[Constructing biomass models for natural Quercus mongolica based on Bayesian seemingly unrelated regression]. / åŸºäºŽè´å¶æ–¯ä¼¼ä¹Žä¸ç›¸å ³å›žå½’æ–¹æ³•çš„å¤©ç„¶è’™å¤æ Žç”Ÿç‰©é‡æ¨¡åž‹æž„å»º.

In this study, the biomass models for natural Quercus mongolica in Heilongjiang Province were constructed based on the predictors of diameter at breast height (D) and tree height (H) by several methods including multivariate likelihood analysis and seemingly unrelated regression. The results showed that the H could significantly improve the stem biomass model, with the coefficient of determination (R2) being increased from 0.953 to 0.988 and the root mean square error (RMSE) being reduced by 14 kg, but it had no significant improvement for the biomass model of branch, foliage, and root. The error structures of both biomass model systems (only D and D-H) were multiplicative, indicating that the linear models after logarithmic transformation were more appropriate. The R2 for the biomass models of stem, branch, foliage and root were 0.953-0.988, 0.982-0.983, 0.916-0.917, and 0.951-0.952, while the RMSE were 13.42-27.03, 6.84-7.00, 1.95-1.97 and 9.71-9.84 kg. Compared with the feasible generalized least squares (FGLS) approach, Bayesian estimation had similar fitting performance and provided parameter estimates with different variations. The standard errors of parameters for FGLS were 0.054-0.211. There were similar variations (standard deviations of 0.055-0.221) for the two Bayesian estimation with no prior information (DMC and Gibbs1). The Gibbs sampler with a multivariate normal distribution with a mean vector of 0, variances of 1000 and covariances of 0 (Gibbs2) or the prior information from the historical researches summary for Quercus trees biomass models (Gibbs3) produced greater variation than those of FGLS, DMC, and Gibbs1 (stan-dard deviations were 0.080-0.278), while Gibbs sampler with the prior information obtained from own data (Gibbs4) provided the lower variations than others (standard deviations were 0.004-0.013). The Gibbs4 approach provided the narrowest 95% prediction interval and produced the smaller prediction biases, with the average absolute error percentage (MAPE) for stem, branch, foliage, root and total of the only-D biomass model being 19.8%, 24.7%, 24.6%, 29.0% and 13.1%, while MAPE for the corresponding components of D-H biomass model kept same except for stem and total decreased to 10.5% and 9.8%, which indicated that Gibbs4 could provide more accurate biomass predictions. Compared with classical statistics, accurate prior information made Bayesian seemingly unrelated regression an advantage in estimation stability and uncertainty reduction.

[Analysis of carbon concentration and allometric growth model of carbon content for Larix olgensis]. / 长白落叶松含碳率分析及含碳量异速生长模型.

Forest carbon storage accounts for about 45% of terrestrial carbon storage. Accurate assessment of forest carbon storage is of great significance to the scientific management and planning of forests. Based on the data of 77 sampling Larix olgensis trees from Mengjiagang, Shangzhi Maoershan, Xiaojiu Forest Farm and Dongjing, Lin-kou Forestry Bureaus of Jiamusi, Heilongjiang Province from 2015 to 2018, we analyzed the partition of carbon content and variation of carbon concentration for five tree components (i.e., wood, bark, branch, leaf, and root). The mono-element and dual-element additive models of carbon content for each component of L. olgensis were deve-loped. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive models, while the jackknife resampling technique was used to verify and evaluate the developed models. The results showed that the weighted mean carbon concentration of each component differed significantly, branches (49.3%) > bark (48.7%) > foliage (48.5%) > wood (48.2%) > root (47.1%). The aboveground and belowground carbon content accounted for about 80% and 20% of the total carbon content, respectively. The adjusted coefficient of determination (Ra2) of additive models of carbon content was greater than 0.89, the mean absolute error was less than 4.1 kg, and the mean absolute error percentage for most models was less than 30%. Adding tree height in the additive models of carbon content could significantly improve model fitting performance and predicting precision. The additive models of carbon content of total, aboveground, wood and bark were better than that of carbon content of branch, foliage, root and crown.

[Effects of mixing proportions on carbon storage and allocation in mixed plantation of Larix olgensis and Fraxinus mandshurica]. / æ··äº¤æ¯”ä¾‹å¯¹é•¿ç™½è½å¶æ¾å’Œæ°´æ›²æŸ³æ··äº¤æž—ç¢³å‚¨é‡åŠå ¶åˆ†é æ ¼å±€çš„å½±å“.

In this study, four types of mixed Larix olgensis and Fraxinus mandshurica plantations were selected according to the rows-mixing proportions (type Ⅰ: 5:3, type Ⅱ: 6:4, type Ⅲ: 5:5, type Ⅳ: 1:1). The see-mingly unrelated biomass models of L. olgensis and F. mandshurica were developed for obtaining biomass values, and the difference and composition of carbon storage in each forest layer and ecosystem were analyzed. The results showed that carbon storage of arbor layer in different stand types was 39.86-50.12 t·hm-2, the carbon storage of arbor layer inⅠ, Ⅱ and Ⅳ was significantly higher than that in type Ⅲ. The carbon storage of understory was 0.10-0.30 t·hm-2, with that in type Ⅱ being significantly higher than other types. Carbon storage of litter layer was 4.43-6.96 t·hm-2, with type Ⅱ and Ⅲ being significantly higher than those of the other types. In the soil layer, carbon storage was 34.97-54.66 t·hm-2. The carbon storage of soil layer in type Ⅱ was significantly greater than those in the other types. At the whole ecosystem level, carbon storage of type Ⅰ-Ⅳ was 90.43, 108.27, 85.83 and 89.92 t·hm-2, respectively. Type Ⅱ had significantly greater carbon storage than the other types. The arbor layer and soil layer were the major carbon pools in the ecosystem, which accounted for 43.3%-55.7% and 38.7%-50.5% of the total, respectively. Our results suggested that mixing by six rows of L. olgensis and four rows of F. mandshurica was better for future planting.

Gallstones and cholecystectomy in relation to risk of liver cancer.

The association between gallstones or cholecystectomy and the incidence risk of liver cancer is controversial. This is a meta-analysis of observational studies on the role of gallstones or cholecystectomy in primary liver cancer. Relevant studies were identified after the literature search via electronic databases until June 2014. A random-effects model was used to generate pooled multivariable adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Heterogeneity among studies was evaluated using Cochran's Q and I statistics. A total of 14 studies (four case-control, 10 cohort) were included in this study. Our study showed the pooled OR was 2.66 (95% CI: 2.05-3.28) for gallstones with liver cancer risk and OR was 1.47 (95% CI: 1.24-1.71) for cholecystectomy. Though there was obvious heterogeneity among these studies, the risk of incidence was consistent in the subgroup analyses and sensitivity analysis. The findings from meta-analysis provided that patients with gallstones or cholecystectomy had significant increased the risk of liver cancer, although the biological mechanisms underlying the link still need to be clarified.

[Predicting models of leaf area for trees in Larix olgensis plantation.] / 人工长白落叶松立木叶面积预估模型.

Leaf area influences dry matter production of trees, as well as the growth of trees and forest stands. The accurate estimation of leaf area plays an important role in analyzing the growth of trees and forest stands. Based on data of 76 Larix olgensis trees in a plantation of Heilongjiang Province, predicting models of branch leaf area (BLA) and crown leaf area (CLA) were constructed, respectively. The results showed that a form of lnBLA=ß1+(ß2+b2)lnBD+(ß3+b3)lnRDINC+ß4lnDBH+ß5lnHT/DBH+(ß6+b6)lnCR was selected as the optimal BLA mixed-effect model with the considera-tion of tree-level random effects, composed of three random-effect on lnBD, lnRDINC and lnCR (ßi represented model fixed parameters, bi represented model random-effect parameters, BD was branch diameter, RDINC was the relative depth into crown from tree apex, DBH was tree diameter at breast height, HT/DBH represented the ratio of tree height to DBH, and CR represented the ratio of crown length to tree height). The adjusted coefficient of determination (Ra2), residual mean squares error (RMSE), mean error (ME), mean absolute error (MAE) and precision estimation (P) of the optimal BLA mixed model were 0.90, 0.5477, -0.03, 0.24 and 91%, respectively, indicating the model had a good performance in predicting. The CLA was calculated by predicted values of all branches based on developed BLA model and the final form of CLA model was as follows: lnCLA=γ0+γ1lnDBH+γ2CR (γi, model parameters). Results of likelihood ratio test (P>0.05) showed that plot-level random effect had no influence on the model performance, which can be ignored. The CLA model got a good-fitting effect with R2 and RMSE being 0.87 and 0.3847, respectively. The CLA predicting model developed in this study could provide a good prediction of CLA for L. olgensis trees and provided a theoretical basis for the research on distribution of leaf area and photosynthesis.

Aberrant Methylation of T-cadherin Can Be a Diagnostic Biomarker for Colorectal Cancer.

BACKGROUND/AIM: T-cadherin is a tumor suppressor gene, its predictive value in colorectal cancer (CRC) still remains controversial. In this study, we aimed to evaluate the association between T-cadherin promoter methylation and CRC by performing a meta-analysis. MATERIALS AND METHODS: The relevant literature was searched using the PubMed, Cochrane Library, Web of Science and Google Scholar databases for articles published until December 2016. The effect sizes were estimated by measuring an odds ratio (OR) with a 95% confidence interval (CI). Sensitivity analysis was performed to examine the heterogeneity and funnel plots were constructed to evaluate publication bias. RESULTS: Nine studies, including 488 samples were included in this meta-analysis. The pooled OR of T-cadherin promoter methylation in cancer tissues was 16.73 (95%CI=6.24-44.87), 19.48 (95%CI=5.64-67.31) and 2.23 (95%CI=1.05-4.75) compared to normal tissues, adjacent tissues and premalignant tissues, respectively. The relationship between T-cadherin promoter methylation and clinicopathological features were also analyzed. However, a significant association was not observed between T-cadherin promoter methylation status and gender, tumor stage, and lymph node status (p>0.05). CONCLUSION: The methylation status of T-cadherin promoter was strongly associated with CRC risk. However, T-cadherin promoter methylation may have a limited prognostic value for CRC patients.

Robotic Verse Laparoscopic Gastrectomy for Gastric Cancer: A Pooled Analysis of 11 Individual Studies.

OBJECTIVE: Robotic surgery is a new technique with the benefits of a 3-dimensional view, the ability to use multidegree-of-freedom forceps, the elimination of physiological tremors, and a stable camera view. The aim of this study was to evaluate the feasibility and short-term outcomes of robotic surgery for gastric cancer, compared with conventional laparoscopic surgery. METHODS: A literature search was performed for comparative studies reporting perioperative outcomes of robotic gastrectomy (RG) and laparoscopic gastrectomy (LG). The methodological quality of the selected studies was assessed. The data were analyzed using Stata (Version 13.0) software. RESULTS: Eleven studies of 3503 patients totally were included for meta-analysis. Compared with LG, RG indicated potentially favorable outcomes in terms of blood loss [weighted mean difference (WMD)=-36.50; 95% confidence interval (CI), -61.39 to -11.61], and time to oral intake (WMD=-0.28; 95% CI, -0.46 to -0.09). But RG suggested longer operative time (WMD=53.48; 95% CI, 38.84-68.12). There were no differences in terms of postoperative flatus, length of hospital stay, postoperative complications, and lymph nodes harvested (P>0.05). CONCLUSIONS: RG for the treatment of gastric cancer is a feasible and safe procedure in the hand of experienced laparoscopic surgeons. But the long-term outcomes between the 2 techniques need to be further examined.

[Optimal model of photosynthesis-light response curve in canopy of planted Larix olgensis tree.] / äººå·¥é•¿ç™½è½å¶æ¾å† å±‚å ‰åˆä½œç”¨-å ‰å“åº”æ›²çº¿æœ€ä¼˜æ¨¡åž‹.

Rectangle hyperbola model (RH), nonrectangle hyperbola model (NRH), exponential model (EM), modified rectangle hyperbola model (MRH), and modified exponential model (MEM) were applied respectively for modeling the photosynthesis-light response curves (PLC) based on four types of curves (photosynthesis-light response curve of inhibition, PLCi, photosynthesis-light response curve of saturation, PLCs, photosynthesis-light response curve of unsaturation, PLCu andphotosynthesis-light response curve in weak light, PLCw) in canopy of 15 years old planted Larix olgensis tree in the Maoershan Forest Farm, Heilongjiang Province. The major photosynthetic physiological indexes including maximum net photosynthetic rate (Pn max), dark respiration rate (Rd), light compensation point (LCP), light saturation point at saturated light intensity (LSP) and apparent quantum yield (AQY) were calculated. All the five candidate models were comprehensively compared by the model goodness-of-fit and the precision estimations of photosynthetic physiological indexes in the four types of curves. The results showed that MEM model was only suitable for fitting the PLCi. MRH model showed the best goodness-of-fit for PLCi and PLCs(Ra2was 0.9986 and 0.9978, respectively). Meanwhile, NRH model expressed the best fitting result in PLCu and PLCw (Ra2 was 0.9996 and 0.9963, respectively). MRH had the lowest mean absolute value of relative error (MAPE) when evaluating Pn max in different types of curves (0.1%