Inactivation of the chlL gene in Synechocystis sp. PCC 6803 resulted in negligible chlorophyll content when the mutant was grown in darkness. Upon phycocyanin excitation at 580 nm, the 77K fluorescence spectrum of dark-grown cells showed three peaks at 648 nm, 665 nm, and 685 nm, this last being the largest. This reflects the functional presence of major components of phycobilisomes, including phycocyanin, allophycocyanin, and the terminal emitter, and efficient energy transfer between these components. As expected, no fluorescence emission peaks corresponding to chlorophyll in the photosystems were observed. Intact phycobilisomes could be isolated from the dark-grown chlL-deletion mutant. However, the phycobilisomes had a lower efficiency of energy transfer than did those isolated from the light-grown mutant, probably because of a decreased phycobilisome stability in the absence of chlorophyll. Exposing the dark-grown chlL-deletion mutant to light triggered the biosynthesis of chlorophyll. For the first 6 h in the light, upon phycocyanin excitation at 580 nm, the 77K fluorescence emission spectrum of greening cells was identical to that of dark-grown cells that lacked significant amounts of chlorophyll. With increased chlorophyll synthesis, gradual energy transfer from phycobilisomes to the two photosystems can be demonstrated.
The Solar Upper Transition Region Imager (SUTRI) onboard the Space Advanced Technology demonstration satellite (SATech-01), which was launched to a sun-synchronous orbit at a height of $\sim$500 km in July 2022, aims to test the on-orbit performance of our newly developed Sc/Si multi-layer reflecting mirror and the 2k$\times$2k EUV CMOS imaging camera and to take full-disk solar images at the Ne VII 46.5 nm spectral line with a filter width of $\sim$3 nm. SUTRI employs a Ritchey-Chrétien optical system with an aperture of 18 cm. The on-orbit observations show that SUTRI images have a field of view of $\sim$ 41.6’$\times$41.6’ and a moderate spatial resolution of $\sim$8” without an image stabilization system. The normal cadence of SUTRI images is 30 s and the solar observation time is about 16 hours each day because the earth eclipse time accounts for about 1/3 of SATech-01’s orbit period. Approximately 15 GB data is acquired each day and made available online after processing. SUTRI images are valuable as the Ne VII 46.5 nm line is formed at a temperature regime of $\sim$0.5 MK in the solar atmosphere, which has rarely been sampled by existing solar imagers. SUTRI observations will establish connections between structures in the lower solar atmosphere and corona, and advance our understanding of various types of solar activity such as flares, filament eruptions, coronal jets and coronal mass ejections.
Lodging is one of the major issues that seriously affects wheat quality and yield. To obtain timely and accurate wheat lodging information and identify the potential factors leading to lodged wheat in wheat breeding programs, we proposed a lodging-detecting model coupled with unmanned aerial vehicle (UAV) image features of wheat at multiple plant growth stages. The UAV was used to collect canopy images and ground lodging area information at five wheat growth stages. The PSPNet model was improved by combining the convolutional LSTM (ConvLSTM) timing model, inserting the convolutional attention module (CBAM) and the Tversky loss function. The effect of the improved PSPNet network model in monitoring wheat lodging under different image sizes and different growth stages was investigated. The experimental results show that (1) the improved Lstm_PSPNet model was more effective in lodging prediction, and the precision reached 0.952; (2) choosing an appropriate image size could improve the segmentation accuracy, with the optimal image size in this study being 468 × 468; and (3) the model of Lstm_PSPNet improved its segmentation accuracy sequentially from early flowering to late maturity, and the three evaluation metrics increased sequentially from 0.932 to 0.952 for precision, from 0.912 to 0.940 for recall, and from 0.922 to 0.950 for F1-Score, with good extraction at mid and late reproductive stages. Therefore, the lodging information extraction model proposed in this study can make full use of temporal sequence features to improve image segmentation accuracy and effectively extract lodging areas at different growth stages. The model can provide more comprehensive reference and technical support for monitoring the lodging of wheat crops at different growth stages.
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