Different Levels Of Spatial Resolution Research Articles

Calculating earthquake damage building by building: the case of the city of Cologne, Germany

The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

Effect of Textural Features in Remote Sensed Data on Rubber Plantation Extraction at Different Levels of Spatial Resolution

The expansion of rubber (Hevea brasiliensis) plantations has been a critical driver for the rapid transformation of tropical forests, especially in Thailand. Rubber plantation mapping provides basic information for surveying resources, updating forest subplot information, logging, and managing the forest. However, due to the diversity of stand structure, complexity of the forest growth environment, and the similarity of spectral characteristics between rubber trees and natural forests, it is difficult to discriminate rubber plantation from natural forest using only spectral information. This study evaluated the validity of textural features for rubber plantation recognition at different spatial resolutions using GaoFen-1 (GF-1), Sentinel-2, and Landsat 8 optical data. C-band Sentinel-1 10 m imagery was first used to map forests (including both rubber plantations and natural forests) and non-forests, then the pixels identified as forests in the Sentinel-1 imagery were compared with GF-1, Sentinel-2, and Landsat 8 images to separate rubber plantations and natural forest using two different approaches: a method based on spectral information characteristics only and a method combining spectral and textural features. In addition, we extracted textural features of different window sizes (3 × 3 to 31 × 31) and analyzed the influence of window size on the separability of rubber plantations and natural forests. Our major findings include: (1) the suitable texture extraction window sizes of GF-1, Sentinel-2, and Landsat 8 are 31 × 31, 11 × 11 to 15 × 15, and 3 × 3 to 7 × 7, respectively; (2) correlation (COR) is a robust textural feature in remote sensing images with different resolutions; and (3) compared with classification by spectral information only, the producer’s accuracy of rubber plantations based on GF-1, Sentinel-2, and Landsat 8 was improved by 8.04%, 9.44%, and 8.74%, respectively, and the user’s accuracy was increased by 4.63%, 4.54%, and 6.75%, respectively, when the textural features were introduced. These results demonstrate that the method combining textural features has great potential in delineating rubber plantations.

On Segmentation of Pectoral Muscle in Digital Mammograms by Means of Deep Learning

Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography. Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle). Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confusable with some elements of breast anatomy due to their morphological and photometric similarity. As a result, the problem of automatic detection and segmentation of pectoral muscle in MLO mammograms remains a challenging task, innovative approaches to which are still required and constantly searched for. To address this problem, the present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction (deep learning) and graph-based image processing. In particular, the proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary at different levels of spatial resolution. Subsequently, the predictions are used by the second stage of the algorithm, in which the desired boundary is recovered as a solution to the shortest path problem on a specially designed graph. The proposed algorithm has been tested on three different datasets (i.e., MIAS, CBIS-DDSm, and InBreast) using a range of quantitative metrics. The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing. The average values of dice similarity coefficient (DSC) and accuracy (ACC) on the mentioned three datasets are 97.22 ± 1.96% and 99.64±.27%, respectively.

Enzymatic complexes across scales.

An unprecedented opportunity to integrate ~100 years of meticulous in vitro biomolecular research is currently provided in the light of recent advances in methods to visualize closer-to-native architectures of biomolecular machines, and metabolic enzymes in particular. Traditional views of enzymes, namely biomolecular machines, only partially explain their role, organization and kinetics in the cellular milieu. Enzymes self- or hetero-associate, form fibers, may bind to membranes or cytoskeletal elements, have regulatory roles, associate into higher order assemblies (metabolons) or even actively participate in phase-separated membraneless organelles, and all the above in a transient, temporal and spatial manner in response to environmental changes or structural/functional changes of their assemblies. Here, we focus on traditional and emerging concepts in cellular biochemistry and discuss new opportunities in bridging structural, molecular and cellular analyses for metabolic pathways, accumulated over the years, highlighting functional aspects of enzymatic complexes discussed across different levels of spatial resolution.

Multiscale remote sensing of urbanization in Ho Chi Minh city, Vietnam - A focused study of the south

This study focuses on monitoring and mapping the urbanization process in the south of Ho Chi Minh City, Vietnam. Among the most vulnerable cities worldwide due to climate change, Ho Chi Minh city is experiencing the fast urbanization in last decades, especially in the south, a wetland area. The severe environmental impact such as flood has occurred more frequently implying the critical infrastructures development is far behind the expansion of newly built-up residences and hence, growing population. To what extent, the newly built-up areas have contributed to accelerating the environmental impacts in the context of climate change? The aim of this study is twofold. Firstly, we experiment three different levels of spatial resolution, sub-meter, 2.5 m and 10–15 m in mapping the landcover changes of a selected typical fast urbanization area in 2000–2010. With all about 80% classification accuracy achievement, we found that sub-meter resolution is the best for a reliable identification of land cover changes in such complex and fast-growing built-up areas whereas 2.5 m resolution is sufficient to identify the extent of built-up areas and 10–15 m is unsuitable. We also confirmed a consistent expansion, over 25%, of built-up area in our study area, using three different resolutions in spite of small differences in other landcover types. Secondly, in comparison of ground data, we revealed that most of the newly built-up areas in 2000–2010 were built on the higher land and no flood incident was recorded obviously. However, the nearby lowland experienced more floods due to the changes. A slight increase of sea level and the sinking of the city due to the groundwater extraction may also contribute to more flood incidents. Remote sensing proved to be effective in revealing the temporal urbanization and its possible consequences. However, a holistic approach to investigation needs to be developed to better understand the contribution of anthropogenic activities to environmental impacts in the climate change context.

Probing the Complexities of Structural Changes in Layered Oxide Cathode Materials for Li-Ion Batteries during Fast Charge-Discharge Cycling and Heating.

The rechargeable lithium-ion battery (LIB) is the most promising energy storage system to power electric vehicles with high energy density and long cycling life. However, in order to meet customers' demands for fast charging, the power performances of current LIBs need to be improved. From the cathode aspect, layer-structured cathode materials are widely used in today's market and will continue to play important roles in the near future. The high rate capability of layered cathode materials during charging and discharging is critical to the power performance of the whole cell and the thermal stability is closely related to the safety issues. Therefore, the in-depth understanding of structural changes of layered cathode materials during high rate charging/discharging and the thermal stability during heating are essential in developing new materials and improving current materials. Since structural changes take place from the atomic level to the whole electrode level, combination of characterization techniques covering multilength scales is quite important. In many cases, this means using comprehensive tools involving diffraction, spectroscopy, and imaging to differentiate the surface from the bulk and to obtain structural/chemical information with different levels of spatial resolution. For example, hard X-ray spectroscopy can yield the bulk information and soft X-ray spectroscopy can give the surface information; X-ray based imaging techniques can obtain spatial resolution of tens of nanometers, and electron-based microcopy can go to angstroms. In addition to challenges associated with different spatial resolution, the dynamic nature of structural changes during high rate cycling and heating requires characterization tools to have the capability of collecting high quality data in a time-resolved fashion. Thanks to the advancement in synchrotron based techniques and high-resolution electron microscopy, high temporal and spatial resolutions can now be achieved. In this Account, we focus on the recent works studying kinetic and thermal properties of layer-structured cathode materials, especially the structural changes during high rate cycling and the thermal stability during heating. Advanced characterization techniques relating to the rate capability and thermal stability will be introduced. The different structure evolution behavior of cathode materials cycled at high rate will be compared with that cycled at low rate. Different response of individual transition metals and the inhomogeneity in chemical distribution will be discussed. For the thermal stability, the relationship between structural changes and oxygen release will be emphatically pointed out. In all these studies being reviewed, advanced characterization techniques are critically applied to reveal complexities at multiscale in layer-structured cathode materials.

Antibiotics to outpatients in Norway-Assessing effect of latitude and municipality population size using quantile regression in a cross-sectional study.

High antibiotic consumption rates are associated to high prevalence of antimicrobial resistance. Geographical differences in dispensing rates of antibiotics are frequently analysed using statistical methods addressing the central tendency of the data. Yet, examining extreme quantiles may be of equal or greater interest if the problem relates to the extremes of consumption rates, as is the case for antimicrobial resistance. The objective of this study was to investigate how geographic location (latitude) and municipality population size affect antibiotic consumption in Norway. We analysed all outpatient antibiotic prescriptions (n>14000000) in Norway between 2004 and 2010 using quantile regression. Data were stratified by year, and we aggregated individual data to municipality, county, or latitudinal range. We specified the quantile regression models using directed acyclic graphs and selected the model based on Akaike information criteria. Yearly outpatient antibiotic consumption in Norway varied up to 10-fold at municipality level. We found geographical variation to depend on the number of inhabitants in a municipality and on latitude. These variables interacted, so that consumption declined with increasing latitude when municipality population sizes were small, but the effect of latitude diminished as the number of inhabitants increased. Aggregation to different levels of spatial resolution did not significantly affect our results. In Norway, outpatient antibiotic dispensing rates decreases with latitude at a rate contingent on municipality population size. Quantile regression analysis provides a flexible and powerful tool to address problems related to high, or low, dispensing rates.

A comparison of estimation methods for multilevel models of spatially structured data

Two recent contributions (Dong et al., 2015; Osland et al., 2016) point to the relevance of multilevel models for spatially structured data. In Osland et al. (2016) these models are used to examine the importance of district-level covariates for house prices in Stavanger, Norway, in Dong et al. (2015) similarly for land parcel prices in Beijing; we use these data sets in our comparison. In Osland et al. (2016), a district-level spatial random effect was fitted using an intrinsic CAR model estimated using WinBUGS. Dong et al. (2015) used R code provided in supplementary materials to their article, and subsequently improved in an R package (Dong et al., 2016a); computation there used custom MCMC C++ code to fit a SAR district-level spatial random effect. This article compares approaches to estimating models of this kind, using the R packages R2WinBUGS, HSAR, INLA, R2BayesX, hglm and the new package mclcar for Monte Carlo maximum likelihood estimation (Sha, 2016b). We show that multilevel models of spatially structured data may be estimated readily using a variety of approaches, not only the intrinsic CAR model more typically found in the existing literature. We also point to a range of issues for further research in situations in which data acquired at different levels of spatial resolution are combined.

Using DEM to predict Abies faxoniana and Quercus aquifolioides distributions in the upstream catchment basin of the Min River in southwest China

The species distribution model (SDM), which is used to spatially predict species distributions, can also identify the probable causes of the location of certain species (i.e. the mathematical description of habitat requirements). Therefore, SDM has the potential to guide resource management and biodiversity conservation. In the topographically complex terrain, SDMs are often complicated by the lack of environmental data; however, the first information that is typically obtained for these analyses is a topographic map. Here, the possibility of using 16 predictor variables derived from the digital elevation model (DEM) to model the distributions of Abies faxoniana and Quercus aquifolioides in the mountainous upstream catchment basin of the Min River (UCBM) in southwest China was investigated. In particular, with the ensemble modeling approach based on eight niche models and nine model-training and -testing datasets, changes in model performance and shifts in the explanatory power of the predictor variable over five different levels of spatial resolution (30m, 90m, 120m, 240m, 900m) were assessed. Almost all models succeed in predicting the distributions of both species, although predictive accuracies differed significantly among spatial scales and model classes. On average, model accuracies increased to the highest level at the meso-scale (120m and 240m for A. faxoniana and Q. aquifolioides, respectively) and then decreased as resolution became coarser, indicating that high spatial resolution does not imply a better model. The relative importance rankings for each topographical variable were consistent across all spatial scales, but their explanatory powers did differ significantly among spatial scales. Elevation and terrain-distributed solar radiation for growing season (SRG) drive the distributions of A. faxoniana and Q. aquifolioides with a much higher level of confidence than other predictors across all spatial scales; the former tended to decrease, and the latter tended to increase when spatial resolution became coarse. Our findings confirm that DEM can be used exclusively and effectively to predict species distribution. Multi-scale analysis is needed to detect highly subtle variations in species habitat requirements, and to select the spatial scale that corresponds to known spatial characteristics of the species habitat. This has broad implications for distribution modeling of species in rugged terrain.

Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions

Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models, which provide considerable flexibility when testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and has therefore been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that can generate synthetic populations and can match household- and person-level constraints at a specified geographic resolution (e.g., a census block group). However, although information for some control variables may be available at the specified spatial resolution, information for other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that would not be representative of the underlying population. However, there has been limited progress in the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions when generating a synthetic population. The method is an extension of the iterative proportional updating algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.

Cross-scale contradictions in ecological relationships

Not accounting for spatial heterogeneity in ecological analyses can cause modeled relationships to vary across spatial scales, specifically different levels of spatial resolution. These varying results hinder both the utility of data collected at one spatial scale for analyses at others and the determination of underlying processes. To briefly review existing methods for analyzing data collected at multiple scales, highlight the effects of spatial heterogeneity on the utility of these methods, and to illustrate a practical statistical method to account for the sources of spatial heterogeneity when they are unknown. Using simulated examples, we show how not accounting for the drivers of spatial heterogeneity in statistical models can cause contradictory findings regarding relationship direction across spatial scales. We then show how mixed effects models can remedy this multiscaling issue. Ignoring sources of spatial heterogeneity in statistical models with coarse spatial scales produced contradictory results to the true underlying relationship. Treating drivers of spatial heterogeneity as random effects in a mixed effects model, however, allowed us to uncover this true relationship. Mixed effects models is advantageous as it is not always necessary to know the influential explanatory variables that cause spatial heterogeneity and no additional data are required. Furthermore, this approach is well documented, can be applied to data having various distribution types, and is easily executable using multiple statistical packages.

Macro-, Meso- and Microscale Segregation: Modeling Changing Ethnic Residential Patterns in Auckland, New Zealand, 2001–2013

Most world cities can now be characterized as multiethnic and multicultural in their population composition, and the residential patterning of their major component ethnic groups remains a topic of substantial research interest. Many studies of the degree of residential segregation of ethnic groups recognize that this is multiscalar in its composition, but few have incorporated this major feature into their analyses: Those that do mostly conclude that segregation is greater at the microscale than at the macroscale. This article uses a recently developed alternative procedure for assessing the degree of segregation that differs from all others in that it analyzes the geography of all groups simultaneously, providing a single, synoptic view of their relative segregation; can incorporate data for more than one date and therefore evaluate the statistical significance of the extent of any change over time; operates at several geographical scales, allowing appreciation of the extent of clustering and congregation for the various ethnic groups at different levels of spatial resolution; and—most important—is based on a firm statistical foundation that allows for robust assessments of differences in the levels of segregation for different groups between each other at different scales over time. This modeling procedure is illustrated by a three-scale analysis of ethnic residential segregation in Auckland, New Zealand, as depicted by the country's 2001, 2006, and 2013 censuses.

The effect of spatial aggregation on performance when mapping a risk of disease

BackgroundSpatial data on cases are available either in point form (e.g. longitude/latitude), or aggregated by an administrative region (e.g. zip code or census tract). Statistical methods for spatial data may accommodate either form of data, however the spatial aggregation can affect their performance. Previous work has studied the effect of spatial aggregation on cluster detection methods. Here we consider geographic health data at different levels of spatial resolution, to study the effect of spatial aggregation on disease mapping performance in locating subregions of increased disease risk.MethodsWe implemented a non-parametric disease distance-based mapping (DBM) method to produce a smooth map from spatially aggregated childhood leukaemia data. We then simulated spatial data under controlled conditions to study the effect of spatial aggregation on its performance. We used an evaluation method based on ROC curves to compare performance of DBM across different geographic scales.ResultsApplication of DBM to the leukaemia data illustrates the method as a useful visualization tool. Spatial aggregation produced expected degradation of disease mapping performance. Characteristics of this degradation, however, varied depending on the interaction between the geographic extent of the higher risk area and the level of aggregation. For example, higher risk areas dispersed across several units did not suffer as greatly from aggregation. The choice of centroids also had an impact on the resulting mapping.ConclusionsDBM can be implemented for continuous and discrete spatial data, but the resulting mapping can lose accuracy in the second setting. Investigation of the simulations suggests a complex relationship between performance loss, geographic extent of spatial disturbances and centroid locations. Aggregation of spatial data destroys information and thus impedes efforts to monitor these data for spatial disturbances. The effect of spatial aggregation on cluster detection, disease mapping, and other useful methods in spatial epidemiology is complex and deserves further study.

2D numerical simulation of density currents using the SPH projection method

Density currents (DCs) or gravity currents are driven by gravity in a fluid environment with density variation. Smoothed Particle Hydrodynamics (SPH) has been proved to have capabilities such as free surface modeling and accurate tracking of the immiscible-fluids interface that can be useful in the context of gravity currents. However, SPH applications to gravity currents have been limited to often-coarse simulations of high density-ratio currents. In this work, the SPH projection method is tried to solve currents with very low density-ratios (close to one), at a resolution, that captures the Kelvin–Helmholtz instabilities at the fluids interface. Existing implementations of the SPH projection method do not allow for an efficient solution of the currents with very low density-ratios. We found that a pressure-decoupling scheme inherently changes the way of distinguishing between the light and dense fluids in the projection method. Using this technique, SPH results, in a 2D lock-exchange flow at a Grashof number of 1.25×106 and Schmidt number of unity, are successfully compared with a previous grid-based simulation. Although these types of problems are well addressed in the past through grid-based methods, reproducing them with SPH reveals method’s capabilities such as free surface modeling as advantages that can be benefited from in addressing relevant currents. DCs are often turbulent, but unfortunately, turbulence modeling is not yet well established in the SPH method. Nevertheless, there are opinions in the literature that SPH possesses some built-in mechanisms that compensate for the missing energy dissipation at the sub-particle scales. The results of our simulation and energy budget analysis of another lock-exchange flow at different levels of spatial resolution support this hypothesis, although do not provide conclusive evidence.

Elasticity Imaging and Sensing Using Targeted Motion: From Macro to Nano

The methods used to estimate tissue elasticity based on the motion of local inhomogeneities such as laser-induced gas microbubbles, magnetic nanoparticles and other targets under an externally applied force are reviewed. The theoretical bases of the motion of a target in a viscoelastic medium are described. Given various targets differing in size, these methods allow for elasticity measurements at different levels of spatial resolution. Short acoustic radiation force and magnetic field pulses were used to initiate the motion of various test objects (solid spheres, laser-induced microbubbles and magnetic nanoparticles) with sizes ranging from several millimeters to tens of nanometers. The induced motion was monitored by conventional pulse-echo ultrasound systems adapted for these measurements. The elasticity of the medium was evaluated based on a theoretical model describing the motion of a particle in a viscoelastic medium. Shear elasticity, evaluated using the developed approach, agrees well with independent measurements of mechanical properties of the medium. In this paper we review our recent theoretical and experimental results and discuss the applications of elasticity imaging and sensing using targeted motion, i.e., motion induced via the external forces acting on local inhomogeneities.

Influence of using texture information in remote sensed data on the accuracy of forest type classification at different levels of spatial resolution

We evaluated the influence of texture information from remote sensed data on the accuracy of forest type classification at different spatial resolutions. We used 4-m spatial resolution imagery to create five different sets of imagery with lower spatial resolutions down to 30 m. We classified forest type using spectral information alone, texture information alone, and spectral and texture information combined at each spatial resolution, and compared the classification accuracy at each resolution. The classification and regression tree method was used for classification. The accuracy of all three tests decreased slightly with lower spatial resolution. The accuracy with the combined data was generally higher than with either the spectral or texture information alone. At most resolutions, the lowest accuracy was with texture information alone. However, there was no clear difference in accuracy between the combined data and spectral data alone at 25- and 30-m spatial resolution. These results indicate that adding texture information to spatial information improves the accuracy of forest type classification from very high resolution (4-m spatial resolution) to medium resolution imagery (20-m spatial resolution), but this accuracy improvement does not appear to hold for relatively coarse resolution imagery (25- to 30-m spatial resolution).

Accuracy comparison of cabbage coverage estimated from remotely sensed imagery using an unmixing method

In this study, accuracy comparisons of the cabbage coverage estimated from AVNIR-2 and QuickBird imagery using an unmixing method were carried out to increase the amount of remote sensing data obtained at different levels of spatial resolution on different observation days. The accuracy rates of the cabbage coverage estimated using an unmixing method from AVNIR-2 and QuickBird imagery were almost the same. This result is very interesting, because it shows that we may be able to evaluate cabbage coverage using remote sensing data obtained at different spatial resolutions on different observation days. Thus, it would be possible to use different remote sensing imagery systems to evaluate cabbage conditions during the growing period using this proposed method.

Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France

BackgroundZoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The disease's causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus.MethodsSandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km). Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R 0 map for leishmaniasis was constructed within a Geographical Information System. R 0 is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission.ConclusionsTo our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R 0 model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

Effects of Ginkgo biloba on cerebral blood flow assessed by quantitative MR perfusion imaging: a pilot study

Extract of Ginkgo biloba (EGb), a dietary supplement used for a number of conditions including dementia, has been suggested to increase cerebral bloodflow (CBF). The purpose of this study was to determine if changes in CBF could be detected by dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI)in elderly human subjects taking EGb. DSC-MRI was performed in nine healthy men(mean age 61±10 years) before and after 4 weeks of 60 mg EGb taken twice daily. One subject underwent six consecutive scans to evaluate intrasubject reproducibility. CBF values were computed before and after EGb, and analyzed at three different levels of spatial resolution, using voxel-based statistical parametric mapping (SPM), and regions of interest in different lobes, and all regions combined. Normalized intrasubject CBF (nCBF) measurements had a standard deviation of 7% and 4% in gray and white matter (WM) regions, respectively. SPM using an uncorrected, voxel-level threshold of P≤0.001 showed a small CBF increase in the left parietal-occipital region.CBF in individual lobar regions did not show any significant change post-EGb, but all regions combined showed a significant increase of non-normalized CBF after EGb (15% in white and 13% in gray matter, respectively, P≤0.0001). nCBF measured by DSC-MRI has good intrasubject reproducibility. In this small cohort of normal elderly individuals, a mild increase in CBF is found in the left parietal-occipital WM after EGb, as well as a small but statistically significant increase in global CBF.

Phase-Sensitive Reflective Imaging Device in the mm-wave and Terahertz Regions

Two Free Electron Laser sources have been developed at ENEA-Frascati for a variety of applications: A Compact Free Electron Laser (C-FEL) that provides coherent radiation in the frequency range between 90 and 150 GHz Gallerano et al. (Infrared Phys. and Techn. 40:161, 1999), and a second source, FEL-CATS, which utilizes a peculiar radio-frequency structure to generate coherent emission in the range 0.4 to 0.7 THz Doria et al. (Phys. Rev. Lett 93:264801, 2004). The high peak power of several kW in 15 to 50 ps pulses, makes these sources particularly suitable for the assessment of exposure limits in biological systems and for long range detection. In this paper we present a phase-sensitive reflective imaging device in the mm-wave and THz regions, which has proven to be a valuable tool in the biological Ramundo-Orlando et al. (Bioelectromagnetics 28:587–598, 2007), environmental Doria et al. (2005) and art conservation fields Gallerano et al. (2008). Different setups have been tested at different levels of spatial resolution to image objects from a few centimeter square to larger sizes. Images have been compared to identify and characterize the contrast mechanism.