No abstract

Interactive high quality volume rendering is becoming increasingly more important as the amount of more complex volumetric data steadily grows. While a number of volumetric rendering techniques have been widely used, ray casting has been recognized as an effective approach for generating high quality visualization. However, for most users, the use of ray casting has been limited to datasets that are very small because of its high demands on computational power and memory bandwidth. However the recent introduction of the Cell Broadband Engine (Cell B.E.) processor, which consists of 9 heterogeneous cores designed to handle extremely demanding computations with large streams of data, provides an opportunity to put the ray casting into practical use. In this paper, we introduce an efficient parallel implementation of volume ray casting on the Cell B.E. The implementation is designed to take full advantage of the computational power and memory bandwidth of the Cell B.E. using an intricate orchestration of the ray casting computation on the available heterogeneous resources. Specifically, we introduce streaming model based schemes and techniques to efficiently implement acceleration techniques for ray casting on Cell B.E. In addition to ensuring effective SIMD utilization, our method provides two key benefits: there is no cost for empty space skipping and there is no memory bottleneck on moving volumetric data for processing. Our experimental results show that we can interactively render practical datasets on a single Cell B.E. processor.

Image analysis is vital for extracting quantitative information from biological images and is used extensively, including investigations in developmental biology. The technique commences with the segmentation (delineation) of objects of interest from 2D images or 3D image stacks and is usually followed by the measurement and classification of the segmented objects. This chapter focuses on the segmentation task and here we explain the use of ImageJ, MIPAV (Medical Image Processing, Analysis, and Visualization), and VisSeg, three freely available software packages for this purpose. ImageJ and MIPAV are extremely versatile and can be used in diverse applications. VisSeg is a specialized tool for performing highly accurate and reliable 2D and 3D segmentation of objects such as cells and cell nuclei in images and stacks.

Mangrove is a woody plant which grows in intertidal zones. Mangrove forests are mainly distributed in subtropical and tropical regions, and are valuable for ecosystems and for society. Even in dry regions such as Qatar, mangrove forests provide ecosystem services despite their small area and low productivity compared with tropical mangroves. Carbon sequestration by mangroves is one of the main ecosystem services mitigating climate change, as mangrove forests are carbon-rich ecosystems. In mangrove forests, there is a natural gradient in soil environments between land and sea. Soil salinity and water availability are the major factors influencing mangrove productivity. The objectives of this study were (1) to investigate the change in biomass of Avicennia marina, the only mangrove species in Qatar, along the distance from coast and the relationship between biomass and soil characteristics, and (2) to estimate carbon storage of biomass in a natural mangrove forest in Qatar. Three plots were established in a natural mangrove forest of A. marina in Al-Thakira, Qatar (25°42»15.9»N 51°32»18.4»E), at a distance of 0 m (D0), 50 m (D50), and 100 m (D100) from the coast. Plot size was 2 ×  2 m2, 3 ×  3 m2 and 4 ×  4 m2 at D0, D50, and D100, respectively. Plant abundance was determined by counting the number of individual seedlings ( ≤ 1.3 m high) and trees (>1.3 m high) per plot. Diameter at breast height (DBH) was measured on trees in each plot, and above-ground biomass (AGB) and below-ground biomass (BGB) of trees were estimated using allometric equations for A. marina. Carbon storage in biomass was calculated by carbon fraction of 45.1%. Soil samples at 0-10 cm depth were collected at three random points per plot. Soil samples were air-dried and sieved through a 2 mm mesh screen, and then pH, salinity, water content and nitrogen (N) concentration of each soil sample were measured. Differences in biomass of trees, pH, salinity, water content and N concentration of soil at each distance were analyzed using t-test (SAS 9.4 software). Seedling abundance (no. m-2) was 2.4 ± 0.1 at D0, 7.9 ± 0.8 at D50, and 1.9 ± 0.2 at D100, while tree abundance was 0.9 ± 0.1 at D50 and 1.2 ± 0.1 at D100. There were no trees at D0, so comparisons with this site were excluded. AGB was significantly higher at D100 (41.4 ± 1.9 Mg ha-1) than at D50 (7.3 ± 0.9 Mg ha-1). BGB was higher at D100 (44.9 ± 0.6 Mg ha-1) than at D50 (19.4 ± 2.3 Mg ha-1), but there was no significant difference between two distances. Salinity, water content, and N concentration of soil were 125.1%, 196.3%, and 114.5% higher, respectively, at D100 than at D50 (all differences were significant). Soil pH was significantly 3.1% lower at D100 than at D50. It was reported that growth and biomass of mangroves increased slightly and then decreased continuously along the salinity gradient. However, salinity at the study site was low compared with that in other mangrove forests and might be within the range that biomass increases with salinity. A. marina required a large amount of water and nutrients in high salinity condition (Naidoo, 2009). High water content and N concentration of soil at D100 could meet the requirement for water and nutrient in high salinity condition, and thereby result in the biomass increment. In this study, carbon storage (Mg C ha-1) was 7.3 ± 1.1 for AGB, 9.7 ± 1.1 for BGB, and 18.1 ± 2.4 for total biomass of A. marina. These values are lower than those reported for A. marina in temperate regions (AGB: 57.7 Mg C ha-1, BGB 69.8 Mg C ha-1), subtropical regions (AGB: 49.6-73.1 Mg ha-1, BGB: 49.2-56.8 Mg ha-1), and even dry regions (20.7-66.6 Mg C ha-1). To conclude, biomass of A. marina increased as the distance from the coast and was affected along the gradient of soil characteristics. A better understanding of mangrove biomass distribution between land and sea will contribute to estimate biomass and carbon storage in intertidal zones. * This study was supported by Korea Ministry of Environment (2014001810002).

Abstract-We discuss a new efficient out-of-core multidimensional indexing structure, information-aware 2 n -tree, for indexing very large multidimensional volumetric data. Building a series of (n-1)-Dimensional indexing structures on n-Dimensional data causes a scalability problem in the situation of continually growing resolution in every dimension. However, building a single n-Dimensional indexing structure can cause an indexing effectiveness problem compared to the former case. The informationaware 2 n -tree is an effort to maximize the indexing structure efficiency by ensuring that the subdivision of space have as similar coherence as possible along each dimension. It is particularly useful when data distribution along each dimension constantly shows a different degree of coherence from each other dimension. Our preliminary results show that our new tree can achieve higher indexing structure efficiency than previous methods.