text:
- Research article
- Open Access
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies
- Raphaël Mourad1Email author,
- Christine Sinoquet2Email author and
- Philippe Leray1
© Mourad et al; licensee BioMed Central Ltd. 2011
- Received: 15 April 2010
- Accepted: 12 January 2011
- Published: 12 January 2011
Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a challenging task.
We present an accurate modeling of dependences between genetic markers, based on a forest of hierarchical latent class models which is a particular class of probabilistic graphical models. This model offers an adapted framework to deal with the fuzzy nature of linkage disequilibrium blocks. In addition, the data dimensionality can be reduced through the latent variables of the model which synthesize the information borne by genetic markers. In order to tackle the learning of both forest structure and probability distributions, a generic algorithm has been proposed. A first implementation of our algorithm has been shown to be tractable on benchmarks describing 105 variables for 2000 individuals.
The forest of hierarchical latent class models offers several advantages for genome-wide association studies: accurate modeling of linkage disequilibrium, flexible data dimensionality reduction and biological meaning borne by latent variables.
- Linkage Disequilibrium
- Latent Variable
- Bayesian Network
- Directed Acyclic Graph
- Latent Class Model
Genetic markers such as SNPs are the key to dissecting the genetic susceptibility of common complex diseases, such as asthma, diabetes, atherosclerosis and some cancers [1]. The purpose is identifying combinations of genetic determinants which should accumulate among affected subjects. Generally, in such combinations, each genetic variant only exerts a modest impact on the observed phenotype, whereas, in contrast, the interaction between genetic variants and, possibly, environmental factors is determinant. Decreasing genotyping costs now enable the generation of hundreds of thousands of SNPs, spanning the whole human genome, across cohorts of cases and controls. This scaling up to genome-wide association studies (GWASs) makes the analysis of high-dimensional data a hot topic [2]. Despite recent technological advances and extensive research effort, the genetic basis of the aforementioned diseases remains to a large extent unknown. Yet, the search for associations between single SNPs and the variable describing case/control status requires carrying out a large number of statistical tests. Since SNP patterns, rather than single SNPs, are likely to be determinant for complex diseases, a high rate of false positives as well as a perceptible statistical power decrease, not to mention intractability, are severe issues to be overcome.
The simplest type of genetic polymorphism, single nucleotide polymorphism (SNP), involves only one nucleotide change, which occurred generations ago within the DNA sequence. To fix ideas, we emphasize that one single individual can be uniquely defined by only 30 to 80 independent SNPs and unrelated individuals differ in about 0.1% of their 3.1 billion nucleotides [3]. Compared with other kinds of DNA markers, SNPs are appealing because they are abundant, genetically stable and amenable to high-throughput automated analysis. Consistently, advances in high-throughput SNP genotyping technologies lead the way to various down-stream analyses, including GWASs.
Exploiting the existence of statistical dependences between neighboring SNPs, also called linkage disequilibrium (LD), is the key to association study achievement [4]. Indeed, a causal variant (i.e. a genetic factor) may not be a SNP. For instance, insertions, deletions, inversions and copy-number polymorphisms may be causative of disease susceptibility. Nevertheless, a well-designed study will have a good chance of including one or more SNPs that are in strong LD with a common causal variant. In the latter case, indirect association with the phenotype, say affected/unaffected status, will be revealed (see Additional file 1).
Interestingly, LD also offers solutions to reduce data dimensionality in GWASs. In the human genome, LD is highly structured into the so-called "haplotype block structure" [5]: regions where statistical dependences between contiguous markers (called blocks) are high alternate with shorter regions characterized by low statistical dependences (see Additional file 2). The most likely explanation of this phenomenon is related to the presence of large regions with low recombination rates separated by recombination hotspots (i.e. small specific regions with high recombination rates) [6]. Relying on this feature, various approaches were proposed to achieve data dimensionality reduction: testing association with haplotypes (i.e. inferred data underlying genotypic data) [7], partitioning the genome according to spatial correlation [8], selecting SNPs informative about their context, or SNP tags [9] (for more references, see [10] for example). Recent methods, such as HaploBuild [11], have permitted to construct more biologically relevant haplotypes where the "haplotype cluster structure", instead of the "haplotype block structure", is assumed: haplotypes are not constrained by contiguous orientation. Unfortunately, these methods do not take into account all existing dependences since they miss higher-order dependences. Actually, these methods do not consider the fuzzy nature of LD: the LD block boundaries are not accurately defined over the genome (see Additional file 3).
Due to their ability to represent conditional independences between variables, probabilistic graphical models (PGMs) offer an adapted framework for an accurate modeling of dependences between SNPs. A PGM is a probabilistic model relying on a graph representing conditional independences within a set of random variables. Inherently, this model simplifies the description of the joint distribution of the set of variables. Several subclasses of PGMs exist such as Markov random fields (MRFs) and Bayesian networks (BNs). The main difference between these two subclasses remains in the nature of the graph: in contrast with MRFs, Bayesian networks are directed graphs. Although the observed variables (OVs) are often sufficient to describe their joint distribution, sometimes, additional unobserved variables, also named latent variables (LVs), have a role to play.
Only few research works have been dedicated to SNP dependence modeling through PGMs. A hard task because of high data dimensionality, tackling this modeling issue through PGMs nevertheless offers an attractive lead. Approaches based both on MRFs [12] and BNs have been designed. Regarding the latter, some methods only consider observed variables [13, 14] whereas other models include latent variables [15, 16]. In particular, hierarchical BNs are the most promising models for LD representation: their hierarchical structure supported by LVs allows flexible information synthesis, thus efficiently reducing the data dimensionality. To our knowledge, modeling LD through hierarchical BNs in order to reduce SNP data dimensionality has not yet been designed. Notably, scalability remains a crucial issue for GWASs.
In this paper, we emphasize the interest of using a forest of hierarchical latent class models (FHLCMs), to reduce the dimension of the data to be further submitted to statistical analyses devoted to the discovery of genetic factors potentially involved in the disease. Such studies encompass single-SNP analysis [17], multiple-SNP analysis [18], SNP-SNP interaction analysis [19] and analysis integrating gene expression [20, 21]. An FHLCM is a hierarchical BN with discrete observed and latent variables. Basically, latent variables capture the information borne by underlying markers. In their turn, latent variables are clustered into groups and, if relevant, such groups are subsequently subsumed by additional latent variables. Iterating this process yields a hierarchical structure. First, the great advantage to GWASs is that further statistical analyses can be chiefly performed on latent variables. Thus, a reduced number of variables will be examined. Second, a model based on a hierarchical structure provides a flexible data mining tool. For example, different degrees of data dimensionality reduction are available to the statistician. Moreover, the hierarchical structure is meant to efficiently conduct refined association testing: zooming in through narrower and narrower regions in search for a stronger association with the disease ends pointing out the potential markers of interest.
However, most algorithms dedicated to the learning of hierarchical latent class models (HLCMs) fail the scalability criterion when the data describe thousands of variables and a few hundreds of individuals. In a previous work-on progress paper [22], we designed an algorithm devoted to learning FHLCMs. This algorithm was named CFHLC, which stands for Construction of Forests of Hierarchical Latent Class models. The contribution brought in the present extended version is the following: (i) we advocate the use of FHLCMs to model LD; (ii) we provide a detailed description of the main concepts underlying our approach; (iii) using real data, we show that the FHLCM graph is representative of the haplotype cluster structure; (iv) in addition, we compare the haplotype cluster structure obtained through CFHLC with those output by four other algorithms; (v) relying on both real and simulated data, we demonstrate the ability of FHLCMs to concisely model SNP dependences, showing that the multiple layers of the model can take into account different LD degrees and haplotype diversity; (vi) finally, we present a thorough study focused on both scalability and impact of adjustment of the input parameters of CFHLC algorithm.
As a prerequisite to further understanding, Section Preliminaries provides an informal definition of Bayesian networks, focusing on latent class models and hierarchical latent class models. Then the Section dedicated to the state of the art first points out the few anterior works devoted to HLCM learning in general. This section ends with a short review of the few attempts to implement probabilistic graphical models for the specific purpose of LD modeling. Section Methods motivates the modeling of LD through FHLCMs and informally describes such models. Then, the focus is set on the general outline of the method proposed for FHLCM learning. The next Section depicts the sketch of algorithm CFHLC. The last Section is dedicated to experimental results and discussion. In this Section, we first test and discuss the ability of FHLCMs to accurately represent the haplotype cluster structure of genetic data. Then, we compare our algorithm to other methods with respect to faithfulness in LD modeling and data dimension reduction. We end the Section with a thorough study centered on scalability and influence of the input parameters of the CFHLC algorithm.
Bayesian networks are probabilistic graphical models. They are defined by a directed acyclic graph (DAG), G(X, E), and a set of parameters, θ. The set of nodes = {X1, ..., X n } represents n random variables and the set of edges E captures the conditional dependences between these variables (i.e. the structure). The variables are either observed or latent. The set of parameters θ is a matrix of conditional probability distributions where denotes node i's parents. If a node has no parent, then it is described by an a priori probability distribution. For further understanding, we now briefly introduce the concepts of marginal independence and conditional independence between two variables.
Definition 1 The marginal independence between two variables X i and X j is defined referring to the joint distribution P(X i , X j ): P(X i , X j ) = P(X i ) P(X j ).
A non-equality implies that X i and X j are marginally dependent.
Definition 2 More restrictive, the definition of conditional independence between two variables X i and X j given a subset of variables ⊆ X\ {X i , X j } is the following: P(X i , X j |) = P(X i |) P(X j |).
A non-equality implies that X i and X j are conditionally dependent given S.
State of the art
HLC model learning
Various methods have been conceived to tackle HLCM learning. These approaches differ by the following points: (i) structure learning; (ii) determination of the latent variables' cardinalities; (iii) learning of parameters, i.e. a priori and conditional probabilities; (iv) scalability; (v) main usage.
As for general BNs, besides learning of parameters (θ), i.e. a priori and conditional probabilities, one of the tasks in HLCM learning is structure () inference. This task generally remains the most challenging due to the complexity of the search space. To address this issue, two main categories of HLCM learning methods have been developed. The first category, structural expectation maximization (SEM), successively optimizes θ conditional on and conditional on . Amongst a few proposals, greedy search [24] and dynamic programming [25] were designed. They explore the space of possible graphs guided by a scoring function, such as the Bayesian information criterion (BIC) [26]. When using maximum likelihood estimation, the BIC score prevents model overfitting through a penalty term on the number of parameters in the model. As regards greedy search, the search space of HLCM structures can be visited through two operations: a structure in the neighborhood of the current structure may either result from the addition or the removal of latent nodes or from the addition or the dismissing of states, for existing nodes. In the other solution, implemented in Gerbil algorithm, dynamic programming discovers the best segmentation of a genomic region into blocks of contiguous SNPs. Then, for each previously learned block, an LCM is learned. Alternative approaches implement ascending hierarchical clustering (AHC), which provides clusters within which the SNPs are not necessarily contiguous. In the following, we will use the terms "blocks" and "clusters" to distinguish between these two possibilities. Relying on pairwise dependence strength, Wang and co-workers first build a binary tree; then they apply regularization and simplification transformations which may result in subsuming more than two nodes through a latent variable [27]. Hwang and collaborators' approach confines the HLCM search space to binary trees augmented with possible connections between siblings (nodes sharing the same parent into immediate upper layer) [28]. To construct the tree, they design an AHC strategy. First, a partition of the observed variables into clusters of size 2 is performed, based on a mutual information criterion. Any such cluster then defines a new LCM (thus a new LV) in the upper layer under construction. Second, the parameters of each LCM are learned. Thus missing values of LVs can be imputed. Therefore these LVs can be considered as observed variables for the next step. A tree is completed through the iteration of these two steps (partitioning, missing value imputation).
Parameter learning requires the determination of the LVs' cardinalities, e.g. the number of possible states (or classes) for each LV. A simple method is to arbitrarily set a small value for the cardinality. Following this idea, Hwang and collaborators constrain LVs to binary variables. This method is very fast but presents several drawbacks: on the one hand, a too small cardinality can lead to a loss of information in the process subsuming child variables into a unique LV; on the other hand, a too large cardinality can entail model overfitting and heavy computational burden. Wang and co-workers propose a regularization step to reduce the cardinality of an LV Y, knowing the cardinality of its neighbor variables . Other authors use a greedy search approach, starting with a preset value and incrementing or decrementing it to meet an optimal criterion. The latter method has the drawback of entailing computational overload because it runs several steps of the expectation maximization (EM) algorithm, implementing an iterative procedure.
Usually, the EM algorithm is used for parameter learning in the presence of LVs or missing data, but it is computationally expensive and does not guarantee that the global optimum will be reached. To speed up the EM process, Hwang and collaborators implemented a heuristic based on partial imputation of binary LVs' missing values. Thus, the EM algorithm is actually run on partially imputed data. For an LCM containing two child variables Y j and Y k , the heuristic is the following: all individuals showing the most probable configuration of {Y j , Y k } are assigned an LV value of 0. Similarly, the individuals characterized with the second most probable configuration are assigned an LV value of 1. To avoid getting trapped in local optima while running the EM algorithm to learn a set of latent models, other authors adapted a simulated annealing approach [16].
Hwang and co-workers' approach is the only one we are aware of that succeeds in processing high-dimensional data: in an application dealing with a microarray dataset, more than 6000 genes have been processed for around 60 samples. To the best of our knowledge, no running time was reported for this study. Nevertheless, the twofold binarity restriction (binary tree, binary LVs) and the lack of control for information decay as the level increases are severe drawbacks to reach our aims: i.e. to achieve realistic SNP dependence modeling and perform subsequent association study with sufficient power.
Graphical models for LD modeling
To address LD modeling through a probabilistic graphical model framework, various models were proposed: hidden Markov models (HMM), Markov random fields and Bayesian networks with or without latent variables. HMMs represent simple but efficient models to partition a SNP sequence into blocks, because no structure learning step is required [29] and the latent states may represent common haplotypes. Verzilli and co-workers modeled SNP dependences using Markov random fields [12]. They designed an MCMC (Markov Chain Monte Carlo) method to sample over the space of possible graphs while exploiting prior biological knowledge. Their approach allows to discover cliques of dependent SNPs, to further allow the identification of causal relations between markers and the disease status indicator. To implement a tractable method for genome-wide data, Verzilli and co-workers reduce the space of possible graphs by specifying a maximal physical distance between SNPs belonging to the same clique, as well as a maximal size of 8 SNPs for any clique. In the family of Bayesian networks without LVs, HaploBlock implements a statistical model of haplotype block variation [13]. This model's advantage lies in integrating population genetics concepts such as recombination hotspots, bottleneck, genetic drift and mutations. Another method, BNTagger, was developed for SNP tag selection; it exploits conditional independence between variables [14]. To learn the structure, BNTagger implements a greedy search with random restarts; then it determines a subset of independent and highly predictive SNPs. The two latter methods were only tested on a small number of SNPs (less than 1000) and the authors reported running times of 40 h for 97 SNPs [13] and between 2 and 4 h for only 52 SNPs [14]. Thus, these methods do not seem fitted to GWAS data processing. Regarding the family of Bayesian networks with LVs, Nefian modeled SNP dependences through embedded Bayesian networks. Her model is indeed a set of LCMs augmented with SNP-SNP dependences and LV-LV dependences [15]. To learn the model, the SNP data sequence is split into contiguous windows of fixed common size. Then, for each window, an LCM is created. The lack of flexibility of the SNP partitioning method used remains a severe draw-back. Zhang and Ji also proposed to model LD through a set of LCMs, using an SEM strategy [16]. Their method does not require splitting the sequence into fixed-size windows. Nevertheless, the number of LCMs has to be specified. As far as we know, no execution times were reported for the two latter approaches when run on high-dimensional data.
Other methods are based on regularization, such as the graphical Lasso [30], and have been applied to learning sparse PGMs for proteomics or gene expression studies, whose data dimensionality is high (around 5000 variables) but lower than that of genome-wide data (above 100000 variables). The basic idea is to consider that the observations follow a multivariate Gaussian distribution with mean µ and covariance matrix Σ (graphical Gaussian model). If the ij th partial correlation coefficient of the precision matrix Σ-1 equals zero, variables i and j are conditionally independent, given the other variables. The use of Lasso aims at restraining the learning task to sparse PGMs through finding a least-square solution under the following constraint: ∑ν|β ν | ≤ t, meaning that the sum, over the whole variable set, of the absolute values of the regression coefficients v has to be inferior or equal to a constant t. This Lasso-based approach has been extended to the case where it is reasonable to assume that the variables can be clustered into groups sharing similar correlation patterns (corresponding to underlying biological modules in gene expression) and where sparse block-structured precision matrices are estimated [31].
To our knowledge, Verzilli et al' s method is the only one whose tractability regarding GWAS data is known. However, in practice, their MRF modeling reveals a drawback. The LD is modeled through cliques containing a maximum number of 8 SNPs, whereas, generally, several tens or hundreds of SNPs may be dependent. Furthermore, no dependences between cliques are taken into account. In contrast, BNs with LVs offer a crucial advantage over other models: they provide synthesizing variables useful to reduce data dimensionality. However, when the number of variables exceeds several hundreds, implementing the SEM approach for LD modeling leads to prohibitive computational burden. When dealing with genome-wide data, the imperious requirement for tractability leads us to choose a hierarchical clustering approach, in the line of Hwang and co-workers.
Motivation of the FHLC model for GWASs
The HLCMs offer several advantages for GWASs. First, beside data dimensionality reduction, they allow a simple test of direct dependence between an observed variable and a target variable such as the phenotype, conditional on the latent variable, parent of the observed variable. Note that the phenotype variable is not included in the HLCM. In the context of GWASs, this test helps find the markers which are directly associated with the phenotype, i.e. causal markers, should there be any. Second, HLCMs can deal with the fuzzy nature of LD blocks. Indeed, HLCMs can take into account various degrees of LD strength between any two SNPs, depending on the height of their lowest common LV node ancestor in the tree. Thirdly, the hierarchical structure allows zooming in through narrower and narrower regions in search for a stronger association with the disease, thus offering a data mining tool. This zooming process ends pointing out the potential markers of interest. Finally, the latent variables may be interpreted in terms of biological meaning. For instance, in the case of haplotypes, that is, phased genotypes, the latent variables are likely to represent the so-called haplotype block structure of LD. To a certain extent, an LV might be interpreted as the shared ancestry of the haplotypes defined by the observed variables, namely, the contemporary haplotypes of the tree rooted in the LV. Each state of an LV may represent a group of similar haplotypes. In the situation of limited ancestral recombination, similar haplotypes tend to share recent common ancestry. Although this situation is not guaranteed along the genome, it is very likely for low-level LVs, since they are expected to cover very small genomic regions showing strong LD. Thus the directed edges, LV → SNP, can represent causal effects and provide a biological sense. Besides, it has to be noted that when the latent variables capture dependences between distant SNPs (or distant groups of markers), they can be viewed as population structure.
However, SNP dependences would better be more wisely modeled through a forest of HLCMs. In the case of a forest, higher-order dependences are captured only when relevant, i.e when meeting a strength criterion. Therefore, FHLCMs allow to model a larger set of configurations than HLCMs do. Typically, an HLCM is limited to represent clusters of close dependent SNPs. Actually, in this model, variables are constrained to be dependent upon one another, either directly or indirectly. Consequently, HLCMs cannot account for potential independence between groups of distant SNPs or SNPs located on different chromosomes. But realistic modeling requires a more flexible framework. For instance, the LD plot of the 2 Mb sequence shown in Additional file 5 reveals that the greatest part of LD is observed between SNPs in vicinity. LD rarely exceeds 500 kb between SNPs.
Principle of FHLC model construction
Our method can process both genotypic (unphased) and haplotypic (phased) data. It takes as an input a matrix defined on a finite discrete domain, say {0, 1, 2} for unphased SNPs or {0, 1} for phased SNPs, describing n individuals through p variables = {X1, ..., X p }. Algorithm CFHLC yields an FHLCM, that is a forest structure and θ, the parameters of a set of a priori distributions and local conditional distributions allowing the definition of the joint probability distribution. Two search spaces are explored: the space of directed forests and the probability space. In addition, the whole set of latent variables H of the FHLCM is output, together with the associated imputed data matrix.
Along with a hierarchy-based proposal of Hwang and collaborators [28] developed for gene expression studies, our method also implements data subsumption, meeting the two following additional requirements: (i) a more flexible thus more faithful modeling of underlying reality, (ii) a control of information decay due to subsumption.
Node partitioning
An algorithm meeting this purpose has already been described in the literature: BenDor and co-authors designed CAST, a clique partitioning algorithm devoted to variable clustering [33]. They especially applied CAST for gene expression clustering. As an input, CAST requires a binary similarity matrix. The adaptation of CAST to our case is straightforward: the dependence measure between two SNPs, evaluated through mutual information, is used as a similarity measure. All mutual information values less than a threshold t MI are assigned a similarity value of 0, whereas the others are assigned a value of 1. As a threshold t MI , the median value (or another quantile value) of the mutual information matrix can be used. Then, the CAST algorithm constructs the clusters one at a time. The authors define the affinity a(x) of an element x to be the sum of similarity values between x and the elements present in the current cluster . x is an element of high affinity if it verifies inequality a(x) ≥ t CAST ||, where t CAST is a specified similarity threshold. Otherwise, x is considered an element of low affinity. To summarize, the algorithm alternates between adding high affinity elements to and removing low affinity elements from it. When the process stabilizes, is closed. A new cluster can be started.
Determining cardinalities for latent variables
A steep task is choosing - ideally optimizing - the cardinality of each LCM's latent variable. This problem cannot be remedied using greedy search because of its intractability regarding high data dimensionality. Although the regularization method of Wang and collaborators has the advantage of being very quick (see Subsection HLC model learning), in our context, their method is impracticable. For instance, let us consider an HLCM learned from genome-wide data. The first layer of the HLCM contains the majority of the LVs in the model. In our case, an LV in the first layer can subsume more than 10 child OVs (i.e. SNPs). As the cardinality is the same for all OVs (3 possible genotypes: 0, 1, 2), the resulting cardinality of the LV after regularization remains generally very large. For example, for an LCM containing 10 OVs {X1 , ..., X10 } of cardinalities equal to 3, the cardinality of the LV H would be : |H| = 310/3 = 39 = 19683. The simplest solution remains to arbitrarily set a small value for LV cardinalities, but it has several drawbacks (see Subsection HLC model learning). Instead of using an arbitrary constant value common to all latent variables, we propose that the cardinality be estimated for each latent variable through a function of the underlying cluster's size. The rationale for choosing this function is the following: the more child nodes a latent variable has, the larger the total number of possible combinations is for the values of the child variables and the larger also is the expected number of such combinations observed over all individuals (when the number of individuals is sufficiently high). Therefore, the cardinality of this latent variable should depend on the number of child nodes. Nonetheless, to keep the model complexity within reasonable limits, a maximum cardinality is fixed.
Parameter learning and imputation
Parameter learning is carried out step by step, each time generating additional latent variables and imputing their values for each individual. At i th step, this task simply amounts to performing parameter learning for as many LC models as there are clusters of variables identified. We recall that the nodes in the topology of an LCM are reduced to a unique root and leaves. Therefore, at i th step, each LCM's structure is rooted in a newly created latent variable. When latent variables are the source nodes in a BN, parameter learning may be performed through a standard EM procedure. This procedure takes as an input the cardinalities of the latent variables and yields the probability distributions, that is, prior distributions for those nodes with no parents and distributions conditional to parents for the remaining nodes. After imputing the missing data corresponding to latent variables, new data are available to seed the next step of the FHLCM construction: latent variables identified through step i will be considered as observed variables during step i + 1.
It has to be noted that designing an imputation method to infer the values of the latent variable for each individual is a matter for investigation. Once the prior and conditional distributions have been estimated for a given LCM, probabilistic inference in BNs may be performed. A straightforward way would consist in imputing the latent variable value for each individual as follows: . However, in the framework of probabilistic models, this deterministic approach is disputable. In contrast, a more convincing alternative will draw a value h for latent variable H, knowing the probabilities for each individual.
Controlling information decay
Conversely to Hwang and co-workers' approach, which mainly aims at data compression, information decay control is required: in step i, any candidate latent variable H which does not bear sufficient information about its child nodes must be invalidated. As a consequence, such child nodes will be seen as isolated nodes in step i + 1.
Let us consider two variables X and H. Basically, the mutual information measures the difference of entropies between the independent model P(X) P(H) and the dependent model P(X|H) P(H): . Therefore, the mutual information measures the dependence of the two variables. The larger the difference between entropies, the higher is the dependence. Now, let us consider a set of child variables = {X1, X2, ..., X n } and the parent variable H. In our case, we want to compare the two models: P(X1) P(X2) ... P(X n ) P(H) and P(X1|H) P(X2|H) ... P(X n |H) P(H). Thus, Δ, the difference of entropies between the two models is: . Δ corresponds to the sum of mutual information values over all LCM's edges.
Normalization through entropy and averaging are performed to provide a more intuitive criterion: , with S H the size of cluster (H). represents the average percentage of information captured by the LV with respect to its child variables.
The sketch of CFHLC is presented in Algorithms 1 and 2. The user may tune seven parameters. Window size s specifies the number of contiguous SNPs - or variables - spanned per window. The aforementioned criterion C is meant to estimate information decay, thus allowing information dilution to be constrained to a minimal threshold t. Parameters a, b and card max participate in the calculus of the cardinality of each latent variable. Finally, parameter PartitioningAlg enables flexibility in the choice of the method dedicated to clustering highly-correlated variables into non-overlapping groups.
Within each window i, the AHC process is initiated from the first layer consisting of univariate models. Each such univariate model is built for any observed variable in the set (lines 6 to 8). The AHC process stops if each cluster identified is reduced to a singleton (line 13) or if no cluster of size strictly greater than 1 could be validated (line 31). Each cluster containing at least two nodes is subject to LCM learning (lines 19 and 20) followed by validation (line 23 to 28). In order to simplify the FHLCM learning, the cardinality of the latent variable is estimated as an affine function of the number of variables in the corresponding cluster (line 19). Algorithm learn_latent_class_model is plugged into this generic framework (line 20). After validation through threshold t (lines 23 and 24), the LCM is used to enrich the FHLCM associated with the current window (line 25): a specific merging process links the additional node corresponding to the latent variable to its child nodes, themselves already present in the FHLCM structure under construction; the prior distributions of the child nodes are replaced with distributions conditional on the latent variable. The newly created latent variable, , is added to the set of latent variables, whereas its imputed values, , are stored (line 26). In , the variables in are now replaced as a whole with the corresponding latent variable; data matrix is updated accordingly (line 27). In contrast, the nodes in unvalidated clusters are kept isolated for the next step. Finally, the collection of forests, DAG, is successively augmented with each forest built within a window (line 36). In parallel, due to assumed independence between windows, the joint distribution of the final FHLCM is merely computed as the product of the distributions associated with the windows (line 36).
s , a window size,
t, a threshold used to constrain information dilution, based on criterion ,
PartitioningAlg, an algorithm dedicated to partition a set of variables into non-overlapping clusters of variables,
a; b and card max , parameters used to estimate the cardinality of latent variables.
DAG and θ, respectively the DAG structure and the parameters of the FHLCM constructed,
L, the whole set of latent variables identified through the construction (L = {L1,...,L m }), , the corresponding data imputed for n individuals.
1: nbw← p/s /* computation of the number of contiguous windows */
2: DAG ←∅; θ ← ∅; L← ∅; D L ← ∅
4: for i = 1 to nbw
5: /* processing of layer 0 */
10: step ← 1
11: while true
13: if all clusters are singletons then break end if
16: nbValidClusters ← 0
18: for k = 1 to
19: card LV ← min(a × number_of_variables () + b; card max )
20: {DAG jk , θ jk , L jk ,
22: /* validation of current cluster - see Subsection Controlling information decay */
23: if
24: incr(nbValidClusters)
25: DAG i ← merge_structures(DAG i , ); θ i ← merge_parameters(θ i , )
28: end if
29: end for
31: if (nbValidClusters = 0) then break end if
33: incr(step)
34: end while
36: DAG ← DAG ∪ DAG i ; θ ← θ × θ i
37: end for
Algorithm 1: CFHLC
card LV : the cardinality of the latent variable to be created.
a latent class model described by:
DAG u and θ u , respectively the structure and the parameters of the latent class model,
L u , a latent variable,
1: Lu ← create_latent_variable()
2: DAG u ← build_structure_of_latent_class_model(L u , )
3: θ u ← run_standard_EM(DAG u , , card LV )
4: ← impute_data(θ u , )
Algorithm 2: learn latent class model
Experimental results and discussion
Algorithm CFHLC has been developed in C++, relying on the ProBT library dedicated to BNs http://bayesian-programming.org. We have plugged into CFHLC a C++ implementation of CAST based on the original implementation provided in JAVA by Ben Fry http://benfry.com/clustering/. Regarding the visualization of the DAGs, the software Tulip http://tulip.labri.fr/TulipDrupal/ was chosen, meeting both high representation quality and compactness requirements. CFHLC was run on a standard PC (3.8 GHz, 3.3 GB of RAM).
Experimental protocol
The performance of the FHLCM-based method is evaluated using real phased and unphased genetic data, on the one hand, and simulated phased and unphased genetic data on the other hand.
For real data analysis, the well-known Daly et al. dataset [29], available at http://www-genome.wi.mit.edu/humgen/IBD5/index.html, was used. This dataset consists of 129 trios, each composed of two parents and one child. For each individual, 103 SNPs are genotyped in the 5q 31 region and cover 617 kb. We only analyzed the child data.
Regarding simulated data, two well known programs were used: HAPGEN and HAP-SIMU. Using HAPGEN http://www.stats.ox.ac.uk/~marchini/software/gwas/hapgen.html, we generated 2000 unrelated individuals (i.e. 4000 haplotypes) for a several hundreds kb region containing around 20-30 SNPs. The haplotypes used as references come from the HapMap phase II and concern U.S. residents of northern and western European ancestry (CEU) http://hapmap.ncbi.nlm.nih.gov/. Five sequences showing variable LD degrees (median(r2)) ranging from 0.007 to 0.5 were generated.
HAPSIMU http://l.web.umkc.edu/liujian/ was used to simulate genotypes with simulation parameters described in Additional file 6. Three sample sizes were chosen with respect to the number of observed variables: 1 k, 10 k and 100 k SNPs (in all cases, the number of individuals was set to 2000). For each sample size, twenty benchmarks were generated. For these experimentations, imputation of LVs' values was achieved by assigning the most probable values given the observations. A drawback of this method is the loss of probabilistic relation between a variable and its parent variable. A definite advantage lies in its running in around half the time required by imputation through simulation (results not shown).
LD modeling
Real data
Regarding the Daly benchmark, our aim was to evaluate how the forest obtained keeps up with the real structure of the biological data. Moreover, the CFHLC algorithm was compared to four other methods.
Running time
Dimension reduction rates
Entropy compression rates
Daly et al.'s method
40 s
158 mn
Zhang et al.'s algorithm
168 s
84 s
We expected to observe a correspondence between the three plots. The outstandingly clear correspondence demonstrates the ability of FHLCMs to accurately model multiple levels of LD strength: the overall majority of the LD plot dependences are also present in the MRCA level matrix. Interestingly, modeling from genotype data leads to quite good results compared to haplotype data.
Simulated data
Scalability for GWASs
Scalability has been studied through the data simulated with HAPSIMU. In the hardest case (100 k SNPs), Additional file 7 shows that only 15 hours are required with a window size s set to 100. For the same dataset processed in the cases "s = 200" and "s = 600", running times are 20.5 h and 62.5 h, respectively. For the same number of OVs (100 k), Wang et al. report running times of about two months. Regarding the 10 k case, running times are 1.3 h, 2 h and 5.8 h for "s = 100", "s = 200" and "s = 600", respectively.
Further analysis of CFHLC algorithm
Finally, many other experimentations are reported with their commentaries in additional files 8, 9, 10, 11, 12, 13, 14 and 15. Additional file 8 focuses on the impact of window size on running time. In additional file 9 data dimension reduction is evaluated from the distribution of LVs over the forest's layers. Additional files 10, 11, 12 and 13 study the impact of window size on the number of roots in the forest, the number of LVs, the number of layers and the distribution of LVs over the forest's layers, respectively. Additional files 14 and 15 analyze how information fades while the layer number increases.
An important result is that CFHLC can achieve a data dimensionality reduction of more than 80% of the number of observed variables (see Additional file 10). Regarding spatial complexity of CFHLC algorithm, the entire FHLCM does not require to be stored in RAM because each window can be saved on the hard disk.
Our contribution in this paper is twofold: (i) a new framework has been described, which was tested on both real and simulated data and was proven able to concisely model LD and to reduce SNP data dimensionality; (ii) CFHLC, an algorithm dedicated to learn FHLCMs, has been shown to be efficient when run on genome-scaled benchmarks.
Compared to Verzilli and co-workers' works, our algorithm provides a more accurate modeling of LD and synthesizes genetic marker information through LVs. In addition, unlike Nefian or Zhang and Ji, our method does not require to specify the number of LCMs and can capture multiple levels of dependences, thus taking into account the fuzzy nature of LD. To our knowledge, our hierarchical method is the first one shown to achieve fast model learning for genome-scaled data sets, while maintaining satisfying information scores and relaxing the twofold binarity restriction of Hwang and collaborators' model (binary trees, binary latent variables). Hwang and collaborators' purpose is only data compression. We are faced with a more demanding challenge: to make a sufficiently powerful down-stream association analysis possible.
In discussing the biological interpretation of latent variables, we mentioned the potentiality of FHLCMs for population substructure description. In essence, using hierarchical models is highly appealing to take into account the long-range LD expected in substructured populations. However, this interesting use of such hierarchical models as FHLCMs is somewhat precluded by the technical necessity to partition the genome into small regions. As a first palliative, we indicated two strategies (adjusting the window size, diminishing the density) to cope with this current technical limitation. However, the strong expectation for faithful substructure modeling through FHLCMs advocates further efforts to clear the hurdle on path to realistic long-range LD modeling.
A bottleneck currently lies in the clique partitioning method chosen, which forbids window sizes encompassing more than 600 observed variables. In addition to investigating alternative partitioning methods, a lead to cope with this bottleneck may be to adapt the specific processings at the limits of contiguous windows or use overlapping windows.
In short, FHLCMs can be used to resolve several major problems in the GWASs' context. Beside flexible data dimension reduction through FHLCMs' LVs, fine mapping of causal SNPs is expected thanks to conditional independence properties encoded in such models. For instance, FHLCMs' LVs can be used to condition tests for independence between a SNP and the phenotype. Moreover, due to their hierarchical structure, FHLCMs represent an original and appropriate solution to study long-range LD in substructured populations, a recurring problematic in GWASs. Finally, genome-wide visualization of LD can be easily achieved with these models using a graph visualization tool and will provide an intuitive representation of SNP - SNP dependences as well as information synthesis through latent variables.
In this current version of CFHLC, when processing haplotype data, we were not interested in knowing the sequence of each ancestral haplotype. We just wanted to know from which ancestral haplotype (i.e. from which haplotype cluster) a contemporary haplotype comes. Nevertheless, it is feasible to infer the sequence of each ancestral haplotype using probabilistic inference.
Finally, although our modeling is designed for GWAS data, we emphasize that it could be applied to other data presenting spatial dependences between variables, in particular sequential data. Beyond this specific case, it would be interesting to assess the model's generality, in order to determine if it can be applied to generic graphical model learning.
The authors are grateful to two anonymous referees for constructive comments and help in improving their manuscript. The authors would also like to thank Christian Dina and Jean-Jacques Schott (INSERM, French National Institute for Health and Medical Research) for useful discussions regarding genetics and statistical genetics. Yulong Zhang (Tsinghua University, Beijing) has to be thanked for his precious help in the comparison between his algorithm and CFHLC algorithm. RM's work was supported by a grant from the BIL Bioinformatics Research Project of Pays de la Loire Region, France.
Authors' Affiliations
- Morris AP, Cardon LR: Handbook of statistical genetics, Whole genome association. Volume 2. 3rd edition. Wiley Interscience; 2007:1238–1263.View ArticleGoogle Scholar
- Balding DJ: A tutorial on statistical methods for population association studies. Nature Genetics 2006, 7: 781–790. 10.1038/nrg1916View ArticleGoogle Scholar
- International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 2007, 449(7164):851–861. [http://dx.doi.org/10.1038/nature06258] 10.1038/nature06258View ArticleGoogle Scholar
- DeWan A, Klein RJ, Hoh J: Linkage disequilibrium and association mapping: analysis and applications, Linkage disequilibrium mapping for complex disease genes. Volume 376. Humana Press; 2007:85–107.Google Scholar
- Patil N, Berno AJ, Hinds DA, Barrett WA, Doshi JM, Hacker CR, Kautzer CR, Lee DH, Marjoribanks C, McDonough DP, Nguyen BT, Norris MC, Sheehan JB, Shen N, Stern D, Stokowski RP, Thomas DJ, Trulson MO, Vyas KR, Frazer KA, Fodor SP, Cox DR: Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science (New York, N.Y.) 2001, 294(5547):1719–1723. [http://dx.doi.org/10.1126/science.1065573] 10.1126/science.1065573View ArticleGoogle Scholar
- International HapMap Consortium: The international HapMap project. Nature 2003, 426(6968):789–796. [http://www.nature.com/nature/journal/v426/n6968/full/nature02168.html] 10.1038/nature02168View ArticleGoogle Scholar
- Schaid DJ: Evaluating association of haplotypes with traits. Genetic Epidemiology 2004, 27: 348–364. 10.1002/gepi.20037View ArticlePubMedGoogle Scholar
- Pattaro C, Ruczinski I, Fallin DM, Parmigiani G: Haplotype block partitioning as a tool for dimensionality reduction in SNP association studies. BMC Genomics 2008, 9: 405. [http://dx.doi.org/10.1186/1471–2164–9-405] 10.1186/1471-2164-9-405PubMed CentralView ArticlePubMedGoogle Scholar
- Han B, Kang H, Seo M, Zaitlen N, Eskin E: Efficient association study design via power-optimized tag SNP selection. Annals of Human Genetics 2008, 72(6):834–847. [http://dx.doi.org/10.1111/j.1469–1809.2008.00469.x] 10.1111/j.1469-1809.2008.00469.xPubMed CentralView ArticlePubMedGoogle Scholar
- Liang Y, Kelemen A: Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases. Statistics Surveys 2008, 2: 43–60. 10.1214/07-SS026View ArticleGoogle Scholar
- Laramie JM, Wilk JB, DeStefano AL, Myers RH: HaploBuild: an algorithm to construct non-contiguous associated haplotypes in family based genetic studies. Bioinformatics 2007, 23: 2190–2192. 10.1093/bioinformatics/btm316PubMed CentralView ArticlePubMedGoogle Scholar
- Verzilli CJ, Stallard N, Whittaker JC: Bayesian graphical models for genome-wide association studies. The American Journal of Human Genetics 2006, 79: 100–112. 10.1086/505313View ArticlePubMedGoogle Scholar
- Greenspan G, Geiger D: High density linkage disequilibrium mapping using models of haplotype block variation. Bioinformatics 2004, 20: 137–144. 10.1093/bioinformatics/bth907View ArticleGoogle Scholar
- Lee PH, Shatkay H: BNTagger: improved tagging SNP selection using Bayesian networks. Bioinformatics 2006, 22(14):211–219. 10.1093/bioinformatics/btl233View ArticleGoogle Scholar
- Nefian AV: Learning SNP dependencies using embedded Bayesian networks. IEEE Computational Systems, Bioinformatics Conference 2006.Google Scholar
- Zhang Y, Ji L: Clustering of SNPs by a structural EM algorithm. International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing 2009, 147–150.Google Scholar
- Freidlin B, Zheng G, Li Z, Gastwirth JL: Trend tests for case control studies of genetic markers: power, sample size and robustness. Human Heredity 2002, 53: 146–152. 10.1159/000064976View ArticlePubMedGoogle Scholar
- Hoggart CJ, Whittaker JC, De Iorio M, Balding DJ: Simultaneous analysis of all SNPs in genome-wide and resequencing association studies. PLoS Genetics 2008, 4: 1–8. 10.1371/journal.pgen.1000130View ArticleGoogle Scholar
- Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003, 19: 376–382. 10.1093/bioinformatics/btf869View ArticlePubMedGoogle Scholar
- Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ: An integrative approach to infer causal associations between gene expression and disease. Nature Genetics 2005, 37: 710–717. 10.1038/ng1589PubMed CentralView ArticlePubMedGoogle Scholar
- Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KCC, Taylor J, Burnett E, Gut I, Farrall M, Lathrop MG, Abecasis GR, Cookson WOC: A genome-wide association study of global gene expression. Nature Genetics 2007, 39: 1202–1207. 10.1038/ng2109View ArticlePubMedGoogle Scholar
- Mourad R, Sinoquet C, Leray P: Learning hierarchical Bayesian networks for genome-wide association studies. 19th International Conference on Computational Statistics (COMPSTAT) 2010, 549–556.Google Scholar
- Zhang NL: Hierarchical Latent Class models for cluster analysis. The Journal of Machine Learning Research 2004, 5: 697–723.Google Scholar
- Zhang NL, Kocka T: Efficient learning of Hierarchical Latent Class models. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2004, 585–593. full_textGoogle Scholar
- Kimmel G, Shamir R: Gerbil: genotype resolution and block identification using likelihood. Proceedings of the National Academy of Sciences of the United States of America 2004, 102: 158–162. 10.1073/pnas.0404730102PubMed CentralView ArticlePubMedGoogle Scholar
- Schwartz G: Estimating the dimension of a model. The Annals of Statistics 1978, 6(2):461–464. 10.1214/aos/1176344136View ArticleGoogle Scholar
- Wang Y, Zhang NL, Chen T: Latent tree models and approximate inference in Bayesian networks. Machine Learning 2006, 32: 879–900.Google Scholar
- Hwang KB, Kim BH, Zhang BT: Learning hierarchical Bayesian networks for large-scale data analysis. ICONIP 2006, 670–679.Google Scholar
- Daly MJ, Rioux JD, Schaffner SF, Hudson TJ, Lander ES: High-resolution haplotype structure in the human genome. Nat Genet 2001, 29(2):229–232. [http://dx.doi.org/10.1038/ng1001–229] 10.1038/ng1001-229View ArticlePubMedGoogle Scholar
- Friedman J, Hastie T, Tibshirani R: Sparse inverse covariance estimation with the graphical Lasso. Biostat 2008, 9(3):432–441. [http://dx.doi.org/10.1093/biostatistics/kxm045] 10.1093/biostatistics/kxm045View ArticleGoogle Scholar
- Marlin BM, Murphy KP: Sparse Gaussian graphical models with unknown block structure. ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning New York, NY, USA: ACM; 2009, 705–712. [http://dx.doi.org/10.1145/1553374.1553465]Google Scholar
- Martin J, Vanlehn K: Discrete factor analysis: learning hidden variables in Bayesian networks. Tech. rep Department of Computer Science, University of Pittsburgh; 1995. [http://www.public.asu.edu/~kvanlehn/Not%20Stringent/PDF/94TR_JDM_KVL.pdf]Google Scholar
- Ben-Dor A, Shamir R, Yakhini Z: Clustering gene expression patterns. Proceedings of the third annual international conference on Computational molecular biology 1999, 33–42.Google Scholar
- Stephens M, Scheet P: Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. American journal of human genetics 2005, 76(3):449–462. [http://dx.doi.org/10.1086/428594] 10.1086/428594PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
All Seasons Roofing & Construction
Serving the Treasure Valley for 31 Years
All Seasons Roofing is a full service roofing company with high quality workmanship, over ten years in the Treasure Valley, and strong dedication to customer relations. Because of these strengths, we are able to offer you a 5-year guarantee for complete customer satisfaction.
All Seasons Roofing & Construction has served hundreds of Residential and Commercial customers throughout Boise, Nampa, Caldwell, Meridian, Eagle, Star and the entire Treasure Valley. We provide timely and thorough service through project completion.
All Seasons Roofing & Construction's full service roofing includes: Tear Offs, Recovers, Wind Damage, Leaks, Shake Repair and Oil. We also do rain gutters and Roof Inspection & Certifications.
Call All Seasons Roofing & Construction today
to discuss all your roofing needs.

subset: commoncrawl


text:
The Advantages of Purchasing Foreclosed Homes for Sale in Southwood
If ever you have plans of purchasing homes for sale, which would you like newly built properties or foreclosed ones? What are the reasons for their preference of buying foreclosed homes for sale? No matter what you opted to purchase, foreclosed and newly constructed homes for sale have its own advantages. In case you have the intent of purchasing houses for sale in the coming days but still confused on what type of real estate property to purchase, then you can consider reading this article further. Check out
to get started.
Unveiling More Information About Foreclosed Homes for Sale
Foreclosed homes for sale are real estate properties which are previously owned by homeowners but are foreclosed by banks and lending companies for failure to pay their regular mortgage amortization. These properties are placed on sale by these finance companies to recoup the money they lend to these homeowners. Yes, it is true that you can come across plenty of doubts and skepticisms of lots of property owners, investors and homebuyers but it hasn't stopped those from buying these properties simply because of the rewards they can reap from these properties. Though, most of these real estate properties are offered for sale in the market due to negative reasons, these are all erased the moment you know the wonderful benefits that you can reap from investing in these properties. You wouldn't believed that there comes a time when these properties are more popular and in demand due its practicality and profitability compared to purchasing newly constructed or regular homes. Detailed below are the different rewards that you can obtain from investing and purchasing foreclosed homes for sale in Southwood.
Knowing the Perks of Investing and Buying Southwood Foreclosed Homes for Sale
1. It is advantageous to invest in foreclosed properties simply because property owners and homeowners can obtain at least 50% discount from these properties.
2. It is one effectual method of investing and purchasing your dream property at affordable prices. With research and diligence, it is possible for you to find your dream apartment units, condominium units, townhouses with
community amenities
and mansions in Southwood. In fact, investing in foreclosed homes for sale are not only famous and widely sought after in the United States but also in other nations around the globe.
3. You can obtain much lower interest rates when you decided to buy foreclosed properties by means of financing or mortgage loans.
4. You can find growing number of investors who decided to buy foreclosed houses for sale simply because they can obtain higher profits from it, especially when they purchase these properties and offer it at much higher price to interested homebuyers.
If you want to reap all the benefits promised and detailed above, then you can invest in foreclosed properties.
Check out
for more information.


text:
April 7-14,2014 Mercury charges into Aries. Fresh, brave, bold, thoughts will rush your mind for the next two weeks. This is a wonderful chance to assert yourself, your thoughts and ideas are in need of authentic expression with this Aries emphasis you will have a chance to be you, uncensored. This is the path to happiness.
We are feeling the influence of the Cardinal Grand Cross that is coming into tight formation towards the end of the Month. Action and revolution is occurring to crumble old structures and launch new ideas into reality. Pushing, pulling, change. Good stuff. Tricky transformational business if you resist the evolutionary flow. So don't resist. What you resist persists.
Monday is a day of fresh energy- There can be a tension in the air, but it's likely that things will get done. Your mind starts to look forward, impulsive acts release you from constricting situations. Wonderful! Harness a little discipline for best results. Self-assertion is the key to your next move.
Tuesday relationship focused Mars in Libra is at odds with the self focused Aries Sun. Is it meme me, or should I be thinking of you you you. That is the question you might be grapplling with in your head. For a smoothing sorting of this dilemma make sure you do some physically demanding exercise. Mars and the Sun are active energies and if cooped up can cause accidents and injuries. So do what's hard for you. Maybe it's three push-ups and a walk around the block, or dancing all night at the Salsa club. Find something to do this day to keep the energies in check and running smoothly.
Friday is a dreamy day with Venus and Neptune sliding into each others personal space, merging energy, in Pisces. Expect to get it. Perhaps deep spiritual experiences will confirm that life goes on after death, or you might meet a soulmate or your Spirit guide. If you have something to create, now is a day filled with inspiration. Be prepared to capture this shimmery new reality in art of all kinds.
The energies are powerful now. Keep returning to this very second. Follow your breath. Ask for guidance and clarity and to know what to do next. Then wait: You are poised, powerful and magnetic. All good things are coming your way. Occupy your life to claim your freedom!


text:
The forgotten renewable energy source8/11/2014
Ask anyone to name top renewable energy sources and the answer invariably starts with Solar and Wind. The third place tie up could be between geo-thermal, "everything else" and "I don't know." Hardly ever anyone mentions the bio-energy. The energy that we generate from human and animal waste among other things. This is remarkable as the benefits of bio energy are far more with far fewer side effects. Consider…
- Requires low technology and skills
- Waste recycling
- Manure generation
- Natural gas that can easily be consumed in modern vehicles and other systems
- Low investment
I could go on but you get the picture. Many developing countries could entirely meet their energy needs just by efficiently using the animal waste.


text:
Longford conquer Cavan
Longford . . . . . 0-10 Cavan . . . . . 0-7
Trailing by 0-4 to 0-3 at the break, Longford produced a strong second half performance to get the better of Cavan at Breffni Park on Saturday night and thus consolidate their place at the top of the Division 3 table with full points from their two games played to date in the National Football League.
The introduction of substitutes James McGivney and Donal McElligott at half-time made the desired impact in attack as Longford gradually got on top to run out worthy winners and there is now a three-week break to the next game on March 4th against another of the neighbouring rivals Roscommon who are also unbeaten.
Read the full report of Longford's win over Cavan in the next edition of The Longford Leader.


text:
Today I thought I'd talk about xkcd, the online comic strip featuring stick figures and geek humor. Sadly, I don't understand a lot of the science-centered strips because my science smarts tops out at Mr. Wizard (or Bill Nye the Science Guy if you're too young to know who Mr. Wizard is. And if you are don't tell me.), but fortunately for people like me, there's a website called explain xkcd which does just as its title suggests: provides detailed explanations for those in need.
But fortunately, there have been several strips for which I do not require an explanation. They're the pop culture offerings. There was an amazing strip one day featuring Movie Narrative Charts from Lord of the Rings and Star Wars and Jurassic Park and a couple other films. I really want to post it here, but this little blog would not do it any justice because it's the kind of thing that requires a large screen and time to peruse it.
This one is my absolute favorite:
And one last thing before I go...when I was struggling to think of a post for today, The Man, in his helpful manner, suggested the song Xanadu by his favorite band Rush. So here in his honor is the video (please note it's super long):


text:
- Yield
- 8 Bowls
- Active Time
- 1 1/2 hours
- Total Time
- 3 hours
- 2 tbsp butter to start, 3 tbsp to finish.
- 2 tbsp olive oil
- 1 heaping cup of diced onions
- 2 cups diced carrots
- 8 cloves garlic
- 4 sprigs of fresh rosemary
- 6 stalks of fresh thyme
- Salt and Pepper, Garlic Salt
- 2 1/2 pounds of ground meat - use 1/2 pound bison, 1 pound each of veal and lamb.
- Cracked pepper and Cracked sea salt.
- 1 Cup Port, ruby.
- 2 Cups beef stock with 1 tsp paste mixed in to stock.
- 1 can tomato paste.
- 1 small can tomato sauce.
- 2 tsp dried oregano.
- 2 tsp Worcestershire.
- heat butter and oil over medium high heat (4-6 on gas, depending on how strong your burner is) You want the combo of the butter and olive oil, the butter adds the bas layer of richness to the dish and browns the onions and carrots. Add onions, stir 1-2 minutes, just until fragrant. Add carrots, stir 2-3 mins Add garlic, rosemary, thyme mixture, stir 1 minute. Add nice shake of black pepper and salt, stir in well, cook another 1 minute. Add the meat one part at a time. I like to go bison, then veal, then lamb. Make sure you stir each portion in well before adding the next. You are still medium high heat (4-6). Once all meat is thoroughly combined with carrots, onions and garlic/herb mix add a nice helping of cracked black pepper and cracked sea salt and ga - it is important to use the cracked stuff here because you really want to infuse the meat with the salt and pepper. Also put in a good shake of garlic salt. Stir, cook the mixture for 3-5 minutes until you see only a little bit of pink in the meat. Add the ruby port, let cook 5 minutes Add the beef stock, cook another 5 minutes. Add the tomato paste, cook another 5 minutes. Each time you add the prior 3 ingredients you are bringing the mixture back up to a nice bubble and then adding the next. Add the tomato sauce, worcestershire, dried oregano, more cracked pepper, salt and garlic salt, once the mixture starts to bubble reduce heat to low (0-2) and partially cover. Cook for an hour, stirring every 15 minutes. At 45 minutes start your water for the potatoes, make sure to salt the water, don't be shy with the salt. Turn the oven on, 400. Peel and dice potatoes. Last 15 minutes, cover completely. Remove lid & before stirring again, skim oil from top. Add butter and cook on low until potatoes are whipped and ready to go.
Related Video
Nutritional Info
- Calories357
- Carbohydrates6 g(2%)
- Fat35 g(54%)
- Protein4 g(9%)
- Saturated Fat18 g(88%)
- Sodium281 mg(12%)
- Polyunsaturated Fat2 g
- Fiber1 g(6%)
- Monounsaturated Fat14 g
- Cholesterol39 mg(13%)


text:
BIRD SAFE SPRING CLEANINGThere is hardly a corner of our world that is unpolluted by man-made chemicals.
A good place to start to eliminate this problem is at home, especially when spring-cleaning. Many chemical contaminants are acutely toxic. Their effects may lead to a bird's death. But even at low levels they can affect biological functions by disrupting the endocrine or hormone system. As a result, few chicks hatch because embryos fail to develop properly and babies can be deformed. Also, young birds may mature slowly and their parents may reject them. Often contaminants interfere with the chick's ability to fight infection making them susceptible to disease.
So now that the bad is stated, let's try to get to the good of cleaning safely. Using non-toxic cleaners is a good way to insure the safety of your feathered friends.
All Purpose Cleaners
Grapefruit seed extract is a relatively new disinfectant that kills staph, strep, candida, salmonella, and e-coli microorganisms. Add 20 drops of grapefruit seed extract to a 32-ounce spray bottle filled with water. Use on all surfaces around the house. Grapefruit seed extract is hard to find, but the health food stores can order it for you.
We use a lot of vinegar, water, and Pink Solution throughout our home in different solutions. We use a light solution for spritzing cupboards and washing them down. Use heavier solutions for the heavier jobs and straight on for those nasty places that are left.
Baking soda with water is a good all-purpose cleaner (1/4 cup baking soda to 1 quart water). Sprinkle baking soda on surfaces to be cleaned or make a paste with baking soda and a small amount of liquid soap. Scrub with a damp nylon scrubbing pad, soft cloth, sponge, fine steel wool or my favourite is home-made scrubbies.
White distilled vinegar diluted in water removes baking soda residue. Dry with a soft cotton cloth. Recycled cotton clothing serves as a good cleaning and drying cloth.
Vegetable-based liquid soap diluted in water makes a good all-purpose cleaner.
Add 1 cup of soap flakes to 1 quart of boiling water to make soap jelly. Stir until dissolved. Pour into jar and let cool. Mix with water as needed.
Multi chemical sprays and solutions are not necessary to clean appliances and surfaces in the kitchen.
Mix a small amount of liquid soap with water in a spray bottle. Mist surfaces and clean with a sponge or cloth. Wipe dry.
For stains on counters, squeeze fresh lemon juice on the stain and allow it to stand for 45 minutes. Sprinkle on baking soda and rub with a sponge or soft cloth.
Burned or baked on food in cookware - 2 tablespoons liquid dishwashing detergent and 3 teaspoons baking soda. Add ingredients to enough water to cover the burned on food; boil for 15 to 20 minutes and wash normally. Or sprinkle cookware liberally with baking soda and add just enough water to moisten. Let stand for 3 hours and lift the burned food out of the pan.
Stained cookware - use steel wool or scrubbie and solution of lemon juice and baking soda. Cover the discoloration for 30 minutes; then scrub and rinse clean. Also you can use pink solution for this job as well as all of the above jobs.
Ovens - for lightly soiled ovens, make a thick paste of water and baking soda or Pink Solution straight on with use of a scrubbie or nylon scrubbing pad. If the oven is greasy, add a small amount of liquid soap. To remove spots, use very fine steel wool, which can also be used for the toughest spots as well. Commercial oven cleaners are available that are non-toxic and do not contain lye, but read the label carefully.
To clean underneath the refrigerator, tie a sock around the end of a yardstick. When one side is dirty, turn the sock inside out and repeat. This is a good way to use those socks with holes in them.
To clean the sink, shower, tub and tile grout, soak with diluted liquid soap, sprinkle on baking soda, scrub with a nylon scrubbing pad and rinse. For this job I use Pink Solution with a scrubbie.
Use a stiff tooth brush or scrub brush for tiles. If any residue occurs, rinse with vinegar and cold water. Remember always rinsing with cold water removes soap faster. For a serious soap scum, use very fine steel wool. Rinse with cold water and, if necessary, add vinegar to rinse. Buff dry.
To disinfect bathroom surfaces, spray with a solution of one quart of water and 20 drops of grapefruit seed extract.
Mildew remover - spray mildew with hydrogen peroxide and open windows.
Prevention: Keep surfaces dry. Bacteria, viruses, mildew, and mold cannot live without dampness. Wipe down surfaces with white vinegar after cleaning. It prevents the growth of mold and mildew.
Lime deposit remover: spray a solution of 1 cup apple cider vinegar, 2 tablespoons salt, and 1 cup water on lime deposits and let stand overnight. Rinse with cold water.
Vinyl Floors
Fill a bucket with a gallon of warm water and add ˝ cup of white vinegar. To cut grease on vinyl floors, dilute a small amount of liquid soap with water. To remove black heel marks on floors, rub with a pencil or typewriter eraser. To prevent water spots, dry with a cloth after mopping.
Products like 'Carpet Fresh' have been linked to bird deaths. The producers of 'Carpet Fresh' say it is safe for animals, but they admit that birds have a complicated immune system and because they are different from dogs and cats that it may be harmful re: their site.
For spills, immediately blot with a clean cloth or paper towel.
For most spots, mix liquid soap with water in a spray bottle. Spray lightly and blot with a cloth or towel.
For grease, cover with baking soda. Allow the grease to be absorbed by the baking soda before vacuuming.
For odours, sprinkle carpet with plan baking soda. Let sit for 15 minutes, then vacuum.
Carpet shampoos may contain toxic ingredients such as ammonia and perchlorethylene, a known carcinogen. Plant-based cleaning products such as citrus cleaners are preferable. Use a steam-cleaning machine with 10 to 15 drops of grapefruit seed extract per gallon of water. Many hospitals are now starting to use this extract or products made from this extract for carpet cleaning.
Wood Furniture
Use a soft cloth or sponge moistened with water and a little all-purpose cleaner or even a plain damp cloth to clean finished furniture.
To remove water stains on wood furniture, bad white toothpaste onto stain. Allow the paste to dry and then gently buff off with a soft cloth.
To dust, wipe along the grain with a soft cloth.
To clean most spills and fingerprints, rub with a soft cloth, lightly moistened with water. Buff immediately with a soft dry cloth.
A furniture polish: mix Ľ cup linseed oil, Ľ cup vinegar and 1/3 cup lemon juice. Rub into wood with a soft cloth.
Another furniture polish: mix 2 parts olive oil with 1 part lemon juice. Apply mixture to furniture with a soft cloth and wipe it dry.
Nicks and scratches: can be covered by mixing granular instant coffee with a little water and applying with a clean, cotton cloth.
A solution of lemon juice and warm water to clean glass-top tables will make them sparkle. Dry them with a linen towel and then go over them with a crumpled newspaper to remove all lint and make the glass shine.
Use your hair-dryer on the cool setting to dust pleated lampshades and other hard to reach areas. Also ok for vertical blinds.
Window and glass cleaner: 1 gallon water, 1 cup white vinegar. Apply with cloth or spray bottle and clean with a linen cloth. Make sure your bird is secured when cleaning windows so that they do not fly out while you are cleaning.
For extra sparkle, polish clean windows with a crumpled piece of newspaper when nearly dry. Or crumple up black and white newspaper and dip in white vinegar. Wipe the glass until almost dry, then shine with dry newspaper. The drawback to this method is that newspaper print may rub off on the wood surrounding the glass. Best used for inner portions of large expanses of glass.
When polishing windows use up and down strokes on one side of the window and side-to-side strokes on the other to tell which side has streaks.
Lemon juice removes greasy fingerprints on windows.
Cage cleaning
There are 3 main ingredients for cage cleaning - soap, water, and elbow grease. Very little elbow grease is needed if cages are wiped down daily and cleaned well once a week. This method is best to preserve the finish on cages.
Grapefruit seed extract is a good anti-bacterial cleaner for cages. It can be added to a spray bottle and used for pre-soaking the cage and on a sponge to wipe down the cage. Also, Quescell D is good and we also use Pink Solution.
To catch droppings you can use newspaper rather than bedding which can hold moisture and rust out cage bottoms. We use another product from bed o cob to pine shavings, but you must have a grate in your cage so that the birds are not able to get to this.
If you use detergent in the washer, eliminate soap residue by adding 1 cup of white vinegar to the final rinse.
Laundry starch: dissolve 1-tablespoon cornstarch in 1 pink cold water. Place in a spray bottle. Shake before using.
Chrome cleaner: mix Ľ cup baking soda with enough water to make a paste. Rub on, rinse with water, dry.
Drain cleaner: when hair and debris clog drains, make a solution of equal parts of baking soda, vinegar and salt. Place in the drain and let it foam for 15 to 20 minutes. Then rinse with boiling water.
Air fresheners
There have been numerous reports of serious toxicity and even death of parrots exposed to "essential oils" so their use around birds should be avoided. Scented candles should not be used in the air space of parrots. Unscented beeswax candles that contain no chemical additives are safe. See Beeswax Candles on this website.
Air fresheners are dangerous to parrots. Rather than freshen the air, they contaminate it with a synthetic fragrance, coat nasal passages with an undetectable oil film, or diminish your sense of smell with a nerve-deadening agent, which can also cause liver and nerve damage. The only way to really freshen the air is to open windows and to circulate fresh air with a fan, but make sure your bird is secured in a cage and does not fly into the fan.
Natural air fresheners


text:
Celebrating San Diego Air & Space Museum
by San Diego Air & Space Museum
The Museum is thrilled to announce a new publication: Celebrating San Diego Air & Space Museum. This illustrated guidebook shares not just the history of the institution, but brings to life the stories of its remarkable, one-of-a-kind collections. Organized into six major sections – Dawn of Flight, World War I, Golden Era, World War II, Jet Age, and Space Exploration – each section spotlights the Museum's collections and their significance, using unique images from the archives to help tell their stories, as well as exploring important San Diego area contributions to the history of aerospace. From Da Vinci's flying machines to the Apollo 9 Command Module, Celebrating is a guide, a colorful and informative reminder for those who visit the museum, and documentation for those who could not visit.purchase today for $17.95
San Diego International Airport, Lindbergh Field
by Katrina Pescador, Alan Renga & Pamela Gay
Now formally known as San Diego International Airport, Lindbergh Field was named in honor of Charles Lindbergh and has been a center of aeronautic activity since its dedication in 1928. Many famous personalities and events have been associated with the airstrip, which quickly grew to include a Coast Guard Air Station, three airlines, two flying schools, and Ryan Aeronautical. In 1935, Consolidated Aircraft relocated to Lindbergh Field, transforming it into an aviation manufacturing center. Situated just three miles north of downtown San Diego, Lindbergh Field serves more than 50,000 travelers a day, making San Diego International Airport the busiest single-runway commercial airport today in the United States.
The San Diego Air & Space Museum preserved this history, featuring unique photographs from its archives—one of the largest aviation photographic collections in the world. Authored by museum staff members Katrina Pescador, Alan Renga, and Pamela Gay, this publication features many photographs from that collection, some which have never before been published, including rare images taken during construction.
Retails for $21.99.purchase today for $21.99
Just Plane Delicious
by San Diego Air & Space Museum
Enjoy first class fare from the San Diego Air & Space Museum while supporting the Museum's world class Library & Archives. Soar to new heights with hundreds of delectably delicious dishes contributed by the Museum's staff, volunteers, and friends.
From the pioneering days of Glenn Curtis and naval aviation at North Island, the building of Charles Lindbergh's Spirit of Saint Louis by Ryan Airways, the journey to space with Convair's Atlas rocket, to the present-day Predator and Global Hawk unmanned aerial vehicles, San Diego has become known as the "Air Capital of the West."
The San Diego Air & Space Museum has been the keeper of this rich heritage for nearly 50 years. Through preservation of significant artifacts, presentation of dynamic exhibits, and extensive resources of its Library & Archives, the Museum commemorates the men and women who forged aerospace history. This cookbook will help us continue those daring and important achievements. Click here to download a printable order formpurchase today for $20.00
Pacific Southwest Airlines (Image of Aviation Series)
by Alan Renga and Mark E. Mentges
The Museum is proud to announce a new book, Pacific Southwest Airlines¸ through Arcadia Publishing. The book features unique photos, many of them never seen before, highlighting the history of this historic airline. With its low fares and friendly service, Pacific Southwest Airlines (PSA ) was one of the most successful regional airlines in American history. Its distinctive orange, red, and white planes, complete with a beaming smile were immediately recognizable to those living on the West Coast. The airline was also known for employing beautiful and sociable flight attendants. Kenny Friedkin, the founder of PSA , started in 1949 with one leased DC -3 and expanded his fleet to serve millions of passengers each year. Although PSA is no longer in operation, its successful business model of low-priced, efficient service was copied by other airlines and today is considered the norm. In addition, former PSA employees still gather annually to relive the camaraderie they experienced as being a part of one of the most unique airlines of all time.purchase today for $20.00
Wheels to Wings - San Diego's Ford Building
by Katrina Pescador and Karen Garcia Raines
The Museum is pleased to announce the publication of Wheels to Wings: San Diego's Ford Building. This limited edition was released for the 75th anniversary in May 2010. The book narrates the history of the last Ford Exposition building still in existence. Starting with Henry Ford's interest in industrial exhibitions and ending with the building contemporary status as home to the San Diego Air & Space Museum, the book features numerous detailed photos illustrating the building's history from 1935 to the present. Wheels to Wings presents the fascinating history of San Diego's Ford building in a simple, well articulated manner that all can enjoy.purchase today for $15.00
American Combat Planes of the 20th Century
by Ray Wagner
This book is the only complete reference to all the airplanes that created American airpower. It tells the story of the marriage between aircraft and war. Every combat type built in the United States for the Air Force, Navy, or for foreign governments is included, together with foreign aircraft bought for American fighting units. All the planes designed to attack an enemy with guns, bombs, or rockets are described, along with armed reconnaissance aircraft, and those derived from fighter designs, providing a quick reference for each type. Along with famous mass-production types, like the Liberator, Mustang, and Hellcat, with their major modifications, all the little-known experimental projects are included and compared. Ray Wagner's definitive work arranges all those planes by their fighting purpose and appearance in history. Even by simply viewing their photographs page by page, the reader sees the story of combat plane design over the years. Below each picture are the vital facts of each type: the engine's name and power, how big and how heavy that plane is, as well as how fast, how high, and how far it can go. Profusely illustrated with 1700 photographs, American Combat Planes of the 20th Century is 758 pages, hardcover, printed on high quality coated stock, with a section of color photographs showing the history of military airplane color configurations. Retails for $60.purchase today for $60.00
Aviation in San Diego (Images of Aviation Series)
by Katrina Pescador and Alan Renga
For nearly a century, San Diego has been a hub of aviation development, air power, and flying adventure. The city's ideal weather and protected bay allowed San Diego to have an aviation history unrivaled by any local community. From the pioneering days of Glenn Curtiss and naval aviation at North Island to the present cutting-edge aerospace technology, Aviation in San Diego captures it all. With many never-before-published photographs, Aviation in San Diego documents the people and events that made San Diego's aviation heritage unique. From Ryan to Consolidated, Curtiss to Lindbergh, and everything in between, Aviation in San Diego is the preeminent photographic record of flight in "America's Finest City." The book contains 127 pages of rare photographs taken from the San Diego Air & Space Museum's archive collection. Retails for $19.99.purchase today for $20.00
Consolidated Aircraft Corporation (Images of America Series)
by Katrina Pescador and Mark Aldrich
Founded by Reuben H. Fleet in 1923, Consolidated Aircraft Corporation (later Convair) became one of the most significant aircraft manufacturers in American history. For roughly sixty years this prolific company was synonymous with San Diego and, except for the United States Navy, the largest single employer in the County. Whole sections of the city were designed to meet the needs and provide homes for the workers and their families. When shifts at the plant changed the streets were full of the men and women who were responsible for building some of the most significant aircraft in aviation history, including the PBY Catalina, B-24 Liberator, Convair 880 airliner, F-102 Delta Dagger, as well as the reliable Atlas missile, which was vital in launching America into space. To this day, nearly a decade after the company passed from the San Diego scene, tens of thousands of San Diegans can celebrate a seminal connection with Reuben Fleet, his company and his popular slogan, Nothing short of right is right.purchase today for $20.00
Prelude to Pearl Harbor - The Air War in China 1937-1941
by Ray Wagner
The Sino-Japanese war of 1937-1941 pointed to the future of air warfare. Four years before the attack on Pearl Harbor, Japan had been fighting an air war over China, and this experience gave the Japanese Navy confidence that it could win an air war with America. This 44 page book describes the fighting that developed Japanese naval air power from 1937 to 1941, and the way American military aircraft were also changed by the war in China.purchase today for $4.00
San Diego - Air Capital of the West
by Mary L. Scott
This beautiful pictorial history chronicles the history of aviation in San Diego. Originally published in 1991, this classic is now expanded and updated to bring the history current. Lavishly illustrated with more than 300 vintage and modern photographs, its 212 pages of lively text make this unique story come alive for readers of all ages. San Diego: Air Capital of the West will be a great family heritage piece, a wonderful gift, and a treasured keepsake.purchase today for $20.00
San Diego's North Island 1911-1941 (Images of Aviation Series)
by Katrina Pescador and Mark Aldrich
San Diego's North Island is one of the most significant venues of aviation in the world. Starting in 1911, it was the home to one of the nation's first aviation schools, founded by Glenn Curtiss, who pioneered seaplane flight. He trained the nucleus of America's future air forces there, including Lt. Theodore Ellyson, the first naval aviator. When the United States entered World War I, the government took over the island with plans to build a training center for the nation's armed forces. The new army base was named Rockwell Field, and the navy portion was named Naval Air Station San Diego. By 1937, the army moved out and the navy became the sole tenant. Today NAS North Island is part of the largest aerospace-industrial complex in the navy and is headquarters for the Naval Air Force, U.S. Pacific Fleet. The book contains 127 pages of rare photographs taken from the San Diego Air & Space Museum's archive collection.purchase today for $20.00
The Story of the PBY Catalina
by Ray Wagner
The Story of the PBY Catalina chronicles the most widely-used flying boat ever built. The PBY traces its roots back to the Curtiss flying boats of World War I which were twin engine biplanes with open cabins. It was the PBY that replaced this style with its own streamlined monoplane look. This book includes discussion of the PBY Model Differences, U.S. Navy Production & Serial Numbers, Dimensions, and Serial Numbers assigned to U.S. Army Catalina Aircraft. Also included is description of PBY's built on "Non-Navy" Contracts. This 21 page softcover book includes beautiful color three view drawings and many unique photos. The historical and technical data is thoroughly researched. The Story of the PBY Catalina is the perfect addition to the library of the PBY enthusiast. Book was published in 1972 by Flight Classics.purchase today for $4.00
These We Honor - The International Aerospace Hall of Fame
by Linda Spekelmeyer
Only a select few pioneers, leaders, and pilots have been responsible for the key breakthroughs in aviation and space history from the first manned balloons through today's era of space exploration. The most distinguished of these folks have earned a place of honor in the International Aerospace Hall of Fame Gallery in the San Diego Air & Space Museum. The publication These We Honor: The International Aerospace Hall of Fame, contains portraits, biographies of all those honored in the IAHF.purchase today for $5.00


text:
Week Long Activities
|Description [venue]
VIC: Site Unseen
An interactive multimedia theatre piece, Site UnSeen literally walks its audience through the darkened streets of St Kilda, crafting with dark humour and wit, the sensate moments that constitute a life of homelessness. For further information on times and venues visit the website.
City of Port Phillip
VIC: Somali AppealUnited Voice Staff to donate to APHEDA's Somali appeal (gold coin donation).
Cyndy (United Voice)
VIC: DisplayAnti-poverty and youth homelessness display to be set up in reception area of Lighthouse offices. (Lighthouse Foundation)
Kane Bowden (Lighthouse Foundation)
QLD: Link-Up DVD and FlyerDVD on Link-Up's work with Stolen Generation people and an accompanying brochure that talks about the link between the Stolen Generation's people's hardship and poverty will be in Westpac Bank Branches in Westend and Brisbane CBD. (Westpac Bank West End & Brisbane CBD)
Melisah Feeney (Link-Up QLD)
NSW: Change BoxesMoney boxes will be placed in local businesses encouraging customers to donate any spare change to benefit those living in poverty.
Emily Price (State Student Equity Advisory Team)
NAT: Vinnies Social Justice BlogThe Vinnies Social Justice Blog is a brand new initiative to mark Anti-Poverty Week. The blog is designed to engage the public around societal issues that the St Vincent de Paul Society plays a role in, such as social justice, mental health, poverty and homelessness. Articles on social justice will be published from all States and Territories.
Vincent Nguyen Cuu (St Vincent de Paul Society)
NAT: The $35 ChallengeThe $35 Challenge aims to raise awareness of poverty and raise money for a worthy charity, OzHarvest. During Anti-Poverty Week participants will have just $5 a day to spend on food. By experiencing poverty for just 7 days we can come to a better understanding of the reality of living in poverty, and raise awareness of an issue so often swept under the rug. Participants are also encouraged to donate money that they would usually spend on food to OzHarvest.
Lauren Quinn (Corridor Kitchen)
NAT: Australian Volunteer AssignmentsConsider making a humanitarian career move during Anti-Poverty Week. Red Cross has several overseas volunteer assignments available: from climate change to human resources, communications and community engagement. Visit the AusAID website and search for the Red Cross assignments. (Online)
Zayne D'Crus (Australian Red Cross)
VIC: Street to Home Registry WeekHomeground provides housing and support for people sleeping rough and will run its second Registry Week during Anti-Poverty Week from Monday 17th - Friday 21st October.
Stephen Nash (Homeground)
NSW: Survey on Perceptions of HomelessnessTo coincide with Anti-Poverty Week, the St Vincent de Paul Society NSW will be sending out a survey on the perceptions of homelessness. The survey findings will be released early next year at the launch of the Vinnies CEO Sleepout.
Julie McDonald (St Vincent de Paul Society)
TAS: Anti-Poverty Week Christmas Food and Toy DriveThe food and toy drive, organised by Senator Catryna Bilyk and Colonly 47, will begin during Anti-Poverty Week and will conclude on 18 December. The drive will assist Colony 47 with Christmas Lunch and childrens' gift giving. Donations can be made at:
Bronwyn Lysaght (Colony 47)
NSW: . . . Because Children MatterAll week, UnitingCare Burnside will be highlighting stories and snapshots from the experiences of people we work with.
These stories will start a conversation about poverty, and to put a human face on the issue. We invite everyone to check out the stories and share their thoughts at www.becausechildrenmatter.org.au or via the facebook page: www.facebook.com/UCBurnside
Romola Hollywood (UnitingCare Burnside)
NSW: Food DriveFood drive items will be donated to the Salvation Army who work in our school as part of the National Chaplaincy Program. (Prestons Public School, Kurrajong Rd, Prestons)
Gail McHugh (Prestons Public School)
NAT: Make a Loan and Help Us Change the Lives of 75 WomenGood Return is participating in Anti-Poverty Week by challenging our supporters to help us fully fund 75 small loans to women in the Asia Pacific. These loans help women to start or grow a small business and lift themselves and their families out of poverty.
Joni Morris (Good Return)
QLD: APW Stall at Jimboomba LibraryHead to Jimboomba Library to get some more information about Anti Poverty Week and a list of activities near you. You will also be able to find information about local organisations:- The Benevolent Society, YourTown, Red Cross, Link Up. (Jimboomba Library)
Marea Cantrill (The Benevolent Society)
SA: Forgotten MigrantsThe Forgotten Migrants brochure will be distributed through the SACOSS network to all SACOSS members and member agencies.
Julia McGrath (Australian Red Cross)
SA: Static Display for Consumers in Receipt of Low IncomesThis is an opportunity for us as low incomes support workers in the southern area to have a static display at the Uniting Church Corner Church. We are going to provide a range of resources for consumers who are in receipt of low incomes - lots of info from smart shopping to mortgage information and information about rights and responsibilities with debts. (Corner Church)
Sue Heathcote (Uniting Care Wesley)
WA: YouTube Calls For Action Against PovertyMembers of the WA community are getting behind Anti-Poverty Week by recording a short YouTube clip of themselves speaking up about poverty. (APW YouTube Channel)
Dr Lucy Morris
ACT: Help for HomelessWanted: Big-hearted locals willing to hand over a spare sleeping bag, blanket, toothbrush or any essential items to Canberra's poorest people. Donations will be distributed through Road House, a Red-Cross affiliated place of support for the homeless. (ANU Student Place)
Rohana Prince (Red Cross Youth Action)


text:
Inspired by our one pot supper that goes out on McKenna's Wagon every night, One Pot Suppers offers the opportunity for Martha's Table's friends and supporters to make an impact on the community. One Pot Suppers puts the event in your hands, creating a fun, exciting, rewarding experience for an important cause. What better way to celebrate and support the vital work of Martha's Table than to gather with family and friends for a special evening?
Thanks to all who participated in our 2016 One Pot Suppers event on Sunday, October 23rd! Your generosity raised awareness of hunger in the District, as well as vital funds that will help us increase access to healthy food, and spread the joy of healthy eating.
For more information on One Pot Suppers, please contact our Special Events Manager, Robin Jackson, at email@example.com or 202-808-8015.


text:
As principal of a strategic planning consulting firm, Salij advises advertising agencies and marketers.
We're here to answer your questions and serve as your mentors through the application process.
We break the conventional classroom structure to operate like a true agency. Our Advertising program is the oldest in the country.Gary GoldsmithChair, Advertising
ArtCenter students develop powerful campaigns across traditional and new media, preparing them to work as creative leaders at top agencies around the world.


text:
Good Shepherd Is Growing with the Right Partners
Recently, Good Shepherd Rehabilitation Network announced a contract to manage all medical rehabilitation services provided at Children's Hospital of Philadelphia (CHOP). Read the press release here.
This is an exciting collaboration with one of the country's foremost health-care systems. CHOP consistently ranks as one of the top three children's hospitals in the country by US News & World Report. It is ranked first in 2009 for pediatric cancer, diabetes and endocrine disorders, neonatal care, respiratory disorders and urology.
Collaborating with an organization that is so highly respected is clearly an affirmation for Good Shepherd, and it is sure to benefit our organization and the people we serve.
In recent years, we have partnered with some extraordinary health-care organizations, including Penn Medicine in Philadelphia. We have sought out these mutually beneficial partnerships, and we will continue to do so, because these collaborations are in line with two of Good Shepherd's strategic directions.
Develop new management contracts and health system partnerships
Management contracts have proven quite successful for Good Shepherd. They have connected Good Shepherd's expert rehabilitation care with those who need it, while providing vital financial support for our mission.
Grow in new markets by identifying the right partners
We look for opportunities to partner with the "right" organizations, including those that have similar values to Good Shepherd and a consistent culture.
Partnerships, such as the management contract with CHOP, will enable Good Shepherd to live our mission and to provide excellent care and outcomes to more people in more places for years to come.
The management contract with CHOP, and the partnerships that we have developed over the past few years, would not be possible were it not for the expertise of our staff. It is our clinicians, our leaders and our support staff who position Good Shepherd to grow, to thrive and to help more men, women and children overcome their challenges to achieve independence and optimal function.