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As one of the most salt-sensitive crops, strawberry production is severely limited by salt stress. γ--aminobutyric acid (GABA) has been reported to play an important role in the immune response of plants. In this study, the physiological and transcriptomic changes in strawberry seedlings treated with GABA under salt stress were investigated to explore the effect of GABA on salt tolerance. The results showed that exogenous GABA maintained high osmolyte levels, increased antioxidant capacity, and decreased the ROS levels in strawberry leaves under salt stress; the MDA was reduced by 3.27–31.46%, with 10 mM being the most significant effect; the total (Spd + Spm)/ Put ratio was upregulated after GABA treatments. More strikingly, the plants treated with 10 mM GABA significantly increased chlorophyll content and net photosynthetic rate in salt-stressed plants, which was explained by the transcriptomic data showing that the expression levels of most of chlorophyll metabolism and photosynthesis-related genes were upregulated. Furthermore, 38 potential TFs belonging to the WRKY, AP2/ERF, and MYB families were identified that may be positively involved in GABA-induced salt tolerance. Co-expressed network analysis revealed that some of these TFs, such as RAP2.7, WRKY46, and MYB306, were significantly positively correlated with chlorophyll metabolism. These findings provide an important basis for the use of GABA in the breeding of strawberry resistant to salt stress.

In order to investigate the causes of the differences in heat tolerance (‘Lu He Hong’ and ‘Zhi Hong’), we studied the physiological changes, photosynthetic properties and regulatory mechanism of the two peony cultivars at high temperature. The results showed that the physiological changed of different peony cultivars varied significantly under high temperature stress. With the extension of high temperature stress time, MDA content of 'Lu He Hong' increased,while 'Zhi Hong' rised first and then decreased, SOD activity of 'Lu He Hong' rised first and then decreased, that of 'Zhi Hong' kept rising, POD activity of 'Lu He Hong' kept decreasing, while 'Zhi Hong' rised. The photosynthetic instrument records the change of peony photosynthesis parameters at high temperature; the chlorophyll A (Chla) fluorescence transient is recorded using the plant efficiency analyzer (PEA), analyzed according to the JIP test (O-J-I-P fluorescence transient analysis), and several parameters were derived to explain the photosynthetic efficiency difference between different peony cultivars. The tested cultivars responded differently to the survey conditions, and the PCA analysis showed that the ‘Zhi Hong’ was more well tolerated and showed better thermal stability of the PSII. The reduced efficiency of the ‘Lu He Hong’ PSII antenna leads to higher heat dissipation values to increase the light energy absorbed by unit reaction center (ABS/RC), the energy captured by unit reaction center (TR 0 /RC), and the energy dissipated by unit reaction center (DI 0 /RC), which significantly leads to its lower total photosynthetic performance (PI total ). The light capture complex of the variety ‘Zhi Hong’ has high connectivity with its reaction center, less damage to OEC activity, and better stability of the PSII system. The results show that ‘Zhi Hong’ improves heat resistance by stabilizing the cell membrane, a strong antioxidant system, as well as a more stable photosynthetic system. The results of this study provide a theoretical basis for the screening of heat-resistant peonies suitable for cultivation in Jiangnan area and for the selection and breeding of heat-resistant cultivars.

To investigate the effects of high-temperature stress on the chlorophyll fluorescence induction kinetics of peony and to determine indicators for the rapid screening of varieties responding to high temperatures, three four-year-old peony variety, ‘Fengdanbai’, ‘Huhong’, and ‘Yinhongqiaodui’, were selected as materials. The photosynthetic curves (Pn-PAR) and fast chlorophyll fluorescence curves (OJIP curves) of peony leaves were measured at different times under high-temperature stress conditions (40 °C), the changes in the photosynthetic characteristics of different peony varieties under high-temperature stress were analyzed, and the heat tolerance of peony was evaluated. The results showed that ‘Huhong’ grew well within 16 days, while all of the other varieties showed obvious wilting at 6–9 days. High temperatures damaged the structure and function of the photosystem of peony leaves, indicating that the maximum net photosynthetic rate (Pnmax), apparent quantum efficiency (AQE), maximum photochemical efficiency (Fv/Fm), and photosynthetic performance index (PIABS) all tended to decrease under high-temperature stress, while the rate of closing the PS II reaction center (Mo) and the absorption per reaction center (ABS/RC), the capture (TRo/RC), and the dissipation (Dio/RC) of light energy per reaction center showed an overall increasing trend. The ability to cope with high-temperature stress differed among varieties, and the heat tolerance was determined to be in the descending order of ‘Fengdanbai’ < ‘Yinhongqiaodui’ < ‘Huhong’. The correlation analysis among the parameters and the analysis of the morphological change patterns in peony leaves concluded that PIABS, Dio/RC, and Mo could be used as indicators of peony tolerance to high-temperature stress. The results of the study can provide a basis for the screening of heat-tolerant peony species and peony heat defense in the Jiangnan area.

This study explored the changes in the photosynthetic characteristics of the Fengdan peony under high-temperature stress to provide a reference for understanding the tolerance of peony plants under heat stress. In this study, the effects of high-temperature stress (40 °C) on the photosynthetic characteristics of the Fengdan leaves were studied. At 25 °C, the net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (Tr) of the leaves decreased gradually with the increase in heat stress time, and intercellular CO2 concentration (Ci) decreased first and then increased. High-temperature stress reduced the light energy absorption (ABS/RC) and capture (TRO/RC), light energy for electron transport (ETO/RC), and heat dissipation (DIO/RC) per unit leaf area. The maximum photochemical efficiency (FV/FM), leaf photochemical performance index (PIabs), the probability that captured excitons can transfer electrons to other electron acceptors in the electron transport chain beyond QA (ψO), and the quantum yield for electron transport (φEo), decreased gradually. The results showed that high temperatures damaged the photosynthetic capacity of the peony leaves and destroyed the photosynthetic apparatus of leaves. This study provides a reference for understanding the photosynthetic characteristics and tolerance of peony plants under heat stress.

Superoxide dismutases (SODs), as the first line of defense against reactive oxygen species (ROS), play an essential role in protecting plants from adverse elicitors during plant growth and development. However, little is known about the SOD gene family and their response to salinity stress and fruit development in cultivated strawberries (Fragaria × ananassa). In this study, 32 SOD genes consisting of 16 Cu/ZnSODs, 11 FeSODs, and 5 MnSOD were identified, which presented three well-resolved clades in the phylogenetic tree. Each clade had similar motifs, and exon–intron structures, which in turn supported the evolutionary classification. Cis-acting element analysis suggested that FaSOD genes might be involved in the plant response to abiotic and biotic stresses, hormones, and light. The analysis of previously published transcriptome data revealed that FaSOD genes are expressed variably under salt stress. Among these SODs, FaMSD5 was expressed at relatively high levels in strawberry root and leaf, and its transcript abundance significantly increased after salt treatment. Some transcription factors related to photomorphogenesis, hormone signaling pathways, and hyperosmotic salinity response were predicted to bind to the FaMSD5 promoter. These outcomes implied that FaMSD5 might play an important role in protection against salt stress. In addition, the comprehensive transcriptome analysis of FaSOD genes in strawberry fruit showed that almost all FaCSDs and FaMSDs were more highly expressed than FaFSDs at different developmental stages, and the expression patterns of FaCSD1, FaCSD2, FaCSD7, FaCSD8, and FaCSD10 suggested that they were likely to be involved in fruit development and ripening. This study provides a basis for further exploration of the function of the FaSOD gene family in strawberry and provides candidate FaSOD genes for enhancing salinity tolerance and regulating fruit development and ripening.

Ocean Bottom Node (OBN) is a seismic data acquisition technique, comprising a hydrophone and a three-component geophone. In practice, the vertical component is susceptible to high-amplitude, low-velocity, and low-frequency shear wave noise, which negatively impacts the subsequent processing of dual-sensor data. The most commonly used method is adaptive matching subtraction, which estimates shear wave noise in the vertical component by solving an optimization problem. Neural networks, as robust nonlinear fitting tools, offer superior performance in resolving nonlinear mapping relationship and exhibit computational efficiency. In this paper, we introduce a self-supervised shear wave suppression approach for 3D OBN seismic data, using a neural network in place of the traditional adaptive matching subtraction operator. This method adopts the horizontal components as the input to the neural network, and measures the output and the noisy vertical component to establish a loss function for the network training. The network output is the predicted shear wave noise. To better balance signal leakage and noise suppression, the method incorporates a local normalized cross-correlation regularization term in the loss function. As a self-supervised method, it does not need clean data to serve as labels, thereby negating the tedious work of obtaining clean field data. Extensive experiments on both synthetic and field data demonstrate the effectiveness of the proposed method on shear wave noise suppression for 3D OBN seismic data.

Conventional urea nitrogen (N) fertilizers are characterized by low use efficiency, resulting in substantial economic losses and environmental degradation. To address this issue, we developed a novel carbon-based stabilized coated urea by incorporating biochar, the urease inhibitor NBPT, and the nitrification inhibitor DCD through a low-energy in situ coating process. This study evaluated the effects of this fertilizer on N transformation and loss via soil column leaching and ammonia volatilization experiments, as well as its impact on choy sum (Brassica chinensis L.) yield, N use efficiency (NUE), and product quality under field conditions. Results indicated that coatings containing both NBPT and DCD (specifically, formulations with 0.5%NBPT + 1.0%DCD, and 1.0%NBPT + 1.5%DCD) significantly reduced cumulative ammonium-N leaching by 41.5–53.8% and nitrate-N leaching by 45.3–59.4% compared to conventional urea. All coated treatments suppressed ammonia volatilization by over 10%, with the highest inhibition (26.92%) observed in the treatment with 1.0%NBPT + 1.5%DCD. The synergistic coating also modulated key soil enzyme activities involved in N cycling. Field trials demonstrated that the formulations with 0.5%NBPT + 1.0%DCD and 0.5%NBPT + 1.5%DCD increased choy sum yield by 56.1% and 58.1%, respectively, while significantly improving NUE and agronomic efficiency. Moreover, these treatments enhanced vegetable quality by reducing nitrate content and increasing vitamin C and soluble sugar concentrations. In conclusion, this carbon-based stabilized coated urea, which integrates biochar with NBPT and DCD, represents a promising strategy for minimizing N losses, improving NUE, and advancing sustainable vegetable production.

Ocean Bottom Node (OBN) acquisition is a technique for marine seismic survey that has gained increased attention in recent years. The removal of shear wave noise from the vertical component of receivers plays a crucial role in the subsequent processing and interpretation of OBN data. Previous solutions suffer from noise residue or signal impairment for complex noise and signal overlap scenarios. In this work, we present and explore a self-supervised deep learning approach to attenuate shear wave noise in OBN data. It applies a deep neural network (DNN) to perform adaptive subtraction and comprises two steps to remove the noise associated with the two horizontal components of receivers, respectively. The two horizontal components are considered as noise reference and are sequentially fed into the DNN, and the DNN predicts the actual leaked noise from the contaminated vertical components data. The self-supervised method achieves improvements in the signal-to-noise ratio (SNR) on a set of synthetic data. The implementation of our method on field data demonstrates that it effectively attenuates the shear wave noise and preserves the valid signal.