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Evolution of transcriptional networks in yeast: alternative teams of transcriptional factors for different species
Volume 17 Supplement 10
Proceedings of the 14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop: genomics
Adriana Muñoz1,2,4,
Daniella Santos Muñoz1,2,5,6,
Aleksey Zimin1 &
James A. Yorke1,2,3
The diversity in eukaryotic life reflects a diversity in regulatory pathways. Nocedal and Johnson argue that the rewiring of gene regulatory networks is a major force for the diversity of life, that changes in regulation can create new species.
We have created a method (based on our new "ping-pong algorithm) for detecting more complicated rewirings, where several transcription factors can substitute for one or more transcription factors in the regulation of a family of co-regulated genes. An example is illustrative. A rewiring has been reported by Hogues et al. that RAP1 in Saccharomyces cerevisiae substitutes for TBF1/CBF1 in Candida albicans for ribosomal RP genes. There one transcription factor substitutes for another on some collection of genes. Such a substitution is referred to as a "rewiring". We agree with this finding of rewiring as far as it goes but the situation is more complicated. Many transcription factors can regulate a gene and our algorithm finds that in this example a "team" (or collection) of three transcription factors including RAP1 substitutes for TBF1 for 19 genes. The switch occurs for a branch of the phylogenetic tree containing 10 species (including Saccharomyces cerevisiae), while the remaining 13 species (Candida albicans) are regulated by TBF1.
To gain insight into more general evolutionary mechanisms, we have created a mathematical algorithm that finds such general switching events and we prove that it converges. Of course any such computational discovery should be validated in the biological tests. For each branch of the phylogenetic tree and each gene module, our algorithm finds a sub-group of co-regulated genes and a team of transcription factors that substitutes for another team of transcription factors. In most cases the signal will be small but in some cases we find a strong signal of switching. We report our findings for 23 Ascomycota fungi species.
One of the several ways that species evolve and diverge from each other is through changes in regulatory networks and more specifically through changes in the regulation of genes by transcription factors. The 23 species with an established phylogeny in Fig. 1 are collectively an excellent environment or model for the study of gene regulation in general. To investigate evolutionary changes, we generally compare regulation in the species in one branch of the phylogenetic tree and compare that with the remaining species. A group of functionally linked and co-regulated genes is called a "regulon". A regulon (and its function) may be preserved across a family of related species despite changes in regulation. In the review [1], Li and Johnson propose three different scenarios for the evolution of transcriptional networks in yeast. Their scenarios are (1) "transcription factor turnover" where the transcription factor is conserved (as well as the transcription factor binding probability), but membership of genes in the regulon can change; (2) "transcription factor rewiring" or "switching" where the regulon members are conserved, but the regulation switches from one transcription factor to another transcription factor; (3) evolution of combinatorial interactions between transcription factors due to direct protein-protein contacts between DNA binding proteins.
Tree phylogeny for 23 species of yeast. We test each of the 12 selected branches (marked as #4, #5, #6, #10, #114, etc.) to partition the species in the tree for rewiring events. Note that the partition numbers that are one or two digits indicates the branch includes all species up to that species number. A whole genome duplication is indicated in branch #10. Each branch partitions the set of species into two sets M (the species on that branch) and M ⋆ (the remaining species). The 23 species are: Saccharomyces (S.) cerevisiae (1), S. paradoxus(2), S. mikatae (3), S. bayanus (4), Candida (C.) glabrata (5), S. castellii (6), Kluyveromyces (K.) waltii (7), S. kluyveri (8), K. lactis (9), Ashbya gossypii (10), Clavispora lusitaniae (11), Debaryomyces hansenii (12), C. guilliermondii (13), C. tropicalis (14), C. albicans (15), C. parapsilosis (16), Lodderomyces elongisporus (17), Yarrowia lipolytica (18), Aspergillus nidulans (19), Neurospora crassa (20), Schizosaccharomyces japonicus (21), Schizosaccharomyces octosporus (22), Schizosaccharomyces pombe (23)
In this paper we are interested in Scenario 2. Hogues et al. [2] report an example of scenario (2) change in regulation, namely that in Saccharomyces cerevisiae the transcription factor RAP1 regulates ribosomal RP genes, while in the same conditions in Candida albicans the regulation of the same ribosomal RP genes is done by the transcription factor TBF1 (and sometimes also CBF1). There one transcription factor for certain species is replaced by another transcription factor for different species, carrying out the regulation of the same collection of genes. In order for a collection of related genes to preserve their function, we must expect change in transcription factors to be carried out for a collection of genes. Additional such cases have been documented for yeast genes involved in mating [3] and in galactose metabolism [4, 5]. See also cases discussed in [6] and references therein.
Scenario (2) can also be discussed in terms of "motifs". A motif is a short segment in the DNA sequence, between 6–20 nucleotide pairs, usually fewer than 10, that can be positioned at different locations within the regulatory region of a gene [7]. Tanay et al. [8] focus on identifying motifs that are "enriched", i.e., the motif occurs in multiple species, controlling analogous regulons in those species.
Sarda and Hannenhalli [9] present a method for detecting rewiring, switching one transcription factor to another transcription factor in the same 23 yeast species we investigate.
Nocedal and Johnson [7] analyze more complex cases of transcription factor rewiring in yeast and concludes that future research is needed to understand transcription factor rewiring in regulatory networks that involve multiple transcription factors and larger regulons. They also say that it is important to consider evolution in the study of transcription factor rewiring. For us that means considering how regulation in a branch differs from the regulation in the other species of the tree. Our algorithm automatically finds a collection of genes for which switching occurs.
What our method does While it has been demonstrated that one transcription factor can be replaced by another (e.g., [2]), our algorithm looks for larger scale replacements. We present the first computational method that finds a regulon (denoted G) and two teams of transcription factors (denoted T and T ∗) for which there has been rewiring over evolutionary time for a specified branch M of the phylogenetic tree.
We use 53 evolutionarily conserved co-expression modules detected in [10] based on S. cerevisiae and C. albicans. Additional file 1: Our supplementary material lists the genes in each module (those modules for which there was a full set of orthologs for all the species). Some modules are contained in larger modules. The number of genes in each S. cerevisiae module ranged from 1 to 614 with an average of 54 and a total of 2840 genes for all the S. cerevisiae modules. We study the 23 Ascomycota fungi species with an established phylogenetic tree from [8] shown in Fig. 1. Our yeast species includes Saccharomyces cerevisiae, Candida albicans and Ashbya gossypii. All 23 yeast species names are provided in Additional file 2: Supplementary material.
We used the orthology mapping of corresponding genes across the 23 yeast species from [11]. In some cases there is no gene for a given species, but we chose genes that had the representatives (or orthologs) in all or almost all of the species. "Orthologs" are genes in related species that have similar nucleotide sequences, suggesting they came from the same ancestral gene by speciation. When a gene has multiple copies in one species, we pick one copy at random, resulting in 2557 genes of S. cerevisiae – plus the orthologous genes across the other 22 Ascomycota species.
This paper is based on our calculation and analysis of transcription factor binding probabilities, the computed probability that a transcription factor binds somewhere in the 600-base region preceding a gene of one of our species (we obtained those regions from [11]). We refer to that region as the "upstream promoter region". The set of 126 yeast transcription-factor-DNA binding-motifs (represented as Positional Weight Matrices (PWM)) was obtained from Transfac DB Database [12, 13]. While there are many factors determining whether a gene is activated or deactivated, it seems likely to be significant if the probability of a transcription factor is high for a branch of the phylogenetic tree and lower for the remaining species, or vice versa. We computed a binding probability for each of 126 transcription factors binding to each of 2557 genes in each of 23 Ascomycota species for a total of approximately 126×2557×23 probabilities, i.e., approximately 7 million probabilities (provided in Additional file 3: Supplementary material). Each of the genes that we selected was present in S. cerevisiae. We used the same 23 Ascomycota fungi and phylogeny [8], and our set of 126 transcription factors includes most of the 88 transcription factors that [14] uses, so we safely use 126 transcription factor binding motifs associated to S. cerevisiae and applied them to the other yeast species as [14] has demonstrated that most transcription factors have conserved their DNA motifs over large evolutionary distances.
Our skewness method
For each species, gene, and transcription factor, we examine the "binding probability", the probability that the transcription factor binds to the upstream promoter region of the gene.
If a particular branch of the phylogenetic tree has been selected, we say a transcription factor-gene pair is (positively) skewed toward that branch if the binding probabilities are on the average higher for species in that branch than for the species in the complement. Later we will define our function skew that measures how much it is skewed; (see Eq. 3). We say the pair is negatively skewed toward a branch if the reverse is true, that the binding probabilities are lower for the branch than in the complement. We usually average the skewness of a transcription factor over a collection of genes.
Computing skewness We pick a group M of species representing some branch of species in the phylogenetic tree in Fig. 1 (e.g., species 1−10). We use M ⋆ to designate the remaining species, 11−23 in this case. Hence M defines a branch (or partition) of species in the tree.
All calculations use some choice of M but we often omit mention of M and M ⋆ to simplify the notation.
For a collection of genes G we say a transcription factor is skewed towards M if it binds more strongly (averaging over the genes in G) for species in M than for species in M ⋆, and similarly it is skewed towards M ⋆ if the reverse holds. We aim at finding a branch and some related genes G in some module R and two collection of transcription factors that we denote T and T ⋆ so that on the average, transcription factors in T are skewed towards M for genes in G, while transcription factors in T ⋆ are skewed towards species in M ⋆.
To make that precise, we define the skewness, a measure of the difference in the average binding probabilities between M and M ⋆. Specifically, for a given branch M (with complement M ⋆) and each transcription factor x and each gene g, we compute the skewness skew(x,g,M) as follows in Eq. 4. We write 〈… 〉 for an average. We note that the average binding probability is computed by averaging over those species that have an ortholog of g; we exclude those species that do not have an orthologous gene from the average. All of the following depend on the choice of M. First we define P x,g,s = the binding (or occupancy) probability for transcription factor x to bind to the promoter of gene g in species s. (See Additional file 4: Supplementary methods, Section: Estimating transcription factor binding probabilities). We will use " ∗" to indicate M ∗, the complement of M is being used in a calculation.
Now we present a formula for the extent to which the binding probability of one transcription factor to one gene is "skewed", that is, stronger on the species in M than in M ⋆,
$$ skew(x,g,M) = \langle P_{x,g,s}\rangle_{s\in M} - \langle P_{x,g,s}\rangle_{s\in M^{\star}}, $$
Here "skew" measures how much x is skewed towards M for g. It is greater than 0 if x is skewed towards M and is less than 0 if x is skewed towards M ⋆.
Figure 2 is a prime example of our findings. It shows what we find when we investigate the module of RP genes focusing on the branch of the phylogeny tree denoted by '10' in Fig. 1 and consisting of the leftmost 10 species in that Figure. The dashed vertical line separates that branch from the rest of the tree. We see that for the 19 genes, transcription factor TBF1 (blue dots) has generally lower binding probabilities in M than in M ⋆ while the three transcription factors (the red team) are higher in M than in M ⋆ for those genes. Hence the overall dominance between the two teams is opposite for the red and blue teams. Note that the literature discusses this kind of switch for transcription factor TBF1 versus transcription factor RAP1 (a member of the red team), but here we find the switch apparently involves two other transcription factors as well, transcription factor FHL1 and transcription factor SFP1, members of the red team.
Transcription factor rewiring for Module 51, Ribosomal Protein (RP) genes. Here we describe the meaning of this and several following graphs. The species tree is partitioned into two groups: M is the set of species in one branch (labeled "10" in Fig. 1) and M ⋆ consists of the rest of the 23 species. In this and related figures, a dashed vertical line (or two) separates M from M ⋆. For each of the 23 species on the horizontal axis, we plot two dots, each of which is an average of binding probabilities that a transcription factor binds to a gene. Here for example each red dot is the average of 57 (=3 transcription factors in the red team times 19 genes) binding probabilities for the species in question, i.e., averaging over the genes in G and the transcription factors in team T (red dots) or in team T ⋆ (blue dots). The two dots for each species are connected with a solid line using the color of the upper dot. The first row in Table 1 reports on this case. Note that the box in the lower right specifies first the blue team T ⋆ (which here consists of a single transcription factor, TBF1), then the red team T (which here consists of three transcription factors, namely RAP1 SFP1 and SFL1), and finally the number of genes in the block. When there are too many transcription factors to fit in the box, only a few are given, but full data is given in the Additional file 5: Supplementary material for this graph (and all related graphs) including the names of the 19 genes that are discussed here
Table 1 Finding max blocks
We also define the skewness for a collection T of transcription factors, a collection of genes G, and a branch M as follows by averaging the skew(x,g) all the the transcription factors x in T and all the genes g in G, as follows.
$$ skew(T,G) = \langle skew(x,g)\rangle_{x\in T,g\in G}. $$
For each branch M and Module R our goal is to identify a group G of genes in R and two teams or groups of transcription factors T and T ⋆ so that
$$ skew(T,T^{\star},G)= skew(T,G)- skew(T^{\star},G) $$
is large. In the cases we care about, skew(T,G)>0 and skew(T ⋆,G)<0.
Blocks and substitution-maximizing blocks
We define a block denoted (T,T ∗,G) to be two groups or teams T and T ∗ of transcription factors and a group G of genes. We say there is a rewiring for a branch M of the tree when transcription factors in T are positively skewed for species in M for the genes in G while the transcription factors in T ∗ are negatively skewed.
We define a "substitution-maximizing block" or more simply a max block to be a block which has the property that if we substitute any gene for one of the genes in G, or any transcription factor for one of the transcription factors in the teams, then the skewness cannot not increase. But discarding a low scoring gene or transcription factor would raise the score of the block. Indeed the blocks with the highest scores are those that that have exactly one gene and one transcription factor in each of T and T ∗.
Finding max blocks by enumerating subsets is clearly out of the question, since we are dealing with candidate sets that may have dozens of genes and dozens of transcription factors.
We can refer to a block (T,T ∗,G) as an (m,m ∗,m G )-block when m,m ∗, and m G are the numbers of elements in T,T ∗, and G respectively.
For any starting collection G 0 of m G genes, the ping-pong algorithm finds some sets T and T ∗ and eventually a max block (T,T ∗,G) by repeatedly making substitutions in the elements of T,T ∗, and G that increase the score skew(T,T ∗,G); and since only substitutions are made, the numbers of elements in T,T ∗, and G remain m,m ∗, and m G respectively. A gene or transcription factor that is eliminated from one of the sets at one stage may later return after the mix of genes and transcription factors has changed.
A sequence of ever-shrinking max blocks
Next one of the numbers m,m ∗, and m G is decreased by 1: the discussion of "importance" below describes which of these is decreased. This decrementing process continues, yielding a sequence of max blocks whose total m+m ∗+m G decreases in steps of 1. When the process is stopped depends on the needs of the user. As discussed below, here we chose to stop when the importance (a ratio) reaches 0.5.
Our Ping-Pong Algorithm that yields a max block
In the game of ping-pong, the ball goes back and forth between the two sides. Here the block goes back and forth between two steps. The ping-pong algorithm consists of alternating between steps TF and G below repeatedly with skew(T,T ∗,G) increasing at each step until the process stops in the sense that skew reaches an equilibium, a max block.
A key point is that T and T∗ are generated from G without knowledge of previous versions of T and T∗. Similarly G is generated purely from T and T∗ without reference to any previous versions of G.
The ping-pong algorithm requires three positive integers, m,m ∗,m G and a set G of m G genes in a regulon R. The first time the ping-pong algorithm is applied, m G is the number of genes in the Module R and m+m ∗ is the total number of transcription factors. At least one of these three numbers will decrease during the attrition step described below.
Step TF: choosing transcription factors T and T ∗
Given a set G of genes, we compute the skew(x,G) scores of every transcription factor x and let the new T be the m highest scoring transcription factors and let T ∗ be the m ∗ lowest scoring transcription factors. Since skew(T,G) is the average of skew(x,G) for x in T, it follows that skew(T,G) is increased (or equal) by this new T. Similarly −skew(T ∗,G) is increased by the new choice of T ∗ and so is skew(T,T ∗,G).
Step G. choosing G
Note that skew(T,T ∗,G) is the average over the m G genes in G of the terms
$$skew(T,g)-skew(T^{*},g). $$
Next compute that term for each gene g in R and we set the new G to be the m G highest scoring genes in R. That increases (or possibly makes no change) in skew(T,T ∗,G).
Lemma: Steps G and TF never decrease the skew score.
To see this, let m be the number of transcription factors in T and m G be the number of genes in G. Notice that skew(T,G) can be written three ways, namely as the average of the m terms skew(x,G), averaging over all x in T, or as the average of the m G terms skew(T,g), averaging over all g in G. Both are equal to the average of the m×m G items skew(x,g).
$$ skew(T,G) = \langle skew(X,g)\rangle_{g\in G} = \langle skew(x,G)\rangle_{X\in T} $$
Hence if any gene g is introduced by Step G, it must have a higher skew scores
$$skew(T,T^{\star},g) = \langle skew(T,g)\rangle_{g\in G} - \langle skew(T^{\star},g)\rangle_{g\in G} $$
than each gene that is replaced. Similarly each transcription factor changed by step TF must increase the skew score.
In the above transcription factor step the algorithm is supposed to select the highest m scoring transcription factors for T, but for some choices of G there are fewer than m that have positive scores, or similarly with T ∗ there can be too few with negative scores. In such cases we terminate the ping-pong run. There are ways around this as long as there are some transcription factors with positive scores and others with negative scores: just decrease m or m ∗ as needed, but our goal was to present the algorithm in its simplest form. It is also possible to encounter sets of genes G for which there are no transcription factors with positive scores or none with negative scores.
Ping-pong stops at a max block
After applying this algorithm repeatedly, there will be no substitution of a single transcription factor or a single gene that would increase skew(T,T ∗,G) so that T,T ∗,G is a max block.
The algorithm alternates back and forth between the two steps repeatedly, letting T and T ∗ determine the set of genes G, and then letting G determine transcription factor teams T and T ∗. Each step increases the overall score skew(T,T ∗,G) until it stops at a max block: the only changes in the sets are those that increase the overall score. Since there are only a finite number of choices, the procedure must eventually stop at a max block, where the G that is used in step G is the G that is produced in the TF step.
Ping-Pong pseudocode
Input: (all_Gs,G 0,all_TFs,m,m ⋆,mG): all_Gs is the set of all genes in some module R; G 0 is an initial gene set of mG genes; all_TFs is the set of all transcription factors; m,m ⋆,mG remain constant; and m,m ⋆ are the numbers of transcription factors in team T and T ⋆ and mG is the number of genes
Output: The output is the max block (G, T, T ⋆) and its skewness score
new_score=0
G=G 0
score=new_score
Compute Step TF: Choose transcription factor teams T and T ∗
Compute Step G. Choose genes G
new_score=skew(T,T ⋆,G)
while new_score>score: (score is increasing)
return G, T, T ⋆, score
Stopping condition: Neither Step G nor Step TF ever decreases the skew score, so it must reach an equillibrium
The attrition step
For each x in T we define the "importance" of x to be the ratio of skew(x,G) divided by the highest score of the transcription factors in T; similarly for each y in T ∗, the "importance" of y is the ratio of skew(y,G) divided by the lowest score of the transcription factors; and for each g in G, the "importance" of g is the ratio of skew(T,T ∗,g) divided by the highest score in G. We now compare all of the importance scores and delete the one with the lowest score. In other words, we decrease by 1 one of the m,m ∗,m G . That increases the overall skew score. Now again we play ping-pong with the new reduced numbers, starting the game with our current G, possibly reduced by one gene.
As we proceed decreasing the numbers, we may lose some transcription factor or gene that later becomes more important to a reduced set of genes and transcription factors and so it enters back in. That is why we choose new teams from all transcription factors, not just the ones that were included on the last step, and the same holds for genes, using any genes in the specified regulon. We compute binding probabilities with 8 digit precision to avoid having tie scores, but if there is a tie score and one transcription factor or gene must be chosen, we retain the one(s) that comes first alphabetically.
When should attrition stop?
When we start, it is likely that some skew scores will be near 0, much smaller than other skew scores, so their importance will be near 0. The scientist who wishes to find many involved interacting genes and transcription factors might stop when the importance has risen to 0.25 (meaning that all the importance scores lie between 0.25 and 1.0). The experimentalist might wish to deal with fewer transcription factors and genes and so might stop at 0.75. In this paper and in the Additional file 5: Supplementary material we stopped when the importance reached 0.5.
We have examined the 12 largest branches of the species tree for each of the above mentioned modules using this approach. We indicated the branches with a slash and labeled them with a number as shown on Fig. 1. We determined a "max block" for each module and branch. For some, we found strong indications of rewiring.
Table 1 shows the cases with the largest skewness for the block of the module and branch, sorted by descending skewness. Columns 4 shows the difference Dif(M ⋆) between the two teams, T and T ⋆, on M ⋆,
$$ \begin{aligned} \text{Dif}(M^{\star})&=\text{Dif}^{\star}(T,T^{\star},G) \\ &=\langle P_{x,g,s}\rangle_{x\in T,g\in G,s\in M^{\star}} - \langle P_{x,g,s}\rangle_{x\in T^{\star},g\in G,s\in M^{\star}} \end{aligned} $$
while column 5 shows the difference Dif(M) on M,
$$ \begin{aligned} {}\text{Dif}(M)&=\text{Dif}(T,T^{\star},G) \\&= \langle P_{x,g,s}\rangle_{x\in T,g\in G,s\in M} - \langle P_{x,g,s}\rangle_{x\in T^{\star},g\in G,s\in M} \end{aligned} $$
Transcription factor rewiring for Module-51 genes Module 51 (see Fig. 2) consists of Ribosomal Protein (RP) genes exclusively. In the Introduction we noted that [2] reported that one transcription factor substitutes for another on some collection of genes in two species, namely Rap1 in Saccharomyces cerevisiae substitutes for TBF1 in Candida albicans for ribosomal RP genes. We find for a branch of 10 species, RAP1, FHL1, and SFP1 substitute for TBF1. Indeed we find that their skewness scores are similar: skew(Tbf1,Rap1,G)=0.777;skew(Tbf1,Fhl1,G)=0.713;skew(Tbf1,Sfp1,G)=0.711, where our algorithm finds the regulon G consists of 19 of the 31 RP genes in Module 51. See the Additional file 5: Supplementary material for a list of the 19 genes and other detailed information about the most significant block that was found for each module. Note that FHL1 is mentioned in [6] as a "a key player" in the regulation of RP genes in S. cerevisiae. We find it is involved in rewiring, according to our calculations.
Transcription factor rewiring for Module-59 genes Module 59 consists of conserved, co-expressed genes related to the biological function RNA methylation. Here there are two genes in the module and both are in the rewiring block. In Fig. 3 we see a much more complicated apparent rewiring than in Fig. 2.
Transcription factor rewiring for Module-59 (RNA methylation) genes. Here M is branch 112 from the phylogeny tree, so M={14,⋯,17} and M ⋆={1,⋯,13;18,⋯,23}. In Mred dominates blue, while elsewhere blue mostly dominates red
What is striking is that in 13 of the 19 species in M ⋆ transcription factor MATA1 (red dots) has binding probabilities near 0, (though in two M ⋆ species it is high). In contrast in the branch M, it is higher than the T ⋆ team (blue dots, consisting of 9 transcription factors.
Transcription factor rewiring for the Module-55 gene YMR290C Module 55 consists of a conserved, co-expressed gene related to the biological function ribosomal subunit assembly.
Here in Fig. 4 if the tree is cut at the bottom, separating the right branch of 19 species from the left-most branch of 4, it is arbitrary as to which of the two branches is called M and we have called it the left branch. If however we had called it the right branch, the graph and results would be the same. What we see is that in the four species of M, the 12 transcription factors of the T team (red dots) very clearly dominate the 14 transcription factors of the T ⋆ team. In contrast on the right side, the binding probabilities of the two teams are much closer, apparently all active. So the apparent switching behavior here is that the T clearly dominates T ⋆ on the left, while on the right all the transcription factors interact at similar levels (remembering that each dot is only an average).
Transcription factor rewiring for Module-55 genes. Here M is branch 4 from the phylogeny tree, so M={1,2,3,4}, and M ⋆={5,⋯,23}. Notice the large difference between red and blue dots in all species in M, while blue mostly dominates in M ⋆
Transcription factor rewiring for the Module-40 gene Module 40 consists of conserved, co-expressed genes related to the biological function actin cortical patch assembly. The phenomenon seen in Fig. 5 is somewhat similar to what is seen in the previous figure for Module 55. The branch of three species has one team turned on and one turned off, or at least at much lower binding probabilities, while for each of the other species, the two teams have similar binding probabilities.
Transcription factor rewiring for Module-40 genes. Here M is branch 20 from the phylogeny tree, so M={1,⋯,20}, and M ⋆={21,⋯,23}. Branch 20 is special in that it cuts the tree at the root separating the tree into two branches: M ⋆ is also a branch. Hence the roles of M and M ⋆ can be switched and the red and blue colors could be reversed. Notice then that branch M ⋆ has a wide separation between red and blue, while for the rest of the species of the tree, red and blue are closer together
Here in Fig. 5 if the tree is cut at the top, separating the left branch of 20 species from the right-most branch of 3, it is arbitrary as to which of the two branches is called M and we have called it the left branch. What we see is that in the three species of M ⋆, the two transcription factors of the T ⋆ team (blue dots) very clearly dominates the 33 transcription factors of the T team for the 3 genes in the block. In contrast on the left side, the binding probabilities of the two teams are much closer, apparently all active. So the apparent switching behavior here is that the M ⋆ clearly dominates M on the right, while on the left all the transcription factors interact at similar levels (remembering that each dot is only an average).
Transcription factor rewiring for Module-56 genes Module 56 (Fig. 6) consists of conserved, co-expressed genes related to the biological function purine ribonucleotide biosynthetic process. This example is most similar to Module 55 above in that there is an extreme difference between red and blue in M but not in M ∗.
Transcription factor rewiring for Module-56 genes. Here M is branch 113 from the phylogeny tree, so M={11,⋯,13}, and M ⋆={1,⋯,10;14,⋯,23}. Notice the large difference between red and blue dots in all species in M, while blue mostly dominates in M ⋆
Our method can address questions such as the following: Can different groups of genes in related species be regulated by the same group or "team" of transcription factors (as in Scenario 1)? Another question: Can a team of transcription factors become dominant for a collection of related genes in a tree branch while a second team is dominant on the other species (as in Scenario 2)? In this paper we focus in Scenario 2.
Our approach differs from that of Sarda and Hannenhalli [9] in that we define our skewness for each transcription factor while they define a function that compares the skewness of two transcription factors. They require more computation than our approach since they must make a complex computation of rewiring scores for each pair of transcription factors. We use extensive computation instead to look for more complicated situations where there can be several transcription factors that switch with one or more transcription factors. That is we find collections or teams of transcription factors which are positively skewed, averaging over the genes in a regulon, and for those transcription factors which are negatively skewed. We vary the selection of genes in the regulon and the teams of positively skewed transcription factors and the teams of negatively skewed transcription factors.
One of our colleagues, Chris Dock, tested a module (#2) of 40 genes. He picked G 0 to have 36 randomly selected genes, and repeated this process 100 times. The process always arrived at the same max block (using importance = 0.5). That suggests the process is robust, but does not guarantee a unique result.
A module consists of related genes and one can imagine simplistically that the module represents a process with just two stages; first one set of genes is activated, and later another set. If there is rewiring, each might have its own max block, the union of which might be the max block that the above process finds. These can be found by using a modified approach, where instead of starting with a large set of genes and contracting it as we have described above, one can start with one gene and expand the collection of genes until importance 0.5 is reached. This 'expanding" approach would often yield a subset of the "contracting" approach and the subset would depend on the initial gene. Here we chose to keep our report simple by restricting attention to the expanding max block approach which gives an overview.
Note that while T* consists of the transcription factors for which skew(x,G) is smallest (most negative), they are not necessarily all negative, and we have excluded some cases where not all x in T* had negative skew score. This was an optional choice, but it seemed appropriate in view of the concept of rewiring.
Nocedal and Johnson [7] write "We do not yet understand how a large network, composed of many transcription regulators and hundreds or thousands of target genes, forms in the first place." We believe that considering only cases in which one transcription factor is switched with another will be inadequate to describe the evolution of networks. They also write "A change even in the regulation of a single gene can have important consequences in modern species'.... However, most biological processes require the coordinated expression of many genes rather than a single gene" to produce a useful phenotype.
Our investigation aims at providing a new approach to thinking about the very complex idea of rewiring, freeing us from the constraint of considering only one transcription factor substituting for one transcription factor (or one gene for one gene).
All the examples in this paper discuss rewiring (Scenario 2) via one team of transcription factors substituting for another team on a collection of genes. However, it is an equivalent problem mathematically - using the same set of binding probabilities - to have one team of genes substitute for another team of genes for a collection of transcription factors (the turnover problem, Scenario 1). In an example of a Scenario 1, Habib et al. [14] present a method for tracing the evolutionary history of regulatory interactions of 88 regulatory DNA motifs associated with transcription factors across 23 Ascomycota fungi, (the same 23 that we study). They use their method to explain the evolution of transcription factor turnover for a collection of genes. Here the transcription factor changes which genes it regulates while preserving the function of the genes. Gasch et al. [15] also study changes in which regulon members that are regulated by certain transcription factors.
We further expect to be able to investigate more complicated problems with very similar ideas in which there is simultaneously a rewiring of transcription factors and a turnover of genes.
No numerical investigation such as ours can produce definitive biological results, but the fact that our first case in Table 1, top row, is similar to a well known case is promising since our results find a team of three transcription factors instead of one in the published results. Table 1 shows the 12 cases with the highest skew scores, and for the top 5 we have included figures. These seem to suggest rewiring of teams of transcription factors. Of course it is desirable to have some of these cases checked experimentally.
It may be significant that eight of the twelve cases in Table 1 involve only two branches, namely branches 4 and 112. While branch 4 includes Saccharomyces cerevisiae and relatives, branch 112 includes Candida albicans and relatives. Those species have been reported in cases of rewiring in the literature.
For each module and each branch of the tree we have computed a block (except for a small number of cases). The existence of a max block is not evidence of significant rewiring, and in fact there is no apparent test of statistical significance. Our solution to this complication is to examine the blocks that have the greatest skewness. For each module and each branch of the tree we compute the skewness of the resulting max block. Figure 7 is the histogram. The distribution has a long tail consisting of high skew scores. We believe that several cases in this flat tail represent actual cases of rewiring.
Histogram of Skewness Scores. The histogram of all skewness scores for the max blocks of all modules and all branches is shown in blue. The horizontal axis reports the two-digit truncation of skew and the height is the number of max blocks that had that score. The moving average in a sliding window of size 7 is shown in red. Note the long tail on the right, which corresponds to the high skew max blocks that we are most interested in
We believe that the understanding of the evolution of transcription networks will have to invoke teams of transcription factors and teams of genes in some essential form.
Li H, Johnson AD. Evolution of transcription networks - Lessons from yeast. Curr Biol. 2010; 17(20):R746–R753.
Hogues H, Lavoie H, Sellam A, Mangos M, Roemer T, Purisima E, Nantel A, Whiteway M. Transcription factor substitution during the evolution of fungal ribosome regulation. Mol Cell. 2008; 29(5):552–62.
Tsong AE, Tuch BB, Li H, Johnson AD. Evolution of alternative transcriptional circuits with identical logic. Nature. 2006; 443(7110):415–420.
Lavoie H, Hogues H, Whiteway M. Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi. Curr Opin Microbiol. 2009; 12(6):655–63.
Martchenko M, Levitin A, Hogues H, Nantel A, Whiteway M. Transcriptional rewiring of fungal galactose-metabolism circuitry. Curr Biol. 2007; 17(12):1007–13.
Weirauch MT, Hughes TR. Conserved expression without conserved regulatory sequence: the more things change, the more they stay the same. Trends Genet. 2010; 26(2):66–74.
Nocedal I, Johnson AD. How Transcription Networks Evolve and Produce Biological Novelty. CSH Symposia on Quant. Bio. 2015; LXXX:1–10. doi:10.1101/sqb.2015.80.027557.
Tanay A, Regev A, Shamir R. Conservation and evolvability in regulatory networks: The evolution of ribosomal regulation in yeast. PNAS. 2005; 102(20):7203–8.
Sarda S, Hannenhalli S. High-throughput identification of Cis-regulatory rewiring events in yeast. Mol Biol Evol. 2015; 32(12):3047–63.
Field Y, Fondufe-Mittendorf Y, Moore IK, Mieczkowski P, Kaplan N, Lubling Y, Lieb JD, Widom J, Segal E. Gene expression divergence in yeast is coupled to DNA-encoded nucleosome organization. Nat Genet. 2009; 41(4):438–45.
Wapinski I, Pfeffer A, Friedman N, Regev A. Natural history and evolutionary principles of gene duplication in fungi. Nature. 2007; 449(7158):54–61.
Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, et al.TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006; 34:D108–110.
MacIsaac KD, Wang T, Gordon DB, Gifford DK, Stormo GD, Fraenkel E. An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinforma. 2006; 7:113.
Habib N, Wapinski I, Maragalit H, Regev A, Friedman N. A functional selection model explains evolutionary robustness despite plasticity in regulatory networks. Mol Syst Biol. 2012; 619(8):1–18.
Gasch AP, Moses AM, Chiang DY, Fraser HB, Berardini M, Eisen MB. Conservation and evolution of cis-regulatory systems in ascomycete fungi. PLoS Biol. 2004; 2:e398.
The authors would like to thank Chris Dock, S. Hannenhalli, and M. Roberts for suggestions, and we thank the referees for their many useful comments.
The publication charges for this article were supported by National Science Foundation Plant Genome Research Program Grant 1444893.
This article has been published as part of BMC Genomics Vol 17 Suppl 10, 2016: Proceedings of the 14th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-17-supplement-10.
This project was supported in part by National Research Initiative Competitive grants 2009-35205-05209 from the United States Department of Agriculture National Institute of Food and Agriculture. This research was also supported by National Institutes of Health grant R01-HG002945 and National Science Foundation Plant Genome Research Program Grant 1444893.
The overview of the supplementary material and methods is provided in Additional file 6: Supplementary material. The database of the computed binding probabilities of 126 transcription factors to 2557 genes shared by 23 species is included in Additional file 3: Supplementary Material. Additional supporting data is included in additional files.
AM and JY formulated the problem. AM and JY developed this novel mathematical approach. AM designed this computational tool and the MySQL relational database. AM, and DSM implemented this computational tool in Matlab. AM selected and extracted the data sets from the public databases, pre-process and loaded them into our MySQL database. AM and AZ loaded the datasets into Matlab. DS prepared figures and supplementary information. AM, JY conducted the interpretation of the results. AM, JY, and AZ contributed equally to the writing of this manuscript. All authors read, revised and approved the final version of the manuscript.
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, 20742, USA
Adriana Muñoz, Daniella Santos Muñoz, Aleksey Zimin & James A. Yorke
Department of Mathematics, University of Maryland, College Park, Maryland, 20742, USA
Adriana Muñoz, Daniella Santos Muñoz & James A. Yorke
Department of Physics, University of Maryland, College Park, Maryland, 20742, USA
James A. Yorke
Cold Spring Harbor Laboratory, 1 Bungtown Rd., Cold Spring Harbor, 11724, NY, USA
Adriana Muñoz
Faculty of Sciences, University of Ottawa, Ottawa, K1N 6N5, ON, Canada
Daniella Santos Muñoz
Faculty of Engineering, University of Ottawa, Ottawa, K1N 6N5, ON, Canada
Aleksey Zimin
Correspondence to Adriana Muñoz.
Additional file 1
Supplementary material: all genes present in each module. We report all modules and for each module, we list all genes in the module. Each gene entry includes identifier and name. (PDF 91 kb)
Supplementary material: yeast species. We report the yeast species names and identifier. (PDF 16 kb)
Supplementary material: database of the computed transcription factor binding probabilities. We report the database of the computed binding probabilities of 126 transcription factors to 2557 genes shared by 23 species. (TXT 415 kb)
Supplementary methods. We describe the method for estimating transcription factor binding probabilities for each PWM (equivalently, transcription factor) on the gene promoter, for each gene in each species. (PDF 135 kb)
Supplementary material: the transcription factors and genes, and their skewness scores of the rewiring blocks. For each module, results are reported for its top scoring branch. Each module lists the highest scoring block. (PDF 155 kb)
Supplementary material overview. We give an overview of the supplementary material and methods provided in this paper. (PDF 74 kb)
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Muñoz, A., Santos Muñoz, D., Zimin, A. et al. Evolution of transcriptional networks in yeast: alternative teams of transcriptional factors for different species. BMC Genomics 17 (Suppl 10), 826 (2016). https://doi.org/10.1186/s12864-016-3102-7
Transcription factor
Transcriptional networks


Fiever, an allusion to FIVE (the fifth brand in the group) and FEVER, was born in 2015 as a cool, unpretentious urban brand aimed at a young audience. The path it treads includes involving its customers in building the brand, seeking always to innovate and to keep pace with this generation. The icon product, the white sole trainers, distils the essence of the brand: practical, cool and versatile.


text:
Kristen Visbal
Find sources: "Kristen Visbal" – news · newspapers · books · scholar · JSTOR (April 2017) (Learn how and when to remove this template message)
(1962-12-03) December 3, 1962 (age 57)
Fearless Girl
Kristen Visbal (born December 3, 1962) is an American sculptor living and working in Lewes, Delaware.[1] She specializes in lost-wax casting in bronze.[2]
1.1 Fearless Girl replicas lawsuit
2 Other selected works of public art
Visbal was born in Montevideo, Uruguay, the daughter of American Ralph Albert and Elizabeth Krystyniak Visbal; the father was in foreign service there at the time of her birth. She attended the University of Arizona in Tucson 1980-1982 and University of Maryland 1983-1984. She's a Bachelor of Arts summa cum laude at Salisbury State University of 1995.[3][2] She was an apprentice of lost wax fine art casting at Johnson Atelier Foundry, Mercerville, New Jersey, 1995—1998, and is the owner and manager of Visbal Fine Bronze Sculpture in Lewes, Delaware since 1998.
Her most well-known work of public art is Fearless Girl (2017), a 50-inch (1,300 mm) bronze figure installed temporarily on the Bowling Green in Manhattan's Financial District, stirring much international attention and controversy, as it challenges the Charging Bull sculpture of 1989.[4] Visbal has said "The piece is pungent with Girl Power!"[1] Kristen Visbal has since been sued for taking $28,102 from the US Coast Guard Alumni Association to make an Alexander Hamilton statue but allegedly failing to produce the work by the contracted deadline.[5]
Fearless Girl replicas lawsuit[edit]
On February 14, 2019, State Street Global Advisors filed a lawsuit against Kristen Visbal, claiming that she has made and sold replicas of the Fearless Girl statue in violation of her contract with the company. The suit claims the artist made at least three unauthorized Fearless Girl reproductions that could damage the company's global campaign in support of female leadership and gender diversity.[6]
Other selected works of public art[edit]
Goddess of the Sea, a mermaid with two dolphins framed in water, Myrtle Beach, South Carolina[7][8]
The Cradle of Coaches (2009-2011 and 2014), a series of ten 120% lifesize statues of celebrated football coaches (Thomas Van Voorhis, Carmen Cozza, Weeb Ewbank, Paul Dietzel, Red Blaik, Paul Brown, Bo Schembechler, Ara Parseghian, John Pont, John Harbaugh) at Miami University's Cradle of Coaches Plaza in Oxford, Ohio.[9][10][11]
In Search of Atlantis (2009), a girl swimming with a green sea turtle, Atlantic Beach, Florida[12][13]
The American Cape (2004), a 12 feet 4 inches (3.76 m) statue of Alexander Hamilton in Hamilton, Ohio; Hamilton's full-length cape represents the 13-star American flag of the time.[14][15]
Sea Express (2003), a man riding on a bottlenose dolphin, Jacksonville Beach, Florida[16][17]
Passing the Torch (2002), a statue of Olympic athlete Bob Hayes (1942-2002), Jacksonville, Florida; Hayes wears his 1964 Olympics clothing and carries the Olympic torch.[18][19][20]
Girl Chasing Butterflies (1998), Merrill Lynch HQ in Plainsboro, New Jersey, and (2005) revised version in Hershey Gardens, Pennsylvania[21][22]
^ a b Flood, Chris (March 8, 2017). "Lewes' Kristen Visbal creates Fearless Girl". Cape Gazette. Retrieved 10 March 2017.
^ a b MacArthur, Ron (August 7, 2012). "Kristen Visbal's artistic journey continues". Cape Gazette. Retrieved 10 March 2017.
^ "PICS: Artist in uproar as 'Fearless Girl' rivals NYC's bronze bull as major Wall Street attraction". Traveller24. 27 March 2017. Retrieved 31 March 2017.
^ "'Fearless Girl': New statue stares down Wall Street's bull". The Mercury News. Associated Press. March 8, 2017.
^ "Fearless Girl's Kristen Visbal sued for breach of contract". Cape Gazette. Retrieved 2019-02-04.
^ Kinsella, Eileen (2019-02-19). "The Investment Firm That Commissioned Wall Street's 'Fearless Girl' Is Suing the Artist for Making Replicas". artnet news. Retrieved 2019-02-20.
^ "The Goddess of the Sea". Myrtle Beach Downtown Public Arts Initiative. Retrieved 10 March 2017.
^ "Public Art Initiative to hold reception for city sculpture project". WMBF News. 2 December 2014. Retrieved 10 March 2017.
^ "Historic Cradle of Coaches weekend draws near". Miami Redhawks. October 12, 2010. Retrieved 10 March 2017.
^ "John Harbaugh statue unveiled at Miami's 'Cradle of Coaches'". nfl.com. April 19, 2014. Retrieved 10 March 2017.
^ "Cradle of Coaches". Kristen Visbal. Retrieved 10 March 2017.
^ Dixon, Drew (30 May 2009). "Publicly funded statue is erected in Atlantic Beach". Retrieved 10 March 2017.
^ "In Search of Atlantis". Kristen Visbal. Retrieved 10 March 2017.
^ "Alexander Hamilton". Hey! Hamilton. Retrieved 10 March 2017.
^ "The American Cape". Kristen Visbal. Retrieved 10 March 2017.
^ "Oceanfront Park". City of Jacksonville Beach. Retrieved 10 March 2017.
^ "Sea Express". Kristen Visbal. Retrieved 10 March 2017.
^ "Bullet' Bob Hayes Commemorated in Bronze". City of Jacksonville. November 7, 2002. Retrieved 10 March 2017.
^ Emery, Glenn. "Have Track Shoes, Will Travel". Jacksonville Historical Society. Retrieved 10 March 2017.
^ "Passing the Torch". Kristen Vispal. Retrieved 10 March 2017.
^ "Statuesque Beauty in the Gardens" (PDF). Twigs & Gigs: A publication for members of Hershey Gardens. 8 (2): 5. Fall 2014. Retrieved 10 March 2017.
^ "Girl Chasing Butterflies". Kristen Visbal. Retrieved 10 March 2017.
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21st-century American sculptors
American women sculptors
People from Lewes, Delaware
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21st-century American women artists
20th-century American sculptors
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redpajama_set_name: RedPajamaCommonCrawl


text:
How to Get Striker 45 UMP in Call of Duty: Modern Warfare
Call of Duty: Modern Warfare will see the addition of the Striker 45 UMP when Season 2 kicks off.
Published Feb. 10, 2020, 11:42 a.m. about Call of Duty: Modern Warfare
by Nicholas Barth
Season 2 of Call of Duty: Modern Warfare will be kicking off on Tuesday, February 11th. A treasure trove of new content will be added to the first-person shooter. One of the most significant additions that will be made to the game in Season 2 will be the Striker 45 submachine gun that players are likely to remember to be the UMP from past entries in the franchise. Fortunately, we have everything you need to know to get the Striker 45 UMP covered for you.
The previous period of seasonal content for the game had two new weapons be available for players to earn for free be leveling their Battle Pass. These two weapons were the Holger-26 and the RAM-7 and were available for players to unlock from the Battle Pass even if they did not purchase it.
This same formula will be used for Season 2 of Call of Duty: Modern Warfare with the GRAU 5.56 and Striker 45 UMP weapons. The specific Battle Pass tiers that players must reach to unlock the Striker 45 UMP is Tier 31. Once a player has reached Tier 31 on the Season 2 Battle Pass, the Striker 45 UMP will automatically be added to their inventories for use in future battles.
The Holger-26 and RAM-7 weapons were unlocked when players reached Tier 15 and Tier 31 of the Season 1 Battle Pass, respectively. Infinity Ward is using these same tiers for the two new weapons in the Season 2 Battle Pass.
There is no doubt that plenty of players will be excited to see how much damage they can do with the Striker 45 UMP when they first unlock it heading into the future of Call of Duty: Modern Warfare Season 2.
Do you want to learn more about Call of Duty: Modern Warfare besides how to get the Striker 45 UMP? If so, be sure to check out our dedicated hub for the popular first-person shooter or three of our most recent pieces of coverage below:
Call of Duty: Modern Warfare Season 2 Battle Pass Rewards
Call of Duty: Modern Warfare Season 2 Patch Notes Guide
How to Get Ghost in Call of Duty: Modern Warfare
How excited are you to get your hands on the Striker 45 UMP in Call of Duty: Modern Warfare Season 2? Let us know over on Twitter and Facebook!
Nicholas Barth
Guides Writer Nicholas enjoys helping fellow members of the video game community learn more about their favorite games. First-person shooters and Battle Royale games are what you can usually find Nicholas playing when he isn't writing. We will congratulate you if you can find a bigger Call of Duty fan than Nicholas, who has been playing since he got Call of Duty 2 with his first Xbox 360. nicholas.barth@greenlitcontent.com


TIP! Prepare as much of your recipe as you can ahead of time. Save time by doing the prep work before you begin cooking the meal.
Do you go out to eat and wonder about the recipes that were used to create your meal? Have you ever tried to make the food by yourself at home? This article will give you the knowledge you need to create these dishes, or even new ones, from your own home. Use this article to discover how enjoyable cooking can be, and what a wonderful hobby it is.
TIP! When you season meats, try cooking a little piece prior to the entire piece. Certain foods like hamburgers, meatloaf and meatballs have certain kinds of seasonings.
Before attempting to prepare food with skewers, take a few things to heart. Try to buy twisted or other shaped skewers when they are metal, the food will stay on.
TIP! Invest in a knife sharpener to make cutting any ingredient easier. It might seem odd at first, but it makes perfect sense.
Find a dark, cool area in which to store herbs and spices. When exposed to light and heat, spices will lose their sharpness and flavor. Herbs and ground spices usually retain their flavor up to a year. Spices that are not ground can keep their flavors for 3 to 5 years. If you store them properly, they will stay fresh for a longer period of time.
TIP! The process of boiling vegetables will remove a portion of their nutrients. To preserve their vitamins and minerals, cook vegetables quickly by steaming them or frying them.
Don't add the dressing until right before serving. This is why it's called prep work, because this is what you want to have done before you begin cooking. That way, when you're ready to cook, it takes some of the stress out of it, especially if you have a deadline to meet. Get everything prepared at least a day ahead of time.
TIP! It is alright to use it on other foods besides meats. Spread it on pumpkin seeds that are roasted to make yourself a savory snack.
In order to make it easier to slice meat into thin strips, you should first marginally freeze the meat. The thin sliced meat is great in many dishes including many Chinese and Thai meals. When you freeze the meat, fibers stretch less making it cut easier. However, to ensure that the meat cooks evenly, make sure that the meat is thawed before you cook it.
They will act somewhat like a sponge that soaks up all the water. Wipe off each mushroom with a clean wet cloth to prepare them for cooking.
TIP! Always make sure that your flour, sugar and baking additives are stored in airtight containers. Reducing exposure to air and insects will allow food to remain fresh and edible for longer periods of time.
TIP! You should try and dry your tomatoes on your own. To be able to dry them yourself, cut the tomatoes half an inch thick, or you should cut Romas lengthwise in half.
When you need to store dry ingredients such as flour, or sugar in your kitchen, use airtight containers. When you store food in airtight containers, it will not spoil so readily, and bugs won't be able to get into it. You can buy these nearly anywhere, so they're a worthwhile investment.
TIP! It is crucial that you buy new spices around every six months. When spices are stored for too long, their flavor degrades, resulting in a less potent spice.
You are almost an amazing at-hone chef! Use your creativity to create fabulous meals and memorable new recipes. There are some many great foods to choose from and culinary recipes that can make cooking interesting and fun.


Who is this advice for? This advice is for parents and carers about cyberbullying. It provides advice and information about how they can protect their child from cyberbullying and how to tackle it if it happens.
Computers are widely used in school and support learning across the curriculum. During the course of their studies, students will use a variety of software programmes from word processing and presentations to animation and website design.
Use of the internet is essential for many subjects so that students can research and develop project work. The school's internet service is provided by Yorkshire and Humberside Grid for Learning (YHGfL) and a filtering system is in place to prevent access to inappropriate online content. While every effort is made to ensure safe use of the internet, we recognise that there may be instances of inappropriate use. If students are uncomfortable with anything relating to computer use they should immediately contact a member of staff.
The ICT facilities provided by the school are a whole school resource and are updated and maintained by the ICT Support team.
Students also have access to the internet via their mobile devices: phones and music players. While in the school buildings students are asked to have these switched off and out of sight.
Year 7 pupils have a module on E-safety in their ICT lessons. By the time they arrive in Year 7 pupils may already have their own online accounts on a social networking site such as Facebook. There are many useful tips on how to stay safe online. The sites shown on this page are just a few that give information on what to do and who to contact if worried about any websites or online contacts.
Awareness of the responsibilities that come with online activities are part of the learning process and students are encouraged to what their digital footprint looks like. It's important to remember that whatever goes online, stays online. When applying to college, university or a job, one's online presence is available for anyone to check. 'Think before you click' is good advice.


This article summarizes the most important changes introduced to the service between November 2015 and January 2016. Alongside tons of bugfixes and numerous improvements to the UI, it represents the way that Buddy passed since it officially launched and changed its name from Meat! to Buddy.
Make sure to follow Buddy on Twitter to stay up to date with the latest changes and announcements. If you prefer the good ol' email, you can always drop a line to our email - we love to talk with you guys!


text:
The Douglas and Charlotte Grant House is a historic building located in Marion, Iowa, United States.  Located on  of land, this Frank Lloyd Wright designed Usonian-style dwelling was constructed from 1949 to 1951, with some construction continuing to about 1960.  This is one of the first houses in Iowa built in this style, having been completed a year after the Lowell E. Walter House located near Quasqueton.  The two houses are very similar in style.  The characteristics that mark this as a Wright-designed house include: the house integrated into the site and opened to the outdoors; the use of window walls and horizontal bands of windows; natural lighting and ventilation; use of natural materials; a horizontal emphasis in mass and proportion; a car port in place of a garage; slab-on-grade construction with radiant heat system embedded in the slab; a flat roof; an open-plan interior; varied ceiling heights on the interior; built-in
furniture; and a large scale fireplace with a central hearth.  The limestone for the house was quarried on the property.  The house was listed on the National Register of Historic Places in 1988.

References

 Storrer, William Allin. The Frank Lloyd Wright Companion. University Of Chicago Press, 2006,  (S.288)

External links

Photos on Arcaid
Grant house on waymarking.com

Houses completed in 1951
Modernist architecture in Iowa
Houses in Marion, Iowa
National Register of Historic Places in Linn County, Iowa
Houses on the National Register of Historic Places in Iowa
Frank Lloyd Wright buildings

redpajama_set_name: RedPajamaWikipedia


text:
How to Password Protect Text Files Using Vim on Linux (or macOS)
April 7, 2017, 11:23am EDT
The vim text editor, a standard tool included on Linux and macOS, can quickly encrypt text files with a password. It's faster and more convenient than encrypting a text file with a separate utility. Here's how to set it up.
Make Sure Your System's Vim Has Encryption Support
Some Linux distributions, including Ubuntu, include a minimal version of vim by default, intended only for basic text editing. For example, Ubuntu calls this package "vim-tiny". If you try to use encryption in such a minimal version of vim, you'll see a "Sorry, this command isn't available in this version" message.
You may need to install the full version of vim to get this feature on your Linux distribution. For example, on Ubuntu, you can get the full version of vim that by running the following command:
The version of vim included by default with macOS does include encryption support, so you don't need to install anything else on a Mac. Just launch a terminal window from Finder > Applications > Utilities > Terminal and the commands will work the same on macOS as they do on Linux.
How to Encrypt a File With a Password
RELATED: A Beginner's Guide to Editing Text Files With Vi
The basic process is relatively simple if you know how to use vi. If you don't, you might get hung up on vi's modal interface. When you open a text file in vim, there are two modes. By default, you're in a "command mode" where you can use the keys on your keyboard to perform commands. You can also press "i" to enter "Insert mode", where you can type normally and move the cursor around with the arrow keys, as you would in other text editors. To leave insert mode, press "Esc" and you'll be back to command mode.
First, launch vim. For example, the following command will launch vim and point it at a file named "example" in the current directory. If that file doesn't exist, vim will create a file named "example" in the current directory when you save it:
vi example
You can also point vi at another path with a command like the below one. You don't have to create a file in the current directory.
vi /path/to/file
Edit the file normally. For example, you can press "i" to enter insert mode and then type text normally. While editing a file, press Esc to ensure you're in command mode and not insert mode. Type :X and press Enter.
You'll be prompted to enter a password, which the text file will be encrypted with. Type the password you want to use, press Enter, and type it again to confirm. You'll need to enter this password any time you want to open the file in the future.
Vim will warn that you're using a weak encryption method by default. We'll show you how to use a more secure encryption method later.
A password will be associated with the current text file in Vim, but you'll need to save your changes before the password is actually assigned to the file. To do this, press Esc to ensure you're in command mode, and then type :wq and press Enter to write the file to disk and quit Vim.
The next time you attempt to open the file in Vim—for example, by running "vi example"—Vim will ask you for the password associated with the file.
If you enter the wrong password, the contents of the file will be gibberish.
Warning: Don't save the file if you open it and see gibberish. This will save the corrupted data back to the file and overwrite your encrypted data. Just run :q to quit Vim without saving the file to disk.
There's one other shortcut you can use here. Rather than creating or opening a file with "vim /path/to/file", you can run the following command to have vim create or open a file and make it immediately prompt you to encrypt the file with a password:
vi -x /path/to/file
Note that you need to use a lower-case x here, while you need to use an upper-case X when running the associated encryption command from inside Vim.
How to Enable Stronger Encryption in Vim
By default, Vim uses very bad encryption for these files. The default "zip" or "pkzip' encryption method is backwards compatible with versions 7.2 and below of vim. Unfortunately, it can be cracked very, very easily—even on hardware from the 90's. As the official documentation puts it: "The algorithm used for 'cryptmethod' "zip" is breakable. A 4 character key in about one hour, a 6 character key in one day (on a Pentium 133 PC)."
You should not use pkzip encryption for your text documents if you want any security at all. However, Vim provides better encryption methods. Version 7.3 of Vim released in 2010 added a "blowfish" encryption method, which is better. Version 7.4.399 released in 2014 included a new Blowfish encryption method that fixes security problems in the original "blowfish" encryption method, and dubs it "blowfish2".
The only problem is that files you create with stronger encryption methods require these newer versions of Vim. So, if you want to use "blowfish2" encryption, you'll only be able to open that file with Vim versions 7.4.399 and above. As long as you're fine with that, you should use the strongest encryption method possible.
To check which encryption method a file is using, open the file in vim, press the Esc key to ensure you're in command mode, type the following command, and press Enter.
:setlocal cm?
The "cm" here stands for "cryptmethod".
You'll see the encryption method used for the current file displayed at the bottom of the vim screen.
To choose an encryption method, run one of the following commands. The "blowfish2" encryption is best for security.
:setlocal cm=blowfish2
:setlocal cm=blowfish
:setlocal cm=zip
Once you've selected your encryption algorithm, use the :w command to write the file to disk or the :wq command to write the file to disk and quit.
The next time you re-open the file in Vim, it won't complain about a weak encryption algorithm. You'll also see the encryption algorithm you selected at the bottom of the vim screen when you open the file.
How to Change or Remove a Password
To remove a password from a file, open that file in Vim and run the :X command. You'll be prompted to provide a new encryption key. Enter the new password you want to use here. To remove the password completely, leave the password field blank and just press Enter twice.
Save the file and quit afterwards with :wq . The file will be decrypted, so you won't be prompted to enter a password when you open the file in the future.
Be sure to remember whatever password you set or you won't be able to access the contents of the file in the future.


Doors open at 7:30 am for early morning drop off of students. Students must be in the building by 7:45 am. Our academic day begins at 7:50 am with prayer, pledge, and announcements. Classes begin at 7:55am. Periods are 40 minutes long. Lunch period is from 12:00 pm until 12:30pm. Before dismissal at 1:55pm, each homeroom returns to their class for our end of the day prayer. After school activities begin promptly at 2:05pm.
When weather permits, the students are taken outside for recess to play.


AgileCraft Professional is for product-focused teams taking their Lean/Agile practices to the next level. After teams become predictable with quality, the next step is to enable a deep customer-centric approach. With Professional, program managers, product managers and release train engineers get what they need to empower their teams to deliver the right things quickly and respond to market change.
While it's great when programs and release trains start humming and product teams start delivering, but there comes a point where transformation needs to go up a notch to create a truly lean, product-focused organization. In the AgileCraft Professional package, you'll get a set of fundamental features that brings teams together to start working smoothly at the Program level. Get better visibility into financials at the epic level and learn how they relate to delivery. Unlock, capture, and never lose innovation with business canvases, Lean UX, mindmaps, and ideation tools.
We understand what a value-driven organization needs. When multiple programs or release trains exist in an organization, you need to ensure that you have visibility into the work that is delivered and can confidently communicate that the right things are being built for your customers. No matter what you've encountered in your transformation into a product-driven company, AgileCraft has a solution for your most challenging obstacles.
Roadmaps enable product managers to create and communicate a shared vision for what the organization will build over time and connect that vision to the actual work the engineering teams are doing. Customers will love getting customized roadmaps for briefings that give them unprecedented visibility in to your plans. And, you will love never having to manually update roadmaps in presentations or spreadsheets, nor present an out-of-date roadmap again.
Capture the big value using interactive backlogs with ideation portals, mind maps, vision maps, and business canvases. Quickly build backlogs of epics, features, and stories in easily-to-generate mind maps and vision maps so that ideas are never lost. Keep stakeholders and executives informed on the key strategic elements of your business strategy with ease and precision. Compact, easy-to-read, graphical formats make visualizing ideas easy and straightforward.
When you have reports floating around in spreadsheets, there is never certainty that the news is accurate or up to date. When your data is centralized from the team level to the product level, you will always know what's happening, up to the minute , and what to expect.
The product room is your command center for ensuring the right thing is getting done. This is where you manage the entire product planning and execution flow, including actions, impediments, dependencies, and objectives. Product managers plan, manage, and report on work in a unified view that the team can observe and monitor, keeping them engaged in the bigger picture.
The goal of forecasting is to allocate epics, stand-alone features, or capabilities among teams and achieve a good fit within the release. The forecasting tool will help you allocate epics, stand-alone features among teams and achieve a good plan for each release. Forecast the probability of a successful release. View the current release forecast to compare the size of the backlog with the release date.
With epic financials, program managers can gain the insights to track spending against delivery. As programs mature, it becomes important to be able to report delivery against what's being spent. It's also important to know what is going to be spent in the future, so we get you started tracking spending at the epic level. This allows you to calculate the cost and budget of the entire epic, and even assess the epic's exposure to risk.
Visualize and communicate a shared vision for what is built over time and connect that vision to work being done.
Place better bets by measuring the real time cost versus value across work, programs and products.
Calculate the cost and budget of the entire epic, and assess the epic's exposure to risk factors.
Define the core direction and vision of your product or organization in a simple, graphical format.
Create business personas and get visibility into what's planned and what's delivered for customers.
Stakeholders vote on the design of a lean use case tied to personas and experience maps.
Lay out the key elements of your business strategy and define what will make your product successful.
Define, gather, and analyze intelligence about your competitors and their products.
Enable community collaboration and automatically convert the best ideas into backlog items.
Quickly build backlogs of epics, features, and stories in an easy-to-brainstorm mind map.
Manage the program or release planning and execution workflow through a set of dashboards in a central location.
Allocate epics, features, or capabilities among teams and achieve a good fit within the release.
Organize your product backlog and frame the user journey as it is tied to execution.
Refine strategy, allocate budget to investment themes, manage program funding, and assess budget vs. value.
Drive and measure the effectiveness of your agile and / or digital transformation.
Visualize the work flowing through the system with custom swim lanes, cards and flows for a full custom experience.
Observe detailed trends for products and programs driving better customer satisfaction and planning predictability.


2018-07-09 · Transcending the predominant paradigm of the controversial 'brutalization' thesis (George L. Mosse), the article relies on a transnational perspective .
as a teacher and scholar, George Mosse has posed challenging questions about what it means to be an intellectual engaged in the world.
In 1998 he graduated with a thesis on the treatment of Jews in occupied Poland. The Brutalization of the German-Soviet War in the Summer of 1941).
© Best essays Essay writing | Essay examples Brutalization thesis.


Axogon Mutator is a looping texture generator program. It allows you to generate nice-looking moving texture backgrounds for your needs. The main features are: looping textures, directional texture mutation, formula editor, coloring control, texture collection files, output resolutions from small to HD, unlimited export movie length, and textures uniqueness. Version 2.0.5 has improved texture generator, Formula editor, and QuickTime export.


The new Novell Data Synchronizer allows personal information management (PIM) data such as tasks, events, and calendar schedules to synchronize between enterprise systems such as Novell GroupWise and third-party enterprise systems.
In this session we will introduce the New Novell Data Synchronizer Mobility Pack with focus of the Hands-on labs on installation of the new Data Synchronizer Engine and configuration of the Novell GroupWise and ActiveSync Connector. Additional Connectors for connection to various CRM solutions will also be covered.
Prerequisites: This session is intended for those who want their Novell GroupWise system to connect and sync information to Mobile devices or with CRM systems.
This entry was posted on Friday, May 11th, 2012 at 4:33 pm	and is filed under ATT Live May 2012. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.


text:
Tools of the Mind Shows Lackluster Results in Experimental Trials
By Sarah D. Sparks — March 09, 2012 3 min read
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Tools of the Mind, an early- childhood education curriculum, has drawn a lot of attention in recent years for its potential to increase young students' ability to self-regulate. Yet new research suggests the play-based program may be no better than traditional early education at improving self-control or later school readiness.
Thursday's Society for Research on Educational Effectiveness panel on Tools of the Mind drew a packed audience—including Institute of Education Sciences Director John Easton and James Griffin, director of the early learning and school readiness program of the National Institute of Child Health and Human Development. Tools is certainly an intriguing concept: Children role play in different scenarios based on their own written "play plans," to increase their patience, focus and self-control.
Ultimately, the experimental studies found few to no benefits for the program.
Vanderbilt University's Peabody Research Institute found, in a study of 30 rural preschool classes in Tennessee and North Carolina, that there were "clear, observable differences" between Tools of the Mind classrooms and others; for example, the teachers in the control group were less likely to know what literacy curriculum they were using, and the teachers in Tools classes rated their students higher in interpersonal skills. Yet researchers found no significant differences in the students' literacy, language or math skills, based on tests at the beginning and the end of a year of preschool, and moreover, no significant differences in self-regulation, self-control or working memory. The study will follow students into kindergarten this year to look for any later effects.
Study co-author Sandra Jo Wilson said the group did not start out thinking Tools would simply meet par with other preschool programs. "We were really surprised with what we found," she said.
Christopher J. Lonigan, associate director of the Florida Center for Reading Research at Florida State University, pitted Tools and standard preschool programs against the Literacy Express Comprehensive Preschool program, a more teacher-directed, skills-based curriculum developed at the center. In a randomized, controlled trial of 118 preschools in New Mexico and Massachusetts, Lonigan found mixed results. Literacy Express outperformed Tools in expressive vocabulary and phoneme awareness, but Tools had improved results in its second year of implementation, and none of the programs provided children an edge in self-regulation.
It could be that Tools does exactly what it was intended to do—encourage students toward deeper, more mature play—but that the play doesn't automatically transfer to academic achievement and self-control as educators have hoped.
In a University of Buffalo study of three urban school districts in California, researcher Douglas Clements found that students in the Tools classes did score significantly higher than other students in using so-called "mature play," e.g. using props and role play, as well as being able to talk about their play. Yet, Clements noted, "There was no evidence that the increase in mature play led to any increases in self-regulation."
I was a bit disappointed at the lackluster results for Tools (a fourth study, on the use of Tools with English-language learners, is still at a preliminary stage but so far also seems to show middling benefits.) Yet I found the discussion refreshing. The authors themselves noted that they will be unlikely to have the chance to publish their studies beyond the conference because there were such limited positive results, but this sort of housecleaning is needed in education research. While studying what works and where, it's also important to show people what doesn't work in a given situation, and start figuring out why.
Sarah D. Sparks covers education research, data, and the science of learning for Education Week.
Opportunity/Achievement Gap
A version of this news article first appeared in the Inside School Research blog.
Inside School Research
Student Well-Being Explainer How Should Schools Quarantine Students Exposed to Coronavirus? An Explainer
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Students are reminded to wear a mask amidst other chalk drawings on the sidewalk as they arrive for the first day of school at Union High School in Tulsa, Okla., Monday, Aug. 24, 2020.
Mike Simons/Tulsa World via AP
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text:
Album Other People's Greatest Hits
Mr. Brightside Lyrics
[The Killers cover]
I'm coming out of my cage
And I've been doing just fine
Gotta, gotta be down because I want it all
It started out with a kiss, how did it end up like this?
It was only a kiss, it was only a kiss
Now I'm falling asleep
And she's calling a cab
While he's having a smoke and she's taking a drag
Now they're going to bed and my stomach is sick
And it's all in my head, but she's touching his
[Pre-Chorus]
Chest now, he takes off her dress now
I just can't look, it's killing me
And taking control
Jealousy, turning saints into the sea
Swimming through sick lullabies, choking on your alibis
But it's just the price I pay
Destiny is calling me
Open up my eager eyes, 'cause I'm Mr. Brightside
'Cause I just can't look, it's killing me
"Mr. Brightside" Track Info
Cover Of
Mr. Brightside by The Killers
Other People's Greatest Hits Patent Pending
1. Wasted / Wake Me Up
2. Spice Up Your Life
3. Can't Take My Eyes Off You
4. Never Gonna Give You Up
5. Shout Out To My Ex
6. Livin' La Vida Loca
7. All Time Low
8. Mr. Brightside
9. Shape of You
10. See You Again


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text:
The following is a list of current United States governors by age.  This list includes the 50 state governors, the five territorial governors, as well as the mayor of Washington, D.C. in office as of .

State governors

Territorial governors

Mayors of federal districts

Demographics of state governors

Note: The following information for currently serving state governors is correct as of 2021.

 Statistics (not counting territorial governors):
 The median age is .
 The median age at inauguration is .
 The median term length is .
 The average age among Republicans is .
 The average age among Democrats is .

 Age Ranges:
 11 governors are in their 70s;
 20 governors are in their 60s;
 12 governors are in their 50s; and
 7 governors are in their 40s.

 Political Party:
 28 Republicans (56%)
 22 Democrats (44%)

 Sex:
 41 Males (82%)
 9 Females (18%)

See also
 List of current United States lieutenant governors by age
 List of current United States governors

References

Governors
Lists of current office-holders of country subdivisions
Lists of political office-holders by age
 Current age