Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines

In recent years, China has made great efforts to control air pollution. During the governance process, it is found that fine particulate matter (PM2.5) and ozone (O3) change in the same trend among some areas and the opposite in others, which brings some difficulties to take measures in a planned way. Therefore, this study adopted multi-year and large-scale air quality data to explore the distribution of correlation between PM2.5 and O3, and proposed a concept called dynamic similar hu lines to replace the single fixed division in the previous research. Furthermore, this study discussed the causes of distribution patterns quantitatively with geographical detector and random forest. The causes included natural factors and anthropogenic factors. And these factors could be divided into three parts according to the characteristics of spatial distribution: broadly changing with longitude, changing with latitude, and having local characteristics. Overall, regions with relatively more densely population, higher GDP, lower altitude, higher humidity, higher atmospheric pressure, higher surface temperature, less sunshine hours and more accumulated precipitation often corresponds to positive correlation coefficient between PM2.5 and O3, no matter in which season. The parts with opposite conditions that mentioned above are essentially negative correlation coefficient. And what's more, humidity, global surface temperature, air temperature and accumulated precipitation are four decisive factors to form the distribution of correlation between PM2.5 and O3. In general, collaborative governance of atmospheric pollutants should consider particular time and space background and also be based on the local actual socio-economic situations, geography and geomorphology, climate and meteorology and other comprehensive factors.Comment: Our research group have decided to withdraw this preprin

Author Correction:Causal inference from cross-sectional earth system data with geographical convergent cross mapping (Nature Communications, (2023), 14, 1, (5875), 10.1038/s41467-023-41619-6)

Correction to: Nature Communications, published online 21 September 2023 The original version of this Article contained an error in the results, which incorrectly read: ‘The zero ρ of temperature xmap farmland NPP indicate that farmland NPP is not a cause of temperature. And the much-smaller ρ of precipitation xmap farmland NPP majorly result from the above-introduced enslaved effect from the strong causal influence of precipitation on farmland NPP. In other words, farmland NPP can partially reflect precipitation.’ The correct version now reads: ‘The zero ρ of precipitation xmap farmland NPP indicate that farmland NPP is not a cause of precipitation. And the much-smaller ρ of temperature xmap farmland NPP majorly result from the above-introduced enslaved effect from the strong causal influence of temperature on farmland NPP. In other words, farmland NPP can partially reflect temperature.’ This has been corrected in both the PDF and HTML versions of the Article.</p

DeepCBS: shedding light on the impact of mutations occurring at CTCF binding sites

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption of the CTCF binding sites (CBS) will lead to the destruction of insulated neighborhoods, which in turn can cause dysregulation of the contained genes. In a recent study, it is found that CTCF/cohesin binding sites are a major mutational hotspot in the cancer genome. Mutations can affect CTCF binding, causing the disruption of insulated neighborhoods. And our analysis reveals a significant enrichment of well-known proto-oncogenes in insulated neighborhoods with mutations specifically occurring in anchor regions. It can be assumed that some mutations disrupt CTCF binding, leading to the disruption of insulated neighborhoods and subsequent activation of proto-oncogenes within these insulated neighborhoods. To explore the consequences of such mutations, we develop DeepCBS, a computational tool capable of analyzing mutations at CTCF binding sites, predicting their influence on insulated neighborhoods, and investigating the potential activation of proto-oncogenes. Futhermore, DeepCBS is applied to somatic mutation data of liver cancer. As a result, 87 mutations that disrupt CTCF binding sites are identified, which leads to the identification of 237 disrupted insulated neighborhoods containing a total of 135 genes. Integrative analysis of gene expression differences in liver cancer further highlights three genes: ARHGEF39, UBE2C and DQX1. Among them, ARHGEF39 and UBE2C have been reported in the literature as potential oncogenes involved in the development of liver cancer. The results indicate that DQX1 may be a potential oncogene in liver cancer and may contribute to tumor immune escape. In conclusion, DeepCBS is a promising method to analyze impacts of mutations occurring at CTCF binding sites on the insulator function of CTCF, with potential extensions to shed light on the effects of mutations on other functions of CTCF

Maintain the structural controllability under malicious attacks on directed networks

The directedness of the links in a network plays a critical role in determining many dynamical processes among which the controllability has received much recent attention. The control robustness of a network against malicious attack and random failure also becomes a significant issue. In this paper, we propose a novel control robustness index motivated by recent studies on the global connectivity and controllability. In its general form, the problem of optimizing the control robustness index is computationally infeasible for large-scale networks. By analysing the influences of several directed topological factors on the dynamical control process, we transform the control robustness problem into the problem of transitivity maximization for control routes, and propose an efficient greedy algorithm to make control routes transitive. A series of experiments on real-world and synthetic networks show that the global connectivity and controllability can be improved simultaneously and we can mitigate the destruction of malicious attack through backing up the control routes

Spatial Structure Change Analysis of Cultivated Soil Nutrients in Urban Fringe of North China

International audienceSpatial structure analysis is beneficial to guide soil nutrients management. This paper developed a method for spatial structure of soil nutrients and analyzed these change characteristics from 2000 to 2007 using geographic information system (GIS) technology for Daxing district of Beijing, China. The results of spatial structure were obtained and occupied space proportions of total kjeldahl nitrogen (TN), alkali-hydrolyzable nitrogen (AN), organic matter (OM), available phosphorus (AP) and available potassium (AK) were 0.33, 0.22, 0.25, 0.03, 0.16 for 2000 and 0.32, 0.25, 0.23, 0.03, 0.17 for 2007, respectively. The change characteristics and influence factors for spatial structure of soil nutrients were systematically analyzed. Increased soil nutrients were exhibited three belts on the whole, whereas decreased soil nutrients were located in other regions. Natural factors and human activities drove these changes of soil nutrients. This study provides a reference for future related research

The spatial statistic trinity: A generic framework for spatial sampling and inference

Geospatial referenced environmental data are extensively used in environmental assessment, prediction, and management. Data are commonly obtained by nonrandom surveys or monitoring networks, whereas spatial sampling and inference affect the accuracy of subsequent applications. Design-based and model-based procedures (DB and MB for short) both allow one to address the gap between statistical inference and spatial data. Creating independence by sampling implies that DB may neglect spatial autocorrelation (SAC) if the sampling interval is beyond the SAC range. In MB, however, a particular sampling design can be irrelevant for inferential results. Empirical studies further showed that MSE (mean squared error) values for both DB and MB are affected by SAC and spatial stratified heterogeneity (SSH). We propose a novel framework for integrating SAC and SSH into DB and MB. We do so by distinguishing the spatial population from the spatial sample. We show that spatial independence in a spatial population results in independence in a spatial sample, whereas SAC in a spatial population is reflected in a spatial sample if sampling distances are within the range of dependence; otherwise, SAC is absent in the spatial sample. Similarly, SSH in a population may or may not be inherited in data, and this depends on the sampling method. Thus, the population, sample, and inference constitute a so-called spatial statistic trinity (SST), providing a new framework for spatial statistics, including sampling and inference. This paper shows that it greatly simplifies the choice of method in spatial sampling and inferences. Two empirical examples and various citations illustrate the theory