Impacts of road network expansion on landscape ecological risk in a megacity, China: A case study of Beijing

AbstractRoad networks affect the spatial structure of urban landscapes, and with continuous expansion, it will also exert more widespread influences on the regional ecological environment. With the support of geographic information system (GIS) technology, based on the application of various spatial analysis methods, this study analyzed the spatiotemporal changes of road networks and landscape ecological risk in the research area of Beijing to explore the impacts of road network expansion on ecological risk in the urban landscape. The results showed the following: 1) In the dynamic processes of change in the overall landscape pattern, the changing differences in landscape indices of various landscape types were obvious and were primarily related to land-use type. 2) For the changes in a time series, the expansion of the road kernel area was consistent with the extension of the sub-low-risk area in the urban center, but some differences were observed during different stages of development. 3) For the spatial position, the expanding changes in the road kernel area were consistent with the grade changes of the urban central ecological risk, primarily because both had a certain spatial correlation with the expressways. 4) The influence of road network expansion on the ecological risk in the study area had obvious spatial differences, which may be closely associated with the distribution of ecosystem types

Author Correction: The disease resistance protein SNC1 represses the biogenesis of microRNAs and phased siRNAs.

The original version of this Article contained an error in the spelling of the author Beixin Mo, which was incorrectly given as Beixing Mo. This has now been corrected in both the PDF and HTML versions of the Article

Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study

IntroductionDiabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.MethodsFor this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values.ResultsThe mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort.DiscussionThe model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function

A Research on Hot Spots of Psychological Warning Based on Co-word Analysis and Visualization

Offering reference for sustainable researches of psychological warning field by analyzing hotspots in it in the latest decade. 589 literatures relevant to psychological warning conformed to standard, were extracted keywords. Cooccurrence matrix and lexical matrix were exported by using python software, and the correlations among high frequency keywords were received by analyzing the combination between social network and semantic network which also drew the semantic network graph. The hierarchical clustering function was used to draw the high frequency keywords clustering analysis diagram, and the Eucidean range model was used to draw the two-dimensional scale analysis graph. The combination between co-word analysis and knowledge graph revealed, explored, and summarized the current research hotspots and their relationships in the field of psychological warning. Hotspots research showed the necessity of researches in the psychological warning field. The construction of psychological warning evaluation system and the analysis of key elements of psychological warning should be attached importance

Carbonation Resistance and Pore Structure of Mixed-Fiber-Reinforced Concrete Containing Fine Aggregates of Iron Ore Tailings

The disposal of industrial by-product tailings has become an important issue in solving environmental pollution. In this study, 15%, 30%, 50%, and 70% iron tailings were used to replace the natural sand in concrete, and 1.5% steel fiber and 0–0.75% PVA fibers were added to the iron tailings concrete. The effects of the iron tailings replacement rate and the fiber content on the mechanical properties, carbonization depth, and concrete porosity were studied in a carbonization environment. The results demonstrated that the compressive and splitting tensile strengths of concrete first increased and subsequently decreased with an increase in the iron tailings replacement rate, while the carbonation depth and porosity initially decreased and subsequently increased. When the replacement rate of iron tailings was 30%, the compressive strength and split tensile strength were increased by 7.6% and 17.7%, respectively, and the porosity was reduced by 8.9%. The compressive strength, carbonation depth and porosity of single-doped steel-fiber concrete were superior to those of ordinary iron tailings concrete. However, compared with single-doped steel fiber, the performance of steel-PVA fiber was further improved. Based on the mechanical properties, the carbonation depth test results of the three aforementioned types of concrete, the mathematical expression of the uniaxial compression stress–strain curve of iron tailings concrete, and the prediction equation of the carbonation depth of mixed-fiber iron tailings concrete were proposed. This study provides a reference for the application and popularization of fiber-reinforced iron tailings concrete in carbonization environments

Automatic Distributing Schemes of Physical Cell Identity for Self-Organizing Networks

This paper presents and puts forward an optimal automatic distributing of physical cell identity (ADPCI) scheme for the self-organizing network (SON). Considering the high number and the layered structure of the evolved node B (eNodeB, eNB) in the initial rollout phase, the assigning of PCI for cells would be quite complex. The PCI self-distributing problem is mapped to the well-known minimum spanning tree (MST) problem in order to optimize the PCI reuse distance and decrease the multiplexing interference. The correlation property of PCI is analyzed and taken into consideration in the assigning phase. Moreover, a suboptimal algorithm (SADPCI) is presented as it performs approximately to ADPCI but the computational complexity is lower. To demonstrate the proposal validity, performances of ADPCI and SADPCI are evaluated. Simulation results illustrate that these schemes can achieve significantly higher performance even under the condition of severe PCI deficiency