High spatial-temporal resolution data across large scales are needed to transform our understanding of ecosystem services

Editorial for special issue. Many assessments of ecosystem services are based on maps of land cover. For example, Costanza et al. estimated the value of global ESs using economic valuations based on land cover and land use data. This method consists of matching an ecosystem type with the potential ESs that they provide. However, within the different types of land cover or land use considered, various environmental factors occurring at finer temporal or spatial scales (e.g., climatic variation) are not well captured. Thus, ES assessments are largely scale dependent, often missing important variables at both large and small scales. More in-depth studies should be encouraged to elucidate the roles of variables other than land cover

A Comprehensive Analysis of 5G Heterogeneous Cellular Systems operating over κ\kappa-μ\mu Shadowed Fading Channels

Emerging cellular technologies such as those proposed for use in 5G communications will accommodate a wide range of usage scenarios with diverse link requirements. This will include the necessity to operate over a versatile set of wireless channels ranging from indoor to outdoor, from line-of-sight (LOS) to non-LOS, and from circularly symmetric scattering to environments which promote the clustering of scattered multipath waves. Unfortunately, many of the conventional fading models adopted in the literature to develop network models lack the flexibility to account for such disparate signal propagation mechanisms. To bridge the gap between theory and practical channels, we consider $\kappa$-$\mu$ shadowed fading, which contains as special cases, the majority of the linear fading models proposed in the open literature, including Rayleigh, Rician, Nakagami-m, Nakagami-q, One-sided Gaussian, $\kappa$-$\mu$, $\eta$-$\mu$, and Rician shadowed to name but a few. In particular, we apply an orthogonal expansion to represent the $\kappa$-$\mu$ shadowed fading distribution as a simplified series expression. Then using the series expressions with stochastic geometry, we propose an analytic framework to evaluate the average of an arbitrary function of the SINR over $\kappa$-$\mu$ shadowed fading channels. Using the proposed method, we evaluate the spectral efficiency, moments of the SINR, bit error probability and outage probability of a $K$-tier HetNet with $K$ classes of BSs, differing in terms of the transmit power, BS density, shadowing characteristics and small-scale fading. Building upon these results, we provide important new insights into the network performance of these emerging wireless applications while considering a diverse range of fading conditions and link qualities

The flows of nature to people, and of people to nature: applying movement concepts to ecosystem services

To date, the provision of ecosystem services has largely been estimated based on spatial patterns of land cover alone, using benefit transfer analysis. Although it is increasingly being recognised that the distribution of the human population affects whether a potential service translates into a realised service, this misses key steps in the process and assumes that everyone accesses ecosystem services in the same way. Here we describe a conceptual approach to ecosystem services in terms of movement and flows. We highlight that ecosystem service flows can be broken down into ‘nature to people’ (the movement of nature towards beneficiaries) and ‘people to nature’ (the movement of beneficiaries towards nature). The former has been relatively well described. Here, we explore the latter by reviewing research on human migration, animal foraging and landscape connectivity. We assess if and how existing theories might be useful in describing how people seek out ecosystem services. We consider some of the ways in which flows of people to nature can be measured. Such measurements may reveal which movement theories best represent how people seek out and access ecosystem services. Overall, our review aims to improve the future modelling of ecosystem services by more explicitly considering how people access potential services and therefore realise them

Remote sensing methods for the biophysical characterization of protected areas globally: challenges and opportunities

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale

Xanthinuria: a rare cause of urolithiasis in the cat

Xanthinuria is a very rare disease in cats. Its etiology may have a genetic origin or may be due to an iatrogenic xan- thine-dehydrogenase inhibition that nally results in urolithiasis. The present work reports two cases of xanthine uro- lithiasis in European Shorthair unrelated male and female cats. Both uroliths were analyzed by stereoscopic microsco- py, infrared spectroscopy and scanning electron microscopy. Besides the report of these two clinical cases, a detailed pathophysiologic review and some updated recommendations for diagnosis and treatment for this condition were done.La xantinuria es una patología que se presenta raramente en los gatos. Su etiología puede tener origen genético o de- berse a una inhibición yatrogénica de la enzima xantina deshidrogenasa, que generalmente se mani esta con urolitiasis. En este trabajo se informa el hallazgo de dos urolitos de xantina en dos gatos, un macho y una hembra, de raza Euro- pea de pelo corto, no emparentados. Los urolitos fueron analizados mediante microscopía estereoscópica, espectrosco- pía infrarroja y microscopía electrónica de barrido. Además de informar sobre estos casos clínicos, se hace una revisión detallada de la siopatología y de las recomendaciones actuales para el diagnóstico y manejo médico de esta patología.Se agradece al Consejo Nacional de Ciencia y Tecnolo- gía de México (CONACyT) y al Programa de Mejora- miento del Profesorado de la Secretaria de Educación Pública de México 2011 (PROMEP-SEP), el apoyo complementario para la realización de este trabajo

Towards globally customizable ecosystem service models

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed

Software Provision Process for EGI

he European Grid Initiative (EGI) provides a sustainable pan-European Grid computing infrastructure for e-Science based on a network of regional and national Grids. The middleware driving this production infrastructure is constantly adapted to the changing needs of the EGI Community by deploying new features and phasing out other features and components that are no longer needed. Unlike previous e-Infrastructure projects, EGI does not develop its own middleware solution, but instead sources the required components from Technology Providers and integrates them in the Unified Middleware Distribution (UMD). In order to guarantee a high quality and reliable operation of the infrastructure, all UMD software must undergo a release process that covers the definition of the functional, performance and quality requirements, the verification of those requirements and testing in production environments

Beam characterisation studies of the 62 MeV proton therapy beamline at the Clatterbridge Cancer Centre

The Clatterbridge Cancer Centre (CCC) in the United Kingdom is the world's first hospital proton beam therapy facility, providing treatment for ocular cancers since 1989. A 62 MeV beam of protons is produced by a Scanditronix cyclotron and transported through a passive delivery system. In addition to the long history of clinical use, the facility supports a wide programme of experimental work and as such, an accurate and reliable simulation model of the treatment beamline is highly valuable. However, as the facility has seen several changes to the accelerator and beamline over the years, a comprehensive study of the CCC beam dynamics is needed to firstly examine the beam optics. An extensive analysis was required to overcome facility related constraints to determine fundamental beamline parameters and define an optical lattice written with the Methodical Accelerator Design (MAD-X) and the particle tracking Beam Delivery Simulation (BDSIM) code. An optimised case is presented and simulated results of the optical functions, beam distribution, losses and the transverse rms beam sizes along the beamline are discussed. Corresponding optical and beam information was used in TOPAS to simulate transverse beam profiles and compared to EBT3 film measurements. We provide an overview of the magnetic components, beam transport, cyclotron, beam and treatment related parameters necessary for the development of a present day optical model of the facility. This work represents the first comprehensive study of the CCC facility to date, as a basis to determine input beam parameters to accurately simulate and completely characterise the beamline

Machine learning for ecosystem services

Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues

Viruses under the Antarctic Ice Shelf are active and potentially involved in global nutrient cycles

Viruses play an important role in the marine ecosystem. However, our comprehension of viruses inhabiting the dark ocean, and in particular, under the Antarctic Ice Shelves, remains limited. Here, we mine single-cell genomic, transcriptomic, and metagenomic data to uncover the viral diversity, biogeography, activity, and their role as metabolic facilitators of microbes beneath the Ross Ice Shelf. This is the largest Antarctic ice shelf with a major impact on global carbon cycle. The viral community found in the cavity under the ice shelf mainly comprises endemic viruses adapted to polar and mesopelagic environments. The low abundance of genes related to lysogenic lifestyle (<3%) does not support a predominance of the Piggyback-the-Winner hypothesis, consistent with a low-productivity habitat. Our results indicate a viral community actively infecting key ammonium and sulfur-oxidizing chemolithoautotrophs (e.g. Nitrosopumilus spp, Thioglobus spp.), supporting a “kill-the-winner” dynamic. Based on genome analysis, these viruses carry specific auxiliary metabolic genes potentially involved in nitrogen, sulfur, and phosphorus acquisition. Altogether, the viruses under Antarctic ice shelves are putatively involved in programming the metabolism of ecologically relevant microbes that maintain primary production in these chemosynthetically-driven ecosystems, which have a major role in global nutrient cycles