The mechanisms of community assembly are a central focus in the field of microbial ecology. However, to what extent these mechanisms differ in importance by traits of groups is poorly understood. Here we quantified the importance of neutral and niche processes in community assembly for bacteria, habitat specialists and generalists in 21 plateau lakes of China. Results showed that both neutral and niche processes played a critical role in the assembly of entire bacterial communities, shaping a unique biogeographical pattern. A few habitat generalists and many specialists were identified. Interestingly, habitat specialists were only governed by niche process, with seven significant environmental variables-salinity, dissolved oxygen, water transparency, total phosphorus, ammonium-nitrogen, temperature and total nitrogen-independently explaining 40.3% of the biological variation. By contrast, habitat generalists were strongly driven by neutral process, with 50.9% of the variation of detection frequency explained in neutral community model. Only three environmental variables-salinity, total nitrogen and dissolved oxygen-significantly affected the distribution of habitat generalists, independently explaining 13.6% of the variation. Governed by different assembly mechanisms, habitat specialists and generalists presented disparate biogeographical patterns. Our result emphasizes the importance of investigating the bacterial community assembly at more refined levels than entire communities.

Background The present study aims to investigate the gene expression changes in papillary renal cell carcinoma(pRCC) and screen several genes and associated pathways of papillary renal cell carcinoma progression. Methods The papillary renal cell carcinoma RNA sequencing (RNA-seq) data set was downloaded from TCGA (The Cancer Genome Atlas). We identified the differentially expressed mRNAs between cancer and normal tissues and performed annotation of differentially expressed mRNAs to figure out the functions and pathways they were enriched in. Then, we constructed a risk score that relied on the 5-mRNA. The optimal value for the patients’classification risk level was identified by ROC analysis. The relationship between mRNA expression and prognosis of papillary renal cell carcinoma was evaluated by univariate Cox regression model. The 5-mRNA based risk score was validated in both complete set and testing set. Result In general, the 5-mRNA (CCNB2, IGF2BP3, KIF18A, PTTG1, and BUB1) were identified and validated, which can predict papillary renal cell carcinoma patient survival. This study revealed the 5-mRNA expression profile and the potential function of a single mRNA as a prognostic target for papillary renal cell carcinoma. Conclusion In addition, these findings may have significant implications for potential treatments options and prognosis for patients with papillary renal cell carcinoma.

Metastasis is the major cause of prostate cancer (PCa)-related mortality. Epithelial-mesenchymal transition (EMT) is a vital characteristic feature that empowers cancer cells to adapt and survive at the beginning of metastasis. Therefore, it is essential to identify the regulatory mechanism of EMT in metastatic prostate cancer (mPCa) and to develop a novel therapy to block PCa metastasis. Here, we discovered a novel PCa metastasis oncogene, DEP domain containing 1B (DEPDC1B), which was positively correlated with the metastasis status, high Gleason score, advanced tumor stage, and poor prognosis. Functional assays revealed that DEPDC1B enhanced the migration, invasion, and proliferation of PCa cells in vitro and promoted tumor metastasis and growth in vivo. Mechanistic investigations clarified that DEPDC1B induced EMT and enhanced proliferation by binding to Rac1 and enhancing the Rac1-PAK1 pathway. This DEPDC1Bmediated oncogenic effect was reversed by a Rac1-GTP inhibitor or Rac1

Disruption means a sudden loss of confinement during a discharge in fusion reactors. Due to the huge electromagnetic loading and thermal loading on the facility and a large number of runaway electrons generated during disruptions, it is essential to find a method to predict the disruptions, so that measures like massive gas injection can be taken to mitigate or to avoid these harmful effects. In this research, a machine learning model mainly based on a 1.5-dimensional convolutional neural network, which is good at dealing with signals from multi-channels with great divergence, is trained to predict disruptions in the HL-2A tokamak. The disruption predictor uses shots 20000–29999 in HL-2A to train the machine learning model, and uses shots 30000–31999 to optimize hyper parameters. When tested on shots 32000–36000 in HL-2A, it reaches a true positive rate of 92.2% and a true negative rate of 97.5% with 30 ms before the disruption.