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Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.
Citation: Guney E, Oliva B (2014) Analysis of the Robustness of Network-Based Disease-Gene Prioritization Methods Reveals Redundancy in the Human Interactome and Functional Diversity of Disease-Genes. PLoS ONE 9(4): e94686. https://doi.org/10.1371/journal.pone.0094686
Editor: Vince Grolmusz, Mathematical Institute, Hungary
Received: February 13, 2014; Accepted: March 13, 2014; Published: April 14, 2014
Copyright: © 2014 Guney, Oliva. 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: Spanish Ministry of Science and Innovation (MICINN) FEDER BIO2011-22568 (http://www.mineco.gob.es/portal/site/mineco/); and by EU grant EraSysbio+ (SHIPREC http://www.erasysbio.net/index.php?index=279) Euroinvestigación (EUI2009-04018). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
A fundamental characteristic of biological systems is tolerance to noise. The ability to counteract both internal mechanistic failures and changes in environmental conditions plays a central role in the survival of the organism. The main components of robustness are controlling the system through negative and positive feedback [1], splitting the parts of the system as functional units [2] (modularity and decoupling), and phenotypic plasticity [3] (typically achieved by redundancy). In a biological system, groups of genes are optimized in functional decoupling, redundancy and diversity such that the effects of perturbations are minimized [4]. However, complex biological systems have to balance between robustness and fragility which implies that a small number of rare perturbations can substantially disrupt the system [5]. In particular, some mutations are the main cause of diseases by exploiting the fragility of the biological system.
During the past decade, genome-wide efforts such as linkage analysis and association studies have successfully associated numerous causal loci with human disorders [6]. Still, much effort needs to be taken to fully understand the complex implications on the whole system. For this purpose, several methods have been developed recently to amplify available disease-gene associations using the principle of "guilt-by-association" through underlying biomolecular networks. These methods typically exploit relationships of the disease causing genes with other candidate genes, using the neighborhood of known associations in the physical [7], [8] or functional [9] interaction network. Recent methods extend the definition of the neighborhood to account for the global topology of the underlying network (network-based disease-gene prioritization) [10], [11]. Such global topology based methods have been showed to improve disease-gene association prediction [12]–[15].
Following the emergence of high-throughput experimental techniques that produce large amount of biological data, several studies have investigated robustness of a complex system in respect to the underlying network topology. Different types of networks have been studied with this purpose, such as metabolic networks [16], protein-protein interaction networks [17], [18] and regulatory interaction networks [19]. However, to our knowledge, robustness of network-based disease-gene prioritization methods, where the underlying network itself is perturbed, has not been extensively investigated. Due to the fact that network-based disease-gene prioritization methods use the connectivity between genes associated with the disease, we hypothesize that they may serve to distinguish diseases with respect to the predictability of causative genes via examining the behavior of network-based prioritization under noisy network models. The definition of robustness is problem specific [20] and here we define the robustness as the observed change in the prediction capacity of the prioritization methods when perturbing the underlying network.
In this work, our main goal has been to test the quality and robustness of several network-based disease-gene prioritization methods against perturbations introduced either in the underlying network or to known disease-gene associations. Next, to look into the relationship between robustness, functional diversity and modularity, we have examined the capability of these methods to identify disease modules (groups of genes that are enriched with the functions relevant to the disease). Through the analysis of the prediction performance of the network-based prioritization method under in silico perturbations and adopting a jackknifing scheme, we have categorized various disease-phenotypes (pathophenotypes) in Online Mendelian Inheritance in Man (OMIM) database [21] based on the level of recovery of the hidden disease-gene associations. Our results suggest that hidden disease-gene associations in several pathophenotypes, particularly the ones with high prevalence such as breast cancer and diabetes, can be recovered easier than the rest of the compared pathophenotypes, even when the underlying network is substantially perturbed. We have found that this was independent of the number of initial genes associated with the disease and rather mediated by the diversity of functions deduced by them. These findings provide evidence on the role of functional diversity in defining robustness of biological systems.
Results and Discussion
Network-based prioritization is sensitive to known disease-gene associations
Network-based disease gene prioritization methods rank the relevance of genes to a disease using known disease-gene associations (seeds) and the network topology. Any perturbation in the network topology or seeds induces a change on the ranking of the genes for the disease in concern. Systematically introducing perturbations at different levels and analyzing the changes in the ranking of the disease-genes provide a way to measure the robustness of the prioritization (see Figure 1 for an overview of the perturbations applied in this study). In order to assess the tolerance of a prioritization method to the noise in the underlying network or in the known disease-genes we used the jackknifing technique. This is a blind test in which we hid the disease-gene association of some seeds and we used the remaining seeds and a prioritization method to predict genes associated with the disease. Then, we checked whether the hidden seeds were predicted accurately by the prioritization. We tested five prioritization algorithms: NetShort, NetZcore and NetScore, three algorithms we have recently proposed that use global network topology [15]; and two existing algorithms, Functional Flow [22] and PageRank with priors [11]. The human protein-protein interaction (PPI) network (referred simply as network hereafter, unless otherwise stated) was obtained by integrating protein-protein interactions from several publicly available repositories. The network consisted of 11250 nodes (gene products) and 59220 edges (physical interactions) connecting them. We analyzed 19 disease phenotypes that were compiled among the human disorders in OMIM [21]. The phenotypes were created by merging disorders in OMIM using keywords describing the phenotype (see Methods and Table S1).
(A) Original network, (B) seed swapping, (C) interaction rewiring, (D) interaction removal. N(p,k) represents the kth instance of the network with a perturbation level of p in a random ensemble of networks for a given perturbation type (see Methods).
We first questioned the robustness of the prioritization methods by means of testing the quality of the genes associated with a disorder. To address the dependence on the number of seeds, we replaced the seeds with non-seeds in the network at varying percentages (10% to 80%). That is, we disturbed the initial disease-gene associations at different levels by introducing wrong associations between genes and pathophenotypes. Then we calculated the area under Receiver Operating Characteristic (ROC) curve of network-based prioritization methods using a five-fold cross-validation setting on the perturbed disease-gene annotations. An increased percentage of mis-annotated seeds reduced the reliability of predictions for all methods (Figure 2a). If more than 70% of seeds were false, the area under ROC curve (AUC) reduced to less than 50% for all methods. Only NetShort resulted in an AUC higher than 50% when using 40% false seeds. In conclusion, all methods were dependent on the quality of the initial associations, but NetShort was less affected, compared to the rest, in predicting new genes associated with the disease. In order to ensure that this was not an artifact of the network, we repeated the analysis on the network used by Goh et al. [23]. This network contained a set of high confident protein-protein interactions in human (we refer to this network as Goh network hereafter, see Methods). The AUCs for the methods at different perturbation percentages using Goh network are given in Figure S1a and confirmed our observation.
The interactions of the bPPI network were perturbed (A) by swapping the links in order to make false interactions or (B) by removing links. Plots show the average AUC and 95% confidence intervals calculated for the prediction of gene-disease associations for 19 diseases using NetScore (red), NetZcore (yellow), NetShort (green), Functional Flow (blue), and ToppGene (purple). The percentage of interactions swapped or removed varied between 0 and 80%. (C) Dependence on the number and quality of seeds. The average AUCs are given as the percentage of mis-annotated seeds goes from 0% to 80%.
In silico analysis highlights the existence of alternative routes connecting disease-genes
To examine the effect of the quality of the interactions in prioritization, we randomly swapped the edges of the network. Also, to observe the relevance of the number of interactions, we randomly deleted edges of the network. The variation in the edges of the network ranged between 10% and 80%. We applied the prioritization methods to these perturbed networks and calculated the average AUC over all diseases as before. Figure 2b shows the decrease in AUC produced using false interactions (randomly swapped edges) for all methods. It is noteworthy that PageRank was the most robust method, the prediction performance of which was less affected from the perturbation of edges than the rest. On the other hand, edge deletion decreased the AUC for NetScore, Functional Flow and PageRank, but NetZcore and NetShort improved the prediction quality by increasing the AUC, and it only began to drop after more than 60% of the interactions were removed (Figure 2c). Repeating the same analysis with the Goh network revealed that the prioritization approaches exhibited a similar behavior (Figures S1b and S1c), indicating that these features were independent of the underlying network. Although this behavior was unexpected, it could be explained by the way the prioritization algorithms work [15]. These algorithms used the seed nodes to disseminate information through the network. For each disease, there were very few number of seeds compared to non-seeds in the network (varying in the range of 0.1–0.9% of all nodes, see Table S1). Therefore, random deletion of edges disconnected fewer seeds because the number of edges connecting a seed (either one seed with another seed or one seed with a non-seed) was much less than the number of edges connecting two non-seeds (only <2% of all edges involve a seed, see Table S2). Consequently, Functional Flow and NetScore were more affected than others because of their dependence on the number of paths that connected seeds with each other (Functional Flow simulates flow of information through links of the network and NetScore exploits multiple shortest paths connecting seeds). However, the effect of deletion diminished in the case of NetZcore since it normalized the score using random networks. Considering that disease-genes tend to be highly connected to each other, the scores of the nodes connected with seeds and thus the prioritization of the hidden disease-genes was improved. To understand this with an example, let's take a node u that is relatively more connected to seeds in comparison to any of the random networks. The random deletion of an edge would be more likely to remove a link connecting at least one non-seed. Hence, it would be more likely that node u would remain relatively more connected to seeds in comparison to random networks. NetShort, which accounted for the number of seeds involved in a path to identify shortest paths leading to seeds, also improved the quality of the predictions, due to the seeds being connected by alternative routes unaffected by the deletion of links (see Table S3 for the number of all shortest paths between pairs of seeds in each pathophenotype). Such backup circuits constitute a fail-safe mechanism and explain the resilient nature of cells [4].
In order to gain an insight on the consequences of the changes in the network and explain the behavior of the prioritization algorithms independent of the jackknifing test described above, we focused on Alzheimer's Disease (AD), a relatively well studied pathophenotype for which we had an expert curated set of disease-gene associations [24]. We analyzed the connectedness of the genes associated with the AD in the perturbed networks introduced above. We used the genes associated with AD in OMIM [21] as seeds (AD-seeds). We took the neighbors of seeds in the network and checked how many of their neighbors are implicated in AD using an independent set of genes taken from literature (AD-related genes) [24]. We repeated this procedure on perturbed networks, interactions of which were either randomly swapped (permuted) or deleted (pruned) at different percentages (see Methods). Figure 3 shows the total number of genes and AD-related genes in the neighborhood of seeds. Not surprisingly, as the percentage of perturbation increased, the number of AD-related genes in the neighborhood of AD-seeds decreased. However, in the case of interaction pruning, the ratio of AD-related genes versus the total number of genes in the neighborhood of AD-seeds increased. This suggested that AD-related genes tended to remain connected with at least one AD-seed in the network. To confirm that this behavior was arising from the AD phenotype and not an artifact of the perturbations in the network, we checked the expected number of genes that would be covered at each perturbation level using 100 random sets of genes with sizes equal to the set of AD-related genes. The ratio of random gene sets covered in the neighborhood was substantially lower compared to the observed ratio of coverage of AD-related genes (Figure 3).
The interactions of bPPI network were perturbed (A) by swapping the links in order to make false interactions or (B) by removing interactions. The percentage of interactions swapped or removed varied between 0 and 80%. The bars correspond to the average number of genes over perturbed networks (except the first bar in which the number of genes in the original network are given) whereas the line shows the ratio of the number of AD-related genes in the neighborhood of AD-seeds to the number of all genes in the neighborhood in the perturbed networks.
Discovery of disease-genes under noisy network models varies among pathophenotypes
The analysis of the prediction performance caused by perturbations showed that disease-gene associations could still be discovered using network-based prioritization methods even when half of the interactions were perturbed (see Figures 2b and 2c). We questioned whether the emergence of such network-centric robustness of the prioritization methods depended on the pathophenotype. For this purpose, we defined, "prioritization tolerance" of a disease as the amount of perturbations required to cause a "critical" AUC change when we applied a network-based prioritization approach to predict its associated genes (see Methods for details). Among the different approaches to prioritize candidate genes we used NetScore for this analysis, since it had good overall prediction accuracy (Figure 2) and produced the smallest number of clusters coherently enriched in the functions associated with the corresponding disease (see Supplementary Results in Text S1 and Figure S2). Note that NetZcore had also similar properties, however the perturbation analysis above shoved that NetZcore was robust against perturbations whereas the AUC for NetScore fell down linearly as the percentage of perturbations increased (Figure 2). In order to minimize the bias on the prediction performance due to using a robust prioritization method and focus on the network-centric tolerance of the disease, we selected NetScore over NetZcore.
We grouped diseases into two categories based on the differences of the prioritization performance (assessed by the AUC) between the original network and perturbed networks using NetScore. For each pathophenotype, we checked the amount of perturbation (both for edge swapping and removal) required in the network that caused the prioritization performance fall below the critical AUC (table 1). If after perturbing more than 50% of the interactions, the prioritization of a disease still achieved a performance higher than the critical AUC, we labeled this disease as bearing high tolerance (referred as "tolerant" hereafter) in prioritization. On the other hand, if the critical AUC was reached with perturbations affecting less than 50% of the interactions in the network, we labeled the disease as bearing low tolerance (referred as "non-tolerant" hereafter). See Figure S3 for the changes in the NetScore prioritization AUC upon perturbations in the network for tolerant and non-tolerant diseases. According to our criteria, pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, obesity and parkinson disease were tolerant and thus higher capability of adaptation against perturbations in the underlying network than the rest of the pathophenotypes. Tolerant pathophenotypes have been extensively studied and might have a larger number of known disease-gene associations than the rest. Therefore, it may be argued that this is the cause to be classified as tolerant diseases under our criterion. However, in the following section we show that this is not true.
Comparison between pathophenotypes implies a role for functional diversity in determining prioritization tolerance
We checked whether the diseases in different categories bore similar properties: 1) in terms of known disease annotations associated with disease (seeds); 2) in terms of connectivity of these seeds; and 3) in terms of functions enriched among these seeds. In principle, it is reasonable to think that the prioritization tolerance might depend on the number of seeds of the phenotype. This would also suggest an explanation to the fact that diseases of high prevalence were categorized as tolerant, simply because they were more studied. Tolerant diseases had slightly higher number of seeds, nevertheless, there was not a significant difference between the number of seeds of tolerant and non-tolerant diseases (Figure 4a, associated p-value with the two-sided Wilcoxon rank sum test, p = 0.27). In order to make sure that number of seeds has no significant effect on the categorization, we took the diseases with less than 50 seeds in each category and confirmed that tolerant diseases (with less than 50 seeds) still had higher AUC values on average compared to non-tolerant diseases (with less than 50 seeds) upon perturbations (one-sided p-values were 5.5e-3 and 0.04 for interaction swapping and deletion respectively). In our previous study [15], we had shown that the prediction performance of network-based disease-gene prioritization methods was inversely correlated with the average shortest path length between seeds of a phenotype. Nevertheless, seeds of tolerant pathophenotypes were not distinguished significantly from the rest with respect to the average length of shortest paths between seeds (Figure 4b, p = 0.13). We have to note that the average length of shortest paths connecting two seeds is independent of the number of seeds. We also investigated the difference between the number of shortest paths connecting seeds (normalized by the number of seeds per pathophenotype) among two groups of diseases and found not significant discrepancy (p = 0.15). No significant difference between the number of interaction partners (degrees) of the seeds of tolerant and non-tolerant diseases were observed either (p = 0.24).
With respect to (A) known disease annotations associated with disease, (B) the connectivity of these seeds (assessed by the average shortest path length between seeds), (C) the ratio of the number of seed GO terms to the number of seeds associated with the diseases, and (D) the number of GO terms enriched within the high scoring subnetwork of the diseases.
Next, we looked at the functional enrichment of seeds involved in the two groups of diseases. We calculated the enrichment of GO terms of seeds (i.e., seed GO terms, see Methods for details) for both categories. In order to avoid possible bias towards the number of seeds, we normalized the number of seed GO terms dividing them by the total number of seeds for each pathophenotype. Considering that tolerant diseases had slightly more number of seeds (as mentioned above), normalizing by total number of seeds ensures that the observed number of GO terms are penalized more for tolerant diseases. Still, pathophenotypes with high prioritization tolerance contained higher ratio of seed GO terms per seed (around 3 times more than that of non-tolerant pathophenotypes), proving that a larger number of biological functions associated with seeds were involved in tolerant diseases than in pathophenotypes with low prioritization tolerance (Figure 4c, p = 3.29e-3). Likewise, on average there were almost twice as many GO terms enriched within the high scoring subnetworks (top 5% scored nodes in the network using NetScore) of tolerant diseases compared to non-tolerant diseases (Figure 4d, p = 0.03). These findings suggested that tolerant pathophenotypes tended to be functionally more diverse, being involved in a larger number of functions compared to non-tolerant phenotypes. Further analysis on the GO terms shared between pathophenotypes suggested that tolerant diseases tended to share more GO terms among themselves than non-tolerant diseases (Figure S4 and Figure S5).
Differential network analysis reveals a connected core machinery in breast cancer
Breast cancer was one of the pathophenotypes that was observed to bear high prioritization tolerance upon the analysis of network-based prioritization on the ensemble of randomly perturbed networks. To gain insights on the interactions giving rise to network-centric tolerance in breast cancer, we focused on the perturbed networks in which 80% of the interactions were randomly removed and chose among the random networks those with highest and lowest prediction accuracy when the network-based prioritization method was applied. We selected two networks in each case, two networks with AUCs of 82% and 73.9% and two networks with AUCs of 46.1% and 46.9%. Then, we checked the set of interactions yielding the difference between highest and lowest AUC. For this, we considered the interactions that were common in the two networks with highest AUC but were not among the common interactions of the two networks with lowest AUC. These interactions potentially caused that breast cancer phenotype was classified as tolerant. The network consisting of these interactions was named differential network (see Figure 5a). One key observation in this network was that the largest connected component (referred as module hereafter) of the differential network contained one third of the seed genes and this included proteins encoded by BRCA1, TP53, ESR1, AR, AKT1. This is an interesting result, because the networks from which the differential network was obtained contained only one out of five of the original interactions. Moreover, the GO terms enriched in this module covered 65.7% of all 35 seed GO terms of breast cancer (see Tables S4 and S5). Next, we checked the GO terms enriched using only non-seeds in this module. Among 312 GO terms enriched with these genes, 22 were seed GO terms yielding a highly significant enrichment of seed GO terms (p = 5.97e-7). This confirmed that the enrichment was not originated solely from the 10 seeds of the component. Figure 5b shows the seed GO terms covered by seed and non-seed genes of this module. We concluded that the observed prioritization tolerance was due to i) redundancy, being difficult to disconnect seed proteins with the random deletion of interactions and ii) functional diversity, the existence of a large connected module involving genes with the characteristic functions of the disease.
(A) The network containing interactions common to the two pruned networks with which the NetScore obtained the best prediction performance for breast cancer but did not exist among the common interactions in the worst performing two pruned networks. The red nodes are seeds. (B) The seed GO terms that are covered in the largest connected component of this differential network via using only seed genes (red) and only non-seed genes (blue) in the component.
Implications of this study and future directions
In this study, we have analyzed the robustness of several disease-gene prioritization algorithms using known gene-disease associations and protein-protein interaction networks. Our analysis on randomly perturbed interaction networks pointed to the existence of backup circuits within the PPI network constituting a fail-safe mechanism. Strikingly, the performance of the methods might tolerate up to 50% of interactions being removed, the point at which methods using alternative paths start suffering from low prediction performance. NetZcore and NetShort showed to be consistently effective in ranking genes when interactions were removed, while PageRank was more robust against the introduction of false interactions (we have to note that though it was more robust than NetShort, its overall performance was worse). Furthermore, NetShort, the method that calculated the distance to another gene in the network taking into account number of known disease-genes included in the path, yielded better prioritization performance when the known disease-gene information was noisy.
We categorized diseases with respect to their prioritization tolerance based on the performance of a network-based prioritization method under the perturbation of the network. Interestingly, pathophenotypes with high prioritization tolerance included many diseases with high prevalence in society. We further investigated whether there were characteristic differences between diseases involved in these categories. Neither the number of seeds, nor the average length of the shortest paths between them were significantly different between tolerant and non-tolerant diseases. On the other hand, the number of GO-terms enriched among seed genes (known disease-genes) per seed stood out as the most important factor in defining prioritization tolerance of a pathphenotype. That is, a disease was more likely to be tolerant to noise in the interaction network if the genes involved in that pathophenotype were functionally more diverse. Futhermore, tolerant diseases showed a higher tendency to share functions with other pathophenotypes. Such common features at the functional level might be useful for understanding the etiology of complex genetic disorders [25]. We have presented a way of systematically assessing the prioritization tolerance of pathophenotypes emerging from the underlying protein-interaction network. Although the robustness of a disease cannot possibly be explained only by the topology of the network, intuitively the prioritization tolerance of a disease could imply its robustness. Previous studies have argued that cancer [26] and diabetes [27] are robust diseases through a holistic analysis pointing out the redundancy and feedback mechanisms in these diseases. These studies are in agreement with our results on several diseases with high prioritization tolerance such as breast cancer, diabetes and leukemia. Furthermore, in addition to functional redundancy and decoupling, two key features mediating robustness of biological systems [26], [28], functional diversity (i.e. involving multiple pathways) may be related to the robustness of diseases. On the other hand, we did not find lung and prostate cancers as bearing high prioritization tolerance. Considering the heterogeneity among different cancer types, it is not very surprising to see such differences with respect to prioritization tolerance for various types of cancer. In fact, even within a certain type of cancer, such as breast cancer, a certain level of heterogeneity is expected at the patient level. In this sense, the genes involved in the pathophenotype and the underlying PPI network might be significantly different compared to another patient [29]–[31]. Therefore, the major limitations of our study are the incompleteness and inaccuracy of the disease-gene associations and protein-protein interactions. Nonetheless, the developments in high throughput genomics and proteomics screening, as well as experimental and computational efforts in identifying context-specific interaction networks (e.g., tissue-specific) [32], [33], will help to build more comprehensive models explaining the plasticity of diseases.
The findings presented here may help developing novel network-medicine approaches that try to characterize the interconnected pathways implicated in diseases and possibly suggest points of action to compensate the changes induced by the disease [34]. Functional diversity can explain why polypharmacological approaches, which typically target many gene products simultaneously via administration of multiple drugs [35], may work better on some diseases such as cancer [36]–[38].
Gene–phenotype associations
Genes and their associated disorders were taken from Online Mendelian Inheritance in Man (OMIM) database [21]. OMIM is one of the most comprehensive and reliable repositories of genes with Mendelian mutations and the disorders associated with them. Phenotypic associations for genes were extracted from the OMIM Morbid Map (ftp://ftp.ncbi.nih.gov/repository/OMIM/morbidmap retrieved August 27, 2009) by searching for keyword entries associated with the disorders given in Table S1. A disorder was considered if at least 25 genes were associated with it in the Morbid Map after merging several diseases under the same keyword (e.g., for Alzheimer's disease we collected Alzheimer's disease types 1, 2, etc. using the keyword "alzheimer"). Table S1 summarizes all diseases used under the context of this study, the number of genes associated with them and number of protein products these genes encode in the PPI network. Asthma, neuropathy and spastic paraplegia also satisfied the criterion of having at least 25 genes, however they were excluded from the robustness analysis since the AUCs using the original network were lower than 50% (see below).
Genes associated with a disorder were mapped to their products (proteins) in the PPI network and assigned an initial score for their phenotypic relevance. Thus, proteins translated by genes known to be involved in a particular pathology were termed seeds and have the highest score (1.0) in the network. All other proteins in the network were assigned a non-seed score (lowest score in the network: 0.01). The correspondence between genes and their products (proteins) was determined using the data integration protocol of BIANA [39].
Protein–protein interaction network
We used the human PPI network presented in our recent work [15]. The interaction network was compiled from publicly available major interaction data repositories using BIANA integration tool (Table S6). First, protein-protein interactions from different sources were integrated with BIANA [39] to obtain a human interactome. High throughput pull down interaction detection methods introduce many indirect relationships (such as being involved in the same complex) in addition to direct physical interactions. Thus, we removed the subset of interactions obtained by tandem affinity purification and called this network as the bPPI (binary protein-protein interaction) network. We also used the human interactome from Goh et al. [23] (referred to as the Goh network), which combined two high quality yeast two-hybrid experiments [40], [41] and protein-protein interactions obtained from the literature.
Network-based prioritization of disease-genes
To assess the tolerance of a given phenotype to the noise in the underlying network or in the seeds, we used five network-based prioritization algorithms available in GUILD software package. GUILD (Genes Underlying Inheritance Linked Disorders) is a network-based disease gene prioritization framework [15], [42]. The prioritization methods rank the nodes of the network according to their implication in the pathopheontype. The network-based prioritization approaches obtain this rank by disseminating the information of seeds through the network. GUILD framework provides several methods that use known disease-genes and their interactions to rank the relevance of genes in a disease or disorder. The basic hypothesis is that genes whose proteins interact with each other tend to exhibit similar features, such as function and/or phenotype. These methods require an initial set of genes associated with a particular phenotype (e.g., a Mendelian disorder) and interactions between the products of these genes. We chose three topology-based ranking algorithms: NetShort, NetZcore, and NetScore (see Supplementary Methods in Text S1 for details); and two state-of-the-art algorithms PageRank with priors [43] (as used in ToppNet [11]) and Functional Flow [22]. PageRank with priors has recently been proven its success in network-based disease-gene prioritization [11], [12], [14].
To evaluate the prioritization methods, we used five-fold cross validation and calculated area under ROC curve (AUC). The AUCs were averaged over all folds. The details of the evaluation has been described elsewhere [15].
Dependency of prioritization methods on network features and gene associations
We evaluated the dependencies of the methods by modifying the input data using three tests: 1) permuting interactions at random, 2) randomly removing interactions of the network, and 3) permuting the seeds at random. We tested the effect of the modifications on the bPPI and Goh networks using OMIM gene-phenotype associations. The degree of modifications ranged from 10% to 80% for each network and seed set. Ensembles of 100 random networks and random seed-sets were used to assess average prediction performance for each perturbation level. For the first test, nine groups of 100 networks were generated by swapping the edges of the original network (randomly creating new edges and removing old ones), and each group contained a different number of random permutations corresponding to the 10% to 80% of the number of edges. For the second test, the edges were randomly deleted to create nine groups of 100 networks in which the number of deletions varied between 10% and 80% of the number of edges. For the third test, a varying percentage of seed nodes (10% to 80%) was replaced with non-seed nodes 100 times, yielding nine groups of seed-sets and the percentage of non-seeds in each group ranged between 10 and 80. The prioritization methods were applied to these modified data sets and for each group, the AUC was averaged over the 100 randomly modified networks or seed sets. That is, for each level of perturbation p (%), the average AUC over an ensemble of randomly perturbed networks of size n can be written as follows;where N(p,k) is kth instance of the network with a perturbation level of p in a random ensemble of networks for a given perturbation type
Functional enrichment analysis
GO terms enriched among genes corresponding to high scoring proteins in a network (top 5% nodes in the network with respect to their prioritization score) were identified using the FuncAssociate2.0 [44] web service. Proteins in the network were mapped to the genes using the gene symbols provided by UniProt, and these symbols were fed to the web service. All genes in the network were used as the background gene list. A GO term was associated with the gene set, if and only if, the adjusted p-value associated with the term was less or equal to 0.05. Similarly, seed GO terms of pathophenotypes (or disease GO terms) were defined as the GO terms enriched among the seeds used in the prioritization method for that disease. For the comparison analysis of tolerant and non-tolerant pathophenotypes, the semantically non-redundant GO terms that belonged to biological process ontology were taken into consideration. GoSemSim R package [45] used to calculate the semantic similarity based on the metric proposed by Wang et al. [46] and semantically non-redundant GO terms were identified by removing the term that had at least 90% similarity to another term and that was closer to the root of the ontology (in case of a tie, one of the two terms was taken randomly). In the differential network analysis of breast cancer, a p-value was calculated assuming a hypergeometric distribution and using 1320 GO biological process terms enriched among all the genes in the original network as the set of all possible GO terms (all human genes were used as background in this case).
Defining network-centric tolerance of a pathophenotype
For the analysis of network-centric tolerance, we used the NetScore method of the GUILD package since 1) it had the highest prediction performance, 2) it produced clusters that are functionally more relevant to the disease compared to the rest of the prioritization methods, and 3) the method itself was not robust against perturbations (Figure 2 and Figure S2). We defined network-centric tolerance based on the amount of interaction perturbation required to cause a "critical AUC change" in network-based prioritization of a disease. For each disease, the "critical AUC change" was set as half of the AUC difference and the expected AUC that would be obtained by random predictions (i.e. 0.5). That is, for each pathophenotype, the "critical AUC change" was calculated using the following formula:and accordingly the "critical AUC" of a pathophenotype was given by:Figure S6 gives a schematic explanation of the critical AUC change. A disease phenotype was called tolerant if the amount of interaction perturbation (both interaction swapping and removal) required to cause a "critical AUC change" was lower than 50%. Similarly we called a disease phenotype non-tolerant if the amount of interaction perturbation for "critical AUC change" was higher than 50%. This can be formulated as follows:For testing the significance of differences in the distribution of values between robust and non-robust diseases we used Wilcoxon rank-sum test. Alpha values were set to 0.05. R software (http://www.r-project.org) was used to compute the statistics.
Supporting Information
Figure S1.
Robustness of the methods against the perturbation of the edges of the Goh network. Plot of the average AUC (shown in bars) and confidence interval (shown with error lines) calculated for the prediction of gene-disease associations by NetScore (red), NetZcore (yellow), NetShort (green), Functional Flow (blue), and PageRank (purple). The interactions of Goh network were perturbed (A) by swapping the links in order to make false interactions or (B) by removing interactions. The percentage of interactions swapped or removed varied between 0 and 80%. (C) Plot of the average AUC and confidence intervals calculated for the prediction of gene-disease associations as the percentage of mis-annotated seeds goes from 0% to 80%.
Figure S2.
Module-based functional enrichment analysis of prioritized subnetworks. (A) Number of modules identified in the neighborhood of known disease associations (N.hood) and in high scoring subnetworks identified by NetScore (N.Score), NetZcore (N.Zcore), NetShort (N.Short), Functional Flow (F.Flow) or PageRank with priors (P.Rank) prioritization methods. (B) Percentage of seed GO terms (GO terms significantly enriched in the set of genes associated with the disease) among all GO terms significantly enriched in the identified modules.
Figure S3.
AUC change at different levels of perturbations in the network. (A) Tolerant and (B) non-tolerant pathophenotypes. Solid lines correspond to AUC of the disease when NetScore prioritization method is applied to the interaction network whose edges are randomly swapped (averaged over all such networks) and dashes correspond to AUC of the disease when the prioritization method is applied to the interaction network whose edges are randomly deleted.
Figure S4.
Heatmap representation of Jaccard indices calculated for every pair of pathophenotypes. We used GO terms in the high scoring (top 5%) modules identified by NetScore prioritization method. Tolerant pathophenotypes are on the top of the heatmap (green), whereas non-tolerant pathophenotypes are at the bottom (orange). No overlap is represented with gray color and the degree of overlap increases as color gets darker (from white to blue).
Figure S5.
GO terms shared in the high scoring modules of at least 4 diseases. Tolerant pathophenotypes are on the left of the heatmap (green) and non-tolerant pathophenotypes are on the left (orange).
Figure S6.
Schematic explanation of the critical AUC change.
Table S1.
The number of genes associated with the diseases used in this study and the number of gene products corresponding to these genes in the interaction networks.
Table S2.
The number of seed connecting and non-seed connecting edges in the interaction networks for each disease.
Table S3.
The number of shortest paths connecting pairs of seeds in each disease.
Table S4.
GO term enrichment of the genes in the largest connected component of the differential network for breast cancer.
Table S5.
Seed GO terms (GO terms enriched by the seed genes) for all diseases.
Table S6.
The interaction sources used to create the human protein-protein interaction network.
Author Contributions
Conceived and designed the experiments: EG BO. Performed the experiments: EG. Analyzed the data: EG. Wrote the paper: EG BO.
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A few things in my hospital bag + recommendations from other moms
If you've already read this 1,000 times on my blog I'm sorry to keep repeating myself but it's hard to gauge what people know and what they may have missed! For example, it's so funny to me to read comments from Instagram followers asking for a hair tutorial because they have no idea that I have hundreds of them on my YouTube channel! So many blog readers discovered me through those tutorials on Pinterest, so I assume most people know that about me, but not everyone does!
Anyway, I'm having a third scheduled c-section for this baby because I've already had two. History has shown that I recover quickly from these and I can only hope for an easy recovery just like the last two!
Since this is my third, I have a pretty good idea of what the hospital stay is like. They let me out early last time because everything was normal and good, but traditionally it's a 3 day stay. I overpacked BIG TIME for the very first hospital stay, and overpacked a little bit the second time around.
I'm sure I'll overpack for this time, but I'm trying to remember that I can always send Justin home to grab something for me in case I need it. Plus, he appreciates the opportunities for fresh air!
A few things I'm packing in my bag for this hospital stay are:
- Comfortable nursing bras (more like a bralette). I've packed nursing tanks in the past but I like the skin to skin contact better when I'm just wearing a bralette. Plus my incredibly flattering mesh underwear reaches above my belly button so I've got extra coverage there anyway.
- A sweater in case I get cold. I'll pack my robe as well, but that can be a lot of fabric for when I'm laying in the bed. A sweater is easy to throw on or drape over the front of me.
- Comfortable onesies for baby! I have a few grey and white options, but I'm also bringing some pink and blue options!
- Some toiletries. A shower feels wonderful, but I typically don't wash my hair until I get home. I've usually washed it fresh right before we head in, and laying in bed all day doesn't really mess with it or create much need for it to be washed. I'll pack a few hair products, hair ties, a brush, etc. just to get me through! Other toiletries here would be my skincare products and some simple makeup.
- White blankets. The hospital blankets are great for swaddling but not my top choice for a blanket to use in a photo. I'll bring one to lay him or her down on in order to take a few photos!
- Weekender Bag. This bag is a good size and keeps me from overpacking. I'll be able to fit everything that baby and I need in here!
While I was thinking through what I needed to pack, I reached out to a few other influencers that I love to follow to ask what their MUST HAVE items are when they have their babies.
from left: Abby, Lauren, Anna, and Kailee
Abby Smith, Twist Me Pretty : I make sure to bring my maternity sweat pants and miralax! After four babies and three c-sections I've learned the hospital is not the time to be glamorous!
Lauren McBride, Lauren McBride -the blog: Depends! If you thought the mesh panties and the big bulky pad were good, just wait until you try Depends! The Always Discreet are thin so if it feels a little less like a diaper, and they do a way better job of handing that post-birth flow. So much more secure and comfortable, even for c-section moms! Game changer!
Anna Liesemeyer, In Honor Of Design: Something I was sooo thankful I stuck in my bag was my own comfy support leggings and soft robe. There is nothing better than getting out of that hospital gown to cuddle your babe with.
Kailee Wright, Kailee Wright : My go-to for every baby are my granny panties or now Spanx high waisted underwear. I put them on right after I can get up and moving. The just suck everything in and hold all those pads right next to you so you don't feel like anything is falling out. They help give my back extra support too. Plus, I swear the tightness helped bring my hips back down a lot faster after birth!
It's so good to hear from other moms about what they really appreciated having in the hospital. Whether it's your first or your fifth, there is so much to think about that it's easy to forget even simple things, like a robe, but those are the things that go a long way in making you feel a bit more comfortable in the hospital.
P.S. This is such a cute top for spring! This looks easy for throwing on over a nursing tank, and has lots of really high reviews too!

subset: commoncrawl


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Skills Training UK shortlisted for 'Training Provider of the year' at the prestigious Tes FE Awards.
November 21st, 2018
Skills Training UK is delighted to announce it has been selected as a finalist in the 'Training Provider of the Year' category for the annual Tes FE Awards
The awards, which will take place in London on 22 March 2019, will celebrate the exceptional and innovative individuals and organisations that have made an outstanding contribution to Further Education in 2017-18.
The 'Training provider of the year' category will be awarded to a provider that has demonstrated exceptional performance in the past academic year, covering high quality teaching, learner achievement and business performance.
Skills Training UK's entry for Training Provider of the Year highlighted high performance in the three core streams of its delivery: Apprenticeships and meeting employer training needs; achieving positive destinations for 16-24s and working with Local Enterprise Partnerships (LEPs) and local communities to boost skills and achieve social mobility.
Apprenticeships and skills minister Anne Milton said those shortlisted "should be really proud". She added: "The Tes FE Awards are a fantastic way to highlight the exceptional and innovative work going on in the sector. I know this will continue, develop and even get better. I want everyone to be encouraged and inspired by your example."
Martin Dunford OBE, Chief Executive, Skills Training UK, commented: "We are very pleased to be recognised as a finalist in the Tes FE awards following a strong year of growth in which we have provided and delivered high quality, high performing provision. Through our innovative approach we have continued to develop genuine partnerships with learners, employers and other education and training providers."
Skills Training UK's Apprenticeships growth has focused on STEM sectors that remain a Government priority and are aligned to the introduction of technical education in 2019. A team of over 60 Trainer-Assessors with relevant industry backgrounds design and deliver Apprenticeships training programmes in partnership with employers which help them to upskill existing staff, recruit new talent and develop their workforce in line with defined goals.
82% of Skills Training UK's employer clients are levy payers and the team adds value by advising employers on how to best manage and use their levy fund for training in line with their business goals.
Skills Training UK's delivery for 16-24-year-olds empowers young people and adults to improve their lives by developing the skills, qualifications and self-belief needed to progress. Vocational Study Programme courses with technical qualifications as well as Traineeships which are developed in partnership with employers have supported socio-economic inclusion, creating real opportunities for disadvantaged and hard to reach 16-24-year-olds in London, the West Midlands and South East.
Through individual mentoring, skills development and high-quality work experience, 62% of 921 learners in 2017/18 progressed to a positive destination such as an Apprenticeship, further education or employment (compared to a national average of 48% - DfE SFR).
Innovation has included Mentor Me, for 16-24-year-olds who face challenging personal circumstances and are struggling with mental health. Skills Training UK has invested in accredited Mental Health First Aid training for tutors so they can provide 1-2-1 mentoring sessions alongside workshops in money management, confidence building, and health and wellbeing.
Skills Training UK also works with LEPs and local communities in London, South East and West Midlands to design and deliver skills and social mobility programmes funded by European Social Fund 2014-20 contracts and due to high performance has won contract extensions. High performance is achieved through identifying local business partners, agencies and charities that know their local areas, local economy and the issues they face – so that the training can be designed and delivered to address their individual needs.
Other key achievements in 2017/18 include being named as one of London Stock Exchange Group's 1,000 Companies to Inspire Europe – a celebration of fast growing and dynamic SMEs and also becoming a finalist for 'Skills Provider of the Year' at the Education Investor 2018 awards.


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Social enterprise headlines recent roundtable
Posted on July 15, 2009 by jvaconsulting
At the recent Progress Group Responsible Economy Roundtable, held at Mi Casa Resource Center in Denver, social enterprise professionals and nonprofits using social enterprise to help their clients got together to discuss what the "Responsible Economy" is, strategies for how … Continue reading →
Posted in Economy, Social enterprise | Tagged Ashoka's Youth Venture, Ceyl Prinster, Christine Marquez-Hudson, Colorado Enterprise Fund, Jenny Spencer, Matt Nathan, Mi Casa Resource Center, Progress Group Responsible Economy Roundtable, Rob Smith, Rocky Mountain MicroFinance Institute, Tamra Ryan, Women's Bean Project | Leave a comment


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Culture Trip stands with
Black Lives Matter
As soon as you set foot in Bariloche, it will immediately become apparent that this town is built on chocolate. Chocolate shops and cafes line every street, and one of the best is certainly Mamuschka, a local stalwart on the chocolate scene and a favourite with tourists. Fill up a small pack of chocolate goodies that you can nibble at on the go, and for your friends and family, order a selection box with different varieties of chocolate to keep them happy for days.
Mamuschka, Mitre 298, San Carlos de Bariloche, Río Negro, Argentina +54 294 442-3294
Following hot on the heels of Mamuschka is Rapa Nui, another legendary chocolate shop just a few doors down. Complete with a chocolate fountain and ice-skating rink, these guys know a thing or two about chocolate, and apart from the traditional selection boxes, you can also pick up some chocolate animals, buildings and pre-packaged chocolate bars to keep you ticking over until next Christmas.
Rapa Nui, Mitre 202, 8400 San Carlos de Bariloche, Río Negro, Argentina +54 294 443-3999
Bariloche's most popular hike is famous for its world class views once you reach the top of the 30 minute trek. Not only will you be rewarded with panoramic vistas out over the whole lake district, but you can also treat yourself to a coffee and a snack in the cafe on top of the summit. In the cafe you will find lots of Patagonian-themed trinkets, so pick yourself up a baseball cap or a keyring and you'll never forget that time you conquered Cerro Campanario.
Cerro Campanario, Av. Exequiel Bustillo 17000, San Carlos de Bariloche, Río Negro, Argentina +54 294 442-7274
Argentina's premier manufacturer of alfajores, a local cake-slash-biscuit, Havanna are big on the scene in Bariloche. Not only do they have a number of cafes, but they also have their very own chocolate museum! Check out the museum and the factory, and afterwards grab a box of different flavoured alfajores. Eat them at home with a cup of coffee and the memories of Bariloche will come flooding back.
Museum, Av. Exequiel Bustillo 1200, San Carlos de Bariloche, Río Negro, Argentina +54 294 443-3575
Argentina's answer to Switzerland is located on the Circuito Chico in Bariloche. This tiny Alpine enclave has a central square around which are dotted chocolate shops, ice-cream vendors and waffle stands, but you can also pick up local beauty products like face creams and essential oils. Go for the rosehip oil, which is made locally from the bushes that grow in the forests surrounding the lakes.
Beer lovers rejoice, Chilean beer kings Kunstmann have a fully stocked merchandising stand at the back of the Bustillo beer campus. Located within the cavernous Kunstmann beer hall, you can browse their selection of ceramic mugs, t-shirts and caps, and go home with your very own beer mug from which any old beer will taste just as good as a Kunstmann original.
Kunstmann, Av. Exequiel Bustillo 7966, R8402AFN San Carlos de Bariloche, Río Negro, Argentina +54 294 446-4996
Patagonia beer's Bariloche HQ has one of the best views in the area, and a merch table to match. If Kunstmann's offerings didn't satisfy your thirst for beer souvenirs, head over to Patagonia and pick yourself up some locally-themed sweaters, hoodies and t-shirts. Not to be confused with the Patagonia clothing line, this is purely beer-related.
Patagonia, Circuito Chico Km 24,7, 8400 San Carlos de Bariloche, Río Negro, Argentina


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Prosecute Vijayakala? Be very careful
(Courtesy of The Island)
My former boss Tyrell Gunatillake, then Director CID has told me, whenever he took a tricky matter to the Late L.B.T. Premaratne, the then AG for advice, he would look at the law first, and then would say, " That is what the Law says, now let us see what Prudence would say?". This is what I am concerned about here – prudence.
There is no question that Mrs. Vijayakala could be prosecuted, given the facts as Constitutional lawyer Manohara de Silva had pointed out. But by doing so, are we playing into her hands? The Northern Provincial Chief Minister also says the same thing as Vijayakala in different ways but without transgressing the Law, he being a legal eagle. Vijayakala is a cruder version. But they are both addressing the same audience – the disgruntled/disillusioned former LTTE elements and their sympathizers. They have nothing new to offer to the people in the North and the East other than the same old wine in new bottles – Communalism. They have no new vision to offer the Tamil people, like to suggest to them that they come into the mainstream politics and march together with the other communities as one nation. On the other hand, as seen in the recent Col. Priyath Bandu incident in Kilinochchi, ordinary people may be already inclined to move in a different direction. If that happens on a larger scale, the likes of Vigneswaran, Shivajilingam and Vijayakala will run out of business. So they need to keep home fires burning. Thinking in that line, we need to think, is she deliberately courting arrest and prosecution? If that happens she would become a martyr before the Tamil people in the short run, and her future political role is settled. This talk of Reconciliation is a problem to them. Reconciliation to them is nothing short of separatism or at least Federalism. I think this is a political minefield. Taking action against her, we must, but the question is how, must be carefully deliberated on. In 1983, the UNP government bungled on the question of where to have the funerals of the 13 dead soldiers, and we had the Riots. This may be not such a serious issue in the short run, but emotional and knee-jerk reaction is not the way. This is where statesmanship would come to play. But do we have statesmen now other than politicians; who are hunting for their daily bread?
There is another aspect for the people to consider. The Yahapalana government, and the UNP in particular, are contending with the daily fall out of the Bond Scam. They have been pulling out various new rabbits form their bag to keep the people's attention diverted away from the main allegation. A little earlier it was the 'Hitler' story. Last week, it was the New York Times story. And this week we have the Vijayakala story. We do not know what they will come up with next week! But mind you in the Vijayakala story, the obnoxious utterance was made in the presence of three UNP Ministers! What does that mean?
Also, this government does this kind of thing to confuse the people by passing a dummy, when they want to pass some law which is not in the interest of the people, to satisfy some Western Powers. They have done that several times now. So my second point is, while we go to town on this Vijayakala issue, let us be watchful on what dummy the government is planning to get through in this confusion!
By GAMINI GUNAWARDANE
537 Viewers


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Procurement organisation, Foodbuy, has entered into a strategic five-year partnership deal with international textile, hygiene and facility services solutions provider, Elis.
Under the terms of the contract, Elis will deliver a full range of hospitality laundry services, including rental and maintenance of chefs' uniforms, kitchen porter uniforms, table linen, event linen, kitchenware and laundry services to more than 600 locations in the UK, including corporate catering, schools, universities and sports and leisure sites.
Elis will support Foodbuy by working with its clients, including Foodbuy's parent company and largest client, Compass Group UK & Ireland.
Ian Murphy, Managing Director of Foodbuy said: "We're really excited to be forming a strategic partnership with Elis. They're a multi-service provider which means they can accommodate a wide range of requests, but they also offer tailor-made solutions. This means we can provide our clients with a more efficient service, saving them both time and money, whilst helping them focus on their core businesses."
Operating in more than 28 countries, Elis directly sources its garments from manufacturers and offers full supply chain transparency. Elis has invested in radio-frequency identification (RFID) and barcoding technology to allow Foodbuy and its clients to manage and track inventory, no matter where a garment is in the supply chain.
Join 12,000 other FMs from over 46 countries at ExCeL London from the 18-20 June 2019. You and your team will learn from skilled trainers in CPD-accredited seminars and workshops at the only IWFM supported exhibition. You'll test, trial and source smart solutions across technology, cleaning, FM services, waste and energy management. You'll meet enthusiastic suppliers to identify new collaborations. By registering, you'll also gain access to three neighbouring shows to enhance your specialisms across fire, security and health and safety. Get your free ticket for Facilities Show.


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What we do:
Cinema in the 21st century is at the forefront of moving image, sound and performance; it is the future of audiovisual storytelling and creative expression.
Housed in the School of Creative Arts, Film Production & Media Arts courses offer students a complex and compelling education in the craft and theory of filmmaking, sonic and time-based video art, alongside exciting courses in photography, sculpture, drawing, music, architecture and visual culture. Students have the opportunity to learn and grow in a vibrant and artistic inter-disciplinary environment that fosters experimentation, creativity and excellence
Who we are:
Film at the University of Windsor is about close-knit, hands-on learning. Students in Film specialize in the audiovisual in the areas of filmmaking, sonic art and time-based media, through technical and artistic approaches to moving images and sound. We focus on developing filmmakers, sound and image artists, in a collaborative environment, featuring a strong working relationship with professors across the disciplines of film, music and time-based art.


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LONDON, June 3, (RTRS): British singer-songwriter Yusuf Islam, formerly known as Cat Stevens, launched a campaign on Friday to highlight the fate of child refugees in Europe with the release of a new single and plans for a one-off charity concert. The single, He Was Alone, was released with a video that follows a boy who loses his family before dying on the road with his gravestone bearing just one word, walad, meaning boy in Arabic. Save the Children said it was estimated that 95,000 refugee and migrant children travelled to Europe on their own last year, or lost their families on the journey. Yusuf, 67, best known for his 1960s and 1970s hits such as "Moonshadow" and "Wild World", said he had become involved after visiting a refugee camp in southern Turkey near Syria.
"It was difficult to stand by just watching this tragedy without trying to do something," Yusuf said in a statement. "My hope is to open people's hearts to the sadness experienced by thousands of young souls whose voices are not heard … in the midst of the refugee crisis, generated by the wars and conflicts blazing through their homelands." Yusuf, who converted to Islam in 1977 after almost drowning and largely quit the music industry for nearly 30 years, said the single and London concert on June 14 were part of a broader campaign #YouAreNotAlone. He said his charity Small Kindness, that he founded in 2000 to provide humanitarian aid worldwide, was working with the charities Save the Children and Penny Appeal to raise funds for child refugees. "I simply decided to help humanise the narrative and lend my voice to the call for keeping hearts and doors open to every refugee, especially youngsters, who have lost what future they might have once hoped for," said Yusuf who now lives in Dubai.


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This is a little keepsake activity I like to do in September – it incorporates the apple theme and the "school pictures" I take of each child in the fall. Each one chooses what color to paint their cardstock apple shape. After it dries we cut it out and glue it to a page printed with their name & date. Below is the apple template I used.
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B-vitamins are essential components of numerous key enzyme systems' without which the body cannot function. Each B-vitamin fills a different complementary need' which is why they should most often be taken together. B-vitamins act as coenzymes to catalyze numerous biochemical reactions.
These statements have not been evaluated by the Food and Drug Administration (FDA). These products are not meant to diagnose' treat or cure any disease or medical condition. Please consult your doctor before starting any exercise or nutritional supplement program or before using these or any product during pregnancy or if you have a serious medical condition.
Serving Size: 1 Tablet
Amount Per Serving / % DV
Thiamin (vitamin B-1) 50mg / 3'333%
Riboflavin (vitamin B-2) 50mg / 2'941%
Niacinamide 50mg / 250%
Vitamin B-6 (as pyridoxine HCI) 50mg / 2'500%
Folate (as folic acid) 800mcg / 200%
Vitamin B-12 (as cyanocobalamin) 50mcg / 833%
Biotin 50mcg / 17%
Pantothenic Acid 100mg / 1'000%
(as calcium D-pantothenate)
Choline (as choline bitartate) 50mg / *
Inositol 50mg / *
PABA (as para-amino benzoic acid) 30mg / *
*Daily Value not established.
Other Ingredients:
Sorbitol' stearic acid' colloidal silicon dioxide' modified cellulose gum' and magnesium stearate.
1 to 2 tablets daily.
If you are pregnant' may become pregnant' or breastfeeding' consult your health care professional before using this product.Do not use if either tamper-evident seal is broken or missing. Keep out of the reach of children.
Allergy / Dietary Needs
- Made in the USA


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This text discusses the overall procedure and requirements set by the US Citizenship and Immigration Companies (USCIS) for withdrawing varied applications. You should also state in you letter the place did you get such info that the corporate is in search of extra worker, and that you're qualified for the position. My mother and father had been thrilled with my choice, regardless that I had by no means even paid the campus a go to through the application process. The continued presence of the applications on the cell units of their prospects, permits the brands to be visible always.
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Beef is a common food allergen for dogs and can cause uncomfortable skin disorders as well as chronic conditions such as indigestion, ear infections, and breathing troubles. Allergies are due to an abnormally high defensive response to a protein, in this case, beef, that the immune system considers to be an intrusive substance. The process of digestion breaks down our foods into amino acids which are then absorbed by enterocytes, a type of white blood cell. If the digestive system doesn't completely break down the proteins, the enterocytes see them as intruders and attack by releasing histamine into the system.
An allergy to beef is an over-reaction of your dog's immune system to an unwelcome protein, in this case, a beef protein, that is present in a particular ingredient of their diet.
Beef Allergies Average Cost
From 551 quotes ranging from $200 - $800
Average Cost
Canine food allergies generally don't develop until after 3 years old, although there are exceptions, and this can affect the young dog's growth if not addressed. Skin reactions are usually more concentrated around the face, groin, toes, and under the front legs.
There are many types of proteins that can take the place of beef in the patient's diet. Using limited ingredient foods with novel protein sources should relieve the symptoms. Some foods may include beef as a flavoring even when it is not listed on the label, especially alternative red meats such as venison. New allergies are more likely to develop to related meats like venison or bison than dissimilar proteins like pheasant or duck. Alternative protein sources for your dog could include one or more of the following:
Food allergy
- An allergy to food is the immune system defending itself against what it perceives as a threat. An allergy response can't happen the first time an individual is exposed to the allergen as the immune system has to encounter the protein more than once for enough enterocytes to recognize it as an invader. If your pet is sensitive to a particular food, an intolerance to the food may predict that an allergy is developing. Any individual ingredient can cause an allergic reaction, but certain foods, including beef, dairy, chicken, and eggs, tend to cause allergies in canines more often than others.
Food intolerance
- Food intolerance is different from a food allergy because the reactions in food intolerance are not caused by histamine. Additional symptoms, including changes in the consistency or color of the stools, gurgling sounds from the digestive system, and abdominal pain, are common with a food intolerance. Food intolerance, although different from an allergy, can lead to allergies as the intolerance may cause the food to be improperly broken down by the digestive system.
The symptoms of food allergy in canines show up mostly on the skin and will prompt your veterinarian to collect skin scrapings from areas that have been affected by the allergic reaction, which will then be used to perform a cutaneous cytology. Cutaneous cytology is a procedure in which the affected skin cells are examined microscopically for signs of disease, yeast infections, or mites. This evaluation is likely to lead your veterinarian to suspect a food allergy.
The most common diagnostic tool for confirming and later pinpointing food allergies is an elimination diet. This involves changing the dog's food to either unseasoned human grade food or reduced ingredient commercial foods. Novel ingredients are generally the best choice for an elimination diet, meaning proteins and carbohydrates that are not included in the dog's current food. All of the ingredients in the current food should be avoided when determining the proper replacement food. The elimination diet should cause the signs and symptoms to disappear and then additional ingredients will be added back into the diet one at a time, until the allergen is uncovered. It is essential to ensure your dog does not ingest anything other than the food used for the elimination diet during this diagnostic treatment. A single treat with the allergen can cause the allergy to resurface. Beef flavoring can be found in items as diverse as:
Several weeks are often required before the elimination diet reveals the allergen. During this time your pet may continue experiencing symptoms. Corticosteroids and antihistamines can sometimes reduce swelling and control itching, however, use of these remedies can also make it harder to determine which ingredient in your dog's diet is causing the allergic reactions by masking obvious symptoms, so many veterinarians prefer to complete the elimination diet before applying these types of medications.
Antibiotics may be prescribed to combat the secondary skin infections that are often seen with food allergies. Once the allergen has been ascertained, the initial course of action is the removal of the ingredient from the dog's diet. Supplements, such as Omega-3 oils and probiotics, are often recommended to support the immune system and to protect the skin itself. The additions of these supplements are intended to assist your canine's body in handling any accidental exposure to allergens as well as to prevent the cultivation of new allergies.
Although allergies to foods are not strictly curable, signs and symptoms should be relieved by the removal of the ingredient from their diet. Relapses can occur from any exposure to the allergen, so it is important that you are selective in the type of treats and flavorings you give your dog. Allergic responses to one type of food often indicate a likelihood of developing an allergy to the ingredients in the replacement diet over time as well.The approach to combat this situation varies within the veterinary profession, with some doctors advocating that your pet remain on a single source of food, while others maintain that a steady rotation of three of four novel protein foods is optimal.
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Beef Allergies Average Cost
From 551 quotes ranging from $200 - $800
Average Cost
Ask a Vet
Miniature Pinscher
3 Years
Moderate severity
5 found helpful
Moderate severity
Has Symptoms
My min-pin had goat milk allergy in the past, he was born on a goat farm, the breeder fed him goat milk, we continued feeding him goat milk because it is supposedly beneficial (our other two dogs did not have any reaction), he licked his feet constantly, his feet were bald, red rash, and oozing. We did not know that it was from the goat milk. When we stopped giving it to him, his feet cleared up within a few months - this was 8 months ago. At the end of May we started feeding him beef (home made dog food recipe) every third week, we alternated 3 different types of meat - one week chicken meat, next week donkey meat (never had any reaction to these two types of meat), and we added third week beef. We only fed him beef 3 times and he developed the same symptoms he had with goat milk. We strongly suspect the beef is the culprit. Is it possible that since he had a reaction to goat products that he is having the same reaction to beef?
July 30, 2018
Tommy's Owner
3320 Recommendations
A dog may have a reaction to any ingredient in food and beef allergies are actually more common than most people realise, you should eliminate the beef from the rotation and substitute for another protein source; if you're making your own dog food you should ensure that it is nutritionally balanced for your dogs and discuss the ingredients with your Veterinarian. Regards Dr Callum Turner DVM
July 31, 2018
Thank you, Dr. Turner.
Aug. 1, 2018
Tommy's Owner
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Captain Jack
JR / ShihTzu mix
5 years
Moderate severity
0 found helpful
Moderate severity
Has Symptoms
my dog often licks his feet and under his legs, also scratches these areas to the point where his white fur is discolored. Can you advise what could be the cause and what can be done to improve his condition? He was given antibiotics which greatly improved his condition, but obviously this cannot be ongoing.
Sept. 28, 2017
Captain Jack's Owner
3320 Recommendations
Whilst paw licking may be due to an infection (bacterial or more commonly fungal) it may also be a symptom of atopy/food allergies; determining the environmental allergies or food which is causing the paw licking can be a long and unrewarding process with no single 'fix all' solution to the problem. Food elimination trials and allergy testing by your Veterinarian are the best course of action if your Veterinarian suspects this is the issue. Regards Dr Callum Turner DVM https://wagwalking.com/condition/atopy www.msdvetmanual.com/integumentary-system/atopic-dermatitis/canine-atopic-dermatitis
Sept. 28, 2017
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Beef Allergies Average Cost
From 551 quotes ranging from $200 - $800
Average Cost
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A new report from the Environmental Working Group has found that more than 500 different foods in U.S. grocery stores contain the potentially dangerous industrial plastics chemical azodicarbonamide (ADA).
The chemical, which is approved by the FDA for use in food, is a flour bleaching and oxidizing agent used in dough. However, the World Health Organization says that there is "abundant evidence that azodicarbonamide can induce asthma, other respiratory symptoms, and skin sensitization" for people who use it. It is banned in food in Australia, as well as some countries in Europe. Read more at NBC News.
{Andy Heller-Matzav.com Newscenter}


text: HomeBIRD WITH ROSEHIPS£29.00PricePainted by artist Ivanka this gorgeous print from an original painting would make a lovely gift for a bird enthusiast. Add to CartDetailsPrinted using Giclee ink to maintain those vibrant colours onto fine art paper, and mounted and framed under glass. The solid wood frame is black and measures overall 235mm x 285mm.


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We are really pleased to launch our volunteering programme today!
Snapping the Stiletto' will only work if we can create a community of people to support the project and its partner museums. We are encouraging all of our partners to look at their collections for new stories but their existing staff and volunteer teams only have so much time and have museums to run. I'm always fascinated by how many objects, photographs and documents museums have to carefully store (what you see when you visit is only ever a small percentage of what a museum has to care for) and we need to find ways to help them look at what they have.
We are excited to be using Volunteer Makers to create a team of volunteers to help the project. There will be lots of different challenges posted on the site http://snappingthestiletto.volunteermakers.org/ from all of the partner museums across Essex.
The Volunteer Makers approach looks at volunteering in a slightly different way and encourages people to help out in a range of ways from micro-volunteering- where you can sign up to follow social media accounts and retweet key messages to your followers- to offering regular help where you spend time at a museum and help them to research their collections and uncover new stories.
We have some training challenges on offer so, for example, if you think you'd like to help us by researching stories at the Essex Record Office but aren't sure how to go about this you can sign up here to come along to a session and find out more
More challenges will be added to the site so do keep an eye on it. At the moment we are focusing on discovering stories so a lot of the challenges are linked to research but later in the year we'll be looking for people to help with events, designing displays and posters, helping with touring exhibitions and visiting and reviewing these events.
We want to find ways for you to get involved so if you have an idea for a volunteering challenge that would help to uncover and tell stories of strong Essex woman from the last 100 years please get in touch and we'll see if we can come up with something.
So if you want to support a project celebrating strong Essex Women but don't have much time check out the challenges under in 'Just a Minute' and an 'Hour or Two' – you don't even need to leave your house for some of these. If you have time and think you'd like to get more involved and maybe spend time at a museum then look for 'Give a Day' and 'Regular help'.
You can find out more about Volunteer Makers and their approach and platform here http://volunteermakers.org/