Global Transcriptional Analyses of the Wnt-Induced Development of Neural Stem Cells from Human Pluripotent Stem Cells.

The differentiation of human pluripotent stem cells (hPSCs) to neural stem cells (NSCs) is the key initial event in neurogenesis and is thought to be dependent on the family of Wnt growth factors, their receptors and signaling proteins. The delineation of the transcriptional pathways that mediate Wnt-induced hPSCs to NSCs differentiation is vital for understanding the global genomic mechanisms of the development of NSCs and, potentially, the creation of new protocols in regenerative medicine. To understand the genomic mechanism of Wnt signaling during NSCs development, we treated hPSCs with Wnt activator (CHIR-99021) and leukemia inhibitory factor (LIF) in a chemically defined medium (N2B27) to induce NSCs, referred to as CLNSCs. The CLNSCs were subcultured for more than 40 passages in vitro; were positive for AP staining; expressed neural progenitor markers such as NESTIN, PAX6, SOX2, and SOX1; and were able to differentiate into three neural lineage cells: neurons, astrocytes, and oligodendrocytes in vitro. Our transcriptome analyses revealed that the Wnt and Hedgehog signaling pathways regulate hPSCs cell fate decisions for neural lineages and maintain the self-renewal of CLNSCs. One interesting network could be the deregulation of the Wnt/ß-catenin signaling pathway in CLNSCs via the downregulation of c-MYC, which may promote exit from pluripotency and neural differentiation. The Wnt-induced spinal markers HOXA1-4, HOXA7, HOXB1-4, and HOXC4 were increased, however, the brain markers FOXG1 and OTX2, were absent in the CLNSCs, indicating that CLNSCs have partial spinal cord properties. Finally, a CLNSC simple culture condition, when applied to hPSCs, supports the generation of NSCs, and provides a new and efficient cell model with which to untangle the mechanisms during neurogenesis.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.