Sensing of Soil Organic Matter Using Laser-Induced Breakdown Spectroscopy Coupled with Optimized Self-Adaptive Calibration Strategy.

Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288-950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM-PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60-80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R2 = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg-1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy.

In-situ rapid monitoring of nitrate in urban water bodies using Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) coupled with deconvolution algorithm.

Urban river and lake systems show important ecological function, and eutrophication frequently occurs and results from human activities due to the limited self-regulating ability. Since nitrate (NO3-) is one of the key factors causing water eutrophication, its rapid qualification plays critical role in the eutrophication control and management. In this study, water samples were collected from typical water bodies from Nanjing in different seasons, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) was employed for the quantitative determination of NO3- coupled with algorithms of deconvolution and partial least squares regression (PLSR). Results indicated that the typical absorption band of NO3- at 1500-1200 cm-1 was observed and the intensity of the band around 1360 cm-1 was positively correlated with the concentration of NO3- through spectra deconvolution. PLSR models were established based on the deconvolution spectra, which were excellent with the correlation coefficients (R2) of more than 0.8886 and the ratio of prediction to deviation (RPD) of more than 2.76; it was found that the carbonate in water might impact the prediction due to its absorption around 1450 cm-1, but the prediction model performed well in condition that the carbonate content in a low level with less than 10 mg L-1. Significant temporal and spatial variations of NO3- were observed in the typical water bodies, and the Qinhuai River having the highest NO3- content, which mainly was influenced by human activities, and the impact of water pH and temperature were not significantly observed. Therefore, FTIR-ATR combined with deconvolution and PLSR, allowed a rapid determination of NO3- in urban water bodies, providing an alternative option for the monitoring of nitrate in natural water body, which will benefit the prevention and control of eutrophication.

Synthesis and evaluation of 4-(1,3,4-oxadiazol-2-yl)-benzenesulfonamides as potent carbonic anhydrase inhibitors.

Human Carbonic anhydrase (hCA) I and II are crucial targets for anti-acute mountain sickness. Twenty-one 4-(1,3,4-oxadiazol-2-yl) benzenesulfonamides were synthesized and screened against these two isoforms. The results illustrated that 5c, 5g, 5h, 5k were more potent against both hCA I and II than clinical drug AAZ. In particular, the value of compound 5c with hCA I (18.08 nM) was over 84-fold more than of AAZ with hCA I. The data of docking simulations were also in accord with the tendency of inhibitive activities. Furthermore, compound 6h, the methanesulfonate of 5h, showed better anti-hypoxia activity than AAZ in vivo, making it interesting lead compound.

Optimization of activity localization of quinoline derivatives: Design, synthesis, and dual evaluation of biological activity for potential antitumor and antibacterial agents.

A novel of quarternary amine around a quinolinium iodide combined with even number alkyl chain were prepared in a several step in moderate yield starting from malonic ester and benzo[d][1,3]dioxol-5-amine. All of the active structure compounds were identified by nuclear magnetic resonance (NMR), such as 1H NMR, 13C NMR, infrared radiation (IR), high resolution mass spectrometry (HR-MS) and Carlo Erba Instruments CHNS-O EA1108 spectra analysis. With regard to the anticancer properties, the in vitro cytotoxicity against three human cancer cell lines (A-549, Hela and SGC-7901) were evaluated. The antibacterial properties against two human bacterial strains, Escherichia coli (ATCC 29213) and Staphylococcus aureus (ATCC 8739), along with minimum inhibitory concentration (MIC) values were evaluated. The target compounds, 5-12, exhibited significant antitumor and antibacterial activity, of which compound 12 was found to be the most potent derivative with IC50 values of 5.18 ± 0.64, 7.62 ± 1.05, 17.59 ± 0.41, and 54.45 ± 4.88 against A-549, Hela, SGC-7901, and L-02 cells, respectively, stronger than the positive control 5-FU and MTX. Furthermore, compound 12 had the most potent inhibitory activity. The MIC of this compound against Escherichia coli (ATCC 29213) and Staphylococcus aureus (ATCC 8739) was 3.125 nmol·mL-1, which was smaller than that of the reference agents, amoxicillin and ciprofloxacin.

N-Quinary heterocycle-4-sulphamoylbenzamides exert anti-hypoxic effects as dual inhibitors of carbonic anhydrases I/II.

Acute mountain sickness (AMS) affects approximately 25-50% of newcomers to high altitudes. Two human carbonic anhydrase isoforms, hCA I and II, play key roles in developing high altitude illnesses. However, the only FDA-approved drug for AMS is acetazolamide (AAZ), which has a nearly 100 times weaker inhibitory activity against hCA I (Ki = 1237.10 nM) than hCA II (Ki = 13.22 nM). Hence, developing potent dual hCA I/II inhibitors for AMS prevention and treatment is a critical medical need. Here we identified N-quinary heterocycle-4-sulphamoylbenzamides as potent hCA I/II inhibitors. The newly designed compounds 2b, 5b, 5f, 6d, and 6f possessed the desired inhibitory activities (hCA I: Ki = 16.95-52.71 nM; hCA II: Ki = 8.61-18.64 nM). Their hCA I inhibitory capacity was 22- to 76-fold stronger than that of AAZ. Relative to the control group for survival in a mouse model of hypoxia, 2b and 6d prolonged the survival time of mice by 21.7% and 29.3%, respectively, which was longer than those of AAZ (6.5%). These compounds did not display any apparent toxicity in vitro and in vivo. In addition, docking simulations suggested that the quinary aromatic heterocycle groups stabilised the interaction between hCA I/II and the inhibitors, which could be further exploited in structure optimization studies. Hence, future functional studies may confirm 2b and 6d as potential clinical candidate compounds with anti-hypoxic activity against AMS.

Synthesis and Evaluation of the Antitumor Activity of Novel 1-(4-Substituted phenyl)-2-ethyl Imidazole Apoptosis Inducers In Vitro.

Novel imidazole derivatives were designed, prepared, and evaluated in vitro for antitumor activity. The majority of the tested derivatives showed improved antiproliferative activity compared to the positive control drugs 5-FU and MTX. Among them, compound 4f exhibited outstanding antiproliferative activity against three cancer cell lines and was considerably more potent than both 5-FU and MTX. In particular, the selectivity index indicated that the tolerance of normal L-02 cells to 4f was 23-46-fold higher than that of tumor cells. This selectivity was significantly higher than that exhibited by the positive control drugs. Furthermore, compound 4f induced cell apoptosis by increasing the protein expression levels of Bax and decreasing those of Bcl-2 in a time-dependent manner. Therefore, 4f could be a potential candidate for the development of a novel antitumor agent.

Synthesis, activity evaluation, and pro-apoptotic properties of novel 1,2,4-triazol-3-amine derivatives as potent anti-lung cancer agents.

In this study, a series of 4,5-bis(substituted phenyl)-4H-1,2,4-triazol-3-amine compounds was designed, synthesised, and evaluated to determine their potential as anti-lung cancer agents. According to the results of screening of lung cancer cell lines A549, NCI-H460, and NCI-H23 in vitro, most of the synthesised compounds have potent cytotoxic activities with IC50 values ranging from 1.02 to 48.01 µM. Particularly, compound 4,5-bis(4-chlorophenyl)-4H-1,2,4-triazol-3-amine (BCTA) was the most potent anti-cancer agent, with IC50 values of 1.09, 2.01, and 3.28 µM against A549, NCI-H460, and NCI-H23 cells, respectively, meaning many-fold stronger anti-lung cancer activity than that of the chemotherapeutic agent 5-fluorouracil. We also explored the effects of BCTA on apoptosis in lung cancer cells by flow cytometry and western blotting. Our results indicated that BCTA induced apoptosis by upregulating proteins BAX, caspase 3, and PARP. Thus, the potential application of compound BCTA as a drug should be further examined.

Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging.

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R2) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m2) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R2 = 0.63, MAE = 7.04 gC/m2, and RMSE = 8.51 gC/m2), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R2 = 0.72, MAE = 8.09 gC/m2, and RMSE = 9.89 gC/m2) was higher than that of the LR method (R2 = 0.70, MAE = 8.99 gC/m2, and RMSE = 10.69 gC/m2). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R2 = 0.69, MAE = 7.40 gC/m2, and RMSE = 9.01 gC/m2, while the LR method reaches the highest model accuracy of R2 = 0.53, MAE = 9.20 gC/m2, and RMSE = 11.10 gC/m2. The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 ⅹ 104 Mg C, and the mean carbon stock density was 47.44 gC/m2. The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China.

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011-2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years.

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m-2 day-1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m-2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr-1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method.

Understanding the spatial variability of soil organic matter (SOM), soil total nitrogen (STN), soil total phosphorus (STP), and soil total potassium (STK) is important to support site-specific agronomic management, food production, and climate change adaptation. High-resolution remote sensing imageries have emerged as an innovative solution to investigate the spatial variation in agricultural soils with machine learning (ML) algorithms. However, the predictive power of the individual and combined effects of Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral images for mapping soil properties, especially STN, STP, and STK, have rarely been investigated. Moreover, single ML model may achieve unstable performance for predicting multiple soil properties due to strong spatial heterogeneity. This study explored the combine use of S1, S2, and DEM derivatives to map SOM, STN, STP, and STK content of a sloped cropland of northeastern China. A two-step method with a weighted sum of four ML models was proposed to improve the accuracy and robustness in predicting multiple soil properties. Our results showed that single ML model has various performance in predicting the four soil properties. The optimal ML models could explain approximately 56 %, 53 %, 56 % and 37 % of the variability of SOM, STN, STP, and STK, respectively. Using the weights estimated through a 10-fold cross-validation procedure, the two-step ensemble learning model was retrained and showed more robust performance than the four ML models, in which the prediction accuracy was improved by 2.38 %, 1.40 %, 3.52 %, and 3.29 % for SOM, STN, STP, and STK, respectively. Our results also showed that the optical S2 derived features, especially the two S2 short-wave infrared bands, enhanced vegetation index, and soil adjusted vegetation index, were more important for soil property prediction than S1 data and DEM derivatives. Compared with individual sensor, a combination of S1 and S2 data yielded more accurate predictions of STN and STP but not for SOM and STK. The results of this study highlight the potential of high-resolution S1 and S2 data and the two-step method for soil property prediction at farmland scale.

Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China.

Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.

Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe.

The Eurasian steppe is the largest steppe region in the world and is an important part of the global grassland ecosystem. The eastern Eurasian steppe has favorable hydrothermal conditions and has the highest productivity and the richest biodiversity. Located in the arid and semi-arid region, the eastern Eurasian steppe has experienced large-scale grassland degradation due to dramatic climate change and intensive human activities during the past 20 years. Hence, accurate estimation of aboveground biomass (AGB, gC m-2) and belowground biomass (BGB, gC m-2) is necessary. In this study, plenty of AGB and BGB in-situ measurements were collected among dominated grassland types during summer in 2013 and 2016-2018 in the eastern Eurasian steppe. Vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) and climate variables were chosen as independent variables to establish predictive models for AGB and BGB with random forest (RF). Both AGB (R2 = 0.47, MAE = 21.06 gC m-2, and RMSE = 27.52 gC m-2) and BGB (R2 = 0.44, MAE = 173.02 gC m-2, and RMSE = 244.20 gC m-2) models showed acceptable accuracy. Then the RF models were applied to generate spatially explicit AGB and BGB estimates for the study area over the last two decades (2000-2018). Both AGB and BGB showed higher values in the Greater Khingan Mountains and decreased gradually to the east and west sides. The mean values for AGB and BGB were 62.16 gC m-2 and 531.35 gC m-2, respectively. The climatic factors were much more important in controlling biomass than anthropogenic drivers, and shortage of water and raising temperature were the main limiting factor of AGB and BGB, respectively, in the peak growth season. These findings provide scientific data for the scientific management of animal husbandry and can contribute to the sustainable development of grassland ecology in the eastern Eurasian steppe.

Design, Synthesis, and Dual Evaluation of Quinoline and Quinolinium Iodide Salt Derivatives as Potential Anticancer and Antibacterial Agents.

A series of novel quinoline and quinolinium iodide derivatives were designed and synthesized to discover potential anticancer and antibacterial agents. With regard to anticancer properties, in vitro cytotoxicities against three human cancer cell lines (A-549, HeLa and SGC-7901) were evaluated. The antibacterial properties against two strains, Escherichia coli (ATCC 29213) and Staphylococcus aureus (ATCC 8739), along with minimum inhibitory concentration (MIC) values were evaluated. The target alkyliodine substituted compounds exhibited significant antitumor and antibacterial activity, of which compound 8-((4-(benzyloxy)phenyl)amino)-7-(ethoxycarbonyl)-5-propyl-[1,3]dioxolo[4,5-g]quinolin-5-ium (12) was found to be the most potent derivative with IC50 values of 4.45±0.88, 4.74±0.42, 14.54±1.96, and 32.12±3.66 against A-549, HeLa, SGC-7901, and L-02 cells, respectively, stronger than the positive controls 5-FU and MTX. Furthermore, compound 12 had the most potent bacterial inhibitory activity. The MIC of this compound against both E. coli and S. aureus was 3.125 nmol ⋅ mL-1 , which was smaller than that against the reference agents amoxicillin and ciprofloxacin.