Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.
Copyright: © 2012 de Matos Simoes, Emmert-Streib. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project is partly supported by the Department for Employment and Learning through its "Strengthening the all-Island Research Base" initiative and the Engineering and Physical Sciences Research Council (EPSRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Gene networks represent the blueprint of the causal interplay between genes and their products on all molecular levels –. Gene regulatory networks (GRN) inferred from large-scale gene expression data aim to represent signals from these different levels of the gene network. The inference, analysis and interpretation of a GRN is a daunting task due to the fact that the concentrations of mRNAs provide only indirect information about interactions occurring between genes and their gene products (e.g., protein interactions). The reason for this is that DNA microarrays measure only the concentration of mRNAs rather than the binding, e.g., between proteins or between a transcription factor and the DNA. Despite the increased community effort in recent years ,  and a considerable number of suggested inference methods – there is an urgent need to further advance our current methods to provide reliable and efficient procedures for analyzing the increasing amount of data from biological, biomedical and clinical studies –. For this reason, this field is currently vastly expanding. A detailed review for many of the most widely used methods can be found in , , –.
A major problem for the inference of regulatory networks are the intricate characteristics of gene expression data. These data are high-dimensional, in the order of the genome size of the studied organism, and nonlinear due to the intertwined connection of the underlying complex regulatory machinery including the multilevel regulation structures (DNA, mRNA, protein, protein complexes, pathways) and turnover rates of the measured mRNAs, products and proteins. Further, gene expression data for network inference are large-scale, although, the "Large Small "  problem holds, because the number of explanatory variables ( genes) exceeds the number of observations ( microarray samples). In addition, technical noise and outliers can make it difficult to gain access to the true biological signal of the expression measurement itself.
The main contribution of this paper is to introduce a new network inference method for gene expression data. The principle idea of our method is based on bootstrap aggregation , , briefly called bagging, in order to create an ensemble version of the network inference method C3NET . For this reason we call our new method bagging C3NET (BC3NET). The underlying procedure of BC3NET is to generate an ensemble of bootstrap datasets from which an ensemble of networks is inferred by using C3NET. Then the obtained inferred networks are aggregated resulting in the final network. For the last step we employ statistical hypotheses tests removing the need to select a threshold parameter manually. Instead, a significance level with a clear statistical interpretation needs to be selected. This is in contrast with other studies, e.g., .
Given the challenging properties of gene expression data, briefly outlined above, BC3NET is designed to target these in the following way. First, BC3NET is based on statistical estimators for mutual information values capable of capturing nonlinearities in the data. Second, in order to cope with noise and outliers in expression data, we employ bagging because it has the desirable ability to reduce the variance of estimates . Computationally, this introduces an additional burden, and a necessary prerequisite for any method to be used in combination with bagging is its tractability to be applicable to a bootstrap ensemble. C3NET is computationally efficient to enable this, even for high-dimensional massive data.
There are a few network inference methods that are similar to BC3NET. The method GENIE3, which was best performer in the DREAM4 In Silico Multifactorial challenge , employs also an ensemble approach, however, in combination with regression trees, e.g., in form of Random Forests . In  a bootstrap approach has been used in combination with Bayesian networks to estimate confidence levels for features. However, we want to emphasize that, in contrast to BC3NET, both methods ,  do not provide a statistical procedure for determining an optimal confidence threshold parameter. Finally, we note that also for ARACNe a bootstrap version has been introduced , which has so far been used for inferring subnetworks around selected transcription factors , .
In general, mutual information based gene regulatory network inference methods consists of three major steps. In the first step, a mutual information matrix is obtained based on mutual information estimates for all possible gene pairs in a gene expression data set. In the second step, a hypothesis test is performed for each mutual information value estimate. Finally, in the third step, a gene regulatory network is inferred from the significant mutual information values, according to a method specific procedure.
The basic idea of BC3NET is to generate from one dataset , consisting of samples, an ensemble of independent bootstrap datasets by sampling from with replacement by using a non-parametric bootstrap  with . Then, for each generated data set in the ensemble, a network is inferred by using C3NET . From the ensemble of networks we construct one weighted network (1)which is used to determine the statistical significance of the connection between gene pairs. This results in the final binary, undirected network . Fig. 1 shows a schematic visualization of this procedure.
is inferred from a bootstrap ensemble generated from a single gene expression dataset . For each generated dataset in the ensemble, , a network, , is inferred using C3NET. From an aggregated network is obtained whose edges are used as test statistics to obtain the final network .
A base component of BC3NET is the inference method C3NET introduced in , which we present in the following in a modified form to obtain a more efficient implementation. Briefly, C3NET consists of three main steps. First, mutual information values among all gene pairs are estimated. Second, an extremal selection strategy is applied allowing each of the genes in a given dataset to contribute at most one edge to the inferred network. That means we need to test only different hypotheses and not . This potential edge corresponds to the hypothesis test that needs to be conducted for each of the genes. Third, a multiple testing procedure is applied to control the type one error. In the above described context, this results in a network .
In order to test the statistical significance of the connection between gene pairs BC3NET utilizes the edge weights of the aggregated network as test statistics. The edge weights of are componentwise defined by (2)Here is the indicator function which is if its argument is and otherwise. This expression corresponds to the number of networks in which have an edge between gene and . For brevity, we write in the following . From Eqn. 2 follows that assumes integer values in . Based on the test statistic , we formulate the following null hypothesis which we test for each gene pair .
The number of networks in the ensemble with an edge between gene and is less than .
Here the cut-off value depends on the significance level . Due to the independence of the bootstrap datasets we assume the null distribution of to follow a binomially distributed , whereas corresponds to the size of the bootstrap ensemble and is the probability that two genes are connected by chance. The parameter relates to a population of networks, estimated from randomized data by using BC3NET, and corresponds to the fraction of randomly inferred edges in the bootstrap population ( ) divided by the total number of possible edges in this population ( ) that means (3)The maximal number of gene pairs that can be formed from genes in bootstrap datasets is given by (4)This value is independent of the sample size. corresponds to the expectation value of the number of randomly inferred edges for a population of an ensemble of bootstrap datasets of size . Because is a random variable it is necessary to average over all possible bootstrap datasets of size with sample size . On a theoretical note we remark that these bootstrap datasets constitute a population that specifies a probability mass function (pmf) for which the expectation of needs to be evaluated. Due to the fact that this pmf is unknown the value of needs to be estimated.
In order to estimate we randomize the data to estimate the number of edges randomly inferred in an bootstrap ensemble of size , (5)Using as plug-in estimator for Eqn. 3 we obtain an estimate for . This allows us to calculate a p-value for each gene pair and a given test statistic , given by Eqn. 2, from the null distribution of by (6)Here is the probability to observe or more edges by chance in a bootstrap ensemble of size and sample size .
Because we need to test hypotheses simultaneously (one for each gene pair) we need to apply a multiple testing correction (MTC) , . For our analysis we are using a Bonferroni procedure for a strong control of the family-wise error rate (FWER). Typically, procedures controlling the FWER are more conservative than procedures controlling, e.g., the false discovery rate (FDR) by making only mild assumptions about the underlying data , . Based on these hypotheses tests the final network is componentwise defined by (7)That means if the connection between a gene pair is statistically significant they are connected by and edge, otherwise there is no connection.
In order to determine the statistical significance of the mutual information values between genes we test for each pair of genes the following null hypothesis.
The mutual information between gene and is zero.
Because we are using a nonparametric test we need to obtain the corresponding null distribution for from a randomization of the data. Principally, there are several ways to perform such a randomization which conform with the formulated null hypothesis. For this reason, we perform different randomizations and compare the obtained results with respect to the performance of the inference method to select the most appropriate one. Two randomization schemes (RM1 and RM2) permute the expression profiles for each gene pair separately. RM1 permutes only the sample labels and RM2 permutes the sample and the gene labels. In contrast, the randomization scheme RM3 permutes the sample and gene labels for all genes of the entire expression matrix at once.
Due to the expected nonlinearities in the data we use mutual information estimators to assess the similarity between gene profiles instead of correlation coefficients. In a previous study, we found that for normalized microarray data the distribution among individual gene pairs can strongly deviate from a normal distribution . This makes it challenging to judge by theoretical considerations only which statistical estimator is most appropriate for gene expression data because most estimators were designed assuming normal data. For this reason we compare eight different estimators and investigate their influence on the performance of C3NET.
In our study, we use four MI estimators based on continuous data and four MI estimators based on discretized data. The MI estimators for discretized data are the empirical estimator , Miller-Madow , shrinkage  and the Schürmann-Grassberger  mutual information estimator. For the emipirical estimator, the entropy is estimated from the observed cell frequencies for each bin of a random variable discretized into bins, i.e., (9)With an increasing number of bins, the empirical estimator underestimates the true entropy due to undersampling of the cell frequencies . The different estimators attemp to adjust the undersampling bias by a constant factor , estimate cell frequencies by a shrinkage function between two models  or add a pseudo count from a probability distribution to the cell frequencies .
Mutual information can also be estimated from continuous random variables. The B-spline estimator considers the bias induced by the discretization for values falling close to the boundaries of a bin. For each bin, weights are estimated for the corresponding values from overlapping polynomial B-spline functions . Hence, this method allows to map values to more than one bin.
For normal data, there is an analytical correspondence between a correlation coefficient and the mutual information , (10)In this equation, the coefficient could be the Pearson correlation coefficient , Spearman rank correlation coefficient or the Kendal rank correlation coefficient .
We use the S. cerevisiae Affymetrix ygs98 RMA normalized gene expression compendium available from the Many Microbe Microarrays Database M3D . The yeast compendium dataset comprises probesets and samples from experimental and observational data from anaerobic and aerobic growth conditions, gene knockout and drug perturbation experiments. We map the yeast affymetrix probeset IDs to gene symbols using the annotation of the ygs98.db Bioconductor package. Multiple probesets for the same gene are summarized by the median expression value. The resulting expression matrix comprises a total of features for gene symbols and probesets that cannot be assigned to a gene symbol.
We simulate a variety of different gene expression datasets for Erdös-Rényi networks  with an edge density of . An Erdös-Rényi network is generated by starting with unconnected vertices. Then, between each vertex pair an edge is included with a pre-selected probability. The generated networks contain genes of which genes are unconnected. For each network, simulated gene expression datasets were created for various sample sizes of by using Syntren  including biological noise. We generate also simulated gene expression datasets for different subnetworks from the E.coli transcriptional regulatory network obtained from RegulonDB. The giant connected component (GCC) of the transcriptional regulatory network of E.coli consists of 1192 genes. We sample seven connected subnetworks from the GCC of sizes . Again, using Syntren we simulate different expression datasets including biological noise with sample size for each of these seven networks.
To test the enrichment of GO-terms in the inferred yeast BC3NET network we adopt a hypergeometric test (one-sided Fisher exact test) for edges (gene pairs) instead of genes in the following way. For genes there is a total of different gene pairs. If there are genes for a given GO-term then the total number of gene pairs is . Suppose the inferred yeast BC3NET network contains edges of which are among genes from the given GO-term, then a p-value for the enrichment of this GO-term can be calculated from a hypergeometric distribution by (11)Here the p-value estimates the probability to observe or more edges between genes from the given GO-term. For all GO:0032991 (macromolecular complex) offspring terms from Cellular Component that correspond to protein complexes, the above null hypothesis reflects the expected connection in a protein complex which is a clique (fully connected). For all other GO categories that we test, e.g., from the category Biological Process, the above is a very conservative assumption.
The influence of the randomization scheme on the performance of BC3NET is shown in Fig. 2. Here we use simulated data from a Erdös-Rényi network consisting of genes, of which are unconnected. The figure shows results for RM1-RM3 with and without MTC for five different sample sizes, shown in the legend of the figure. As one can see, all three randomization schemes with a Bonferroni correction perform similarly good. Also RM1-RM3 without MTC perform similarly, however, significantly worse indicating the importance to correct for multiple hypotheses testing. Due to the fact that RM3 is from a computational point of view more efficient than RM1 or RM2 we use this randomization scheme for our following investigations.
Figure 2. Influence of different randomization schemes (RM1, RM1 and RM3) and the multiple hypothesis testing correction on the network inference performance, measured by the F-score.
The legend shows the used sample sizes. Each randomization scheme is used with and without a Bonferroni correction. The boxplots labeled 'random' correspond to randomly permuted data to get an impression for random F-scores.
Fig. 2 includes also the F-scores obtained from the randomization of the expression data itself (right-hand side) to obtain baseline values for a comparison with the results from RM1-RM3. This is interesting because, e.g., in contrast to the AU-ROC , the F-score for data containing only noise is not as for the AU-ROC. From this perspective, one can see that even the results without MTC are significantly better than expected by chance.
To study the influence of the statistical estimators of the mutual information values, we use simulated data for several different network topologies. Fig. 3 shows results for eight different estimators and different sample sizes for a Erdös-Rényi network with an edge density of . The three continuous estimators, Pearson, Spearman and Kendall as well as B-spline, perform better for smaller sample sizes. For large sample sizes the empirical, Miller-Madow, shrinkage and Schürmann-Grassberger perform slightly better. We want to note that for different parameters of the Erdös-Rényi network and different network types we obtain qualitatively similar results (not shown). Considering the size of the studied networks we used for our analysis, which contain genes, sample sizes up to lead to a realistic ratio of which one can also find for real microarray data. Larger ratios are currently and the near future hard to achieve. For this reason, we assess the results for smaller sample sizes as more important, due to their increased relevance for practical applications. Based on these results we use for the following studies the B-spline estimator.
Figure 3. Influence of the statistical mutual information estimators (x-axis) on the network inference performance, measured by the F-score.
The legend shows the used sample sizes. Gene expression data were simulated for an Erdös-Rényi network with .
In  the computational complexity of C3NET has been estimated as , where corresponds to the number of genes. For BC3NET this means that its computational complexity is . Here is the number of bootstraps. In order to provide a practical impression for the meaning of these numbers, we compare the computational complexity between the ARACNe bootstrap network approach, described in , and BC3NET. We performed an analysis for a gene expression data set with genes and samples. The ARACNe algorithm needed hours for a single run that means to analysis one bootstrap data set. This results in a total time of hours ( hours) for bootstraps, which are about days. In contrast, the BC3NET algorithm completed this task in only minutes for all bootstraps.
Comparative analysis using simulated data.
In order to gain insight into the quality of BC3NET we study it comparatively by contrasting its performance with GENIE3 and C3NET. In Fig. 4 we show results for three different Erdös-Rényi networks each with genes, of which genes are unconnected. The edge density of these networks is . We use these edge densities because regulatory networks are known to be sparsely connected . The F-score distributions for all studied conditions are larger for BC3NET. We repeated the above simulations for subnetworks from the transcriptional regulatory network of E. coli and obtained qualitatively similar results. This demonstrates the robustness of the results with respect to different network types and network parameters.
. The x-axis shows the sample size .
To emphasize the actual gain in the number of true positive edges with respect to C3NET, on which BC3NET is based, we present in Fig. 5 the percentage of the increase of inferred true positive edges for various network sizes ranging from to genes for subnetworks from E. coli. For the results shown in the left figure, we use a fixed sample size of and for the right figure the sample size equals the number of genes, i.e., . For a fixed sample size ( ) the BC3NET networks show an increase of true positives edges , with a more prominent increase for the larger networks. Quantitatively, this observation is confirmed by a linear regression which gives a none vanishing positive slope of and an intercept of . Both parameters are highly significant with p-values . For the datasets with variable sample sizes ( ) the percentage of inferred true positive edges remains constant with an increasing network size and is around . We want to note that the results for assess the asymptotic behavior of BC3NET because the number of samples increases linearly with the number of genes . That means, asymptotically, the gain of BC3NET over C3NET is expected to be . On the other hand, for real data for which holds, the expected gain is much larger, as one can see from the left figure, reaching .
Figure 5. Gain in the number of true positive edges in BC3NET compared with C3NET.
The x-axis shows the size (number of genes ) of the used subnetwork of E.coli. A: Influence of network size on TP gain with constant sample size ( ). B: Influence of network size on TP gain with sample size .
Using BC3NET, we infer a regulatory network for a large-scale gene expression dataset of Saccharomyces cerevisiae. Due to the fact that for Saccharomyces cerevisiae no gold standard reference network is available to assess the quality of the inferred GRN we evaluate the resulting network by using functional gene annotations and experimentally validated protein interactions.
The yeast network inferred by BC3NET is a connected network that contains genes and edges with an edge density of . The degree distribution of this network follows a power-law distribution, , with an exponent of . We tested a total of GO-terms from the category Biological Process, whereas each GO-term contains less than annotated genes. From these, ( ) test significant using a Bonferroni procedure indicating an enrichment of gene pairs for the corresponding GO-terms. The strongest enrichment of gene pairs we find in our analysis are for ribosome biogenesis, ncRNA and rRNA processing, mitochondirial organization, metabolic and catabolic processes and cell cycle. See Table 1 for an overview of the top results.
Table 1. Top GO-terms for a GPEA for the category Biological Process.
One of the most reliable to detect (biochemical) interaction types that can be experimentally tested and that correspond to causal interactions, are protein-protein interactions from protein complexes. The reason therefore is that protein-protein interactions establish a direct connection between the proteins by forming physical bonds. Therefore we study the extend of protein complexes, as defined in the GO database , that are present in the yeast BC3NET network. We perform GPEA for GO-terms, which correspond to different protein complexes. From these we identify protein complex terms with significantly enriched gene-pairs. The top GO-terms of protein complexes we find are listed in Table 2. Some of the largest protein complexes detected in the BC3NET network are ribonucleoprotein complexes ( edges) including the cytosolic ribosome ( edges) and mitochondrial ribosome ( edges). Further protein complexes present in the yeast BC3NET network are the proteasome complex ( edges), proton-transporting ATP synthase complex ( edges) and DNA-directed RNA polymerase complex ( edges).
Table 2. Top GO-terms for a GPEA for protein complexes.
Finally, we study experimentally evaluated protein-protein interactions extracted from the BioGrid database (release )  and compare them with our yeast BC3NET network. First, we find that the yeast PPI network from BioGrid and our yeast BC3NET network have genes in common. Further, we find a total of BioGrid interactions among genes that are present in the yeast BC3NET network. These interactions are distributed over a total of separate network components, each consisting of or more genes. Among these, we find network components with a significant component size, where the largest significant component includes genes and the smallest significant component includes genes. Significance was identified from gene-label randomized data generating a null distribution for the size of connected network components of the genes. The resulting p-values were Bonferroni corrected. For each BioGRID component that is nested in the yeast BC3NET network, we conduct a GO enrichment analysis. From this analysis we use the GO-term with the highest enrichment value to annotate the individual network components, see Table 3.
Table 3. Shown are significant BC3NET network components nested in the BioGrid PPI yeast network.
One of the most extensively studied biological processes in Saccharomyces cerevisiae is the cell cycle. For cell cycle the GPEA gives a gene-pair enrichment p-value of , see Table 1. In Fig. 6 we show the largest network component of the cell cycle inferred by BC3NET that includes genes and edges. From this network, edges are confirmed in BioGrid (violet edges), edges are from protein complex units (GO) (green edges) and edges are present in both databases (orange edges).
Figure 6. The largest network component of the yeast BC3NET network nested in cell cycle (GO category Biological Process: GO:0007049).
The network component comprises 304 genes and edges (GPEA cell cycle ). The violet edges correspond to interactions present in BioGrid, green edges correspond to protein-protein interactions in protein complexes (GO) and the orange edges are present in both databases.
From the analysis of BC3NET for the gene expression data set from S. cerevisiae, we find in addition to a significant enrichment of over GO-terms in the category Biological Process, the significance of GO-terms in Cellular Component for protein complexes. The largest complexes we identified are the ribosome ( ) and proteasome protein complex ( ). There are two main reasons why edges of these protein complexes are highly abundant in the yeast BC3NET network. First, the ribosome and proteasome protein complexes are well annotated because they have been extensively studied in yeast . Second, the ribosome and proteasome protein complex are mainly regulated on the gene expression level and, where observed, having highly dependent gene expression patterns . Therefore, it is plausible that GRN inference methods can also pick-up signals from physical interactions between protein subunits of protein complexes.
We want to note that we are not the first to recognize that gene expression data contain information about protein-protein interactions. For example, ,  provide evidence that proteins from the same complex show a significant coexpression of their corresponding genes. Also in  it is mentioned that inferred interactions from gene expression data 'may represent an expanded class of interactions' . However, when it comes to the experimental assessment of the inferred networks, usually, only interactions related to the transcriptional regulation are studied, e.g., with ChIP-chip experiments , . To our knowledge we are the first to provide a large-scale analysis of an inferred GRN from gene expression data with respect to the presence of protein-protein interactions.
BC3NET is an ensemble method that uses as base network inference algorithm C3NET , . As for other ensemble methods based on bagging, e.g., random forests, the interpretability and characteristics of the base method does usually not translate to the resulting ensemble method , . In our case this means that the inferred network can actually have more than edges, despite the fact that networks inferred by C3NET can not. However, in our case this is a desirable property because it improves BC3NET leading ultimately to a richer connectivity structure of the inferred network. Specifically, our numerical results demonstrate that BC3NET gains in average more than true positive edges compared to C3NET (see Fig. 5). Another more general advantage of an ensemble approach is that it is straight forward to use on a computer cluster because a parallelization is naturally given by the base inference methods. Given the increasing availability of computer clusters this appears to be a conceptual advantage over none ensemble methods, likely to gain even more importance in the future. In this paper we pursued a conservative approach by using a Bonferroni procedure for MTC to demonstrate that even in this setting our method is capable of inferring many significant interactions that can be confirmed biologically. However, there is certainly potential to use more adopted MTC procedures that are less conservative. For example, procedures controlling the false discovery rate (FDR) could be investigated , .
Further, we want to note that despite the fact that the network inference method C3NET is no Bayesian method , , BC3NET is. The reason for this is that it is known for the bootstrap distribution of a parameter to correspond approximately to the Bayesian posterior distribution for a noninformative prior, and the bagged estimate thereof is the approximate mean of the Bayesian posterior . Hence, BC3NET can be considered as a Bayesian method with noninformative priors for the connectivity structure among the genes. Given the problem to define informative priors for a Bayesian approach in a genomics context, either because not enough reliable information about a specific organism is available or because it is difficult to select this information in an uncontroversial manner, a noninformative prior is in the current state of genomics research still a prevalent choice. From a theoretical point of view, a bootstrap implementation is easier to accomplish than the corresponding (full) Bayesian method. Hence, our approach is more elementary . Employing a similar argument as above, one can also see that BC3NET performs a model averaging of the individual networks inferred by C3NET.
From a conceptual point of view, one may wonder if an inferred GRN using BC3NET corresponds to a causal or an association network , . Here, by causal we denote an edge that corresponds to a direct interaction between gene products, e.g., the binding of a transcription factor to the promoter region on the DNA for regulating the expression of this genes. The quantitative evaluation of our simulated data, provide actually a quantification of the causal content of the inferred networks in the form of F-scores. It is clear that due to the statistical nature of the data, any inference is accompanied by a certain amount of uncertainty leading to an inferred GRN that contains false positive as well as false negative edges. However, as demonstrated by our numerical analysis, BC3NET is an important improvement toward the inference of causal gene regulatory networks.
Despite the fact that the presented inference method BC3NET was introduced by using gene expression data from DNA microarray experiments, it can also be used in connection with data from RNA-seq experiments. Given the rapidly increasing importance of this new technology we expect that within the next few years datasets with sufficient large sample size are available to infer GRN.
We would like to thank Gökmen Altay, Dirk Husmeier and Shailesh Tripathi for fruitful discussions. For our simulations we used R  and the network was visualized with igraph .
Conceived and designed the experiments: FES. Performed the experiments: RMS FES. Analyzed the data: RMS FES. Contributed reagents/materials/analysis tools: RMS FES. Wrote the paper: RMS FES.
1. Barabási AL, Oltvai ZN (2004) Network biology: Understanding the cell's functional organization. Nature Reviews 5: 101–113.
2. Emmert-Streib F, Glazko G (2011) Network Biology: A direct approach to study biological function. Wiley Interdiscip Rev Syst Biol Med 3: 379–391.
3. Kauffman S (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22: 437–467.
4. Palsson B (2006) Systems Biology. Cambridge; New York: Cambridge University Press.
5. Vidal M (2009) A unifying view of 21st century systems biology. FEBS Letters 583: 3891–3894.
6. Waddington C (1957) The strategy of the genes. Geo, Allen & Unwin, London.
7. Stolovitzky G, Califano A, editors. (2007) Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference. Wiley-Blackwell.
8. Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, et al. (2010) Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences 107: 6286–6291.
9. Altay G, Emmert-Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Systems Biology 4: 132.
10. Bulashevska S, Eils R (2005) Inferring genetic regulatory logic from expression data. Bioinformatics 21: 2706–2713.
11. Faith J, Hayete B, Thaden J, Mogno I, Wierzbowski J, et al. (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5: e8.
12. de la Fuente A, Bing N, Hoeschele I, Mendes P (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20: 3565–3574.
13. Hache H, Lehrach H, Herwig R (2009) Reverse engineering of gene regulatory networks: A comparative study. EURASIP J Bioinform Syst Biol 2009: 617281.
14. Luo W, Hankenson K, Woolf P (2008) Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information. BMC Bioinformatics 9: 467.
15. Markowetz F, Spang R (2007) Inferring cellular networks–a review. BMC Bioinformatics 8: S5.
16. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky , et al. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7: S7.
17. Meyer P, Kontos K, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP journal on bioinformatics and systems biology 2007: 79879.
18. Werhli A, Grzegorczyk M, Husmeier D (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 22: 2523–31.
19. Xing B, van der Laan M (2005) A causal inference approach for constructing transcriptional regulatory networks. Bioinformatics 21: 4007–4013.
20. Barabási AL (2007) Network Medicine – From Obesity to the "Diseasome". N Engl J Med 357: 404–407.
21. Emmert-Streib F, Dehmer M, editors. (2010) Medical Biostatistics for Complex Diseases. Weinheim: Wiley-Blackwell.
22. Zanzoni A, Soler-Lopez M, Aloy P (2009) A network medicine approach to human disease. FEBS Letters 583: 1759–1765.
23. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nature Reviews Microbiology 8: 717–729.
24. Emmert-Streib F, Glazko G, Altay G, de Matos Simoes R (2012) Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Frontiers in Genetics 3: 8.
25. Olsen C, Meyer P, Bontempi G (2009) On the impact of entropy estimator in transcriptional regulatory network inference. EURASIP Journal on Bioinformatics and Systems Biology 2009: 308959.
26. Penfold CA, Wild DL (2011) How to infer gene networks from expression profiles, revisited. Interface Focus 1: 857–870.
27. West M (2003) Bayesian factor regression models in the "large p, small n" paradigm. Bayesian Statistics 7. Oxford University Press. pp. 723–732.
28. Breiman L (1996) Bagging Predictors. Machine Learning 24: 123–140.
29. Zhang H, Singer BH (2010) Recursive partitioning and applications. Springer; New York: Springer.
30. Friedman N, Goldszmidt M, Wyner A (1999) Data Analysis with Bayesian Networks: A Bootstrap Approach. Proc Fifteenth Conf on Uncertainty in Artificial Intelligence (UAI). Society for Artificial Intelligence in Statistics. pp. 196–205.
31. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5: e12776.
32. Breiman L (2001) Random Forests. Machine Learning 45: 5–32.
33. Margolin A, Wang K, Lim W, Kustagi M, Nemenman I, et al. (2006) Reverse engineering cellular networks. Nat Protoc 1: 662–71.
34. Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, et al. (2010) A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Molecular Systems Biology 6: 377.
35. Zhao X, D Arca D, Lim WK, Brahmachary M, Carro MS, et al. (2009) The N-Myc-DLL3 cascade is suppressed by the ubiquitin ligase Huwe1 to inhibit proliferation and promote neurogenesis in the developing brain. Developmental Cell 17: 210–221.
36. Efron B, Tibshirani RJ (1993) An Introduction to the Bootstrap. Chapman et Hall.
37. Dudoit S, van der Laan M (2007) Multiple Testing Procedures with Applications to Genomics. New York; London: Springer.
38. Farcomeni A (2008) A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Stat Methods Med Res 17: 347–88.
39. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological) 57: 125–133.
40. Ge Y, Dudoit S, Speed T (2003) Resampling-based multiple testing for microarray data analysis. TEST 12: 1–77.
41. Emmert-Streib F, Altay G (2010) Local network-based measures to assess the inferability of different regulatory networks. IET Systems Biology 4: 277–288.
42. Paninski L (2003) Estimation of entropy and mutual information. Neural Computation 15: 1191–1253.
43. Schäfer J, Strimmer K (2005) A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics. Statistical Applications in Genetics and Molecular Biology 4: 32.
44. Schürmann T, Grassberger P (1996) Entropy estimation of symbol sequences. Chaos 6: 414427.
45. Daub C, Steuer R, Selbig J, Kloska S (2004) Estimating mutual information using B-spline functions–an improved similarity measure for analysing gene expression data. BMC Bioinformatics 5: 118.
46. Faith J, Driscoll M, Fusaro V, Cosgrove E, Hayete B, et al. (2008) Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res 36: D866–70.
47. Erdos P, Renyi A (1960) On the evolution of random graphs. Publ Math Inst Hungary Acad Sci 5: 17–61.
48. Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, et al. (2006) SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7: 43.
49. Husmeier D (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks. Bioinformatics 19: 2271–82.
50. Leclerc RD (2008) Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol 4: 213.
51. Ashburner M, Ball C, Blake J, Botstein D, Butler , et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25: 25–29.
52. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, et al. (2008) The BioGRID Interaction Database: 2008 update. Nucl Acids Res 36: D637–640.
53. Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with proteinprotein interactions. Genome Res 12: 37–46.
54. Grigoriev A (2001) A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res 29: 3513–9.
55. Margolin A, Califano A (2007) Theory and limitations of genetic network inference from microarray data. Ann N Y Acad Sci 1115: 51–72.
56. Altay G, Emmert-Streib F (2011) Structural Influence of gene networks on their inference: Analysis of C3NET. Biology Direct 6: 31.
57. Storey J (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B 64: 479–498.
58. Bernardo JM, Smith AFM (1994) Bayesian Theory. Wiley.
59. Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian Data Analysis. Chapman & Hall/CRC.
60. Haste T, Tibshirani R, Friedman J (2009) The elements of statistical learning: Data mining, inference and prediction. New York: Springer.
61. Opgen-Rhein R, Strimmer K (2007) Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8: S3.
62. R Development Core Team (2008) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
63. Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems 1695.


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World Cup 2014 Circus: Suarez Loses 888, While Neymar Briefs
Posted on: June 28, 2014, 10:00h.
Last updated on: June 28, 2014, 01:55h.
Casino.org Staff Writer Read More
The Bite Heard Round the World cost Luis Suarez his World Cup and his 888poker sponsorship this week, but 888 may need to thank the toothy Uruguayan in the end. (Image: bbc.com)
It's all over but the shouting: as we reported earlier this week, the Bite That Rocked the World Cup, aka Luis Suarez loses his mind, now has 888poker officially cutting ties with the Uruguayan striker. Following a four-month playing ban by FIFA that effectively ends his World Cup play, Suarez must now face the sobering reality that he bit off more than he could chew this time, career-wise.
It was only last month that 888 signed the, um, colorful character that is Suarez to its ambassador program, and even then, it looked like a shaky bet on the part of the online poker giant; after all, this wasn't exactly the first, or second, or third (you get the idea) time that Suarez sank his teeth into a very bad idea.
"888poker signed Luis Suárez following a fantastic season for which his achievements were widely recognised," said the company in an officially issued statement. "Regrettably, following his actions during Uruguay's World Cup match against Italy on Tuesday, 888poker has decided to terminate its relationship with Luis Suárez with immediate effect."
In case you live in a bubble and haven't heard the gory details, the FIFA ban and 888 shunning follow a much-publicized dental assault on Italian player Giorgio Chiellini during the pair's match up between Uruguay and Italy as the final Group D showdown last week. Italy was later knocked out by South America, making it a doubly bad day for Chiellini.
Is it just us or is 888.com the big winner in all this? By becoming the first of Suarez's sponsors to officially ditch him over the incident, they pretty much got a mention in every newspaper and news website in THE WHOLE WORLD. Way to make lemonade out of lemons, 888. That bite seems to have left you with plenty of zest. (Sorry, we are on a pun-athon right now and cannot be stopped).
Speaking of branding, the World Cup is a maelstrom of corporate sponsorship and the rules are exceedingly strict on brands, which is why FIFA has launched a very serious investigation into Neymar's underpants. It appears that the little Brazilian playmaker with the dazzling feet and toilet brush hairdo was seen wearing Speedo underwear during the customary post-match exchange of shirts when Brazil played Cameroon last week.
Now, Speedo, while it does makes very nice underwear, is not a licensed FIFA brand and herein lies the problem.
At the heart of the investigation is whether Neymar purposefully exposed the Speedo logo on his underwear or whether it was pure accident that the offending articles had ridden up slightly above his shorts (it happens!). How they propose to determine this we have no idea.
Were Neymar's underpants illegal, and, if so, should he be forced to remove them? Maybe he should be banned for life. We'll be sure to bring you updates from the investigation as it progresses.
The Big Payoff
As we also reported in our earlier story, some clever bettors made bank on the Big Bite Theory before it happened; we now have some more details on how that all played out.
In fact, important news reaches us from Sweden, where bookies have paid out just under 1,400 kroner ($2,070) to one prescient punter who predicted that Luis Suarez would sink his Bee Gee-like ivories into the soft, tempting flesh of an opponent at the 2014 World Cup.
Jonathan Brack, a 23-year-old teacher from Stenungsund, placed the 80 kroner ($12) bet at odds of 174 to 1, and the Uruguayan duly obliged by attaching his gleaming dentition to the shoulder of Chiellini during the Uruguay Italy game, before rolling on the floor feigning injury to his fangs. It seems that over a 150 people across Europe have won money on similar bets.
"I was looking through some odds before the tournament started and I saw this bet and thought I had to take a chance," Jonathan told the BBC. "I wanted to place hundreds but my friend told me not to waste my money on such a stupid thing."
But we still say 888 will be laughing all the way to the bank on this one. After all, Suarez has pretty much given them a lifetime's publicity in one mad, hellish, impulsive moment. And they don't have to pay him anymore.
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redpajama_set_name: RedPajamaCommonCrawl


Published on 7 March 2005.
I watched the DVD of Chicago tonight, and I really wasn't very impressed, and I certainly don't even begin to comprehend why it got six Oscars. It was marginally than the stage version, but that's not a particularly difficult achievement. Some of the acting was so wooden that I almost expected to catch glances of strings from the hands, and some of the miming was equally appalling. I've never been a fan of the music in Chicago either, which didn't really help.
People have compared this with Moulin Rouge – it doesn't even come close. Moulin Rouge was cinema at its best, reminding us all that cinema can be a true artform, and that it's only made mundane by the manufactured poop that normally comes out of Hollywood. Chicago was just terrible. The way that the story was integrated with the stage songs-and-dances was clever, and worked well, but that's about the only good thing I can think of from the whole film.
Not really worth spending a couple of hours of your life with, much less worth buying.
This 421st post was filed under: Reviews.


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Rights And Wrongs - Round 4
Tue 29 Mar 2016, 10:34 AM
Right: Kicking Game
Every shot landed - especially the last one. In fact the majority of the Broncos' points came from shots at goal on the night (13 in total). Corey Parker who came into this game kicking at 50% (5/10) was flawless on the night (6/6) with the constant penalty kicks the Cowboys gave away on their line. Anthony Milford also now has more field-goals than any player in the NRL this season with three from four games.
Right: Busting Ahead
Anthony Milford is always hard to bring down and currently averages 6.25 tackles busts a game. More impressively he has made 10 tackle busts two weeks in a row V Panthers and Cowboys. James Roberts also tore through the Cowboys with six – one of which led to a try for Anthony Milford. Being the most elusive Broncos on the night it's no surprise that they set up both tries scored by Brisbane.
Right: Golden Point Winners
Once the game went to golden point a sense of deja vu no doubt came over both Broncos' and Cowboys' fans. Flashbacks to last year's Grand Final loss would also be in the minds of the Broncos' fans but this time the story had a happy ending – well at least for Brisbane. This time it was the Cowboys who had to watch on as a drop goal from 40 metres out flew through the air and weaved its way between the uprights to end the game.
Wrong: Focus On The Bunker
The bunker has only been implemented for three rounds previously so there is bound to be the odd mistake or controversial call here or there. Unfortunately much of the focus has been on the no tries of Matt Gillett and Johnathan Thurston and not on how incredible the actual game was on the night. The jet 'James Roberts' finally took off setting up a try and scoring one and Anthony Milford's drop goal gave us another unforgettable moment. To have two golden point thrillers in a row shows there is absolutely nothing between the Broncos and Cowboys and who knows we may be in for another when they meet in Round 11.


Tilhill Forestry has an immediate opportunity to join the business and drive it forward in an area of significant growth potential.
This senior position involves the procurement of standing timber for Tilhill Forestry working closely with a range of clients, Tilhill Forest Managers and customers for the purchase and sale of all roundwood products to market. The successful candidate will be responsible for the valuation and purchase of standing timber parcels, engagement and supervision of teams to undertake the harvesting and haulage, together with the management of staff as required to fulfil the work programme.
We are seeking someone with relevant experience who is self-motivated, can lead a team with enthusiasm, manage our relationship with key suppliers and customers, and be ambitious in developing the business further. You should possess excellent communication and management skills, demonstrate a good safety and environmental awareness, and be a good team player.
Candidates should hold a degree or diploma in a relevant discipline and /or have at least 5 years operational and management experience in forestry. Professional membership of the Institute of Chartered Foresters or another relevant professional body is also desirable.
In return we offer an attractive salary and benefits package, autonomy to manage your own operational area, together with excellent opportunities for personal and career development in a truly national and integrated company.
Company's Job Applicant Privacy Notice is available on www.tilhill.com/about-us/careers.


NEMI is a Mexican organization founded in 1996 with the aim of participating actively in Mexico's social development through school and community programs. Throughout its 20 years of experience, NEMI has professionalized its entire operation with high quality standards, broadening its scope and improving its results with a uniquely designed model of on-site operations, earning the support of the most important strategic allies in the country and becoming the organization with the greatest reach and operating capacity in Mexico.
Thanks to the quality and success of its programs, NEMI has extended its efforts to 32 states in Mexico and directly benefited more than 17 million people.
Humanism: the comprehensive advancement of every human being is at the core of our mission.
High impact social action: we are committed to society and carry out high impact actions by professionalizing the quality of every program and working closely with each group and community.
Transparency: Each program stresses visibility, scope and follow-up.
Sustainable development: all groups served are active participants in their programs.
Efficacy: we are constantly optimizing processes to improve the cost-benefit ratio.
To serve as a transcendental engine of change for Mexico's social development, through education and the formation of complete individuals, encouraging a social commitment toward and in communities, and through transparency and accountability.
By creating and implementing school, community and business programs across the nation in direct contact with beneficiary communities, NEMI addresses social development needs in partnership with government institutions and private enterprise.
Education, prevention and training programs for primary and secondary schools.
Professional development programs geared toward university students.
Fundraising and donation administration programs.
Systems for follow-up, transparency and accounting of programs.


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Q: If $f$ is a function of moderate decrease then $\delta \int f(\delta x) dx = \int f(x) dx$ A function of moderate decrease is a map from $\mathbb{R}$ into $\mathbb{C}$ such that there exists $A \in \mathbb{R}$ such that $\forall x\in \mathbb{R}, \ |f(x)| \lt \frac{A}{1 + |x|^{1+\epsilon}}$.  Let $\epsilon$ be fixed, then such functions form a vector space over $\mathbb{C}$ called $\mathcal{M}_{\epsilon}(\mathbb{R})$.  Improper integrals are well-defined for such functions.  I want to prove the property called "Scaling under dilations":
For any $\delta \gt 0$,
$$
\delta \int_{-\infty}^{\infty} f(\delta x) dx = \int_{-\infty}^{\infty}f(x) dx
$$
My problem is showing that $f(\delta$x) is in $\mathcal{M}_{\epsilon}(\mathbb{R})$.  Or do we have to?  I.e. could we just show that the improper integral on the left is valid?

A: You don't have to show that $x \mapsto f(\delta x)$ belongs to $\mathcal{M}_\varepsilon(\mathbb{R})$, but it's not hard. If $\lvert \delta\rvert \geqslant 1$, then
$$\lvert f(\delta x)\rvert \leqslant \frac{A}{1 + \lvert \delta x\rvert^{1+\varepsilon}} \leqslant \frac{A}{1+\lvert x\rvert^{1+\varepsilon}},$$
and if $0 < \lvert \delta\rvert < 1$, then
$$\lvert f(\delta x)\rvert \leqslant \frac{A}{1 + \lvert \delta x\rvert^{1+\varepsilon}} \leqslant \frac{A}{\lvert\delta\rvert^{1+\varepsilon}+\lvert \delta x\rvert^{1+\varepsilon}} = \frac{A\cdot \lvert \delta\rvert^{-(1+\varepsilon)}}{1 + \lvert x\rvert^{1+\varepsilon}},$$
so in either case, $x \mapsto f(\delta x)$ belongs to $\mathcal{M}_\varepsilon(\mathbb{R})$.

A: Do you have a continuity requirement on $f$? Then use the change of variables formula, which guarantees that $\int_{\phi(a)}^{\phi(b)}f(x)=\int_{a}^bf(\phi(u))\phi'(u)du$ for continuiously differentiable $\phi$.

redpajama_set_name: RedPajamaStackExchange


The story of a modest dream. A widow, Florence Green, wants to open a bookshop in the small town of Hardborough, in which she has lived for ten years.
After the death of her husband she simply wants to make a living and be independent. However, the decision to open a bookshop without due attention to Mrs Gamart's aspirations proves to be Florence Green's downfall. The former is a well connected and ruthless woman with plans of her own. However, one senses her objection to the bookshop is less significant than her objection to the unyielding FG.
This brilliant little Booker-shortlisted gem shows the best and worst of parochial life. It illustrates how people can be easily swayed with little consideration as to principles. The humour (unintentionally provided by self-important people) and the tensions found in the politics of this small town dictates the tone of this tale.
FG has some significant allies, but disappointingly the majority go with the influential flow and at the end, with a Hardy-esque touch, her arch-defender unwittingly plays into the hands of her arch-enemy. This vivid chapter in the life of a kindly and courageous woman is, sadly, described with great credibility.
Desperate for English language books in France?
What is it about manga comics?


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Q: Apache Tomcat webapp favicon Suppose that, in an Apache Tomcat webapp, in a JSP, we have an anchor for a PDF document.
When I click the link, the PDF document opens but the favicon is the Tomcat favicon (the cat).
What can I do to have the webapp favicon instead?
Note 1: replacing the contents of the ROOT webapp favicon.ico file with the contents of my webapp favicon doesn't solve the problem.
Note 2: deleting ROOT/favicon.ico doesn't solve the problem either.
Thanks.


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Q: How would you add salt to your existing password hashes? I have a database of hashed passwords that had no salt added before they were hashed.  I want to add salt to new passwords.  Obviously I can't re-hash the existing ones.
How would you migrate to a new hashing system?

A: Sure you can. Just add a salt to the existing hash and hash it again. Of course this will require any future logins to go through the same process meaning two hash functions will need to be called but lots of legitimate patterns do this anyway so it doesn't smell as bad as you might think.
Salting a password is an effort to defend against rainbow tables. In this case the salt does not need to be a secret.
http://en.wikipedia.org/wiki/Rainbow_tables#Defense_against_rainbow_tables
You can actually see in the article
hash = MD5 (MD5 (password) . salt)

Which is the same exact method you would be using. (Except a different hashing function.)

A: You could add a column, consisting of a flag showing whether the user has an old (no salt) or a new (with salt) hash.
A good idea is, at that point, to force all users to change their passwords upon sign in. This way you can get rid of that column eventually.

A: As a quick fix, you could create a salt column in the database, and when a user logs in correctly matching the old hash, you can then use that password that they entered with a salt and create a new hash.

A: I dealt with a similar issue involving multiple hashing techniques. I used the approach of encoding a hash method type in the database as well (i.e. 'alpha', 'beta', 'gamma', 'delta'). I marked all current hashes with the appropriate level. As users logged in, I validated their passwords and re-hashed them using the updated methods. Our passwords expire after 90 days, so it was just a matter of holding on for 3 months until all passwords using the old methods could be reset.

A: There are some ways here that may work for you.
Remember, any constant pattern you add into the existing hash is useless (one of the tricks on that link is suggesting something like that). There should be no identifiable pattern that can be used to isolate the salt.
Of course, the best way would be to migrate to a salted hash table.

A: Create a new field in you're database named "salted" with a type of true/false (or whatever the equivalent is in your DBMS). Set all the values to false for the existing hashes. Whenever a new, salted, hash is added, set the "salted" field to true.
Then, all you have to do is handle the two types of hashes differently in your code.
This is more of a general solution than a specific one, but it should solve your problem.

A: If you are storing the salt inside the hash, it should be fairly straight forward to determine if a salt is included by checking the length of the hash.  If there isn't a salt, just hash the password, if there is a salt, hash the password + salt.
You shouldn't need a boolean column in your database.

A: The best way I store my salt is that I embed the salt value within the password hash + salt I have just created.  I don't append the salt string to the beginning or end of the hash, I literally embed the salt into the hash.


Exactly what are the very best whole house best whole house reverse osmosis system in the Valley for residence usage? Well, as a result of technical concerns and also loan, you'll have fewer choices when looking for one of these, compared to you would if you were getting a filter for simply one tap. That's all right, however, because there are some really good choices available, which for an appliance, are not that pricey.
To start with, I'm glad you're considering filtering all your water instead of just the water that comes out of your cooking area tap. We can consume equally as several toxic substances from the water we bathe in as we could from the water we consume alcohol. Also, I have actually obtained much more good news for you! From a simply personal pleasure perspective, you just have not lived until you have actually bathed in filtered water!
Whole house water filtration systems utilized to be a genuine deluxe, yet no more– that is, if you get the ideal system. There are primarily 4 innovations that are made use of in home water purification, purification, reverse osmosis, ultraviolet light filtration, and also carbon/ceramic filtering. Of these 4, only ultraviolet light and carbon/ceramic filtration are affordable for whole house usage.
Distillers are pricey– and slow! Anon, or under, the counter unit could set you back anywhere from nearly $1,000 to $3,000! Because they work by heating water and letting it condense, it takes an actually long period of time making a gallon of water. Unless you purchase an actually large distiller, you're not going to have the ability to make enough water to bathe in.
Reverse osmosis is faster than distillers, yet it's an extremely wasteful technology. Inning accordance with which design and make you get, a reverse osmosis filter can take anywhere from 3 to ten gallons of water to earn one pure gallon! Wow! If you filter all your water with reverse osmosis, then your water bill is going to be from three to 10 times as huge! I don't know concerning you, but I don't seem like paying money to the water energy each month.
The only two entire home water purification systems that actually make good sense are ultraviolet light and also carbon/ceramic filtration. UV functions well, however just for a minimal variety of living contaminants, particularly bacteria and also viruses. There are even some microbes it cannot kill because they exist in cyst kind when beyond our bodies. This leaves us with entire residence water filtration systems that are made with carbon and ceramic filters. This excels! Because of this filtering innovation functions wonderfully! It can filter both organic and also inorganic impurities, and it can filter also the tiniest pollutants out of your water.


text:
CSBM proves the icing on Angeleens cake
A Whāngarei woman who gave up a nursing career to go into business making raw cakes is working with Air New Zealand to have her products used on the airline.
And it's largely thanks to the skills she received from Te Wānanga o Aotearoa.
Angeleen Lewers completed the Certificate in Small Business Management in May and says without the course she would never have had the confidence or knowledge to successfully pitch her rawfood cakes to the national airline.
"I couldn't have approached Air New Zealand without doing this course," she says.
"Definitely Te Wānanga o Aotearoa helped me hugely. The course gave me a lot more mana, strength and knowledge to be a successful businesswoman."
When she first enrolled on the course, Angeleen says all she wanted was a "piece of paper to say I was a business woman and that I have the right to be a business woman".
But that all changed during the nine-month course.
"At the end, the bit of paper was obsolete. I didn't need it because the course helped me to become that business woman. I had the strength and confidence I needed," she says.
"I had reservations about what I would get out of it but it really nurtured my inner self. It made me a lot more focussed so that whatever comes up, I know I've got the knowledge and the resources to be able to help."
Angeleen launched her raw cakes business 14 months ago, giving up a 10-year career as a registered nurse so she could work with her mother.
"Whānau is very important to me," she says.
"It's not all about the money and having lots of power, it's about being true to yourself, it's your own journey and I feel like I can make a difference to clients."
She deals with her own allergies so raw cakes was an obvious area to focus on. It's also a market with strong potential.
"Allergies aren't going to go away and there will be more people making these sorts of decisions in the future. We're one of a kind, we're the only business in New Zealand doing this so we're going to run with it."
Angeleen says she also knows she can still talk to her kaiako for advice if she needs to.
"Even though I graduated in May I still have that support behind me. I know I can talk to her at any time and that's great. It's given me so much strength and if I can do this, other people can do it too."


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Q: Using the Conditional operator in asp.net web forms markup I have a repeater, that is bound is the level is equal to one, this works great, however if the level is equal to something else, I print out a blank line. How do I get it to not print out anything if Level != 1?
<%# ((programsRepeat)Container.DataItem).Level == 1 ? ((programsRepeat)Container.DataItem).ProgramName : "" %>

A: Suggestions:

*

*Modify your query (filter WHERE level = 1)

*Use code-behind (datatable, etc.) to modify your dataset prior to binding it to the repeater

*Use the databinding event (http://msdn.microsoft.com/en-us/library/system.web.ui.control.databinding%28v=vs.110%29.aspx)

...


Garth Brooks is performing at Encore Resort Casino, Las Vegas. How much longer will He continue to perform? He won't say so do not take a chance that this is the last time. Breaking sales records is nothing to Garth Brooks. He took off for many years and took care of His farm, never performing during that time. He would sit on His porch and strum His guitar on occasion but unless You were hiding in the bushes, You didn't get to hear. Then He came back to perform at the Encore, then He left again. Do not miss the opportunity of a lifetime to see this legend. He is a good family man and has more money than he can ever spend so he has no motivation to earn more. Some People are greedy and want more and more, but not Garth.


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The Weekly Scenario
The Weekly Scenario: Virtual Maryland Wills and Trusts
May 20, 2022 | Share:
Effective April 21, 2022, people can now sign their Maryland Wills and Trusts virtually. Senate Bill 36 is new legislation…
The Weekly Scenario: Governor Hogan Signed into Law the Maryland Tax Reduction Act on Friday, April 1st.
The act will cut retirement taxes by eliminating all state tax on the first $50,000 of income for retirees making…
The Weekly Scenario: New FDIC Rule to Simplify Banking for Trusts
April 1, 2022 | Share:
On January 21, 2022, the Federal Deposit Insurance Corporation ("FDIC") approved a new rule (going into effect on April 1,…
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March 4, 2022 | Share:
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February 24, 2022 | Share:
When you are nominating the guardian of your minor children, the goal is to provide each child as little disruption…
The Weekly Scenario: Identity Theft and Protection of the Estate
February 4, 2022 | Share:
Stolen identities and fraudulent usage of personal identifying information continues to be a big problem. These concerns grow when an…
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The Build Back Better Act did not pass in 2021. However, this is not to say that Congress won't try…
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Estate planning attorneys frequently see certain common mistakes in an estate plan. Here are the three estate planning mistakes that…
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January 7, 2022 | Share:
In certain situations where a person has a large retirement plan account, such as an IRA, and a substantial estate…
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December 24, 2021 | Share:
When an account owner dies, the assets go directly to the beneficiaries named on the account. This overrides the will…


text:
KBRI Bangkok
Embassy of the Republic of Indonesia
In Speeches
FM says Islamophobia in world declining
Author Catherine JimenezPosted on February 1, 2017 April 24, 2017
Indonesian Foreign Minister Hassan Wirajuda said Islamophobia in the world had decreased but the Islamic community needs to continue its efforts to correct wrong perceptions about Islam.
The foreign minister made the remarks in an annual coordination meeting of countries grouped in the Organization of the Islamic Conference (OIC) at the United Nations` headquarters here on Friday (Saturday in Indonesia).
"When we met here last year we were concerned over the negative image of Islam in various parts of the world. Now the frequency of incidents related to Islamophobia has declined," the Indonesian foreign minister said.
He said the decline in Islamophobia was the result of better communications made by various parties in convincing the world that Islam is a peace-loving and compassionate religion.
In this regard , Indonesia itself, he said, had continued to make efforts to convince the world about the true teachings of Islam.
The efforts included the holding of the Third International Conference of Islamic Scholars in Jakarta in July, a Global Intermedia Dialog and an Interfaith Youth Camp.
"Efforts made by other Islamic countries have also begun to yield results," Hassan said.
During the coordination meeting, which was held on the sidelines of the 63rd session of the United Nations General Assembly, the Indonesian foreign minister also referred to the world`s food crisis and what role the OIC could play to overcome it.
He said food availability was decreasing in line with the increasing world population growth while prices of basic necessaries such as rice and wheat had also increased two-fold.
"As most of us, OIC members, are agricultural countries, we can play a role by increasing our food production," he said calling on OIC members to put emphasis on development of the agricultural sector.
"Let us empower our farmers," he said.
Thailand: Your Honeymoon Destination
Palambang Get ASEAN award
Democratization in un security council
OIC praises Ri for democratic credentials
RI does not want to be trapped
President Receives South Thailand delegation
Riot-prone because of hunger
Various events keep tourist number high
© 2021 KBRI Bangkok


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Q: Crear .exe para código python pyinstaller, cx_freeze, etc Estoy utilizando estas librerias para una GUI, necesito crear un ejecutable para windows, estoy trabajando con windows 7 32bits.
utilice pyinstaller, si me corre el programa pero al correrlo me abre el proceso detras de la GUI (una ventana negra), el archivo que uso para la GUI es .pyw pero aun así me hace esto.
También utilicé cx_freeze pero no me corre la aplicación.
¿Alguna solución para lo que ocurre con pyinstaller o alguna otra librería para crear un ejecutable?
import pandas as pd
import numpy as np
import threading
import time
from tkinter import *


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Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)
Korean Society for Atmospheric Environment (한국대기환경학회)
Environment > Air Quality Management
The journal is mainly devoted to the advancement of knowledge in the fields of atmospheric measurements and analysis, current status and management, dispersion and reaction, control technology, global environment, health and ecological effect, indoor air quality and other related fields.
http://www.jkosae.or.kr KSCI KCI
Characterization of NOx Reduction on Filter Bag Support System at Low Temperature using Powder Type MnOx and V2O2/TiO2 Catalysts
Kim, Byung-Hwan;Kim, Jeong-Heon;Kang, Pil-Sun;Yoo, Seung-Kwan 1
https://doi.org/10.5572/KOSAE.2010.26.1.001 PDF KSCI
In this study, the selective catalytic reduction of $NO_x$ with ammonia was carried out in a filter bag support reactor. The experiments were performed by powder type $MnO_x$ and $V_2O_5$/$TiO_2$ catalyst at low temperature between 130 and $250^{\circ}C$. Also, the effect of $SO_2$ and $H_2O$ on the NO conversion was investigated under our test conditions. The powder type catalysts were analyzed by X-ray photoelectron spectrum (XPS), X-ray diffraction(XRD), scanning electron microscopy (SEM) and thermal gravimetric analysis (TGA). It was observed that NO removal efficiency of the powder type $V_2O_5$/$TiO_2$ catalyst was 85% at low temperature($200^{\circ}C$) under presence of oxygen and that of $MnO_x$ was 50% at the same condition. The powder type $V_2O_5$/$TiO_2$ catalyst, in conclusion, was found to be available for SCR reaction in a filter bag support system.
Improvement of Atmospheric PM2.5 Levels and Related Premature Deaths in Seoul, Korea
Bae, Hyun-Joo;Shin, JI-Young;Park, Chan-Koo;Jung, Kweon;Lee, Sang-Yol;Kim, Min-Young;Park, Jeong-Im 10
In recent studies, $PM_{2.5}$ has been reported to be more harmful to human health than $PM_{10}$ because it penetrates more deeply into the lung. We estimated $PM_{2.5}$ related health benefits in Seoul from implementing the World Health Organization (WHO)'s guidelines (annual average $10{\mu}g/m^3$, 24-hour average $25{\mu}g/m^3$) and U.S. Environmental Protection Agency (EPA)'s National Ambient Air Quality Standard (annual average $15{\mu}g/m^3$, 24-hour average $35{\mu}g/m^3$). U.S. EPA's Environmental Benefits Mapping and Analysis Program was utilized for the analysis. It was predicted that the attainment of the WHO annual guideline and U.S. EPA's annual standard, relative to the concentration in 2006, would result in reduction of 2,333~2,895 premature deaths and 1,703~2,121 premature deaths, respectively. If the WHO and EPA's daily standard for $PM_{2.5}$ are attained, 1,211~1,394 and 1,012~1,165 premature deaths could be avoided, respectively. Sensitivity analyses indicated that the estimates were robust regardless of air quality simulation methods for attaining the $PM_{2.5}$ goals. This study provides a quantitative approach to evaluate health risks from air pollution as well as to assess the potential health benefits of improving atmospheric $PM_{2.5}$ concentration. Even considering the intrinsic limitations and uncertainties of the analysis, it is an important information to rationalize the enforcement of $PM_{2.5}$ management policies and measures in Seoul, Korea.
Source Identification and Estimation of Source Apportionment of Ambient PM2.5 at Western National Park Site in USA
Hwang, In-Jo 21
The objective of this study was to estimate the $PM_{2.5}$ source apportionment at the Pinnacles National Monument IMPROVE site in western coastal USA. The PMF was applied to identify the existing sources and apportion the $PM_{2.5}$ mass to each source. To analyze local source impacts from various wind directions, the NPR analysis was performed using source contribution results with the wind direction values measured at the site. Also, PSCF was applied to identify the locations by point sources relative to the back trajectories. A total of 1,634 samples were collected from March 1988 to May 2004 by IMPROVE sampler and 32 chemical species were analyzed by PIXE, PESA, XRF, IC, and TOR methods. The PMF modeling identified seven sources and the average mass was apportioned to gasoline vehicle, secondary sulfate, aged sea salt, secondary nitrate, wood/field burning, diesel emission, and soil, respectively. In this study, the average mass was apportioned to gasoline vehicle (33.0%), secondary sulfate (25.7%), aged sea salt (17.8%), and secondary nitrate (10.1%). Also, this study suggests the possible role for source apportionment study of $PM_{2.5}$ at similar areas such as wildness, national park, and coastal areas in Korea.
Analysis of Seasonal Characteristics about Long-Range Transport and Deposition of Sulfur
Hong, Sung-Chul;Lee, Jae-Bum;Moon, Kyung-Jung;Song, Chang-Keun;Bang, Cheol-Han;Choi, Jin-Young;Kim, Jeong-Soo;Hong, You-Deog 34
Long-range transport of air pollutants was simulated using Comprehensive Acid Deposition Model (CADM) and Yonsei University-Sulfuric Acid Deposition Model (YU-SADM). For the simulation, weather patterns that represent the four seasons were derived through a clustering analysis with 5-years of meteorological data. The simulation result showed that in spring, influenced by strong low pressure from China, air pollutants of moved to the Korean Peninsula. In summer, humid air moved into the Korean Peninsula across the Yellow Sea while the north pacific high pressure extended, making the concentration of air pollutants lower than that in the other seasons. In autumn, air pollutants were transported by the northwest wind caused by the movement of high pressure over the Yellow Sea, while in winter air pollutants were influenced by northwest winds from continental highs. The amount of air pollutants in each season showed that high amount of pollutants were transported in winter due to the strong northwest wind. The in-flows were 3 to 8 times higher than those of the other seasons, and out-flows were about as twice as high. The amount of wet deposition in summer and autumn increased significantly compared to the amount in the other seasons due to the increase of rainfall. Source-receptor relationship analysis for sulfur showed that 70 to 91 precent of the total deposition came from the self-contribution by the Korean Peninsula. In winter, contribution from China was about 25 percent of the total deposition which was higher amount than any other season.
Estimation of Vehicle Kilometers Travelled and Air Pollution Emission from Motorcycles
Jang, Young-Kee;Kim, Jeong;Kim, Pil-Su;Shin, Yong-Il;Kim, Woon-Soo;Choi, Yu-Jin 48
Recently it has reported that the motorcycle is a major source of air pollution in urban area by many studies. But the air pollution emission from motorcycle has been high uncertainty due investigation of a lack of activity data and emission factors in Korea. So in this study, the population of moped and VKT (Vehicle kilometers travelled) of motorcycle are investigated for calculation of the emission from this source categories. As the results, the population of moped is estimated as about 400, 000 and corresponded as 23% of registrated motorcycle which is larger than 50 cc in engine displacement. And it is found that the VKT of moped and motorcycle are investigated as 19.1 km/day and 32.3 km/day. Annual air pollution emission from motorcycle and moped are estimated by investigated VKT and updated emission factors. The nationwide emissions of PM_{10}, CO, $NO_x$, VOC are calculated as 910 ton/yr, 208, 198 ton/yr, 3, 032 ton/yr and 25, 575 ton/yr in 2008. The contribution ratio of CO, VOC emission from these sources are estimated as 29%, 24% in on-road transport sector and it is confirmed that motorcycle and moped are major air pollution sources in urban area.
Estimation of Food Miles and CO2 Emissions of Imported Food
Ju, Ok-Jung;Lee, Jae-Bum;Seong, Mi-Ae;Kim, Su-Yeon;Ryu, Ji-Yeon;Kim, Dai-Gon;Hong, Yoo-Deog 57
Increase in greenhouse gas emissions during the last century has led to remarkable changes in our environment and climate system. Many policy measures have been developed to reduce greenhouse gas emissions across the world, many of which require our lifestyle changes from energy-intensive to energy-saving. One of the changes in our living patterns is to consider food miles. A food mile is the distance food travels from where it is produced to where it is consumed. Providing information of food miles will help people choose low mileage food, helping promote a "green consumption" action and lead to a low carbon society with emission reduction systems. In this study, 10 items are selected from 23 Harmonized commodity description and 2-digit coding system (HS) to estimate their food miles, and $CO_2$ emissions released in the transportation of imported food. For the estimation, four countries are chosen-Korea, Japan, United Kingdom (UK) and France, with Korea and Japan's 2001, 2003, and 2007 trade statistics and UK and France's 2003 and 2007 trade statistics used. As a result, Korea showed in 2007 the highest level of food miles and $CO_2$ emissions per capita among 4 countries. That suggests that Korea should make an effort to purchase local food to reduce food miles and use low-carbon vehicles for food transport, contributing to reducing greenhouse gas emissions.
Estimation of Air Pollutant Emission Factors for Motorcycle
Lim, Jae-Hyun;Kim, Hyun-Min;Lee, Sang-Moon;Kang, Hee-Jun;Lim, Yoon-Sung;Seo, Choong-Yeol;Kim, Jong-Choon 69
While increased use of motor cycles in the recent years for various demands could worsen air quality, only few studies have been conducted on estimation of emission factors and characterization of emissions from motorcycle. In this study, emissions from selected six models of motorcycle based on largest market share and production rate were investigated. To investigate gaseous and carbonaceous air pollutants, such as carbon monoxide (CO), total hydrocarbon (THC), nitrogen oxide ($NO_x$), elemental carbon (EC) and organic carbon (OC), total 124 motorcycles between 2003 and 2007 model year were tested with regulatory driving conditions, such as CVS-40 and CVS-47 mode. These motorcycles were further sub-categorized based on their displacement (< 50 cc, 50~150 cc, and $\geq$ 150 cc), type of stroke (2- and 4 strokes) and model year (2003~2005 and 2006~2007). Tested motorcycles with recent model year (2006~2007) exhibited less emissions of regulatory gaseous and carbonaceous air pollutants compared to old model year (2003~2005). Chemical analysis showed that CO present in highest concentration followed by THC and $NO_x$ for all tested motorcycles. Interestingly, two strokes motorcycle produced higher THC emission but less CO and $NO_x$ than those of four strokes. For all types of displacement and stroke, emission factors (gram per kilometer) of THC and CO except $NO_x$ with recent model year (2006~2007) showed decreased trend compared to old model year (2003~2005). In addition to this, due to mixed combustion between gasoline fuel and lubricant, two strokes motorcycle showed OC > EC emission trend.
The Determination of Diffusion and Partition Coefficients of PUF
Park, Jin-Soo;Little, John C.;Kim, Shin-Do;Lee, Hee-Kwan;Kong, Boo-Ju 77
The diffusion and partition coefficients of polyurethane foam (PUF) are estimated using a microbalance experiment and small chamber test. The microbalance is used to measure sorption/desorption kinetics and equilibrium data. When the diffusion condition is controlled in the chamber of the sample, interactions between volatile organic compounds (VOCs) and PUF can lead to the estimation of a relatively homogenous rate of mass transfer in the interiors and surfaces of PUF. The estimates of the material/air partition coefficient (K) and the material-phase diffusion coefficient (D) are shown to be independent of the concentrations of VOCs. This approach, if applied to a diffusion-controlled or physically-based model, can facilitate more precise prediction of their source/sink behavior. Although further research and more rigorous validation is needed, an emission model applied with the diffusion and partition coefficients from this research holds promise for the improvement of reliability in predicting the behavior of VOCs emitted from porous building materials by D and K.
CF4 Treatment Characteristics using an Elongated Arc Reactor
Kim, Kwan-Tae;Lee, Dae-Hoon;Lee, Jae-Ok;Cha, Min-Suk;Song, Young-Hoon 85
$CF_4$ removal characteristics were investigated using an elongated arc reactor. The advantage of the elongated arc reactor includes direct use of treated gas as plasma operating gas and the enhancement of the removal reaction by using a thermo-chemistry and a plasma induced chemistry at the same time. Geometrical configurations, such as the length of the reactor and the shape of a throat, were tested to get an optimized removal efficiency with low power consumption. As results, over 95% of $CF_4$ removal was obtained with 300 lpm of total flowrate for various $CF_4$ concentration (0.1~1%). Corresponding specific energy density (SED), which means required electrical energy to treat the unit volume of treated gas, is about 3.5 kJ/L, The present technique can be applied to real applications by satisfying three major concerns, those are the high flowrate of treated gas, high removal efficiency (> 95%), and low power consumption (< 10 kJ/L).
Seasonal Characteristics of PM2.5 Water Content at Seoul and Gosan, Korea
Lee, Hyung-Min;Kim, Yong-Pyo 94
Water content of $PM_{2.5}$ (particles in the atmosphere with a diameter of less than or equal to a nominal $2.5{\mu}m$) was estimated by using a gas/aerosol equilibrium model, SCAPE2, for the particles collected at Seoul and Gosan, Korea. From measured and analyzed characteristics of the particles, the largest difference between Seoul and Gosan is the proportions of total ammonia (t-$NH_3$=gas phase $NH_3$+particle phase ${NH_4}^+$), total nitric acid (t-$HNO_3$=gas phase $HNO_3$+particle phase ${NO_3}^-$) and sulfuric acid ($H_2SO_4$). Even though both sites have sufficient t-$NH_3$ to neutralize acidic species such as $H_2SO_4$, t-$HNO_3$, and t-HCl (total chloric acid=gas phase HCl+particle phase $Cl^-$), equivalent fraction of t-$NH_3$ and t-$HNO_3$ are higher at Seoul and $H_2SO_4$ is higher at Gosan. Based on the modeling result, it is identified that the $PM_{2.5}$ at Seoul is more hygroscopic than Gosan if the meteorological conditions are the same. To reduce water content of $PM_{2.5}$, and thus, mass concentration, control measures for ammonia and nitrate reduction are needed for Seoul, and inter-governmental cooperation is required for Gosan.


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Matthew Ryan Simmons
Matthew Ryan Simmons was born on February 7, 1980 in Fullerton, California to his biological parents Hope Cornejo and Kevin Rainey but adopted by John and Cindi Simmons. Matthew went home to be with the Lord on December 12, 2008 in Savannah, Georgia at the age of 28. Matthew grew up in Mission Viejo, California with his parents and brothers and sisters Nathan, Alyssa, Bethany and Taylor. He grew up loving trains, match box cars, cats, challenging puzzles, playing cards and board games. He loved growing up going camping with his family during the summer in Lake Tahoe. He would not give anything to sit around camp fires with his family, go hiking, watch the stars, and swim in the lake. Matthew always kept everything very organized. He graduated from Mission Viejo Highschool in 1999 and shortly after joined the Air Force. He was in the Air Force for seven years and during that time was deployed to South Korea for a year in 2004. Matthew met and married his wife Michelle Simmons of seven years in 2001 and than started a beautiful family. He was a wonderful father to Ariana, Shakira, Cayson, Elijah, and Jeremiah. Matt loved being with his kids and would do anything for them. His kids were his world. Matthew left the Air Force to join the Army in June 2006 and was stationed at Fort Stewart Army base in Savannah, Georgia with his family. He was than deployed to Afghanistan in November 2006 for fifteen months. While over seas he was a chaplain assistant to Chaplain Greg Mc Vey who Matt became good friends and battle buddies with. They stuck together and learned alot from each other. Matthew returned to Fort Stewart February 2008. He loved being with his wife and kids. Matthew is remembered as a loving dad, a great friend, and a hero to many. His favorite carton was Spongebob Squarepants. He loved watching a good movie and got a kick out of inviting everyone to his gun show. Matthew was extremely proud to serve his country. He loved to travel and see new places and learn about new things. He loved the San Diego Chargers. They were his team. He loved watching and following the Chargers wins/losses. Matthew will be missed by so many people. He had touched so many lives in his 28 years here on earth. We love you Matthew Ryan Simmons.
8 responses to Matthew Ryan Simmons
Ken, Sally, Grandpa says:
As a soldier, Matt was one of many heroes. His true measure of courage, determination, loyalty, devotion, and love came as a dad. That's what made him a hero in our eyes. We love you, Matt.
Bethany Simmons says:
Happy 4th of July Matt! thank you for serving our country and for your hard work. You are my hero and so many others as well. i love you and miss you. <3
Bruce M Chapnick says:
I never knew Matthew Simmons. I was in traffic the other day and saw a decal with his Memorial on the back windshield of a vehicle driving in Lake Forest. I thought how beautiful gesture to remember someone. I searched the internet and learned a bit about him. Hope this helps sharing this with those who may still grieve.


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Filtracija aktivnim ugljem jedna je od metoda koja se koristi za prečišćavanje vode, vazduha i gasova, obradu tečnosti (u hemijskoj, farmaceutskoj i prehrambenoj industriji) i za posebne namene (kod prerade zlata, membranske filtracije, u duvanskoj industriji, u uređajima za klimatizaciju, kod raznih katalitičkih procesa i drugo).

Osnovni pojmovi

Aktivni ugalj

Aktivni ugalj je posebno pripremljena vrsta ugljenika porozne kristalne strukture, s vrlo velikom unutrašnjom površinom (samo jedan gram aktivnog uglja može imati površinu i preko 500 m2). Prostori pora u aktivnom uglju ne prelaze veličinu od 10 Angstrema.[1][2]

Sirovine za proizvodnju su razne vrste prirodnog uglja, drvo, i koštice biljnih plodova. Proizvodi se fizičko-hemijskim postupkom aktiviranja, delovanjem vodene pare i ugljen-dioksida na visokim temperaturama (700 – 900°C), pri čemu nastaju brojne pore različite veličine. Fizikalna aktivacija podrazumieva karbonizaciju, odnosno pirolizu u odsutnosti kiseonika na temperaturama manjim od 700°C. Aktivacija se odvija oksidirajućim gasovima na temperaturi od 800 do 900°C. hemijska aktivacija je kombinacija karbonizacije i aktivacije sa različitim reagensima poput kalijum hidroksida i natrijum hidroksida. Tokom proizvodnje se vrši odstranjivanje vode (dehidratacija) a potom aktivacija (uklanjanje neugljeničnih jedinjenja). Aktivacija se vrši na visokoj temperaturi u posebnim pećima djelovanjem hemijskih materija aktivatora (cink-hlorid, fosforna kiselina, amonijum rodanid, kalijum karbonat).

Oplemenjivanje aktivnog uglja sa rastvorom srebra koristi se za aktivaciju antibakterijske aktivnosti aktivnog uglja.

Aktivni ugalj se u vojsci koristi za izradu cedila gas-maske i filtera za vazduh u skloništima. Radi bolje zaštite od bojnih otrova aktivnom uglju se najčešće dodaju hemoadsorpcioni i katalitički dodaci.

Aktivni ugljen koji se koristi za adsorpciju može biti u obliku praha ili u obliku granula. Granulirani aktivni ugalj ima veći efekat adsorpcije, a njegova veća cena može biti umanjena boljim dejstvom. Može se koristiti za adsorpciju posebnih organskih, prirodnih i sintetskih molekula, ali i određenih anorganskih materija. Učinak adsorpcije zavisi od niza faktora, od kojih su najvažniji: kvalitet i granulacija adsorbensa, kvalitet i količina početnog uzorka, predtretmanu uzorka, vreme adsorpcije, pH vrednosti uzorka, i osobine hemijske supstance koja se adsorbira.

Sorpcija u kondicioniranju vode

Sorpcija je proces koji se dešava kada se gasni ili tečni rastvor akumulira na površini nekog čvrstog materijala ili, ređe, tečnosti (sorbenta), formirajući molekularni ili atomski film (sorbat). Ovaj pojam se često zamenjuje sa pojmom apsorpcije, međutim reč je o sasvim drugačijem procesu. Oba ova procesa se mogu nazvati sorpcijama, dok je suprotan proces nazvan desorpcija.

Sorpcija koja je operativna u većini prirodnih, bioloških i hemijskih sistema, široko se upotrijava u industriji, kao npr. pri prečišćavanju voda, princip aktivnog uglja itd. U analitičkoj hemiji, naročito u tehnikama poput hromatografije i izmene jona, adsorpcija igra važnu ulogu tako da od odabira selektivno određenih adsorbensa, zavisi krajnji rezultat ispitivanja.

Osnova sorpcije leži u energiji površine materijala. U većini materijala, svi atomi i molekule imaju određene veze (bilo one jonske, kovalentne ili metalne prirode) sa drugim atomima i molekulama. Izuzetak predstavljaju atomi koji se nalaze na površini materijala, jer nisu potpuno okruženi sa drugim atomima, i zato ti atomi imaju afinitet (veći ili manji) da formiraju veze s nekim drugim atomom koji se nađe u blizini.

Prednosti primene filtracije aktivnim ugljem
Prednosti primene filtracije aktivnim ugljem su:
 odstranjuje vodonik sulfid i metan koji može nastati u vodi ili u pesku;
 odstranjuje hlor (hloramine) iz slavine;
 odstranjuje organske zagađivače nastale biološkim procesima;
 odstranjuje spoljašnje zagađivače koji mogu dospeti u vodu (dezodoransi, pesticidi, dim cigareta);
 podiže redoks vode;
 stvara kristalno čistu vodu;
 podiže koncetraciju kiseonika u vodi;
 vrši hemijsku, mehaničku, biološku i denitrifikacionu filtraciju (denitrifikaciju vrši samo u sporim filterima).

Izvori

Spoljašnje veze

Вода
Кондиционирање воде за пиће

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