Regeneration in gap models: priority issues for studying forest responses to climate change

Recruitment algorithms in forest gap models are examined with particular regard to their suitability for simulating forest ecosystem responses to a changing climate. The traditional formulation of recruitment is found limiting in three areas. First, the aggregation of different regeneration stages (seed production, dispersal, storage, germination and seedling establishment) is likely to result in less accurate predictions of responses as compared to treating each stage separately. Second, the relatedassumptions that seeds of all species are uniformly available and that environmental conditions are homogeneous, are likely to cause overestimates of future species diversity and forest migration rates. Third, interactions between herbivores (ungulates and insect pests) and forest vegetation are a big unknown with potentially serious impacts in many regions. Possible strategies for developing better gap model representations for the climate-sensitive aspects of each of these key areas are discussed. A working example of a relatively new model that addresses some of these limitations is also presented for each case. We conclude that better models of regeneration processes are desirable for predicting effects of climate change, but that it is presently impossible to determine what improvements can be expected without carrying out rigorous tests for each new formulation

Effect of Specific Immunoglobulin E Response and Comorbidities on Effectiveness of MP-AzeFlu in a Real-Life Study

Acknowledgements: We would like to thank the subjects who participated in the trial. Funding Sources: This study was supported by MEDA Pharma GmbH & Co. KG (a Mylan Company), Bad Homburg, Germany. Technical, editorial, and medical writing assistance were provided under the direction of the authors by Strategix, an affiliate of The Lynx Group, LLC. This assistance was supported by MEDA Pharma GmbH & Co. KG (a Mylan Company).Peer reviewedPublisher PD

Making geology relevant for infrastructure and planning

The urban population is projected to rise to 66% in 2050 to 7.6 billion. This has had, and will have, a profound effect on the geological and geomorphological character of the Earth’s shallow geosphere. It is important to know the character and geometries of the geological deposits so that infrastructure is planned sensibly and sustainably, and urban areas can be reused responsibly to ensure that they help facilitate economic and social development. This brings major challenges for our cities, where there is increased pressure on resources, space and services. The geosciences have an important part to play in securing sustainable global cities - they can support urban innovation and city performance, reduce our environmental footprint and ensure greater resilience to natural hazards such as flooding and ground instability. For more than 30 years the British Geological Survey has advanced the geoscientific understanding and 3D characterisation of urban environments, producing multi-themed spatial datasets for geohazards and ground investigation used across the environmental, planning and insurance sectors. The BGS have collaborated with the University of Cambridge to better integrate geological data with landuse and infrastructure to look at the long-term impact on these types of activities at surface and subsurface. A 3D GeoLanduse layer was produced from the geological framework model of London. This vector-based grid means that many soil and rock properties (e.g. foundation conditions, groundwater levels, volume change potential), can be represented alongside landuse statistics and infrastructure type and correlated in the XYZ domain. Focus has been at geothermal potential of the ground surrounding residential basements and the broader correlation between geology, energy consumption and landuse at city scale using principle component analysis and cluster recognition

Genetic Variation among Temporally and Geographically Distinct West Nile Virus Isolates, United States, 2001, 2002

Analysis of partial nucleotide sequences of 22 West Nile virus (WNV) isolates collected during the summer and fall of 2001 and 2002 indicated genetic variation among strains circulating in geographically distinct regions of the United States and continued divergence from isolates collected in the northeastern United States during 1999 and 2000. Sequence analysis of a 2,004-nucleotide region showed that 14 isolates shared two nucleotide mutations and one amino acid substitution when they were compared with the prototype WN-NY99 strain, with 10 of these isolates sharing an additional nucleotide mutation. In comparison, isolates collected from coastal regions of southeast Texas shared the following differences from WN-NY99: five nucleotide mutations and one amino acid substitution. The maximum nucleotide divergence of the 22 isolates from WN-NY99 was 0.35% (mean = 0.18%). These results show the geographic clustering of genetically similar WNV isolates and the possible emergence of a dominant variant circulating across much of the United States during 2002

The Intentional Use of Service Recovery Strategies to Influence Consumer Emotion, Cognition and Behaviour

Service recovery strategies have been identified as a critical factor in the success of. service organizations. This study develops a conceptual frame work to investigate how specific service recovery strategies influence the emotional, cognitive and negative behavioural responses of . consumers., as well as how emotion and cognition influence negative behavior. Understanding the impact of specific service recovery strategies will allow service providers' to more deliberately and intentionally engage in strategies that result in positive organizational outcomes. This study was conducted using a 2 x 2 between-subjects quasi-experimental design. The results suggest that service recovery has a significant impact on emotion, cognition and negative behavior. Similarly, satisfaction, negative emotion and positive emotion all influence negative behavior but distributive justice has no effect

Cellular and molecular mechanisms involved in the neurotoxicity of opioid and psychostimulant drugs

Substance abuse and addiction are the most costly of all the neuropsychiatric disorders. In the last decades, much progress has been achieved in understanding the effects of the drugs of abuse in the brain. However, efficient treatments that prevent relapse have not been developed. Drug addiction is now considered a brain disease, because the abuse of drugs affects several brain functions. Neurological impairments observed in drug addicts may reflect drug-induced neuronal dysfunction and neurotoxicity. The drugs of abuse directly or indirectly affect neurotransmitter systems, particularly dopaminergic and glutamatergic neurons. This review explores the literature reporting cellular and molecular alterations reflecting the cytotoxicity induced by amphetamines, cocaine and opiates in neuronal systems. The neurotoxic effects of drugs of abuse are often associated with oxidative stress, mitochondrial dysfunction, apoptosis and inhibition of neurogenesis, among other mechanisms. Understanding the mechanisms that underlie brain dysfunction observed in drug-addicted individuals may contribute to improve the treatment of drug addiction, which may have social and economic consequences.http://www.sciencedirect.com/science/article/B6SYS-4S50K2J-1/1/7d11c902193bfa3f1f57030572f7034

Search for direct production of charginos and neutralinos in events with three leptons and missing transverse momentum in √s = 7 TeV pp collisions with the ATLAS detector

A search for the direct production of charginos and neutralinos in final states with three electrons or muons and missing transverse momentum is presented. The analysis is based on 4.7 fb−1 of proton–proton collision data delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in three signal regions that are either depleted or enriched in Z-boson decays. Upper limits at 95% confidence level are set in R-parity conserving phenomenological minimal supersymmetric models and in simplified models, significantly extending previous results

Jet size dependence of single jet suppression in lead-lead collisions at sqrt(s(NN)) = 2.76 TeV with the ATLAS detector at the LHC

Measurements of inclusive jet suppression in heavy ion collisions at the LHC provide direct sensitivity to the physics of jet quenching. In a sample of lead-lead collisions at sqrt(s) = 2.76 TeV corresponding to an integrated luminosity of approximately 7 inverse microbarns, ATLAS has measured jets with a calorimeter over the pseudorapidity interval |eta| < 2.1 and over the transverse momentum range 38 < pT < 210 GeV. Jets were reconstructed using the anti-kt algorithm with values for the distance parameter that determines the nominal jet radius of R = 0.2, 0.3, 0.4 and 0.5. The centrality dependence of the jet yield is characterized by the jet "central-to-peripheral ratio," Rcp. Jet production is found to be suppressed by approximately a factor of two in the 10% most central collisions relative to peripheral collisions. Rcp varies smoothly with centrality as characterized by the number of participating nucleons. The observed suppression is only weakly dependent on jet radius and transverse momentum. These results provide the first direct measurement of inclusive jet suppression in heavy ion collisions and complement previous measurements of dijet transverse energy imbalance at the LHC.Comment: 15 pages plus author list (30 pages total), 8 figures, 2 tables, submitted to Physics Letters B. All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/HION-2011-02

Developing a predictive modelling capacity for a climate change-vulnerable blanket bog habitat: Assessing 1961-1990 baseline relationships

Aim: Understanding the spatial distribution of high priority habitats and developing predictive models using climate and environmental variables to replicate these distributions are desirable conservation goals. The aim of this study was to model and elucidate the contributions of climate and topography to the distribution of a priority blanket bog habitat in Ireland, and to examine how this might inform the development of a climate change predictive capacity for peat-lands in Ireland. Methods: Ten climatic and two topographic variables were recorded for grid cells with a spatial resolution of 1010 km, covering 87% of the mainland land surface of Ireland. Presence-absence data were matched to these variables and generalised linear models (GLMs) fitted to identify the main climatic and terrain predictor variables for occurrence of the habitat. Candidate predictor variables were screened for collinearity, and the accuracy of the final fitted GLM was evaluated using fourfold cross-validation based on the area under the curve (AUC) derived from a receiver operating characteristic (ROC) plot. The GLM predicted habitat occurrence probability maps were mapped against the actual distributions using GIS techniques. Results: Despite the apparent parsimony of the initial GLM using only climatic variables, further testing indicated collinearity among temperature and precipitation variables for example. Subsequent elimination of the collinear variables and inclusion of elevation data produced an excellent performance based on the AUC scores of the final GLM. Mean annual temperature and total mean annual precipitation in combination with elevation range were the most powerful explanatory variable group among those explored for the presence of blanket bog habitat. Main conclusions: The results confirm that this habitat distribution in general can be modelled well using the non-collinear climatic and terrain variables tested at the grid resolution used. Mapping the GLM-predicted distribution to the observed distribution produced useful results in replicating the projected occurrence of the habitat distribution over an extensive area. The methods developed will usefully inform future climate change predictive modelling for Irelan