Semi-Automated Monitoring of a Mega-Scale Beach Nourishment Using High-Resolution TerraSAR-X Satellite Data

This paper presents a semi-automated approach to detecting coastal shoreline change with high spatial- and temporal-resolution using X-band synthetic aperture radar (SAR) data. The method was applied at the Sand Motor, a “mega-scale” beach nourishment project in the Netherlands. Natural processes, like waves, wind, and tides, gradually distribute the highly concentrated sand to adjacent beaches. Currently, various in-situ techniques are used to monitor the Sand Motor on a monthly basis. Meanwhile, the TerraSAR-X satellite collects two high-resolution (3 × 3 m), cloud-penetrating SAR images every 11 days. This study investigates whether shorelines detected in TerraSAR-X imagery are accurate enough to monitor the shoreline dynamics of a project like the Sand Motor. The study proposes and implements a semi-automated workflow to extract shorelines from all 182 available TerraSAR-X images acquired between 2011 and 2014. The shorelines are validated using bi-monthly RTK-GPS topographic surveys and nearby wave and tide measurements. A valid shoreline could be extracted from 54% of the images. The horizontal accuracy of these shorelines is approximately 50 m, which is sufficient to assess the larger scale shoreline dynamics of the Sand Motor. The accuracy is affected strongly by sea state and partly by acquisition geometry. We conclude that using frequent, high-resolution TerraSAR-X imagery is a valid option for assessing coastal dynamics on the order of tens of meters at approximately monthly intervalsOptical and Laser Remote SensingMathematical Geodesy and PositioningCoastal Engineerin

HLA class II associations with Type 1 diabetes mellitus: A multivariate approach

The association of HLA class II phenotype with the development of insulin-dependent (Type 1) diabetes mellitus (IDDM) is well established but the contribution of the various HLA-DR and -DQ alleles and haplotypes to disease predisposition is not fully understood. We have determined haplo-type and genotype odds ratios, and further employed multivariate tree analysis to explore the contribution of individual HLA-DRDQ haplotypes to the genetic risk for developing IDDM in the Dutch population. Next to haplotype and genotype odds ratios, multivariate tree analysis techniques provide overall risk calculations for each modeled parameter, and offer insight in the interaction of the model parameters via tree-shaped reports, in which subsequent stratifications on the data can easily be followed. We compared 206 Dutch IDDM patients with 840 serologically typed random healthy unrelated Dutch Caucasoid controls. The multivariate tree analysis showed that the HLA-DR7DQ9 and DR15DQ6 haplotype were strongly associated with disease protection (OR=0.04, P=0.0003, and OR=0.07, P= <0.0001, respectively). The highest ORs were found for the DR4DQ8/ DR8DQ4 genotype (OR=21.04, P=0.001), followed by DR4DQ8/DR17DQ2 (OR= 12.45, P< 0.0001) and DR9DQ9/DR17DQ2 (OR= 10.87, P=0.02). DR4DQ8 homozygous and DR17DQ2 homozygous individuals have a disease OR of 9.0 and 3.0 (P=0.01 and 0.03), respectively. In conclusion, the results from haplotype, genotype, and tree analyses provide insight into the disease associations for combinations of HLA-DRDQ haplotypes. We confirm that the DR9DQ9/DR17DQ2 genotype is associated with susceptibility in the Dutch population, which has previously been noticed as a HLA risk genotypes in Asian populations only

Salivary protein/peptide profiling with SELDI-TOF-MS.

Contains fulltext : 53048.pdf (publisher's version ) (Closed access