Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis.

PURPOSE: Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression in patients with glioma. METHOD: Using the TCGA database, we examined 614 patients diagnosed with glioma. First, the relationship between the SYP gene expression level and outcome of survival rate was investigated using partial correlation analysis. Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images (T2WI), and contrast-enhanced T1WI images in the TCIA database. Finally, a radiomics features-based model was built using a convolutional neural network (ConvNet), which can perform autonomous learning classification using a ROC curve, accuracy, recall rate, sensitivity, and specificity as evaluation indicators. RESULTS: The expression level of SYP decreased with the increase in the tumor grade. With regard to grade II, grade III, and general patients, those with higher SYP expression levels had better survival rates. However, the SYP expression level did not show any significant association with the outcome in Level IV patients. CONCLUSION: Our multiparametric MRI radiomics model constructed using ConvNet showed good performance in predicting the SYP gene expression level and prognosis in low-grade glioma patients.

Assessment of the eco-environmental quality in the Nanweng River Nature Reserve, Northeast China by remote sensing.

Scientific and rapid assessment of the ecological environment quality based on remote sensing can provide basis for regional protection, management and planning. In this study, using Landsat TM/OLI/TIRS in 1990, 2000 and 2015 as data sources, four indices including the wetness component of the tasseled cap transformation (WET), normalized difference vegetation index (NDVI), normalized difference built-up and soil index (NDSI), and land surface temperature (LST) were derived, with the weights being determined by principal components analysis (PCA). Remote sensing ecological index (RSEI) was used to evaluate the eco-environmental quality of Nanweng River Nature Reserve from 1990 to 2015. Results showed that RSEI was an effective indicator for evaluating spatio-temporal dynamics of eco-environmental quality. From 1990 to 2015, the mean value of RSEI of Nanweng River Nature Reserve increased from 0.55 to 0.83, the proportion of area with excellent and good levels of RSEI trended to increase, probably due to the increase of forests at those levels. Meanwhile, 91.4% of the total land area in the reserve had an improved eco-environmental quality. Such improvement was attributed to the establishment of the reserve and implement of a series of engineering and non-engineering protection measures. In the north of core area and buffer area, decrease of eco-environmental quality was due to forest fires which destroyed a large area of forest vegetation. In the road side and southeast of the experimental area, intensified human activities led to the decrease of regional eco-environmental quality.

[Dynamics of habitat suitability for waterfowls from 1990 to 2015 in the ecological function zone of Sanjiang Plain, Northeast China]. / 1990­2015å¹´ä¸‰æ±Ÿå¹³åŽŸç”Ÿæ€åŠŸèƒ½åŒºæ°´ç¦½æ –æ¯åœ°é€‚å®œæ€§åŠ¨æ€.

As a wetland of international importance, the ecological function zone of the Sanjiang Plain (EFZSP) plays an important role in maintaining waterfowl habitat. With the increasing disturbance of human beings, land use and climate changes, habitat suitability for waterfowls (HSW) has drawn greater attention of ecologists. In this study, Landsat TM/ETM+/OLI and HJ-1B images in 1990, 2000, 2010 and 2015 were used to obtain land cover classification by object-based image analysis, and the entropy and analytic hierarchy process methods were adopted to determine the factors and their weights, the factors selected in this study included water condition (lake and river density), disturbance factor (residence and road density), shelter condition (land cover type and slope), and food abundance (NDVI). Finally, the evaluation results of habitat suitability of year 1990, 2000, 2010, and 2015 were achieved based on the habitat suitability index (HSI) system, and the spatio-temporal dynamics and driving forces causing the changes were analyzed. The results showed that the excellent level of HSW mainly distributed in abundant water areas, especially along the riparian zone of the Heilongjiang River, Naoli River, Ussuri River, Muling River and Xingkai Lake. Its area decreased by 3.2% from 1990 to 2015 because of wetland reclamation. The good level of HSW concentrated in Raohe County over the past 25 years, the condition of Hulin County and Fuyuan County were improved to a good level from 2010 to 2015 due to the substantial increase of paddy field areas. The fair level of HSW distributed dispersedly, and the areas first increased from 1990 to 2000 and then decreased during 2000-2010 and 2010-2015 periods. The areas of poor le-vel increased by 6.7% from 1990 to 2000 and decreased by 3.1% from 2000 to 2015. The change of HSW level in this study area was dominated by land cover change, and both of the increasing population and economy and the drying and worming climate resulted in the decline of the HSW level, whereas the establishment of nature reserves is crucial to protect habitat for waterfowls.

The marine metabolite SZ-685C induces apoptosis in primary human nonfunctioning pituitary adenoma cells by inhibition of the Akt pathway in vitro.

Nonfunctioning pituitary adenoma (NFPA) is one of the most common types of pituitary adenoma. The marine anthraquinone derivative SZ-685C has been isolated from the secondary metabolites of the mangrove endophytic fungus Halorosellinia sp. (No. 1403) which is found in the South China Sea. Recent research has shown that SZ-685C possesses anticancer and tumor suppressive effects. The tetrazolium-based colorimetric assay (MTT assay) to investigate the different effect of the marine compound SZ-685C on the proliferation of primary human NFPA cells, rat normal pituitary cells (RPCs) and rat prolactinoma MMQ cell lines. Hoechst 33342 dye/propidium iodide (PI) double staining and fluorescein isothiocyanate-conjugated Annexin V/PI (Annexin V-FITC/PI) apoptosis assays detected an enhanced rate of apoptosis in cells treated with SZ-685C. Enhanced expression levels of caspase 3 and phosphate and tensin homolog (PTEN) were determined by Western blotting. Notably, the protein expression levels of Akt were decreased when the primary human NFPA cells were treated with SZ-685C. Here, we show that SZ-685C induces apoptosis of human NFPA cells through inhibition of the Akt pathway in vitro. The understanding of apoptosis has provided the basis for novel targeted therapies that can induce death in cancer cells or sensitize them to established cytotoxic agents and radiation therapy.

[Wetlands of priority restoration in Northeast China based on spatial analysis].

By using GIS/RS technology, and from the aspects of landscape structure, river- and road densities, wetness index, geomorphology, and cultivated land productivity, a spatial analysis was made on the potentiality of wetland restoration in Northeast China, with the regions of priority and secondary priority restoration wetlands determined. Then, by using the coordinated development index of crop production and wetland as well as the landscape indices, the wetland restoration effect was verified. In Northeast China, the wetland area of priority restoration was 1.78 x 10(6) hm2, among which, farmland and grassland were the main types for restoration, accounting for 96.7% of the total, and mainly located in the Sanjiang Plain in the northeastern part and the Songnen Plain in the central part of Northeast China. The wetland area of secondary priority restoration was 1.03 x 10(6) hm2. After the restoration of the wetlands, the wetland area in Northeast China would be increased by 37.4%, compared with the present wetland area, and the value of the coordinated development index of crop production and wetland would increase from 0.539 before restoration to 0.733 after restoration. The landscape pattern would be more benefit to the performance of the ecological functions of the wetlands. This study revealed that the restoration scheme of the wetlands in Northeast China based on spatial analysis was practicable, which could provide data support for the implement of wetland restoration and the improvement of ecological environment in Northeast China.

[Dynamic changes of vegetation net primary productivity in permafrost zone of Northeast China in 1982-2009 in response to global change].

As one of the high-latitude cold regions, the permafrost zone of Northeast China (PZNC) is more sensitive to global change. Based on the AVHRR GIMMS NDVI and MODIS NDVI, and by using CASA model, this paper simulated the net primary productivity (NPP) in the PZNC from 1982 to 2009. In 1982-2009, the mean annual air temperature, annual solar radiation and sunshine duration, and atmospheric CO2 concentration and its annual increasing rate in the PZNC increased significantly while the annual precipitation had a significant decrease, and the annual NPP showed an up-and-down trend, with the turning point in 1998. The annual NPP had a high spatial heterogeneity, with an average of 623 g C X m(-2). Comparing with air temperature, the precipitation in growth season was the main factor affecting the NPP. Land use change altered land cover, and thus, the spatiotemporal characteristics of NPP. A significant positive correlation was observed between the NPP and atmospheric CO2 concentration. The impact of permafrost degradation on NPP differed with regional environment. The NPP had a significant positive correlation with the mean annual ground temperature, but a negative correlation with the annual maximum permafrost depth.

[Spatial and temporal variability of soil C/N ratio in Songnen Plain maize belt].

The C/N ratio of soils is a sensitive indicator of soil quality, and an indicator for assessing carbon and nitrogen nutrition balance of soils. Its variation is significant in reflecting the carbon and nitrogen cycling of soils. Based on field investigation, sample collection and analysis, and application of geostatistics and GIS technology, spatial and temporal variation of C/N ratio was analyzed and studied from 1980 to 2005 in Songnen Plain maize belt. The results indicated that the mean value of C/N ratio is 10.56 and 12.30 in 1980 and 2005, respectively. Spatial correlation distance of soil C/N ratio in two periods is 196.3 km and 51.1 km, showing a decreasing trend, which indicated that farming management factors were enhancing. In the past 25 years, 84.88% of soil C/N ratio was on rise with the highest value in the west of the study area, but parts of Dehui County and Jiutai County decreased. As for different land use types, soil C/N ratios in the upland, paddy land, forest and woodland and grassland showed upward trends, with the highest increase from 10.03 +/- 1.12 in 1980 to 12.61 +/- 0.87 in 2005 in grassland and higher in upland and paddy land than the national average. The increasing soil C/N ratio illustrated that soil carbon increased faster than nitrogen. To maintain the steady growth of soil C/N ratio, it is suggested that the return of carbon be paid more attention when the input of nitrogen, such as incorporating crop residues into soil and inputting more organic fertilizers into soils for future farming practices.

[Comparison of methods for estimating soybean chlorophyll content based on visual/near infrared reflection spectra].

The estimation of crop chlorophyll content could provide technical support for precision agriculture. Canopy spectral reflectance was simulated for different chlorophyll levels using radiative transfer models. Then with multiperiod measured hyperspectral data and corresponding chlorophyll content, after extracting six wavelet energy coefficients from the responded bands, an evaluation of soybean chlorophyll content retrieval methods was conducted using multiple linear regression, BP neural network, RBF neural network and PLS method. The estimate effects of the three methods were compared afterwards. The result showed that the three methods based on wavelet analysis have an ideal effect on the chlorophyll content estimation. R2 of validated model of multiple linear regression, BP neural network, RBF neural network and PLS method were 0. 634, 0. 715, 0. 873 and 0.776, respectively. PLS based on Gaussian kernel function and RBF NN methods were better with higher precision, which could estimate chlorophyll content stably.

[Light absorption by suspended particulate matter in Chagan Lake, Jilin].

Spectral characteristics and the magnitudes of light absorption by suspended particulate matter were determined by spectrophotometry in this optically complex Lake Chagan waters for the purpose of surveying the natural variability of the absorption coefficients to parameterize the bio-optical models for converting satellite or in-situ water reflectance signatures into water quality information. Experiments were carried out on seasonal frozen Lake Chagan, one representative inland case-2 water body in Northeast of China. Particulate absorption properties analyzed using the field data on July 15th and October 12th 2009 were measured using the quantitative filter technique to produce absorption spectra containing several fractions that could be attributed to two main optical active constituents (OACs) phytoplankton pigments and non-algal particulates (mineral sediments, and organic detritus). Results suggested that the suspended particulate matter (SPM) concentration was higher while phytoplankton biomass (chlorophyll-a concentration) was lower in July and that in October. The spectral shape of total suspended particulate matter resembled that of non-algal particulates which contributed greater than phytoplankton in total particulate absorption during both periods. An obvious absorption peak occurring at around 440 nm exhibited an increase in phytoplankton contribution in October. Non-algal particulate absorption at 440 nm (a(NAP) (440)) had better correlation with total suspended particulate matter concentration than that with chlorophyll-a over the two periods. Light absorption by phytoplankton pigments in the Chagan lake region was generally lower than that of non-algal components. Chl. a dominating phytoplankton pigment composition functioned exponentially with its absorption coefficients at 440 and 675 nm specifically, the average values of which in July were 0.146 8 m2 x mg(-1) and 0.050 3 respectively while in October they were 0.153 3 and 0.013 2 m2 x mg(-1) varying regionally and seasonally due to the changes in specific composition, light and nutrient conditions.

[Spatial characteristics of soil organic carbon and nitrogen storages in Songnen Plain maize belt].

By using the data of 382 typical soil profiles from the second soil survey at national and county levels, and in combining with 1:500000 digital soil maps, a spatial database of soil profiles was established. Based on this, the one meter depth soil organic carbon and nitrogen storage in Songnen Plain maize belt of China was estimated, with the spatial characteristics of the soil organic carbon and nitrogen densities as well as the relationships between the soil organic carbon and nitrogen densities and the soil types and land use types analyzed. The soil organic carbon and nitrogen storage in the maize belt was (163.12 +/- 26.48) Tg and (9.53 +/- 1.75) Tg, respectively, mainly concentrated in meadow soil, chernozem, and black soil. The soil organic carbon and nitrogen densities were 5.51-25.25 and 0.37-0.80 kg x m(-2), respectively, and the C/N ratio was about 7.90 -12.67. The eastern and northern parts of the belt had much higher carbon and nitrogen densities than the other parts of the belt, and upland soils had the highest organic carbon density [(19.07 +/- 2.44) kg x m(-2)], forest soils had the highest nitrogen density [(0.82 +/- 0.25) kg x m(-2)], while lowland soils had the lower organic carbon and nitrogen densities.

[Soil taxonomy on the basis of reflectance spectral characteristics].

Soil spectral reflectance is the comprehensive representation of soil physical and chemical parameters, and its study is the physical basis for soil remote sensing and provides a new way and standard for soil properties themselves' research. Soil room spectra significantly correlate with that derived from hyperspectral images. So the room spectra are very important for soil taxonomy and investigation. To seek for the feasibility of soil taxonomy on the basis of topsoil reflectance spectral characteristics, and provide the theory foundation for quick soil taxonomy based on remote sensing methods, the spectral reflectance in the visible and near infrared region (400-2 500 nm) of 248 soil samples (black soil, chernozem, meadow soil, blown soil, alluvial soil) collected from Nongan county, Jilin province was measured with a hyperspectral device in room, and the soil spectral characteristics were determined with continuum removal method, and soil spectral indices (spectral absorption area, depth and asymmetry) were computed, which were introduced into BP network models as external input variables. The models consist of three layers (input, output and hidden layer), the training function is "TRAINLM", learning function "LEARNGDM", and transferring function "TAN SIG". The results showed that: (1) There are some differences among different soils in their spectral characteristics, but with similar parental matrix and climate, the spectral differences of soils in Nongan county are not significant. So it's difficult to analyze soil spectral characteristics based on soil reflectance. (2) The curves after continuum removal strengthened soil spectral absorption characteristics, and simplified soil spectral analysis. The soil spectral curves in Nongan county mainly have five spectral absorption vales at 494, 658, 1 415, 1 913 and 2 206 nm, and the former two vales are caused by soil organic matter, Fe and mechanical composition, the latter three are due to soil moisture; the differences of the latter three vales among different soils are not apparent, and the significant differences are in the former two vales region. (3) Soil reflectance is sensitive to organic matter, soil moisture, Fe, mechanical composition, roughness, and so on. The sensitivity of soil spectral indices derived with continuum removing method is decreased. Then the models with these indices as input variables are more stable and general. As the input variables were external, the BP network model based on the former two vales' shape characteristics was better than that based on reflectance values or all five vales, the classifying accuracy of the main three soils (chernozem, meadow soil, blown soil) was bigger than 60%, and the model could be used for soil taxonomy. However, this work still needs further study, and to improve classifying accuracy, auxiliary data, such as topography, vegetation, and land use should be introduced.

[Black soil organic matter content prediction based on reflectance simulation models].

The hyperspectral reflectance characteristics of black soil in Heilongjiang province were analyzed quantitatively, and then the main characteristic controlling points of reflectance were determined and used to build soil reflectance prediction models; the relationship between organic matter content and reflectance and the coefficients of simulating models were studied, Black soil organic matter content spectral prediction models were built, and the feasibility of hyperspectral reflectance simulatiib method was discussed. The results are as follows (1) Organic matter content is the determining factor of black soil reflectance characteristics in the range less than 1000 nm. When the content is low, the covering effect of organic matter on the black soil parent matrix reflectance characteristics is very weak, there are two absorption vales at 500 and 640 rim; when the content reaches a certain content (about 5%), the reflectance characteristics of black soil parent matrix are totally covered by organic matter, and there is only one large absorption vale in the region caused by organic matter. (2) The spectral characteristic controlling points of black soil hyperspectral reflectance in the range of 450-930 om are located at 450, 500, 590, 660 and 930 nrn, and divide the black soil reflectance into four parts. (3) Simulation models (linear, quadratic) rightly describe the characteristics of black soil hyperspectral reflectance, and the linear piecewise model shows a better performance. (4) The organic matter content prediction models with the coefficients of reflectance simulation models as independent variables are more precise than that based on soil reflectance and its derivate, which indicates that the characteristic controlling points for reflectance simulation models are selected reasonably and representatively, and the simulation models partly solve the data redundancy problem of soil hyperspectral reflectance, and improve the precision of black soil organic matter content prediction models with remote sensing methods. Reflectance simulating method can be used for data simplification and compression, data redundancy removal, organic matter and other soil pararneters remote sensing studies.

[Comparison of methods for estimating soybean leaf area index].

Leaf area index (LAI) is an important biophysical parameter, and is the critical variable in many ecology models, productivity models and carbon circulation study. Based on the field experiment data, an evaluation of soybean LAI retrieval methods was conducted using NDVI (normalized difference vegetation index) and RVI (ratio vegetation index), principle component analysis (PCA) and neural network (NN) methods, and the estimate effects of three methods were compared. The results showed that the three methods have an ideal effect on the LAI estimation. R2 of validated model of vegetation indices, PCA, NN were 0.753 (NDVI), 0.758 (RVI), 0.883, 0.899. PCA and NN methods were better with higher precision, and PCA method was the best, as its RMSE (0.202) was slower than the two vegetation indices (RMSEs of NDVI and RVI were 0.594 and 0.616) and NN (RMSE was 0.413) method. While the LAI was small, vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained the good evaluation result. PCA showed better effect for all LAI. LAI affected the estimating result of NN method moderately. As for the NN method, modeled LAI value and measured LAI regression formula slope was the nearest to 1 with R2 of 0.949, which showed a great potential for LAI estimating. As a whole, PCA and NN methods were the prior selection for LAI estimation, which should be attributed to the application of hyperspectral information of many bands.

[Bio-optical model of total suspended matter based on reflectance in the near infrared wave band for case-II waters].

From August to October, 2006, reflectance spectra were measured in a turbid Case-Il waters condition with an ASD FieldSpec spectrometer for a total of 58 samples. Based on the observation of reflectance curves, spectral analysis was carried out over 400-1200 nm. Showing the typical character of Case-II waters, the reflectance values were generally higher than those in other similar studies. Strong backscattering of high concentration total suspended matter (TSM) contributed considerably to the total reflectance spectra in water. Two obvious TSM reflectance peaks were observed in the near infrared wave bands, i.e., 808 and 1067 nm, especially the latter one that was never reported before. The highest correlation coefficient between reflectance and concentrations of TSM existed at 873 nm. Based on the simplification of water inherent optical parameters in the near-infrared wave band, including absorption of TSM, Chlorophyll-a (Chl-a) and chromophoric dissolved organic matter (CDOM), and backscattering of pure water, Chl-a and CDOM, three empirical equations of the bio-optical model using reflectance at 808, 873 and 1067 nm respectively were established to estimate the concentrations of TSM. Compared with linear and exponential models, the bio-optical model showed fairly good performance with comparatively high determination coefficient (r2) and low root mean squared error (RMSE), which confirmed the applicability of the bio-optical model to retrieve concentrations of TSM effectively in turbid Case-II waters.