Integrative study of transcriptome and microbiome to reveal the response of Rhododendron decorum to cadmium stress.

The anomalies of cadmium (Cd) in karst region pose a severe threat to plant growth and development. In this study, the responses of Rhododendron decorum to Cd stress were investigated at physiological, molecular, and endophytic microbial levels, and the potential correlation among these responses was assessed. The Cd stress impeded R. decorum growth and led to an increase in malondialdehyde (MDA) and hydrogen peroxide (H2O2) levels, as well as enhanced superoxide dismutase (SOD) and catalase (CAT) activities. Meanwhile, Cd stress increased the Cd (up to 80 times compared to the control), sodium (Na), aluminum (Al), and zinc (Zn) contents, while decreased the magnesium (Mg) and manganese (Mn) contents in R. decorum leaves. Transcriptome suggested that Cd significantly regulated the pathways including "protein repair", "hormone-mediated signaling pathway", and "ATP-binding cassette (ABC) transporters". Additionally, q-PCR analysis showed that Cd stress significantly up-regulated the expressions of ABCB19-like and pleiotropic drug resistance, while down-regulated the expressions of indole-3-acetic acid-amido synthetase and cytokinin dehydrogenase. The Cd stress influenced the composition of endophytic microbial communities in R. decorum leaves and enhanced the interspecific bacterial associations. Furthermore, the bacterial genera Achromobacter, Aureimonas and fungal genus Vishniacozyma exhibited a high degree of connectivity with other nodes in networks constructed by the metal element contents, differentially expressed genes (DEGs), and microbial communities, respectively. These findings provide a comprehensive insight into the response of R. decorum to Cd-induced stress, which might facilitate the breeding of the Cd-tolerant R. decorum.

Water use efficiency of China's karst ecosystems: The effect of different ecohydrological and climatic factors.

Water use efficiency (WUE) is an important indicator for understanding the coupled ecosystem carbon and water cycles. However, the effect and contributions of factors on WUE variations in China's karst ecosystems for different climatic conditions have not been extensively studied. Our studies on WUE variations of China's karst ecosystems from 2001 to 2021 based on evapotranspiration and net primary productivity (NPP) from Moderate-resolution imaging spectroradiometer revealed the contributions of soil moisture (SM), leaf area index (LAI), precipitation (P), temperature (T), vapor pressure deficit (VPD), and CO2 concentration (CO2). Results showed that the trend of WUE was similar to that of NPP in terms of the latitude, longitude, and elevation, and WUE started abruptly decreasing after an elevation >3000 m until it reached 0 at 4500 m. WUE was primarily "slightly increased" in the humid region (H) and "slightly decreased" in the semi-humid region (SH), arid and semi-arid regions (ASA), and Qinghai-Tibet plateau region (QTP). CO2 (0.34), LAI (0.60), P (0.58), and LAI (0.55) exhibited the strongest positive direct effects on WUE in H, SH, ASA, and QTP, while VPD exhibited the strongest negative direct effect. VPD (0.26), VPD (0.28), SM (0.47), and P (0.39) had the strongest positive indirect effect, while T (-0.24), T (-0.18), VPD (-0.35), and P (-0.03) had the strongest negative indirect effect on WUE. The positive contributions of WUE variations in H, SH, ASA, and QTP were dominated by T (47.96 %), CO2 (26.36 %), P (8.81 %), and CO2 (52.97 %), whereas the negative contributions were dominated by P (-7.95 %), LAI (-26.57 %), CO2 (-35.98 %), and VPD (-9.59 %), respectively. This study quantifies the spatial and temporal distribution patterns of WUE in China's karst ecosystems and the regional differences between the multiple ecohydrological factors, thereby facilitating in-depth understanding and effective regulation for the carbon and water cycles in karst ecosystems.

Effects of varying the spatial configuration and scale of terraces on water and sediment loss based on scenario simulation within the Chinese Loess Plateau.

To optimize soil and water conservation measures, it is important to consider the spatial configuration and construction scale of terraces on the Loess Plateau in China. However, there are few existing efficient technology frameworks for assessing the impact of changing the spatial configuration and scale on reducing water and sediment loss at the basin scale. To address this gap, this study proposes a framework that employs a distributed runoff and sediment simulation tool coupled with multi-source data and scenario setting methods to identify the impacts of constructing terraces with different spatial configurations and scales on reducing water and sediment loss at the event scale on the Loess Plateau. Four scenarios (i.e. baseline, realistic, configuration changing and scale changing scenarios) were established to evaluate the associated impacts. The results show that, under the realistic scenario, the average water loss reductions within Yanhe Ansai and Gushanchuan Basins are 15.28 % and 8.68 %, respectively, and average sediment reduction rates are 15.97 % and 7.83 %, respectively. The effect of reducing water and sediment loss in the basin is highly related to the spatial configuration of terraces and that terraces should be built as low as possible on hillslopes. The results also show that, if terraces are disorderly constructed, the threshold of the terrace ratio that effectively contains the sediment yield in the hilly and gully regions of the Loess Plateau is approximately 35 %, whereas if the scale of terraces is increased, the sediment reduction effect is not significantly improved. Furthermore, if terraces are configured near the downslope, the threshold of the terrace ratio that can effectively contain sediment yield is further reduced to approximately 25 %. This study can be used as a scientific and methodological reference for optimizing terrace measures at a basin scale in the Loess Plateau and in other similar regions in the world.

GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model.

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win-win results in terms of efficiency and accuracy.

Study of coordinated development of county urbanization in arid areas of China: The case of Xinjiang.

Urbanization is a comprehensive process of mutual influence among the population, economy, society and living environment, and it depends on the synergy of a series of factors. This paper uses the statistical data of 76 counties in Xinjiang from 1996 to 2018 to construct a comprehensive urbanization evaluation system. Based on the entropy method, comprehensive evaluation model and coupling coordination model, from the scales of time and space, this paper discusses the current situation of the coordinated development of population, economy, society and living environment factors in counties in Xinjiang in the process of urbanization. Local spatial autocorrelation analysis is used to further study the spatial agglomeration effect of the coupling and coordination of urbanization development in the counties. The results show the following: (1) The comprehensive urbanization level of 76 counties in Xinjiang has the characteristics of "center-periphery" development, and high-level counties are clustered on the northern slopes of the Tian Mountains. (2) Most counties are in a serious state of imbalance; notably, the degree of population-economy-society-living environment coupling and coordination in the border counties and towns is in an unsatisfactory state. (3) The county-level cities in Northern Xinjiang belong to the diffusion and spillover areas, the county-level cities in southern Xinjiang belong to the polarization benefit areas, and most other counties are in the state of no spillover effect.

Remote sensing hydrological indication: Responses of hydrological processes to vegetation cover change in mid-latitude mountainous regions.

Hydrological processes in mid-latitude mountainous regions are greatly affected by changes in vegetation cover that induced by the climate change. However, studies on hydrological processes in mountainous regions are limited, because of difficulties in building and maintaining basin-wide representative hydrological stations. In this study, a new method, remote sensing technology for monitoring river discharge by combining satellite remote sensing, unmanned aerial vehicles and hydrological surveying, was used for evaluating the runoff processes in the Changbai Mountains, one of the mid-latitude mountainous regions in the eastern part of Northeast China. Based on this method, the impact of vegetation cover change on hydrological processes was revealed by combining the data of hydrological processes, meteorology, and vegetation cover. The results showed a decreasing trend in the monitored river discharge from 2000 to 2021, with an average rate of -5.13 × 105 m3 yr-1. At the monitoring section mainly influenced by precipitation, the precipitation-induced proportion of changes in river discharge to annual average river discharge and its change significance was only 6.5 % and 0.23, respectively, showing the precipitation change was not the cause for the decrease in river discharge. A negative impact of evapotranspiration on river discharge was found, and the decrease in river discharge was proven to be caused by the increasing evapotranspiration, which was induced by the drastically increased vegetation cover under a warming climate. Our findings suggested that increases in vegetation cover due to climate change could reshape hydrological processes in mid-latitude mountainous regions, leading to an increase in evapotranspiration and a subsequent decrease in river discharge.

Spatial Pattern Analysis of Xinjiang Tourism Resources Based on Electronic Map Points of Interest.

This study considers the Point of Interest data of tourism resources in Xinjiang and studies their spatial distribution by combining geospatial analysis methods, such as the average nearest neighbor index, standard deviation ellipse, kernel density analysis, and hotspot analysis, to explore their spatial distribution characteristics. Based on the analysis results, the following conclusions are made. Different categories of tourism resource sites have different spatial distributions, and all categories of tourism resources in Xinjiang are clustered in Urumqi city. The geological landscape resource sites are widely distributed and have a ring-shaped distribution in the desert area of southern Xinjiang. The biological landscape resources are distributed in a strip along the Tianshan Mountains. The water landscape resources are concentrated in the northern Xinjiang area. The site ruins are mostly distributed in the western region of Xinjiang. The distributions of the architectural landscape and entertainment and shopping resources are highly coupled with the distribution of cities. The distributions of the six categories of tourism resource points are in the northeast-southwest direction. The centripetal force and directional nature of the resource points of the water landscape are not obvious. The remaining five categories of resource points have their own characteristics. The distribution of resources in the site ruins is relatively even, and there are many hotspot areas in the geomantic and architectural landscapes, which are mainly concentrated in Bazhou and other places. The biological landscape has many cold-spot areas, distributed in areas such as Altai in northern Xinjiang and Hotan in southern Xinjiang. The remaining four categories have cold-spot and hotspot areas with different distributions. Tourism is an important thrust for economic development. The study of the distribution of tourism resources on the spatial distribution of tourism resources has clear guidance for later tourism development, can help the tourism industry optimize the layout of resources, and can promote tourism resources to achieve maximum benefits. The government can implement effective control and governance.

[Responses of soil moisture at different slope positions to rainfall in dry-hot valley]. / 干热河谷区不同坡位土壤水分对降雨的响应特征.

The study of short-term dynamics of soil moisture in the dry-hot valley area during rainfall process will help identify soil hydrological function. In this study, we analyzed the short-term responses of soil moisture to rainfall in Huajiang dry-hot valley of Guizhou, using in-situ monitoring method to yield high-frequency soil moisture monitoring data of different slope positions. The results showed that, during the whole monitoring period, soil moisture at each layer was at a moderate variation level (15.2%≤coefficient of variation CV≤29.7%), for both upper slope and middle slope. The fluctuation range of soil moisture of the upper slope (CV=21.1%) was greater than that of the middle slope (CV=19.1%), and that of the 0-5 cm soil layer (CV=26.2%) was greater than 20-40 cm layer (CV=16.5%). Compared with the middle slope, soil moisture of the upper slope had a faster response to rainfall. The supplement amount of rainfall was bigger and the supplement speed of rainfall was faster at the upper slope than that at the middle slope. The difference between the supplement speed and the depletion speed of soil moisture of the upper slope (2.3%·h-1) was greater than that of the middle slope (1.8%·h-1). With the increase of soil depth, the responses of soil moisture to rainfall in subsoil layer was earlier or synchronous with that in topsoil layer. When the supplement amount of soil moisture decreased and the supplement speed slowed down, the depletion speed slowed down. Compared with the middle slope, soil at the upper slope had greater water infiltration capacity and better water retention capacity. The responses of soil moisture to rainfall in dry-hot valley were influenced by micro-environment and microclimate, and the rapid recharge of dominant flow at rock-soil interface accelerated the response speed of subsoil moisture to rainfall, which made the slopes in this area easier to form mixed runoff generation mechanism.

Effect of human disturbances and hydrologic elements on the distribution of plant diversity within the Shamu watershed, Mt. Yuntai Nature Reserve, China.

This paper explores how human disturbance and hydrologic elements affect the spatial distribution pattern of plant diversity in the watershed, taking Shamu watershed in the World Natural Heritage Site as a case study. Spatial analysis of multisource remote sensing and plant diversity plots data were conducted using linear mixed effects models and structural equation models. Results revealed that the distribution of plant diversity in the watershed is mainly affected by human disturbance. However, under similar human disturbance levels, hydrologic elements also affect the plant diversity within the watershed. The topographic undulation and surface runoff significantly promote plant diversity, while the river network density, the watershed shape factor, the river longitudinal gradient do not. The influence of topographic undulation is more obvious than that of runoff on plant diversity, but the effect of topographic undulation and runoff on plant diversity is getting weaker from upstream to downstream within the watershed. In addition, the impact of hydrologic elements on plant diversity is mainly regulated by environmental factors Pre and Tem. The findings clarify how human disturbance and hydrologic elements affect plant diversity distribution within the watershed, optimizing the conservation theory of plant diversity resources and scientifically guiding the region's sustainable development.

Assessing the factors influencing water quality using environment water quality index and partial least squares structural equation model in the Ebinur Lake Watershed, Xinjiang, China.

Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable "land use/cover types" (path coefficient = - 0.600) and "Environmental factor" (path coefficient = - 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R2 = 0.727). In the latent variable "Environmental factors", the "NDVI" and "night light brightness value" have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the "farmland" and "forest land" within the latent variable of "Land use/cover type" have a considerable impact water quality, with the weights of 0.361 and - 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.

Impacts of storm events on chlorophyll-a variations and controlling factors for algal bloom in a river receiving reclaimed water.

Harmful algal bloom is prevalent in the reclaimed-water-source (RWS) river caused by the excessive nutrient's inputs. Rainfall water may be the sole nutrient-diluted water source for the RWS river. However, the effects of storm events on the algal bloom in the RWS river are poorly understood. This study presents chlorophyll-a (Chl-a) variations before, during, and after the initial storm events (Pre-storm, In-storm, and Post-storm) at four representative sites with distinct hydraulic conditions in a dam-regulated RWS river system, Beijing. The response of Chl-a to the initial storm events mostly depends on the ecosystem status that caused by the river hydraulic properties. The upstream is more river-like and downstream is more lake-like. In the river-like system, elevated water temperature (WT, increased by 2 %) could support the dominating algae (diatom) growth (Chl-a increased by 130 %) from Pre-storm to In-storm period. In the lake-like system, the dominant algae (blue algae) declined (Chl-a sharply decreased by 96%-99 %) due to the lower WT (decreased by 3%-7%) and increased flow velocities from Pre-storm to In-storm period. During the Post-storm period, the dominant algae break out (Chl-a surged by 20%-319 %) in the lake-like system caused by the recovered WT (increased by 3%-6%) and flow velocity. The occurrence of algal bloom can be predicted by the Random Forest (RF) model based on water quality parameters such as total nitrogen (TN). The thresholds of algal bloom for the Pre-storm, In-storm, and Post-storm periods were identified as 30 µg/L, 10 µg/L, and 10 µg/L, respectively. The two driven factors were WT and nitrate (NO3-N) for the Pre-storm period and were WT and TN for the In- & Post-storm periods. A higher risk of algal bloom is highlighted during the initial storm events in the RWS river. We propose recommendations for improving water quality in the RWS river systems under the climatic change.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China.

BACKGROUND: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. METHODS: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). RESULTS: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15-35 mm, and MAAP showed a positive correlation in the range of 925-945 hPa. HFMD incidence increased with MAT in the range of 15-23 °C, and the effective value of MAWS was in the range of 1.3-1.7 m/s, which was positively correlated with incidences of HFMD. CONCLUSIONS: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.

Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China.

Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m-1) has become a surrogate of soil salinity (EC, dS m-1) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional-scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional-scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0-20 cm, R2 = 0.65, RMSE = 5.59) and deeper soil depth (60-80 cm, R2 = 0.63, RMSE = 2.00, and 80-100 cm, R2 = 0.61, RMSE = 1.73), lower at transitional zone (20-40 cm, R2 = 0.55, RMSE = 2.66, and 40-60 cm, R2 = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%-61.54%, of which the most obvious depths are 60-80 cm and 0-20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.

Lake water volume fluctuations in response to climate change in Xinjiang, China from 2002 to 2018.

Climate change has a global impact on the water cycle and its spatial patterns, and these impacts are more pronounced in eco-fragile regions. Arid regions are significantly affected by human activities like farming, and climate change, which influences lake water volumes, especially in different latitudes. This study integrates radar altimetry data from 2002 to 2018 with optical remote sensing images to analyze changes in the lake areas, levels, and volumes at different altitudes in Xinjiang, China. We analyzed changes in lake volumes in March, June, and October and studied their causes. The results showed large changes in the surface areas, levels, and volumes of lakes at different altitudes. During 2002-2010, the lakes in low- and medium-altitude areas were shrinking but lakes in high altitude areas were expanding. Monthly analysis revealed more diversified results: the lake water levels and volumes tended to decrease in March (-0.10 m/year, 37.55×108 m3) and increase in June (0.03 m/year, 3.48×108 m3) and October (0.04 m/year, 26.90×108 m3). The time series lake water volume data was reconstructed for 2011 to 2018 based on the empirical model and the total lake water volume showed a slightly increasing trend during this period (71.35×108 m3). We hypothesized that changes in lake water at high altitudes were influenced by temperature-induced glacial snow melt and lake water in low- to medium-altitude areas was most influenced by human activities like agricultural irrigation practices.

Impact of human activities on coastal groundwater pollution in the Yang-Dai River plain, northern China.

Overexploitation of groundwater has resulted in seawater intrusion in many semiarid and arid coastal areas. This study illustrates the origin of groundwater salinity and assesses seawater intrusion/extrusion process in the Yang-Dai River plain aquifer, by analyzing hydrochemical and stable isotopic compositions of surface water, groundwater, geothermal water, and seawater. A cone of depression in groundwater is caused by intensive groundwater pumping formed in the late 1980s in the alluvial Yang-Dai River plain. In the northern part, groundwater exploitation has caused seawater intrusion identified by Ca-Cl type water. However, the widely distributed silty clay prevented the seawater intrusion in the southern part, evidenced by Ca-HCO3 type water with depleted δ2H (-60 to -46‰) and Î´18O (-8.9 to -4.7‰). Anthropogenic pollution also plays a significant role in groundwater salinization. The positive correlation between Cl and NO3- for most groundwater and the extremely high nitrate concentrations (up to 652.7 mg/L) indicate that fertilizer from agricultural activities has greatly influenced groundwater quality. Irrigation return flow evaporation during agricultural activities also accounts for groundwater salinity. Besides the intensive fertilizer usage, seawater intrusion and the established anti-tide dams reduced the surface water and groundwater discharge to the sea and then resulted in the extremely high nitrate concentration. This study may improve the understanding of the groundwater salinization processes in a complex coastal aquifer, which is greatly influenced by anthropogenic activities.

Exploring the spatial-temporal characteristics of the aerosol optical depth (AOD) in Central Asia based on the moderate resolution imaging spectroradiometer (MODIS).

Central Asia has become a key node of the belt and road corridor. It is located in arid and semi-arid climate regions, and it is a region where the contribution of global aerosols of sand and dust is continuous. However, few studies have been conducted on the Central Asian aerosol optical depth. Therefore, this paper relied on the belt and road sustainable development dataset to analyze the spatial-temporal variations in the AOD in Central Asia and provide spatial-temporal characteristics of the AOD for environmental services. We analyzed the spatial and temporal variation in the aerosol optical depth (AOD) in Central Asia by using MODIS/AQUA C6 MYD08_M3 images from 2008 to 2017. The results showed that (1) the annual average AOD in Central Asia in the past decade varied from 0.183 to 0.232, which indicated a slow decline starting in 2014. The percentage of average annual decline was approximately 0.18%, and the regular distinct revealed the distribution characteristics of AOD. In different years, the Central Asian region exhibited the similar monthly change characteristics: from July to December, the AOD decreased, and from December to February, it increased. In different seasons, the Central Asian region exhibited the different seasonal change characteristics: the AOD value was higher in the spring and summer. The mean values in the spring, summer, autumn, and winter were 0.273, 0.240, 0.155, and 0.183, respectively, and the standard deviations were 0.036, 0.038, 0.025, and 0.048, respectively. (3) Based on spatial distribution characteristics, the Tarim Basin, Aral Sea region, and Ebinur Lake area were high value areas, and Kazakhstan was a low value area. The AOD of the surrounding area of the Aral Sea had increased in the last 5 years, while that of Kazakhstan, Uzbekistan, and Turkmenistan had decreased. The AOD of the Taklamakan area exhibited an inter-annual change. Sand dust aerosols were the largest contributors to the AOD in the Taklamakan area. The rising trend of the AOD in the Aral Sea area was clear, with an average annual increase of 0.234%, and the contribution of salt dust aerosols to the AOD increased. The average annual AOD in the Ebinur Lake area remained stable.

Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions.

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg-1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China.

Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An example of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R2), Lin's concordance correlation coefficient, root mean square error, and relative root mean squared error values, were 0.78, 0.87, 9.59, and 0.76, respectively, for the Cubist and 0.78, 0.86, 9.79, and 0.78, respectively, for RF models. The slope of the linear fitting equation was higher for the Cubist model (0.75) than for RF (0.69). The explanatory power of Cubist and RF for soil salinity variation were 33.22% and 31.41% in the agroecosystem, and 72.25% and 71.66% in desert-oasis ecotone, respectively. For the agroecosystem, the range of the predicted values for 89.13% (Cubist) and 84.78% (RF) of sample was controlled within the same observational range at an interval of 0-5 dS m-1. Compared to single-year data (from 2013 to 2018), the ability to account for model spatial variability in soil salinity based on multi-year Landsat images was increased by 16%-35%. According to the variable importance evaluation, soil-related indices are the most important predictor variables, followed by vegetation, topography, landform, and land use, with relative importance values of 60%, 21%, 16%, and 3%, respectively. The predicted map was also broadly consistent with those obtained for Xinjiang in the Harmonized World Soil Database (HWSD) from the second national soil survey of China conducted from 1984 to 1997. The results also showed that the average value of the study area is 8.10 dS m-1 based on the Cubist-based map whereas that of the HWSD is 10.60 dS m-1, this implied that the overall salinity level has reduced by 23.58%. The methodological framework presented covers all prediction process steps and has considerable potential to be used in future soil salinity mapping at large scales for other similar region as OADoEs. The map derived from the Cubist/RF model revealed more detailed variation information about spatial distribution of the soil salinity compared to HWSD, and can further assist with decision-making when planning and utilising on existing soil and water resources in OADoEs.

Size Matters and How You Measure It: A Gram-Negative Antibacterial Example Exceeding Typical Molecular Weight Limits.

Monobactam antibiotic 1 is active against Gram-negative bacteria even though it has a higher molecular weight (MW) than the limit of 600 Da typically applied in designing such compounds. On the basis of 2D NMR data, the compound is able to adopt a compact conformation. The dimensions, projection area, and dipole moment derived from this conformation are compatible with porin permeation, as are locations of polar groups upon superimposition to the crystal structure of ampicillin bound to E. coli OmpF porin. Minimum inhibitory concentration (MIC) shifts in a porin knock-out strain are also consistent with 1 predominately permeating through porins. In conclusion, we describe a carefully characterized case of a molecule outside default design parameters where MW does not adequately represent the 3D shape more directly related to permeability. Leveraging 3D design criteria would open up additional chemical space currently underutilized due to limitations perceived in 2D.

A Unified Framework for the Incorporation of Bioorthogonal Compound Exposure Probes within Biological Compartments.

Compartmentalization is a crucial facet of many biological systems, and key aspects of cellular processes rely on spatial segregation within the cell. While many drug targets reside in specific intracellular compartments, the tools available for assessing compound exposure are generally limited to whole-cell measurements. To address this gap, we recently developed a bioorthogonal chemistry-based method to assess compartment-specific compound exposure and demonstrated its use in Gram-negative bacteria. To expand the applicability of this approach, we report here novel bioorthogonal probe modalities which enable diverse probe incorporation strategies. The probes we developed utilize a cleavable thiocarbamate linker to connect localizing elements such as metabolic substrates to a cyclooctyne moiety which enables the detection of azide-containing molecules. Adducts between the probe and azide-bearing compounds can be recovered and affinity purified after exposure experiments, thus facilitating the mass-spectrometry based analysis used to assess compound exposure. The bioorthogonal system reported here thus provides a valuable new tool for interrogating compartment-specific compound exposure in a variety of biological contexts while retaining a simple and unified sample preparation and analysis workflow.