High mobility group box 1 as a mediator of endotoxin administration after hemorrhagic shock-primed lung injury

High mobility group box 1 (HMGB1) was discovered as a novel late-acting cytokine that contributes to acute lung injury (ALI). However, the contribution of HMGB1 to two-hit-induced ALI has not been investigated. To examine the participation of HMGB1 in the pathogenesis of ALI caused by the two-hit hypothesis, endotoxin was injected intratracheally in a hemorrhagic shock-primed ALI mouse model. Concentrations of HMGB1 in the lung of the shock group were markedly increased at 16 h (1.63 ± 0.05, compared to the control group: 1.02 ± 0.03; P < 0.05), with the highest concentration being observed at 24 h. In the sham/lipopolysaccharide group, lung HMGB1 concentrations were found to be markedly increased at 24 h (1.98 ± 0.08, compared to the control group: 1.07 ± 0.03; P < 0.05). Administration of lipopolysaccharide to the hemorrhagic shock group resulted in a notable HMGB1 increase by 4 h, with a further increase by 16 h. Intratracheal lipopolysaccharide injection after hemorrhagic shock resulted in the highest lung leak at 16 h (2.68 ± 0.08, compared to the control group: 1.05 ± 0.04; P < 0.05). Compared to the hemorrhagic shock/lipopolysaccharide mice, blockade of HMGB1 at the same time as lipopolysaccharide injection prevented significantly pulmonary tumor necrosis factor-alpha, interleukin-1beta and myeloperoxidase. Lung leak was also markedly reduced at 16 h; blockade of HMGB1 24 h after lipopolysaccharide injection failed to alter lung leak or myeloperoxidase at 48 h. Our observations suggest that HMGB1 plays a key role as a late mediator when lipopolysaccharide is injected after hemorrhagic shock-primed ALI and the kinetics of its release differs from that of one-hit ALI. The therapeutic window to suppress HMGB1 activity should not be delayed to 24 h after the disease onset.

Immune cells and oxidative stress in the endotoxin tolerance mouse model

Sepsis is a systemic inflammatory response that can lead to tissue damage and death. In order to increase our understanding of sepsis, experimental models are needed that produce relevant immune and inflammatory responses during a septic event. We describe a lipopolysaccharide tolerance mouse model to characterize the cellular and molecular alterations of immune cells during sepsis. The model presents a typical lipopolysaccharide tolerance pattern in which tolerance is related to decreased production and secretion of cytokines after a subsequent exposure to a lethal dose of lipopolysaccharide. The initial lipopolysaccharide exposure also altered the expression patterns of cytokines and was followed by an 8- and a 1.5-fold increase in the T helper 1 and 2 cell subpopulations. Behavioral data indicate a decrease in spontaneous activity and an increase in body temperature following exposure to lipopolysaccharide. In contrast, tolerant animals maintained production of reactive oxygen species and nitric oxide when terminally challenged by cecal ligation and puncture (CLP). Survival study after CLP showed protection in tolerant compared to naive animals. Spleen mass increased in tolerant animals followed by increases of B lymphocytes and subpopulation Th1 cells. An increase in the number of stem cells was found in spleen and bone marrow. We also showed that administration of spleen or bone marrow cells from tolerant to naive animals transfers the acquired resistance status. In conclusion, lipopolysaccharide tolerance is a natural reprogramming of the immune system that increases the number of immune cells, particularly T helper 1 cells, and does not reduce oxidative stress.

Edaravone protects endotoxin-induced liver injury by inhibiting apoptosis and reducing proinflammatory cytokines

Studies have shown that edaravone may prevent liver injury. This study aimed to investigate the effects of edaravone on the liver injury induced by D-galactosamine (GalN) and lipopolysaccharide (LPS) in female BALB/c mice. Edaravone was injected into mice 30 min before and 4 h after GalN/LPS injection. The survival rate was determined within the first 24 h. Animals were killed 8 h after GalN/LPS injection, and liver injury was biochemically and histologically assessed. Hepatocyte apoptosis was measured by TUNEL staining; proinflammatory cytokines [tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)] in the liver were assayed by ELISA; expression of caspase-8 and caspase-3 proteins was detected by Western blot assay; and caspase-3 activity was also determined. Results showed that GalN/LPS induced marked elevations in serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT). Edaravone significantly inhibited elevation of serum AST and ALT, accompanied by an improvement in histological findings. Edaravone lowered the levels of TNF-α and IL-6 and reduced the number of TUNEL-positive cells. In addition, 24 h after edaravone treatment, caspase-3 activity and mortality were reduced. Edaravone may effectively ameliorate GalN/LPS-induced liver injury in mice by reducing proinflammatory cytokines and inhibiting apoptosis.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Photograph of Robert Bell, Airborne Regiment, World War II [n.d.]

A photograph of Robert Bell (kneeling, front row, third left) with his airborne regiment in the Second World War. The date and location of the photo is unknown. This photograph was among those recovered from the attic of Iris Sloman Bell, of St. Catharines, in the 1980s and kept in the possession of her son, Rick Bell. The Bell- Sloman family is descended from former Black slaves from the United States.

Motion Segmentation And Meanshift Assisted Contour Refinement For Airborne Video

This paper presents methods for moving object detection in airborne video surveillance. The motion segmentation in the above scenario is usually difficult because of small size of the object, motion of camera, and inconsistency in detected object shape etc. Here we present a motion segmentation system for moving camera video, based on background subtraction. An adaptive background building is used to take advantage of creation of background based on most recent frame. Our proposed system suggests CPU efficient alternative for conventional batch processing based background subtraction systems. We further refine the segmented motion by meanshift based mode association.

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, Digital Surface Models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub-metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. The paper describes the development of a LiDAR post-processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post-processing produces a Digital Terrain Model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially-distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR-derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ~1m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a 2D finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features such as buildings and roads as well as taller vegetation features such as trees and hedges. This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features.

Using airborne laser altimetry to improve river flood extents delineated from SAR data

Flood extent maps derived from SAR images are a useful source of data for validating hydraulic models of river flood flow. The accuracy of such maps is reduced by a number of factors, including changes in returns from the water surface caused by different meteorological conditions and the presence of emergent vegetation. The paper describes how improved accuracy can be achieved by modifying an existing flood extent delineation algorithm to use airborne laser altimetry (LiDAR) as well as SAR data. The LiDAR data provide an additional constraint that waterline (land-water boundary) heights should vary smoothly along the flooded reach. The method was tested on a SAR image of a flood for which contemporaneous aerial photography existed, together with LiDAR data of the un-flooded reach. Waterline heights of the SAR flood extent conditioned on both SAR and LiDAR data matched the corresponding heights from the aerial photo waterline significantly more closely than those from the SAR flood extent conditioned only on SAR data.

Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops

High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modelling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.