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SARS-CoV-2 vaccination has been launched worldwide to build effective population-level immunity to curb the spread of this virus. The effectiveness and duration of protective immunity is a critical factor for public health. Here, we report the kinetics of the SARS-CoV-2 specific immune response in 204 individuals up to 1-year after recovery from COVID-19. RBD-IgG and full-length spike-IgG concentrations and serum neutralizing capacity decreases during the first 6-months, but is maintained stably up to 1-year after hospital discharge. Even individuals who had generated high IgG levels during early convalescent stages had IgG levels that had decreased to a similar level one year later. Notably, the RBD-IgG level positively correlates with serum neutralizing capacity, suggesting the representative role of RBD-IgG in predicting serum protection. Moreover, viral-specific cellular immune protection, including spike and nucleoprotein specific, persisted between 6 months and 12 months. Altogether, our study supports the persistence of viral-specific protective immunity over 1 year. The quality of immune response to SARS-CoV-2 is thought to wane over time, but it is unclear how long it can persist. Here the authors show persistent immune responses in a large number of patients over the course of a 1-year follow-up from the time of recovery from COVID-19.

The underlying mechanism of chronic hepatitis B virus (HBV) functional cure by interferon (IFN), especially in patients with low HBsAg and/or young ages, is still unresolved due to the lack of surrogate models. Here, we generate a type I interferon receptor humanized mouse (huIFNAR mouse) through a CRISPR/Cas9-based knock-in strategy. Then, we demonstrate that human IFN stimulates gene expression profiles in huIFNAR peripheral blood mononuclear cells (PBMCs) are similar to those in human PBMCs, supporting the representativeness of this mouse model for functionally analyzing human IFN in vivo. Next, we reveal the tissue-specific gene expression atlas across multiple organs in response to human IFN treatment; this pattern has not been reported in healthy humans in vivo. Finally, by using the AAV-HBV model, we test the antiviral effects of human interferon. Fifteen weeks of human PEG-IFNα2 treatment significantly reduces HBsAg and HBeAg and even achieves HBsAg seroconversion. We observe that activation of intrahepatic monocytes and effector memory CD8 T cells by human interferon may be critical for HBsAg suppression. Our huIFNAR mouse can authentically respond to human interferon stimulation, providing a platform to study interferon function in vivo. PEG-IFNα2 treatment successfully suppresses intrahepatic HBV replication and achieves HBsAg seroconversion. There is increasing evidence that treatment of hepatitis B with interferon alpha can be beneficial. Here, Wang et al, present a type 1 interferon receptor humanized mouse model and characterize it as a platform in which to study interferon function in vivo.

Long-term continuous cropping in cotton fields of Southern Xinjiang has limited crop productivity. To investigate how subsoiling depth regulates ecosystem-level water partitioning and thereby enhances water productivity mechanisms, a two-year field experiment was conducted in a mulched drip irrigation cotton field in Southern Xinjiang. Using a non-subsoiled field in the current season (CT) as the control, three subsoiling depths were established: subsoiling at 30 cm (ST1), 40 cm (ST2), and 50 cm (ST3). Changes in evapotranspiration partitioning and water use efficiency were analyzed. The results showed that subsoiling enhanced the utilization of deep soil water. Compared with CT, the ST2 and ST3 treatments significantly reduced soil water storage in the 0–60 cm layer during the flower opening to boll-setting stages, while soil water consumption increased by 26.4 mm and 28.8 mm, respectively. We demonstrate that subsoiling depth exerts a predominant control on the partitioning of evapotranspiration. Increasing subsoiling depth systematically shifted water loss from non-productive soil evaporation to productive plant transpiration, with the ST2 and ST3 treatments decreasing seasonal soil evaporation by 24.1% and 25.1%, respectively, and increasing plant transpiration by 21.9% and 22.8%, and lowering the Es/ET (where Es is soil evaporation and ET is evapotranspiration) ratio by 22.1% and 27.1%. However, this maximal physiological water-saving did not yield the optimal agronomic return. We established a non-linear relationship in which the ST2 treatment uniquely achieved the maximum seed cotton yield, WUE (water use efficiency), and IWUE (irrigation water use efficiency) (increasing by up to 34.4%, 17.2%, and 23.4%, respectively). This optimal depth better balances water resource allocation and reproductive growth. We conclude that under sandy loam soil conditions in typical mulched drip-irrigated cotton areas of Southern Xinjiang, implementing an optimal subsoiling depth (40 cm) can engineer a more resilient soil–plant–water continuum, providing a feasible pathway toward enhancing water and crop production sustainability.

Due to random sampling and the unpredictability of moving obstacles, it remains challenging for mobile robots to effectively learn navigation policies and accomplish obstacle avoidance safely. Overcoming such challenges can reduce the time cost required for navigation model training and validation, improving the safety and credibility of autonomous navigation in medical service and industrial patrol. This article proposes an improved soft actor–critic model to enhance the autonomous navigation performance of robots. We first introduce a prioritized experience replay method to reduce the randomness of sampling. The performance of the navigation policy can be enhanced by prioritizing the learning of high-value experiences. Moreover, we also design a network with long short-term memory abilities to store historical environmental information. In this way, temporal characteristics of obstacle motion can be obtained to optimize obstacle avoidance policy. Experimental results in simulation and real-world show that the proposed model significantly improves learning speed, success rate, and trajectory smoothness while exhibiting excellent obstacle avoidance performance in dynamic environments.

Freshwater scarcity severely limits sustainable cotton production in arid regions. This study aimed to establish the optimal salinity threshold for staged saline water supplemental irrigation (SWSI) and elucidate its canopy-level mechanisms in optimizing within-boll yield components and fiber quality. A two-year field trial (2023–2024) was conducted in Awati County, Xinjiang, using mulched drip irrigation at five SWSI levels (3.5–9.5 g L−1) and a freshwater control (CK). Compared with CK, 3.5 g L−1 treatment significantly increased lint yield by 31.4%, boll number per plant by 22.45%, and fibers per seed by 6.01–10.59%, while fiber length and strength rose by 6.98–10.38% and 2.69–6.00%, respectively. When salinity reached 8.0 g L−1, yield declined by 8.5%, and a salinity of 9.5 g L−1 reduced yield by 24.52%. Spatially, mid-fruiting branches (nodes 4–6) remained stable, maintaining high lint mass per seed even under high salinity, whereas upper branches (≥node 7) were most sensitive; at 9.5 g L−1, the boll number (0.36) was 56.6% lower than at 3.5 g L−1 (0.83), and the Q-score decreased by 6.7%. These results demonstrate that SWSI with ≤5.0 g L−1 salinity (optimum 3.5 g L−1) simultaneously enhances lint yield and fiber quality, providing a practical strategy for efficient saline water use in arid cotton regions.

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R2 = 0.811; RMSE = 2.931 t ha−1). Texture features alone also showed strong predictive capability (R2 = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring.

The optimization of plant density plays a crucial role in cotton production, and deficit irrigation, as a water-saving measure, has been widely adopted in arid regions. However, regulatory mechanisms governing nitrogen absorption, transportation, and nitrogen use efficiency (NUE) in cotton under deficit irrigation and high plant density remain unclear. To clarify the mechanisms of N uptake and NUE of cotton, the main plots were subjected to three irrigation amounts based on field capacity (Fc): (315 [W1, 0.5 Fc], 405 [W2, 0.75 Fc, farmers’ irrigation practice], and 495 mm [W3, 1.0 Fc]). Subplots were planted and applied at three densities: (13.5 [M1], 18.0 [M2, farmers’ planting practice], and 22.5 [M3] plants m−2). The results revealed that under low-irrigation conditions, the cotton yield was 5.1% lower than that under the farmer’s irrigation practice. In all plant densities and years, the nitrogen uptake of cotton increased significantly with the increase in irrigation. However, excessive irrigation resulted in nitrogen accumulation and migration, mainly concentrated in the vegetative organs of cotton, which reduced the NUE by 9.2% compared with that under farmers’ irrigation practice. Concerning the interaction between irrigation and plant density, under low irrigation, the nitrogen uptake of high-density planting was higher, and the yield of seed cotton was only 2.9% lower than that of the control (the interaction effect of farmers’ irrigation × plant density), but the NUE was increased by 10.9%. Notably, with the increase in irrigation amount, the soil nitrate nitrogen at the 0–40 cm soil layer decreased, and high irrigation amounts would lead to the transfer of soil nitrate nitrogen to deep soil. With the increase in plant density, the rate of nitrogen uptake and the amount of nitrogen uptake increased, which significantly reduced the soil nitrate nitrogen content. In conclusion, deficit irrigation and high plant density can improve cotton yield and NUE. We anticipate that these findings will facilitate optimized agricultural management in areas with limited water.

The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R2 = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R2 = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R2 and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture.

High-density planting is an effective technique to optimize yields of mulched cotton. On the other hand, deficit irrigation is an emerging water-saving strategy in cotton cultivation, especially suitable for arid and water-scarce areas. However, the relationships between deficit irrigation, high-density planting, and regulation mechanisms of canopy light radiation and light use efficiency (LUE) in cotton is not yet clear. To clarify the mechanism of light interception (LI) and the LUE of cotton canopies, three irrigation treatments [315 (50% Fc), 405 (75% Fc, farmers’ irrigation practice), and 495 mm (100% Fc), where Fc was the field capacity] with three plant densities [13.5, 18.0 (farmers’ planting practice), and 22.5 plants m2] were applied. The findings of this research revealed that, under deficit irrigation, the above-ground dry matter (ADM) was reduced by 5.05% compared to the farmers’ irrigation practice. Over both years and across all plant densities, LI and LUE under deficit irrigation decreased by 8.36% and 4.79%, respectively, relative to the farmers’ irrigation practices. In contrast, LI and LUE for the highest irrigation level increased by 10.59% and 5.23%, respectively. In the case of the interaction (plant density and irrigation level), the ADM under deficit irrigation and high-density combination increased by 7.69% compared to the control (farmers’ irrigation × sowing practices interaction effects). The LI and LUE also exhibited an increase in 1.63% and 6.34%, respectively. Notably, the LI effect of the middle and upper cotton canopy under film drip irrigation reached 70%. A lower irrigation level resulted in a higher percentage of LI in the lower canopy region. The leaf area index, light interception rate, and extinction coefficient escalated with the increase in plant density. Under deficit irrigation treatment, the LI of the 0–30 cm canopy in high plant density settings increased by 8.6% compared to the control (farmers’ irrigation × sowing practices interaction effects). In conclusion, deficit irrigation and increased plant density improved the interception of LI and LUE of cotton canopy. These findings may help the farmers to optimize their agricultural management strategies in water-deficient areas.

Many pathogens are virulent because they specifically interfere with host defense responses and therefore can proliferate. Here, we report that virulent strains of the bacterial phytopathogen Pseudomonas syringae induce systemic susceptibility to secondary P. syringae infection in the host plant Arabidopsis thaliana. This systemic induced susceptibility (SIS) is in direct contrast to the well studied avirulence/R gene-dependent resistance response known as the hypersensitive response that elicits systemic acquired resistance. We show that P. syringae-elicited SIS is caused by the production of coronatine (COR), a pathogen-derived functional and structural mimic of the phytohormone jasmonic acid (JA). These data suggest that SIS may be a consequence of the previously described mutually antagonistic interaction between the salicylic acid and JA signaling pathways. Virulent P. syringae also has the potential to induce net systemic susceptibility to herbivory by an insect (Trichoplusia ni, cabbage looper), but this susceptibility is not caused by COR. Rather, consistent with its role as a JA mimic, COR induces systemic resistance to T. ni. These data highlight the complexity of defense signaling interactions among plants, pathogens, and herbivores.