Search

Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how to accurately assess climate stresses (especially the low-temperature stress) suffered on crops at the regional scale in a systematic approach has not been extensively explored. In this study, we developed a climate vegetation stress index (CVSI) to assess and quantify the impacts of climate stress on crops at large scales by combining TROPOspheric Monitoring Instrument (TROPOMI) SIF and land surface temperature (LST) data through an easy-to-operate approach. This index was employed to identify low-temperature stress conditions in Henan Province’s winter wheat in 2018. Results indicate that, influenced by climate characteristics, crops in the northern part of Henan Province experienced more severe low-temperature stress than those in the southern part. The daily average SIF values experienced reductions of 0.74, 0.45, 0.61, and 0.86 mW $\cdot ~\text{m}^{-2}~\cdot $ sr $^{-1}~\cdot $ nm−1 during the four cooling episodes within the two phenological periods, respectively. As low-temperature stress intensified, winter wheat growth was hindered, reducing grain yield. Indeed, the CVSI provides an accurate depiction of crop stress levels and patterns. In areas with high-CVSI values, yield losses are particularly severe. In addition, the significant positive correlation between the CVSI and net primary productivity (NPP), along with the similar spatial intensity pattern, shows the effectiveness of CVSI in monitoring low-temperature stress. CVSI provides a new approach to understand the impacts of climate change on overwintering crops and offers a practical reference for climate stress effects monitoring at the regional scale.

The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences (DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm (NDVI-DID) and the disease severity level (SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels (SL < 20%), the R2of the regression of NDVI-DIDon SL was 38.8% higher than that of the NDVI and the root mean square error (RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level (SL > 40%). According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes (VIS) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VISfor monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum, increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.

Recent progress in chimeric antigen receptor-modified T-cell (CAR-T cell) technology in cancer therapy is extremely promising, especially in the treatment of patients with B-cell acute lymphoblastic leukemia. In contrast, due to the hostile immunosuppressive microenvironment of a solid tumor, CAR T-cell accessibility and survival continue to pose a considerable challenge, which leads to their limited therapeutic efficacy. In this study, we constructed two anti-MUC1 CAR-T cell lines. One set of CAR-T cells contained SM3 single chain variable fragment (scFv) sequence specifically targeting the MUC1 antigen and co-expressing interleukin (IL) 12 (named SM3-CAR). The other CAR-T cell line carried the SM3 scFv sequence modified to improve its binding to MUC1 antigen (named pSM3-CAR) but did not co-express IL-12. When those two types of CAR-T cells were injected intratumorally into two independent metastatic lesions of the same MUC1+seminal vesicle cancer patient as part of an interventional treatment strategy, the initial results indicated no side-effects of the MUC1 targeting CAR-T cell approach, and patient serum cytokines responses were positive. Further evaluation showed that pSM3-CAR effectively caused tumor necrosis, providing new options for improved CAR-T therapy in solid tumors.