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Soil microorganisms are important components of terrestrial ecosystems, affecting soil formation and fertility, plant growth and stress tolerance, nutrient turnover and carbon storage. In this study, we collected soil samples (humus layer, 0-10 cm, 10-20 cm, 20-40 cm, and 40-80 cm) from Caragana jubata shrubland in Shanxi subalpine to explore the composition, diversity, and assembly of soil bacterial communities at different depths across the soil profile. The results showed that Actinomycota (19%-28%), Chloromycota (10%-36%) and Acidobacteria (15%-24%), and Proteobacteria (9%-25%) were the dominant bacterial phyla. a-diversity of soil bacterial community significantly decreased with the increases of soil depth. Soil bacterial β-diversity varied across different soil depths. Soil pH, water content, and enzyme activity were the main ecological factors affecting the dis-tribution of soil bacterial communities. Soil bacterial communities had more complex interactions in humus layer and 0-10 cm layer. On the whole, soil bacterial communities were dominated by coexistence in C. jubata shrubland, and the soil bacterial community assembly was driven by random process. [ABSTRACT FROM AUTHOR]

Background: Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient. Purpose: To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model. Study Type: Retrospective. Population: A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts. Field Strength/Sequence: T2‐weighted imaging, contrast enhanced‐T1‐weighted imaging using fast spin echo sequences at 1.5 T scanner. Assessment: Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single‐factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Modelclinic), radiomics features (Modelradiomics), and clinical factors + radiomics signatures using logistic (Modelcombined), and BPNN (ModelBPNN) methods were established, and model performances were compared. Statistical Tests: Student's t‐test, Mann–Whitney U‐test, and Chi‐square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance. Results: Three significant clinical factors: Epstein–Barr virus‐DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969–3.171), sex (OR = 2.883; 95% CI, 1.364–6.745), and T stage (OR = 1.853; 95% CI, 1.201–3.052) were identified via univariate and multivariate logistic models. Twenty‐four radiomics features were associated with treatment response. ModelBPNN demonstrated the highest performance among Modelcombined, Modelradiomics, and Modelclinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695). Conclusion: A machine‐learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]