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As a macroelement to plant, potassium (K) absorption mechanism has been widely studied. However, as for cotton genotypes with different K efficiency, how they related to the absorption patterns under K starvation is not fully understood. In this hydroponic experiment, plants were grown at different K levels: low (K1, 2 mg/L) and adequate K level (K2, 20 mg/L) for 2 weeks. K + absorption kinetic parameters were got by Michaelis-

Background: To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms. Methods: PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable. Results: Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet. Conclusions: Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Selenite is a form of selenium (Se) commonly found in Se-excessive soils. To regulate the Se content in plants in high-Se areas, a potted soil experiment was performed on oilseed rape (Brassica napus L.) to evaluate the effects of varied amounts of sulfur (S) on the biomass, accumulation and distribution of Se in B. napus under the conditions of different amounts of Se in the soil. The results showed that the seedlings of B. napus were more sensitive to Se than the mature plants were. The addition of S significantly alleviated the growth inhibition in seedlings and facilitated the growth of mature plants under higher Se (15 mg kg −1) conditions. S treatment significantly decreased soil pH within the range of 0.22-0.60. An appropriate moderate amount (150 mg kg −1) of S exerted the strongest inhibition on Se concentration and accumulation in B. napus at the seedling stage, but a higher amount (300 mg kg −1) of S led to a more significant decrease in the mature plants under higher Se conditions, with the maximum reduction in various parts of B. napus reaching 51.3-60.9% and 42.5-53.4%, respectively. The application of S only affected the uptake of Se, and not the translocation of Se; the accumulation of Se in B. napus follows the sequence of pod ≈ stem > rapeseed > root, and the distribution ratio is approximately 1.00:0.97:0.69:0.49. Overall, the application of S alleviated the inhibitory effect on growth caused by excessive Se by reducing the Se concentration in B. napus and facilitating its growth, suggesting that S treatment is a suitable and highly cost-effective method to regulate the content of Se in B. napus.