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Intel's microprocessors are powered by high-frequency fully-integrated switching regulators implemented on die. Current products use non-magnetic inductors designed in the package substrate. This article investigates the use of magnetic thin film inductors fabricated on silicon wafers as an alternative that improves conversion efficiency while achieving higher inductance per area density. This article builds on the earlier work done on thin film inductors at Intel, and explores the feasibility of using them in a high volume product. A number of different design variations are studied to look at tradeoffs between efficiency, saturation characteristics, transient response, and voltage ripple. This is accomplished through a combination of passive measurements using a vector network analyzer (VNA) and active measurements on a fully functioning integrated voltage regulator system.

The central nervous system (CNS) plays a key role in the sense-and-respond system which maintains proper energy stores. However, the molecular, cellular, and circuit mechanisms through which the CNS achieves this goal is poorly understood. We have recently identified the dorsal raphe nucleus (DRN) as a key node in the CNS, responsible for regulating numerous aspects of energy balance. Neurons within the DRN can potently regulate feeding, thermogenesis, and locomotor activity, and prolonged changes in their activity can ultimately drive changes in body weight. Despite playing a key role in regulating energy homeostasis, the molecular heterogeneity of the DRN had, until recently, hindered further progress in understanding it’s intricate role in the regulation of body weight. Here, we use a combination of nuclear labeling, sorting, and single-nuclear RNA-sequencing to identify the component cell types of key DRN populations, themselves known to regulate the various facets of energy balance. In particular, we identify over twenty candidate subpopulations of DRN neurons, principally defined by the neurotransmitters serotonin, glutamate, and GABA. Many of the subpopulations of these ‘principal’ cell types differentially express peptides, receptors, and signaling molecules that are directly implicated in the regulation of energy balance. We have begun to validate the expression and neuroanatomy of these molecules, including such neuropeptides as thyrotropin-releasing hormone and neuropeptide Y, and we have leveraged available recombinase-driver lines to modulate the activity of neurons expressing these genes to effectively assay their function. Together, this work represents important progress towards identifying the key cell subtypes that regulate the various aspects of energy balance. Disclosure V. M. Bhave: None. M. J. Hill-oliva: None. N. Kamatkar: None. S. Chowdhury: None. A. R. Nectow: None. Funding American Diabetes Association/Pathway to Stop Diabetes (1-18-ACE-49 to A.R.N.)

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

Assessing the effects of human land use and management decisions requires an understanding of how temporal changes in biodiversity influence the rate of ecosystem functions and subsequent delivery of ecosystem services. In highly modified anthromes, the spatial distribution of natural vegetation types is often unknown or coarsely represented challenging comparative analyses seeking to assess changes in biodiversity and potential downstream effects on ecosystem processes and functions. In this context, the objectives of this study were to construct a multi-resolution representation of potential natural vegetation at four hierarchical classification levels of increasing floristic and physiognomic detail for the state of Minnesota, USA. Using a collection of natural/near-natural vegetation releves, a series of Random Forest classification models were used to project the potential distribution of natural vegetation types based on their association with a variety of environmental variables. Model performance varied within and between classification levels with overall accuracy ranging between 64–99% (kappa 0.44–0.99). Model performance tended to decrease and become more variable with increasing floristic complexity at finer classification levels. Classwise performance metrics including precision and sensitivity were also reported. A method for exploring potential class confusion resulting from niche overlap using Random Forest proximities and Nonmetric Multidimensional Scaling is demonstrated. Collectively, the results presented here provide an analytically supported baseline representation of potential natural vegetation for the state of Minnesota, USA. These data can provide a backdrop to further analyses surrounding the influence of human activity on ecosystem processes and services as well as inform future conservation and restoration efforts.

Colloidal gels are three-dimensional networks of microgel particles and can be utilized to design microtissues where the differential adhesive interactions between the particles and cells, guided by their surface energetics, are engineered to spatially assemble the cellular and colloidal components into three-dimensional microtissues. In this work we utilized a colloidal interaction approach to design cell-polyurethane (PU) microgel bimodal microtissues using endothelial cells (ECs) as a normal cell model and a nonmalignant breast cancer cell line (MCF-7) as a cancer cell model. PU microgels were developed from a library of segmental polyurethanes with poly(ethylene glycol) soft segment and aliphatic diisocyanate/l-tyrosine based chain extender as hard segment to modulate the interactions between PU colloidal particles and cells. The surface energies of the microgel particles and cells were estimated using Zisman's critical surface tension and van Oss-Good-Chaudhury theory (vOGCT) from liquid contact angle analysis. Binary interaction potentials between colloidal PU particles and cells and the ternary interaction between colloidal PU particle, cell, and collagen I/Matrigel were calculated to explain the formation of microtissues and their spreading in extraneous biomatrix respectively by using classical and extended DLVO theory (XDLVO). Furthermore, rheological analysis and in silico simulations were used to analyze the assembly and spreading of the PU microgel based microtissues. In vitro experiments showed that ECs and MCF-7 displayed more differentiated (EC spreading/MCF-7 lumen formation) character when mixed with microgel particles that were stable in aqueous medium and more undifferentiated character (EC nonspreading/MCF-7 spreading) when mixed with microgel particles unstable in aqueous medium.

The Mediterranean-type oak/grass savanna of California is composed of widely spaced oak trees with understory grasses. These savanna regions are interspersed with large areas of more open grasslands. The ability of remotely sensed data (with various spatial resolutions) to monitor the phenology in these water-limited oak/grass savannas and open grasslands is explored over the 2012–2015 timeframe using data from Landsat (30 m), the MODerate resolution Imaging Spectroradiometer (MODIS – gridded 500 m), and the Visible Infrared Imaging Radiometer Suite (VIIRS – gridded 500 m) data. Vegetation phenology detected from near-ground level, webcam based PhenoCam imagery from two sites in the Ameriflux Network (long-term flux measurement network of the Americas) (Tonzi Ranch and Vaira Ranch) is upscaled, using a National Agriculture Imagery Program (NAIP) aerial image (1 m), to evaluate the detection of vegetation phenology of these savannas and grasslands with the satellite data. Results show that the Normalized Difference Vegetation Index (NDVI) time series observed from the satellite sensors are all strongly correlated with the PhenoCam NDVI values from Tonzi Ranch (R 2 > 0.67) and Vaira Ranch (R 2 > 0.81). However, the different viewing geometries and spatial coverage of the PhenoCams and the various satellite sensors may cause differences in the absolute phenological transition dates. Analysis of frequency histograms of phenological dates illustrate that the phenological dates in the relatively homogeneous open grasslands are consistent across the different spatial resolutions, in contrast, the relatively heterogeneous oak/grass savannas display has somewhat later greenup, maturity, and dormancy dates at 30 m resolution than at 500 m scale due to the different phenological cycles exhibited by the overstory trees and the understory grasses. In addition, phenologies derived from the MODIS view angle corrected reflectance (Nadir BRDF-Adjusted Reflectance – NBAR) and the newly developed VIIRS NBAR are shown to provide comparable phenological dates (majority absolute bias ≤2 days) in this area.

International audience; The semi-empirical, kernel-driven, linear RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is used to generate the routine MODIS BRDF/Albedo product due to its global applicability and the underlying physics. A challenge of this model in regard to surface reflectance anisotropy effects comes from its underestimation of the directional reflectance signatures near the Sun illumination direction; also known as the hotspot effect. In this study, a method has been developed for improving the ability of the RTLSR model to simulate the magnitude and width of the hotspot effect. The method corrects the volumetric scattering component of the RTLSR model using an exponential approximation of a physical hotspot kernel, which recreates the hotspot magnitude and width using two free parameters (C1 and C2, respectively). The approach allows one to reconstruct, with reasonable accuracy, the hotspot effect by adjusting or using the prior values of these two hotspot variables. Our results demonstrate that: (1) significant improvements in capturing hotspot effect can be made to this method by using the inverted hotspot parameters; (2) the reciprocal nature allow this method to be more adaptive for simulating the hotspot height and width with high accuracy, especially in cases where hotspot signatures are available; and (3) while the new approach is consistent with the heritage RTLSR model inversion used to estimate intrinsic narrowband and broadband albedos, it presents some differences for vegetation clumping index (CI) retrievals. With the hotspot-related model parameters determined a priori, this method offers improved performance for various ecological remote sensing applications; including the estimation of canopy structure parameters.

Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

The Western Prairie Fringed Orchid ( Platanthera praeclara ) is a threatened species found on the Sheyenne National Grassland (SNG) in southeast North Dakota, USA. The SNG is subject to management for multiple uses including biodiversity conservation, livestock grazing and recreation. Therefore, there is a need for the development of indicators of suitable orchid habitat. The orchids are continuously monitored, but understanding of the relationship between landscape properties and orchid locations is limited. In this study data that characterize topography, moisture, and groundwater were used to construct indicators of landscape suitability and an overall Habitat Suitability Index (HSI) for the orchid. A LiDAR-derived DEM and groundwater well observations were used to develop landscape indicators. The Topographic Wetness Index (TWI: a measure of moisture on the landscape), the Topographic Position Index (TPI: a measure of position on the landscape), and the distance to groundwater (DTG: a measure of the distance from the land surface to the groundwater surface) provided the best set of indicators of orchid habitat. Point-based field observations of orchid occurrence were used to develop Orchid Suitability Metrics (OSMs) that identified the range of indicator values most strongly associated with orchids. These OSMs were used to define year by year suitability zones for each indicator that were combined to create the HSI. Comparison of orchid locations with groundwater elevations showed that orchids occurred on average 0.98 ± 0.39 (2 σ ) m above the water table. TWI and TPI demonstrated that orchids occur near flow paths and areas of lower elevation than their surroundings. HSI values of 0.67 and above were associated with 89.8% of all orchid observations used in the analysis. The landscape indicators, OSM concept and HSI could be generally applied to monitoring and conservation management of orchid habitat and the concept may be applicable to other valued species with similar niche properties.

Understanding of the surface energetic aspects of the spontaneously deposited proteins on biomaterial surfaces and how this influences cell adhesion and differentiation is an area of regenerative medicine that has not received adequate attention. Current controversies surround the role of the biomaterial substratum surface chemistry, the range of influence of said substratum, and the effects of different surface energy components of the protein interface. Endothelial cells (ECs) are a highly important cell type for regenerative medicine applications, such as tissue engineering, and In-vivo they interact with collagen I based stromal tissue and basement membranes producing different behavioral outcomes. The surface energetic properties of these tissue types and how they control EC behavior is not well known. In this work we studied the surface energetic properties of collagen I and Matrigel

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

Suites of spectral indices may be derived from hyperspectral sensors such as Hyperion on EO-1. Spectral indices linked to vegetation and landscape function that are scalable to multi-spectral global sensors, could provide “fingerprints” for vegetation states in tropical savannas. In this study, Hyperion images were acquired on three occasions throughout the dry season over each of two consecutive years in the tropical savanna near Darwin, Northern Territory, Australia (12°25'N,130°50'E) during 2005 and 2006. This paper examines the changes in fractional cover of photosynthetic and non-photosynthetic vegetation and bare soil and key diagnostic narrow band vegetation indices for major land cover/land use (LCLU) types over two contrasting post-monsoon seasons. The fractional cover proportions and vegetation indices responded strongly to the additional month of full monsoon rains in 2006 versus 2005. There were differences in vegetation indices sensitive to pigments, canopy water and cellulose between LU and LC classes, but within class variation was very high for large sized sample areas. When fine scale variation in vegetation indices and fractional cover were examined as “fingerprints” for small, more uniform areas of specific LC, distinct differences were evident. Vegetation indices and derived vegetation properties can be used to characterize vegetation states at the scale of natural and management-induced variation. The vegetation indices and fractional cover methods used here can be translated and scaled-up to current and new global sensors to improve description of vegetation structure and function in savannas.

Development of predictive relationships between hyperspectral reflectance and the chemical constituents of grassland vegetation could support routine remote sensing assessment of feed quality in standing pastures. In this study, partial least squares regression (PLSR) and spectral transforms are used to derive predictive models for estimation of crude protein and digestibility (quality), and lignin and cellulose (non-digestible fractions) from field-based spectral libraries and chemical assays acquired from diverse pasture sites in Victoria, Australia between 2000 and 2002. The best predictive models for feed quality were obtained with continuum removal with spectral bands normalised to the depth of absorption features for digestibility (adjusted R 2 = 0.82, root mean square error of prediction (RMSEP) = 3.94), and continuum removal with spectral bands normalised to the area of the absorption features for crude protein (adjusted R 2 = 0.62, RMSEP = 3.18) and cellulose (adjusted R 2 = 0.73, RMSEP = 2.37). The results for lignin were poorer with the best performing model based on the first derivative of log transformed reflectance (adjusted R 2 = 0.44, RMSEP = 1.87). The best models were dominated by first derivative transforms, and by limiting the models to significant variables with “Jack-knifing”. X -loading results identified wavelengths near or outside major absorption features as important predictors. This study showed that digestibility, as a broad measure of feed quality, could be effectively predicted from PLSR derived models of spectral reflectance derived from field spectroscopy. The models for cellulose and crude protein showed potential for qualitative assessment; however the results for lignin were poor. Implementation of spectral prediction models such as these, with hyperspectral sensors having a high signal to noise ratio, could deliver feed quality information to complement spatial biomass and growth data, and improve feed management for extensive grazing systems.

Understanding the physicochemical interactions between endothelial cells and biomaterials is vital for regenerative medicine applications. Particularly, physical interactions between the substratum interface and spontaneously deposited biomacromolecules as well as between the induced biomolecular interface and the cell in terms of surface energetics are important factors to regulate cellular functions. In this study, we examined the physical interactions between endothelial cells and segmental polyurethanes (PUs) using l-tyrosine based PUs to examine the structure-property relations in terms of PU surface energies and endothelial cell organization. Since, contact angle analysis used to probe surface energetics provides incomplete interpretation and understanding of the physical interactions, we sought a combinatorial surface energetics approach utilizing water contact angle, Zisman's critical surface tension (CST), Kaelble's numerical method, and van Oss-Good-Chaudhury theory (vOGCT), and applied to both substrata and serum adsorbed matrix to correlate human umbilical vein endothelial cell (HUVEC) behavior with surface energetics of l-tyrosine based PU surfaces. We determined that, while water contact angle of substratum or adsorbed matrix did not correlate well with HUVEC behavior, overall higher polarity according to the numerical method as well as Lewis base character of the substratum explained increased HUVEC interaction and monolayer formation as opposed to organization into networks. Cell interaction was also interpreted in terms of the combined effects of substratum and adsorbed matrix polarity and Lewis acid-base character to determine the effect of PU segments.

Estimating the proportion of woody-to-total plant material ‘α’ is an essential step to convert Plant Area Index ‘PAI’ estimates into Leaf Area Index ‘LAI’. α has also been shown to have a significant impact on the passive optical remote sensing signal for retrieval of biophysical parameters in forests, woodlands, and savannas. However, benchmarked indirect α retrieval methods are lacking and thus it is common for this pivotal correction to be ignored. In this paper we validate an α retrieval method using a 3D radiative transfer simulation framework, enabling the retrieval method to be benchmarked against a known and precise model truth. The 3D framework consists of a representative and highly detailed 3D explicit Eucalypt forest reconstructed from field measurements. The 3D structure is coupled with a 3D scattering model to enable simulation of remote sensing instruments. The retrieval method utilises classified hemispherical photography ‘HP’, but is applicable to all ground-based optical instruments that can separate leaf and woody elements. The method is applicable to evergreen forests and thus independent of the estimation of PAI or LAI. The unknown degree of mutual shading or occlusion of leaf and woody elements was traditionally a key impediment to the operational use of this method and was therefore closely examined. The indirect α method utilising classified HP imagery agreed on average to within 0.01 α of the reference (αref = 0.37). In addition, the method demonstrated robustness to a range of LAI, stem density, and stem distribution values, matching to within ±0.05 α of the reference. Angular dependence on indirect α retrieval was also found; where the entire HP image (180° FOV) was needed to produce the most accurate estimate. Conversely, the classified narrow view zenith angle range around 55−60° zenith also provided an α estimate matching the reference. At this narrow zenith angle the method is insensitive to leaf angle distribution. As such, careful consideration of zenith angle range utilised from the instrument is recommended. The results demonstrate the method’s applicability for accurate indirect estimation of α in single-storey forest types. The simple and efficient method can be used to convert estimates of PAI into LAI from a variety of optical ground-based instruments. Quantitative α estimates can and should be used to aid interpretation of the remote sensing signal from satellite imagery, which has been shown to be sensitive to the proportion and spatial distribution of woody canopy materials.

The current agricultural systems of broad areas of Australia are unsustainable, with large projected increases in salinization, decreases in water quality, wind erosion, and losses of biodiversity. It is well known that these problems can be partially resolved by farmland reforestation; however, a major issue is financing the scale of activity required. The international response to global warming, the United Nations Framework Convention on Climate Change and its Kyoto Protocol, includes provisions that enable greenhouse sinks (sequestration of carbon in soils and vegetation) to be used by parties to fulfil their obligations. The Kyoto Protocol also allows for trading in emission reductions, and this opens the possibility that investment in carbon sinks may help underwrite broader natural resource management objectives. This paper examines the possibilities for improved land management in Western Australia arising from the development of carbon sinks by considering: (a) the likelihood of a carbon market developing and the likely depth of that market as a result of current national and international policies, (b) the data available to provide estimates on different types of

Richard Brown,1 Gaétan Chevalier,2 Michael Hill31Department of Human Physiology, University of Oregon, Eugene, OR, 2Developmental and Cell Biology Department, University of California at Irvine, Irvine, CA, 3Garden Way Chiropractic Center and Sports Injury Clinic, Eugene, OR, USAAbstract: Grounding a human to the earth has resulted in changes in the physiology of the body. A pilot study on grounding and eccentric contractions demonstrated shortened duration of pain, reduced creatine kinase (CK), and differences in blood parameters. This follow-up study was conducted to investigate the effects of grounding after moderate eccentric contractions on pain, CK, and complete blood counts. Thirty-two healthy young men were randomly divided into grounded (n=16) and sham-grounded (n=16) groups. On days 1 through 4, visual analog scale for pain evaluations and blood draws were accomplished. On day 1, the participants performed eccentric contractions of 200 half-knee bends. They were then grounded or sham-grounded to the earth for 4 hours on days 1 and 2. Both groups experienced pain on all posttest days. On day 2, the sham-grounded group experienced significant CK increase (P

In this study, we sought to address the issue of how to derive an extended synthetic record of fire incidence and timing at regional scale that would be representative of a short remotely-sensed calibration record. We used annual rainfall and simulated annual ground stratum growth to develop multiple regression relationships for prediction of annual fire probability and proportion of late (August-November) fires from AVHRR NDVI fire footprint data across Australian tropical savannas. Relationships were examined using spatial averaging in moving windows varying from 3 X 3 to 61 X 61 pixels in size. Model fits as measured by R 2 improved as window size increased, but output layers became smoother and less representative of natural heterogeneity. A 25 X 25 pixel window was selected as the best compromise between model fit and smoothing. A 113-year synthetic record of annual fire probability and proportion of late fires was generated using the spatially explicit layers of model coefficients. The statistical properties of the synthetic fire probabilities were compared with those derived from the available fire footprint record, using a simple vegetation classification based on ground stratum type for spatial stratification. The two data sets showed a strong correspondence for both burned area and fire probability; spatial variation in mean and coefficient of variation of fire probability was representative of that observed in the historical record. There was significant temporal variation in the synthetic annual fire probability for different vegetation zones across the tropical savanna region for the full 113-year length of record. This simple approach could readily be applied to other areas of the world provided rainfall data are available and annual ground stratum growth can be simulated with a suitable model or estimated with remote sensing. Crown Copyright (c) 2005 Published by Elsevier Ltd. All rights reserved.

The principle enzymes in the metabolism of both the substituent groups and the nucleus of the bile acids and of cholesterol are reviewed, together with their possible relevence to the etiology of large bowel cancer. Since the incidence of the disease appears to be related to the bile acid concentration and the activity of the 7-dehydroxylase and the delta4-dehydrogenase enzymes, the study of bile acids with a 4,6-dien-3-one structure is strongly recommended.

This paper uses a scenario analysis system - Range-ASSESS - to examine the potential for gains and losses of soil carbon in the Australian rangelands as affected by grazing and climate. The analysis involves a factorial examination of the effect of stocking rates and all possible 5-year historical climates between 1889 and 1999. The analysis also looks at the sensitivity of results to the method of calculation of safe carrying capacity, and to the thresholds used to calculate grazing and dryness indices that drive transitions in state and transition models. The analysis showed that different vegetation zones produced different responses to changes in stocking depending upon the spatial distribution of dryness index, nature of carbon state and transition model, rules governing transitions, and relative significance of soil carbon. At a stocking density equivalent to 100% of 1997 levels, the soil carbon loss from rangelands was about 400 Mt C in 40% of the 5-year periods using a sensitive growth deviation threshold to determine dryness index. If a less sensitive threshold was used, potential loss was reduced to about 200 Mt C. If the grazing pressure threshold for a grazing index of four is adjusted to a more generous level, then potential losses in the dry periods are substantially reduced. The analysis is intended to be indicative of a likely approximate outcome rather than a quantitative measure of system response. The results indicate that the interpretation of the effect of the drought-grazing pressure interaction on perennial plant survival, and consequent organic carbon input to soils, is a major source of uncertainty and a critical area for more experimental measurement.

The purpose of this study was to help to inform policy development for the Australian rangelands, and provide a proof of concept for application of a multi-criteria analysis approach to assessment of competing resource use at continental scale. The study aimed to identify and locate key natural resource and agricultural production assets in the rangelands, define a number of measures of potentially threatening processes, and use a multi-criteria approach to identify areas where threatening processes, agricultural production problems, or valuable natural resources coincided. The analysis used 35 readily available, continental spatial data layers at 5-km pixel resolution ranked from 1 (low) to 5 (high) under three themed groupings: natural resource base, production base, and threatening processes. These measures were aggregated into composite indicators to define attributes such as environmental sensitivity and total grazing pressure. The composites were then compared in a two-way analysis to explore particular interactions between threatening processes such as pastoralism and mining, and the condition of production and natural resource assets. These interactions were defined as "tensions" for purposes of this analysis. Example "tensions" included the association of high grazing intensity with areas of high environmental sensitivity, indicating a risk of land degradation under adverse climatic conditions. A summary of patterns of tension was obtained by extracting area proportions of high-tension classes for selected Natural Heritage Trust Regions, which are a basis for Australian Government funding of improved environmental management. The study provides a basis for further examination of trade-offs in the use of natural assets, opportunities for improved productivity and sustainability, and social and economic implications.

In this study, independent classifications of Landsat Thematic Mapper imagery and Jet Propulsion Laboratory AirSAR were combined to create an integrated classification of pasture and other vegetation types for a study area in the agricultural zone of Western Australia. The resulting classification combines greenness and brightness information from optical data with structure and water content information from synthetic aperture radar (SAR). Field observations of vegetation type, botanical composition, ground cover percentage, wet and dry biomass, canopy height, and soil water content were collected at 34 sites representing a range of pastures, browse shrubs, and crops. An unsupervised version of the Complex Wishart classification procedure, based on preserving scattering characteristics from the Freeman and Durden backscatter decomposition, was applied to the C-, L-, and P-band polarimetric SAR data. The optical classification was carried out using a principle component analysis on the green, red, and near-infrared bands and clustering on the basis of a class centroid distance measure and knowledge of ground targets. These two classification results were then fused together. Assessment of a confusion matrix using the individual sites showed that identification of more uniform, dense, and structurally distinct canopies was better than that of more diverse, sparse, and structurally ambiguous canopies, as the former were better represented by the canopy height attribute used in the SAR classification component. The optical classification enabled correction of SAR misclassification of vegetation due to surface roughness and soil moisture effects, or similar backscatter responses from herbaceous or arboreal canopies. The results show that simplification of vegetation into groups based upon properties with sensitive responses in both the optical and SAR domains, and combination of separate SAR and optical classifications, has potential for improving classification of diverse and heterogeneous herbaceous and browse cover in grazing lands. However, collection of ground calibration data must be at an appropriate spatial scale and include canopy and surface measurements directly related to backscatter mechanisms and spectral sensitivity.

Decision support systems (DSS) that are simple, spatial, flexible, non-deterministic and have a long track record of practical application in a policy environment have until recently been uncommon. There has been a rapid expansion in the development and description of both quantitative and soft system methods that can be applied to decision-making processes and many of these have application in the spatial domain. This paper examines the case for inclusion of new methods in spatial systems for multi-criteria decision analysis (MCDA) in the context of the history and application of ASSESS (A System for Selecting Suitable Sites). As a spatial implementation of the Analytic Hierarchy Process (AHP), ASSESS has been used extensively for MCDA in a policy environment, but has not previously been described in peer-reviewed literature. ASSESS provides an interface in the ArcInfo Grid GIS environment that accesses GIS functionality and enables simple linear addition and combination of data layers, quantised into rankings from 1 to 5 corresponding to suitable/good to unsuitable/bad, for the development of output scenarios that may be constructed from different user viewpoints. Results from assessment of catchment condition for the intensive land use zone of Australia are used to illustrate issues surrounding incorporation of new methods and spatially explicit operations into the simple ASSESS AHP MCDA process. The knowledge and methods base is outlined diagrammatically using AHP MCDA as the core process, and new methods and spatial approaches as adjuncts or inputs at various stages. New methods can assist with correlation of input data layers, subjective weightings, and mixing of qualitative and quantitative data. The merits of the inclusion of quantitative methods based on logical empiricism for explicit definition of input errors and uncertainty, approaches to quantisation of input data, and optimisation of outputs are contrasted with soft systems approaches that incorporate more linguistic and information theory into landscape analysis. The potential role for spatial analysis both in providing static input factor layers and in dynamic optimisation and seamless integration in the decision process are explored.

Guanylin and uroguanylin induce natriuresis in mice lacking guanylyl cyclase-C receptor. Background Guanylin (GN) and uroguanylin (UGN) are intestinally derived peptide hormones that are similar in structure and activity to the diarrhea-causing Escherichia coli heat-stable enterotoxins (STa). These secretagogues have been shown to affect fluid, Na + , K + , and Cl − transport in both the intestine and kidney, presumably by intracellular cyclic guanosine monophosphate (cGMP)-dependent signal transduction. However, the in vivo consequences of GN, UGN, and STa on renal function and their mechanism of action have yet to be rigorously tested. Methods We hypothesized that intravenous administration of GN, UGN, or STa would cause an increase in natriuresis in wild-type mice via cGMP and guanylyl cyclase-C (GC-C, Gucy2c ), the only known receptor for these peptide-hormones, and that the peptide-induced natriuresis would be blunted in genetically altered mice devoid of GC-C receptors (GC-C (−/−) null). Results In wild-type mice using a modified renal clearance model, GN, UGN, and STa elicited significant natriuresis, kaliuresis, and diuresis as well as increased urinary cGMP levels in a time- and dose-dependent fashion. Absolute and fractional urinary sodium excretion levels were greatest ∼40 minutes following a bolus infusion with pharmacologic doses of these peptides. Unexpectedly, GC-C (−/−) null mice also responded to the GN peptides similarly to that observed in wild-type mice. Glomerular filtration rate (GFR), blood pressure, and plasma cGMP in the mice (wild-type or GC-C (−/−) null) did not significantly vary between the vehicle- and peptide-treatment groups. The effects of UGN may also influence long-term renal function due to down-regulation of the Na + /K + ATPase γ-subunit and the Cl − channel ClC-K2 by 60% and 75%, respectively, as assessed by differential display polymerase chain reaction (PCR) (DD-PCR) and Northern blot analysis of kidney mRNA from mice treated with UGN. Conclusion GN, UGN, and STa act on the mouse kidney, in part, through a cGMP-dependent, GC-C–independent mechanism, causing significant natriuresis by renal tubular processes. UGN may have further long-term effects on the kidney by altering the expression of such transport-associated proteins as Na + /K + ATPase and ClC-K2.

In this study, a well bounded and validated biogeochemical model, CENTURY, is used to mimic behaviour of soil carbon under different agricultural management regimes in Australian conditions to provide simple soil carbon response signals for use in a multi-criteria, spatial framework for analysis of management effects on carbon storage. The study is concerned with relative change in soil carbon resulting from change in management, and not concerned with absolute values either of change or final stock. Simulations were run for a range of starting soil carbon levels, soil physical properties, and climate sites using two carbon pool allocations within the model. A simplified set of agricultural land management options was used consisting of warm season native grassland, warm/cool season mixed native grassland, improved pasture, continuous wheat, conservation tillage, and long and short cereal rotations with lucerne. There were large differences in simulation results between crop and pasture systems and management types within systems. Within management types, starting soil carbon levels had the major influence on simulation outcomes. The derived carbon response signals for agricultural management practices, summarised over soils and climate, tended to underestimate losses under cropping and overestimate gains from perennial grassland when compared to field data. The value of these signals in a scenario analysis is discussed.

Management of grazing lands has been included in land-based mechanisms for reduction of greenhouse gas emissions. This paper describes a spatial system for scenario analysis of the effect of changes in grazing management on rangeland carbon balances. The system is based on identification of alternative biophysical carbon states and incorporates the effects of management changes and socio-economic and cultural barriers to changes. The management factors include grazing pressure, fire management, control or spread of woody weeds and introduction of browse shrubs. The impact of these factors may be influenced by frequency of good and poor growth years, frequency of droughts and be discounted due to social and economic barriers to adoption. The system appeared to plausibly represent the rangeland responses to management when tested for responses to climate variability and changes in stocking rate, and the impact of prescribed burning in the Tropical Woodlands. Responses are highly sensitive to the knowledge-based estimates of proportion of area in zones for each carbon state and the value of the relative carbon index for each state. A complete and ecologically sound representation of this simple model of carbon state dynamics and climate/vegetation interactions is needed to ensure that scenario analysis is useful and valid for scoping studies.

We analysed the geographic distribution of native grassland and introduced grassland plants and the association of species abundance with climate and site characteristics in southern Alberta using a database of range and pasture assessments collected during 1991, 1992 and 1993. Distribution maps of native and introduced grassland plants could be inferred from association between site data recording botanical composition or presence and mean monthly climate surfaces for May and August. Introduced species formed three main geographical patterns with alfalfa, smooth bromegrass and crested wheatgrass most widespread, Russian wildrye restricted to the south eastern quarter and timothy, creeping red fescue, bluegrass and clovers restricted to the northern half. Native plants formed four main patterns characterised by Stipa comata and Bouteloua gracilis – south eastern; Agropyron spp. – widespread; Festuca spp. – northern and foothills; and Stipa viridula – sparse. Relative abundance of Stipa comata and Bouteloua gracilis were related primarily to latitude and longitude and variables describing temperature during the growing season. Biomass increased and range condition decreased with increasing latitude reflecting the decline in dominant native species, the increase in introduced and invader species and the better growing season moisture in northern areas. The maps provide a recent snapshot of the condition of commercially utilised native and cultivated grasslands in Alberta. They constitute a valuable reference point for comparison with future climate or management-induced change.

In early epidemiological studies of diet and cancer the stress was on the search for causal factors. Population (ecological) studies tended to show a strong correlation between meat intake, particularly red meat, and the risk of colo-rectal cancer. They also tended to show meat to be strongly inversely correlated with cancers of the stomach and oesophagus and liver. Early case- control studies tended to support the postulated role for red meat in colo-rectal carcinogenesis, although more recent case-control studies, particularly those from Europe, have tended to show no relationship. The cohort studies in general failed to detect any relationship between meat intake and colo-rectal cancer risk. The available evidence points to the intake of protective factors such as vegetables and whole-grain cereals being the main determinants of colo-rectal cancer risk, with meat intake only coincidentally related.

A mechanistic model of persistence and production was combined with a geographic information system to define zones of adaptation for white clover ( Trifolium repens ) within an area encompassing most of the Temperate High Rainfall Zone on the eastern Australian mainland. Using a digital elevation model as co-variate data, monthly climate surfaces for rainfall, rain days, maximum and minimum temperature, evaporation and solar radiation were constructed using the Australian Climate Surfaces and the esoclim program. A broad, subjective zone where white clover could grow was defined by simple logical modelling, and subdivided using factors such as annual rainfall and elevation as threshold constraints. Simulations were run with a white clover model for 91 climate stations having > 2 years of evaporation data. A surface describing annual white clover dry matter production was created by using latitude, longitude, elevation, annual rainfall, January maximum temperature as independent variables in the thin plate spline interpolation routine anusplin . The production map was reclassified in the GIS to define a six-category white clover zone based on the relative magnitude of modelled annual dry matter production. The map produced offers an ecological view of the white clover zone with boundaries based on a better spatial representation of climatic constraints.

Background. High salt and nitrate intake are considered as risk factors for stomach cancer, but little is known about possible interactions. This ecological study examines the respective importance of both factors for stomach cancer mortality at the population level using data obtained under standardized conditions and with biochemical analyses performed in the same laboratories. Method. Randomly selected 24-hour urine samples from 39 populations, sampled from 24 countries (N = 5756 people for sodium, 3303 for nitrate) were obtained from the INTERSALT study. Median sodium and nitrate levels were age- and sex-standardized between ages 20-49 years and averaged per country. Ecological correlation-regression analyses were done in relation to national stomach cancer mortality rates. Results. The Pearson correlation of stomach cancer mortality with sodium for the 24 countries was : 0.70 in men and 0.74 in women (both P < 0.001), and with nitrate : 0.63 (P = 0.001) in men and 0.56 (P < 0.005) in women. In multiple regression of stomach cancer mortality, using sodium and nitrate as independent variables, the adjusted R 2 was 0.61 in men and 0.54 in women (both P < 0.001). Addition of the interaction term (sodium x nitrate) to the previous model increased the adjusted R 2 to 0.77 in men, and to 0.63 in women. The analysis of this model showed that the importance of nitrate as risk factor for stomach cancer mortality increased markedly with higher sodium levels. However, the relationship of stomach cancer mortality with sodium was always stronger than with nitrate. Conclusions. Salt intake, measured as 24-hour urine sodium excretion, is likely the rate-limiting factor of stomach cancer mortality at the population level.