passive accumulation of silicon (Si) generally depends on the regulation of plant transpiration rates. It is challenging to comprehend how plants use Si to adapt to shaded habitats where they have low transpiration rates. Therefore, we hypothesized that Si accumulation is partly due to physiological demand rather than being entirely dependent on transpiration regulation. To test this hypothesis, the concentrations of Si and total minerals at various positions of shaded foliage and branches within the crowns of Juniperus chinensis L. trees were examined to determine whether shaded foliage and branch had a physiological demand allocation to resist shade. The concentrations of total minerals and Si in the foliage were consistently higher in shaded areas than in sunny areas within the same crown, regardless of the position within the crown or foliar age. However, foliar Si accumulation displayed more dependent on available light, which is linked to crown orientation. Additionally, branch Si accumulation displayed a weak dependence on the available light. These results suggested that Si, an energy-saving element that supplements cell walls, could also supplement carbon-based components in photosynthetic organs to balance energy limitations in shaded habitats. Thus, the high Si accumulation in shaded foliage, not in shaded branches, was explained by the physiological demand to make up for the decreased energy supply caused by shade.

Block Copolymer‐Stabilized Metal–Organic Framework Hybrids Loading Pd Nanoparticles Enable Tumor Remission Through Near‐Infrared Photothermal Therapy

“…The heat and ROS generated by the NIR irradiation can synergistically suppress the growth of cancer cells. [44] In this study, Pd@MIL-100(Fe) showed photothermal effects that can inhibit the growth of cancer cells. Based on the previous study, these anticancer effects of Pd@MIL-100(Fe) and Pd@MIL-100(Fe)/polymer hybrids may be the outcome of both photothermal and photodynamic effects, as these effects usually occur simultaneously and cannot be separated.…”

Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images

“…Therefore, we visualized response regions of our model to produce different localization maps visualized by CAM in images from 2 perspectives: (1) randomly selecting several cases, visualizing the region of interest (ROI), and (2) visualizing the self-learned multilevel (6 levels) features from shallow to deep layers. Our code for proposed Trisomy21Net has been made available in the Github repository …”