ABSTRACT
To explore the taxon-dependent contribution of dispersal limitation versus environmental filters to bryophyte assemblages. We investigated bryophytes and six environmental variables on 168 islands in the Thousand Island Lake,China. We compared the observed beta diversity with the expected values based on six null models (EE, EF, FE, FF, PE, and PF), detected the partial correlation of beta diversity with geographical distances. We quantified the contributions of spatial versus environmental variables and island isolation per se to species composition (SC) using variance partitioning. We modeled the species-area relationships (SARs) for bryophytes and the other eight biotas. To explore the taxon-dependent effects of spatial versus environmental filters on bryophytes, 16 taxa including five categories (total bryophytes, total mosses, liverworts, acrocarpous, and pleurocarpous mosses) and 11 species-richest families were included in the analyses. The observed beta diversity values were significantly different from the predicted values for all 16 taxa. For all five categories, the observed partial correlations between beta diversity and geographical distance after controlling environmental effects were not only positive, but also significantly different from the predicted values based on the null models. Spatial eigenvectors are more important in shaping SC than environmental variables for all 16 taxa except Brachytheciaceae and Anomodontaceae. Spatial eigenvectors contributed more to SC variation in liverworts than in mosses and in pleurocarpous mosses than in acrocarpous mosses. The effects of island isolation on SC were significant for all five categories, highly varied at the family level. The z values of the SARs for the five bryophyte categories were all larger than those of the other eight biotas. In subtropical fragmented forests, dispersal limitation exerted significant, taxon-dependent effects on bryophyte assemblages. It was dispersal limitation rather than environmental filtering that predominantly regulated the SC patterns of bryophytes.
ABSTRACT
OBJECTIVE: The kinetic analysis of (11)C-acetate PET provides more information than routine one time-point static imaging. This study aims to investigate the potential of dynamic (11)C-acetate hepatic PET imaging to improve the diagnosis of hepatocellular carcinoma (HCC) and benign liver lesions by using compartmental kinetic modeling and discriminant analysis. METHODS: Twenty-two patients were enrolled in this study, 6 cases were with well-differentiated HCCs, 7 with poorly-differentiated HCCs and 9 with benign pathologies. Following the CT scan, all patients underwent (11)C-acetate dynamic PET imaging. A three-compartment irreversible dual-input model was applied to the lesion time activity curves (TACs) to estimate the kinetic rate constants K1-k3, vascular fraction (VB) and the coefficient α representing the relative hepatic artery (HA) contribution to the hepatic blood supply on lesions and non-lesion liver tissue. The parameter Ki (=K1×k3/(k2 + k3)) was calculated to evaluate the local hepatic metabolic rate of acetate (LHMAct). The lesions were further classified by discriminant analysis with all the above parameters. RESULTS: K1 and lesion to non-lesion standardized uptake value (SUV) ratio (T/L) were found to be the parameters best characterizing the differences among well-differentiated HCC, poorly-differentiated HCC and benign lesions in stepwise discriminant analysis. With discriminant functions consisting of these two parameters, the accuracy of lesion prediction was 87.5% for well-differentiated HCC, 50% for poorly-differentiated HCC and 66.7% for benign lesions. The classification was much better than that with SUV and T/L, where the corresponding classification accuracy of the three kinds of lesions was 57.1%, 33.3% and 44.4%. CONCLUSION: (11)C-acetate kinetic parameter K1 could improve the identification of HCC from benign lesions in combination with T/L in discriminant analysis. The discriminant analysis using static and kinetic parameters appears to be a very helpful method for clinical liver masses diagnosis and staging.
