Macroalgae and Eelgrass Mapping in Great Bay Estuary Using AISA Hyperspectral Imagery.

Results Increases in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending eutrophication for New Hampshire’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the Piscataqua Region Estuaries Partnership adopted the assumption that eelgrass survival can be used as the target for establishing numeric water quality criteria for nutrients in NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that an eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To determine the extent of this effect, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral image was made by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was then used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. Here we outline the procedure for mapping the macroalgae and eelgrass beds. Hyperspectral imagery was effective where known spectral signatures could be easily identified. Comprehensive eelgrass and macroalgae maps of the estuary could only be produced by combining hyperspectral imagery with ground-truth information and expert opinion. Macroalgae was predominantly located in areas where eelgrass formerly existed. Macroalgae mats have now replaced nearly 9% of the area formerly occupied by eelgrass in Great Bay

Using Moored Arrays and Hyperspectral Aerial Imagery to Develop Eelgrass-based Nutrient Criteria for New Hampshire\u27s Great Bay Estuary

Increasing nitrogen concentrations and declining eelgrass beds in the Great Bay Estuary, NH are clear indicators of impending problems for the state’s estuaries. A workgroup established in 2005 by the NH Department of Environmental Services and the NH Estuaries Project (NHEP) adopted eelgrass survival as the water quality target for nutrient criteria development for NH’s estuaries. In 2007, the NHEP received grant from the U.S. Environmental Protection Agency to collect water quality information including that from moored sensors and hyper-spectral imagery data of the Great Bay Estuary. A second grant in 2008 was directed at determining the influence of nuisance macroalgae proliferation on eelgrass bed extent in the context of eutrophication. Here we present the results of these two projects with the spatial distributions of water quality, shallow water bathymetry, and the extent of eelgrass and macroalgae. The results are discussed with respect to eelgrass survivability models, historical eelgrass distributions, and using eutrophication responses in the Great Bay Estuary as a model for other northern, macrotidal estuaries. The expected outcome of this research will support the development of numeric nutrient criteria for NH’s estuaries

A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Geneenvironment interactions (G x E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G x E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G x E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G x E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant GxBMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer

The Seafloor: A Key Factor in Lidar Bottom Detection

The environmental factors that determine the ability of airborne lidar bathymetry (ALB) to detect the seafloor are not well understood; however, water clarity is often considered the single factor for detection. A comparison of data from two different ALB systems (LADS-MKII and SHOALS-3000) of a small area offshore Gerrish Island, Maine, USA shows a striking correlation (95% overlap) in areas of no bottom detection that is independent of the tide status, the date of collection and the orientation of the survey flight. The laser measurements from the two ALB systems are compared to acoustic measurements of depth, seafloor slope, and backscatter from a Kongsberg EM3002 echosounder. The comparison shows that in water depths deeper than 7 m, there is a close correlation between the ALB detection patterns and bottom features. The study results indicate that lack of bottom detection by ALB does not necessarily indicate that water depths deeper than the surrounding areas have lidar strong bottom detection. No bottom detection in the study area actually reflects a change in bottom characteristics

Identifying subtidal coastal environments using airborne lidar bathymetry (ALB)

Airborne lidar bathymetry (ALB) survey planning differs from one coastal zone to another because environmental factors affect the success of the lidar. Environmental factors are so dominate in the lidar success that the same survey configuration at different times may produce different results. A comparison of results between two different ALB systems (Tenix LADS and Optech SHOALS) in the Portsmouth Harbor, NH and offshore Gerrish Island, ME showed a striking correlation of the lack of bottom detection in shallow waters (3-25 m). This lack of bottom detection is independent of the tide status, the date of data collection, and the direction of the survey flight. Multibeam echosounder measurements (Simrad EM3002) were used as reference measurements. In both the Portsmouth Harbor and Gerrish Island surveys, the lack of bottom detection by the lidar was independent of the bathymetry. Because the water-column environmental factors are directly related to the water depth, these results show that the success of the laser measurements observed here are also independent to the optical properties of the water. A comparison of the laser bottom detection to the seafloor slope shows a close correlation. Steep- sloped features such as bedrock outcrops off Gerrish Island result in a lack of bottom detection by the lidar. The multibeam echosounder backscatter in the Portsmouth Harbor and Gerrish Island surveys shows the acoustic properties of the seafloor also have a high correlation between the backscatter intensity to the areas lacking bottom detection by the lidar surveys. Ground-truth underwater video imagery in the Portsmouth Harbor area show that the seafloor in areas of successful bottom detection by the ALB are composed of sands, whereas the seafloor in areas that produced a lack of bottom detection are composed of pebbles and rock outcrops. To date, the only environmental factor that is considered in ALB survey planning is the water column (diffuse attenuation coefficient, Kd). The observations presented here show that in water depths deeper than 3 m, the surficial characteristics of the seafloor becomes a dominant environmental factor that affects the success of bottom detection with ALB

Macroalgae and Eelgrass Mapping in Great Bay Estuary Using AISA Hyperspectral Imagery

Increases in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending eutrophication for New Hampshire’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the Piscataqua Region Estuaries Partnership adopted the assumption that eelgrass survival can be used as the target for establishing numeric water quality criteria for nutrients in NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that an eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To determine the extent of this effect, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral image was made by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was then used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. Here we outline the procedure for mapping the macroalgae and eelgrass beds. Hyperspectral imagery was effective where known spectral signatures could be easily identified. Comprehensive eelgrass and macroalgae maps of the estuary could only be produced by combining hyperspectral imagery with groundtruth information and expert opinion. Macroalgae was predominantly located in areas where eelgrass formerly existed. Macroalgae mats have now replaced nearly 9% of the area formerly occupied by eelgrass in Great Bay

Energetics of donor-doping, metal vacancies, and oxygen-loss in A-site Rare-Earth-doped BaTiO₃

The energetics of La-doping in BaTiO3 are reported for both (electronic) donor-doping with the creation of Ti3+ cations and ionic doping with the creation of Ti vacancies. The experiments (for samples prepared in air) and simulations demonstrate that ionic doping is the preferred mechanism for all concentrations of La-doping. The apparent disagreement with electrical conduction of these ionic doped samples is explained by subsequent oxygen-loss, which leads to the creation of Ti3+ cations. Simulations show that oxygen-loss is much more favorable in the ionic-doped system than undoped BaTiO3 due to the unique local structure created around the defect site. These findings resolve the so-called “donor-doping” anomaly in BaTiO3 and explain the source of semiconductivity in positive temperature coefficient of resistance (PTCR) BaTiO3 thermistors

Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancerResearch in context

Summary: Background: Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. Methods: A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. Findings: The 3-DF joint test revealed two significant loci with p-value <5 × 10−8. Rs4730274 close to the SLC26A3 gene showed an association with fibre (p-value: 2.4 × 10−3) and G × fibre interaction with CRC (OR per quartile of fibre increase = 0.87, 0.80, and 0.75 for CC, TC, and TT genotype, respectively; G × E p-value: 1.8 × 10−7). Rs1620977 in the NEGR1 gene showed an association with fruit intake (p-value: 1.0 × 10−8) and G × fruit interaction with CRC (OR per quartile of fruit increase = 0.75, 0.65, and 0.56 for AA, AG, and GG genotype, respectively; G × E -p-value: 0.029). Interpretation: We identified 2 loci associated with fibre and fruit intake that also modify the association of these dietary factors with CRC risk. Potential mechanisms include chronic inflammatory intestinal disorders, and gut function. However, further studies are needed for mechanistic validation and replication of findings. Funding: National Institutes of Health, National Cancer Institute. Full funding details for the individual consortia are provided in acknowledgments

Genome-wide interaction analysis of folate for colorectal cancer risk.

BACKGROUND: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folates role in CRC. OBJECTIVES: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency &gt;1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction &lt; 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS: Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding