Establishment of reference intervals for bone turnover markers in healthy Chinese older adults.

BACKGROUND: Reference ranges for bone turnover markers (BTMs) are still lacking in the healthy Chinese population. AIM: To establish reference intervals for BTMs and to investigate the correlations between BTMs and bone mineral density (BMD) in Chinese older adults. SUBJECTS AND METHODS: A community-based cross-sectional study was conducted among 2511 Chinese subjects aged over 50 yrs residing in Zhenjiang, Southeast China. Reference intervals for BTMs (i.e. procollagen type I N-terminal propeptide, P1NP; ß cross-linked C-terminal telopeptide of type I collagen, ß-CTX) were calculated as the central 95% range of all measurements in Chinese older adults. RESULTS: The reference intervals of P1NP, ß-CTX and P1NP/ß-CTX were 15.8-119.9 ng/mL, 0.041-0.675 ng/mL and 49.9-1261.5 for females and 13.6-111.4 ng/mL, 0.038-0.627 ng/mL and 41.0-1269.1 for males, respectively. In the multiple linear regression analysis, only ß-CTX was negatively associated with BMD after adjusting for age and body mass index (BMI) in both sex-stratified groups (all p < .05). CONCLUSION: This study established age- and sex-specific reference intervals for BTMs in a large sample of healthy Chinese participants ≥ 50 and < 80 years of age and explored the correlations between BTMs and BMD, which provides an effective reference for the assessment of bone turnover in the clinical practice of osteoporosis.

[Use of ensemble Kalman smoother algorithm for the time-series retrieval of leaf area index from remote sensing data].

In the present paper, the empirical LAI dynamic model was constructed using the MOD15A2 data set, and the canopy radiative transfer model MCRM2 was coupled with the LAI dynamic model through LAI. The scheme was proposed to retrieve LAI by assimilating MOD09A1 data set into the coupled model. The ensemble Kalman smoother (EnKS) method was first introduced. In order to preferably assess the feasibility of EnKS, the LAI retrieval results of EnKS were compared with the ensemble Kalman filter (EnKF) solutions and MODIS LAI product. The results indicated that the EnKS method achieved ideal results. The retrieved LAI temporal profiles by the EnKS method were smoother and more continuous than the EnKF solutions and the MODIS LAI product, which were in good agreement with the realistic LAI climatology. The developed inversion method in this paper can be applied to retrieve LAI time--continuous profiles effectively.

[The research on bidirectional reflectance computer simulation of forest canopy at pixel scale].

Computer simulation is based on computer graphics to generate the realistic 3D structure scene of vegetation, and to simulate the canopy regime using radiosity method. In the present paper, the authors expand the computer simulation model to simulate forest canopy bidirectional reflectance at pixel scale. But usually, the trees are complex structures, which are tall and have many branches. So there is almost a need for hundreds of thousands or even millions of facets to built up the realistic structure scene for the forest It is difficult for the radiosity method to compute so many facets. In order to make the radiosity method to simulate the forest scene at pixel scale, in the authors' research, the authors proposed one idea to simplify the structure of forest crowns, and abstract the crowns to ellipsoids. And based on the optical characteristics of the tree component and the characteristics of the internal energy transmission of photon in real crown, the authors valued the optical characteristics of ellipsoid surface facets. In the computer simulation of the forest, with the idea of geometrical optics model, the gap model is considered to get the forest canopy bidirectional reflectance at pixel scale. Comparing the computer simulation results with the GOMS model, and Multi-angle Imaging SpectroRadiometer (MISR) multi-angle remote sensing data, the simulation results are in agreement with the GOMS simulation result and MISR BRF. But there are also some problems to be solved. So the authors can conclude that the study has important value for the application of multi-angle remote sensing and the inversion of vegetation canopy structure parameters.

[Hyperspectral inversion models on verticillium wilt severity of cotton leaf].

The correlation of cotton leaf verticillium wilt severity level with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters was analyzed. Using linear and nonlinear regression methods, the hyperspectral remote sensing retrieval models of verticillium wilt severity level with remote sensing parameters as independent variables were constructed and validated. The result showed that spectral reflectance increased significantly in visible and short infrared wave band with the increase in the severity level, and this is especially significant in visible band. The raw spectral reflectance has the maximum coefficient of determination at 694 nm (R2 = 0.461 6) with severity level and the logarithm model constructed with reflectance at this point is the better one as compared to linear model. By the precision evaluation of retrieval models, the linear model with the first derivative reflectance at 717 nm as independent variable was proved to be the best, with R2 = 0.488 9, RMSE = 0.257 1, and relative error = 12.74%, for the estimation of verticllium wilt severity level of cotton leaf. The results provide a good basis for further studying monitoring mechanism of cotton verticillium wilt by remote sensing data, and have an important application in acquiring cotton disease information using hyperspectral remote sensing.

[Estimating leaf area index by fusing MODIS and MISR data].

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.

[Crop geometry identification based on inversion of semiempirical BRDF models].

Investigations have been made on identification of erective and horizontal varieties by bidirectional canopy reflected spectrum and semi-empirical bidirectional reflectance distribution function (BRDF) models. The qualitative effect of leaf area index (LAI) and average leaf angle (ALA) on crop canopy reflected spectrum was studied. The structure parameter sensitive index (SPEI) based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso), was defined in the present study for crop geometry identification. However, the weights associated with the kernels of semi-empirical BRDF model do not have a direct relationship with measurable biophysical parameters. Therefore, efforts have focused on trying to find the relation between these semi-empirical BRDF kernel weights and various vegetation structures. SPEI was proved to be more sensitive to identify crop geometry structures than structural scattering index (SSI) and normalized difference f-index (NDFI), SPEI could be used to distinguish erective and horizontal geometry varieties. So, it is feasible to identify horizontal and erective varieties of wheat by bidirectional canopy reflected spectrum.