ID: 58218
Title: Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets
Author: Andrew O Finley, Sudipto Banerjee, Bruce D Cook, John B Bradford
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: LiDAR, hyperspectral , Bayesian hierarchial spatial models, Gaussian Predictive proces, Forestry
Abstract: In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model ' s assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with commonly applied modeling approaches. This improvement results from explicity modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58217
Title: Area estimation from a sample of satellite images: The impact of stratification on the clustering efficiency
Author: Francisco Javier Gallego, Hand Jurgen Stibig
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Stratified sampling, Area estimation, land cover, cluster sampling, correlogram
Abstract: Several projects dealing with land cover area estimation in large regions consider samples of sites to be analysed with high or very high resolution satellite images. This paper analyses the impact of stratification on the efficiency of sampling schemes of large-support units or clusters with a size between 5 km x 5 km and 30 km x 30 km. Cluster sampling schemes are compared with samples of unclustered points, both without and with stratification. The correlograms of land cover classes provide a useful tool to assess the sampling value of clusters in terms of variance: this sampling value is expressed as "equivalent number of points" of a cluster. We show that the "equivalent number of points" is generally higher for stratified cluster sampling than for non-stratified cluster sampling, whose values remain however moderate. When land cover data are acquired by photo-interpretation of tiles extracted from largerimages, such as Landsat TM, a sampling plan based on a larger number of smaller clusters might be more efficient.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58216
Title: Mapping efficiency and information content
Author: T Hengi, M Nikolic, R A MacMillan
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Scale, Effective information content, Complexity, Compression, Regression-kriging, soil mapping
Abstract: This paper proposes two compound measures of mapping quality to support objective comparison of spatial prediction techniques for geostatistical mapping:(1) mapping efficiency - defined as the costs per area per amount of variation explained by the model, and (2) information production efficiency - defined as the cost per byte of effective information produced. These were inspired by concepts of complexity from mathematics and physics. Complexity ie. the total effective information is defined as bytes remaining after compression and after rounding up the numbers using half the mapping accuracy (effective precision). It is postulated that the mapping efficiency, for an area of given size and limited budget, is basically a function of inspection intensity and mapping accuracy. Both measures are illustrated using the Meuse and Ebergotzen case studies (gstat, plotKML packages). The results demonstate that, for mapping organic matter (Meuse data set), there is a gain in the mapping efficiency when using regression-kriging versus ordinary kriging: mapping efficiency is 7% better and the information production efficiency about 25% better (3.99 vs 3.14 EURB-1 for the GZIP compression algorithm). For mapping sand content (Ebergotzen data set), the mapping efficiency for both ordinary kriging and regression-kriging (37.1 vs 27.7 EUR B-1 for the GZIP compression algorithm). Information production efficiency is possibly a more robust measure of mapping quality than mapping efficiency because: (1) it is scale-independent, (2) it can be more easily related to the concept of effective information content, and (3) it accounts for the extrapolation effects. The limitation of deriving the information production efficiency is that both reliable estimate of the model uncertainty and the mapping accuracy is required.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58215
Title: Sub-pixel land - cover mapping with improved fraction images upon multiple -point simulation
Author: Yong Ge
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Soft classificatiom, Uncertainty, sub-pixel mapping, Multiple-point simulation
Abstract: Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/claass. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensssed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58214
Title: Downscaling in remote sensing
Author: Peter M Atkinson
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Downscaling, super-resolution mapping, area-to-point prediction, area-to-point krigning
Abstract: Downscaling has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation. (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through downscaling or area-to-point prediction) or categories are predicted (super-resolutin mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and downscaling cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany downscaling predictions, and the fundamental limits on the representiveness of downscaling predictions. The paper ends with a look towards the grand challenge of downscaling in the context of time-series image stacks. The challenge here is to use all the available information to produce a downscaled series of images that is coherent between images, and , thus, which helps to distinguish real changes (signal ) from noise.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58213
Title: On the difficulty to delimit disease risk hot spots
Author: M Charras-Garrido, L Azizi, F Forbes, S Doyle, N Peyrard, D Abrial
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Classification, Disease mapping, Epidemology, Generalized Potts model, Spatial clustering, Hidden Markov random field
Abstract: Representing the health state of a region is a helpful tool to highlight spatial heterogeneity and localize high risk areas. For ease of interpretation and to determine where to apply control procedures, we need to clearly identify and delineate homogeneous regious in terms of disease risk, and in particular disease risk hot spots. However, even if practical puposes require the delineation of different risk classes, such a classification does not correspond to a reality and is thus difficult to estimate. Working with grouped data, a first natural choice is to apply disease mapping models. We apply a usual disease mapping model, producing continuous estimations of the risks that requires a post-processing classification step to obtain clearly delimited risk zones. We also apply a risk partition model that build a classification of the risk levels is a one step procedure. Working with point data, we will focus on the scan statistic clustering method. We illustrate our article with a real example concerning the bovin spongiform encephalopathy (BSE) an animal disease whose zones at risk are well known by the epidemiologists. We show that in this difficult case of a rare disease and a very heterogeneous population, the different methods provide risk zones that are globally coherent. But, related to the dichotomy between the need and the reality, the exact delimitation of the risk zones, as well as the corresponding estimated risks are quite different.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58212
Title: Bayesian analysis of zero inflated spatiotemporal HIV/TB child mortality data through the INLA and SPDE approaches: Applied to data observed between 1992 and 2010 in rural North East South Africa
Author: Eustasius Musenge, Tobias Freeman Chirwa, Kathleen Kahn, Penelope Vounatsou
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: GMRF, Big "N", Zero inflated, INLA SPDE, HIV/TB mortality, Spatiotemporal, Agincourt South Africa
Abstract: Longitudinal mortality data with few desths usually have problems of zero -inflation.This paper presents and Bayesian models which cater for zero-inflation, spatial and temporal random effects. To reduce the computational burden experienced when a large number of geo-locations are treated as a Gaussian field (GF) we transformed the field to a Gaussian Markov Random Fields (GMRF) by triangulation. We then modelled the spatial random effects using the Stochastic Partial Differential Equations (SPDEs). Inference was done using a computationally efficient alternative to Markow chain Monte Carlo (MCMC) called Integrated Nested Laplace Approximation (INLA) suited for GMRF. The models were applied to data from 71,057 children aged 0 to under 10 years from rural north-east South Africa living in 15,703 households over the years 1992-2010. We found protective effects on HIV/TB mortality due to greater birth weight, older age and more antenatal clinic visits pregnancy (adjusted RR (95%CI)): 0.73 (0.53;0.99), 0.18(0.14;0.22) and 0.96 (0.94;0.97) respectively. Therefore childhood HIV/TB mortality could be reduced if mothers are better catered for during pregnancy as this can reduce mother-to-child transmissions and contribute to improved birth weights. The INLA and SPDE approaches are computationally good alternatives in modelling large multilevel spatiotemporal GMRF data structures.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58211
Title: Analysis of geographical disparities in temporal trends of health outcomes using space-time joinpoint regression
Author: Pierre Goovaerts
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Cluster analysis, Boundary analysis, Binomial kriging, cancer, urbanization, late-stage diagnosis
Abstract: Analyzing temporal trends in health outcomes can provide a more comprehensive picture of the burden of a disease like cancer and generate new insight about the impact of various interventions. In the United States such an analysis is increasingly conducted using joinpoint regression outside a spatial framework, which overlooks the existence of significant variation among U S counties and states with regard to the incidence of cancer. The paper presents several innovative ways to account for space in joinpoint regression: (1) prior filtering of noise in the data by binomial kriging and use of the kriging variance as measure of reliability in weighted least-square regression. (2) detection of significant boundaries between adjacent countries basedon tests of parallelism of time trends and confidence intervals of annual percent change of rates, and (3) creation of spatially compact groups of countries with similar temporal trends through the application of hierarchial cluster anlaysis to the results of boundary analysis. The approach is illustrated using time series of proportions of prostate cancer late-stage cases diagnosed yearly in every country of Florida since 1980s. The annual percent change (APC) in late-stage diagnosis and the onset years for significant declines vary greatly across Florida. Most countries with non-signifcant average APC are located in the north-western part of Florida, known as the Panhandle, which is more rural than other parts of Florida. The number of significant boundaries peaked in the early 1990s when prostate-specific antigen (PSA) test became widely available, a temporal trend that suggests the existence of geographical disparities in the implementation and /or impact of the new screening procedure, in particular as it began available.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58210
Title: Ecological bias in studies of the short-term effects of air pollution on health
Author: Gavin Shaddick, Duncan Lee, Jonathan Wakefield
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Air pollution, Health effects, Particulate matter ecological bias, exposure modelling
Abstract: There has been a great deal of research into the short-term effects of air pollution on health with a large number of studies modelling the association between aggregate disease counts and environmental exposures measured at point locations, for example via air pollution monitors. In such cases, the standard approach is to avergae the observed measurements from the individual monitors and use this in a log-linear health model. Hence such studies are ecological in nature being based on spatially aggregated health and exposure data. Here we investigate the potential for bias in the estimates of the effects on health when estimating the short-term effects of air pollution on health. Such ecological bias may occur if a simple summary measure, such as a daily mean, is not a suitable summary of a spatially variable pollution surface. We assess the performance of commonly used models when confronted with such issues using simulation studies and compare their performance with a model specifically designed to acknowledge the effects of exposure aggregation. In addition to simulation studies, we apply the models to a case study of the short-term effects of particulate matter on respiratory mortality using data from Greater London for the period 2002-2005. We found a significant increased risk of 3% (95% CI 1-5%) associated with the average of the previous three days exposure to particulate matter (per 10 ?g m-3 PM10).
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58209
Title: Bagging Voronoi classifiers for clustering spatial functional data
Author: Piercesare Secchi, Simone Vantini, Valeria Vitelli
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Spatial statistics, Functional data analysis, Voronoi tessellation, clustering, bagging irradiance data
Abstract: We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction ....). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, compased by lcoal representative of neighbourhood covering the entire investigated area: (ii) analyze the local representatives: (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure- which does not require an explicit model for spatial dependence-is statistically and computationally efficient.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58208
Title: Night on Earth: Mapping decadal changes of anthropogenic night light in Asia
Author: Christopher Small, Christopher D Elvidge
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Urban, Night light, DMSP-OLS, Landsat, Zipf, Asia, India, China, Nightsat
Abstract: The defense meteorological satellite program (DMSP) operational linescan system (OLS) sensors have imaged emitted light from Earth ' s surface since the 1970s. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years (Elvidge et al. 2009). The resulting image time series captures a spatiotemporal signature of the growth and evolution of lighted human settlements and development. We use empirical orthogonal function (EOF) analysis and the temporal feature space to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOFs and spatial structure of the PCs provides a basis for distinguishing between deterministic multi-year trends and stochastic year-to-year variance. The low order EOFs and principal components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict potentially deterministic decadal trends. The most pronounced changes occur in Asia. At critical brightness threshold we find an 18% increase in the number of spatially distinct lights and an 80% increase in lighed area in southern and eastern Asia between 1992 and 2009. During this time both China and India experienced a ~20% increase in number of lights and a ~270% increase in lighted area - although the timing of the increase is later in China than in India. Throughout Asia a variety of different patterns of brightness increase are apparent in tri-temporal brightness composites- as well as some conspicuous areas of apparently decreasing background luminace and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution Landsat imagery verifies multiple phases of urban growth in several cities as well as the consistent presence of low DN (<~15) background luminance for many agricultural areas. Lights also allow us to quantify changes in the size distribution and connectedness of different intensities of development. Over a wide range of brightness, the size distributions of spatially contiguous lighted area are consistent with power laws with exponents near -1 as predicted by Zipf ' s Law for cities. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development (Small et al., 2011).
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58207
Title: Integration of spatial functional interaction in the extrapolation of ocean surface temperature anomalies due to global warming
Author: M D Ruiz-Medina, R M Espejo
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Global warming, Ocean surface temperature anomalies, Spatial functional stochastic extrapolation, spatial functional time seriesmodels, wavelet transform
Abstract: The aim of this paper is to derive spatiotemporal extrapolation maps of ocean surface temperature to investigate two global warming effects: On the other hand, the reduction of daily heat fluxes from the sea into the air at the end of the day and during the night, in tropical regions. On the other hand, the strengthening of ocean current flows, due to the increase of ocean surface minimum daily temperature differences between two connected ocean regions. These maps are constructed from the spatial functional time series framework. Specifically, the spatial functional extrapolation of ocean surface temperature in the last 15 years, caused by the reduction of daily heat fluxes from the sea into the air. Furthermore, for the two connected regions of Indian Ocena, and the eastern coast of Australia, the spatial functional extrapolation results derived show more pronounced differences between ocean surface minimum daily temperatures in the year 2003 than in the years 1995-1997. Thus, a strengthening of the flow of the East Australia Current is appreciated.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58206
Title: A Gibbs sampling disaggregation model for orographic precipitation
Author: P Gagnon, A N Rousseau, A Mailhot, D Caya
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Statistical disaggregation, Gibbs sampling, Orographic precipitation, Olympic Mountains, Cascade Range
Abstract: Hydrological applications in complex topographic areas need high spatial resolution precipitation data. Some daily high-resolution products are now available for recent past data, even in complex terrain. While the spatial resolution of Regional Climate Models (RCMs) and operational meterological models are becoming increasingly fine, there still exists a mismatch between the spatial resolutions of observed or estimated recent past data and simulated or forecasted precipitation.
Statistical disaggregation models can generate precipitation on a high-resolution grid using as input a mesoscale precipitation grid (e.g., RCM or meteorological grid). In this paper, a Gibbs sampling disaggregation model previously developed for flat areas is adapted to account for topography. Only one variable, the topographic anomaly, is added to the original model. The model is applied on a 300 km x 300 km area in the northwestern United States, covering the Olympic Mountain and the Cascade Range. Daily high-resolution precipitation data for the 2002-2005 period are used to estimate the model parameters. Using 750 days taken from the 2006-2008 period, 36, 52-km grid boxes are disaggregated on 4.3-, 8.7-, 13-, 17.3- and 26- km grids; each day being simulated nine times. Thank to the Gibbs sampling algorithm, the original model, which does not account for topography, is able to capture the mesoscale topographic structure of the daily precipitation, while the adapted model accounting for topography is better suited to recreate the local impact of topography on interannual means, interday standard deviation, and maximum values. The model outputs could be used by hydrological moderates who need high-resolution precipitation data in complex topographic area application.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58205
Title: Bayesian hierarchial ANOVA of regional climate-change projections from NARCCAP phase II
Author: Emily L Kang, Noel Cressie
Editor: F D van der Meer
Year: 2013
Publisher: Elsevier, Vol 22, June 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Posterior distribution, regional climate model (RCM), Spatial Random Effects (SRE) model, Spatial statistics
Abstract: We consider current (1971-2000) and future (2041-2070) average seasonal surface temperature fields from two regional climate models (RCMs) driven by the same atmosphere-ocean general circulation model (GCM) in the North American Regional Climate Change Assessement Program (NARCCAP) Phase II experiment. We analyze the difference between future and current temperature fields for each RCM and include the factor of season, the factor of RCM, and their interaction in a two-way ANOVA model. Noticing that classical AnOVA approaches do not account for spatial dependence, we assume that the main effects and interactions are spatial processes that follow the Spatial Random Effects (SRE) model. This enables us to model the spatial variability through fixed spatial basis functions, and the computations associated with an ANOVA of high-resolution RCM outputs can be carried out without having to resort to approximations. We call the resulting model a spatial two-way ANOVA model. We implement it in a Bayesian framework, and we investigate the variability of climate-change projections over seasons, RCMs, and their interactions. We find that projected temperatures in North America are credibly higher, that the associated warming effects differ in spatial areas and in seasons, and that they are of much larger magnitude than the variability between RCMs.
Location: TE12, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None
ID: 58204
Title: Ayurvedic formulations as therapeutic radioprotectors: preclinical studies on Brahma Rasayana and Chyavanaprash
Author: Menon Aditya and C K K Nair
Editor: P Balaram
Year: 2013
Publisher: Current Science Association, Vol 104, No 7, 10 April 2013
Source: Centre for Ecological Sciences
Reference: None
Subject: Current Science
Keywords: Antioxidant, Brahma Rasayana, Cellular repair index, Chyavanaprash, DNA damage
Abstract: Exposure to ionizing radiation reduces the cellular antioxidants and causes damage to genomic DNA. In the mammalian system, this results in various radiation syndromes depending on the radiation dose. Commercially available ayurvedic formulations, Brahma Rasayana (BRM) and Chyavanaprash (CHM) were analysed for their ability to restore the cellular antioxidant status and enhance the repair of radiation-induced DNA damages. The antioxidant status in various tissues of mice was restored when these formulations were orally administered, following wholebody exposure to gamma radiation. Administration of these formulations to 4Gy whole-body gamma irradiated mice resulted in faster cellular DNA repair, as revealed from the increased cellular repair index and decrease in the formation of micronucleus. This work suggests the possibility of using BRM or CHM as a therapeutic radioprotector during unplanned, accidental ionizing radiation exposure scenario.
Location: TE15, New Biological Sciences, IISc
Literature cited 1: None
Literature cited 2: None