Soil visible-near infrared diffuse reflectance spectroscopy （vis-NIR DRS） has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be ...particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression （PLSR）. This combination significantly improved the predictions of soil organic matter （R^2= 0.899; RPD = 3.158） compared with using PLSR alone （R^2 = 0.697; RPD = 1.817）.
In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and ...following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R2>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R2<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils.
Glioblastoma (GBM) is a highly aggressive brain tumor characterized by increased proliferation and resistance to chemotherapy and radiotherapy. Recently, a growing body of evidence suggests that ...glioma‐initiating cells (GICs) are responsible for the initiation and recurrence of GBM. However, the factors determining the differential development of GICs remain poorly defined. In the present study, we show that curcumin, a natural compound with low toxicity in normal cells, significantly induced differentiation of GICs in vivo and in vitro by inducing autophagy. Moreover, curcumin also suppressed tumor formation on intracranial GICs implantation into mice. Our results suggest that autophagy plays an essential role in the regulation of GIC self‐renewal, differentiation, and tumorigenic potential, suggesting autophagy could be a promising therapeutic target in a subset of glioblastomas. This is the first evidence that curcumin has differentiating and tumor‐suppressing actions on GICs. (Cancer Sci 2012; 103: 684–690)
•3D digital soil maps were prepared at 10-depths (1-m total) for 12 soil properties.•In-situ depth-wise proximally sensed soil data was used for mapping.•3 linear and nonlinear regression techniques ...were compared for prediction ability.•3D regression kriging was used to map soil properties and calculate uncertainty.•Depth-specific mapping accuracy was observed for all soil properties.
Three-dimensional digital soil mapping (3D-DSM) quantifies both the horizontal and the vertical variability of soil properties. Most current studies in 3D-DSM were based on either one-dimensional profile depth functions or two-dimensional horizontal interpolation techniques, which did not allow true 3D visualization of spatial soil heterogeneity. Only a few studies have utilized the 3D variograms for mapping. Recent advances in proximal soil sensing technologies allow measurement and prediction of soil properties rapidly at multiple depths which could serve as input data for DSM. Various soil physical and chemical properties have already shown either direct or indirect relationships with the proximal soil sensing data. This study aims to test the methodology of 3D-DSM by incorporating a 3D regression kriging (RK) with multiple proximal soil sensing techniques. In this study, vis-NIR spectra were collected in-situ at 148 locations to about 1-m depth using the Veris® P4000 soil profiler at Field 26 of Macdonald Farm, McGill University. Additionally, 32 soil cores were collected out of the 148 locations to 1-m maximum depth and sectioned at 10-cm depth intervals for laboratory analysis of volumetric water content (VWC), soil organic matter (SOM), and clay content. Cubist spectral models were developed for each soil property at the 32 locations and then predicted to the 148 locations, which were then randomly split into calibration (70%, 103 locations) and validation (30%, 45 locations) datasets for mapping. The 3D-RK method included a trend prediction between calibration dataset and environmental covariates (including apparent soil electrical conductivity, gamma-ray radiation, and elevation) and a residual kriging. The generalized linear model (GLM), regression tree (RT), and random forest (RF) models were compared for trend prediction. The covariates were also simulated 100 times using sequential Gaussian simulations to fit into 3D-RK and calculate model uncertainty. As a result, complete 3D digital soil maps with uncertainty were developed. We found that the RF model outperformed GLM and RT in regard to interpreting non-linear soil-landscape relationships and resulting in marginally higher validation accuracy and smaller prediction uncertainty for VWC and clay. The GLM model resulted in slightly better validation results and smaller model uncertainty for SOM only. SOM and clay showed large horizontal and vertical variability and affected the spatial distribution of VWC. The validation accuracy was higher in the soil surface for most soil properties due to the uniform environment in the plow layer and sufficient environmental covariates collected at the soil surface. The mapping uncertainty increased with depth for VWC and clay content but decreased with depth for SOM because SOM content decreases with depth.
•Model performance was stable for a large calibration size.•Independent validation had a large uncertainty of accuracy for a small data set.•One-time random sampling for independent validation is ...appropriate for a large data set.•Cross validation and/or repeated independent validation are suggested for a small data set.
Visible-near infrared (vis–NIR) spectroscopy has been widely used to characterize soil information from field to global scales. Before applying a calibrated spectral predictive model to acquire soil information, either independent validation or k-fold cross validation is used to evaluate model performance. However, there is no consensus on which validation strategy is more suitable and robust when evaluating model performance for the studies in different scales. The objective of this study is to evaluate and compare the model performance of two validation strategies coupling different calibration sizes (a ratio of calibration to validation of 2:1, 4:1 and 9:1) and calibration sampling strategies (random sampling (RS), rank, Kennard-Stone (KS), rank-Kennard-Stone (RKS) and conditioned Latin hypercube sampling (cLHS)) across scales. A total of 17,272 vis–NIR spectra of mineral soils from LUCAS data (continental scale) and their soil organic carbon (SOC) and clay contents were used in this study, and the dataset was further split into national (2761 samples in France) and five regional datasets (110 to 248 samples from five French administrative regions). To eliminate the effect of changing validation set on the model performance, a consistent test set (20% of total samples at each scale) was split to evaluate all the combinations involved in two validation strategies. The Lin’s concordance correlation coefficient (CCC) of the cubist model were stable for both SOC and clay for different calibration sizes, calibration sampling and validation strategies for a large calibration size (>1400) at the national and continental scales. A larger calibration size can potentially improve model performance for a small dataset (<300) at the regional scale, and a wider calibration range would result in better model performance. No silver bullet was found among the different calibration sampling strategies at the regional scale. For five French regions (small data set), we found a high variation (95th percentile minus the 5th percentile) in the CCC among the models built from 50 repeated RS (0.10–0.44 for SOC, 0.16–0.52 for clay) and cLHS (0.08–0.40 for SOC, 0.12–0.36 for clay). This finding indicates that a one-time RS or cLHS for selecting the calibration set has high uncertainty in model evaluation for a small dataset and therefore should be used with caution. Therefore, we suggest the following: (1) for a large data set (thousands), either one-time random sampling for independent validation or k-fold cross validation would be appropriate; (2) for a small data set (dozens to hundreds), k-fold cross validation and/or repeated random sampling for independent validation would be more robust for spectral predictive model evaluation.
Canagliflozin, an inhibitor of sodium glucose co-transporter (SGLT) 2, has been shown to reduce body weight during the treatment of type 2 diabetes mellitus (T2DM). In this study, we sought to ...determine the role of canagliflozin in body weight loss and liver injury in obesity. C57BL/6J mice were fed a high-fat diet to simulate diet-induced obesity (DIO). Canagliflozin (15 and 60 mg/kg) was administered to DIO mice for 4 weeks. Orlistat (10 mg/kg) was used as a positive control. The body weight, liver weight, liver morphology, total cholesterol (TC) and triglyceride (TG) levels were examined. Signaling molecules, including diacylgycero1 acyltransferase-2 (DGAT2), peroxisome proliferation receptor alpha-1 (PPARα1), PPARγ1, PPARγ2 mRNA levels and the protein expression of SGLT2 were evaluated. Canagliflozin reduced body weight, especially the high-dose canagliflozin, and resulted in increased body weight loss compared with orlistat. Moreover, canagliflozin reduced the liver weight and the ratio of liver weight to body weight, lowered the serum levels of TC and TG, and ameliorated liver steatosis. During the canagliflozin treatment, SGLT2, DGAT2, PPARγ1 and PPARγ2 were inhibited, and PPARα1 was elevated in the liver tissues. This finding may explain why body weight was reduced and secondary liver injury was ameliorated in response to canagliflozin. Together, the results suggest that canagliflozin may be a potential anti-obesity strategy.
•The DS algorithm is used to remove the effects of soil moisture on vis–NIR spectra.•A spectral library of dry ground soils can be used to predict pH, OM, and TN simultaneously with field ...spectra.•Only a small subset of local samples needs to be collected, dried and ground before prediction.
Organic matter (OM), total nitrogen (TN), and pH are essential soil properties for assessing the fertility of paddy soils. They can be measured with visible and near infrared (vis–NIR) spectroscopy effectively in the field. However, environmental factors e.g., soil moisture and particle size distribution affect the accuracy of spectroscopic measurement and successful calibration transfer between laboratory and field spectra. Large spectral libraries derived from dried and ground soil samples thus could not be used to predict soil properties using spectra of fresh (non-processed) samples. In this paper, we investigated the possibility of using the Chinese soil spectral library (CSSL) of dry ground soils to predict OM, TN and pH of paddy soils in the Yangtze River Delta using spectra of fresh (non-processed) soil samples measured in situ, after removing the influences of the environmental factors with direct standardization (DS). The locally weighted regression (LWR) model built on the CSSL was then used to predict with the DS-transferred field spectra. The CSSL consists of vis–NIR spectra of over 3993 samples collected local dataset from 19 Chinese provinces. Two hundred and twenty-five soil samples independent from the CSSL (local dataset) were collected from 20 target sites in the Yangtze River Delta, China and their spectra were measured in both field and laboratory conditions. Using DS, a subset of the corresponding field and laboratory spectra from the independent set (designated as the transfer set) was used to derive the DS transfer matrix, which characterized the differences between the field and laboratory spectra. The field spectra of the 225 samples were then transferred to match characteristics of laboratory measured spectra of processed soil samples. The predictions of soil properties were performed on the DS-transferred field spectra using a LWR model derived with the CSSL. Results showed that DS effectively removed the effects of moisture from field spectra, and led to simultaneous improvement in the predictions of pH, OM, and TN to an acceptable level (pH: R2=0.611, root mean square error (RMSE)=0.73 and ratio of performance to inter-quartile range (RPIQ)=2.30; OM: R2=0.641, RMSE=6.82gkg−1 and RPIQ=1.79; TN: R2=0.658, RMSE=0.39gkg−1 and RPIQ=1.81). We recommended the use of DS combined with CSSL models for the efficient prediction of soil pH, OM, and TN simultaneously using field scans of paddy soils.
National identity constitutes the psychological basis and important force for the survival and development of a country. How to shape and construct national identity and maintain the unity and ...stability of multi-ethnic countries is an important subject that multi-ethnic countries must face. Citizenship education is considered to be the basic path to cultivate and strengthen national identity. The construction of national identity affects the direction of citizenship education, which in turn affects the cultivation of national identity. National identity and citizenship education interact and shape each other. It should be pointed out that both national identity and citizenship education are related to citizenship, and citizenship is the connection point between national identity and citizenship education.
Soil quality in alpine ecosystems requires regular monitoring to assess its dynamics under changes in climate and land use. Visible near‐infrared (vis‐NIR) spectroscopy could offer an option, as ...sampling and transporting large numbers of soil samples in the Qinghai‐Tibet Plateau is extremely difficult. However, the potential for in situ vis‐NIR spectra and the optimal algorithms need to be defined in this region. We have therefore evaluated the performance of a deep learning method, multilayer perceptron (MLP), for in situ spectral measurement of soil organic carbon (SOC) with in situ vis‐NIR spectroscopy in southeastern Tibet, China. A total of 39 soil cores (maximum depth 1 m), including 547 soil samples taken from each 5‐cm depth interval, were collected. The spectra were also measured at each 5‐cm depth interval accordingly. After spectral preprocessing, 4,096 MLP models were generated by taking all the combinations from six parameters defined in the MLP. The 10‐fold‐core cross‐validation showed that MLP had a good performance for in situ SOC prediction, and the best MLP model had an R2 of .92, which were much better than those of the partial least squares regression model (R2 = .80). The results also suggested that the number of epochs, number of neurons, and dropout rate were the most important parameters in the MLP model. We concluded that in situ vis‐NIR spectroscopy coupled with an MLP model has high potential for large‐scale SOC monitoring in the Qinghai‐Tibet Plateau. Our results also provide a reference for rapid hyperparameter optimization using MLP for future soil spectroscopic modeling.
Highlights
We evaluated the in situ measurement of SOC using vis‐NIR spectra.
A multilayer perceptron was used to predict SOC in alpine soils.
Hyperparameter optimization was conducted by grid searching.
A multilayer perceptron had good performance for in situ SOC prediction.
The most vital parameters for a multilayer perceptron model were identified.
This study analyzed the rupture risk of intracranial aneurysms (IAs) according to aneurysm characteristics by comparing the differences between two aneurysms in different locations within the same ...patient. We utilized this self-controlled model to exclude potential interference from all demographic factors to study the risk factors related to IA rupture. A total of 103 patients were diagnosed with IAs between January 2011 and April 2015 and were enrolled in this study. All enrolled patients had two IAs. One IA (the case) was ruptured, and the other (the control) was unruptured. Aneurysm characteristics, including the presence of a daughter sac, the aneurysm neck, the parent artery diameter, the maximum aneurysm height, the maximum aneurysm width, the location, the aspect ratio (AR, maximum perpendicular height/average neck diameter), the size ratio (SR, maximum aneurysm height/average parent diameter) and the width/height ratio (WH ratio, maximum aneurysm width/maximum aneurysm height), were collected and analyzed to evaluate the rupture risks of the two IAs within each patient and to identify the independent risk factors associated with IA rupture. Multivariate, conditional, backward, stepwise logistic regression analysis was performed to identify the independent risk factors associated with IA rupture. The multivariate analysis identified the presence of a daughter sac (odds ratio OR, 13.80; 95% confidence interval CI, 1.65-115.87), a maximum aneurysm height ≥7 mm (OR, 4.80; 95% CI, 1.21-18.98), location on the posterior communicating artery (PCOM) or anterior communicating artery (ACOM; OR, 3.09; 95% CI, 1.34-7.11) and SR (OR, 2.13; 95% CI, 1.16-3.91) as factors that were significantly associated with IA rupture. The presence of a daughter sac, the maximum aneurysm height, PCOM or ACOM locations and SR (>1.5±0.7) of unruptured IAs were significantly associated with IA rupture.
PDF
