Oral administration of a select mixture of Bacillus probiotics generates Tr1 cells in weaned F4ab/acR- pigs challenged with an F4+ ETEC/VTEC/EPEC strain.

Although breeding of F4 receptor - negative (F4R(-)) pigs may prevent post-weaning diarrhea, the underlying immunity is poorly understood. Here, various doses of a Bacillus licheniformis and Bacillus subtilis mixture (BLS-mix) were orally administered to F4ab/acR(-) pigs for 1 week before F4 (K88) - positive ETEC/VTEC/EPEC challenge. Administration of BLS-mix increased the percentage of Foxp3(-)IL-10(+) T cells but not of Foxp3(+)IL-10(+) regulatory T (Treg) cells among peripheral blood CD4(+) T cells. A low dose of BLS-mix feeding resulted in increased the expression of IL-6, TNF-α, IL-10, and the transcription factors Foxp3 and T-bet mRNAs in the jejunum. Administration of either a low or high dose BLS-mix also led to an increase in the percentage of CD4(+)Foxp3(+) Treg cells among intraepithelial lymphocytes and CD4(+)IL-10(+) T cells in the small intestinal Peyer's patches and the lamina propria of F4ab/acR(-) pigs following F4(+) ETEC/VTEC/EPEC challenge. The increased number of IL-10-producing CD4(+) T cells was attributed to an increase in the proportion of Foxp3(-)IL-10(+) Treg cells rather than Foxp3(+)IL-10(+) Treg cells. Our data indicate that oral administration of BLS-mix to newly weaned F4ab/acR(-) pigs ameliorates enteritis in an F4(+) ETEC/VTEC/EPEC model; however, induction of IL-10-producing Foxp3(-) Treg cells by BLS-mix administration cannot account for the protection of newly weaned F4ab/acR(-) pigs from F4(+) ETEC/VTEC/EPEC infection, and that excessive generation of CD4(+)IL-10(+) T cells following consumption of BLS-mix during episodes of intestinal inflammation that is caused by enteric pathogens might prohibit clearance of the pathogen. Select probiotic mixtures may allow for tailoring strategies to prevent infectious diseases.

Spatial scale applicability of 4-Scale geometrical optics model canopy reflectance simulation.

Accurately clarifying the applicable spatial scale of 4-Scale model is conducive to improving the accuracy of its application in canopy reflectance simulation of different vegetation types, and to further improving the inversion accuracy of leaf area index, canopy density, and other parameters. Two forest plots (one for broad-leaved forest and one for mixed forest) with each area of 100 m×100 m in Maoershan Experimental Forest Farm, Shangzhi, Heilongjiang, were divided into the spatial scales of 10, 20, 30, 40 and 50 m, respectively. The 4-Scale model was used to simulate forest canopy reflectance. Local mean method, the nearest neighbor method, bilinear interpolation method, and cubic convolution method were used to convert Sentinel-2 images with spatial resolution of 10 m to other scales, with the results being evaluated. The simulated canopy reflectance and remote sensing pixel reflectance were compared and analyzed. The spatial scale of mixed forest and broad-leaved forest suitable for high-precision inversion parameters of 4-Scale model was determined. The results showed that the 4-Scale model underestimated the pixel forest canopy reflectance as a whole. The canopy reflectance of mixed forest and broad-leaved forest had the worst simulation effect at the 20 m scale. Both the root mean square error (RMSE) and the mean absolute error from (MAE) of red and near-infrared band were large. When the scale was >20 m, the simulation effect became better. The applicability of the model was the best when the mixed forest was 40 m and the broad-leaved forest was 30 m. The mean and standard deviation of the reflectance difference between the simulated value and the remote sensing pixel were the minimum in the red and near near-infrared bands, with the minimum RMSE and MAE. The simulation results of mixed forest and broad-leaved forest at 10 m scale were not stable, the rule of mean and standard deviation was inconsistent, and the difference between RMSE and MAE was large under the same band.

Therapeutic effects of high-dose vitamin C supplementation in patients with COVID-19: a meta-analysis.

CONTEXT: Coronavirus disease 2019 (COVID-19) could induce the "cytokine storm" due to overactivation of immune system and accompanied by acute respiratory distress syndrome as a serious complication. Vitamin C has been effective in improving lung function of patients by reducing inflammation. OBJECTIVE: The aim was to explore the therapeutic effects of high-dose vitamin C supplementation for patients with COVID-19 using meta-analysis. DATA SOURCES: Published studies were searched from PubMed, Cochrane Library, Web of Science, EMBASE, and China National Knowledge Infrastructure databases up to August 2022 using the terms "vitamin C" and "COVID-19". Data analyses were performed independently by 2 researchers using the PRISMA guidelines. DATA EXTRACTION: Heterogeneity between the included studies was assessed using I2 statistics. When I2 ≥50%, the random-effects model was used; otherwise, a fixed-effects model was applied. Stata 14.0 software was used to pool data by standardized mean differences (SMDs) with 95% CIs or odds ratios (ORs) with 95% CIs. DATA ANALYSIS: The 14 studies had a total of 751 patients and 1583 control participants in 7 randomized controlled trials and 7 retrospective studies. The vitamin C supplement significantly increased ferritin (SMD = 0.272; 95% CI: 0.059 to 0.485; P = 0.012) and lymphocyte count levels (SMD = 0.376; 95% CI: 0.153 to 0.599; P = 0.001) in patients with COVID-19. Patients administered vitamin C in the length of intensive care unit staying (SMD = 0.226; 95% CI: 0.073 to 0.379; P = 0.004). Intake of vitamin C prominently alleviate disease aggravation (OR = 0.344, 95%CI: 0.135 to 0.873, P = 0.025). CONCLUSIONS: High-dose vitamin C supplementation can alleviate inflammatory response and hinder the aggravation of COVID-19.

[Correlation of angiotensin-converting enzyme 2 gene polymorphism with antihypertensive effects of benazepril].

OBJECTIVE: To explore the correlation of rs2106809 from angiotensin-converting enzyme 2 gene with antihypertensive effects of benazepril, as well as its interactions with polymorphisms of angiotensinogen(AGT) and angiotensin II type 1 receptor(AGTR1) gene. METHODS: Correlation between rs2106809 and blood pressure reduction was estimated based on a field trail with 1 831 hypertensive patients using benazepril for 2 weeks. Generalized multifactor dimensionality reduction (GMDR) was used to explore the interactions of rs2106809 and 8 single nucleotide polymorphisms (SNPs) of AGTR1 gene and 3 SNPs of AGT gene. RESULTS: rs2106809 was found to be associated with reduction in systolic blood pressure and pulse pressure in women, as well as pulse pressure reduction in men. T allele carriers presented more blood pressure reduction (1.4, 1.3 and 0.9 mmHg/T allele respectively). Gene-gene interactions involving rs2106809 were found in systolic blood pressure reduction of men, and the response to benazepril of non-sensitive genotypes carriers was 8.2 (95% confidence interval: 6.6-9.7) mmHg, lower than that of sensitive genotypes carriers. CONCLUSION: rs2106809 might act as an independent influencing factor or component of gene-gene interaction in blood pressure reducing effects of benazepril.

[Estimation of forest canopy chlorophyll content based on PROSPECT and SAIL models].

The forest canopy chlorophyll content directly reflects the health and stress of forest. The accurate estimation of the forest canopy chlorophyll content is a significant foundation for researching forest ecosystem cycle models. In the present paper, the inversion of the forest canopy chlorophyll content was based on PROSPECT and SAIL models from the physical mechanism angle. First, leaf spectrum and canopy spectrum were simulated by PROSPECT and SAIL models respectively. And leaf chlorophyll content look-up-table was established for leaf chlorophyll content retrieval. Then leaf chlorophyll content was converted into canopy chlorophyll content by Leaf Area Index (LAD). Finally, canopy chlorophyll content was estimated from Hyperion image. The results indicated that the main effect bands of chlorophyll content were 400-900 nm, the simulation of leaf and canopy spectrum by PROSPECT and SAIL models fit better with the measured spectrum with 7.06% and 16.49% relative error respectively, the RMSE of LAI inversion was 0. 542 6 and the forest canopy chlorophyll content was estimated better by PROSPECT and SAIL models with precision = 77.02%.

[Using fractal dimensions of hyperspectral curves to analyze the healthy status of vegetation].

The reflectance spectral curves of leaves can reflect many information of vegetation growth, and its variation maybe means that the healthy status of vegetation will change. Many spectral feature parameters such as red edge position, height of green peak, depth of red band absorption, the area of red edge and some vegetation index have been used to describe this change. However, the change of vegetation healthy status is not some feature parameters, but a comprehensive variation of the whole curve. So, a comprehensive index maybe has more value to describe the change of hyperspectral curve of vegetation and indicates its healthy status. Fractal is an appropriate mathematical tool, and fractal dimension can be used to explain the comprehensive variation of a curve. Therefore, in the present study, fractal theory was used to analyze the healthy status of different vegetation. Firstly, analytical spectral devices (ASD) were used to measure the hyperspectral curves of different vegetations with different healthy status. Secondly, spectral curves were analyzed, and some parameters which can really reflect different healthy status were obtained. Finally, the fractal dimension of reflectance spectral curves inside a spectral band zone between 450 and 780nm was computed by variation method, and the relationship between fractal dimensions and spectral feature parameters was established. The research results showed that (1) the hyperspectral curves of vegetation have fractal feature, and their fractal dimensions gradually decrease with the health deterioration of leaves, (2) fractal dimension has positive correlation with the height of green peak, the depth of red band absorption and the area of red edge, (3) multivariate analysis showed that fractal dimensions have a significant linear relationship with the three spectral feature parameters just mentioned above. So, the fractal dimension of hyperspectral curve can serve as a new comprehensive parameter to analyze quantitatively the healthy status of vegetations.

[Study on relationships between total chlorophyll with hyperspectral features for leaves of Pinus massoniana forest].

In the present study, the authors built the relationships between the total chlorophyll and hyperspectral features of P. massoniana. The research results showed that (1) chlorophyll content has a good linear relationship with spectral reflectance around 527, 703, 1 364 and 1 640 nm, and this result is helpful for us to select some important bands when monitoring P. massoniana by remote sensing image; (2) all of the nine kinds of spectral feature parameters including red edge position, mean reflectance of red edge, mean reflectance around red edge position, red edge slope, red edge area, absorption depth of red band, green peak height, red edge normalized difference vegetation index and red edge vegetation stress index, have exponential function relationship (r = 0.5-0.7) with the total chlorophyll; (3) the total chlorophyll content can be predicted by multivariate model by the nine spectral feature parameters, and partial least-squares regression model have higher prediction accuracy than the traditional multivariate linear model. The model's root mean square (RMS) is 0.008 8, and mean absolute percentage error is 0.761 7%. During the growth of vegetation, biochemical parameters such as chlorophyll have vital function, for example, it can indicate the health status or pathological feature. So, the models mentioned just above will help us understand the ecological process of P. massoniana forest and provide valuable reference for monitoring P. massoniana and pine wood nematode disease by remote sensing technique.

Quantitative analysis of impact factors on net primary productivity of Tahe forest based on InTEC model.

Net primary productivity (NPP) is an important character reflecting forest carbon source/sink capability, with the spatial-temporal change of which being affected by meteorological change (air temperature, precipitation, etc.), changes in atmospheric composition (CO2 concentration, nitrogen deposition) and various disturbances. However, the key factor driving the changes of forest NPP is still unclear. In order to explore this issue, the relationship between NPP and forest age under different site class index (SCI) was re-simulated and embedded the forest fire data in 1987-2015 to simulate the change characteristics of average NPP of Tahe forest from 1901 to 2015 by comprehensively considering the disturbance and non-disturbance factors of InTEC model, nine scenarios were considered to quantitatively analyze the contribution of different influence factors to the changes of NPP in Tahe forest from 1961 to 2015, and to explore the main influencing factors of the interannual and chronological changes of the Tahe forest NPP to provide a guiding strategy for forest management. The results showed that NPP in Tahe forest was relatively stable from 1901 to 1960, but had a significant trend with the disturbance factors after 1960. The embedding of forest fire and SCI data had different effects on the distribution characteristics of NPP at different times. After 1960, the main reason for the significant change of NPP in Tahe forest was stand age and fire disturbance, with an average annual contribution rate of -49%, followed by precipitation and CO2, which were -28% and 17% respectively. The average contribution rates of temperature and nitrogen deposition were only 5% and 1%, respectively.

[Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales.] / 基于不同空间尺度遥感影像估算森林叶面积指数的差异.

There are several important issues in quantitative remote sensing and product authenticity testing, including how well do the ground measurement points represent the remote sensing pixels, how to obtain the relative truth value of pixels, and how much spatial resolution can truly reflect fore-st leaf area index (LAI). In this study, the measured space scope of two plant canopy analyzers [LAI-2200 and tracing radiation and architecture of canopies (TRAC)] were calculated, which were combined with remote sensing images with three different spatial resolutions: GF-2 with 4.1 m spatial resolution, the Sentinel-2 with 10 m spatial resolution, and Landsat-8 OLI with 30 m spatial resolution, to get the relative true value of pixel at each scale. Under the condition of keeping the real observed area consistent with that obtained by remote sensing, the effects of different spatial resolution images for estimating forest LAI were compared and analyzed based on the unary exponential and multiple regression statistical models. Moreover, the optimal statistical models of the three images were tested on 30 m and 100 m scales and the spatial representation of dataset were evaluated, to find the most suitable scale for the description of forest LAI in the study area. The results showed that high resolution did not necessarily fully reflect LAI of forests. The statistical model based on three kinds of resolution images could well estimate forest LAI. Among the three models, the model based on the Sentinel-2 image had the highest accuracy, and the one based on the GF-2 images had the lowest. The test results at 30 and 100 m scales indicated that the forest LAI was overestimated by the GF-2 inversion model, and underestimated by the Landsat-8 inversion model. The statistical model based on Sentinel-2 could well estimated forest LAI in the study area.

[Foliage clumping index of main vegetation types in Daxing'an Mountains, Northeast China]. / å¤§å ´å®‰å²­åœ°åŒºä¸»è¦æ¤è¢«ç±»åž‹çš„å¶ç‰‡èšé›†åº¦ç³»æ•°.

The foliage clumping index quantifies the cluster degree of the leaf spatial distribution under random canopy. It is of comparable importance for establishment of ecological models. MODIS BRDF model parameter products (MCD43A1 data) and land cover types (MCD12Q1 data) were used in this study to simulate the reflectivity of the hot spots and dark spots, and calculate the normalized difference between hotspot and darkspot (NDHD) based on the Ross-Li semi-empirical model. Least square method was then used to simulate the relationship between NDHD and the foliage clumping index and foliage clumping index products of 500-m resolution in August 2014 were retrieved. Measurements of the foliage clumping index in Daxing'an Mountains were conducted by using the TRAC (Tracing Radiation and Architecture of Canopies) sampling instrument for mo-del validation and analysis. Results showed that it was a feasible algorithm to retrieve clumping index from MCD43A1 product with the correlation of simulated data and the measured data of significance (R2=0.8879). The MODIS near infrared wave band was more sensitive than that on red band to foliage clumping index change. With the increase of the solar zenith angle, the clumping index retrieved by Ross-Li model had a linear increase (R2=0.9699), which indicated that the foliage clumping index related to the solar zenith angle.

Proparacaine induces cytotoxicity and mitochondria-dependent apoptosis in corneal stromal cells both in vitro and in vivo.

Proparacaine (PPC) is a widely used topical anaesthetic in the eye clinic; its abuse may damage the cornea and result in impairment of vision. Although PPC has been reported to be cytotoxic to human keratocytes, there is no scientific report about its toxic mechanisms in human corneal stroma. Here, we evaluated the cytotoxicity of PPC to corneal stroma in an in vitro model of human corneal stromal (HCS) cells and an in vivo model of cat corneas. To postulate the cellular and molecular mechanisms involved in PPC toxicity, changes in the hallmarks of apoptosis as well as in pro-apoptotic signaling pathways were investigated. Our results showed that PPC at concentrations varying from 5.0 to 0.15625 g L-1 induced dose- and time-dependent cell atrophy, vacuolation, cytopathic effects, and viability decline in vitro. Moreover, PPC induced G1 phase arrest, plasma membrane permeability elevation, phosphatidylserine externalization, DNA fragmentation, chromatin condensation, and apoptotic body formation of HCS cells. Furthermore, PPC could induce caspase-2, -3 and -9 activation, and mitochondrial transmembrane potential disruption. Expression of Bcl-xL and Bax were downregulated and upregulated, respectively, and cytoplasmic cytochrome c and apoptosis inducing factor were upregulated remarkably after PPC treatment. The cytotoxicity and pro-apoptotic effects of PPC were also proven by induced corneal edema, apoptotic-like ultrastructural alterations and DNA fragmentation of keratocytes in cat corneas in vivo. These results suggest that PPC above 1/32 of its clinical dosage has remarkable cytotoxicity to corneal stromal cells, which is achieved by inducing death receptor-mediated mitochondria-dependent apoptosis of HCS cells.

[Parameter optimization of BEPS model based on the flux data of the temperate deciduous broad-leaved forest in Northeast China.] / 基于东北温带落叶阔叶林通量数据的BEPS 模型参数优化.

Keeping other parameters as empirical constants, different numerical combinations of the main photosynthetic parameters Vc max and Jmax were conducted to estimate daily GPP by using the iteration method in this paper. To optimize Vc max and Jmax in BEPSHourly model at hourly time steps, simulated daily GPP using different numerical combinations of the parameters were compared with the flux tower data obtained from the temperate deciduous broad-leaved forest of the Maoershan Forest Farm in Northeast China. Comparing the simulated daily GPP with the observed flux data in 2011, the results showed that optimal Vc max and Jmax for the deciduous broad-leaved forest in Northeast China were 41.1 µmol·m-2·s-1 and 82.8 µmol·m-2·s-1 respectively with the minimal RMSE and the maximum R2 of 1.10 g C·m-2·d-1 and 0.95. After Vc max and Jmax optimization, BEPSHourly model simulated the seasonal variation of GPP better.

[Collaborative application of BEPS at different time steps.] / 不同步长尺度下BEPS碳循环模型的协同应用.

BEPSHourly is committed to simulate the ecological and physiological process of vegetation at hourly time steps, and is often applied to analyze the diurnal change of gross primary productivity (GPP), net primary productivity (NPP) at site scale because of its more complex model structure and time-consuming solving process. However, daily photosynthetic rate calculation in BEPSDaily model is simpler and less time-consuming, not involving many iterative processes. It is suitable for simulating the regional primary productivity and analyzing the spatial distribution of regional carbon sources and sinks. According to the characteristics and applicability of BEPSDaily and BEPSHourly models, this paper proposed a method of collaborative application of BEPS at daily and hourly time steps. Firstly, BEPSHourly was used to optimize the main photosynthetic parameters: the maximum rate of carboxylation (Vc max) and the maximum rate of photosynthetic electron transport (Jmax) at site scale, and then the two optimized parameters were introduced into BEPSDaily model to estimate regional NPP at regional scale. The results showed that optimization of the main photosynthesis parameters based on the flux data could improve the simulate ability of the model. The primary productivity of different forest types in descending order was deciduous broad-leaved forest, mixed forest, coniferous forest in 2011. The collaborative application of carbon cycle models at different steps proposed in this study could effectively optimize the main photosynthesis parameters Vc max and Jmax, simulate the monthly averaged diurnal GPP, NPP, calculate the regional NPP, and analyze the spatial distribution of regional carbon sources and sinks.

[Detecting the moisture content of forest surface soil based on the microwave remote sensing technology.] / 基于微波遥感技术探测森林地表土壤含水率.

The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.

[An object-based information extraction technology for dominant tree species group types].

Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution remote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird image data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herbaceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership functions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.

[Quantitative estimation of evapotranspiration from Tahe forest ecosystem, Northeast China].

Evapotranspiration (ET) is an important parameter of agriculture, meteorology and hydrology research, and also an important part of the global hydrological cycle. This paper applied the improved DHSVM distributed hydrological model to estimate daily ET of Tahe area in 2007 using leaf area index and other surface data extracted TM remote sensing data, and slope, aspect and other topographic indices obtained by using the digital elevation model. The relationship between daily ET and daily watershed outlet flow was built by the BP neural network, and a water balance equation was established for the studied watershed, together to test the accuracy of the estimation. The results showed that the model could be applied in the study area. The annual total ET of Tahe watershed was 234.01 mm. ET had a significant seasonal variation. The ET had the highest value in summer and the average daily ET value was 1.56 mm. The average daily ET in autumn and spring were 0.30, 0.29 mm, respectively, and winter had the lowest ET value. Land cover type had a great effect on ET value, and the broadleaf forest had a higher ET ability than the mixed forest, followed by the needle leaf forest.

[Comparison of three daily global solar radiation models].

Three daily global solar radiation estimation models ( Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al.) were analyzed and compared using data of 13 weather stations from 1982 to 2012 from three northeastern provinces and eastern Inner Mongolia. After cross-validation analysis, the result showed that mean absolute error (MAE) for each model was 1.71, 2.83 and 1.68 MJ x m(-2) x d(-1) respectively, showing that Å-P model and model provided by Liu Ke-qun et al. which used percentage of sunshine had an advantage over Thornton-Running model which didn't use percentage of sunshine. Model provided by Liu Ke-qun et al. played a good effect on the situation of non-sunshine, and its MAE and bias percentage were 18.5% and 33.8% smaller than those of Å-P model, respectively. High precision results could be obtained by using the simple linear model of Å-P. Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al. overvalued daily global solar radiation by 12.2%, 19.2% and 9.9% respectively. MAE for each station varied little with the spatial change of location, and annual MAE decreased with the advance of years. The reason for this might be that the change of observation accuracy caused by the replacement of radiation instrument in 1993. MAEs for rainy days, non-sunshine days and warm seasons of the three models were greater than those for days without rain, sunshine days and cold seasons respectively, showing that different methods should be used for different weather conditions on estimating solar radiation with meteorological elements.

[Simulation of leaf inclination angle distribution of main tree species in Daxing'an Mountains of China based on the Campbell ellipsoid distribution function].

Leaf inclination angle distribution directly decides the amount of radiation interception by vegetation canopy, and also, decides the size and direction of the incident radiation, being the key parameter in quantitative remote sensing. This paper simulated the leaf inclination angle distribution of the main tree species in Daxing'an Mountains forest region based on the Campbell ellipsoid distribution model and iterative method, and quantitatively analyzed the fitting results of canopy with and without leaf stratification as well as the effects of tree age group on the leaf inclination angle distribution. For the test 6 main tree species, the leaf inclination angle distribution was in planophile shape, and the mean leaf inclination angle was smaller for coniferous tree than for broadleaved tree. Whether with or without stratify, the fitting result and the measured result were basically identical. For Betula platyphylla and Larix gmelinii, the correlation coefficient between the simulated and measured values was 0.8268 and 0.8192, and the root mean square error was 3.7% and 4.3% respectively, indicating that the Campbell model was reliable applied for forest canopy. Considering the effects of tree age group, though the leaf inclination angle distribution trend with leaf stratification had no correlation with age group, the mean leaf inclination angle of young L. gmelinii was relatively smaller than that mature one, suggesting that age group had positive effects on the numerical design of leaf inclination angle distribution and negative effects on the numerical design of extinction coefficient.

[Forest carbon rates at different scales in Northeast China forest area].

To accurately estimate forest carbon storage is of significance in researching terrestrial ecosystem carbon cycle and global change. Based on the survey data from the representative plots in Northeast China forest area (Da Xing' an Mountains, Xiao Xing' an Mountains, Zhangguangcai Mountains and Changbai Mountains) in 2007 and 2008 and the inventory data in the same period, and by using the models for estimating ground tree biomass, shrub biomass, and grass biomass and the Multi N/C 3000 for measuring forest carbon rate in laboratory, this paper calculated the forest biomass and carbon storage in the forest area, and analyzed the variation and stability of the forest carbon rates at different scales. There was an obvious difference in the carbon rates among tree organs, being the highest in leaf (0.4448), followed by in branch (0.4422), bark (0.4398), and trunk (0.4351). In Changbai and Zhangguangcai Mountains, coniferous forest had a higher carbon rate than broad-leaved forest; whereas in Daxing' an and Xiaoxing' an mountains, it was in adverse. In Northeast China forest area, the forest carbon rates were relatively stable, with a total value of 0.44.

[Influence of chronic diseases on health related quality of life in middle-aged and elderly people from rural communities: application of EQ-5D scale on a Health Survey in Fangshan, Beijing].

OBJECTIVE: To evaluate the health related quality of life (HRQoL) and to explore the influencing factors related to the prevalence of the essential hypertension, diabetes, coronary heart disease (CHD) and stroke in middle-aged and elderly people from rural communities. METHODS: A total of 5345 individuals were chosen from a 2010 Health Survey in Fangshan, Beijing. Self-designed questionnaire was applied to collect the information on general health-related behaviors, chronic conditions, self-rated health, blood pressure and HRQoL with the EuroQoL questionnaire (EQ-5D) were measured. Probit regression model was used to identify the influencing factors on HRQoL, adjusting age, marriage status, education level, smoking, alcohol intake, physical activity and control of blood pressure. Population Attributable Risk Proportion (PARP) for estimating the influence of chronic disease on HRQoL was also calculated. RESULTS: The average EQ-5D index was 0.923 ± 0.145, with 72.1% of the overall respondents in the status of having chronic diseases. Participants with chronic diseases had significantly lower EQ-5D index (0.907 ± 0.155) than those without (0.963 ± 0.105) (t = 15.082, P < 0.001). Significant relations were found between a lower HRQoL and chronic diseases. Relative risk (RR) and 95% confidence interval (95%CI) of poor HRQoL in populations with hypertension, diabetes, CHD and stroke were 1.686 (95%CI: 1.519 - 1.871), 1.725 (95%CI: 1.502 - 1.981), 1.869 (95%CI: 1.663 - 2.099), and 1.759 (95%CI: 1.474 - 2.089) respectively. The most influential disease appeared to be stroke in male (RR = 1.929, 95%CI: 1.462 - 2.546), whereas CHD in female individuals (RR = 1.926, 95%CI: 1.671 - 2.220). Rates of PARP (PAR%) for poor HRQoL in chronic disease subgroups were as follow: hypertension 31.05%, CHD 23.39%, diabetes 4.84% and stroke 4.44%, respectively. CONCLUSION: This study suggested that stroke, at individual level, was the major influential factor to HRQoL. However, hypertension remained the leading preventable influential factor to HRQoL at the population level.