Agitator is one of the essential factors to realize high efficient fermentation for high aerobic and viscous microorganisms, and the influence of different impeller combination on the fermentation process is very important. Welan gum is a microbial exopolysaccharide produced by Alcaligenes sp. under high aerobic and high viscos conditions. Computational fluid dynamics (CFD) numerical simulation was used for analyzing the distribution of velocity, shear rate and gas holdup in the welan fermentation reactor under six different impeller combinations. The best three combinations of impellers were applied to the fermentation of welan. By analyzing the fermentation performance, the MB-4-6 combination had better effect on dissolved oxygen and velocity. The content of welan was increased by 13%. Furthermore, the viscosity of production were also increased.
Alcaligenes ; metabolism ; Fermentation ; Hydrodynamics ; Industrial Microbiology ; methods ; Oxygen ; Polysaccharides, Bacterial ; biosynthesis ; Viscosity

Objective:To explore the mediating roles of rumination and avoidance coping between information anxiety and suicidal ideation.Methods:In March 2022, a total of 896 college students were surveyed by the information anxiety scale(IAS), ruminative responses scale(RRS), coping style questionnaire(CSQ)and self-rating idea of suicidal ideation scale(SIOSS). The ANOVA analysis, Pearson correlation analysis and mediating effect analysis were performed by the SPSS 22.0 software.Results:The detection rate of suicidal ideation among college students was 9.15%(82/896). The information anxiety score was(73.84±17.29), the rumination score was(47.73±12.16), the avoidance coping score was(3.76±2.52), and the suicidal ideation score was(5.41±4.09). Information anxiety was significantly positively correlated with rumination, avoidance coping and suicidal ideation( r=0.49, 0.36, 0.37, all P<0.05). Rumination was significantly positively correlated with avoidance coping and suicidal ideation( r=0.42, 0.59, both P<0.05). There was a significantly positive correlation between avoidance coping and suicidal ideation( r=0.45, P<0.05). Information anxiety affected suicidal ideation of college students through four paths.The direct effect value of information anxiety on suicidal ideation was 0.06, accounting for 16.67% of the total effect. The effect values of the separate mediating effect of rumination and avoid coping were 0.22 and 0.04, and accounting for 61.11% and 11.11% of the total effect respectively. The chain mediating effect value of rumination and avoid coping was 0.04, accounting for 11.11% of the total effect. Conclusion:Information anxiety can directly affect suicidal ideation of college students and indirectly affect suicidal ideation through rumination and avoidance coping.

Objective:To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning.Methods:The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, adenocarcinoma, squamous cell carcinoma and small cell carcinoma, from the Beijing Chest Hospital, the Capital Medical University. The slides were digitized by scanner, and 316 slides were used as training set and 203 as the internal test set. The researchers labeled all the training slides by pathologists and establish a semantic segmentation model based on DeepLab v3 with ResNet-50 to detect lung cancers at the pixel level. To perform transfer learning, the researchers utilized the gastric cancer detection model to initialize the deep neural network parameters. The lung cancer detection convolutional neural network was further trained by fine-tuning of the labeled data. The deep learning model was tested by 203 slides in the internal test set and 1 081 slides obtained from TCIA database, named as the external test set.Results:The model trained with transfer learning showed substantial accuracy advantage against the one trained from scratch for the internal test set [area under curve (AUC) 0.988 vs. 0.971, Kappa 0.852 vs. 0.832]. For the external test set, the transferred model achieved an AUC of 0.968 and Kappa of 0.828, indicating superior generalization ability. By studying the predictions made by the model, the researchers obtained deeper understandings of the deep learning model.Conclusions:The lung cancer histopathological diagnostic system achieves higher accuracy and superior generalization ability. With the development of histopathological AI, the transfer learning can effectively train diagnosis models and shorten the learning period, and improve the model performance.