[Revealing the Effect of Saline Water Drip Irrigation on Soil Microorganisms in Cotton Fields Based on Metagenomics].

Saline water irrigation has become an important means to alleviate the shortage of freshwater in arid areas. However, long-term saline water irrigation can cause soil salinity accumulation, affect soil microbial community structure, and then affect soil nutrient transformation. In this study, we used metagenomics to investigate the effects of long-term saline water drip irrigation on soil microbial community structure in a cotton field. In the experiment, the salinity of irrigation water (ECw) was set to two treatments:0.35 dS·m-1 and 8.04 dS·m-1 (denoted as FW and SW, respectively), and the nitrogen application rates were 0 kg·hm-2and 360 kg·hm-2 (denoted as N0 and N360, respectively). The results showed that saline water irrigation increased soil water content, salinity, organic carbon, and total nitrogen content and decreased soil pH and available potassium content. Nitrogen fertilizer application increased soil organic carbon, salinity, and total nitrogen content and decreased soil water content, pH, and available potassium content. The dominant bacterial phyla in each treatment were:Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes. Saline water irrigation significantly increased the relative abundances of Actinobacteria, Chloroflexi, Gemmatimonadetes, and Firmicutes but significantly decreased the relative abundances of Proteobacteria, Acidobacteria, Cyanobacteria, and Nitrospira. Nitrogen fertilizer application significantly increased the relative abundances of Chloroflexi and Nitrospira but significantly decreased the relative abundances of Acidobacteria, Gemmatimonadetes, Planctomycetes, Cyanobacteria, and Verrucomicrobia. LEfSe analysis showed that saline water irrigation had no significant effect on the number of potential biomarkers, and nitrogen fertilizer application decreased the number of potential biomarkers in soil microbial communities. The correlation network diagram showed that the 20 genera had different degrees of correlation, including 44 positive correlations and 48 negative correlations. The core species in the network diagram were Nocardioides, Streptomyces, Pyrinomonas, Candidatus_Solibacter, and Bradyrhizobium spp. Saline water irrigation increased the relative abundances of the denitrification genes nirK, nirS, nasB, and norC and decreased the relative abundances of the nitrification genes amoB, amoC, and nxrA, whereas nitrogen fertilizer application increased the relative abundances of the nitrification genes amoA, amoB, amoC, hao, and nxrA and decreased the relative abundances of the denitrifying genes narB, napA, nasA, and nosZ. Saline water irrigation could adversely affect soil physicochemical properties; SWC, EC1:5, and BD were the main driving factors affecting soil microbial community structure and function genes; and soil microorganisms adapted to soil salt stress by regulating species composition.

New Helvolic Acid Derivatives with Antibacterial Activities from Sarocladium oryzae DX-THL3, an Endophytic Fungus from Dongxiang Wild Rice (Oryza rufipogon Griff.).

Three new helvolic acid derivatives (named sarocladilactone A (1), sarocladilactone B (2) and sarocladic acid A (3a)), together with five known compounds (6,16-diacetoxy-25-hy- droxy-3,7-dioxy-29-nordammara-1,17(20)-dien-21-oic acid (3b), helvolic acid (4), helvolinic acid (5), 6-desacetoxy-helvolic acid (6) and 1,2-dihydrohelvolic acid (7)), were isolated from the endophytic fungus DX-THL3, obtained from the leaf of Dongxiang wild rice (Oryza rufipogon Griff.). The structures of the new compounds were elucidated via HR-MS, extensive 1D and 2D NMR analysis and comparison with reported data. Compounds 1, 2, 4, 5, 6 and 7 exhibited potent antibacterial activities. In particular, sarocladilactone B (2), helvolinic acid (5) and 6-desacetoxy-helvolic acid (6) exhibited strongly Staphylococcus aureus inhibitory activity with minimum inhibitory concentration (MIC) values of 4, 1 and 4 µg/mL, respectively. The structure-activity relationship (SAR) of these compounds was primarily summarized.

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. METHODS: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. RESULTS: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. CONCLUSION: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.