text:
- Research
- Open Access
A combinatorial optimization approach for diverse motif finding applications
- Elena Zaslavsky1Email author and
- Mona Singh1Email author
© Zaslavsky and Singh; licensee BioMed Central Ltd. 2006
- Received: 02 July 2006
- Accepted: 17 August 2006
- Published: 17 August 2006
Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied.
We introduce a versatile combinatorial optimization framework for motif finding that couples graph pruning techniques with a novel integer linear programming formulation. Our approach is flexible and robust enough to model several variants of the motif finding problem, including those incorporating substitution matrices and phylogenetic distances. Additionally, we give an approach for determining statistical significance of uncovered motifs. In testing on numerous DNA and protein datasets, we demonstrate that our approach typically identifies statistically significant motifs corresponding to either known motifs or other motifs of high conservation. Moreover, in most cases, our approach finds provably optimal solutions to the underlying optimization problem.
Our results demonstrate that a combined graph theoretic and mathematical programming approach can be the basis for effective and powerful techniques for diverse motif finding applications.
- Integer Linear Program
- Motif Finding
- Oxyr
- Integer Linear Program Formulation
- Phylogenetic Footprinting
Motif discovery is the problem of finding approximately repeated patterns in unaligned sequence data. It is important in uncovering transcriptional networks, as short common subsequences in genomic data may correspond to a regulatory protein's binding sites, and in protein function identification, where short blocks of conserved amino acids code for important structural or functional elements.
The biological problems addressed by motif finding are complex and varied, and no single currently existing method can solve them completely (e.g., see [1, 2]). For DNA sequences, motif finding is often applied to sets of sequences from a single genome that have been identified as possessing a common motif, either through DNA microarray studies [3], ChIP-chip experiments [4] or protein binding microarrays [5]. An orthogonal approach, which attempts to identify regulatory sites among a set of orthologous genes across genomes of varying phylogenetic distance, is adopted by [6–10]. For protein sequences, and especially in the case of divergent sequence motifs, it is particularly useful to incorporate amino acid substitution matrices [11, 12]. Often, motif finding methods are either tailor-made to a specific variant of the motif finding problem, or perform very differently when presented with a diverse set of instances.
Numerous approaches to motif finding have been suggested (e.g., [13–24], and those referenced in [1]). These methods differ mainly in the choice of the motif representation, the objective function used for assessing the quality of a motif, and the search procedure used for finding an optimal (or sub-optimal but reasonable) solution. Two broad categories of motif finding algorithms can be identified [25]: stochastic-search methods based on the position-specific scoring matrix (PSSM) representation and combinatorial approaches based on variants of the consensus sequence representation. Both categories come with their own sets of scoring functions (e.g., see [25, 26]), and most variants of the motif finding problem are NP-hard, including those optimizing either the average information content score or the sum-of-pairs score [27]. The performance of these two broad groups of methods seem to be complementary in many cases, with a slight performance advantage demonstrated by representative methods of the combinatorial class (e.g., Weeder [24]), as reported in a recent comprehensive study [1]. However, many combinatorial methods enumerate every possible pattern, and are thus limited in the length of the motifs they can search for. While this may be less of an issue in eukaryotic genomes, where transcriptional regulation is mediated combinatorially with a large number of transcription factors with relatively short binding sites, substantially longer motifs are found when considering either DNA binding sites in prokaryotic genomes (e.g., for helix-turn-helix binding domains of transcription factors) or protein motifs [28, 29].
Here, we introduce a combinatorial optimization framework for motif finding that is flexible enough to model several variants of the problem and is not limited by the motif length. Underlying our approach, we consider motif discovery as the problem of finding the best gapless local multiple sequence alignment using the sum-of-pairs (SP) scoring scheme. The SP-score is one of many reasonable schemes for assessing motif conservation [30, 31]. In the case of motif search, where the goal is to use a set of known motif instances and uncover additional instances, the SP-score has been shown empirically to be comparable to PSSM-based methods [32]. Additionally, unlike the PSSM models, which typically assume independence of motif positions, the SP-score can address the problem of nucleotide or amino acid dependencies in a natural way. This is an important consideration; for example in the case of nucleotides, it has been shown that there are interdependent effects between bases [33, 34]. Moreover, modeling these dependencies using the SP-score leads to improved performance in representing and searching for binding sites; a similar statistically significant improvement is not observed when extending PSSMs to incorporate pairwise dependencies [32]. The SP-score was most recently utilized in the context of motif finding in MaMF [13].
In this paper, we use the SP-score and recast the motif finding problem as that of finding a maximum (or minimum) weight clique in a multi-partite graph, and introduce a two-pronged approach, based on graph pruning and mathematical programming, to solve it. In particular, we first formulate the problem as an integer linear program (ILP) and then consider its linear programming relaxation. In practice, the linear programs (LPs) arising from motif finding applications can be prohibitively large, numbering in the millions of variables. Thus, to reduce the size of the LPs, we develop a number of new pruning techniques, building upon the ideas of [35, 36]. These fall into the broad category of dead-end elimination (DEE) algorithms (e.g., [37]), where sequence positions that are incompatible with the optimal alignment are discarded. In practice, such methods are very effective in reducing problem size; to handle the rare cases where the DEE techniques do not sufficiently prune the problem instance, we also develop a heuristic iterative scheme to eliminate sequence positions. The reduced linear programs are then solved by the ILOG CPLEX LP solver, and in cases where fractional solutions are found, an ILP solver is applied.
Given a motif discovered by any method, it is important to be able to assess its statistical significance, as even optimal solutions for their respective objective functions may result in very poor motifs. We demonstrate how to test the statistical significance of the motifs discovered via the graph pruning/mathematical programming approach by using the background frequencies for each base or amino acid and computing the motif scores' probability distribution [38]. We then assess the number of motifs of the same or better quality that are expected to occur in the data at random. In the few cases where the heuristic DEE procedure is applied, we are able to give a lower bound on the significance value of the optimal solution; this allows us to evaluate how much better an alternate undiscovered motif might be.
We test our coupled mathematical programming and pruning approach, LP/DEE, in diverse settings. First, we consider the problem of finding shared motifs in protein sequences. Unlike commonly-used PSSM-based methods for motif finding (e.g., [15, 18]), our combinatorial formulation naturally incorporates amino acid substitution matrices. For all tested datasets, we find the actual protein motifs exactly, and these motifs correspond to optimal solutions according to the SP scoring scheme. Second, we consider sets of genes known to be regulated by the same E. coli transcription factor, and apply our approach to find the corresponding binding sites in genomic sequence data. We compare our results with those of three popular methods [18, 22, 39], and show that our method is often able to better locate the actual binding sites. Using the same dataset, we also embed E. coli binding sites within sequences of increasingly biased composition, and show that our scoring scheme and motif finding procedure is effective in this scenario as well. Third, we consider the phylogenetic footprinting problem [9], and find shared motifs upstream of orthologous genes. The difficulty of this problem lies in that the sequences may not have had enough evolutionary time to diverge and may share sequence level similarity beyond the functionally important site; incorporation of additional information, in the form of weightings obtained from a phylogenetic tree relating the species, proves useful in this context. Finally, we demonstrate in the context of phylogenetic footprinting that our formulation can be used to find multiple solutions, corresponding to several distinct motifs. In all scenarios, we test the uncovered motifs for statistical significance. We show that our method works well in practice, typically recovering statistically significant motifs that correspond to either known motifs or other motifs of high conservation.
Interestingly, the vast majority of motif finding instances considered are not only effectively pruned by the optimality-preserving DEE methods, but also lead to linear programs whose optimal solutions are integral. These two conditions together guarantee optimality of the final solution for the original SP-based motif finding problem. This is interesting, since the motif finding formulation is known to be NP-hard [27], and nevertheless our approach runs in polynomial time for many practical instances of the problem. Overall, the ability of our LP/DEE method to find optimal solutions to large problems demonstrates the power of the computational search procedures, and its performance in uncovering known motifs illustrates its utility for novel sequence motif discovery.
Broad problem formulation
Motif discovery is modeled here as the problem of finding an ungapped local multiple sequence alignment (MSA) of fixed length with the best sum-of-pairs (SP) score. That is, given N sequences {S1, ..., S N } and a block length parameter l, the goal is to find an l-long subsequence from each input sequence so that the total similarity among selected blocks is maximized. More formally, let refer to the l-long subsequence in sequence S i beginning in position k and let sim(x, y) denote a similarity score between the l-long subsequences x, y. The objective is then to find the set of positions {k1, ..., k N } in each sequence, such that the SP-score ∑i<jsim (, ) is maximized.
This problem can be formulated in graph-theoretic terms [40]. Let G be an undirected N-partite graph with node set V1 ∪ ... ∪ V N , where V i includes a node u for each l-long subsequence in the i-th sequence. Note that the subsequences corresponding to two consecutive vertices overlap in l - 1 positions, and that the V i 's may have varying sizes. Each pair of nodes u ∈ V i and v ∈ V j (i ≠ j), corresponding to subsequences and in S i and S j respectively, is joined by an edge with weight of w uv = sim (, ). By this construction G is a complete N-partite graph. The MSA is achieved by picking the highest weight N-partite clique in graph G.
Similarity between l-long subsequences can be defined using a simple scoring scheme, such as counting up the number of matching bases or amino acids when the subsequences are aligned. However, for DNA sequences, the background distribution of the input sequence can vary substantially, and in order to reward matches of more infrequent bases, instead of using 1 for a match, we assign a score of log(1/f(b)) for a base b pairing, where f(b) is the zero-corrected frequency of base b in the background, and 0 for any mismatch. (We also experimented with a scheme that assigns a score of 1/f(b) for a base b match; both methods perform similarly). In practice, we work with integral scores by scaling the floating point numbers to the desired degree of accuracy and rounding (here, we use the scale factor of 100). For protein sequences, on the other hand, compositional bias is not as major an issue, and instead, to better capture the relationships between the amino acids, we score the similarity between two amino acids using substitution matrices. This assigns higher scores to more favorable substitutions and better reflects shared biochemical properties of such pairings. We experiment with both PAM [11] and BLOSUM [12] matrix families.
Integer linear programming formulation
The motif finding problem can be formulated as an ILP as follows. For a graph G = (V, E) corresponding to the motif finding problem, where V = V1 ∪ ... ∪ V N and E = {(u, v): u ∈ V i , v ∈ V j , i ≠ j}, we introduce a binary decision variable x u for every vertex u, and a binary decision variable y uv for every edge (u, v). Setting x u to 1 corresponds to selecting vertex u for the clique and thus choosing the sequence position corresponding to u in the alignment. Edge variable y uv is set to 1 if both endpoints u and v are selected for the clique. Then the following ILP solves the motif finding problem formulated above:
The first set of constraints ensures that exactly one vertex is picked from every graph part, corresponding to one position being chosen from every input sequence. The second set of constraints relates vertex variables to edge variables, allowing the objective function to be expressed in terms of finding a maximum edge-weight clique. An edge is chosen only if it connects two chosen vertices. This formulation is similar to that used by us [41] in fixed-backbone protein design and homology modeling.
ILP itself is NP-hard, but replacing the integrality constraints on the x and y variables with 0 ≤ x u , y uv ≤ 1 gives an LP that can be solved in polynomial time. If the LP solution happens to be integral, it is guaranteed to be optimal for the original ILP and motif finding problem. Non-integral solutions, on the other hand, are not feasible for the ILP and do not translate to a selection of positions for the MSA problem; in these cases, more computationally intensive ILP solvers must be invoked.
Graph pruning techniques
In this section, we introduce a number of successively more powerful optimality-preserving dead-end elimination (DEE) techniques for pruning graphs corresponding to motif finding problems. The basic idea is to discard vertices and/or edges that cannot possibly be part of the optimal solution.
Basic clique-bounds DEE
The idea of our first pruning technique is as follows. Suppose there exists a clique of weight C* in G. Then a vertex u, whose participation in any possible clique in G reduces the weight of that clique below C*, is incompatible with the optimal alignment and can be safely eliminated (similar to [36]).
For u ∈ V i define star(u) to be a selection of vertices from every graph part other than V i . Let F u be the value induced by the edge weights for a star(u) that form best pairwise alignments with u:
If u were to participate in any clique in G, it cannot possibly contribute more than F u to the weight of the clique. Similarly, let be the value of the best possible star(u) among all u ∈ V i :
Now, if F z , the most a vertex z ∈ V k can contribute to a clique, assuming the best possible contributions from all other graph parts, is insufficient compared to the value C* of an existing clique, i.e. if
z can be discarded. The clique value C* is used with a factor of 2 since two edges are accounted for between every pair of graph parts in the above inequality.
In fact, the values of are further constrained by requiring a connection to z when z is under consideration. That is, when considering a node z ∈ V k to eliminate, and calculating according to Equation 2 among all possible u ∈ V i , the F u of Equation 1 is instead computed as:
The value of C* can be computed from any "good" alignment. We use the weight of the clique imposed by the best overall star.
Tighter constraints for clique-bounds DEE
For a vertex u ∈ V i and every other V j , an edge has to connect u to some v ∈ V j in any alignment. When calculating F u , we can constrain its value by considering three-way alignments and requiring that the vertices in the best star(u) chosen as neighbors of u in graph parts other than V j are also good matches to v. Performing this computation for every pair of u, V j and considering every edge incident on u would be too costly. Therefore, we only consider such three-way alignments for every vertex u ∈ V i and the next part Vi+1of the graph (with the last and first parts paired). Essentially, this procedure shifts the emphasis onto edges, allowing better alignments and bounds, and yet eliminates vertices by considering the best edge incident on it.
For a given edge (u, v) with endpoints u ∈ V i and v ∈ Vi+1we consider an adjacent double star with two centers at u and v, and sharing all the endpoints x j in the other graph parts, denoted as dstar(u, v); the weight of such a dstar(u, v) is . Now consider a clique {u1 ∈ V1, ..., u N ∈ V N } of some value C*, and the sum of its double stars:
This sum is equal to 4C*, as each edge (u i , u j ) is counted four times. We define F uv with for an edge (u, v) with endpoints u ∈ V i and v ∈ Vi+1as
F uv can be viewed as the weight of the best dstar centered at the pair of vertices u, v (or edge (u, v)) and it is the best possible contribution to any alignment, if the edge (u, v) was required to be a part of the alignment. We define F u for u ∈ V i and for part i similarly to the above definitions as
F u is the value of the best dstar centered on vertex u ∈ V i and some vertex v ∈ Vi+1, and is the value of the best dstar centered on any pair of vertices u ∈ V i and v ∈ Vi+1.
For any clique {u1 ∈ V1, ..., u N ∈ V N } of value C* in the graph, by Equations –8 we have
Then Equation , with 2C* replaced by 4C*, can be used to eliminate vertices in the same way as before, eliminating a vertex z in a particular graph part if F z , the value of its best adjacent dstar, is insufficient considering best possible contributions from all other graph parts. For best pruning results the value of C* should be as high as possible; we choose C* as the clique weight induced by the best overall dstar.
Graph decomposition
We also use a divide-and-conquer graph decomposition approach for pruning vertices. For every graph part i and vertex u ∈ V i we consider induced subgraphs G u = (V u , E u ) in turn, where V u = u ∪ V\V i . Application of the clique-bounds DEE technique to graphs G u is very effective since one of the graph parts, contains only one vertex, u, and all the F and F* values that need to be recomputed for the new graph G u are greatly constrained. The process of updating the F and F* values is efficient as the changes are localized to one part in the graph. Importantly, the best known clique value C* remains intact, since the clique of that larger value exists in the original graph and can be used for the decomposed one, helping to eliminate vertices. For some of the vertices u, iterative application of the DEE criterion and re-computation of the F and F* values causes G u to become disconnected, implying that vertex u cannot be part of the optimal alignment. Such a vertex u is marked for deletion, and that information is propagated to all subsequently considered induced subgraphs, further constraining the corresponding F and F* values and helping to eliminate other vertices in turn.
Statistical significance
Once we have found a motif of a particular SP-score, we evaluate its statistical significance by calculating the number of motifs of equal or better quality expected to occur in random data with the same characteristics. Let the score of the motif of length l in question be denoted by s, and let f(b) be the zero-corrected background frequency of nucleotide (or residue) b in the input sequences, and sim(b1, b2) be the integral score computed for all residue pairs as above. We compute P l (X), the probability distribution of scores for a motif of length l in N sequences, in the first two steps of the following, and infer the e-value of score s in the last two:
1. Calculate the exact probability distribution P1(X) for a single column of N random residues. We use the multinomial distribution to compute the probability of observing every combination of bases (or residues) in the column according to the background distribution, and calculate the corresponding SP-score for the column. We then add probabilities for the same scores resulting from different base combinations. To make the computation feasible for the protein alphabet and for large numbers of sequences, we calculate the scores and probabilities in such an order that every new score and probability is computable from the previous one by a local update operation.
2. Calculate the probability distribution P l (X) for l random columns by convolution of P1(X)as in [38], where we inductively construct a distribution for i columns based on the distribution for i - 1 columns, Pi-1(X), and the single column distribution P1(X).
3. For a given score s of interest, we calculate the probability that an l-long pattern has score greater than or equal to s by chance alone. This probability is ∑x>=sP l (x).
4. Finally, we compute the total number of possible motifs of length l in the data. If the sequences have lengths L1, ..., L N , then the search space size L = ∏ i (L i - l + 1). The expected number of alignments with score at least s by chance alone, or the e-value, is equal to L* ∑x>=sP l (x).
Overview of approach
Our basic LP/DEE approach is to: (1) formulate an instance of motif finding as a graph problem (2) apply the DEE techniques described above in the order of increasing complexity so as to prune the graph (3) use mathematical programming to find a solution to the smaller graph problem and (4) evaluate statistical significance.
While applying DEE, if the size of the graph becomes small enough (set at 800 vertices for the described experiments), we submit the appropriate LP to the CPLEX LP solver and, if necessary, to the ILP solver. To reduce the graph to that necessary small size, we apply the DEE variants, running each one of them until either the specified graph size has been reached, or to convergence so that no further pruning is possible. In particular, we first attempt to prune the graph using basic clique-bounds DEE, then we consider tighter bound computations, and lastly we employ graph decomposition in conjunction with the DEE methods.
In rare cases the optimality-preserving DEE procedures are unable to prune the graph, and we perform what we call speculative pruning using higher C* values, which do not necessarily correspond to known cliques in graph G. Three outcomes of such pruning are possible: (i) The graph is eliminated completely. This guarantees that a clique of value C* does not exist in G. (ii) The pruning proves once again insufficient to reduce the graph. (iii) The pruning procedure converges to a small graph. We search the space of possible C* values until we find one that produces outcome (iii). To identify such a value we first translate the possible clique scores into their corresponding e-values, and then perform binary search on the e-value exponent. This method converges quickly, typically locating an appropriate C* in fewer than 10 iterations. If the optimal solution for the final reduced graph is better than the C* used in pruning, then it is also optimal for the original graph. Otherwise, the e-value corresponding to C* provides us with a lower bound on the significance of the actual optimal solution.
Extensions for other motif finding frameworks
Phylogenetic footprinting
An increasingly common way of finding regulatory sites is to look for them among upstream regions of a set of orthologous genes across species (e.g., [9]). In this case additional data, in the form of the phylogenetic tree relating the species, is available and can be exploited. This is especially important when closely related species are part of the input, and, unweighted, they contribute duplicate information and skew the alignment. We use a phylogenetic tree and branch lengths when calculating the edge weights in the graph, with highly diverged sequence pairs getting larger weights. The precise weighting scheme follows the ideas of weighted progressive alignment [42], in which weights α i are computed for every sequence i. The calculation sums branch lengths along the path from the tree root to the sequence at the leaf, splitting shared branches among the descendant leaves, and thereby reducing the weight for related sequences. In essence, we solve a multiple sequence alignment problem with weighted SP-score using match/mismatch, where the computed weight for a pair of positions in sequences i and j is multiplied by α i × α j . The rest of the algorithm operates as in the basic motif finding case above, employing the same LP formulation and DEE techniques.
Subtle motifs
Another widely studied formulation of motif finding is the 'subtle' motifs formulation [17], in which an unknown pattern of a length l is implanted with d modifications into each of the input sequences. The graph version of the problem remains the same except that edges only exist between two vertices that correspond to subsequences whose Hamming distance is at most 2d (since otherwise they cannot both be implanted instances of the same pattern). Edges can either be unweighted, or weighted by the number of mismatches between the corresponding subsequences. Either is easily modeled via slight modification of the ILP given earlier (with variables corresponding to non-existent edges removed, and summations in the edge constraints taken only over existing edges), and the resulting ILP can be used in conjunction with the numerous graph pruning techniques previously developed for this problem (e.g. [17]).
Multiple motifs
Here we give extensions to address the issue of multiple motifs existing in a set of sequences. Discovery of distinct multiple motifs, such as sets of binding sites for two different transcription factors, can be done iteratively by first locating a single optimal motif, masking it out from the problem instance, and then looking for the next one. We mask the previous motif by deleting its solution vertices from the original graph, and then reapplying the LP/DEE techniques to locate the next optimal solution and its corresponding motif.
To identify multiple occurrences of a motif in some of the input sequences, it is possible to iteratively solve several ILPs in order to find multiple near-optimal solutions, corresponding to the best cliques of successively decreasing total weights. At iteration t, we add t - 1 constraints to the ILP formulation so as to exclude all previously discovered solutions:
where S k contains the optimal set of vertices found in iteration k. This requires that the new solution differs from all previous ones in at least one graph part. We note that to use this type of constraint for the basic formulation of the motif finding problem, the DEE methods given above have to be modified so as not to eliminate nodes taking part in near-optimal but not necessarily optimal solutions. For the subtle motifs problem, existing DEE methods (e.g., [17]) only eliminate nodes and edges based on whether they can take part in any clique in the graph, and thus constraint 10 can be immediately applied to iteratively find cliques of successively decreasing weight.
Experimental results
We apply our LP/DEE approach to several motif finding problems. We attempt to discover motifs in instances arising from both DNA and protein sequence data, and compare them with known motifs and those found by other motif finding methods. We then consider the phylogenetic footprinting problem, and demonstrate the discovery of multiple motifs.
Protein motif finding
Descriptions of protein datasets. # Seq. gives the number of input protein sequences. Length gives the length of the protein motif searched for. |V| gives the number of vertices in the original graph constructed from the dataset. DEE gives the methods used to prune the graph, and are denoted by (1) clique-bounds DEE, (2) tighter constrained bounds and (3) graph decomposition. |V DEE | is the number of vertices in the graph after pruning. E-value lists the e-value of the motif found by the LP/DEE algorithm.
# Seq.
3.80 × 10-16
3.88 × 10-67
Tumor Necrosis Factor
1.50 × 10-40
Zinc Metallopeptidase
5.82 × 10-23
Immunoglobulin Fold
3.04 × 10-24
For each of the test protein datasets, our approach uncovers the optimal solution according to the SP-measure. These discovered motifs correspond to those reported by [15, 36, 43], and their SP-scores are highly significant, with e-values less than 10-15 for all of them. As described by [15], the HTH dataset is very diverse, and the detection of the motif is a difficult task. Nonetheless, our HTH motif is identical to that of [15], and agrees with the known annotations in every sequence. We likewise find the lipocalin motif; it is a weak motif with few generally conserved residues that is in perfect correspondence with the known lipocalin signature. We also precisely recover the immunoglobulin fold, TNF and zinc metallopeptidase motifs. The protein datasets demonstrate the strength of our graph pruning techniques. The five datasets are of varying difficulty to solve, with some employing the basic clique-bounds DEE technique to prune the graphs, while others requiring more elaborate pruning that is constrained by three-way alignments (see Table 1). In each case, the size of the reduced graph is at least an order of magnitude smaller. For three of the five datasets, the pruning procedures alone are able to identify the underlying motifs.
In contrast to [36], who limit sequence lengths to 500, we retain the original protein sequences, making the problem more difficult computationally. For example, the average sequence length in the zinc metallopeptidase dataset is approximately 800, and some sequences are as long as 1300 residues. The motif we recover is identical to the motif reported by [36] in nine of ten sequences (see Additional Table 1); yet, with the difference in the last sequence, the motif discovered by our method is superior both in terms of sequence conservation and statistical significance (with an e-value of 5.7729 × 10-23 for us vs 1.12155 × 10-21 for [36]).
Detecting bacterial regulatory elements
Listing of the transcription factors' datasets (columns 1, 2, and 3) and the results of motif finding by LP/DEE. TF is the transcription factor dataset. Seq is the number of input sequences. Len is the length of the motif searched for. The rest of the listed measures refer to the motifs discovered by the LP/DEE algorithm: IC is the average per-column information content [44]; RE is the average per-column relative entropy; E-value is the e-value, computed according to our statistical significance assessment; nPC is the nucleotide level performance coefficient; and sSn is the site level sensitivity. The four starred entries indicate potentially non-optimal solutions; entries marked with † indicated usage of the ILP solver.
9.16 × 10-1
1.15 × 10-3
4.31 × 10-6
1.30 × 10-7
1.09 × 10-5
3.08 × 10-9
2.48 × 10-1
6.12 × 10-6
1.33 × 10-2
1.37 × 10-6
4.79 × 10-3
1.85 × 10-9
5.52 × 10-8
1.28 × 10-8
1.52 × 10-16
2.63 × 10-2
2.26 × 10+8
1.01 × 10-40
6.44 × 10-2
1.73 × 10-1
3.37 × 10-12
6.57 × 10-2
3.95 × 10-4
1.03 × 10-3
8.06 × 10-4
7.48 × 10-4
1.28 × 10-8
4.27 × 10-6
4.14 × 10-9
1.53 × 10-37
1.26 × 10-9
3.74 × 10-6
1.05 × 10-17
3.63 × 10-9
We evaluate the overlap between motif predictions made by our approach and the known motifs using the nucleotide level performance coefficient (nPC) [1, 17]. Let nTP, nFP, nTN, nFN refer to nucleotide level true positives, false positives, true negatives and false negatives respectively. For example, nTP is the number of nucleotides in common between the known and predicted motifs. The nPC is defined as nTP/(nTP + nFN + nFP); it is a stringent statistic, penalizing a method for both failing to identify any nucleotide belonging to the motif as well as falsely predicting any nucleotide not belonging to the motif. Though nPC takes both false positives and false negatives at the nucleotide level into account, we also find it useful to consider site level statistics. Following [1], we consider two sites to be overlapping if they overlap by at least one-quarter the length of the site. Defining site level statistics similarly to the nucleotide level statistics above (e.g., site level true positives, sTP, is the number of known sites overlapped by predicted sites), site level sensitivity sSn is sTP/(sTP + sFN).
Motif finding in genomic data
We compare the performance of our method to three others, MEME [18], Gibbs Motif Sampler [39], and Projection [22]. We choose these particular methods as they are widely-used, readily accessibly for download via the internet, and can handle the lengths of motifs (11–48 bps) in our dataset. While a recent performance evaluation of motif finders [1] indicates that combinatorial methods such as Weeder [24] have somewhat better performance than other methods, most enumerative combinatorial methods, Weeder included, are not able to handle the lengths of the motifs in our bacterial dataset.
Two of the methods we compare against, MEME and Gibbs Motif Sampler, are stochastic-search based algorithms. We run them requiring one motif instance per sequence and limiting the search to the primary sequence strand only, while leaving other parameters at their defaults. Gibbs Motif Sampler is run with 100 random restarts to allow for sufficient sampling of the search space, and MEME is allowed to execute its own algorithm to search the dataset for good starting points for EM. Note that Gibbs Motif Sampler failed to execute on three largest datasets, crp, ihf and purr when run on our local linux machines; these datasets were submitted through the web server.
Projection method has a combinatorial component, combining the idea of locality-sensitive hashing with postprocessing by MEME, and unlike most enumerative combinatorial methods, Projection is not limited by motif length. Since many of the motifs in our dataset are not well conserved, we set the d parameter, assessing the average number of mismatches per motif instance, to it maximum suggested value of 0.25 of the motif length. Length parameters of the known motif are used for each dataset.
We detail the performance of our algorithm on the full set of binding sites for 36 E. coli transcription factors in Table 2. Considering a motif to have been correctly identified if at least half of its sites were found with at least 25% overlap with the known site (as in [1], and essentially corresponding to datasets with sSn of at least 0.5), our method accurately discovers motifs for 22 transcription factors. Setting an e-value threshold at 1.0 (a lower threshold causes other methods to identify too few datasets as having significant motifs), we find statistically significant motifs for 33 datasets. Of the three transcription factor datasets with no significant solutions, one, hns, is a short motif, and the other two, oxyr and ihf, are very poorly conserved motifs with low information content (IC) [44], such that the average per-column IC for the known oxyr and ihf motifs are 0.89 and 0.36, respectively, whereas these values are 0.95 and 0.39 for their discovered motifs. In general, as compared to the motifs corresponding to the actual known transcription factor binding sites, the motifs found by our method exhibit equal or higher IC, measuring motif conservation in isolation from background sequence, as well as higher relative entropy, measuring the difference with the background distribution (Table 2).
MEME reports motifs for all 36 transcription factor datasets, with e-values less than 1.0 for 20 of them. Gibbs Motif Sampler discovers motifs for 34 of the transcription factors, with 15 of them considered significant via their positive logMAP scores (no motifs are found for araC and flhCD). Projection reports motifs with no significance assessment for all 36 transcription factor datasets. The LP/DEE approach described in this paper has the best overall performance. Taking significance assessment into account, and considering all datasets with no significant motifs to have zero sSn and nPC values, our method produces 0.554 averaged sSn and 0.411 nPC values. Indeed, only two datasets, oxyr and ihf, have motifs that are deemed insignificant using our scheme yet have non-zero overlap with the actual motifs. Performance statistics for MEME and Gibbs Motif Sampler are considerably lower with the averaged sSn of 0.338 and nPC of 0.257 for Gibbs, and corresponding sSn and nPC values of 0.382 and 0.285 for MEME. Since Projection does not report significance values, we also note averages of raw coefficients for overlap with the known motifs while ignoring significance assessments. Our method still outperforms the others, though not as significantly, with sSn and nPC values of 0.565 and 0.414 for LP/DEE; 0.550 and 0.402 for Gibbs; 0.501 and 0.358 for MEME; 0.560 and 0.407 for Projection.
Our approach finds provably optimal solutions for 32 of the 36 datasets. Notwithstanding, our method also exhibits excellent runtimes for most problems. Of the 36 transcription factors we considered, 25 were solved in seconds with the application of clique-bounds DEE, some using tighter bounds constrained by three-way alignments. Seven required the application of graph decomposition with tighter clique-bounds DEE, and took a few minutes to three hours to solve. For the remaining four datasets, we used more computationally intensive speculative pruning. Interestingly, we found highly significant solutions for three of these datasets, albeit without the guarantee of optimality, and no significant solution for one. For each of them we provide a bound on the significance value of a potential optimal solution according to the method detailed in the section above. The e-values of the obtained motif and the lower bound in parentheses are: crp, 3.08 × 10-9 (6.04 × 10-33); fis, 1.37 × 10-6 (2.29 × 10-7); soxS, 1.26 × 10-9 (5.25 × 10-14); and ihf, 2.26 × 10+8 (3.98 × 10-31). Finally, in the entire data collection, all but two of the problems resulted in integral solutions to their LPs. The remaining instances with fractional solutions were easily solved by the ILP solver.
Simulated data
We also evaluate the effectiveness of our scoring scheme in finding binding sites for five regulatory proteins when they are embedded in simulated data. Our goals are twofold. First, since our underlying scoring measure is based on counting matches between nucleotides, it is important to see how well it performs in compositionally biased backgrounds. In the E. coli dataset, even a simple scoring scheme that assigns a score of 1 to matches and 0 to mismatches performs competitively (data not shown). However, since other genomes can have considerably more biased nucleotide compositions, our scoring scheme rewards matches between more rare nucleotides, and we test here how it performs in different scenarios. Second, while it is essential to test the performance of motif finding algorithms on genomic data (as above), it is possible that there are other conserved motifs in the data, besides those with which we are evaluating performance, and these conserved motifs lead to lower nPC and sSn measurements. Simulated data is not expected to have other conserved motifs, and thus provides a cleaner, though perhaps optimistic, means for testing motif finding approaches.
Scoring method evaluation in terms of performance coefficient in biased-composition simulated data. Performance of LP/DEE in biased-composition simulated data. The first column identifies the TF dataset. The second column measures the degree of conservation of the known motif, as measured by average per-column information content [44]. The rest of the columns list the nucleotide performance coefficient of the LP/DEE method with the probability of base G indicated in the column heading and the frequencies of all other bases split equally.
Our nPC performance is summarized in Table 3 for various background distributions. We find motifs of very high nPC values in varying biased nucleotide composition, attesting to the fact that our scoring scheme is successfully able to correct for bias in sequence composition. Moreover, as expected, performance on simulated data is better than that for actual genomic sequence. In the narP dataset, for example, the motif is found perfectly in simulated data and not at all in real genomic data. Additionally, an alternate highly conserved site is found by all four methods in genomic data (Table 2), suggesting that while the narP site is well-conserved, the corresponding genomic sequences contain another shared motif of higher conservation.
Phylogenetic footprinting
We also apply our approach to identify motifs among sets of upstream regions of orthologous genes in a number of genomes. Here, the relationships between genomes is incorporated via weighting of the components of the SP-score. The eight datasets come from [9]. All datasets contain vertebrate sequences; some (Interleukin-3 and Insulin datasets) consist of only mammalian genomes, while others contain members from more diverse animal phyla. The number of sequences in the datasets ranges between 4 and 16, and most sequences are shorter than 1000 residues in length.
Motifs identified with use of phylogenetic information. Listing of motifs and details of their host sequences for phylogenetic motif finding. All datasets tested are from [9]. DNA region details the DNA regions considered (PR signifies promoter region). # Seq. gives the number of input sequences. Motif (id) identifies the consensus sequence of the discovered motif and its correspondence with the motifs of [9] where applicable. All listed motifs have been documented as regulatory elements in TRANSFAC [45]. For datasets other than the insulin dataset, only the best motif is reported and for the insulin dataset multiple motifs are reported in order of discovery.
DNA region
# Seq.
Motif (id)
Growth-horm. 5' UTR + PR (380 bp)
Histone H1 5' UTR + PR (650 bp)
C-fos 5' UTR + PR (800 bp)
C-fos first intron (376 to 758 bp)
Interleukin-3 5' UTR + PR (490 bp)
C-myc second intron (971 to 1376 bp)
C-myc 5' PR (1000 bp)
Insulin family 5' PR (500 bp)
This dataset is also an excellent testing ground for finding distinct multiple motifs using our method. We iteratively identify motifs and remove their corresponding vertices from the constructed graphs. As proof of principle, we find multiple motifs for the insulin dataset. In this case, we successfully identify all four motifs reported by [9]. Since our objective function differs from theirs and we require motif occurrences in every input sequence, we recover the motifs in a different order. Of course, we identify numerous shifts of these motifs in successive iterations. In practice, therefore, it may be more beneficial to remove a number of vertices corresponding to subsequences overlapping the optimal solution before attempting to find the next motif.
We have described a combined graph-theoretic and mathematical programming framework for the motif finding problem that provides a flexible approach to tackle many important issues in motif finding. We have successfully applied it to a variety of problems, including discovering statistically significant DNA and protein motifs, and have been able to incorporate phylogenetic information in the context of cross-species motif discovery. A major advantage of our approach for motif finding is the ability to find optimal solutions for many practical problems.
In related follow up work, we have shown how to improve the ILP formulation in the case where there are only a small number of distinct edge weights [47]; while this is not the case with the similarity scores considered here, it comes up in some applications (e.g., when considering scores based on just the total number of exact base-pair or amino acid matches). Further improvements and extensions to the ILP formulation for motif finding may be possible – for example, by incorporating constraints that model cooperative binding of transcription factors by looking for motifs within some distance of one another. While mathematical programming has not traditionally been applied to the motif discovery problem, our work demonstrates that it provides us a powerful alternative to successfully tackle a diverse set of applications.
The authors thank Stephen Altschul for help in assessing the statistical significance of discovered motifs, and the anonymous referees for their helpful suggestions. M.S. thanks the NSF for PECASE award MCB-0093399, DARPA for award MDA972-00-1-0031 and NIH for award GM076275-01.
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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.


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Scotland's Whitelee windfarm has a capacity of 322MW, enough to power 180,000 homes, or a city the size of Glasgow. The opening celebration was enhanced by the news of the Scottish Government's approval for an extension, which will raise the windfarm's power capacity to 452MW and create 300 much-needed green jobs in construction. Whitelee takes the current level of wind operating up to 3.5GW. A further 9.2GW is either under construction or about to start construction, but 8.6GW, of which 7.1GW is onshore, is still awaiting planning permission. In England and Wales the majority of this is onshore at Local Government level, where planning decisions can take up to two years to be made, and the approval rate is only 40%.
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subset: commoncrawl


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I have been looking around at other blogs and it seems that there are so many new ones in the middle school section of TpT. I have also gone around and downloaded a few freebies and a few products that just might work for my own history classroom.
During this time I have also looked thru lots of old "Mailbox" magazines and converted ideas to fit my own classroom. Being that I teach Louisiana history I know that there is almost nothing out there for my subject so I have to be on my game just to create things for my own little world.
I have come up with several items that have worked well for me and have shared them with teacher friends around the state. I have been begging and begging for them to share what they do but little has every come back my way.
Is it just me that has some great ideas or are they afraid to share? I would love to help all the new teachers out there in our state but I have to have items that they think may work for someone else if this is going to be successful.
I love what I do and I am smart enough to know that so many ideas out there have to be reworked just so that they can be used in our own state. Our history is very rich in people, culture, music, and food. We just need to share them with each other. Our state is celebrating our own 200th birthday this year and it would be nice if we all began to "SHARE".


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Judgment day has finally come. Sorry I don't mean the return of a popular deity known as Jesus Christ-a figure in the Christian faith. But Brett Lorenzo Favre; at 40 yrs old. Thanks to teammates on the Viking organization, Brett decided to give another season from his teammates. He says: " if they had forgotten about me, it would've been easier…"
Isn't this what sport should be about? that one extra rep, that one extra play- for the greater good? Favre has passed for over 69000 yards in his career. That's the length of an American football field 690 times. Pretty durable to me.
But, Favre is banged up. What will it take for him to walk away from the game? He is a tough SOB because most of us run the treadmill for five minutes and call it a workout. Is he a walking target? A geriatric playing the game with younger, faster, hungrier players- who for that matter-will not hesitate to ring Favre's bell. This was the case in the NFC championship last year in 2009.
He is a modern day Babe Ruth. A legend for his toughness, grit and tenacity. For a guy who lost his dad,and brother in law,one of his best friends( fellow retired and deceased player Steve McNair) he is definitely an inspiration for those young players who think that 40 is over the hill.
40 is the new 30.


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0800 040 7526
Children can be taught to use their imagination to tackle frequent bouts of stomach pain, research shows. A relaxation-type CD, asking children to imagine themselves in scenarios like floating on a cloud led to dramatic improvements in abdominal pain. The US researchers said the technique worked particularly well in children as they have such fertile imaginations. It has been estimated that frequent stomach pain with no identifiable cause affects up to one in five children. The research, published in the journal Pediatrics, follows on from studies showing hypnosis is an effective treatment for a range of conditions known as functional abdominal pain, which includes things like irritable bowel syndrome.
In this study, the children had 20 minute sessions of "guided imagery" – a technique which prompts the subject to imagine things which will reduce their discomfort. One example is letting a special shiny object melt into their hand and then placing their hand on their belly, spreading warmth and light from the hand inside the tummy to make a protective barrier inside that prevents anything from irritating the belly The researchers, from the University of North Carolina and Duke University Medical Center, said a lack of therapists led them to the idea of using a CD to deliver the sessions. In all 30 children aged between six and 15 years took part in the study – half of whom used the CDs daily for eight weeks and the rest of whom got normal treatment.
As usual at Diets Don't work we must also emphasise the importance of exercise and good healthy nutrition in preventing any illnesses, whether in children or adults. Many of our personal training clients in the London area have families and report that exercise and good nutrition really helps prevent their children getting ill, and it also means that when they do catch something nasty from school it's gone in no time.
This power of mind over matter also plays a vital role in all sports and for those battling illness. whether it's the mental toughness of a marathon runner or the mental calmness of German footballer getting ready to score a penalty!
A female athlete made her mark and proved that you are never too old for exercise or for a personal trainer for that matter. Ruth Firth, a 100 year old great-grandmother was jubilant after winning gold in the shot putt at the world masters games in Sydney. The fact that she was the only competitor in her category (shot putt, women, aged 100-104) does not diminish her achievement. She trains 5 days a week, bench pressing 80lbs and practicing hammer throws and javelin. She is hoping to defend her title in 4 years' time.
At Diets Don't Work personal training many of our clients are over 40, and lots of those are older than that; my personal training client Greta is now in her 60's, I'm doing a 10k with D who has a personal trainer in Sunninghill (me!) who is in her 50's, and the star of the show is B in Reading who has just passed 84!
In many ways the older a person gets the results of structured exercise become more more beneficial and visible. For a 25 year old, there can certainly be improvements in cardiovascular fitness and muscle tone, as well as lots of other benefits, but as a client becomes older these fitness and functional benefits have a more profound effect and make a bigger change to everyday life. B, our 84 year old success story, has gone from needing help to get everyday chores done, including trips to the shops, to now having the functional fitness and strength to do all of these things independently, thus making a huge difference to her life. Similarly, Greta in Holyport retains great posture and stamina, so is able to carry on life just as you or I would, while many of her peers who have become unfit and overweight are suffering form a range of debilitating conditions, from osteoporosis to knee problems.
So even of you are getting on a bit, it really is never too late, and in fact it can be a case of later having more marked benefits and easier improvements!
When a new personal trainer client signs on for a block booking of personal training, the first thing we do is get them to keep an honest and accurate food diary, which we then take away and analyse. More often than not, although the food going in is not too bad many of our clients fall down on sugary drinks. Whether it's coke or sprite, sugary tea, some types of orange squash, there is a lot more energy going in from these sources than you would think. The interesting thing is that although we get clients who say that these drinks give them an energy boost when they get tired at work, it is the drinks themselves that are creating a cycle of excessively high and then low blood sugar levels. Like a cigarette, the drink will initially give a boost, but all of this sugar is used up quickly, creating a successive blood sugar trough. This leads to low energy and cravings for something sweet. So the sugary drink is actually creating the sugar low and not curing it.
The following is taken from an article by Kelly Brownell in the Los Angeles Times, and doesn't just make sense for the U.S, but for anywhere in the western world where obesity is rising.
"If America is looking for a way to raise revenue and cut health care costs, there is an obvious solution:slap a tax on fizzy drinks. Congress has discussed this idea, but shied away from taking action after coming under industry pressure. That was a mistake. By levying a cent per ounce national tax on sugared soft drinks, the US could raise about $150 billion over the next ten years, while also saving an estimated $50 billion in medical bills. That money could go a long way in addressing the country's mounting health care bill. There's no doubt that soda pop is one of the main culprits behind the obesity epidemic in the US. Each year, the average American drinks 50 gallons of coke and other sweet drinks, which are assiduously marketed to children through celebrity endorsements and product placement. Portion sizes have steadily grown: the typical 8-ounce bottle of the 1950s had morphed into a 20 ounce behemoth containing the equivalent of 17 teaspoons of sugar. The industry complains that a soda tax would hurt the poor, but that's a specious argument. As with tobacco, the poor stand to benefit the most, both health-wise and financially by reducing their consumption. It's time to crack down on pop."
So if you are looking for some personal training in London, or just want a good head start and some free advice, there it is. By cutting out sweet drinks and increasing exercise, you have lots more leeway on the eating side of things, and can eat a lot more good things while having treats from time to time.
For more nutritional tips and advice on all things fitness and health, have a look at our knowledge page.
Although all our personal trainers are REPS level 3 and qualified to train special populations (diabetics,elderly,obese,arthritic) we are always looking to expand our knowledge and think of new ways to torture our poor clients.
To this end three of our personal trainers will be getting together over the course of the next few weeks to swap ideas and exchange new personal training techniques, and we will be looking to use each other's specialities too.
Annie Sealy (who is our fab personal trainer for the Windsor, Maidenhead, Ascot and Bracknell areas has a new type of cargo netting that attaches to doors and provides excellent instability for an interesting upper body workout, brilliant for stabilising the joints and increasing functional strength and joint stability. I am (Adam Atkinson, I am our head personal trainer covering the Ascot, Windsor, Bracknell, Sunninghill and Sunningdale areas) now a kettle-bell qualified instructor, so I will be trying to teach the troops about the posterior kinetic chain and how kettle-bell training can make you super toned whilst also providing an elevated heart rate for extended periods of time while doing resistance training. Meanwhile Stephen Hitchcock, who is our trainer for the Egham, Staines, Richmond and Virginia Water areas is a boxercise instructor and also a proper runner (is a member of a running club and everything!) so he will be showing us how it's done.
Although we all have extensive experience in the home personal training field, you can always learn new things form other trainers, and it's really useful in keeping all our trainers and clients interested in the sessions while keeping them varied and fun. All our profiles are on the site, have a look at the meet the trainers page, where there are also some hopefully flattering photos of us too!


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A birthday for Airplane Park
This photo of the F-86L Sabre Jet was taken at Airplane Park on June 25. Photos provided.
The F-86L Sabre Jet being transported from the Air Force base in upstate Rome to Monroe on Aug. 22, 1963.
F-86L Sabre Jet with slide added (pictured on the right side of the aircraft).
Volunteers work on the restoration of the Sabre Jet.
Monroe — It is hard to believe that the F-86L Sabre Jet that stands in Ford R. Dally Airplane Park is 50 years old this year. Although some people may consider it nothing more than an old airplane, it is far more then that — it is a monument to the men who fought and died in the Korean War. From June 25, 1950 to July 25, 1953, the time period of the war, the Department of Defense database lists 54,246 Americans lost their lives fighting for freedom.
Airplane Park has stood as a symbol of Monroe for these 50 years, and has been recognized as part of Monroe just as people recognize the Statue of Liberty in New York Harbor. People who grew up in the area or visit Monroe often seem to have memories of the airplane in the park.
What better way, then, is there to remember Ford Dally and honor the veterans who served and died in the Korean War than to restore the plane and let it continue to be a centerpiece at the end of Crane Park?
The restoration itself has been ongoing for several years, with volunteers putting hundreds of hours into this restoration using their own tools and paying for supplies out of their own pockets. Unfortunately, they have worked without recognition from their community. Their sole reward was their own satisfaction after the eventual restoration of this Sabre Jet.
In 2009, a group of Marines from Stewart Air National Guard Base started restoring the plane. The team leader of this group was Staff Sgt. Claude Gillman, who has since retired from the Marine Corps but continues to be the leader in the completion of this project.
He was joined by 15 volunteer members of Cornerstone Masonic Lodge No. 711, including Robert J. Adams, David N. Roman, David Roman, Nathaniel Roman, Brian Miller, Kylie Miller, Zack Miller, Paul Miller, Claude Gillman, John Ziegler, Greg MacLeod, Robson Vidal, Kevin Grace, Dennis Gerbino and Ricardo Assad. The Masonic Lodge became involved in this project in recognition of Ford Dally, who was a member of the lodge for many decades.
They have had setbacks, including graffiti and destruction of special fabricated parts made for the plane. Vandalism destroyed many hours of work by these volunteers as the work was originally stopped as the behavior continued. Earlier this year, however, the village said they would erect a fence around the Sabre Jet and place the airplane on a permanent concrete pad. Once this fence is erected, the work will continue.
The history
The airplane itself was transported from the U.S. Air Force base in Rome, N.Y. by four residents from Monroe, including Sgt. Ford Dally and Mayor Jay Stahl on Aug. 22, 1963. The tractor and trailer used to transport the plane were also donated by local businesses. The Sabre Jet was reassembled by volunteers from the Air Force stationed at Newburgh's Stewart Air Force Base, originally had a slide added and was a major part of the play area.
A couple of attempts have been made to remove the plane, but the public has come to its aid by opposing those actions and volunteering to aid in its restoration. The last time this was attempted was in 1987 — it was defeated with the help of former Mayor Herb Dorn.
Many residents hope the fence will go up soon, so the volunteers can continue this important project, honoring not only the memory of Ford Dally, but also those 54,246 men who lost their lives as well as the many veterans who served during the Korean War.
- May 20, 2019
- May 19, 2019
- May 19, 2019
- May 19, 2019
- May 16, 2019


text:
Spá Atlantach - Wild Atlantic Seaweed Baths
Experience the Healing Power of the Atlantic
Seaweed Baths
The seaweed is hand harvested from the shores of Ballydavid, washed, cleaned and ready for your bath - you can't get it fresher or purer!
Fresh saltwater is pumped from the Atlantic and heated to your individual sensibility. It's like floating in the Atlantic - just warmer!
Classical Swedish Massage - to relieve tensions and stress in the muscles deep pressure and/or gentle techniques (depending on body conditions)
Sports Massage - to detox the muscles and get them back in form.
Deep tissue massage, stretching and mobilisation
Aromatic oil Massage - to sharpen senses, detox and relax body and soul.
Gentle and slow rhythm techniques combined with different aromatic oils
(Full body only)
Lymphatic Drainage - to activate the lymphatic system, detox
Soft and special pump technique supports the lymphatic system in its function and is specially
recommended for people who suffer from permanent or acute fluid retentions or to reduce
swellings (acute or after surgery).
- Address: Baile na nGall, Trá Lí, Co. Chiarraí
- Telephone: +353 83 4437322
- Website: http://www.spaatlantach.com/
- Facebook Page: https://www.facebook.com/SPÁ-Atlantach-Wild-Atlantic-Seaweed-Baths-1602431049986169/
- Distance from Dingle Town: 5-10km
- Nearest Town or Village: Ballydavid / Baile na nGall


text:
Blade Trinity: Drake's Fun (MF,anal)
by The Fan ([email protected])
Drake, the Lord of all vampires looked at the world of man from the
skyscraper owned by the vampire community. The world had changed since he had
last seen it. 7500 years had passed since the Lord of the Night turned his
back upon the world of the mortals. Drake was not pleased by what he saw. The
men and women of the world were arrogant, selfish creatures as always. The
women were becoming more arrogant due to increased stature in society and the
fact that many men did not stand up to them anymore. Especially in America.
In the eyes of Drake, humans of both sexes needed to be crushed, brought
down beneath the heel of vampire domination. Drake was not the first of the
vampires. He was the last of an ancient species. The race of the true
immortal drinkers of blood.
Long ago, a race of human-like creatures roamed the planet Earth. These
creatures were very ancient. They had been around since shortly after the
fall of the dinosaurs. They were the ultimate predators. They drank the blood
of their prey. Also, they had shapeshifting abilities. These creatures called
themselves the Wamphyr. The Wamphyr existed for millions of years before the
coming of humanity. When the humans evolved, the Wamphyr hunted them.
Sometimes, when a human was bitten by a Wamphyr, he or she became something
else. A creature that drinks blood and fears sunlight. Thus the first
vampires were born. The Wamphyr were the undisputed lords of the earth.
Unfortunately, they fought among themselves and thus their race vanished.
Very few of these supreme predators remained. They learned to masquerade
among humanity. They learned to hide their true nature. The Wamphyr became
the overlords of the ever-increasing vampires. The humans feared the
vampires. The vampires worshiped the Wamphyr. They called them the Blood
Gods. It was the blood of the Wamphyr that gave the vampires their power.
Superhuman strength, super speed, sharp senses and the ability to regenerate.
All those were the powers of the Wamphyr, passed down to their vampiric
Drake was the last surviving member of the Wamphyr race. Once, tens of
thousands of his kind roamed the planet. They hunted anything they wanted.
Nothing could match them in ferocity or physical might. The Wamphyr had a
very long lifespan. Ordinary vampires could live for over 1000 years. Not
many made it beyond that. The lifespan of a Wamphyr was 10,000 years. Drake
was still quite young, by his ancient species's standards. Unfortunately,
there were no others like him. The vampires were creatures that were once
human. In spite of their superhuman strength, speed, agility and healing
abilities, in spite of their craving for blood, all vampires were once human.
Drake was not human. He was never human. He was a true predator. Sentient,
ferocious, adaptable and ruthless. He was a lot more powerful than any
vampire. Drake could walk in broad daylight. No other vampire could duplicate
that feat. The Wamphyr weren't exclusively nocturnal, like their vampire
descendants. The Wamphyr were perfectly at ease in the day. They had
abilities that vampires did not have. The Wamphyr weren't allergic to silver.
They couldn't be killed by a stake through the heart. They did not fear
sunlight. They were ten times stronger than the average vampire. Also, they
had the ability to shapeshift at will. Drake turned his gaze from the city
and looked at Danica Talos.
The female vampire looked at him lustfully. Drake gestured to her, beckoning
her to come to him. Danica Talos had felt strongly attracted to Master Drake
ever since he came back from his exile. Now, it would seem that he wanted
her. Drake stood naked before her. A tall, handsome and powerful-looking man.
Drake had the body of an athlete and the manner of a king. Drake was the kind
of guy who could have whatever he wanted and he knew it. He had a lot of
power. He was a very powerful guy. Something about him was so undeniably
sexy. He called her and she came to him. Drake extended his claws and
lightning-quick he slashed his way through her clothes. Danica Talos found
herself naked. Drake took her. She found herself on her hands and knees.
Drake positioned himself behind Danica Talos. He looked at her sexy butt
cheeks and spread them wide open. Her little asshole stared at him. Drake
grinned wickedly and placed his cock against her asshole. He pushed. Drake's
cock slid into Danica Talos's asshole. Danica shrieked. She didn't like
taking it up the ass but who was she to say no to the lord of all vampires?
He started pumping his cock into her ass. She fingered herself as he fucked
her. He shoved his prick deep into her bowels. Danica screamed and barked
obscenities as he fucked her up the ass. Drake plunged his cock deep inside
her shithole. He fucked her until he came, then he pulled out. Danica Talos
lay on the floor, spent.
Drake ignored her. He stepped out of the building and went for a walk.
Naturally, his mind drifted to thoughts of the half-breed hero who had
challenged him. Blade. Somewhere between human and vampire, mortal and
immortal, man and beast, there was Blade. The ultimate vampire hunter.
Born with the greatest strengths of both human and and vampire, yet
without the limitations of either. Blade was the greatest fear of the
vampire community. Blade was the most interesting individual that Drake
had met in the many thousands of years he had been alive. Tall,
broad-shouldered and muscular. Dark and handsome, clad in black leather.
Armed to the teeth. There was something dangerous about Blade. Something
sexy as well. Drake looked forward to challenging him to a fight to the
death. Finally, he had a worthy adversary.
The End
Submit stories to:
[email protected](dot)com
with the title heading "TSSA Story Submission"
Copyright © 2002-2014 TSSA Click For Celebrity Pics


text: Hi, On Thu, Mar 01, 2007 at 02:01:19PM -0800, David Hendricks wrote: > A trivial patch that has served me well for this particular flash chip. It > functions identically to some already supported Winbond chips so basically > all that was needed was an additional device ID for recognition. I've tested > read, erase, and write functionality. Thanks, the patch looks good. Please sign-off all your patches, though, we cannot commit any patches without a proper sign-off. See http://linuxbios.org/Development_Guidelines#Sign-off_Procedure for details. > diff -Nru -x .svn a/flash.h b/flash.h Just a hint: 'svn diff > foo.patch' in your top-level LinuxBIOSv2 directory is usually a bit simpler and faster than "manual" diffing. Uwe. -- http://www.hermann-uwe.de | http://www.holsham-traders.de http://www.crazy-hacks.org | http://www.unmaintained-free-software.org -------------- next part -------------- A non-text attachment was scrubbed... Name: signature.asc Type: application/pgp-signature Size: 189 bytes Desc: Digital signature URL: <http://www.coreboot.org/pipermail/coreboot/attachments/20070301/2bbcb44b/attachment.sig>


text:
Rannoch Estate Sporting Holidays in Scotland
The 20,000 acre estate surrounds the Scottish home of Lord and Lady Pearson of Rannoch. It provides first class stalking and hind shooting, with about 70 stags and 80 hinds being shot each year. Grouse are shot over pointers. The estate can also provide exceptional trout and pike fishing. There is a tennis court.
The estate lies in one of the most beautiful, secluded, but easily accessible parts of the Highlands. Situated around the west end of Loch Rannoch, Perthshire, it can be reached for breakfast by overnight sleeper train from London, or by road one hour west of Pitlochry on the A9 and the B8019 to Tummel Bridge and then onward to Bridge of Gaur along either the north or the south shore of Loch Rannoch
Travelling by car the longer and more scenic route is to take the A82 along the shores of Loch Lomond north out of Glasgow to Crianlarich and then follow the A85 and A827 pausing to view the Falls of Dochart before taking the road across the Schiehallion mountain and along the south shore of Loch Rannoch to arrive in Bridge of Gaur.


text:
Hi Everyone!!! yep, like my title says..I'm a dork! I made this card late last week using the sketch from Mojo Monday and forgot to blog about it and enter it..LOL! sheesh! not a major crisis but darn it ..I love playing with their sketches and now I missed it..o well :) ..I haven't used Lovely As A Tree in so long but I thought it would be cute for a thank you card :)..what do you think..like the card or not? :) ..I'm glad a lot of people like my Cooking The Witch card..it totally makes my day to get so many "likes" on FB :) ..have a great day!!!
Thank You!!!! Come Again!!!!


text:
Grace and Rachel Martin
Disguised as men, Grace and Rachel Martin, ambushed British officers carrying important documents one night. Returning home, they found the officers enjoying a meal their mother had prepared.
The women's bravery began when the Martin sisters were tired of how the British treated women and children. They sought this opportunity to help out. Knowing that a courier was bringing British documents to South Carolina, they set out to stop the courier and get the documents for the Patriots. Borrowing their husbands' clothes and pistols, they waited in the dark along the road the courier would be travelling. Upon hearing horses, they stopped the British officers, obtaining what they needed without firing a shot. They were in for a real surprise when they arrived home later that night. Luckily for them, men in the dark look different from women in the light.


text:
Adobe Acrobat XI: Fundamentals Training Course
The full version of Acrobat Professional XI (Acrobat 11) is probably one of the most under-utilized Adobe applications. Acrobat is filled with industrial-strength features for creating dynamic documents including: forms, multimedia slideshows, interactive catalogs and more. Acrobat's rich feature set also includes commenting, editing PDF capabilities, and preflighting (checking that a file is print or press-ready.) Acrobat XI is packed with new and improved features to help give you a simpler way to do more with PDF files.
If you haven't upgraded yet, all New Toronto Group classes cover features that work in all previous versions of Acrobat, including Acrobat 10, Acrobat 9 and older. The instructor will let you know if you are seeing something that is new to Acrobat XI (11).
2 Days
There are currently no public training classes scheduled. Call us to schedule training for you or your team.
Because of the amount of material we cover in class, we do require students to be comfortable using a mouse, opening and saving files, and the general operations of computers. While we will make every reasonable effort to help, students with insufficient skills may be required to observe the class lessons or do their best to keep up without slowing down the rest of the class. We highly recommend working with the application tutorial before coming to the course.
Module 1: Introducing Adobe Acrobat XI
- About Adobe PDF
- About Adobe Acrobat
- About Adobe Reader
- Adobe PDF on the web
- A first look at the work area
- Viewing PDF presentations in Full Screen mode
- Viewing PDF files in Read mode
- Designing documents for online viewing
Module 2: Exploring The Work Area
- Opening a PDF file
- Working with the toolbars
- Working with task panes
- Customizing the Quick Tools toolbar
- Using keyboard shortcuts to select tools
- Navigating PDF documents
Module 3: Creating Adobe PDF Files
- About creating Adobe PDF files
- Using the Create command
- Dragging and dropping files
- Converting and combining different types of files
- Using PDFMaker
- Using the Print command to create Adobe PDF files
- Reducing file size
- About compression and resampling
- Scanning a paper document
- Making scanned text editable and searchable
- Converting email messages to PDF (Windows)
- Converting web pages to Adobe PDF
Module 4: Reading And Working With PDF Files
- Changing the opening view
- About the onscreen display
- Reading PDF documents
- Searching PDF documents
- Printing PDF documents
- Filling out PDF forms
- About flexibility, accessibility, and structure
- Working with accessible documents
- Making files flexible and accessible
- Using the Acrobat accessibility features
- Sharing PDF files
Module 5: Using Acrobat With Microsoft Office Files
- About Acrobat PDFMaker
- Converting Microsoft Word files to Adobe PDF
- Converting Excel documents and review Converting PowerPoint presentations
- Converting web pages from Internet Explorer
- Saving PDF files as Word documents
- Extracting PDF tables as Excel spreadsheets
Module 6: Enhancing And Editing PDF Documents
- Examining the work file
- Moving pages with page thumbnails
- Editing Adobe PDF pages
- Renumbering pages
- Editing links
- Working with bookmarks
- Adding multimedia files
- Editing text
- Copying text and images from a PDF file
- Setting document properties and metadata
- Review questions and answers
Module 7: Combining Files In PDF Portfolios
- About PDF Portfolios
- Creating a PDF Portfolio
- Customizing your PDF Portfolio
- Sharing your PDF Portfolio
- Searching a PDF Portfolio
Module 8: Adding Signatures And Security
- Getting started
- Viewing documents in Protected Mode (Windows only)
- About security
- Viewing security settings
- Adding security to PDF files
- About digital signatures
- Creating digital signatures
- Signing a document digitally
- Modifying signed documents
- Certifying PDF files
- Signing certified documents
- Exploring on your own: Using security envelopes
Module 9: Using Acrobat In A Review Cycle
- About the review process
- Adding comments to a PDF document
- Working with comments
- Initiating a shared review
- Exploring on your own: Initiating live collaboration
Module 10: Working With Forms In Acrobat
- Converting PDF files to interactive PDF forms
- Adding form fields
- Distributing forms
- Collecting form data
- Working with form data
- Exploring on your own: Calculating and validating numeric fields
Module 11: Using Action 256
- About actions
- Using predefined actions
- Creating an action
- Sharing actions
Module 12: Using The Legal Features
- Legal features in Adobe Acrobat X
- About Bates numbering and redaction
- Applying Bates numbering
- Defining Bates numbering
- Editing Bates numbering
- Applying redaction
- Changing the appearance of redactions
- Searching text for redaction
- Assembling PDF documents
- Marking redactions across multiple pages
Module 13: Using Acrobat In Professional Printing
- Creating PDF files for print and prepress
- Preflighting files (Acrobat Pro)
- Working with transparency (Acrobat Pro)
- Setting up color management
- Previewing your print job (Acrobat Pro)
- Advanced printing controls


text:
"Thunder River Feud" (1942)
This may not work for everyone, but when I look at this shot from the insanely weird Monogram B-Western "Thunder River Feud," I see a figure that, to me, looks like a circus clown in agony. In fact, I've come to call the figure "Circus Clown in Agony." If you see it, you know what I mean, and if you don't, then I suppose we should all just step away.
Above is a detail of Circus Clown, just in case this is that one last chance you needed to see the clown. Can't say I didn't try.
If you like this sort of thing, click on this link for more such madness.


text:
About Ekata Training Center
Ekata Training Center is a family fitness training center. The name "Ekata" means "oneness" in Sanskrit. The point of learning to fight is learning not to fight – it is to discover peace, good health and tranquility. This is the underlying theme of our facility. We train adults and children to live a healthy, balanced life by training not only the body, but the brain. The Ekata Team Represents Combined Decades Of Training And Experience In Fitness And Martial Arts, And Each Member Is Passionate About Helping You Achieve Total, Lasting Fitness.
Call today at 661-255-1114 to schedule an introductory class and speak with one of our counselors!
Our Afterburn workout is specifically designed to help you burn the most fat for the longest period of time during and after the workout is finished. To achieve this, we have combined the elements of the most effective workouts in the country with the finest machines on the market. We're talking about Woodway, and Versaclimber. Throw into the mix dedicated strength training, plus the motivation of heart-rate monitoring, and you've got the formula for decimating your workout.
High intensity cardiovascular activity alone will help you burn calories. Combine that with targeted strength training, and you're going to burn fat. The right kind of strength work can kick your body's repair cycle into hyper-drive mode. Meaning, you can actually continue to burn fat for 24-36 hours after your workout is finished
Heart monitors will help keep you in the right zone for the right amount of time to ensure that you're pushing hard enough to maximize your workout. You'll see your heart rate on our TV monitors throughout your workout. So will our trainers, who will encourage you along the way.
We Served Clients From These Cities
Ekata is the only family fitness center serving in Santa Clarita CA, Canyon Country CA, Conejo Valley CA, Newhall CA, Santa Clarita Valley CA, Saugus CA & Valencia CA that offers personalized and group training, but addresses nutrition, and your overall health.


text:
Haven't had a lot of time to take anything new but we decide to scrape together a few that we hadn't posted before. These pics span about 9 years. Open to trades or to meet the right couple if you are local.
Get complete and unrestricted access to all photos and videos - Upgrade Today


text:
Enjoy free images of my surreal artwork that you can copy for use as wallpaper. For a look at other great art work I am producing (and links to high quality prints) visit my web site at http://davemartsolf.com . Scroll down to get the latest cool news on worthy subjects (My Kind of News) Thanks everyone!
Saturday, June 9, 2012
Welcome to free images of my surreal artwork. Google my name day or night to view surreal imagery that takes surrealism to the next step of adventurous exploration of the human condition in this extraordinary world of dreams and realities.
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text:
Former Lehman Brothers CEO Dick Fuld, who ran one of the largest Wall Street firms before its 2008 collapse, decided to end his two-year tenure at Legend Securities amid mounting regulatory scrutiny concerning his role at the small brokerage firm, FOX Business Network reported yesterday.
The problem for Fuld centered on regulators' continued questioning of how much business he was generating at the firm and Fuld's inability to obtain brokerage licenses from state regulators, according to people with direct knowledge of the matter.
Anthony Fusco, the CEO of Legend Securities, would neither confirm nor deny the circumstances behind Fuld's departure. Fuld didn't return telephone calls or an e-mail placed through his attorney.
Fuld is one of Wall Street's most controversial players. Known as the "gorilla" for his tough management style, he spent nearly 30 years in the business, all of them at Lehman Brothers, rising to the position of CEO as he built the firm into one of Wall Street's largest. But Lehman's September 2008 collapse, and his role in the firm's demise, remains a stain on his record.
Lehman's bankruptcy sparked the broader collapse of the US financial system. Many blame Fuld for the firm's business model that focused on taking big risks in real estate.
Since then, Fuld has been the focus of numerous regulatory probes over his actions during the firm's final days, but regulators are unlikely to file charges.
In May of 2010 he finally landed a job, when he registered with securities regulators as being an employee of a little-known brokerage firm called Legend Securities, which describes itself as "a full-service brokerage and investment banking firm," according to its Web site.
People who know Fuld tell FOX Business that the controversy surrounding his days at Lehman made it difficult for Fuld to get a job at a more established outfit.


text:
Budget $750-1500 USD
Advanced Wellness Spa Looking for- Bookkeeper using QuickBooks, able to scheduled appointments talk to customers greeting, taking orders and or shipping orders. Looking for someone who is self motivated looking to work with some of the latest concepts in health and wellness. Candidate will have strong problem solving skills, and background in office concepts and software and very outgoing. Background check required...
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