Intercomparison of SCIAMACHY and OMI Tropospheric NO2 Columns: Observing the Diurnal Evolution of Chemistry and Emissions from Space

Concurrent (August 2006) measurements of tropospheric NO2 columns from OMI aboard Aura (1330 local overpass time) and SCIAMACHY aboard Envisat (1000 local overpass time) offer an opportunity to examine the consistency between the two instruments under tropospheric background conditions and the effect of different observing times. For scenes with tropospheric NO2 columns <5.0 × 1015 molecules cm−2, SCIAMACHY and OMI agree within 1.0–2.0 × 1015 molecules cm−2, consistent with the detection limits of both instruments. We find evidence for a low bias of 0.2 × 1015 molecules cm−2 in OMI observations over remote oceans. Over the fossil fuel source regions at northern midlatitudes, we find that SCIAMACHY observes up to 40% higher NO2 at 1000 local time (LT) than OMI at 1330 LT. Over biomass burning regions in the tropics, SCIAMACHY observes up to 40% lower NO2 columns than OMI. These differences are present in the spectral fitting of the data (slant column) and are augmented in the fossil fuel regions and dampened in the tropical biomass burning regions by the expected increase in air mass factor as the mixing depth rises from 1000 to 1330 LT. Using a global 3-D chemical transport model (GEOS-Chem), we show that the 1000–1330 LT decrease in tropospheric NO2 column over fossil fuel source regions can be explained by photochemical loss, dampened by the diurnal cycle of anthropogenic emissions that has a broad daytime maximum. The observed 1000–1330 LT NO2 column increase over tropical biomass burning regions points to a sharp midday peak in emissions and is consistent with a diurnal cycle of emissions derived from geostationary satellite fire counts.Earth and Planetary SciencesEngineering and Applied Science

Effects of climate change on grassland biodiversity and productivity: the need for a diversity of models

There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types

Phase Space Dissimilarity Measures for Structural Health Monitoring

A novel method for structural health monitoring (SHM), known as the Phase Space Dissimilarity Measures (PSDM) approach, is proposed and developed. The patented PSDM approach has already been developed and demonstrated for a variety of equipment and biomedical applications. Here, we investigate SHM of bridges via analysis of time serial accelerometer measurements. This work has four aspects. The first is algorithm scalability, which was found to scale linearly from one processing core to four cores. Second, the same data are analyzed to determine how the use of the PSDM approach affects sensor placement. We found that a relatively low-density placement sufficiently captures the dynamics of the structure. Third, the same data are analyzed by unique combinations of accelerometer axes (vertical, longitudinal, and lateral with respect to the bridge) to determine how the choice of axes affects the analysis. The vertical axis is found to provide satisfactory SHM data. Fourth, statistical methods were investigated to validate the PSDM approach for this application, yielding statistically significant results

Toward Models for the Full Oxygen-Evolving Complex of Photosystem II by Ligand Coordination To Lower the Symmetry of the Mn_3CaO_4 Cubane: Demonstration That Electronic Effects Facilitate Binding of a Fifth Metal

Synthetic model compounds have been targeted to benchmark and better understand the electronic structure, geometry, spectroscopy, and reactivity of the oxygen-evolving complex (OEC) of photosystem II, a low-symmetry Mn_4CaO_n cluster. Herein, low-symmetry Mn^(IV)_3GdO_4 and Mn^(IV_)3CaO_4 cubanes are synthesized in a rational, stepwise fashion through desymmetrization by ligand substitution, causing significant cubane distortions. As a result of increased electron richness and desymmetrization, a specific μ_3-oxo moiety of the Mn_3CaO_4 unit becomes more basic allowing for selective protonation. Coordination of a fifth metal ion, Ag+, to the same site gives a Mn_3CaAgO_4 cluster that models the topology of the OEC by displaying both a cubane motif and a “dangler” transition metal. The present synthetic strategy provides a rational roadmap for accessing more accurate models of the biological catalyst

Resorufin analogs preferentially bind cerebrovascular amyloid: potential use as imaging ligands for cerebral amyloid angiopathy

<p>Abstract</p> <p>Background</p> <p>Cerebral amyloid angiopathy (CAA) is characterized by deposition of fibrillar amyloid β (Aβ) within cerebral vessels. It is commonly seen in the elderly and almost universally present in patients with Alzheimer's Disease (AD). In both patient populations, CAA is an independent risk factor for lobar hemorrhage, ischemic stroke, and dementia. To date, definitive diagnosis of CAA requires obtaining pathological tissues via brain biopsy (which is rarely clinically indicated) or at autopsy. Though amyloid tracers labeled with positron-emitting radioligands such as [¹¹C]PIB have shown promise for non-invasive amyloid imaging in AD patients, to date they have been unable to clarify whether the observed amyloid load represents neuritic plaques versus CAA due in large part to the low resolution of PET imaging and the almost equal affinity of these tracers for both vascular and parenchymal amyloid. Therefore, the development of a precise and specific non-invasive technique for diagnosing CAA in live patients is desired.</p> <p>Results</p> <p>We found that the phenoxazine derivative resorufin preferentially bound cerebrovascular amyloid deposits over neuritic plaques in the aged Tg2576 transgenic mouse model of AD/CAA, whereas the congophilic amyloid dye methoxy-X34 bound both cerebrovascular amyloid deposits and neuritic plaques. Similarly, resorufin-positive staining was predominantly noted in fibrillar Aβ-laden vessels in postmortem AD brain tissues. Fluorescent labeling and multi-photon microscopy further revealed that both resorufin- and methoxy-X34-positive staining is colocalized to the vascular smooth muscle (VSMC) layer of vessel segments that have severe disruption of VSMC arrangement, a characteristic feature of CAA. Resorufin also selectively visualized vascular amyloid deposits in live Tg2576 mice when administered topically, though not systemically. Resorufin derivatives with chemical modification at the 7-OH position of resorufin also displayed a marked preferential binding affinity for CAA, but with enhanced lipid solubility that indicates their use as a non-invasive imaging tracer for CAA is feasible.</p> <p>Conclusions</p> <p>To our knowledge, resorufin analogs are the fist class of amyloid dye that can discriminate between cerebrovascular and neuritic forms of amyloid. This unique binding selectivity suggests that this class of dye has great potential as a CAA-specific amyloid tracer that will permit non-invasive detection and quantification of CAA in live patients.</p

Pointing all around you : selection performance of mouse and ray-cast pointing in full-coverage displays

Funding: SurfNet (NSERC, Canada), EPSRC (Small Equipment Grant).As display environments become larger and more diverse - now often encompassing multiple walls and room surfaces - it is becoming more common that users must find and manipulate digital artifacts not directly in front of them. There is little understanding, however, about what techniques and devices are best for carrying out basic operations above, behind, or to the side of the user. We conducted an empirical study comparing two main techniques that are suitable for full-coverage display environments: mouse-based pointing, and ray-cast `laser' pointing. Participants completed search and pointing tasks on the walls and ceiling, and we measured completion time, path lengths and perceived effort. Our study showed a strong interaction between performance and target location: when the target position was not known a priori the mouse was fastest for targets on the front wall, but ray-casting was faster for targets behind the user. Our findings provide new empirical evidence that can help designers choose pointing techniques for full-coverage spaces.Postprin

Running Biomechanics and Knee Cartilage Health in ACLR Patients

Anterior cruciate ligament reconstruction (ACLR) patients are more likely to subsequently suffer from knee osteoarthritis than non-ACLR counterparts. Exercise is thought to influence articular cartilage, however, it is unclear how running biomechanics are associated with femoral cartilage thickness and composition in ACLR patients. PURPOSE: The purpose of this study was to investigate relationships between running biomechanics and measures of femoral articular cartilage condition (thickness and composition) in ACLR patients and control subjects. METHODS: We used ultrasound and MRI (T2 mapping sequence) to measure articular cartilage thickness and composition, respectively, for 20 ACLR patients (age: 23 ± 3 yrs; mass: 70 ± 10 kg; time post-ACLR: 14.6 ± 6.1 months) and 20 matched controls (age: 22 ± 2 yrs; mass: 67 ± 11 kg). After these measures, all participants completed a 30-minute run on a force-instrumented treadmill. Correlational analyses were used to explore relationships between running biomechanics (vertical ground reaction force (vGRF)) and femoral cartilage thickness and composition (T2 relaxation time). The present procedures were approved by the appropriate institutional board and all subjects provided informed consent before data collection was performed. RESULTS: Significant positive correlations existed for the control subjects only between peak vGRF and overall (r = 0.34; p \u3c 0.01), medial (r = 0.23; p \u3c 0.01), lateral (r = 0.39; p = 0.02), and intercondylar (r = 0.31; p \u3c 0.01) femoral thickness. The ACLR patients showed significant negative correlations between T2 relaxation time for the central-medial region of the femoral condyle, and peak vGRF (r = −0.53; p = 0.01) and vertical impulse due to the vGRF (r = −0.46; p = 0.04). CONCLUSION: These findings offer some limited support for the idea that femoral articular cartilage benefits from increase vGRF during running. This is evidenced by the increased thickness for the control subjects and decreased T2 relaxation time (indicative of increased free-flowing water in the cartilage) for the ACLR patients, as running vGRF increased

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

<p>Abstract</p> <p>Background</p> <p>Autoregressive regression coefficients for <it>Anopheles arabiensis </it>aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of <it>An. arabiensis </it>aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of <it>An. arabiensis </it>aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled <it>Anopheles </it>aquatic habitat covariates. A test for diagnostic checking error residuals in an <it>An. arabiensis </it>aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature.</p> <p>Methods</p> <p>Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4^®was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS^®database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3^®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix.</p> <p>Results</p> <p>By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with <it>An. arabiensis </it>aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled <it>An. arabiensis </it>aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat.</p> <p>Conclusion</p> <p>An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific <it>An. arabiensis </it>aquatic habitats based on larval/pupal productivity.</p