Convalescent plasma (CP) transfusion has been indicated as a promising therapy in the treatment for other emerging viral infections. However, the quality control of CP and individual variation in patients in different studies make it rather difficult to evaluate the efficacy and risk of CP therapy for coronavirus disease 2019 (COVID-19). We aimed to explore the potential efficacy of CP therapy, and to assess the possible factors associated with its efficacy. We enrolled eight critical or severe COVID-19 patients from four centers. Each patient was transfused with 200–400 mL of CP from seven recovered donors. The primary indicators for clinical efficacy assessment were the changes of clinical symptoms, laboratory parameters, and radiological image after CP transfusion. CP donors had a wide range of antibody levels measured by serology tests which were to some degree correlated with the neutralizing antibody (NAb) level. No adverse events were observed during and after CP transfusion. Following CP transfusion, six out of eight patients showed improved oxygen support status; chest CT indicated varying degrees of absorption of pulmonary lesions in six patients within 8 days; the viral load was decreased to a negative level in five patients who had the previous viremia; other laboratory parameters also tended to improve, including increased lymphocyte counts, decreased C-reactive protein, procalcitonin, and indicators for liver function. The clinical efficacy might be associated with CP transfusion time, transfused dose, and the NAb levels of CP. This study indicated that CP might be a potential therapy for severe patients with COVID-19.
Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = −ln (a 1 × nDVi + b 1)/(a 2 × nDVi + b 2)) were related to the plant type of wheat. For the erectophiletype cultivars (LOV ≥ 60°), the parameters for the modified model were, a 1 = 0.306, a 2 = −0.534, b 1 = −0.065, and b 2 = 0.541. For the middle-type cultivars (30° < LoV < 60°), the parameters were, a 1 = 0.392, a 2 = −0.88 1 , b 1 = 0.028, and b 2 = 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a 1 = 0.596, a 2 = −1.306, b 1 = 0.014, and b 2 = 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R 2 = 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI. The leaf area index (LAI), the leaf orientation value (LOV), and the extinction coefficient (k) are important structural parameters of crop populations. By affecting light distribution, they directly affect crop photosynthetic efficiency, and ultimately show an impact on crop biological yield and its distribution in various plant organs 1. Remote sensing technology could provide a practical method for crop LAI estimation, rather than a slow, expensive and complicated chemical method. The advantage of the remote-sensing method is that it can obtain plant canopy information on a large scale without disrupting the normal growth of plants 2,3. Studies using remote sensing to monitor agronomic parameters have been extended from crop soils 4-6 , to fresh leaves 7-9 and entire
Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R 2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha −1 and 786.5 kg ha −1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion. Scientifically and accurately estimating crop yield is of significant importance for formulating plans for social and economic development, determining agricultural products import and export plans, ensuring national food security, guiding and regulating macroscopic planting structure, as well as improving the management skills of relevant agriculture-related enterprises and farmers 1-6. With the improvement of spatial, temporal and spectral resolutions of remote sensing data and the significant reduction of cost, currently remote sensing has been widely used in the estimation of production of all kinds of food crops, and it has become a research focus in the interdisciplinary field combining remote sensing and agriculture 7. At present, there were many methods and means for estimating crop yield, such as crop yield meteorological forecast, artificial sampling survey, statistical simulation model, remote sensing estimation and so on 8,9. Using a Criteria/Wofost simulation model that included the new numerical scheme for soil water balance, some researchers compared field data collected at the university of bologna's experimental farm in 1977-1987 with the median wheat yield, and the predicted value was consistent with the observed value 10. Other researches have suggested that the mars-crop ...
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