Integrated assessment and prediction of ecological security in typical ecologically fragile areas.

In order to safeguard and restore ecological security in ecologically fragile regions, a regionally appropriate land use structure and ecological security pattern should be constructed. Previous ecological security research models for ecologically fragile areas are relatively homogenous, and it is necessary to establish a multi-modeling framework to consider integrated ecological issues. This study proposes a coupled "PLUS-ESI-Circuit Theory" framework for multi-scenario ecological security assessment of the Ningxia Hui Autonomous Region (NHAR). Firstly, the PLUS model was used to complete the simulation of four future development scenarios. Secondly, a new ecological security index (ESI) is constructed by synthesizing ecological service function, ecological health, and ecological risk. Finally, the Circuit Theory is applied to construct the ecological security pattern under multiple scenarios, and the optimization strategy of ecological security zoning is proposed. The results show that (1) from 2000 to 2030, the NHAR has about 80% of grassland and farmland. The built-up area is consistently growing. (2) Between 2000 and 2030, high ecological security areas are primarily located in Helan Mountain, Liupan Mountain, and the central part of NHAR, while the low ecological security areas are dominated by Shapotou District and Yinchuan City. (3) After 2010, the aggregation of high-security areas decreases, and the fragmentation of patches is obvious. Landscape fragmentation would increase under the economic development (ED) scenario and would be somewhat ameliorated by the ecological protection (EP) and balanced development (BD) scenarios. (4) The number of sources increases but the area decreases from 2000 to 2020. The quantity of ecological elements is on the rise. Ecological restoration and protection of this part of the country will improve its ecological security.

Temporal and spatial variation, input fluxes and risk assessment of cyclic methylsiloxanes in Rivers-Bohai Sea System.

In the present study, the total concentrations of three cyclic methylsiloxanes (ΣCMSs), including octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5) and dodecamethylcyclohexasiloxane (D6), in surface water and sediment samples of five main rivers draining into the Bohai Sea were in the range of 1.62-1.39 × 103 ng/L and 1.92-1.69 × 103 ng/g dw, respectively. Riverine input had great influence on the coastal distribution of siloxanes in the Bohai Sea. The concentrations of ΣCMSs in coastal sediments farthest away (40-50 Km) from the estuaries were only 4-33% of those close to the estuaries. But surprisingly, compared with those in coastal sediments (1.03-1.44 × 103 ng/g dw), the concentrations of CMSs (1.56-2.67 × 103 ng/g dw) in some deep-sea sediments were higher, and certain positive correlation existed between sediment ΣCMSs in this area with the total petroleum hydrocarbons concentration (R2 = 0.92, p < 0.05) suggested offshore oil exploitation as one important emission source of siloxanes. Overall, calculated based on their sediment concentrations, D4-D6 had negligible ecological risks to the benthic organisms in river-Bohai Sea system, i.e. HQs < 1. However, sediment-accumulation of siloxanes should be paid attention, especially for some deep-sea sediments nearby drilling platforms, where it will take only less than 1 year for D4 to reach its threshold.

Research on the Impact of Absorption Feature Extraction on Spectral Difference Between Similar Minerals.

Diagnostic absorption features can indicate the existence of specific materials, which is the foundation of mineral analysis with optical remote sensing data. In hyperspectral data processing, the most commonly used method to extract absorption feature, is Continuum Removal (CR). As for multispectral data, Principle Component Analysis and other indirect methods were used to extract absorption information, and little research has been done on full-band absorption feature extraction. Classification of similar minerals is one of the major difficulties in mineral spectral analysis, while there is no valid index for spectral difference between similar mineral groups. Absorption feature extraction may improve the classification accuracy, but there is no research to investigate the impact of absorption feature extraction on spectral difference between similar minerals. This paper summarized the principle of mineral spectral difference, and proposed the concept of Class Separability Ratio (CSR), which was verified to be a valid index for spectral difference between similar mineral categories. Through comparison experiments on alunite and kaolinite spectra, including USGS spectral library spectra and resampled spectra in accordance with the band settings of HYPERION, ASTER and OLI, the impact of absorption feature extraction on spectral difference between similar minerals were investigated. Experimental results show that valid absorption feature extraction can greatly enhance the spectral difference between similar minerals, and the spectral difference is positively correlated with spectral resolution. Besides, the results of CR can be severely affected by spectral resolution and band center positions, and the absorption feature spectra extraction results for multispectral datasets need to be improved. This research laid the foundation of precise identification between similar mineral categories, and provided valuable reference for the band settings of future geology remote sensing sensors.

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity.

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

Glycolysis-Metabolism-Related Prognostic Signature for Ewing Sarcoma Patients.

Ewing sarcoma (EwS) is a malignant sarcoma which occurs in bone and soft tissues commonly happening in children with poor survival rates. Changes in cell metabolism, such as glycolysis, may provide the environment for the transformation and progression of tumors. We aimed to build a model to predict prognosis of EwS patients based on glycolysis and metabolism genes. Candidate genes were obtained by differential gene expression analysis based on GSE17679, GSE17674 and ICGC datasets. We performed GO and KEGG pathway enrichment analysis on candidate genes. Univariate Cox and LASSO Cox regression analyses were conducted to construct a model to calculate the Risk Score. GSEA was done between high-risk and low-risk groups. CIBERSORT was applied to analyze the immune landscape. We got 295 candidate glycolysis-metabolism-related genes which were enriched in 620 GO terms and 18 KEGG pathways. 12 Genes were selected by univariate Cox model and 5 of them were determined by LASSO Cox regression analysis to be used in the construction of the Risk Score model. The Risk Score could be considered as an independent prognosis factor. The immune landscape and immune checkpoints' expression significantly differed between high- and low-risk groups. Our research constructed a new glycolysis-metabolism-related genes (FABP5, EMILIN1, GLCE, PHF11 and PALM3) based prognostic signature for EwS patients and assisted in gaining insight into prognosis to improve therapies further.

Using a combination of nighttime light and MODIS data to estimate spatiotemporal patterns of CO2 emissions at multiple scales.

Accurate mapping spatiotemporal patterns of CO2 emissions and understanding its driving factors are very important, it is useful for the scientific and rational formulation of carbon emission reduction policies. Nevertheless, due to data availability issues, most studies have been limited to the global and national scales, and the models used were relatively simple. In this paper, we used the 500 m Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data and the 250 m Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS NDVI) and proposed an improved CO2 emissions index (ICEI) to calculate CO2 emissions. Compared with the total nighttime light (NTL), the average regression coefficient (R2) can be improve from 0.73 to 0.78. We also used the coefficient of variation, spatial autocorrelation, and geographically weighted regression models to analyze the temporal and spatial variation mode of CO2 emissions, as well as the associated correlation and heterogeneity, at three different administrative unit scales during 2012-2019. Our experimental results demonstrate that: (1) the improved index (ICEI) is better than the traditional variable (NTL) in estimating CO2 emissions; (2) the highest CO2 emissions are primarily gathered in the developed coastal areas in eastern China; and (3) at the provincial level, the added value of the secondary industry is the most significant factor, whereas the added value of the tertiary industry is negatively correlated with CO2 emissions.

Species differences in the green-up date of typical vegetation in Inner Mongolia and climate-driven mechanism based on process-based phenology models.

Different species within the same community may exhibit distinct phenological responses to climate change, so it is necessary to study species differences in the green-up date among abundant species within a wide area, and a suitable phenology model should be introduced to explain the associated climate-driven mechanism. Although various models have been developed, very few studies have aimed to compare their efficiency and robustness, and the relative contributions of climate driving factors have not been sufficiently examined. We analyzed phenology data for 12 species across 17 stations in Inner Mongolia and found that essential spatiotemporal and interspecies differences existed in the green-up date. Five process-based models were established for each species and their performance was comprehensively evaluated. The two-phase models (sequential model, parallel model, unified model and unified model combined with precipitation driving) generally performed better than the one-phase model (thermal time model), and the model considering precipitation performed the best, which indicates that it is necessary to introduce the chilling effect and precipitation driving effect to improve the model accuracy in arid environments. We proposed a method to estimate the contribution rates of various climate driving factors, and significant differences in the relative demand for the various climate driving factors among different species were clearly revealed. The results indicated that for natural vegetation in Inner Mongolia, the need for the chilling and temperature driving is relatively high, and the precipitation driving is very important for herbaceous vegetation, which leads to considerable spatial and interspecies differences in green-up date. We demonstrated the feasibility of quantitatively evaluating the contributions of different climate driving factors with a process-based model, and the contradiction in phenological changes among different studies may eventually be clarified.

Hypothesis: exertional heat stroke-induced myopathy and genetically inherited malignant hyperthermia represent the same disorder, the human stress syndrome.

Exertional heat stroke is usually experienced as a result of a prolonged and intensive exercise. It is a life-threatening condition that is characterized by an increase in core body temperature and rhabdomyolysis. The associated hyperkalemia and metabolic acidosis may lead to an acute renal, cardiac, and hemostatic failure. Exactly, the same symptoms are noticed in case of the anesthesia-induced malignant hyperthermia (MH), an inherited disorder of the skeletal muscle ryanodine receptor. This receptor is a Ca(2+) channel that is activated by the volatile anesthetic agents and depolarizing muscle relaxant. The presence of MH-associated ryanodine receptor variant in the individuals who suffered from EH and improvement of the symptoms with dantrolene has frequently raised the question as to whether the two disorders actually represent one and the same disease. Nevertheless, an exact explanation of the susceptibility of the genetically predisposed MH individuals to ER remains elusive. We have attempted to review the published clinical reports to explore the possibility that ER and EH represent one and the same disorder.