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The Roane State baseball team has added two new members to this year's class of signees.
On Tuesday, it was announced that Trousdale County duo of Luke Fergusson and Marquel Claiborne would be suiting up for the Raiders in 2016.
The duo has helped the Yellow Jackets to a 9-13 record this season.
"It's kind of crazy," Fergusson said. "I always wanted to play at the next level."
For Fergusson, the idea of being able to stay somewhat close to home helped make his decision.
It didn't take long for the CAK offense to get things going on Wednesday afternoon when they traveled to Roane County to take on Kingston.
Of the 11 batters in the first inning for the Lady Warriors, seven of them connected with Kingston pitcher Meredith Garrett.
Kingston allowed five runs in the opening inning, but it would be a while before the Lady Jackets found their way to the scoreboard.
Kingston was held scoreless in the bottom of the first, but Garrett sat down the first three batters CAK sent to the plate in the top of the second.
After losing both matches to Grace Christian more than a week ago, the Rockwood boys and girls tennis teams were looking to get back on track against Pigeon Froge.
Unfortunately for both the Tigers and Lady Tigers that didn't happen as the two fell short, 0-9 and 4-5.
It was tough sledding for the Tigers against Pigeon Forge. In all nine sets, Pigeon Forge racked up 8 points for victories.
Drake Long was the first man up for the Tigers, but lost to Noah Amet, 8-2.
The Oliver Springs High School football team will be hosting a Lift-A-Thon event to help raise money for the 2015-16 football season with new head coach Larry Green. The team is looking for money to help rebuild the program which includes providing equipment, food, transportation and other player necesities. The event will take place April 27-May 1 at Oliver Springs High School. On Friday, May 1, the team will hold a cookout for people to meet the 2015-16 players and coaches.
The Kingston Yellow Jackets soccer team scored six goals on Tuesday night to defeat Oliver Springs, 6-1. Zach McCoig completed the hat trick for Kingston, recording the first three goals. Carson Heidle, Andres Guerrero and Colin Haba each added additional goals. Marcus Miller was responsible for the Bobcats lone goal.
Over the weekend, former Roane State Raider and Coalfield native Joe Ellis and his partner Matthew Townson hauled in 42 pounds, 14 ounces to nab sixth place for Tennessee Tech in the 2015 FLW College Fishing National Championship on Lake Murray.
"I thought we were going to get a top five," Ellis said. "It just didn't happen for us."
The Lady Devils of Harriman took the drive up Harvey H. Hannah Highway to face the Lady Cats of Oliver Springs On Monday, but the Lady Devils would go home with a 3-0 loss.
Courtney Jones of Oliver Springs continued her phenomenal senior campaign as she recorded 10 strikeouts, only gave up three hits and was able to pull the shut out.
Harriman couldn't find their swing in the first inning, only mustering one hit.
Oliver Springs mirrored Harriman's results as they too could only get one runner on base.
There wasn't a winner on Monday night when the Harriman and Oliver Springs' soccer teams faced off, but there wasn't a loser either as the two ended the contest with a 0-0 tie.
"The boys played good," Harriman head coach Joe Wampler said. "I'm proud of them. They've been talking and they're doing a lot of passes and drops like they should. We've still got a long way to go, but we're definitely going to end up getting there."
Harriman was successful moving the ball early against Oliver Springs, but shots were coming nearly as often for the Blue Devils as the were for the Bobcats.
Seven combined hits was all that the Kingston Yellow Jackets and the
Christian Academy of Knoxville Warriors put up in Monday night's baseball game.
However, the Warriors (25-4-1) still walked away with the 10-0 run-rule win.
The Jackets gave up 16 walks and hit four batters on Monday in the loss, with six of those instances happening with the bases loaded.
"We didn't have our stuff tonight," Kingston head coach Bruce Robinette said.
"(The strike zone) was tight on both sides, and they adjusted and we didn't."
Copyright © 2015
The Roane County News is your source for local news, sports, events, and information in Roane County and Kingston, Tennessee, and the surrounding area.


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Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival.
Here we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes.
Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.
Genes and their protein products carry out cellular processes in the context of functional modules and are related to each other through a complex network of interactions [1]. Understanding an individual gene or protein's network properties within such networks may prove to be as important as understanding its function in isolation [2]. Because of this, numerous studies have focused on the large scale modeling of genomic and proteomic data. Utilizing network theory, these studies have yielded insights into biological systems. For example, both protein interaction networks and gene co-expression networks exhibit a strong modularity reflecting functional partitioning. Both of these network types have been frequently observed as having a scale-free topology, with the existence of highly connected hub nodes [3-7].
Scale-free networks are resistant to random perturbations but sensitive to targeted removal of highly connected nodes [8]. Comprehensive efforts to determine the functional consequences of individual gene deletions in yeast provide the opportunity to study the relationship between individual gene network properties and gene deletion lethality [9]. For example, physical interaction studies in yeast have allowed comparison of connectivity to gene essentiality based on gene deletion. [10]. Typical of scale-free networks, there were few highly connected proteins within the network, and the deletion of a protein with a large number of binding partners is more likely to be lethal in yeast. Thus, the relative position of nodes within a protein interaction network is strongly affiliated with distinct biological properties of individual proteins. Similarly, analysis of unweighted gene co-expression networks have revealed a relationship between connectivity and essentiality across all genes [11].
Correlation of gene expression across a wide variety of experimental perturbations has been shown to cluster genes of similar function [12]. Since this guilt-by-association approach may lead to false positive groupings, approaches have been refined by comparing orthologs across divergent species indicating that highly conserved co-expression is a strong predictor that two genes will function in similar pathways [13-15]. This indicates that functionally related genes are under similar expression constraints. The gene co-expression networks that are based on these relationships have been shown in multiple species to be frequently scale-free and exhibit a small world architecture similar to protein interaction networks even though they are generally more strongly connected [3,14]. It is still unclear however, to what degree the network properties of individual genes within a co-expression network can predict relative gene importance for a particular process. To assess this, we have constructed three networks based on a weighted measure of connectivity [16] of correlated gene expression in yeast using three separate microarray data sets. We assessed relationships between essentiality and connectivity of each gene within the whole network. Further, we define 'modules' (groups of highly correlated genes) and determine that in some instances, the relative importance of genes within these modules can be inferred from network connectivity. We demonstrate that genes which have high connectivity (i.e. 'hub' genes) within a weighted co-expression network are significantly more likely to be essential for yeast viability. Furthermore, we demonstrate a relationship between connectivity and a measure of sequence conservation. Finally, we show that certain critical modules are conserved from one network to another, and that in many cases it is possible to extend the relationship between connectivity and essentiality or between connectivity and conservation within given modules. Thus, analysis of gene co-expression networks provides insight into the functional importance of individual genes within modules of co-expressed genes.
Network validation and properties
In order to test the relationship between gene connectivity within the co-expression network and gene essentiality, we first constructed three networks corresponding to three microarray datasets. The yeast microarray data were derived from experiments designed to analyze DNA Damage (DD) (n = 51), Cell Cycle (CC) (n = 44), and Environmental Response (ER) (n = 151) [17-19]. Each network was constructed as conceptually shown in Figure 1. Nodes were defined as individual genes. The co-expression network is based upon the correlation relationships between individual genes across a dataset (Figure 1A). From the microarray data, the absolute value of the Pearson correlation is the initial measure of gene co-expression similarity (Figure 1B). This co-expression similarity was then transformed into an adjacency matrix (Figure 1C) by raising the similarity to a certain power based on a scale free topology criterion as described previously [16]. The powers selected were 12 (DD), 10 (CC), and 18 (ER). These data can also be visualized as a drawn network (Figure 1D).
Figure 1. Generating a gene co-expression network. (A) Illustration to show how genes that are highly correlated (blue) will look across a dataset. (B-C) Flow chart for defining a gene co-expression network based on a simple Pearson correlation matrix. (D) An example visualization of a network produced by using the Fruchterman Rheingold algorithm on the data in Figure 1B and 1C. The node highlighted in yellow, is an example of the kind of highly connected node that this study shows is more likely to be an essential gene for survival.
Each of the datasets resulted in the construction of distinct networks but with global similarities. The differences in the modularity of the three networks is demonstrated by comparing the heat maps of the topological overlap matrices (Figures 3A, 4A, and 5A). A topological overlap matrix is an indicator of the similarity of all the nodes in question and is defined in detail in the methods section of this manuscript. Approximations of all three of the networks are also drawn in Figures 3B, 4B and 5B using the Fruchterman-Reingold algorithm as implemented in Pajek [20,21] and demonstrate obvious modularity with aggregation of genes into strongly co-expressed groups.
Figure 2. Global gene co-expression networks. (A-C) Log-log plots of connectivity distributions in each of the three networks drawn from DNA Damage, Environmental Response, and Cell Cycle, respectively. The linear relationship indicates a scale-free structure. (D-F) Correlation plots showing the relationship between gene group connectivity and essentiality in the same order as A-C. On the y axis of these plots is the percentage of genes determined to be essential in yeast. (G-I) Relationship between the average blastp score of a node and k for all datasets in the same order as A-C. On the y axis of these plots is the average log(e score) of genes within that bin. For plots D-I, the connectivity within each network for each gene was determined and each gene rank ordered by connectivity. 20 equal sized bins for each of the expression datasets were formed and the average connectivity of the genes in each bin plotted on the X axis
Figure 3. A co-expression network of the DNA Damage dataset. For all panels in the figure: blue color represents members of the rRNA processing module, yellow color represents members of the protein synthesis module and red color represents members of the ubiquitin pathway. (A) A hierarchical clustering of the topological overlap matrix for the DD dataset. (B) A drawn network of gene co-expression from the DD dataset. Edges were computed from the Pearson correlation coefficients. Network structure was drawn in Pajek [20]. Each gene is represented as a dot. Edges are drawn as grey lines. Colored dots belong to the module that their color indicates and grey dots indicate all other genes in the network. (C-E) Scatter plots showing the relationship between gene group connectivity and essentiality for rRNA processing, protein synthesis and ubiquitin from the DD network respectively. On the y axis of these plots is the percentage of genes determined to be essential in yeast. The number of essential genes in each module/total number of genes in each module was: 196/390 = 50.3 (C), 122/441 = 27.7 (D), 50/222 = 22.5 (E). (F-H) Relationship between the average blastp score of a node and k for the same module members listed in C-E. On the y axis of these plots is the average log(e score) of genes within that bin. For plots C-H, the connectivity within each module for each gene was determined and each gene rank ordered by connectivity. 20 equal sized bins for each of the expression datasets were formed and the average connectivity of the genes in each bin plotted on the X axis.
Figure 4. A co-expression network of the Environmental Response dataset. For all panels in the figure: blue color represents members of the rRNA processing module, yellow color represents members of the protein synthesis module and red color represents members of the ubiquitin pathway. (A) A hierarchical clustering of the topological overlap matrix for the ER dataset. (B) A drawn network of gene co-expression from the ER dataset. A drawn network of gene co-expression from the DD dataset. Edges were computed from the Pearson correlation coefficients. Network structure was drawn in Pajek [20]. Each gene is represented as a dot. Edges are drawn as grey lines. Colored dots belong to the module that their color indicates and grey dots indicate all other genes in the network. (C-E) Correlation plots showing the relationship between gene group connectivity and essentiality for rRNA processing, protein synthesis and ubiquitin from the ER network respectively. On the y axis of these plots is the percentage of genes determined to be essential in yeast. The number of essential genes in each module/total number of genes in each module was: 317/929 = 34.1 (C), 98/323 = 30.3 (D), 18/37 = 48.6 (E). (F-H) Relationship between the average blastp score of a node and k for the same module members listed in C-E. On the y axis of these plots is the average log(e score) of genes within that bin. For plots C-H, the connectivity within each module for each gene was determined and each gene rank ordered by connectivity.
Figure 5. A co-expression network of the Cell Cycle dataset. For all panels in the figure: blue color represents members of the rRNA processing module, yellow color represents members of the protein synthesis module and red color represents members of the ubiquitin pathway. (A) A hierarchical clustering of the topological overlap matrix for the CC dataset. (B) A drawn network of gene co-expression from the CC dataset. A drawn network of gene co-expression from the DD dataset. Edges were computed from the Pearson correlation coefficients. Network structure was drawn in Pajek [20]. Each gene is represented as a dot. Edges are drawn as grey lines. Colored dots belong to the module that their color indicates and grey dots indicate all other genes in the network. (C-E) Correlation plots showing the relationship between gene group connectivity and essentiality for rRNA processing, protein synthesis and ubiquitin from the CC network respectively. On the y axis of these plots is the percentage of genes determined to be essential in yeast. The number of essential genes in each module/total number of genes in each module was: 154/300 = 51.3 (C), 105/398 = 26.4 (D), 38/312 = 12.2 (E). (F-H) Relationship between the average blastp score of a node and k for the same module members listed in C-E. On the y axis of these plots is the average log(e score) of genes within that bin. For plots C-H, the connectivity within each module for each gene was determined and each gene rank ordered by connectivity.
Individual gene connections can be difficult to visualize in such complex graphs as presented. Thus, in order to probe the structure of the network, we first sought to determine characteristics of the general network topology. We considered two models. To quantify the fit to the scale free topology model or to the truncated exponential model, we used the model fitting index R-squared of the corresponding linear model involving the log transformed variables [16]. The pattern of network connectivity in these datasets approximated a scale-free topology (Figures 2A, 2B, and 2C). These results are similar to the general trend with genetic perturbation networks and other gene co-expression networks [3,22]. However, gene co-expression networks may better fit an exponential power law [23]. For two of the three constructed yeast networks (DD and ER), the distribution of connectivity (k) of genes best fits a power law p(k) ~ k-γ and the other (CC) best fits an exponentially truncated power law p(k) ~ k-γ e-αk [24]. We point out that in general that regardless of the overall structure, the major trend is that the high connectivity genes are few in number, and that most genes have low connectivity (Figure 2A–2C).
Global network connectivity relationships
Network modeling in biological systems [2,8,11] stresses the relative importance of highly connected nodes. Thus, the small number of high connectivity genes within the gene co-expression networks defined here are expected to be more important. To test the relative importance of each gene across the entire network, we determined the relationship between connectivity and gene essentiality. All the genes for a network were first ranked according to their weighted connectivity and then placed into 20 equal sized bins according to their position in this ranking. The weighted connectivity of a given gene is defined as the sum of its connection strengths with all other genes in the network (see the methods section). The percentage of essential genes in each bin was then plotted against the mean connectivity in each bin (Figure 2D–2F). We also calculated the Spearman correlation coefficient based on the ranked unbinned data and found that there is a relationship between the connectivity and the proportion of essential genes in each of the three datasets (R > 0.2, and p value < 10-39) for each network. While the binned plots are well suited for visualizing the results, we calculated the correlation coefficients on the raw, unbinned data to arrive at a valid p-value.
To study whether our conclusions were being unduly influenced by data outliers, we removed genes whose connectivity was more than two standard deviations away from mean connectivity but found no substantial variation in the reported relationships. Thus, there is a clear relationship between connectivity of genes, in these weighted gene co-expression networks, and gene essentiality across the whole network. These results are robust with respect to experimental conditions since the three separate datasets all have the same functional relationship between connectivity and gene essentiality. These results are consistent with a single whole network generated from gene co-expression data and using an unweighted network [11], as well as networks based on protein-protein interactions [10].
Yeast co-expression networks fit an evolutionarily plausible model in which network connection distribution is a natural consequence of gene duplication, mutation and deletions [13]. The emergence of approximate power-law distribution (scale free topology) is intimately linked to the growth of the network in which new nodes are preferentially attached to highly connected already established nodes [2,8]. A direct consequence of this network growth model is that high connectivity 'hub' genes are more likely to be highly conserved across different species. Stated another way, within a gene co-expression network the oldest nodes would be more likely than the newer nodes to be in hub positions. With this in mind, we examined the relationship between connectivity and the relative evolutionary sequence conservation of a node (as evidenced by the average best blastp hit log(e score) against N. crassa C. elegans, D. melanogaster, H. sapiens, and M. musculus). High connectivity genes are plotted in Figure 2G–2I relative to the average of the blastp hit in four other eukaryotic genomes. As before, we calculated the Spearman correlation for the ranked, but still unbinned, data and found that there is a relatively weak but highly significant correlation indicating that high connectivity genes are more evolutionarily conserved (R = -0.15, p value = 1.3 × 10-19, for the DD network; R = -0.22, p value < 1.4 × 10-39 for ER network; and R = -0.18, p value = 4.0 × 10-29 for the CC network). Our results were robust with respect to the removal of outlier genes (i.e. genes whose connectivity was more than two standard deviations away from mean connectivity). This relationship is similar to that reported for gene perturbation networks [22] and in evolutionarily conserved gene co-expression relationships [14].
Identification of modules
In order to identify modules, we performed hierarchical clustering on the topological overlap matrix derived from these networks as described in the methods section. In doing so, we identified modules that involve conserved processes for rRNA processing, protein synthesis, and ubiquitination (Table 1) using an EASE analysis [25]. One prediction about network constructions is that certain critically important modules ought to be repeatedly detected in independent datasets. Within the network modules (groups of high connectivity genes within the network) produced by the different datasets for gene ontology enrichment, 3 distinct modules that were common to the 3 different data sets were detected (Table 1 and Figure 6). Comparing the modules that had an enrichment in rRNA processing, protein synthesis and the ubiquitin pathway terms across the DD, CC and ER datasets resulted in a highly significant overlap of 215 genes (hypergeometric distribution p = 3.1e-173). Similarly, the protein synthesis module also had significantly more overlap than expected by chance in which 172 genes were present in all three modules (p = 5.6e-99). The ubiquitin module had the least overlap of common genes (12 genes, p = 3.4e-07) probably because of its smaller size in general.
Table 1. Identifying modules that repeated from one dataset to another
Figure 6. Module members retrieved from very different datasets are conserved. For all panels in the figure: black color represents modules retrieved from the DD dataset, blue color represents modules retrieved from the ER dataset, and green color represents modules retrieved from the CC dataset. In each panel A-C a different module is examined for overlap of its members in a Venn diagram. In each diagram the relative overlap of the various modules has been diagramed as different amounts of area with the count represented for each zone by a number that quantifies the precise number of matches.
These three processes are well known to be fundamental processes and have been described before in great detail, and so we decided to investigate the nature of these overlapping regions further. Gene members were selected that were shared within similar functional modules from the three datasets and searched for in the KEGG [26] database. In the ubiquitin pathway, 9 of the overlapping 12 genes were found in KEGG, and all of these were found to be in the same proteasome structure. Since the overlap for this module was unusually small due in part to the very small size of the ER dataset's ubiquitin module, we also looked at the overlap just of the CC and DD ubiquitin modules. Of the 57 genes present in both the CC and DD modules, only 14 were present in the KEGG database. Of those 14 members, all of them were in the same proteasome structure. For the Protein Biosynthesis module, 135 of 172 overlapping members were represented in the KEGG database, and of these, 72 out of a possible 86 were components of the KEGG annotated yeast ribosome. Notably, if the 172 overlapping members were first rank ordered by their average network connectivity, and the bottom quartile removed, then 100% of the remaining members are part of this same yeast ribosome structure, which shows that in this overlap set the high connectivity genes were in fact the members of the yeast ribosome. Finally, the overlap of genes detected in the rRNA modules overlap identified purine and pyrimidine synthesis and RNA polymerase 3 pathways. These pathways dominate this list with 30 out of the 49 identified matches to the KEGG database. Unlike the previous two results however, multiple pathways are found that represent this module. The three network modules discussed all contain large protein complexes. This is in agreement with the recent discovery that there is often a correlation between genes that are expressed together and proteins that bind to each other [27]. However, there is no reason to believe that modules in co-expression networks will always correspond to protein complexes since our module detection method should find any set of genes with high pair-wise correlations. Any experimental perturbation that gives rise to a highly correlated gene set is expected to result in a corresponding network module.
Intramodular network connectivity relationships
Having identified 3 functionally similar modules in the three different datasets, we assessed whether these same modules contain the same information as the entire network. Is the connectivity of a gene within the identified modules correlated with the likelihood that a gene was essential by deletion analysis and also whether it was conserved (Figures 3, 4, 5)? To test connectivity correlations within these modules, we define the intramodular connectivity (k.in) as the sum of all connectivity measures only with genes within the same module. Similar to that performed for the total gene connectivity within the whole network, binned values were plotted to observe the possible relationships (Figures 3C–H, 4C–H, and 5C–H). To evaluate significance, Spearman correlation coefficients for the ranked but unbinned data were calculated for relationship of k.in to essentiality and sequence conservation. Interestingly, there was a general trend to similar correlations as that seen within the whole network. The correlation with sequence conservation was generally weak: The ubiquitin module formed from the DD dataset and the rRNA processing modules in the ER and CC were the most correlated with k.in. There was a correlation of k.in and essentiality in all three datasets within the rRNA Processing module (Figures 3C, 4C and 5C), and for two of the datasets (DD and ER but not CC) within the ubiquitin module, and a trend towards a weak anticorrelation within the protein synthesis module (Figures 3D, 4D, and 5D). Thus, some of the relationships between connectivity and essentiality or sequence conservation and not detected when the analysis is restricted within the modules.
Characterizing modules that contain essential hub genes
The relationship between intramodular connectivity and essentiality is not necessarily universal for any module in any dataset. Whether or not a module contains meaningful information for predicting essentiality depends on the expression data (biological perturbations) that were analyzed. One can view the correlation of k.in and essentiality as a test of the modules importance. The key issue is that in order for a module to demonstrate this relationship, there must be a sufficient number of essential genes within the module. As some modules have substantially fewer essential genes than others, there is little power to detect any relationship with k.in within those modules. We calculated the Spearman correlation between the proportion of essential genes within a module and the correlation of k.in with essentiality and show that there is a strong positive correlation (r = 0.48). Since in actual applications the goal will be to find unknown 'essential' genes, it would be preferable to have another metric of 'essential' gene enrichment. In this regard, it is interesting to note that of the three modules studied in the 3 datasets (9 total), there were 4 that had a correlation of k.in with sequence conservation (p < 0.05). Of these 4 modules, all 4 were shown to have a strong relationship of k.in with essentiality in the same modules. Of the 5 without a correlation (p > 0.05), only one showed a correlation between k.in and essentiality (DD, ubiquitin module). Thus, the detection of a relationship between intramodular connectivity and sequence conservation may predict those modules with a relationship between k.in and essentiality.
Our results extend the inferences made from protein and metabolic networks to the more prolific datasets available from gene expression data. These results are made possible due to broad surveys of expression and the comprehensive characterization of the yeast genome with deletion mutants. In contrast to gene expression data, which are based on the assessment of cells in many different states of perturbation, protein interaction measurements are determined in only a limited set of conditions which by nature only capture a subset of all possible protein-protein interactions. Others [22,28,29] have constructed gene perturbation networks by looking at the expression changes when individual genes are eliminated or in relation to transcription factor expression. Microarray based surveys of gene expression allow detailed surveys of gene expression across a wide variety of experimental perturbations. From these data a strong correlation of gene expression for two genes implies that both genes act within a common functional group and are under similar transcriptional control. Since these networks are not based on direct measurements of protein binding/interaction, the presence of two genes that are highly correlated has a different interpretation than that derived from a protein interaction network. In a co-expression network, two closely tied nodes would be highly correlated but not necessarily direct binding partners. In spite of this key difference, numerous other studies have indicated that genes that are correlated with each other across a modest range of experiments tend to function in similar ways and that this functional categorization is strengthened when gene co-expression exists across large evolutionary distances [12,14].
Here, we use a more general weighted gene co-expression network construction method for network construction that can be applied generically to microarray data using simple correlational analyses of individual genes [16]. Advantages of weighted networks over unweighted networks based on dichotomizing the correlation matrix include a) that the continuous nature of the gene co-expression information is preserved and b) that the results of weighted network analyses are highly robust with respect to the choice of the parameter β, [16]. We show that there is a strong correlation with the weighted gene co-expression connectivity measure and gene essentiality and sequence conservation. Our analysis demonstrates that individual conserved modules are identifiable within a network. Moreover, the presence of these modules, which were detected in three independent networks, illustrates that these networks have a structure to them which matches known cellular biology. We have also shown that when sufficient information is available, these correlation modules can provide further information about which of their members are more likely to be important for a given process. The ability to assess whether or not a module is complete enough to be informative using a test like either of the ones employed here will be important for validating the merit of less understood modules in the future.
Our study extends the strong links between the network properties of a gene within co-expression networks and the global importance of each gene to the yeast cell, and may have implications for targeted drug therapy in more complex biological systems like cancer. For instance, one can infer from an analysis of the single gene deletion data that single targets for inhibition can be partially predicted from expression data. However, in biological systems an optimal effect is frequently achieved by inhibiting multiple specific targets. Can we extrapolate network positions and relationships to infer effective combinations of targeted therapeutics? Yeast is an excellent model for establishing the rules for applying network theories to practical predictions. For instance, will double deletions of high connectivity genes within the network which reside in the same module have substantially greater negative consequences to the yeast than double deletions of low connectivity genes? Alternatively, will genes which are connecting distinct modules, and serving as key connections between integrators prove to be better disruption targets? The modeling of such proposed interactions in yeast is attractive because of the relative tractability of yeast studies and the availability of large scale datasets. Technologies like synthetic genetic arrays which automate double deletion screens or chemical genomic profiling provide an expanding capability to survey the effects of multiple target inhibition or deletion [30-32]. However, even with these powerful methods, only a small fraction of the millions of possible combinations will be assayed. For instance in a substantial recent survey of double deletions only 0.02% of all possible double deletions were tested for synthetic lethality [33]. Thus, network based modeling may be a useful adjunct for the design of double knockout experiments or combination inhibition experiments to permit a more efficient search for synthetic lethals. An interplay between predictive models and targeted large scale inhibition studies may be quite useful for surveying pathway analysis in yeast. In the long term, such studies may also be critical in the development of therapies for diseases like cancer.
In conclusion, our study demonstrates that weighted gene co-expression networks can be used to predict both the likelihood of essentiality and the likelihood of conservation for their gene members within the entire network. Furthermore, we show that these networks can be subdivided into identifiable modules, which themselves can often contain enough individual information to indicate which genes are more likely to be crucial for a particular process. We have indicated a means for testing whether this is true or not. Thus, we have illustrated how this process can be applied to common microarray data for the purpose of predicting both functional assignment as well as the ranking of gene significance in a particular process with which it is found to be associated. We believe that these kinds of predictions will be helpful for identifying gene targets for therapeutics and for directing multi-drug therapies.
Data sources
Yeast microarray data were obtained from the Saccharomyces Genome Database in ratio format [34]. In each case, microarray based gene expression analysis was previously reported to have been performed for the analysis of DNA Damage [18], Cell Cycle [17], and Environmental Response [19]. Not all of the data obtained from each datasets listed was used in these experiments. Because missing data is especially confounding for our analysis, arrays where greater than 5% of the gene expression information was missing were removed from our analysis. The exact number of arrays chosen to represent each dataset was: DNA Damage (n = 51), Cell Cycle (n = 44), and Environmental Response (n = 151). Essential genes were determined from the Saccharomyces Genome Deletion Project [35]. Genomic data for conducting the blastp searches were obtained from the following locations: Neurospora genome data (version 7) was obtained from the Broad Institute [36,37]. Drosophila genome data was obtained from the Berkeley Drosophila genome project [38], C. elegans genome data was obtained from the wormbase database [39], while both the human and mouse genomes were obtained from the UCSC genome browser [40]. The blastp algorithm was run locally.
Weighted Network construction
In gene co-expression networks, each gene corresponds to a node. The neighbors of a node i are the nodes that are connected to the node i. Two genes are connected by an edge with a weight indicating the connection strength. A gene co-expression network can be represented by an adjacency matrix A = [aij], where aij is the weight of a connection between two nodes i and j. For all of the networks considered in this paper, the connectivity equals the sum of connection weights being considered.
Our analysis follows a general framework for construction of gene co-expression networks [16]. To transform the co-expression measure (here the absolute value of the Pearson correlation matrix) into measures of pair wise connection strengths, one can make use of an adjacency function. The choice of the adjacency function determines whether the resulting network will be weighted (soft thresholding) or unweighted (hard thresholding). A widely used adjacency function is the signum function which implements 'hard' thresholding involving the threshold parameter tau. Specifically, aij = Ind(|cor(xi, xj)| > τ where the indicator function takes on the value 1 if the condition is satisfied and 0 otherwise. Hard thresholding using the signum function leads to intuitive network concepts (e.g., the node connectivity equals the number of direct neighbors), but it may lead to a loss of information. For instance, if the threshold has been set to 0.8, there will be no connection between two genes if their correlation equals 0.79. To avoid the disadvantages of hard thresholding, Zhang and Horvath [16] proposed a 'soft' power adjacency function: aij = |cor(xi, xj)|β with the single parameter beta. The power adjacency function preserves the continuous nature of the gene co-expression information and, leads to results that are highly robust with respect to the choice of beta.
Zhang and Horvath [16] also proposed a criterion for choosing the power beta of the adjacency function which is based on the fact that despite significant variation in their individual constituents and pathways, metabolic networks have been found to display approximate scale free topology. The linear regression model fitting index R^2 can be used to quantify how well a network satisfies a scale-free topology. While the scale free model has only 1 parameter (γ), the truncated exponential model allows for 2 parameters γ and α. Empirically, we find that the two parameters of the truncated exponential model provide too much flexibility in curve fitting so that the R-squared values are always high [16]. Thus, we focus on the scale free topology model fitting index in this application and emphasize the major trend that the high connectivity genes are few in number, and that most genes have low connectivity (Figure 2A–2C). There is a natural trade-off between maximizing scale-free topology model fit (R^2) and maintaining a high mean number of connections: parameter values that lead to an R^2 value close to 1 may lead to networks with very few connections. These considerations motivated the scale-free topology criterion. To choose the parameters of an adjacency function: Only those parameter values that lead to a network satisfying scale-free topology at least approximately were considered (e.g. signed R^2 > 0.80). When considering the signum and power adjacency functions, we find the relationship between R^2 and the adjacency function parameter (tau or beta) is characterized by a saturation curve. In our applications, we use the first parameter value where saturation is reached as long as it is above 0.8. An R^2 > .80 results in networks that may only approximate the scale free property as is highlighted by the Cell Cycle network in this manuscript.
For each dataset, we first selected the top 4000 most varying transcripts based on the standard deviation. The network adjacency between 2 genes i and j was defined as a power of the correlation coefficient between the corresponding gene expression profiles xi and xj, i.e. aij = |cor(xi, xj)|β where the power beta is chosen using the scale free topology criterion. From this function, the 3000 most-connected genes were selected for further detailed network analysis. Using the 4000 most varying transcripts was not very restrictive since the original data are derived from yeast arrays and therefore only contained only about 6000 genes in total. Many of these genes needed to be removed due to high rates (>40%) of missing data for a gene across a data set. By definition, module genes are highly connected with the genes of their module (i.e. module genes tend have relatively high connectivity). Thus, for the purpose of module detection, restricting the analysis to the most connected genes does not lead to major information loss for the key points of this presentation. However, there may be applications where genes with relatively low connectivity are biologically interesting so that gene filtering based on connectivity would lead to information loss. The adjacency matrix was also visualized as a graph and the number of topological properties of this graph examined. Important network derived concepts include: hub (a node with many strong connections to other nodes), topological overlap (the interconnectivity between two nodes), modules (tightly connected subsets of the network), connectivity (the sum of the adjacencies, ie. ) and intramodular connectivity (the sum of the adjacencies within a module). One potential drawback of soft thresholding is that the network cannot be visualized directly because the number of relationships to display becomes intractable. A soft adjacency matrix specifies pair wise connection strengths and all nodes are connected. To visualize the network with Pajek, one needs to threshold the connection strengths. The resulting dichotomized adjacency matrix can be considered as an unweighted network approximation of the weighted network. Therefore, to make a visual representation, only the strongest correlations (0.9 or greater) were drawn in these renderings. Subsequent to this, networks were then visualized with Pajek [20,41].
Definition of Topological Overlap and Modularity
Topological overlap of two nodes (genes) reflects their relative interconnectivity. For a network represented by an adjacency matrix A = [aij], aij ∈ [0,1], a well-known formula for defining topological overlap was given by [4], and it can be extended to weighted networks[16]:
where, denotes the number of nodes to which both i and j are connected, and k is the number of connections of a node, with and . Since aij ∈ [0,1], we find that . Thus, . Since 1 - aij ≥ 0, we find that ωij is a number between 0 and 1. There are 2 reasons for adding 1 - aij to the denominator in the topological overlap matrix: 1) in this form, the denominator can never be 0 and 2) for an unweighted network, one can show that ωij = 1 only if the node with fewer links satisfies two conditions: (a) all of its neighbors are also neighbors of the other node, i.e. it is connected to all of the neighbors of the other node and (b) it is linked to the other node.
In contrast, ωij = 0 if i and j are unlinked and the two nodes don't have common neighbors. Further, the topological overlap matrix is symmetric, i.e, ωij = ωji. and its diagonal elements are set to 0 (i.e. ωii = 0). The rationale for considering this similarity measure is that nodes that are part of highly integrated modules are expected to have high topological overlap with their neighbors [4].
It is important to note that the definition of topological overlap matrix (equation 1) was taken from the methods supplement of reference [4] since there is a typo in the primary text. Different definitions of the topological overlap matrix are possible, but we have found empirically that many of these possible generalizations lead to very similar modules [42].
Modules are groups of nodes that have high topological overlap. Module identification is based on the topological overlap matrix Ω = [ωij defined as (1). To use it in hierarchical clustering, it is turned into a dissimilarity measure by using the standard approach of subtracting it from one (i.e, the topological overlap based dissimilarity measure is defined by = 1 - ϖij).
In networks involving few nodes, modules can easily be identified by inspecting the network, but for large networks involving hundred of nodes, it is useful to generate a topological overlap matrix (TOM) plot, which we review in the following. A TOM plot provides a "reduced" view of the network that allows one to visualize and identify network modules. The TOM plot is a color-coded depiction of the values of [ ], described above.
Based on the TOM based dissimilarity matrix we can use hierarchical clustering to discriminate one module from another. We used a dynamic cut-tree algorithm for automatically and precisely identifying modules in hierarchical clustering dendrogram [43]. The algorithm makes use of the internal structure in a dendrogram and clips branches of the dendrogram. The algorithm is based on an adaptive process of cluster decomposition and combination and the process is iterated until the number of clusters becomes stable. No claim is made that our module construction method is optimal and future research should compare different method for cluster identification. Each module represents a group of genes with similar expression profiles across the samples and the expression profile pattern is distinct from those of the other modules. In each case, the modules were then assessed for enrichment in ontology terms based on Fisher's exact test as described in [25]. These modules and their relative connectivities were then separated out for further analysis.
Based on the network modules, we can define the intramodular connectivity (k.in) for each node i (i.e., the number of neighbors in the same module as node i). The connectivity of node i across the whole network is denoted as k.all.
The translated sequences of the entire yeast genome were each blasted against the entire translated sequences of the Human, Mouse, Fly, Neurospora and C. elegans genomes. In each case, the best hit was determined using blastp, and the log(e score) scores were averaged across the 5 different comparisons in a manner similar to what was published by [22]. These average blastp scores were then averaged for each point in (Figure 2H–2K, 3F–3H, 4F–4H, 5F–5H). This method is not necessarily an optimal measure of gene conservation, and it is used here simply as an approximate measure of gene conservation, that is independent of annotation. Future research should study the advantages and disadvantages of using different measures for gene conservation in network analysis.
Analysis of module overlap
We used a hyper-geometric p-value to evaluate the significance of the overlap of the module genes across the 3 data sets. Suppose N1, N2 N3 out of N genes are in rRNA processing module of the DNA Damage data, the Environmental Response data, and the Cell Cycle data, respectively. We then denote by 'overlap' the number of genes in the rRNA module that overlap in the 3 data sets.
To arrive at an upper limit of the p-value, we first computed pair wise p-values as follows. Denote as p12 the p-value that by randomly selecting N2 genes from a pool of N genes with N1 module 1 genes, ≥ overlap selected genes are in the rRNA-processing module in both data sets. This p-value can easily be computed using the hypergeometric distribution (dhyper function in R). N1 and N2 were then swapped to obtain a second p-value, which we denoted as p21. The maximum of those two values can be used to assess the significance of overlap between data sets 1 and 2. Similarly, we computed a p-value of overlap between data sets 1 and 3, and data sets 2 and 3. The minimum of the 3 resulting p-values provided an upper limit for assessing the significance of overlap between all 3 data sets, i.e. the final p-value was computed as p = min(max(p12,p21), max(p13,p31), max(p23,p32))
Data and software
Prepared Data and R code are available online [44].
CC, cell cycle; DD, DNA damage; ER, environmental response
Authors' contributions
MC and SFN conceived of and developed the project, and wrote the final manuscript. MC analyzed the data, participated in the construction of all the software tools used in this study and drafted the manuscript and figures. SH, BZ authored the majority of the R code needed for developing these weighted networks, developing network measures, and providing crucial statistical guidance for this project. ZF helped with the statistical analysis. PM and SFN both contributed key ideas and suggestions as the project progressed. All authors participated in the development of the ideas presented in this manuscript. All authors read and approved the final manuscript.
Table 2. Summary of tests for network predictive power
The authors extend their thanks to the SGD for providing data for this analysis. Also, special mention should be made of Allen Day, Ally Perlina and Andy Yip for their input during the analysis of this project. The work was supported by a grant from the NCI (U01 CA881273) as well as the Henry Singleton Brain Tumor Program, the Elliott Family Foundation in memory of Neal Elliott, the Ziering Family Foundation in memory of Sigi Ziering, Accelerate Brain Cancer Cure, and Art of the Brain. MRJC was supported by the UCLA Tumor Cell Biology Training Program, funded by NIH/National Cancer Institute 5T32CA09056. This work was also supported by the UCLA DNA Microarray Facility and the NINDS/NIMH Microarray Consortium.
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Generalized Topological Overlap Matrix and its Applications

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Remedial reading services are provided to students in grades K-8 who meet District criteria. Remedial math services are offered to students in grades 3-8 who meet District criteria. Contact the Pupil Personnel Director (860-349-7208) for information about specific entrance and exit criteria and services offered at each grade level.
The Regional School District 13 Preschool Program housed at Brewster Elementary School (for children ages 3 through 5) integrates children with special needs with typically developing peers within a developmentally appropriate play-based setting. The preschool program follows guidelines and procedures in accordance with the Individuals with Disabilities Education Act which states, "A preschool child requiring special education and related services is entitled to a free and appropriate public education on and after his/her third birthday." Peer models are chosen by a child-find 'birthday party' screening process and have demonstrated age appropriate expectations in the following developmental areas: speech and language, motor, social abilities, and readiness skills. The developmental screenings are conducted by a team of early childhood professionals, through structured observation, throughout the school year. Parents interested in having their child participate in a developmental screening should call Crystal at (860) 349-7985.
Food Service
Regional School District 13 makes lunch available to all students. Breakfast foods are available at Memorial Middle School and Coginchaug Regional High School. The Food Service Program is self-supporting through income derived from the sale of foods and federal funding under the National School Lunch Act and the Child Nutrition Act. School lunch prices are posted at each school.
Transportation Services
DATTCO Transportation provides contracted services for Regional School District 13.
Board of Education
Central Office
Copyright © 2013 Regional School District 13 and its licensors. All rights reserved
Background photograph courtesy Jennifer Schulten Photography at
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Norwegian Transatlantic Cruise Reviews
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72% Loved It
Cruise Critic Overview
- Private Island: Great Stirrup Cay
- Multiple eateries, bars and lounges offer choice
- Mix of tradition and innovation; decor is colorful and fun
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I was on the Norwegian Epic for 22 days, 12 of those days were at sea. I LOVE at sea. I love being on a trivia team and ... Read more
We sailed from Miami-USA to Copenhagen-DK. During the transatlantic cruise, we had seven days for leisure, to sun bath, ... Read more
Just returned from a transatlantic crossing from Miami to Barcelona on the Epic. Had a fantastic time, mostly because ... Read more
Trans-Atlantic cruise from Barcelona to Miami : October 26 to November 9, 2014 We have just completed our seventh ... Read more
Luck provided weather for a first cruise that could not be improved upon. Clear skies, sunshine, warm temperatures were ... Read more
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I went on this cruise with my parents (in their 50's) and brought my nephew along (16). Due to the nature of the ... Read more
I had a fun cruise, because of the company I was with - the ship was horrible. While the crew was great, the ship's ... Read more
This was my second cruise on the Epic but my first TA. Have to say the camaraderie of fellow Cruise Critic members and ... Read more
We recently made our first transatlantic crossing from Barcelona to Miami on Norwegian Epic. It was a two-week cruise ... Read more
My husband and I are platinum members and usually do the fall TA on Norwegian. We both feel this year could be our last ... Read more
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The first thing that you notice about the Epic, is that it is a big,big ship. When you are in the lower levels, you ... Read more
Barcelona To Miami,via Madeira,St Maarten and St Thomas. Embarkation a bit of a nightmare due to NCL insisting on ... Read more
This was our first cruise and we were a little worried, but we had a great time. Our cabin, was an inside cabin, and ... Read more
We have cruised on NCL ships many times but this was our First time on the 4000 passengers plus 1800 crew members EPIC ... Read more
A fantastic experience. The ship was superb and all facilities excellent. Food was excellent in all restaurants we ... Read more
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We had a lovely vacation onboard the Norwegian Epic during our transatlantic cruise. Embarkation went smoothly and ... Read more
We were extremely excited about our two-week trans-Atlantic cruise on the NCL Epic. Although this was not our first ... Read more
This review is for the Fall 2014 westbound transatlantic cruise on Norwegian Epic, from Barcelona to Miami with stops ... Read more
I have been fortunate enough to have sailed on 33 cruises ...more on NCL than any other line ... but this was my first ... Read more
This cruise aboard the Epic was a first for us as both a Transatlantic crossing and our first time on the Epic - in ... Read more
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As seasoned cruisers, we know what to expect on several cruise lines, NCL amongst them. 3 previous Jade cruises ... Read more
Just to set the picture, this was our 12th cruise, 4th with Norwegian but first on the Epic. To say it was a ... Read more
Transatlantic Cruise on Norwegian's Epic, October 26 through November 9, 2014. Barcelona Spain to Miami with ... Read more
We have cruised often with Princess and once each with Crystal and Royal Caribbean and based on reviews and friends' ... Read more
For the most part my husband and I agree with the honeymoon couple. The Star is a terrible, tired ship that needs to be ... Read more
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Kamloops City Centre hotels map Close
Latest deals for City Centre, Kamloops hotelsShow all Kamloops City Centre hotels
Suggested Kamloops hotelsShow all Kamloops hotels
Tenth Bed & Breakfast Kamloops
City Centre 10 /10Superb
Coast Canadian Inn Hotel Kamloops
City Centre 8.7 /10Great
A kilometre from the city centre, a short walk from the main business district, budget friendly and home to a popular Irish pub Telephone, TV, Elevator, Business Centre, Swimming Pool - Outdoor, Car Park, Room Service - 24 Hour, Babysitting
Delta Sun Peaks Resort
City Centre 8.3 /10Very Good
Deluxe European style village ideal for family vacations, set in Thompson Okanagan, features excellent relaxation amenities Bath, Bathroom Amenities, Family Rooms (On Request), TV, Restaurant, Shower, Bathroom, Fitness Centre
Accent Inn Kamloops Hotel
City Centre 8.3 /10Very Good
Situated in the city centre, a pet friendly choice near the BC Wildlife Park and shopping hubs of Kamloops. Work Desk, Business Centre, Air Conditioning - Public Areas, Meeting / Banquet Facilities, Non Smoking Rooms, Microwave, Elevator, Jacuzzi
Plaza Heritage Hotel Kamloops
City Centre 8.3 /10Very Good
Scott's Inn & Restaurant
City Centre 8.3 /10Very Good
Executive Inn Kamloops Hotel
City Centre 8.2 /10Very Good
Near the corporate and shopping hubs of downtown Kamloops, a good value choice with gaming consoles in all rooms and a casino on site. Non Smoking Rooms, Family Rooms (On Request), TV, Restaurant, Refrigerator, Telephone, Reception - 24 Hour, Luggage Storage
Comfort Inn & Suites Kamloops Hotel
City Centre 8.0 /10Very Good
Located a few minutes from the Trans Canada Highway and 7 kilometres from downtown Kamloops, with an indoor swimming pool and a games room. Jacuzzi, Laundry Service, Work Desk, Swimming Pool - Indoor, Business Centre, Air Conditioning - Public Areas, Meeting / Banquet Facilities, Non Smoking Rooms
Thompson Hotel & Conference Centre
City Centre 7.9 /10Good
Country View Motor Inn Hotel Kamloops
City Centre 7.5 /10Good
Acadian Inn Kamloops
City Centre 7.4 /10Good
Travelodge City Centre Kamloops Hotel
City Centre 7.3 /10Good
Kings Motor Inn Kamloops
City Centre 7.1 /10Good
Econo Lodge Inn & Suites Hotel
City Centre 6.4 /10Pleasant
Quality Inn Kamloops Hotel
City Centre 6.3 /10Pleasant
Valleyview Lodge Kamloops
City Centre 6.2 /10Pleasant
Sandman Inn & Suites Kamloops Hotel
City Centre 6.0 /10Pleasant
Knights Inn Kamloops
City Centre
Best Western Kamloops Hotel
City Centre
Downtown affordable property, 10 kilometres from the airport, a short walk from shops and recreation centres, has a saltwater swimming pool Air Conditioning - Public Areas, Microwave, Bathroom, TV, Restaurant, Telephone, Reception - 24 Hour, Swimming Pool - Indoor
Lamplighter Motel Kamloops
City Centre
Tournament Inn Motel
City Centre


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Who has a greater right to work; illegal aliens or lawful immigrants and American citizens?
"I've often asked myself why you should have to register to vote."
Hate radio. Hate speech. Hate websites. Hate politics. Hate crimes. Hate's gone big time and big business and big politics in America.
"My work is a feeble attempt to understand and respond to the guttural cry that erupts from the depths of the soul of each of us."
Last Saturday one individual was shot in the head and two police officers were wounded in an attack on a synagogue in central Copenhagen, Denmark.
Editor's note: This is the first in a series of columns by Santa Clarita Valley native Scott Kanowsky, who is currently living in Madrid, Spain.
The continuous stream of insightful comments made by our pre-school granddaughters never ceases to amaze and delight me. Last week our daughter recounted an exchange with her 3-year old:
A few weeks back, while driving up Newhall Avenue, I witnessed a flurry of activity that included a TV news truck, police cars and an area quarantined by police caution tape.
I was at a Democratic Party Central Committee meeting the other day when a full-sized Hereford cow came flying through the window!
It's safe for me to say that Radio Shack helped form my adolescent life - and might have even saved it. An odd statement, yet true.
Farmers in California have a major stake in water supplies - how much there is and where it comes from.
Politics is not for the faint of heart and successful politicians often put the truth in a lockbox in order to get elected, raise money and get re-elected.
Will you choose to be lonely on Valentine's Day - or make it an empowering, fulfilling day?
Since leaving the Legislature two year ago, I have spent my time looking forward and focused on the future.
I often worry that common sense is a casualty in our current society. It is becoming more and more rare for us to think through the implications of our decisions and try to make better choices now to avoid future problems.
Last month, the City of Santa Clarita in partnership with surrounding cities and towns hosted a community meeting to solicit support for an effort to approach the local (not so) high-speed rail alignment issue from a regional perspective. This rally was the result of an evolving set of alignments being proposed for the section of rail from Palmdale to Burbank. The City of Santa Clarita has maintained that since it does not receive a stop it should not be adversely affected by the system.
Last week I wrote on the challenge of motivating the good folks over at LA County Parks and Recreation to address their disregard leading to the utter devastation of our street medians at Commerce Center Drive in the otherwise shiny Valencia Industrial Center.
Our society is obsessed with many things, but none more so than aging. I don't think a day passes but that I hear someone complaining about the consequences of getting older.
In the early hours of the morning on April 30, after a full day of debate and deliberation, the House Armed Services Committee passed the National Defense Authorization Act for fiscal year 2016.
In the next few months, more than 800,000 California homeowners, including residents of some areas in unincorporated Santa Clarita Valley, will get a bill in the mail requiring them to pay a state fee for fire prevention and safety services.
Rolling hills, a plethora of parks and green landscapes throughout the community draw people into the Santa Clarita Valley.
As long as I can remember, alarmists have bleated and prophesied, "The Big One is coming and California will sink into the ocean depths!"
Just about every local news broadcast, whether by radio or via cable, reminds us that we are in a drought. The social theme being pushed is "to conserve water."
As I travel through life I try to remember to be grateful for the things that have been important to me, and one is my long friendship with the Sheriff's Department as historian for the station, an appointment made in 1996 by Lt. Bob Elson, now retired.
"You've got all the symptoms," Sue said.
While running for president in 2012, former Massachusetts Gov. Mitt Romney was chatting up some students at Otterbein University about how they could get ahead in this world.
Legal rights for animals? Not so fast.
One does not have to look very hard to find victims. History is replete with genocide, natural disasters, cruel and evil despots punishing their citizens.
It's conventional wisdom that the 2016 Republican presidential race is at such an early stage that the polls don't matter. They're just a measurement of name recognition at this point, some observers say, and the only people really paying attention to the campaign are reporters and hard-core party activists.
If you're like me, you sometimes lay in bed after the alarm has sounded, theoretically debating the benefit of leaving the warm comfort of the bed to get up and take on the day. That 30 seconds turns out to be a microcosm of the way we look at life. I suppose many folks get up because of obligations. They simply have to get up because their commitments obligate them to do so, whether they like it or not. On the other hand, some of us get up because we can hardly wait to get a jump on the ...
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Hang tight!
Recipe by Campbell's Kitchen
"Swanson® Stock is an easy way to add flavor to skillet meals like this sauteed chicken, vegetable and noodle homestyle supper."
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skinless, boneless chicken breast halves
1/2 teaspoon
garlic powder
1/8 teaspoon
1 3/4 cups
Swanson® Chicken Stock
1/2 teaspoon
dried basil leaves, crushed
1/4 teaspoon
ground black pepper
2 cups
frozen vegetable combination (broccoli, cauliflower, carrots)
2 cups
uncooked medium egg noodles
Wish I could give this no stars. Not sure what the folks at Campbell's were thinking on this one. I'm a great cook, and my husband and I are not one to throw anything out if it happens to not come out right...but we threw this out. One thing was there wasn't a whole lot of flavor involved in this recipe, other than the paprika on the chicken. The paprika really has no place in this recipe. The veggies ended up coming out overcooked, they should definitely be put in after the noodles are. And there is way too much stuff in one pan for the noodles to really cook properly. But like I said, it tasted terrible, so I wouldn't even bother trying to adjust anything to salvage it.
This recipe was quick, easy, and delicious. Based on the last review which said that this recipe lacked flavor, I made some slight changes/additions. I added Emril's Chicken Rub and Park Hill Maple & Spice Pepper (from Savory Spice Shop) to the chicken before cooking. I also pounded with a meat tenderizer. In the skillet, you'll want to be sure to remove the excess spices that burn/brown. I used fresh vegetables instead of frozen and I added onion and zuchinni. The recipe could have used more than 2 cups of vegetables. I also used rice noodles instead of egg noodles and added dill to the spices at the end. This recipe was great!
7 Ratings
I'll agree that this was a bit on the bland side, but I was going for the comfort food aspect of chicken & noodles, not a highly seasoned dish. I did make it in a different manner as I started with chunks of cooked (rotisserie) chicken. The only vegetable I used was carrot and I used dry parsley flakes for a garnish. (My parsley plant is currently in use as a butterfly caterpillar host plant,)
This is my first review, I felt I had to comment on how much my family DISLIKED this recipe. Cooking broccoli with the noodles allows the flavour to permeate the noodles too much, not a good taste. We did pick out the chicken
* Percent Daily Values are based on a 2,000 calorie diet.
Quick Chicken and Noodles
Serving Size: 1/4 of a recipe
Servings Per Recipe: 4
Amount Per Serving
Calories: 229
Calories from Fat: 31
Rhubarb adds its tangy flavor to everything from salsa to lemonade. Don't forget the pie.
Warm weather calls for a cold, fruity smoothie. Choose from over 400 refreshing recipes.
Delicious recipes, party ideas, and cooking tips! Get a year of Allrecipes magazine for $7.99!
A truly old-fashioned chicken soup with homemade noodles and stock.
See how to make a classic Italian chicken dish.
This chicken Alfredo pasta dish is rich and satisfying.


text: Well Joe tell us something we don't already know. But wait some people think they are MMA know it all, but wait condit had a plan he executed his plan his plan worked lol. Please people the key word is aggression, but wait condit had a plan and obviously the judges had a plan or maybe dana had a plan. Now there is talk about a rematch hmmmm i wonder why. But folks you know who cares because condit will have a plan and everybody knows if you got a plan you will win the match. Some people are just MMA idiots and don't know shit.


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San Francisco Opera
General Director David Gockley's final season features some of his favorites: Verdi's Don Carlo, Sondheim's Sweeney Todd, Janáček's Jenůfa and a new production of Carmen. Secure your seats and subscribe today!
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TA bomb proves embarrassing for peaceniks
Reports that last week's two British suicide bombers, one of whom blew himself up at Mike's Place, entered Israel as activists in the International Solidarity Movement (ISM) has wreaked havoc with the group's self-confidence. Only three to six ISM members remain in Rafah.
One of the group said the peace activists are determined to continue their "passive resistance to the Israeli occupation," but others paint a different picture.
About six weeks ago ISM activist Rachel Corrie was crushed under an Israeli tank in Rafah. Three weeks later activist Brian Avery was critically wounded, shot in the face by Israel Defense Forces fire in Jenin. A week later ISM activist Tom Hurndall was shot in the head by the IDF in Rafah's refugee camp and is now believed to be brain dead with little chance of recovery.
Despite the blow to their morale, the activists, based in Beit Sahour, have been persevering in their mission. The report in Haaretz on Friday, that the IDF has declared war on ISM members and intended to deport them, came as no surprise. For some time now, they have been evading the authorities and using first names only. But over the weekend something seemed to crack in their undaunted spirit.
Some spoke of growing suspicions they have that certain "activists" are planted agents, and of fears that the IDF was following them and listening in on their telephone calls. Both Israeli and British media have suggested the two British citizens involved in the Tel Aviv terrorist attack succeeded in entering Israel by pretending to be ISM activists, going to the Gaza Strip and returning to Tel Aviv for the attack.
ISM spokesman Tom Wallace vehemently denied the two had any connection to the movement and said attending a memorial service for Rachel Corrie was something any Palestinian or stranger could have done, but the denial failed to disperse the cloud over the movement.
Wallace and others in ISM have long said they know various groups are trying to exploit them to bash the IDF, and they tell the Palestinians to keep clear of any provocation. The few ISM members who attended the service for Corrie refuse to talk about Mohammed Hanif and Sharif Khan and whether they had any connections with ISM members apart from attending the ceremony.
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Senator Ben Nelson
Sen. Ben Nelson (D-NE) to retire, boosting GOP chancesComment
Sen. Ben Nelson of Nebraska told his supporters Tuesday he plans to retire rather than seek a third term, a significant setback for Democratic efforts to maintain control of the chamber next year.
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