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Exploring the assortativity-clustering space of a network's degree sequence
Petter Holme Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, U.S.A. Jing Zhao School of Life Sciences & Technology, Shanghai Jiao Tong University, Shanghai 200240, China Shanghai Center for Bioinformation and Technology, Shanghai 200235, China Department of Mathematics, Logistical Engineering University, Chongqing 400016, China
Nowadays there is a multitude of measures designed to capture different aspects of network structure. To be able to say if the structure of certain network is expected or not, one needs a reference model (null model). One frequently used null model is the ensemble of graphs with the same set of degrees as the original network. In this paper we argue that this ensemble can be more than just a null model—it also carries information about the original network and factors that affect its evolution. By mapping out this ensemble in the space of some low-level network structure—in our case those measured by the assortativity and clustering coefficients—one can for example study how close to the valid region of the parameter space the observed networks are. Such analysis suggests which quantities are actively optimized during the evolution of the network. We use four very different biological networks to exemplify our method. Among other things, we find that high clustering might be a force in the evolution of protein interaction networks. We also find that all four networks are conspicuously robust to both random errors and targeted attacks.
89.75.Fb, 82.39.Rt, 89.75.Hc
Network structure mejn:rev ; doromen:book ; ba:rev is usually defined as the way a network differs from what is expected. What "expected" means depends on the fundamental constraints on the network, and this can vary from system to system. For example, if the network is made of units that must be connected to two, and only two, others; then, it is not interesting whether or not a vertex lies on a cycle (we already know that it will). The ensemble of all networks fulfilling the fundamental constraints on the system is usually called null model (or reference model). When we have pinned down the null model we can measure the network structure by standard quantities. If the values of these quantities differs significantly from the null-model average, then we call the network structured. The baseline assumption of complex network theory is that network structure carries information about the forces that have formed the network. Ever since the studies of Barabási and coworkers ba:model ; ba:rev , the degree distribution (or, if referring to the set of degrees of one particular network, degree sequence) has been regarded as the most fundamental network structure. For many networks, the degrees are related to outer factors (not emerging from the network evolution). In such cases the ensemble of all graphs with the same degree sequence as the original network is a natural null model. Another interpretation is that the network structures measured relative to this null model are of higher order than the degree—i.e., what remain after the effects of the more fundamental structure (the degree sequence) is filtered away. The usual way to use a null model is to compare a network measure with the ensemble average value of the null model. In this paper we will argue that one can glean more information about the original network by studying the null model ensemble in greater detail than just measuring averages.
We consider networks that can be modeled as a graph G=(V,E) where V is the set of N vertices and E is the set of M undirected edges. We denote the ensemble of graphs with the same degree sequence as G as G(G). Our basic approach to study G(G) is to resolve its members in the space of higher order network structures. The two such higher order network structures we consider in this paper are: the correlation between the degrees at either side of an edge (measured by the assortative mixing coefficient, r mejn:assmix , or simply assortativity); and, the fraction of triangles in the network (measured by the clustering coefficient, C bw:sw ; mejn:rev ). By mapping out G(G) in the space defined by r and C one can pose questions such as: How large is the region in r-C space where members of G(G) actually exist? (This helps us answer how constrained the network evolution is if the degrees are given.) Is the real network close to G(G)'s boundaries in r-C space? (Which would indicate whether or not r or C are actively optimized.)
The basis for our exploration of an ensemble G(G) is to map out its members in the space defined by some network-structural measures, in our case the assortativity and clustering. We explore the r-C space by successively rewire pairs of edges, (i,j) and (i′,j′) to (i,j′) and (i′,j), that takes the system in a desired direction. Rewiring techniques for studying networks are half a century old gale:rew (randomization for obtaining null models was studied in Ref. katz:cug ). In the physics literature these techniques were first used in Refs. maslov:pro ; alon .
Ii Network structural measures
Before going into details of our algorithm, we will review the network structural quantities that we use to describe our networks: both the independent variables (the assortative and clustering coefficients) that form the basis for our space of interest; and the quantities we use for characterizing the regions of this space.
ii.1 Assortative mixing coefficient
It is quite well accepted that the set of degrees, the degree sequence, is the network quantity that contains most information about both the evolution and function of the network. Degree can (in most contexts) be identified as how influential the vertex is wf (in some sense)—high degree vertices are assumed to be more influential both the formation of the network and the flow of dynamic systems on the network. In this paper we assume the degree sequence is inherent to the system and look at higher order structures arising from how the vertices are linked to one another. The simplest such higher-order structure is the correlations between the degrees of vertices at either side of an edge. Is it the case that high-degree vertices are primarily connected to other high degree vertices, or are they linked to low-degree vertices? A simple way of measuring this tendency is by the assortativity mejn:rev r. Basically speaking, r is the linear correlation coefficient of the degrees at either side of an edge. One complication is that since the edges are undirected, r has to be symmetric with respect to edge-reversal, but the correlation coefficient is not symmetric. The solution is to let one edge contribute twice to the covariance, i.e. represent an undirected edge by two directed edges pointing in opposite directions. If one use an edge list representation internally (i.e., let the edges be stored in an array of ordered pairs (i1,j1),⋯,(iM,jM)) then mejn:assmix
r=4⟨k1k2⟩−⟨k1+k2⟩22⟨k21+k22⟩−⟨k1+k2⟩2 (1)
where, for an edge (i,j), k1 is the degree of first argument (i.e., the degree of i) and k2 is the degree of the second argument. The range of r is [−1,1] where negative values indicate a preference for high connected vertices to attach to low-degree vertices, and positive values means that vertices tend to be attached to others with degrees of similar magnitudes.
ii.2 Clustering coefficient
Several simple random network models (such as the Edrős-Rényi er:on or the model for generating networks of a given r-value in Ref. mejn:assmix ) have rather few triangles (fully connected subgraphs of three vertices). For some classes of real-world networks (notably social networks holl:72 ) there is a strong tendency for triangles to form, which makes such models fail. The network measure of the density of triangles is called clustering coefficient. We use the definition of Ref. bw:sw :
C=3ntriangle/ntriple, (2)
where ntriangle is the number of triangles and ntriple is the number of connected triples (subgraphs consisting of three vertices and two or three edges). The factor three is included to normalize the quantity to the interval [0,1].
ii.3 Distance and component size
Two quantities that are, perhaps more than any other, related to the functionality of dynamic processes on the network are the relative size of the largest component (connected subgraph) s, and the average distance ⟨d⟩. s is simply defined as the number of vertices in the largest component divided by N. The distance d(i,j) between two vertices i and j is defined as the number of edges in the shortest path between these two vertices. ⟨d⟩ is d(i,j) averaged over all vertex pairs (i≠j) in the largest component. In a network with large s and small ⟨d⟩, spreading processes will be fast and far-reaching. This is a good property of information networks, but bad in the context of, for example, disease spreading. Some authors have combined the distance and component size aspects by considering the average reciprocal distances our:attack ; latora:eff . For most purposes, we believe, valuable information gets lost in such a combination (a fragmented network G with short average distances can be something very different from a connected graph of large distances and the same average reciprocal distances as G).
ii.4 Robustness
One line of complex network research is the study of the response of the network to attacks, errors, failures and other events that effectively change the structure. The error response problem is usually formulated as: how does the functionality of the network change if a random fraction of the vertices, or edges, is removed mejn:rev ? The attack problem is the same, except that the vertices are not selected randomly but according to some strategy intended to decrease the networks' functionality as rapidly as possible our:attack ; alb:attack . A frequently used metric for functionality is the ratio of s before and after the event our:attack ; alb:attack ; motter:cascade . In the error and attack robustness problems, this quantity is typically plotted as a function of the number of removed vertices. The idea is that even if one network G is more robust than another network G′ to the removal of, say, 1% of the vertices, G′ can be less vulnerable than G if 10% of the vertices are deleted. Since we aim at mapping out the r-C space of degree sequences, we would like to capture the robustness with just one number. We will use what we call the f-robustness Rf of a network as the expected fraction of vertices that needs to be removed for the relative size of the largest component to decrease to a fraction f∈(0,1) of its original value. The way of removal can either be random (the error problem) or selective (the attack problem). For the rest of the paper we will set f=1/2, and refer to the 1/2-robustness just as "robustness" R. Other f-values give slightly different results, but our conclusions will hold for a range of intermediate f-values.
Figure 1: Illustration of the analysis scheme applied to the C. elegans neural network. (a) shows how the valid region is mapped out: 1. rmin is located. 2. rmax is found and the interval [rmin,rmax] is divided into L segments. 3. Cmin(n) is constructed. 4. Cmax(n) is traced and the interval [Cmin,Cmax] is segmented into L regions. (b) illustrates the sampling of the pixels. The next pixel to go to is chosen from a random permutation of the pixels. In this example n and n′ are chosen to be far apart. The line shows the path taken by the algorithm. The circles indicate every thousandth step on the way from n to n′. The blow-up illustrates the random walk within a pixel to sample the graphs of the pixel more randomly.
Iii The analysis scheme
The fundamental idea of our method is simple: we update the network by choosing pairs of edges randomly, say (i,j) and (i′,j′), and swap one end of them (forming (i,j′) and (i′,j)). This guarantees that the degree sequence stays intact. We navigate in the r-C space by only accepting changes that move us in the desired direction. If an edge-swap would introduce a self-edge (i.e. if i=j′ or i′=j) or a multiple edge (i.e. if (i,j′) or (i′,j) belongs to E before the swapping, or move) it is not performed. There are many other technicalities concerning the convergence to extremes, uniformity of the sampling and more that we discuss in the Appendix.
The members of the ensemble G(G) do not, in general, cover the whole range of (r,C)-values. Indeed, for any finite G, G(G) defines a set of points, rather than a continuous region, in the r-C space. We will perform a more coarse-grained analysis breaking down the r-C space into pixels and average quantities over the graphs of G(G) with (r,C)-values within the pixel. (Thus, a pixel constitute a graph ensemble in itself, our aim is to sample its members with uniform randomness.) For a computationally tractable resolution, the pixels containing members of G(G) typically form contiguous regions. We will refer to the pixels that contain a member of G(G) as valid pixels, and all pixels that are valid or between valid pixels the valid region of G(G).
To trace the valid region of G(G) we start by finding the lowest and highest assortativity value, rmin and rmax respectively. Briefly speaking (more details follow below), to find rmin we rewire edge-pairs that lower r (and vice versa for rmax). After finding the extremal r-values, we splice the region between these into L segments. Then we go through the region and for each region n∈[1,L] we find the minimal and maximal C-values, Cmin(n) and Cmax(n). The region in C-space between the lowest Cmin=min1≤n≤LCmin(n) and highest Cmax=max1≤n≤LCmax(n) observed clustering coefficient is segmented into L regions. (Note that Cmin, without argument, is the global clustering minimum, whereas Cmin(n) is the minimum conditioned on r being in the n'th segment.) Thus we (assuming our method works) obtain an L×L grid of the r-C space that contains the valid region of G(G). The method is illustrated in Fig. 1.
To find the G(G) elements of minimal and maximal assortativity is a non-trivial optimization problem. There are deterministic methods that, if they terminate, are guaranteed to give the maximal (or minimal) assortativity doyle:big ; zj:spectrum . To avoid the such technicalities and to simplify the program, we will use the same kind of optimization algorithm to find rmax and rmin as to find Cmin(n) and Cmax(n). In the Appendix we will argue that this method allows us to come as close to the optimal r-values as we need. A method we find efficient is to repeat the simple edge-pair swapping procedure (where only changes in the desired direction are accepted) with different random seeds until no lower state is found during a number νrep. of repetitions walker_walstedt . Each individual edge-pair is terminated when no lowest state is found for νsame swaps. In general, the larger the network is, the more densely distributed are the points close to the border of the valid region. If one is satisfied with finding a value a certain distance from the extrema, then νrep. and νsame do not need to be increased for larger N. To find Cmin(n) and Cmax(n) almost the same procedure is employed. First, edge-pairs are swapped until the desired segment of r is found. Second, unless r is outside the segment n and the move takes the system yet further from segment, edge-pairs are swapped provided the clustering would decrease (for Cmin(n)), or increase, (for Cmax(n)). When the valid region is traced out and we sample networks of different pixels, we select the pixels randomly. The idea is to sample the space of networks more randomly.
To summarize, the algorithm for finding the extremal assortativity values, rmin and rmax, is:
Choose two undirected edges (i,j) and (i′,j′) at random. If the program makes a difference between the arguments of the edge, the direction of the reading of the edge also has to be randomized (so (i,j) is read as (j,i) with probability 1/2).
Check if swapping these edges to (i,j′) and (i′,j) would introduce a self-edge or multiple edge in the network. If so, go to step 1.
Let Δr be the change in r if the move in step 1 is executed. If r is to be minimized and Δr<0, then accept the change (vice versa for maximization of r).
If no move has been executed during the last νsame executions of step 3, then take the current r as ~rmin (or ~rmax).
Repeat from the beginning νrep. times and return the lowest observed ~rmin during these iterations.
Given rmin and rmax, and a division of the r space into L segments of width (rmax−rmin)/L, we trace the boundaries of the valid region as follows:
Go through the regions sequentially. Say the n'th region is the interval [rn,rn+1).
Perform step 1 and 2 of the assortativity optimization algorithm.
Let ΔC be the change in clustering coefficient during the previous step. If r<rn and Δr>0, r≥rn+1 and Δr<0 or rn≤r<rn+1 and ΔC<0 (for minimization) or ΔC>0 (for maximization), then perform the change of step 6.
If, counting from the first time the system entered the desired r-segment, the minimal (maximal) C-value has been repeated νsame times, take this value as ~Cmin(n) (~Cmax(n)).
Repeat from step 6 νrep. times. Let the lowest ~Cmin(n)-values and largest ~Cmax(n) during these iterations be Cmin(n) and Cmax(n).
Then, when the valid region is mapped out, we split the C-range (between Cmin and Cmax in L segments of equal width, thus forming an L×L-grid enclosing the valid region. This grid is sampled as follows:
Construct a random permutation of the valid pixels.
Pick the next pixel Pn=[rn,rn+1)×[Cm,Cm+1) from the index-list of step 11. Denote the center [(rn+rn+1)/2,(Cm+Cm+1)/2)] of the pixel (rn,0,Cm,0). Let
δ(r,C)=√(r−rn,0rmax−rmin)2+(C−Cm,0Cmax−Cmin)2 (3)
measure the distance in r-C space from the current position (r,C) to the center of the target pixel.
Pick edge-pair candidates according to steps 1 and 2 of the assortativity optimization algorithm.
Calculate Δ(r,C)=δ(r′,C′)−δ(r,C) where r and C are the current assortativity and clustering values, and r′ and C′ are the values after the pending move has been performed. If Δ(r,C)<0 perform the move.
If the updated (r,C) belongs to Pn, then: First, make νrnd. random edge swappings such that (r,C) does not leave Pn. (This is to sample the pixel more uniformly.) Then, measure network structural quantities of Pn, save these values for statistics, and go to step 12.
If not all pixels have been measured go to step 12.
Go to step 11 until each pixel have been sampled νsamp. times.
The parameter values we use in this study are (unless otherwise stated): νsame=105, νrep.=5, νsamp.=100, νrnd.=1000 and L=50. The choice of parameters and further considerations are discussed in the Appendix. Due to the uncertain stopping conditions of steps 4, 5, 9 and 10 it is hard to derive meaningful bounds on the computational complexity. We note, however, that the optimization is faster in r- than in C-direction, this probably relates to the observation in Fig. 1(b) that swapping procedure moves faster in the r- than in the C-direction. (The speed in the C-direction is roughly the same per 1000 steps, but the speed in the r-direction decrease.)
Iv Networks
Our method can be applied to every kind of system that can be modeled as an undirected network. To limit ourselves, we use four networks from biology as examples in this paper. These networks are, nonetheless, representing fundamentally different systems.
protein interaction
291 4168 1905 280
−0.36 −0.13 −0.10 −0.069
0.0016 0.034 0.039 0.20
0.38 0.94 0.87 1
⟨d⟩
Rerror
Rattack
0.012 0.048 0.046 0.38
Table 1: Basic statistical properties of the example networks we use. The number of vertices N, number of edges M, assortativity r, clustering coefficient C, relative size of the largest cluster s, average distance in the largest cluster ⟨d⟩, the error robustness Rerror and the attack robustness Rattack.
iv.1 Gene fusion network
Cancer is a disease that occurs due to changes in the genome. One important process causing such changes is gene fusion—when two genes merge to form a hybrid gene mitelman . In Ref. hoglund the authors construct a network of human genes that have been observed to be fused in the development of tumors in humans. Some genes can fuse with many others but most of the genes have only been observed fusing with one, or a few others. The resulting network structure has a skewed, power-law like degree distribution and is rather fragmented—the largest component spanning only 38% of the vertices. Statistics of this and the other networks are listed in Table 1.
iv.2 Metabolic network
A cell can be regarded as a machine driven by biochemical reactions. The possible reactions of the metabolism (the cellular biochemistry except signaling processes) and its environment determine the state of the cell. The metabolism of an organism is a very complex system—so complex that one has to choose between studying a part of it in detail, or the whole with a coarser method. One approach in the latter category is to construct a network, connecting the chemical substrates occurring in the same reactions to a network, and employ network analysis to characterize the large-scale structure of the metabolism. The way to construct a biochemical network is not entirely straightforward zhao:meta . Should the substances be linked to each other (in a substrate graph), or to the reactions they participate in? If one use a substrate graph, should the substrates be linked only to products, or to all reactants (i.e. in a reaction A + B ↔ C + D, should A be linked to C and D, or to all three other vertices)? Furthermore, some chemical substances (like H2O, ATP, NADH, and so on) are abundant throughout the cell and seldom pose any restriction on the reaction dynamics. For many purposes, one obtains a more meaningful network by deleting such currency metabolites. The biochemical network we use is the human metabolic network of Ref. our:curr . In this network, substrates are linked only to products (A to C and D in the above example). Currency metabolites are identified and deleted according to a self-consistent, graph-theoretic method our:curr .
iv.3 Protein interaction
In protein interaction networks the vertices are proteins and two proteins constitute an edge if they can interact physically. Examples of interaction are the ability to form complexes, carrying another protein across a membrane or modifying another protein. We use the ("physical interaction") data set from Ref. hh:pfp of protein interaction in the budding yeast S. cerevisiae.
iv.4 Neural network
For the biochemistry of an organism, the network representation is a crude model of the system as a whole (as an alternative to a detailed model of a subsystem). Neural networks are yet more complex. For these the choices are either to make a coarse-grained network representation sporns:cortex or study the full network of a very simple organism. In this work, we take the latter approach and study the neural network of C. elegans white:420 . In this data set, the strength of the neuronal coupling has been measured, but we make the network undirected by letting an edge represent a non-zero coupling.
Figure 2: The valid region demarcated by the Cmin(r)- and Cmax(r)-curves (a), and three networks: (b) is the original gene fusion network, (c) shows a random sample with r-C coordinates close to those of the real network. (d) shows a network with high clustering and high assortativity. The largest component of (b), (c) and (d) are indicated with a different color.
V Numerical results
In this section we present numerical results for our four network-structural measures over the G(G) ensembles of the four test graphs. To get a first view, we display the valid region of the gene fusion graph in Fig. 2(a). As seen, the valid region is not covering a large part of the theoretical limits of r (−1≤r≤1) and C (0≤C<1). (Note that only fully connected graphs have C=1, and for these r is undefined.) The requirement that the graph should be simple (no multiple edges or self-edges) puts hard constraints on the actual r-values that can occur (cf. Ref. maslov:inet ). Fig. 2(a) shows that, considering the entire r-C plane, such constraints are even harder. The general shape of the valid region is consistent with the observations that the simple-graph constraint induce a positive correlation between r and C maslov:inet ; mejn:why .
In Fig. 2(b), (c) and (d) we show three example networks of G(G) (where G is the gene fusion network). Fig. 2(b) displays the relatively fragmented real network. Fig. 2(c) is a random network G′ with the almost the same r-C coordinates as the real network (δ(G,G′)≈0.0026). Maybe the biggest visible difference between G and G′ is the larger size of the largest component of G′. Is it true that the gene fusion network is unusually fragmented, given the degree sequence and r-C coordinates? If so, there might be an evolutionary pressure for gene fusion networks to be fragmented. (This will be discussed further in Sect. V.1.) Fig. 2(d) shows, as a contrast, a network far away from G and G′. The network has a well-defined core where high-degree vertices connect to each other. There are also a number of peripheral triangles, which indicates that the network evolves toward a maximal C-value, given its assortativity.
Figure 3: The relative size of the largest component s as a function of r and C. The networks are (a) the network of gene fusions in tumors in humans, (b) protein interaction network of S. cerevisiae, (c) human metabolic network and (d) the C. elegans neural network. The x-like symbols of the main figures and the diamond symbols of the color-bars indicate the values of the real networks. The plus-like symbol indicates the average (r,C)-value of the G(G) ensemble.
v.1 Location in r-C space and size of largest component
In Fig. 3 we plot the relative size of the largest component of the four test networks. We also display the locations of the actual networks in the r-C plane, and the G(G)-averages. (The G(G) averages are obtained from a rewiring sampling of G(G), with step 2 of the algorithm as the only constraint.) We see that the C-value of the gene fusion graph lies close to the Cmin(r)-boundary of its valid region. C averaged over the whole G(G) is about three times larger (⟨C⟩G(G)=0.0061±0.0001) than the observed value (C=0.0017). Furthermore, we see that the assortativity is lower than the G(G) average. This kind of analysis has been used by many authors (following Ref. maslov:pro ). The interpretation is usually that the network is, effectively, disassortative and clustered (i.e., r<⟨r⟩G(G) and C>⟨C⟩G(G)). However, looking at the entire valid region, we can get another perspective: If high clustering really would have been an important goal for the network to obtain (given the degree sequence) there is large room for improvement. For the assortativity, on the other hand, the observed network is rather close to the minimum. This might be telling us that assortativity is a more important factor, than clustering, in the evolution of the gene fusion networks. The protein interaction network of Fig. 3(b) is located quite far from the ensemble average—the assortativity is much lower than the G(G)-average, and given that assortativity, the clustering is maximal. Also the metabolic (Fig. 3(c)) and neural (Fig. 3(d)) networks are more clustered than the average, but here the assortativity is slightly larger than the G(G) average. From Fig. 3 we also note that the density of states is very inhomogeneous distributed—the average (r,C) is close to C=0 and (except for the neural network) left of the middle of the assortativity spectrum. The shapes of the valid regions are rather similar, with an exception for the broader region of the neural network. This can be related to the more narrow degree sequence of the neural network amaral:classes . We have established a correlation between r and C. Ref. mejn:why argues that such correlation occurs in social networks because of their modularity (or "community structure" as the authors call it). However, our large-r networks have no explicit bias towards high modularity, which leads us to conjecture that the correlation between r and C, or more fundamentally the sum ∑(i,j)∈Ekikj (which, given a degree sequence, is the only factor of Eq. 1 that can vary) is a more general phenomenon. Since r is normalized by, essentially, the variance of the degree, it follows that the valid region for G(G) with more narrow degree sequence will appear stretched (larger).
Turning to the average size of the largest component, we observe that the gene fusion network is indeed more fragmented than the average network of the same (r,C)-coordinates (as anticipated from comparing Figs. 2(b) and (c)). The protein interaction and neural networks have no particular bias in this respect, whereas the metabolic network is more fragmented than expected. The relatively low s of the metabolic network can be attributed to the "modularity" of such networks zhao:meta ; our:curr . Such modules are subgraphs that are densely connected within, and sparsely inter-connected. Sometimes they are even disconnected from the largest component (which explains the lower s). In general, s decreases with assortativity. This is natural—in more assortative networks high degree vertices are connected to each other, forming a highly connected core and a periphery too sparse to be connected (viz. Fig. 2(c) and (d)). For the denser networks (the protein interaction, metabolic and neural networks) s increases with C (for a fixed r). For the sparser gene-fusion network s has a peak at intermediate C. We do not speculate further about combinatorial cause of these dependencies; but we note (comparing e.g. Figs. 2(a) and (b)) that even though the shape of the valid regions are similar, the s behavior can be qualitatively different.
Figure 4: The average distance within the largest component ⟨d⟩ as a function of r and C. The panes and symbols correspond to those of Fig. 3.
v.2 Distances in the largest component
In Fig. 4 we display the average distance in the largest component. As mentioned, measuring the distance can give complementary information to the s(r,C) graphs of Fig. 3—while s tells us how much of the network that can be reached, ⟨d⟩ tells us how fast that can happen. For all networks the big picture is that large connected components have large average distances. This is expected from most network models. There is, however, more information than this in Fig. 4: for components of the same size, the average distance is increasing with both r and C. That ⟨d⟩ should increase with C seems quite natural—if one of a triangle's edges is rewired to connect two distant vertices, the distances in the surrounding of the triangle would increase with one, but this would be more than compensated by the connection of the two previously distant areas. Disassortative networks typically lack a well-defined core. Such cores are known to keep the average distance of general power-law networks short chung_lu:pnas . Thus one would expect an increase of r to cause a larger ⟨d⟩, but apparently the clustering related length-increase outweighs this effect. In contrast to the relative size, the average distances of the real networks are close to the G(G)-averages at the same r-C coordinates. For the gene fusion network (with a relatively small largest component), this means the distances are rather large.
Figure 5: The error robustness Rerror as a function of r and C. The panes and symbols correspond to those of Fig. 3.
v.3 Error robustness
Next, we turn to the error robustness problem. As seen in Fig. 5 the gene fusion network (Fig. 5(a)), once again, has a qualitatively different behavior than the other three networks (Fig. 5(b), (c) and (d)). While the gene fusion network is most robust for high r- and C-values the other networks are most robust for low r. A sketchy explanation can be found in the chain-like subgraphs extending from the largest component in a large-r network (cf. Fig. 2)—with a random deletion of vertices, these subgraphs are likely to be disconnected from the core rather soon (whereas in a disassortative network alternative paths may still exist), then if the deletion-robust core is less than half of the original component size it follows that it may soon be isolated. The sparsity of the gene fusion network makes the low-r G(G)-graphs much like trees (i.e., having few cycles), and since cycles provide redundant paths that can make a network robust, it follows that these graphs are fragile. For a fixed r, Rerror is a decreasing function of C for the three largest networks. We believe this is an effect of the local path redundancy induced by triangles—if one vertex of a triangle is deleted, the other two are still connected.
The Rerror-values for the real networks are always markedly higher than the G(G)-averages for the same (r,C)-coordinates. Networks with highly skewed degree distributions (the gene fusion, protein interaction and metabolic networks) are known to be robust to errors by virtue of degree distribution alone alb:attack , now Fig. 5 tells us that all these networks have a yet higher error tolerance which is an indication that error robustness is an important factor in the evolution of these networks.
Figure 6: The error robustness Rattack as a function of r and C. The panes and symbols correspond to those of Fig. 3.
v.4 Attack robustness
The final quantity we measure is the attack robustness (see Fig. 6). Rattack's functional dependence on r and C is quite different from that of Rerror. The gene fusion G(G) has the highest attack robustness at high r- and low C-values. The other networks have higher robustness values for high assortativity, but no clear tendency in the C-direction. The attack mechanism we study targets the high degree vertices. Having all high degree vertices connected to each other is probably the only way to keep the network from instantaneous fragmentation. The observed r-dependence is thus rather expected. The real-world networks all have Rattack-values of the same order of magnitude as the average values for the G(G) networks of the same location in r-C space. We note that for studying the attack problem of metabolic networks, the (less common) enzyme centric graph representation is more appropriate (see Sect. IV.2). The reason being that one can suppress an enzyme much easier than removing the substrates.
gene fusion protein interaction metabolic neural
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Table 2: Summary of the network structural measures of the real world networks relative to the average values of the G(G) a distance δ<0.02 from the real network. "<" indicates that the real network have a lower value than the corresponding G(G)-value. All results are significant with p-values >0.01, except the s-value of the neural network that has a p-value of ∼0.05.
v.5 Comparison between the graphs
Even though all our example networks are constructed from biological data, they represent fundamentally different systems—the neural network is spatial by nature, the protein interaction and (even more so) the metabolic networks are the background topology for an active dynamic system, whereas the gene fusion network is a representation of possible but undesired events. The protein interaction, metabolic and neural networks have one thing in common—the organism needs them to be robust to errors (caused by injuries, mutations, disease etc.) wagner:robu . As mentioned above and summarized in Table 2 the error robustness is indeed higher for the real networks than the G(G)-ensemble at the same (r,C)-coordinates. As mentioned above, the attack robustness of the real network is of the same order as the G(G)-average at the same (r,C)-coordinate, but actually there is a significant tendency that these network also are more robust to attacks. Furthermore, the distances in the largest component, and the relative sizes s are (with the neural network s-value as the only exception) smaller in the real than the G(G) networks.
Despite these similarities between the statistics of the real-world networks the r-C space of the different degree sequences have qualitatively different network structure. Especially, the gene fusion network behaves almost the opposite of the other networks (at least for s, ⟨d⟩ and Rerror). The source of this opposite behavior (as we discuss above) is probably that it is much sparser than the other networks. The neural network is the densest network and the only one that do not have a power-law like degree distribution.
Vi Discussion
Many complex network studies use the ensemble G(G) of graphs with the same degree sequence as the subject graph G as a null model. In contrast to a generative network model, with a few degrees of freedom that has to be fitted approximately, such an ensemble has O(N) degrees of freedom that can be matched exactly with the values of G. We argue that G(G) is more than a null model—by resolving the graphs of G(G) in a space defined by some network-structural measures, one can get a picture of the opportunities and limits there are (or has been) in the evolution of G. In this work we map out G(G) in the two-dimensional space defined by the clustering coefficient and the assortativity. Then we measure other network structural quantities throughout this space. One formal way to see our method is that we resolve G(G) in the (high dimensional) space of all sensible network measures. Then, for simplicity, we project to a few dimensions. (The case of projection to one dimension has been studied in a less formalized way earlier—projection to assortativity zj:spectrum or a "hierarchy" measure rosv:mountain .) An interesting open question is to find the principal components of the space of all sensible network measures. Using four example networks from biology, we measure average values of four network-structural quantities over the r-C space and compare these with the values of the real networks.
The functional characteristics of the r-C spaces varies much between the four example networks. For example, the C. elegans neural network covers a much larger area of the r-C space, something that probably relates to its more narrow degree distribution. The human gene fusion network, on the other hand, has a broad degree distribution similar to the S. cerevisiae protein interaction and human metabolic networks, still the structural dependency on r and C is very different for the gene fusion network compared to the others. We argue that this difference stems from the sparseness of the gene fusion network. To achieve a comprehensive understanding about how the network structure throughout the r-C space depends on the degree sequence, one would need a systematic investigation of different artificial degree sequences. In this paper, we do not pursue this goal beyond the analysis of the four biological data sets. The position of the real networks in the valid region of the r-C space adds some further information. For example, it may have been the case that networks with lower assortativity have been favored during the evolution of the gene fusion network. Clustering, on the other hand, has probably not put any constraint on the network evolution. Furthermore we compare the network structure of the real networks with the average values of networks in G(G) that are close to the (r,C)-coordinates of the real network. From this analysis, we conclude that all our four example networks are more robust to both random errors and targeted attacks than what can be expected from a random network constrained to the same degree distribution, assortativity and clustering coefficient. For all networks, except maybe the gene fusion network, this is in line with robustness being an important factor in the network evolution. Note that in this work we assume the subject network to be accurate. To get more valid error estimates one would need to take the accuracy of the edges into account.
The analysis scheme presented in this paper can be further extended and analyzed. As mentioned, it would be interesting with a quantitative evaluation of the network-structural spaces, and how they depend on the degree sequence. One can also try, for time-resolved data sets, to incorporate dynamic information in the analysis by monitoring the network-evolutionary trajectory in the r-C space.
The authors thank Mikael Huss and Martin Rosvall for helpful suggestions and comments. PH acknowledges financial support from the Wenner-Gren Foundations and the National Science Foundation (grant CCR–0331580).
Figure 7: Convergence of the optimization algorithm. (a) shows the average maximal assortativity ⟨rmax⟩ with νrep.=1. The horizontal line represents the result of the maximization algorithm of Ref. doyle:big . (b) shows the further improvement by finding the maximum over many independent runs (for νsame=10000). The vertical bars indicates the standard deviation of the observed maxima.
Appendix A Convergence and sampling uniformity
In this Appendix, we address some technical issues of our method related to the convergence of our optimization algorithm and uniformity of the sampling. We will also motivate our choice of parameters.
a.1 Assortativity and clustering extremes
To find the extremal assortativity values we use the edge-swapping algorithm described in Sect. III. To find rmin we start from a random member of G(G) and swap random edge-pairs (keeping the graph simple at all times) that lower r. When no graph of lower r has been found for νsame time steps, we break the iteration. To avoid the effect of being trapped in local minima, this process is repeated νrep. times. The main motivation for using this method is that it is at heart the same scheme as for obtaining the extremal clustering values and sampling the valid region (and thus we can re-use the same code for many steps of the calculations). In this section, we argue that the optimization performance of this method is sufficiently good for our purpose.
There is a deterministic method to maximize the assortativity that is, if it exits properly, guaranteed to find rmax doyle:big . The method works as follows: First all vertex-pairs (i,j) are ranked in decreasing order of the product of their degrees, kikj. Then the edges are added in order of this list unless the degree of one of the vertices already is fulfilled. There are some other technicalities from the additional constraint (of the authors) that the network should be connected. Of our networks, only the neural network has such an evolutionary constraint, so we do not impose it.
In Fig. 7 we display the parameter dependence of the convergence for the gene fusion network. The horizontal line is the theoretical maximum obtained by the algorithm of Ref. doyle:big . When νsame=10000 we obtain an average maximal assortativity within 0.001 of the theoretical maximum (Fig. 7(a)). By increasing νrep. the accuracy can be increased further (Fig. 7(b)). The lattice spacing we use is 0.005≲r≲0.02, so we deem a precision of 0.001 sufficient. The gene fusion network is our smallest network but the other networks are not harder to converge. When one edge-pair is swapped so that r decreases, the only term of Eq. 1 that changes is ⟨k1k2⟩. The potential change of the sum ∑(i,j)∈Ekikj, in the calculation of ⟨k1k2⟩ (close to the extrema) is of the order of the typical degree values of the network. These values grow slower than the network itself, which means that a larger network can be closer in r, but further away in number of edge swaps to reach the global optimum, than a smaller network. Some authors doyle:big use ∑(i,j)∈Ekikj to measure the degree correlations, but since we strive for a macroscopic level of description (consistent in the large-N limit), r is a more appropriate quantity for the present work.
The optimization of the clustering to find the minima (maxima) of the segments of assortativity space follows the same pattern as the method to find the minimal (maximal) r. Changes of the parameters (νsame and νrep.) have the same effect as in Fig. 7, and the same values seem sufficient.
Figure 8: Histograms of s for the discussion of sampling uniformity. All the histograms are from the gene fusion network and a pixel centered around r=−0.1, C=0.1 (the dimensions of a pixel is Δr=0.013, ΔC=0.0096. The error bars represent standard errors. Lines are guides for the eyes. (a) shows the histograms with a different numbers of random edge-pair swappings νrnd. within the pixel before the measurements of quantities. (b) illustrates the location of the starting point pixels used in panels (c) and (d). (c) compares histograms for swapping processes starting at W, S with the regular algorithm. (d) compares the average histogram of walks starting in the four peripheral points of (b) with the result of the regular algorithm. In panels (c) and (d) νrnd.=1000. The whole range of the histograms is not shown, which is why the areas under the curves appear different.
a.2 Sampling uniformity
The other technical issue we address in this Appendix is the uniformity of our sampling procedure. Ideally we would like all unique (i.e., non-isomorphic) members of G(G) to be sampled with the same probability. The most important observation is trivial—by edge-pair swapping one can go from one member of G(G) to any other, and thus all members of the ensemble will contribute to the averages. A much harder question is whether or not every member of G(G) is sampled with uniform probability. In this section, we will argue that our algorithm does a reasonably good job in the sense that there are no inconsistencies and parameter values are appropriate.
When the target pixel is found (step 15 of the algorithm) we perform νrnd. additional random edge-pair swaps. The idea is to sample the G(G)-members of the pixel more uniformly (and indeed to be able to reach into the interior of the pixel). In Fig. 8(a) we illustrate the effect of these random moves. We plot a normalized histogram of the relative largest cluster size s for 0, 100 and 10000 random moves. We see that these moves do make a difference (the νrnd.=0 is different from the νrnd.=100) but it does not matter if νrnd.=100 or νrnd.=10000. The same situation is observed for other pixels, networks and quantities. Therefore, we use νrnd.=1000 in this work.
Next, we will illustrate the use of the randomly permuted list in the sampling of the pixels (steps 11 and 12 of the algorithm). The motivation for this procedure is that the network structure can depend on the direction from which the search arrives to the pixel. In Fig. 8(b) we illustrate the test procedure—we sample separate histograms from four starting points in the four cardinal directions with respect to the central (r,C)=(−0.1,0.1) pixel. In Fig. 8(c) we see that the histograms from the W and S pixels are different. There appears to be two regions of G(G) contributing to these histograms (one with s≈0.65, one with s≈0.75). Searches starting from W seem to arrive at the s≈0.75 region more frequently, and searches staring at S ends up around s≈0.65 more frequently. The curve of the actual algorithm weighs the two peaks more equal. The curves from N and E coincides almost completely the curve for the regular algorithm (and are therefore omitted for clarity). The impression we get is that the search from one direction can induce a bias in the network structure (symbolically speaking, the graphs have a preference for ending up in a certain region of G(G)). However, from other directions, or by the random sampling of pixels (step 11), the bias is reduced. This picture is further strengthened in Fig. 8(d) where we show that the average value of the histograms from the four starting points are overlapping with the histogram of the regular algorithm.
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The St George Illawarra Dragons enter Round 2 atop the NRL Telstra Premiership ladder, and will now brace for the Parramatta Eels at WIN Stadium on Sunday, March 12.
A huge 42-10 win over the Penrith Panthers last weekend in Kogarah helped the Dragons get their season underway in the best possible fashion.
A hat-trick from back-rower Joel Thompson and a whopping 22-point haul from skipper Gareth Widdop proved highlights for the Red V.
The Eels started their season with a big win too, a 20-12 effort over the Sea Eagles at Lottoland.
With the Eels' win orchestrated by halves Corey Norman and Clinton Gutherson, the Dragons will have to be wary of the duo's influence.
Both teams are unchanged 1-17 from their respective wins in Round 1.
Dragons: 1. Josh Dugan, 2. Nene Macdonald, 3. Euan Aitken, 4. Tim Lafai, 5. Jason Nightingale, 6. Gareth Widdop (c), 7. Josh McCrone, 8. Russell Packer, 9. Cameron McInnes, 10. Paul Vaughan, 11. Tyson Frizell, 12. Joel Thompson, 13. Jack de Belin. Interchange: 14. Tariq Sims, 15. Leeson Ah Mau, 16. Jacob Host, 17. Jai Field, 18. Kurt Mann, 19. Taane Milne, 20. Luciano Leilua, 21. Hame Sele.
Eels : 1. Bevan French, Semi Radradra, 3. Michael Jennings, 4. Brad Takairangi, 5. Josh Hoffman, 6. Clinton Gutherson, 7. Corey Norman, 8. Suaia Matagi, 9. Kaysa Pritchard, 10. Tim Mannah (c), 11. Manu Ma'u, 12. Tepai Moeroa, 13. Beau Scott (c). Interchange: 14. Nathan Brown, 15. Daniel Alvaro, 16. Rory O'Brien, 17. Frank Pritchard, 18. David Gower, 19.Cameron King, 20. George Jennings, 21. Marata Niukore.
Key Match-Up: Russell Packer v Suaia Matagi – These Kiwi internationals played a huge role in their respective sides' wins in Round 1 of the 2017 NRL Telstra Premiership season. Matagi refused to let Manly's big men damper his spirit as he constantly peppered the Sea Eagles' defensive line. Packer also made a huge impact against the Panthers and finished proceedings with 174 metres.
The History: Played 31; Dragons 14, Eels 15, Drawn 2. The last time the Dragons beat the Eels was coincidentally the last time these two went toe-to-toe in Wollongong, way back in Round 10, 2013. The Dragons have since lost their past four games against the Eels including a 30-18 loss in Round 25 last year.
Match Officials: Referee: Dave Munro. Assistant Referee: Chris Sutton. Sideline Officials: Rohan Best and Phil Henderson.


text:
John Dockendorf
Hospitality and outdoor recreation consultant
ABOUT JOHN DOCKENDORF
He was born in December 1959. The first few years of Dockendorf's life were spent near Baltimore, Maryland. In the long run, he would have a bigger impact somewhere else. When he was a child in the Middle Atlantic states, he thought education was very important. After a very hard education at a friend's school, he packed up his skis and went to the University of Vermont.
First, Dockendorf made a good impression when he was in college for his first two years. He spent the end of the 1970s going through a lot of different university classes quickly, and he graduated from the University of Vermont in 1981 with the best grades. Dockendorf was looking for jobs and roles in recreation and park management, and like many people, he went down different paths and ended up as an assistant for a lot of different professors. Dockendorf went on to get a Master of Management, Hospitality from Cornell University and its School of Hotel Administration in 1992; by the next decade he had earned a PhD in that field. The same thing happened again: Dockendorf was at the top of his class again, and he was also the John E.H. Sherry Scholarship and the head of the student body at Cornell in his field's school.
You get a little personal when you build an institution that lasts for almost 30 years under your direction. Those are things that happen to John Dockendorf, as well. Under his direction, Adventure Treks Inc. was one of the most well-known outdoor programs for teenagers for 28 years. Attendees of Dockendorf's program and project have come from all over the United States and more than 25 countries around the world. As a group, we want everyone to have growth-oriented experiences and take part in wilderness adventures in the best wild natural areas of the western United States and Canada.
The Adventure Treks program was a big deal. It could have easily hired 70 teachers, as well as a full-time team and part-time help. The program was flexible and versatile, even when 2020 and COVID shut down. It was even able to work with restrictions. This was because the program already had a good reputation for being safe that had been around for almost 30 years. In 2021, Dockendorf stopped working at Adventure treks. He passed on the tradition of good outdoor education to his ten-year director, Dave McGlashan.
When John and his wife Jane reopened Camp Pinnacle in 2011, they added Adventure Treks to their business. Founded in 1928, Pinnacle was a summer camp with a long history. It closed in 2010, but it was a well-known summer camp. The Dockendorf family and Steve Baskin, the owner of Camp Champions in Texas, completely renovated Camp Pinnacle over the course of ten years. They restored Camp Pinnacle's history and reputation as one of North Carolina's favorite summer camps. In the past, Pinnacle used to fill its camper list for the summer a year early. People who ran Camp Pinnacle for the Dockendorf family, Fayssoux and J G Moss, took over after the summer of 2021.
Dockendorf first made a name for himself in Western North Carolina when he was on the Flat Rock Park Development Committee, which turned a 62-acre golf course into a great community park. When the playground committee was set up, Dockendorf also led it. Now, the playground in Flat Rock, North Carolina, is the best in the state. If you want to learn more about Mountain Community School, Dockendorf has been a board member there, as well. Then, Dockendorf helped start the French Broad River Academy in Asheville, which is where the school is now.
John Dockendorf's community service grew and stayed as active as Dockendorf's commitment to Adventure Treks, which grew even more. In 2015, Dockendorf added to his role as a civic leader by becoming a member of the Flat Rock Village Council. Transportation representative: Dockendorf was on the French Broad Metropolitan Planning Organization and its Prioritization Committee as a representative for Flat Rock. He was also on the Henderson County transportation advisory committee for Henderson County. Dockendorf played a lot of different roles for his constituents. All of them, each in their own way, made up the bigger contribution he made. By 2019, Dockendorf had made a lot of commitments, so he started to cut back on things a little as he neared early retirement.
Dockendorf's family has also been a big part of his motivation to succeed. When the couple married in 1999, they had four children: Charlie, Ave Rees, Ella, and Audrey. They have raised and been parents to them all. The kids, who are now 15 to 21, are starting their own lives, but John and Jane have built a world of education, training, and efforts to make sure their kids have a good path. The last few years haven't been easy, either. The COVID pandemic was bad for everyone in society and every part of the country. As everyone else, Dockendorf's organization and operations were affected just as much as everyone else, even more so because it ran on adventure socialization as its main service. In fact, what John Dockendorf has known for a long time is that being persistent and flexible pay off in the long run. It's why John Dockendorf has been able to do the same things on a community level again and again.
Can you name the most well-liked summer camp in America where females can go to have a great time?
Find out what to look for and how to evaluate potential summer camps for kid. The most important...
What are Some Instances of Hospitality?
There are many different styles of hospitality. Food, accommodation, and transportation are all...
The Importance of Outdoor Education
Promoting opportunities for children and staff to explore, learn, and grow through outdoor...
© 2022 by John Dockendorf

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text:
DORO: "Fight" Details Unveiled
DORO's brand new studio album, entitled Fight, is set for a September 24th release through SPV/Steamhammer Records. The full track listing for the album is as follows:
01. Fight
02. Always Live To Win
03. Descent (feat. TYPE O NEGATIVE's Pete Steele)
04. Salvaje
05. Undying
06. Legends Never Die (written by KISS' Gene Simmons)
07. Rock Before You Bleed
08. Sister Darkness
09. Wild Heart
10. Fight By Your Side
11. Chained
12. Hoffnung
13. Song For Me (digipack bonus)
Recorded at Pennsylvania's Soundmime Recording Studios by the production team of DORO, Chris Lietz and Dan Malsch, Fight features the group's current line-up of guitarist Joe Taylor, bassist Nick Douglas, drummer Johnny Dee (i.e. BRITNY FOX), and guitarist Oliver Palotai, who joined the fold during the group's most recent tour.
Says DORO: "Compared to previous recordings, Fight sounds even rawer, earthy, totally rooted to the soil. Nothing was polished smooth, there are no loops, hardly any effects, but a lot of pure energy and passion."
GENE SIMMONS To Headline LICENSING LEADERSHIP SUMMIT 2020
JETHRO TULL Drummer DOANE PERRY: NEIL PEART 'Faced Brain Cancer Bravely, Philosophically And With His Customary Humor'
SUICIDE SILENCE Releases Music Video For 'Two Steps'
MACHINE HEAD Joined By Former EXODUS Singer ROB DUKES For 'A Lesson In Violence' Performance In Phoenix (Video)
RONNIE JAMES DIO Hologram To Return To The Road In May
Mind Freeze


text:
Juan Manuel Olivares (Wilde, Buenos Aires, Argentina, 14 de julio de 1988) es un futbolista argentino que juega como centrocampista y actualmente milita en el Club Atlético Aldosivi de Argentina.

En sus primeras apariciones en el Quilmes Atlético Club fue utilizado como volante por sector derecho, pero con la llegada de Alberto Pascutti se le encontró el puesto como la manija del equipo.

Trayectoria
Sus inicios fueron en el Quilmes Atlético Club, equipo de la Primera División de su país. Luego, emigra en 2009 al fútbol chileno. Tras el terremoto en Chile del el 27 de febrero del 2010, este jugador decide dejar aquel país y así mismo Everton de Viña del Mar, donde permaneció un año. Al volver a Argentina, el Club Social y Deportivo Merlo, que militaba en la segunda división, decide contratarlo.

En el 2011 ficha para Platense de la B Metropolitana, tercera división del fútbol argentino, donde fue figura. Permaneció dos años en la institución. En 2013, el Club Mutual Crucero del Norte, la revelación de la Primera B Nacional, lo contrata. En esta temporada convierte 6 goles en 31 partidos disputados.

A mediados de 2014, el Club Olimpo de la Primera División de Argentina decide adquirir sus servicios. Esta era una gran oportunidad para el centrocampista ya que era su vuelta a la máxima categoría de su país. Su debut con la camiseta de Olimpo fue el 22 de julio por Copa Argentina contra Rafaela.

En el 2015 pasa a Los Andes para disputar el Campeonato B Nacional 2015,haciendo una buena campaña con el equipo de Lomas De Zamora, destacando goles a Patronato, Douglas Haig y Guaraní Antonio Franco.

En el 2017 vuelve a Platense en la B Metropolitana y tiene su revancha, ya que el equipo se corona campeón y asciende a la B Nacional.

Jugó en Brown De Adrogué después de un pequeño paso por su ex club Crucero Del Norte.

Actualmente se encuentra jugando en el Defensores de Belgrano.

Clubes

Referencias

Enlaces externos
 Ficha en BDFA
 Perfil del jugador

Futbolistas de Avellaneda (Buenos Aires)
Futbolistas del Club Atlético Platense
Futbolistas de las inferiores del Quilmes Atlético Club
Futbolistas del Quilmes Atlético Club en los años 2000
Futbolistas del Club Social y Deportivo Merlo
Futbolistas de Everton de Viña del Mar en los años 2010
Futbolistas del Club Olimpo
Futbolistas del Club Atlético Brown

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NFL Injuries – Why the increase? Read this doctor's opinion.
returning to playing multiple sports might actually decrease the serious injury rate and also produce better athletes for all sports.
Why can Special Teams be such an issue?


I just completed 45+ hours of yoga for cancer training. I am able to teach clients through every stage of their cancer treatments. I've learned so much about neuropathy, chemotherapy, pranayama and the lymph system. This training helped me realize how important tying together all of what I already do: strength training, yoga, the yoga wall and the value of human touch. Connecting breathing and gravity to help someone full of anxiety, fear or pain makes me feel like I can truly help people. Thank you to Tari Prinster for making me watch videos on breast biopies and tram flap surgeries to help me understand what my clients are going through and how to keep them from getting injured. I'm going to offer donation based one-on-one yoga classes for men and women going through the cancer journey. Namaste.


text:
Laid to Rest ist ein US-amerikanischer Slasherfilm aus dem Jahr 2009.

Handlung
Eine junge Frau wacht in einem Sarg auf und kann sich aus ihm befreien. Da die Türen des Beerdigungsinstituts, in dem sie sich befindet, verschlossen sind, versucht sie mittels Telefon die Polizei zu rufen. Als der ältliche Besitzer des Beerdigungsinstituts erscheint, wird er von einem Mann mit einer silbernen Totenkopfmaske aufgeschlitzt. Die junge Frau kann dem Killer namens Chromeskull entkommen. Sie lässt sich von Tucker, einem vorbeifahrenden Mann, mitnehmen und findet bei ihm und seiner Frau Cindy Unterschlupf. Cindy ist davon zunächst wenig begeistert, könnte es sich doch bei der unter Amnesie leidenden Fremden um eine Prostituierte handeln. Diese will die Polizei alarmieren, doch Tucker und Cindy haben keinen Telefonanschluss mehr und das Benzin reicht nicht, um sie bis zur Polizeistation zu bringen. Während Cindy im Schlafzimmer schläft, schaut sich Tucker eine Wunde an, die die junge Frau am Hinterkopf hat. Als er sich schlafen legen will, entdeckt er, wie Cindy durch das Fenster von Chromeskull festgehalten wird. Hilflos müssen Tucker und die Frau zusehen, wie Chromeskull Cindy ein Messer in den Kopf rammt, ehe sie bestürzt in Tuckers Wagen davonfahren.

Kurz darauf trifft Cindys Bruder Johnny mit seiner Freundin Jamie vor dem Haus seiner Schwester ein. Chromeskull lauert ihnen auf und bringt sie mit seinem Messer zur Strecke. Tucker und seine junge Begleiterin halten derweil vor einem Haus und bitten Steven, den Besitzer, die Polizei zu alarmieren. Steven hat ebenfalls kein Telefon, jedoch einen Computer, mit dem er der Polizei eine E-Mail schickt. Über das Internet findet er zudem heraus, dass es sich bei Chromeskull um einen gesuchten Serienmörder handelt, der seine Morde stets mit einer Videokamera auf seiner Schulter filmt und die Videos anschließend der Polizei zuschickt. Zu dritt fahren sie daraufhin in Stevens Auto zur Polizeistation. Chromeskull, der den Sheriff und einen weiteren Polizisten ermordet hat, stellt ihnen dort eine Falle. Sie können jedoch entkommen und fahren anschließend am Beerdigungsinstitut vorbei. In der Hoffnung, dort Hinweise auf ihre Vergangenheit zu finden, geht die junge Frau in das Gebäude. Steven soll dort auf sie aufpassen, während Tucker zu seinem Haus zurückfährt, um Cindys Leiche vom Fenster weg ins Haus zu bringen und so ihrem Bruder, der am nächsten Morgen bei ihnen vorbeischauen wollte, den Anblick von Cindys Leiche zu ersparen. Cindys Leiche ist jedoch nicht mehr da.

Vom Beerdigungsinstitut aus entdeckt die junge Frau Chromeskull vor einer Scheune. Gemeinsam mit Steven nähert sie sich dem Killer und beobachtet ihn. Chromeskull wird schließlich auf sie aufmerksam und die junge Frau versucht sich in der Scheune zu verstecken. Dort entdeckt sie in zahlreichen Särgen zerstückelte Frauenleichen. Ein gefesseltes Mädchen ist noch am Leben und die junge Frau versucht, es mit einem Messer loszuschneiden. Als sich Chromeskull nähert, läuft die junge Frau panikartig davon. Sie findet jedoch keinen Ausgang. Chromeskull säbelt derweil dem Mädchen den Kopf ab. Danach gibt er der jungen Frau eine Spritze. Diese kommt gefangen in einem Sarg wieder zu sich. Tucker ist indes mit einem Revolver zurückgekehrt und gelangt mit Steven in die Scheune, schießt auf den Killer und befreit die junge Frau aus dem Sarg. Mit dem Wagen des Killers fahren sie gemeinsam zu Stevens Haus. Dort angekommen, finden sie Cindys Leiche im Kofferraum. Tucker bittet Steven, die Leiche seiner Frau ins Haus bringen zu dürfen. Die junge Frau versucht derweil über die Videoaufnahmen des Killers herauszufinden, wer sie ist. Die Batterien der Kamera sind jedoch leer, weshalb sie beschließt, zum nächsten Laden zu fahren. Chromeskull greift sie vor dem Laden an, schickt sie dann jedoch in den Laden, um neue Batterien und ein Videoband zu besorgen. Sollte sie dabei versuchen zu entkommen oder die Polizei zu alarmieren, will er die anderen Kunden des Ladens töten. An der Kasse bemerkt der junge Verkäufer, dass etwas nicht in Ordnung ist. Er nimmt ein Gewehr und geht nach draußen. Chromeskull greift jedoch nach der Schusswaffe, sodass sich der Verkäufer den eigenen Kopf wegschießt. Zwei junge Männer, Tommy und Anthony, sind im Laden bei der jungen Frau. Anthony geht zum Hinterausgang, wo ihm Chromeskull kurz darauf die Kehle durchschneidet.

Tucker und Steven treffen ebenfalls im Laden ein, wo sie sich mit den anderen verbarrikadieren wollen. Im Koffer des Killers, den Steven mit hineingebracht hat, finden sie eine Dose, in die Steven Superkleber umfüllt. Als das Licht ausgeht, taucht Chromeskull hinter der Ladentheke auf, greift nach Steven und tötet ihn mit einer chemischen Substanz. Tucker versucht auf ihn zu schießen, verfehlt jedoch stets sein Ziel. Tommy entkommt zur Vordertür, während sich Tucker auf den Killer stürzt, der ihm ein Messer in die Brust sticht. Die junge Frau verkriecht sich in die Kühlanlage und wehrt mit einem Gitter die Messerattacken des Killers ab. Dieser gibt ihr schließlich die Kamera. Das darin befindliche Videoband zeigt, dass sie eine drogensüchtige Prostituierte ist, die Chromeskull in einem Hotelzimmer mit einem Baseballschläger niedergeschlagen hatte. Als Chromeskull sie erneut attackiert, verliert er seine Totenkopfmaske. Er greift zur Dose aus seinem Koffer und schmiert den Inhalt in die Maske, damit sie an seinem Gesicht wieder haften bleibt. Der Superkleber ätzt ihm jedoch die Haut, worauf er sich die Maske samt Gesicht vom Kopf reißt. Er liegt regungslos auf dem Boden, als ihm die junge Frau mit einem Schläger auf den Kopf schlägt. Tucker öffnet daraufhin kurz die Augen, glaubt Cindy vor sich zu sehen, die ihm sagt, dass sie ihn liebe, und stirbt. Die junge Frau fährt anschließend mit Tommy nach Atlanta. Als die Polizei am Schauplatz des Geschehens eintrifft, ist Chromeskulls Leiche verschwunden.

Hintergrund
Der Regisseur und Drehbuchautor des Films, Robert Green Hall, ist seit den 1990er Jahren hauptsächlich als Maskenbildner im Filmgeschäft tätig. Laid to Rest war nach dem Thrillerdrama Lightning Bug (2004) der zweite Spielfilm unter seiner Regie. Das von ihm gegründete Make-up-Studio Almost Human war an der Produktion von Laid to Rest beteiligt. Die Dreharbeiten fanden in Annapolis, Maryland, statt, wo seine Ehefrau Bobbi Sue Luther herstammt, die die Rolle der jungen Frau spielt. In Nebenrollen sind Lena Headey und Thomas Dekker zu sehen, die Hall durch seine Mitwirkung als Maskenbildner an Terminator: The Sarah Connor Chronicles kennengelernt hatte und die schließlich zusagten, in seinem zweiten Spielfilm jeweils eine Rolle zu übernehmen.

Laid to Rest wurde am 18. März 2009 in Dallas, Texas, uraufgeführt. In Deutschland erschien der Film am 6. November 2009 direkt auf DVD. Im Jahr 2011 wurde die Fortsetzung Chromeskull: Laid to Rest 2 veröffentlicht, bei der erneut Robert Hall die Regie geführt hatte und in der Thomas Dekker und Nick Principe ihre Rollen wiederholten.

Kritiken
Für das Lexikon des internationalen Films war Laid to Rest "überraschungsarm dem Stil und den Klischees der 1980er-Jahre verpflichtet". Kurt Meinicke von moviemaze.de stellte fest, dass der Film "genau wie die meisten seiner Vorbilder, in Sachen Story und Darsteller nicht wirklich überzeugen" könne, jedoch "in puncto Gewalt und Konsequenz wiederum sehr gelungen" sei. "Die überzeugenden Effekte lassen den geneigten Gorehound auch über ein paar Logiklöcher hinweg sehen", so Meinicke. Was den "Einfallsreichtum" betrifft, könne "man an Laid to Rest seine blutige Freude haben".

Weblinks

Einzelnachweise

Filmtitel 2009
Horrorfilm
US-amerikanischer Film


text:
Q: Cloud Formation Wait Condition Timeout but Script executes successfully + Sends CFN Signal I want to setup an EC2 with ubuntu. After the EC2 is setup a script is run which installs several packages. The log (/var/log/cloud-init-output.log) show that the script does not error and sends a successful finishing signal.
Log
+ /usr/local/bin/cfn-signal -e 0 -r 'AWS CodeDeploy Agent setup complete.' 'https://cloudformation-waitcondition-eu-central-1.s3.eu-central-1.amazonaws.com/arn%3Aaws%3Acloudformation%3Aeu-central-1%XXXXXXXXX%3Astack/XXXXXXXXXX/XXXXXXXX-XXXX-11e6-9ec8-50a68ad4f262/WaitHandle?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20160608T172814Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86399&X-Amz-Credential=XXXXXXXXXXXXXX%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=XXXXXXXXXXXXXXX'
/usr/local/lib/python2.7/dist-packages/cfnbootstrap/packages/requests/packages/urllib3/util/ssl_.py:79: InsecurePlatformWarning: A true SSLContext object is not available. This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail. For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.
  InsecurePlatformWarning
CloudFormation signaled successfully with SUCCESS.
Cloud-init v. 0.7.5 finished at Wed, 08 Jun 2016 17:34:00 +0000. Datasource DataSourceEc2.  Up 99.87 seconds

PS: I added some `X, there are real alphanumeric chars in the link.
Code Script
"UserData": {
  "Fn::Base64": {
    "Fn::Join": [
      "",
      [
        "#!/bin/bash -ex\n",
        "apt-get update\n",
        "apt-get -y install python-pip\n",
        "apt-get -y install python-setuptools\n",
        "apt-get -y install ruby2.0\n",
        "apt-get install -y awscli\n",
        "pip install https://s3.amazonaws.com/cloudformation-examples/aws-cfn-bootstrap-latest.tar.gz\n",
        "# Helper function.\n",
        "function error_exit\n",
        "{\n",
          "/usr/local/bin/cfn-signal -e 1 -r \"$1\" '", { "Ref": "WaitHandle" }, "'\n",
          "exit 1\n",
        "}\n",
        "#Install nodejs, npm\n",
        "curl -sL https://deb.nodesource.com/setup_6.x\n",
        "apt-get install -y nodejs npm || error_exit 'Failed to install nodejs.'\n",
        "npm install pm2 -g || error_exit 'Failed to install pm2.'\n",
        "# Install the AWS CodeDeploy Agent.\n",
        "cd /home/ubuntu/\n",
        "aws s3 --region eu-west-1 cp 's3://aws-codedeploy-eu-west-1/latest/install' . || error_exit 'Failed to download AWS CodeDeploy Agent.'\n",
        "chmod +x ./install\n",
        "./install auto\n",
        "/usr/local/bin/cfn-init --stack ", { "Ref":"AWS::StackId" }, " --resource LinuxEC2Instance", " --region ", { "Ref": "AWS::Region" }, "\n",
        "# All is well, so signal success.\n",
        "/usr/local/bin/cfn-signal -e 0 -r \"AWS CodeDeploy Agent setup complete.\" '", { "Ref": "WaitHandle" }, "'\n"
      ]
    ]
  }
},

Code Wait Condition
"WaitHandle": {
  "Type": "AWS::CloudFormation::WaitConditionHandle",
  "Metadata": {
    "AWS::CloudFormation::Designer": {
      "id": "761ddc9a-7c3b-41ca-9fa1-21429046b271"
    }
  }
},
"WaitCondition": {
  "Type": "AWS::CloudFormation::WaitCondition",
  "Properties": {
    "Count": {
      "Ref": "InstanceCount"
    },
    "Handle": {
      "Ref": "WaitHandle"
    },
    "Timeout": "300"
  },
  "Metadata": {
    "AWS::CloudFormation::Designer": {
      "id": "a2c91b03-2c8c-4bd5-9c44-efdb89cf5375"
    }
  }
},

Question
What do I need to change in order to get the script passing.

A: The problem seems not to appear if aws-cfn-bootstrap is not installed with "pip install https://s3.amazonaws.com/cloudformation-examples/aws-cfn-bootstrap-latest.tar.gz\n", instead I use now easy install and call the executables directly with e.g. cfn-signal -e 0 -r \"AWS CodeDeploy Agent setup complete.\" '", { "Ref": "WaitHandle" }, "'\n"

redpajama_set_name: RedPajamaStackExchange


The Consumer Electronics Show 2019 took place last week. The show highlights advancements in technology, as well as, new products soon arriving on store shelves. At the conference, walking the halls, you could find drones, robots, rollable TV's, etc. However, also at CES 2019, you could find trucks. Some trucking companies decided to bring their new models. These trucks ranged from electric vehicles, autonomous vehicles, and slightly autonomous vehicles. Furthermore, there were concept trucks at the show that displayed potential new fuel technologies. Of course, all of these pieces of displayed technology are hopeful. They are hopeful in the sense of how soon they can be rolled out to the masses. Among some expected appearances, CES also featured some more surprising showcases.
Before we get into the ever important topic of trucking fuel, let's take a look at trucking tech. In one of our previous blog entries, we mentioned TuSimple's trucking technology. Well, they went into this year's CES confident, calm, and collected. "CES is becoming an important venue for automotive and trucking because while it's billed as consumer, it's really a technology show, and trucks are getting more and more technology embedded in them. They are becoming the next technology focus," said Chuck Price, vice president of product at TuSimple. TuSimple showcased their autonomous trucks and machine vision.
One of the leading truck manufacturers in the world, Daimler, also appeared at CES. In one of the rarer parts of such a "heavy" concept show, Daimler made some announcements. First, they would begin offering lane-keeping assist with automated steering capabilities. Second, they had hands-on testing of their electronic vehicles. Martin Daum, CEO of Daimler's global truck business, said that he feels Daimler has an obligation to innovate the trucking industry.
When people sit down in a movie theater to watch a movie they're usually shown some variation of the following message: "Silence is Golden." Trucking fuel technologies are taking that message to heart. Many journalists and reporters stated that areas displaying electric truck technology were one of the quietest places in the entire conference. Daimler Truck North America allowed test drives of their various vehicles. All the vehicles, ranging from their eCascadia Semi to their Jouley school bus, were quiet, silent, and vibration-less vehicles traveling the landscape.
However, even with all this good news, the realities can't be escaped. The battery life is still less than optimal. These vehicles won't be able to transport shipments over long distances for a long time. Most electric vehicles are just being used for short distance transportation efforts that have substantial idle time. CES ends up being a hopeful endeavor for those that chose to present. Trucking technology is slowly evolving and, over time, will change the landscape of the trucking industry for the better.


VLC is a very good tool for using your subscription. And you can use the tool on mobile as well as Mac and Windows too. You will have to access the M3U link we send in the subscription email. While the tutorial shared here is for Windows, the same principles and ideas apply to the other platforms as well.
2. Select the Media option, which is located on the top left side of the window.
3. Now you have to select Open Network Stream.
4. Here you have to paste the VLC Player link from our email.
5. The channels will be loading, so it may take a bit of time.
6. Everything will be set after that, so just select the desired channel and enjoy the LeaseIPTV subscription!


text:
Arco opens new Hull store with former Arsenal footballer Paul Merson
Business News Submitted By: News Desk January 13, 2020 No Comments
Safety equipment supplier Arco has officially opened its new retail store in Hull, with help from television pundit and former footballer Paul Merson.
The Hull firm, one of the city's best-known historic family businesses, has relocated its shop as part of a major relocation of its own headquarters.
Customers flocked through the doors of Arco's new store, at Hull's Link 63 Industrial Park, as the UK's leading safety equipment specialist announced a string of opening day offers.
Former Arsenal star and now Sky Sports pundit Paul Merson also dropped in at the new store, to cut the ribbon and open it to the public.
Paul Frewin, head of retail at Arco, said: "The new Hull store will offer customers the best possible selection of products and services with our own in store experts available to offer advice and training.
Inside Arco's new store in Hull
(Image: Peter Harbour)
"We are confident that local tradesmen will enjoy the new location within the busy industrial estate, due to the improved accessibility and convenience to neighbouring stores."
The new store, at Hull's Link 63 industrial park, stocks more than 2,000 of Arco's safety products.
Visitors can also brand workwear in-store and try on workwear and safety footwear for comfort and fit.
Experts from Arco will be on-hand to provide expert advice on safety, and recommend professional safety services including consultancy, training, services and products for working at height, confined spaces and respiratory management.
Paul Merson with Arco staff at the shop
Arco's old store was at its current headquarter site in Waverley Street.
However, later this year Arco will move into new, £16m offices in Hull's thriving Fruit Market area, as its current home is demolished as part of construction of a major infrastructure scheme in the city centre.
Arco has relocated its store ahead of the HQ move later in 2020.
The Hull business has also confirmed its new £25m National Distribution Centre, close to the A63 dual carriageway in Hessle, is expected to be operational in early February.
Speaking about Arco's upcoming headquarters, company MD David Evison said: "We've taken this opportunity to create a modern workplace that has been designed to ensure our colleagues thrive and new talent emerges.
"Features such as open plan, collaborative workspaces, social meeting areas, a glass fronted atrium and an auditorium for colleague presentations will boost our company culture and drive employee engagement.
"We're particularly pleased that our new head office will support the regeneration of Hull's Fruit Market, to ensure the city remains vibrant and commercially successful."
Business Live's Hull & East Yorkshire journalist is Phil Winter, also the business editor at the Hull Daily Mail and Hull Live.
The post Arco opens new Hull store with former Arsenal footballer Paul Merson appeared first on USNewsRank.
Trump's China trade war is 'the biggest risk to the global economy'
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'I won't sleep for over 24 hours …
Highway scheme starts in days to open …
Retailers offer holiday help vacations, kayaks, 'glamour' …
Chancellor Sajid Javid promises to "unite country" …
Apple FaceTime bug lets you listen even …


text:
ACCEPTED

#### According to
Index Fungorum

#### Published in
Bull. Soc. Hist. nat. Doubs 82: 24 (1986)

#### Original name
Acetabula aestivalis R. Heim & L. Remy

### Remarks
null

redpajama_set_name: RedPajamaGithub


The kitchen reno is finally done! Okay, it's almost done. I have to finish sealing the grout (ran out of sealer with 3 feet left) and touch up the paint. But other than that it's done and I love it! Like sit on my couch and just smile as I look at it, love it.
Demo day! Pulling off the tile backsplash destroyed some of the drywall….oops!
Granite install (Please excuse the dishes on the counter and the mess–I live here!) The granite was the only part of the project we contracted out for. Home Depot had great prices, a coupon, and an amazing warranty.
Next up was the painting…Labor Day weekend lived up to its name.
The Rustoleum Cabinet Transformation kit made this easy (well if you consider 30+ hours of painting easy).
Then came wiring for the under cabinet lights, new outlets and switches, and the drywall.
I primed the walls to cover the grey after the drywall mud dried.
And finally the backsplash. I chose to keep it neutral so I can incorporate and change accent colors whenever I want.
Hello 2015 . A trip to Home Goods and Pottry Barn mixed with some family pieces (a jar of my dad's marbles and my great grandma's candlesticks) layered with my Halloween decorations completed the month long renovation.
G surprised me with a new pantry door! We're still deciding if we want to paint the glass with black chalkboard paint. What do you think? I love the glass but hate that you can still sort of see in.
These lights might be my favorite part! They're along the top and bottom cabinets and each section has its own switch.
Awesome, right?! I seriously love it.
Special thanks to my mom who hung out while the granite was installed. And an enormous thank you to G! Without you and your hours of hard work this would not have been possible!
I loved finally getting to do this project but it was a lot of work.
Prepare to spend more time and money than planned. Extra expenses are bound to pop up and some tasks will take longer than expected.
Schedule no work days. The project may have been done a bit sooner but we purposefully took days off to relax and recover.
Know that one project may lead to others. I call this the "If you give a mouse a cookie" effect. I'm already costing out can lights, curtains, and new doors for the rest of the house.
I'm off to stare at my pretty new space!


text:
Military Researching Limb Regeneration, Like The Terminator Cop
One of the signature wounds of the War on Terror has been the traumatic amputation of limbs.
Today, advanced prosthetics help wounded troops recover much of their independence and live their lives more fully than those who've lost limbs in the past.
And while the science and engineering of prosthetics has markedly advanced, the military is working on ways to make those prosthetics flesh and blood.
Army Lt. Col. David Saunders, talks about extremity regeneration at the Military Health System Research Symposium in Kissimmee, Florida, Aug. 28, 2017. (DOD photo)
According to military doctors and scientists, Army medical researchers are trying to figure out how salamanders are able to re-grow their limbs, and apply that to wounded troops who have lost limbs.
"What we're trying to do is develop a toolkit for our trauma and reconstructive surgeons out of various regenerative medicine products as they emerge to improve long-term outcomes in function and form of injured extremities," Lt. Col. David Saunders said during a recent Military Health System research symposium.
Sergeant First Class LEroy Petry, whose right han was amputated by a grenade. (US Army photo)
The symposium also featured technologies closer to current science. A number of projects involving synthetic grafts have shown amazing potential, including one involving bone fillers that are treated to reduce the possibility of infection. Other projects have focused on recovering or preserving nerves, or regrowing muscle.
One researcher is even looking at a mouse to help improve the treatment of burn victims. In this case, the African spiny mouse has been known to lose much of its skin to escape a predator, yet it can quickly recover the skin with a minimum amount of scarring.
Army medics unload a mock casualty from a UH-60 Black Hawk medevac helicopter during a training exercise at the Joint Readiness Training Center. | U.S. Army photo by Sgt. Michael J. MacLeod
"Warfighters and civilians alike suffer large surface [cuts] and burns, and these result in medically and cosmetically problematic scars," said Dr. Jason Brant of the University of Florida. "The ability to develop effective therapies will have an enormous impact not only on the health care system but on the individuals as well."
One Army officer, though, is developing biocompatible sponges that can also reduce scarring by promoting better skin healing. Major Samuel Tahk of Fort Detrick noted that in addition to the sponges being a step along the path towards furthering regenerative medicine, the devices could also cut costs by making treatment of patients simpler.
U.S. Air Force Master Sgt. Israel Del Toro Jr. from Joliet, Ill., listens to instructions for adjusting the sight on his compound bow during the archery competition at the 2016 DoD Warrior Games. (U.S. Air Force Photo by Tech. Sgt. Steve Grever/Released)
With this host of new technologies, it's no wonder Saunders is excited, not only noting that wounds to limbs have become far more survivable, but also about the many advances "emerging in the field of regenerative medicine to restore form and function to our wounded warfighters."
This article originally appeared on WeAreTheMighty.com
READ NEXT: 13 Stress Relief Hobbies For Veterans


The Organic Relaxation and Exotic Gift contains a selection of items. There is a 10ml bottle each of essential oils Organic Rose 5% Blend, Organic Ylang Ylang, and Organic Lavender. The kit also contains 100ml each of Organic Samadhi body massage oil, Organic Neroli Floral water, Organic Blended Base massage oil, and an elegant recycled mixing bottle made of Italian glass.
This kit serves as a perfect gift for newlywed couples as it helps set the mood for their romantic honeymoon. It can also be a great gift for yourself, when you just want to relax and unwind after a long week at work.
Organic Rose 5% Blend Essential Oil is safe to take internally in small dosages – 2 drops on the thumb and rub onto the roof of the mouth. To reduce inflammation, apply 2-4 drops to the affected area on the skin. Apply several drops to the lymph nodes the neck to protect against bacteria, or getting a cold and flu. Apply a drop of on a bee sting or insect bite to stop itching and reduce swelling. Apply 2-3 drops oil on a minor burn or cut to decrease pain, bleeding, clean the wound and kill bacteria.
For an immediate calming effect, rub on the wrists, temples, and feet (or anywhere). You may also rub 2-3 drops of lavender oil in your cupped palms and inhale the scent all the way into your amygdala gland in your brain to calm the mind. As a sleep aid, rub a drop on your palms and smooth on your pillow. For Eczema or Dermatitis, mix several drops of a nut or vegetable oil (coconut, sesame, etc.) and apply to the affected area. Apply a drop of on a bee sting or insect bite to stop itching and reduce swelling. Apply 2-3 drops oil on a minor burn or cut to decrease pain, bleeding, clean the wound and kill bacteria. To stop a nosebleed, put a drop on a tissue and wrap it around a small slice of ice; push the tissue covered ice to the base of the nose and hold as long as comfortable or until the bleeding stops (do not freeze the lip or gum). Rub onto dry, chapped or sunburned skin to soothe. To alleviate the symptoms of motion sickness, place a drop on end of the tongue, behind the ears or around the navel. Rub a drop of lavender oil between your palms and inhale deeply to help alleviate the symptoms of hay fever.
Organic Ylang Ylang Essential Oil is safe to take internally in small dosages – 2 drops on the thumb and rub onto the roof of the mouth. To reduce inflammation, apply 2-4 drops to the affected area on the skin. Apply several drops to the lymph nodes the neck to protect against bacteria, or getting a cold and flu.


text:
package org.hisp.dhis.appstore;

import java.io.IOException;

import org.hisp.dhis.appmanager.AppStatus;

/**
 * @author Lars Helge Overland
 */
public interface AppStoreManager
{
    AppStore getAppStore()
        throws IOException;

    AppStatus installAppFromAppStore( String id );
}