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Accurate spatial estimation of forest carbon stocks and their spatial uncertainties at local, regional, national, and global scales is a critical step in global carbon cycle modeling and management. This study aimed at enhancing the methods that are currently used in this area by combining plot data from the forest inventory and analysis program of the U.S. Forest Service and free landsat thematic mapper image data. Three mapping methods including linear regression, sequential Gaussian co-simulation, and block co-simulation algorithm were compared with respect to the accuracy of forest carbon stock estimates obtained for a study area in Southern Illinois, USA. The results indicated that although the linear regression resulted in smaller prediction errors than the sequential Gaussian co-simulation and the block co-simulation approaches, it also produced both negative and unreasonably large estimates, which is a serious drawback. Moreover, the sequential Gaussian co-simulation and the block co-simulation produced not only accurate carbon predictions, but also uncertainties for the local estimates. In addition, the block co-simulation approach scaled up both forest carbon stocks and the input uncertainties from finer to coarser spatial resolutions as is required for mapping forest carbon at national and global scales. Thus, the co-simulation and block co-simulation algorithms resolved an important current methodological challenge. [ABSTRACT FROM AUTHOR]

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering. This paper develops a Distributed Computing System for Remotely Sensed Images framework (DCSRI) to support distributed and high performance computing for geospatial images. A new algorithm supporting parallel computing with dynamic workload balance for large images, namely Variogram-based Image Texture Extractor (VITE) for extracting image texture from massive and dynamic digital remotely sensed images is presented. The VITE algorithm is used to represent and transform the original data into image textures. The DCSRI framework has the capacity to perform high performance computing on Linux clusters or supercomputers to address the intensive computing challenges arising from large and multiple images. Advanced web technology is also exploited to enable interactive user experience with prompt visual feedback as well as good interoperability. Users can also dynamically steer the visualization and computation process by adjusting the computing parameters on-the-fly. This system leads to a great reduction of image data and provides useful information for knowledge discovery and digital image classification in a user friendly and computing efficient way. [ABSTRACT FROM AUTHOR]

For earthquake and tsunami early warning and emergency response, the earthquake epicenter and magnitude should be determined rapidly and correctly. Using high-rate GPS observations, we can readily obtain precise and high resolution displacement time series and the seismic waveforms during the earthquake. In this paper, a new algorithm is proposed for estimating the earthquake epicenter and magnitude with the seismic waveforms derived from high-rate GPS data during the earthquake. A case study of the 2008 Wenchuan earthquake is conducted from 1 Hz GPS data and the epicenter and magnitude are determined. Compared with the results issued by the China Seismological Bureau, the estimation error of the epicenter and the magnitude is about 12 km and 0.1 magnitude unit, respectively. It has shown that high-rate GPS could be a new tool feasible for estimating the earthquake epicenter and magnitude, independent of or combined with seismometers. [ABSTRACT FROM AUTHOR]

We aimed to investigate whether geniposide, a main component extracted from Gardenia jasminoides Ellis fruit, could exert neuroprotective functions against traumatic brain injury (TBI). Enzyme-linked immunosorbent assay (ELISA) was used for detection of plasma cytokines. Real-time polymerase chain reaction (RT-PCR) was employed for measurements of mRNA levels of cytokines. Neurological outcomes were evaluated by modified neurological severity score (mNSS) and Rota-Rod. Blood–brain barrier (BBB) integrity and brain edema were assessed. Protein expression was tested by Western blot. The plasma levels of interleukin (IL)-1β, IL-6, IL-8 and IL-10 were all elevated in patients with TBI compared to those of healthy controls. TBI rats treated with geniposide showed lower mNSS and longer fall latency time than untreated TBI rats. BBB integrity was maintained and brain edema was reduced by geniposide treatment in TBI rats. Plasma levels of IL-1β, IL-6 and IL-8 were significantly repressed by geniposide treatment in TBI rats, whereas IL-10 level was upregulated. mRNA expression levels of these cytokines in the brain tissues of TBI rats exhibited the same trends of changes. By testing p38 mitogen-activated protein kinase and NF-κB p65 activities, it was observed that phosphorylated (p)-p38 and p-p65 were dramatically inhibited by geniposide. In conclusion, geniposide exerts neuroprotective functions against TBI by inhibiting p-p38 and p-p65. [ABSTRACT FROM AUTHOR]