Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such asMRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat–muscle quantitation system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRATM Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variationwas 1.4%for IAAT and 1.2%for ASAT, the intra-observer correlationwas 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

How coastal strategic planning reflects interrelationships between ecosystem services: a four-step method

Explicit and integrated inclusion of ecosystem services (ESs) and their interrelationships can improve the quality of strategic plans and decision-making processes. However, there is little systematic analysis of how ES interrelationships are framed in policy language, particularly in coastal planning discourse. The objective of this paper is therefore to present a four-step method, based on content analysis, to assess ES interrelationships in coastal strategic planning documents. The method consists of: 1) selecting strategic plans; 2) identifying ESs; 3) identifying drivers, ESs and their effects; and 4) constructing relational diagrams. The four-step method is applied to a case of Jiaozhou Bay in China, demonstrating its capacity of identifying which drivers and ES trade-offs and synergies are formulated in coastal strategic plans. The method is helpful to identify overlooked ES interrelationships, inform temporal and spatial issues, and assess the continuity of plans' attention to interrelationships. The main methodological contributions are discussed by emphasizing its broad scope of drivers and ESs and an explicit distinction among the cause of relationships. The developed method also has the potential of cross-fertilizing other kinds of approaches and facilitating practical planning processes.