Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Background Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients. Materials and Methods The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information. Results A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717–0.819]), 0.778 (95% CI [0.727–0.829]) and 0.870 (95% CI [0.819–0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710–0.812]), 0.801 (95% CI [0.750–0.852]) and 0.883 (95% CI [0.832–0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan–Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001). Discussion The present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients

Noise Reduction Power Stealing Detection Model Based on Self-Balanced Data Set

In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing

Does PM2.5 (Pollutant) Reduce Firms’ Innovation Output?

The potentially serious economic consequences of China’s severe air pollution problem cannot be overlooked, especially the impact on corporate innovation, which is a core driver guiding firms towards efficient and high-quality development. This paper explores the direct effect and mechanism of PM2.5 on firms’ innovation output through the identification strategy of instrument variable. Based on the data of Listed Companies in China from 2003 to 2016, we used thermal inversion as the instrument variable for PM2.5 for estimation. The results show that each 1 ug/m3 increase in PM2.5 concentration causes an average reduction in innovation output of listed companies by about 7.0%. The test of “Porter hypothesis” shows that environmental regulation has not encouraged firms to innovate more. We further used the 2013 China Social Survey (CSS) data to verify the human capital mechanism of PM2.5 affecting firm innovation at micro level. The results show that PM 2.5 deteriorates the healthy human capital in a firm, which reduces the innovation output. This article helps to understand the relationship between air pollution and firms’ innovation and to develop appropriate policies

How to Achieve Carbon Neutrality in Cities? Evidence from China&rsquo;s Low-Carbon Cities Development

Low-carbon city pilots (LCCP) is a key policy for realizing emission peak and carbon neutrality in China, using China&rsquo;s samples from 280 towns from 2006 to 2016. The article utilizes PSM-DID, mediated effects, and moderating effects approach for validating a CO2 reduction effect, mechanisms, and synergistic elements of LCCP. The regression outcomes suggest that (1) LCCP significantly decreases CO2 emissions levels and average annual carbon emissions in LCCP fall by 2.6 percent. (2) LCCP focus on reducing carbon emissions by increasing R&amp;D investment, the efficiency of energy, and decreasing the high CO2 emissions industry. Among them, the reduction of the high carbon emission industry is mainly FDI, while the reduction of local industry is not obvious. (3) LCCP&rsquo;s carbon reduction effects suggest a reversed U-shape relationship with city size. Digitalization and marketization of LCCP are crucial to the carbon reduction effect. Carbon reduction and pollution reduction have a strong synergistic effect

An identification method for UPS rapid switching

For incorrect operation of existing identification methods for UPS switching, an identification method for UPS rapid switching was designed by use of DSP technology. The method uses software lock mode, and adopts AC voltage amplitude and phase signals colleted and processed by DSP to measure power fail and voltage drop. When the measured value exceeds the setting one, USP would be switched. The experimental result shows the method can determine AC power fail and voltage drop, and achieve UPS switching in about 1 ms

Total organic carbon and its environmental significance for the surface sediments in groundwater recharged lakes from the Badain Jaran Desert, northwest China

Total organic carbon (TOC) content in lake sediments is typically used for the reconstruction of paleoenvironments. It remains uncertain, however, whether these sediment variables in lakes supplied by groundwater in the hinterland of the Badain Jaran Desert are applicable. Moreover, it is still uncertain whether the TOC content in these lakes can be used as a proxy to identify past climatic change and environmental evolution studies. In this study, the spatial distributions of the TOC contents and C/N ratios were analyzed for 109 surface sediment samples collected from five lakes without runoff recharge. The results revealed that the TOC content of the lake surface sediments was extremely low (0.03% - 1.01%) and consisted of both allochthonous organic matter carried by wind, as well as autochthonous organic matter generated in the lakes. Within a lake, spatial differences in the amount of TOC found in surface sediments may be caused by several processes including bathymetry topography and wind-induced wave activity. In addition, wind-induced wave activity produces a higher TOC content, which is more pronounced in larger lakes (>0.21 km2) with longer fetches. By contrast, in smaller lakes, organic matter accumulates in the deeper waters, but can be affected by many factors. It is therefore necessary to consider lake area when applying the TOC content of lake sediments for the reconstruction of a paleolake evolution. Furthermore, because the TOC content of lake sediments in hyper-arid regions is extremely low, and the organic matter may have a multiple and varied sources, a single proxy (TOC) cannot be used to reconstruct lake evolution

Content Linking for UGC based on Word Embedding Model

There are huge amounts of User Generated Contents (UGCs) consisting of authors’ articles of different themes and readers’ on-line comments on social networks every day. Generally, an article often gives rise to thousands of readers’ comments, which are related to specific points of the originally published article or previous comments. Hence it has suggested the urgent need for automated methods to implement the content linking task, which can also help other related applications, such as information retrieval, summarization and content management. So far content linking is still a relatively new issue. Because of the unsatisfactory of traditional ways based on feature extraction, we look forward to using deeper textual semantic analysis. The Word Embedding model based on deep learning has performed well in Natural Language Processing (NLP), especially in mining deep semantic information recently. Therefore, we study further on the Word Embedding model trained by different neural network models from which we can learn the structure, principles and training ways of the neural network language model in more depth to complete deep semantic feature extraction. With the aid of the semantic features, we expect to do further research on content linking between comments and their original articles from social networks, and finally verify the validity of the proposed method by comparison with traditional ways based on feature extraction