Hasil untuk "Maps"

Simon Iain Hay, C. Guerra, P. Gething et al.

Background Efficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007. Methods and Findings A total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2–10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2−10 ≤ 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2−10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2−10 ≥ 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk. Conclusions High levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process.

M. Rosvall, D. Axelsson, Carl T. Bergstrom

Abstract Many real-world networks are so large that we must simplify their structure before we can extract useful information about the systems they represent. As the tools for doing these simplifications proliferate within the network literature, researchers would benefit from some guidelines about which of the so-called community detection algorithms are most appropriate for the structures they are studying and the questions they are asking. Here we show that different methods highlight different aspects of a network's structure and that the the sort of information that we seek to extract about the system must guide us in our decision. For example, many community detection algorithms, including the popular modularity maximization approach, infer module assignments from an underlying model of the network formation process. However, we are not always as interested in how a system's network structure was formed, as we are in how a network's extant structure influences the system's behavior. To see how structure influences current behavior, we will recognize that links in a network induce movement across the network and result in system-wide interdependence. In doing so, we explicitly acknowledge that most networks carry flow. To highlight and simplify the network structure with respect to this flow, we use the map equation. We present an intuitive derivation of this flow-based and information-theoretic method and provide an interactive on-line application that anyone can use to explore the mechanics of the map equation. The differences between the map equation and the modularity maximization approach are not merely conceptual. Because the map equation attends to patterns of flow on the network and the modularity maximization approach does not, the two methods can yield dramatically different results for some network structures. To illustrate this and build our understanding of each method, we partition several sample networks. We also describe an algorithm and provide source code to efficiently decompose large weighted and directed networks based on the map equation.

F. Fleuret, J. Berclaz, R. Lengagne et al.

M. Quddus, W. Ochieng, R. Noland

Abstract Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

S. Mansour, W. Matten, April S. Hermann et al.

R. Klemke, S. Cai, A. Giannini et al.

Cell interaction with adhesive proteins or growth factors in the extracellular matrix initiates Ras/ mitogen-activated protein (MAP) kinase signaling. Evidence is provided that MAP kinase (ERK1 and ERK2) influences the cells' motility machinery by phosphorylating and, thereby, enhancing myosin light chain kinase (MLCK) activity leading to phosphorylation of myosin light chains (MLC). Inhibition of MAP kinase activity causes decreased MLCK function, MLC phosphorylation, and cell migration on extracellular matrix proteins. In contrast, expression of mutationally active MAP kinase kinase causes activation of MAP kinase leading to phosphorylation of MLCK and MLC and enhanced cell migration. In vitro results support these findings since ERK-phosphorylated MLCK has an increased capacity to phosphorylate MLC and shows increased sensitivity to calmodulin. Thus, we define a signaling pathway directly downstream of MAP kinase, influencing cell migration on the extracellular matrix.

J. Raingeaud, A. Whitmarsh, T. Barrett et al.

J. Weissenbach, G. Gyapay, C. Dib et al.

R. Treisman

U. Pinkall, K. Polthier

J. Rain, L. Selig, H. D. Reuse et al.

L. Vosshall, H. Amrein, P. Morozov et al.

Y. Kohara, K. Akiyama, K. Isono

Cai Huang, K. Jacobson, M. Schaller

M. Kilgard, M. Merzenich

L. Vosshall, A. Wong, R. Axel

Yin Lou, Chengyang Zhang, Yu Zheng et al.

T. Rankinen, A. Zuberi, Y. Chagnon et al.

G. Sabio, R. Davis

C. McOwen, Lauren V. Weatherdon, J. V. Bochove et al.

Abstract Background Saltmarshes are extremely valuable but often overlooked ecosystems, contributing to livelihoods locally and globally through the associated ecosystem services they provide, including fish production, carbon storage and coastal protection. Despite their importance, knowledge of the current spatial distribution (occurrence and extent) of saltmarshes is incomplete. In light of increasing anthropogenic and environmental pressures on coastal ecosystems, global data on the occurrence and extent of saltmarshes are needed to draw attention to these critical ecosystems and to the benefits they generate for people. Such data can support resource management, strengthen decision-making and facilitate tracking of progress towards global conservation targets set by multilateral environmental agreements, such as the Aichi Biodiversity Targets of the United Nations' (UN's) Strategic Plan for Biodiversity 2011-2020, the Sustainable Development Goals of the UN's 2030 Agenda for Sustainable Development and the Ramsar Convention. New information Here, we present the most complete dataset on saltmarsh occurrence and extent at the global scale. This dataset collates 350,985 individual occurrences of saltmarshes and presents the first global estimate of their known extent. The dataset captures locational and contextual data for saltmarsh in 99 countries worldwide. A total of 5,495,089 hectares of mapped saltmarsh across 43 countries and territories are represented in a Geographic Information Systems polygon shapefile. This estimate is at the relatively low end of previous estimates (2.2-40 Mha), however, we took the conservative approach in the mapping exercise and there are notable areas in Canada, Northern Russia, South America and Africa where saltmarshes are known to occur that require additional spatial data. Nevertheless, the most extensive saltmarsh worldwide are found outside the tropics, notably including the low-lying, ice-free coasts, bays and estuaries of the North Atlantic which are well represented in our global polygon dataset. Therefore, despite the gaps, we believe that, while incomplete, our global polygon data cover many of the important areas in Europe, the USA and Australia.