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Abstract. We propose an original approach to develop rainfall thresholds to be used in civil protection warning systems for the occurrence of landslides at regional scale (i.e. tens of thousands of kilometres), and we apply it to Tuscany, Italy (23 000 km2). Purpose-developed software is used to define statistical intensity–duration rainfall thresholds by means of an automated and standardized analysis of rainfall data. The automation and standardization of the analysis brings several advantages that in turn have a positive impact on the applicability of the thresholds to operational warning systems. Moreover, the possibility of defining a threshold in very short times compared to traditional analyses allowed us to subdivide the study area into several alert zones to be analysed independently, with the aim of setting up a specific threshold for each of them. As a consequence, a mosaic of several local rainfall thresholds is set up in place of a single regional threshold. Even if pertaining to the same region, the local thresholds vary substantially and can have very different equations. We subsequently analysed how the physical features of the test area influence the parameters and the equations of the local thresholds, and found that some threshold parameters can be put in relation with the prevailing lithology. In addition, we investigated the possible relations between effectiveness of the threshold and number of landslides used for the calibration. A validation procedure and a quantitative comparison with some literature thresholds showed that the performance of a threshold can be increased if the areal extent of its test area is reduced, as long as a statistically significant landslide sample is present. In particular, we demonstrated that the effectiveness of a warning system can be significantly enhanced if a mosaic of site-specific thresholds is used instead of a single regional threshold.

The increased demand of food produced through sustainable agriculture has resulted in localised amelioration of intensive management imposed by agroecosystems. However, these newly available niches are often isolated and plant species may not be able to recolonise fragmented agroecosystems from where they have been extirpated. Plant reintroduction can overcome dispersal limitation in agroecosystems but may also generate conflicts that jeopardise conservation efforts. Conflicts arise when reintroductions are perceived to place constraints on the management and productivity of agroecosystems: the translocated plants may require space sharing with crops, may have negative effects on crop yields, and come with the expectation that farmers must modify their farming practices and accommodate legal obligations deriving from protected species status. Benefits include economic incentives that pay farmers through CAP, the conservation of nature, ecosystem services, an effective marketing strategy and increased aesthetic value that might generate ecotourism. We discuss the practical implications of the abovementioned issues by reference to two cases of European species in which different approaches to reintroduction resulted in opposite outcomes (i.e., consensus vs. opposition). Coexistence of threatened plants and crops is possible if farmers and local stakeholders are involved in a conservation project from an early stage and if farmers conservation efforts turn into benefits for their income. Based on these considerations, we propose a strategic framework to promote reintroduction of threatened plants in agroecosystems (land sharing) and policy advancement aimed at recognising the role of farmers in maintaining biodiversity on their lands.

Abstract. HIRESSS (HIgh REsolution Slope Stability Simulator) is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. The hydrological model receives the rainfall data as dynamical input and provides the pressure head as perturbation to the geotechnical stability model that computes the factor of safety (FS) in probabilistic terms. The hydrological model is based on an analytical solution of an approximated form of the Richards equation under the wet condition hypothesis and it is introduced as a modeled form of hydraulic diffusivity to improve the hydrological response. The geotechnical stability model is based on an infinite slope model that takes into account the unsaturated soil condition. During the slope stability analysis the proposed model takes into account the increase in strength and cohesion due to matric suction in unsaturated soil, where the pressure head is negative. Moreover, the soil mass variation on partially saturated soil caused by water infiltration is modeled. The model is then inserted into a Monte Carlo simulation, to manage the typical uncertainty in the values of the input geotechnical and hydrological parameters, which is a common weak point of deterministic models. The Monte Carlo simulation manages a probability distribution of input parameters providing results in terms of slope failure probability. The developed software uses the computational power offered by multicore and multiprocessor hardware, from modern workstations to supercomputing facilities (HPC), to achieve the simulation in reasonable runtimes, compatible with civil protection real time monitoring. A first test of HIRESSS in three different areas is presented to evaluate the reliability of the results and the runtime performance on large areas.

Predation is a major cause of mortality in non-human primates, and considered a selective force in the evolution of primate societies. Although larger body size is considered as protection against predation, evidence for predation on great apes by carnivores comes from chimpanzees (Pan troglodytes), gorillas (Gorilla gorilla), and orangutans (Pongo spp.). Here, we describe the first encounter between wild bonobos (Pan paniscus) and a leopard (Panthera pardus). A single leopard was confronted by a group of habituated bonobos for three hours. Two adult males and one adolescent female bonobo actively harassed the leopard, which remained still for most of the encounter and reacted only to close approaches by bonobos. While no predation was observed, their behaviours confirm that bonobos perceive leopards as potential predators. Our report adds novel information to descriptions from other African ape species, and sheds light on the behavioural repertoire of bonobos' anti-predation strategies. For future investigations, we suggest tagging leopards to remotely monitor their movements and allow assessment of encounter rates as one of several factors influencing predation pressure.

Abstract. This work proposes a methodology to compare the forecasting effectiveness of different rainfall threshold models for landslide forecasting. We tested our methodology with two state-of-the-art models, one using intensity–duration thresholds and the other based on cumulative rainfall thresholds. The first model identifies rainfall intensity–duration thresholds by means of a software program called MaCumBA (MAssive CUMulative Brisk Analyzer) (Segoni et al., 2014a) that analyzes rain gauge records, extracts intensity (I) and duration (D) of the rainstorms associated with the initiation of landslides, plots these values on a diagram and identifies the thresholds that define the lower bounds of the I–D values. A back analysis using data from past events is used to identify the threshold conditions associated with the least number of false alarms. The second model (SIGMA) (Sistema Integrato Gestione Monitoraggio Allerta) (Martelloni et al., 2012) is based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering: the statistical distribution of the rainfall series is analyzed, and multiples of the standard deviation (σ) are used as thresholds to discriminate between ordinary and extraordinary rainfall events. The name of the model, SIGMA, reflects the central role of the standard deviations. To perform a quantitative and objective comparison, these two models were applied in two different areas, each time performing a site-specific calibration against available rainfall and landslide data. For each application, a validation procedure was carried out on an independent data set and a confusion matrix was built. The results of the confusion matrixes were combined to define a series of indexes commonly used to evaluate model performances in natural hazard assessment. The comparison of these indexes allowed to identify the most effective model in each case study and, consequently, which threshold should be used in the local early warning system in order to obtain the best possible risk management. In our application, none of the two models prevailed absolutely over the other, since each model performed better in a test site and worse in the other one, depending on the characteristics of the area. We conclude that, even if state-of-the-art threshold models can be exported from a test site to another, their employment in local early warning systems should be carefully evaluated: the effectiveness of a threshold model depends on the test site characteristics (including the quality and quantity of the input data), and a validation procedure and a comparison with alternative models should be performed before its implementation in operational early warning systems.

Abstract. We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity–duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

Abstract. Although shallow landslides are a very widespread phenomenon, large area (e.g. thousands of square kilometres) early warning systems are commonly based on statistical rainfall thresholds, while physically based models are more commonly applied to smaller areas. This work provides a contribution towards the filling of this gap: a forecasting chain is designed assembling a numerical weather prediction model, a statistical rainfall downscaling tool and a geotechnical model for the distributed calculation of the factor of safety on a pixel-by-pixel basis. The forecasting chain can be used to forecast the triggering of shallow landslides with a 48 h lead time and was tested on a 3200 km2 wide area.

The Nanoscience Foundries and Fine Analysis Project (NFFA) is a FP7-funded Design Study for the definition of a novel European distributed research infrastructure with an advanced standard in modelling, synthesis, characterization and fine analysis facilities. NFFA will provide an advanced platform in nanoscience and nanotechnology by integrating the benefits of advanced fine analysis methods (based on radiation sources at the Large Scale Facilities, LSF) with advanced synthesis and nanofabrication also at the atomic scale. Both academic and industrial users will have access to state-ofthe-art instrumentation and methods for designing, synthesis and fabrication in research programs benefiting from access to LSF: Free-Electron-Laser, Synchrotron X-ray and Neutron Beams for advanced fine analysis experiments in a wide energy and time domain range. Conversely, all fine analysis experiments at LSF that require nanoscale or atomic scale precision in sample definition will greatly benefit from prior or simultaneous access to NFFA centres providing synthesis, complementary experiments and adequate metrology. In full agreement with the EU policy in matter of Research Infrastructures, NFFA will operate in open access mode based on scientific merit of proposals and free of charge.

9 páginas, 2 figuras, 2 tablas. ; In order to characterize the complete range of lesions, especially minimal, affecting mammary gland and viral antigen distribution and target cells using immunohistochemistry in naturally Visna/maedi (VM) 84 infected sheep were studied, forty-four from flocks with clinical cases (A) and 35 randomly sampled from two abattoirs (B) together with five negative controls (C). An immunocytochemistry technique was developed and further milk samples (n = 39) were used to study viral excretion, carrier cells and the role of milk and colostrum in the transmission of the disease. ; This work was supported by LE361A12–1 project of Castilla y León Government and FPU13/01081 grant of the Spanish Government. LE361A12– 1 project financed the materials necessary for collection, analysis and interpretation of data. FPU13/01081 grant financed the pre-doctoral contract of the main performer of experiments and manuscript writer EG.

Background: With the rapid development of innovative anticancer treatments, the optimization of tools able to accelerate the access of new drugs to the market by the regulatory authority is a major issue. The aim of the project was to propose a reliable methodological pathway for the assessment of clinical value of new therapeutic innovative options, to objectively identify drugs which deserve early access (EA) priority for solid and possibly in other cancer scenarios, such as the hematological ones. Materials and methods: After a comprehensive review of the European Public Assessment Report of 21 drugs, to which innovation had previously been attributed by the Italian Medicines Agency (Agenzia Italiana del Farmaco, AIFA), an expert panel formulated an algorithm for the balanced use of three parameters: Unmet Medical Need (UMN) according to AIFA criteria, Added Benefit (AB) according to the European Society for Medical Oncology's Magnitude of Clinical Benefit Scale (ESMO-MCBS) criteria and Quality of Evidence (QE) assessed by the Grades of Recommendation Assessment, Development and Evaluation (GRADE) method. By sequentially combining the above indicators, a final priority status (i.e. EA or not) was obtained using the skip pattern approach (SPA). Results: By applying the SPA to the non-curative setting in solid cancers, the EA status was obtained by 5 out of 14 investigated drugs (36%); by enhancing the role of some categories of the UMN, additional 4 drugs, for a total of 9 (64%), reached the EA status: 2 and 3 drugs were excluded for not achieving an adequate score according to AB and QE criteria, respectively. For hematology cancer, only the UMN criteria were found to be adequate. Conclusions: The use of this model may represent a reliable tool for assessment available to the various stakeholders involved in the EA process and may help regulatory agencies in a more comprehensive and objective definition of new treatments' value in these contexts. Its generalizability in other national contexts needs further evaluation.

We present measurements of the Baryon Acoustic Oscillation (BAO) scale in redshift-space using the clustering of quasars. We consider a sample of 147 000 quasars from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) distributed over 2044 square degrees with redshifts 0.8 0 at 6.6s significance when testing a ΛCDM model with free curvature.C 2017 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society ; AJR is grateful for support from the Ohio State University Center for Cosmology and ParticlePhysics. HGM acknowledges support from the Labex ILP (reference ANR-10-LABX-63) part of the Idex SUPER, and received financial state aid managed by the Agence Nationale de la Recherche, as part of the programme Investissements d'avenir under the reference ANR-11-IDEX-0004-02. GBZ is supported by NSFC Grant No. 11673025, and by a Royal Society Newton Advanced Fellowship. RT acknowledges support from the Science and Technology Facilities Council via an Ernest Rutherford Fellowship (grant number ST/K004719/1) CHC is grateful for support from Leibniz-Institut fur Astrophysik Potsdam (AIP). EB and PZ acknowledge support from the P2IO LabEx (ANR-10-LABX-0038). JLT acknowledges support from National Science Foundation grant AST-1615997. YW is supported by the NSFC grant number 11403034. WJP acknowledges support from the UK Space Agency through grant ST/K00283X/1, and WJP acknowledges support from the European Research Council through grant Darksurvey, and the UK Science & Technology Facilities Council through the consolidated grant ST/K0090X/1. ADM was partially supported by the NSF through grant numbers 1515404 and 1616168. IP acknowledges the support of the OCEVU Labex (ANR-11-LABX-0060) and the A*MIDEX project (ANR-11-IDEX-0001-02) funded by the 'Investissements d'Avenir French government program managed by the AN. JPK acknowledges support from the ERC advanced grant LIDA. GR acknowledges support from the National Research Foundation of Korea (NRF) through NRF-SGER 2014055950 funded by the Korean Ministry of Education, Science and Technology (MoEST), and from the faculty research fund of Sejong University in 2016. Funding for SDSS-III and SDSS-IV has been provided by the Alfred P. Sloan Foundation and Participating Institutions. Additional funding for SDSS-III comes from the National Science Foundation and the U.S. Department of Energy Office of Science. Further information about both projects is available at www.sdss.org. SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions in both collaborations. In SDSS-III, these include the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University. The Participating Institutions in SDSS-IV are Carnegie Mellon University, Colorado University, Boulder, Harvard-Smithsonian Center for Astrophysics Participation Group, Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe Max-Planck-Institut fuer Astrophysik (MPA Garching), Max-Planck-Institut fuer Extraterrestrische Physik (MPE), Max-Planck-Institut fuer Astronomie (MPIA Heidelberg), National Astronomical Observatories of China, New Mexico State University, New York University, The Ohio State University, Penn State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, University of Portsmouth, University of Utah, University of Wisconsin and Yale University. This work made use of the facilities and staff of the UK Sciama High Performance Computing cluster supported by the ICG, SEP-Net and the University of Portsmouth. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.