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KDiamend: a package for detecting key drivers in a molecular ecological network of disease
Volume 12 Supplement 1
Selected articles from the 16th Asia Pacific Bioinformatics Conference (APBC 2018): systems biology
Mengxuan Lyu1,
Jiaxing Chen1,
Yiqi Jiang1,
Wei Dong1,
Zhou Fang1 &
Shuaicheng Li1
BMC Systems Biology volume 12, Article number: 5 (2018) Cite this article
Microbial abundance profiles are applied widely to understand diseases from the aspect of microbial communities. By investigating the abundance associations of species or genes, we can construct molecular ecological networks (MENs). The MENs are often constructed by calculating the Pearson correlation coefficient (PCC) between genes. In this work, we also applied multimodal mutual information (MMI) to construct MENs. The members which drive the concerned MENs are referred to as key drivers.
We proposed a novel method to detect the key drivers. First, we partitioned the MEN into subnetworks. Then we identified the most pertinent subnetworks to the disease by measuring the correlation between the abundance pattern and the delegated phenotype—the variable representing the disease phenotypes. Last, for each identified subnetwork, we detected the key driver by PageRank. We developed a package named KDiamend and applied it to the gut and oral microbial data to detect key drivers for Type 2 diabetes (T2D) and Rheumatoid Arthritis (RA). We detected six T2D-relevant subnetworks and three key drivers of them are related to the carbohydrate metabolic process. In addition, we detected nine subnetworks related to RA, a disease caused by compromised immune systems. The extracted subnetworks include InterPro matches (IPRs) concerned with immunoglobulin, Sporulation, biofilm, Flaviviruses, bacteriophage, etc., while the development of biofilms is regarded as one of the drivers of persistent infections.
KDiamend is feasible to detect key drivers and offers insights to uncover the development of diseases. The package is freely available at http://www.deepomics.org/pipelines/3DCD6955FEF2E64A/.
Assessment and characterization of microbiota are prevalent in human disease studies [1–3]. When the species within the microbial community interact with each other in equilibrium, serving as co-adapted colonists and providing beneficial goods and services, disruption of such alliances may induce health issue [4]. For example, the imbalance in the community could lead to bacterial overgrowth and the development of respiratory infections [5]. In this case, network analysis, for instance, differential network analysis, which identifies biomarker candidates by detecting changes in the correlation relationships between different experimental conditions [6], provides a better understanding towards disease. Thereafter, in microbiome area, molecular ecological networks (MENs) [7] can be constructed to perform network analysis for different types of actors within the microbial community, for examples, species, taxons, or phylogenetic gene markers, and they are referred to as phylogenetic molecular ecological networks (pMENs) where phylogenetic gene markers serve as the actors [8]. Similar to co-expression networks, Deng et al. proved that the MENs are scale-free and small world [7].
In a MEN, the removal of some species could be disproportionately deleterious. These species are referred to as keystone species. Keystone species are topologically important molecules in the MEN. Berry et al. has studied the detection of keystone species in MENs thoroughly [9]. They applied a brute-force leave-one-out strategy to evaluate the keystoneness of a species in a given MEN, and demonstrated the impact of the keystone species on species richness. They also classified keystone species according to their topological properties using linear discriminant analysis. Deng et al. proposed a method to detect keystone species from the MENs by integrating phenotype information [10]. They identified keystone species by calculating the correlation between a phenotype variable and the abundance pattern of species clusters. Researchers also considered species connected to many others in MENs as keystone species (also referred to as hub nodes) [11].
Key drivers, which are major components that drive the disease concerned MENs, provide hints to understand the mechanisms of disease and are intensively studied with RNA data. There are various of methods to identify the key drivers in a co-expression network. One method is to incorporate the annotation of genes and pathways of diseases in order to locate the key drivers by considering enrichment of statistic of genes neighborhood [12, 13]. Another category of method distinguishes important MENs by calculating associations between gene modules with meta information like phenotype and GWAS analysis [14, 15], and then detects the key drivers by measuring the genes topology effect. For example, MEGENA [16] did multiscale hub analysis and Zhang et al. examined the number of N-hob downstream nodes [17]. Those methods on detecting key drivers in RNA data analysis can be adopted to detect key drivers in MENs. Even though Portune et al. locates important microbial species and genes with the assistance of gene annotation to study the MENs [18], the annotation for microbial genes and species yet demands intensive efforts and the pathways of diseases are incomplete.
The distinction between keystone species and key drivers is that the keystone species are only topologically important, while key drivers motivate disease associated networks. MENs of diseases can be different compared to those from healthy individuals. By analyzing the factors driving the differences, we can uncover the development of the disease.
Inspired by key drivers analysis with RNA data and keystone species studies in MENs, we proposed a method to perform key drivers analysis without the availability of annotation information. Given a microbial abundance profile, we first construct the MEN, in which the nodes represent the microbial species or phylogenetic gene markers and the edges capture the associations between their respective nodes. Then we divide the MEN into multiple subnetworks and extract the subnetworks that are most relevant to the disease by calculating the associations between subnetworks and phenotype variables. A single phenotype variable could be insufficient to capture the changes in disease networks from healthy networks and it can be biased. To address this issue, we applied principal component analysis to extract delegated phenotype, which is more robust. Last, our method detects the key driver based on PageRank, which utilizes node topological properties within each extracted subnetwork. It captures the global link structure of subnetworks thus outperforms statistical algorithms that only use local information.
There are multiple ways to calculate inference of MENs, of which two of the most popular ways are Pearson correlation coefficient (PCC) and mutual information (MI) [19]. A review of correlation detection strategies in MENs [20] suggests that although some methods outperform others, the inference calculating method still needs further improvement. To reduce the effect of the high proportion of zero counts, Paulson et al. applied a mixture model that implemented a zero-inflated Gaussian (ZIG) distribution of mean group abundance for each taxonomic feature to do differential abundance analysis. Experiments show the improvement of mixture model compared to other models, for instance, DESeq, edgeR and Kruskal-Wallis test [21, 22]. Inspired by the above trials of solving rare microbes issues with mixture models, we proposed to construct the network by multimodal mutual information (MMI) [23] under the assumption of the Gaussian mixture model. In KDiamend, we implemented both PCC and MMI to infer the associations between nodes in the MENs. However, correlation-based methods, like PCC, have their limitations. To be more specific, it is hard to distinguish correlation with causation [24]. There are many other arbitrary methods to construct networks, like Bayesian network [24] and WGCNA [25], which apply topology overlaps to measure the similarities between nodes. These various methods can be implemented to construct the network as potential options in our framework. Nevertheless, it is out of the range of this work.
Our main contribution is that we refined the framework of key driver detection, and proposed delegated phenotype to capture the changes in disease networks from healthy networks. To validate our method, we performed experiment based on simulated data. Then, we tested KDiamend with two real microbiome datasets. We conducted key drivers analysis on Type 2 diabetes (T2D) and Rheumatoid Arthritis (RA), whose data are from gut microbiome and oral microbiome respectively. For each disease, we also compared experiment using PCC and MMI as two different inference methods, and acquired both consensus and divergence. Experiments of the two inference methods identified multiple identical phylogenetic gene markers and identified consensus pattern of disease-associated networks, indicating the robustness of our framework. On the other hand, the two different inference methods also led to specific findings, providing us with various aspects to study the mechanisms of diseases. We detected six T2D-relevant subnetworks and identified key drivers for each of them correspondingly. The identified key drivers include IPR006047, IPR018485 and IPR003385 related to the carbohydrate metabolic process, while the carbohydrate metabolic process is an important issue during the development of T2D [26]. In addition, we also detected key drivers for RA. Both PCC and MMI experiments located multiple InterPro matches (IPRs) which are related membrane and infection. Six subnetworks were extracted by PCC, containing IPRs concerned with immunoglobulin, Sporulation. Three subnetworks were detected by MMI, with IPRs about biofilm, Flaviviruses, bacteriophage, etc. The result is inspiring since the development of biofilms is regarded as one of the drivers of persistent infections [27] and some biofilms-growing bacterias contribute to RA [28].
Our method is to detect the key drivers which drive the diseases related networks in the microbial community. The key drivers can be microbial species or phylogenetic gene markers. For simplicity, we present our method with nodes as genes in the subsequent descriptions.
The detection of key drivers consists of following steps (see Fig. 1). First, we construct a MEN to represent the relationship between genes based on microbial abundance profiles and infer the weight of each edge. Second, we cluster the genes and partition the MEN into multiple subnetworks. Third, we analyze the phenotype variables and extract the delegated phenotype. By computing the associations between subnetworks and delegated phenotype, we obtain subnetworks that are most related to the disease. Last, based on PageRank, we identify actors with top influence over others in each subnetwork as key drivers.
Flowchart. First, we build a MEN and cluster genes into multiple subnetworks. After that, we summarize the phenotype variables and connect it to subnetworks. Then, we locate key drivers through PageRank
Inference method
In KDiamend, we provide two ways to compute distances between genes. The first one is PCC, which is the most popular way to capture similarities between genes. In addition, inspired from the inference of gene regulatory network in RNA analysis and mixture models in the microbiome analysis, we adopted the MMI and normalization processes in Context Likelihood of Relatedness(CLR) [29]. MI, which uses the mutual dependency and common uncertainty as for the measurement of connection between genes, does not assume linear, or continuous dependence like correlation [19, 30], so it can detect interactions which might be missed by PCC. MMI, under the assumption of the Gaussian mixture model, is for dealing with the high proportion of zero counts issue. The adopted CLR, which considers the context of the whole network and eliminates noises from the background, makes MMI more tolerant for noises when measuring the interactions.
At the beginning, we have an abundance matrix E, which contains abundance value of n genes in m samples. For each gene i, we have a vector of X i =(Ei1,Ei2,…,E im ). The strength of the relationship between gene i and gene j can be measured by PCC:
$$ PCC(i,j)=\frac{cov(X_{i},X_{j})}{\sigma_{i},\sigma_{j} }, $$
where cov(X i ,X j )is the covariance of X i and X j , and σ i is the standard deviation of X i . The adjacency matrix A of network can be generated from A ij =PCC(i,j). Then the distance between gene i and gene j can be interpreted as D ij =1−|PCC(i,j)|.
Apart from PCC, we also implemented MMI [23]. First, we decomposed X i into c i bins as Xi,1, …, \(X_{i, c_{i}}\). In this case, X i was distributed according to the following function:
$$ f_{X_{i}}(x)=\sum_{k=1}^{c_{i}}\pi_{i, k}g_{X_{i, k}}(x), $$
where \(g_{X_{i, k}}(x)\) (1≤k≤c i ) denotes the density function for Ci,k, and πi,k is the proportion for each sample in Ci,k. As proved in former work [23], assuming that Xi,k fits in a Gaussian distribution, we can estimate the mutual information between X i and X j as:
$$ MMI(X_{i},X_{j})=MMI^{O}(X_{i},X_{j})+MMI^{I}(X_{i},X_{j}). $$
The "outer" MI, MMIO(X i ,X j ), captures discretized dependency, while the "inner" MI, MMII(X i ,X j ), refers to the weighted aggregation of MI for each bin.
After computing MMI between all the genes and get a matrix M, we normalized the distance between gene i and gene j by: \(CLR(Z_{i},Z_{j}) = \sqrt {\left (Z_{i}^{2},Z_{j}^{2}\right)}\) where Z i and Z j are z-scores of M ij taking M i and M j as background respectively [29]. Then we applied hierarchical clustering and partitioned the MEN into multiple subnetworks according to the distance between genes.
Delegated phenotype
To best capture phenotype change in disease networks from healthy networks, we generated delegated phenotype by rotating Coordinates in the PC space of phenotype variable matrix S. Each row in S represents a sample while each column refers to a phenotype variable, such as gender, age, disease state, etc. If the properties are nonnumerical, we converted data into numbers before further analysis. Suppose one column v in S indicates whether each sample is collected from a person with or without this disease. That is v=(v1,v2,…,v k ,…,v m ),v k ∈(Y,N),1<k<m where m is the number of samples, Y indicates that this sample is collected from a person with the disease, and N means not.
We applied principal component analysis (PCA) to conclude S. We consider the first two principal components (PCs) to be enough for explaining disease variability, since the number of phenotype variables is relatively small in our test data. When phenotype data are more complicated, we may need extra analysis to decide the number of PCs we use to conclude delegated phenotype. We investigated the first two PCs and plotted samples in the coordinate of PC1 and PC2, regarding every sample as a point. Consequently, we got m points and each point refers to one sample. The coordinates for point k, is expressed as (x k ,y k ). We rotated PC to PC′ and make sure it has the largest correlation with v upon rotation. PC′ can best explain variability related to the disease. The angle of rotation is the θ that maximize f(θ).
$$\begin{array}{*{20}l} {}f(\theta)\! =\!\! \sum_{k, v_{k} \in N}(x_{k} cos \theta_{k} + y_{i} sin \theta_{k}) \,-\, \sum_{k, v_{k} \in Y}(x_{k} cos \theta_{k} + y_{k} sin \theta_{k}) \end{array} $$
That is to say, the angle between PC1 and PC1′ is:
$$\begin{array}{*{20}l} \theta=arctan \frac{\sum_{k, v_{k} \in N}x_{k}-\sum_{k, v_{k} \in Y} x_{k}}{\sum_{k, v_{k} \in N}y_{k}-\sum_{k, v_{k} \in Y} y_{k}} \end{array} $$
For example (see delegated phenotype in Fig. 1), blue points represent v k =Y and red points represent v k =N. By calculating θ, we got the line which implies the direction most correlated to the disease state.
We acquired delegated phenotype PC′, which has the largest correlation with the disease state and outperforms other single variables on explaining the variability of phenotype information at the meantime. For every subnetwork, we concluded the abundance pattern of genes as eigengenes [31]. Then we bridged the subnetworks to phenotype information by calculating the correlation between eigengenes and PC′. Subnetworks which have strong relationships to PC′ are extracted as disease-relevant subnetworks.
Identifying Key driver
Last, in every extracted disease relevant subnetwork, we applied network topology analysis and assigned every gene a PageRank score. PageRank (PR) ranks the nodes in a graph according to the structures of links with others and is used by Google's search engine to compute rankings of websites. In this algorithm, the score for one node can be affected by its neighbors [32] and if one's neighbors have high scores, its score increases iteratively [33].
As stated in [34], letting F u be the nodes linking to the node, B u be the nodes linked from it, and N u =|F u | be the magnitude of F u . Besides, considering there might be other factors towards the ranking, let E(u) be the vector concerned with some of the rank. Then, the PageRank of the node is defined as.
$$ R(u)=c\sum_{\upsilon \in B_{u}} \frac{R(\upsilon)}{N_{\upsilon} }+cE(u) $$
We tested our method with real microbiome datasets and compared PCC with MMI in this framework. First, In order to detect key drivers for T2D, we downloaded processed InterPro matches (IPR) abundance data from EBI (SRP008047), which is gut metagenome (microbiome) data from Chinese samples. InterPro [35] provides a functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites. The phenotype information of the dataset is provided in related paper [1]. We used the 145 samples from stage one.
We first trimmed IPRs with low abundance in relative abundance matrix and then applied quantile normalization [36, 37]. By computing PCC and MMI between pairs of genes, we reconstructed a MEN and conducted clustering to partition the MEN into multiple subnetworks. For all subnetworks, eigengenes were calculated by selecting the first PC of abundance. The eigengene was used for summarizing the abundance pattern in each subnetwork and to bridge it with phenotype information. On the other hand, we digitalized the phenotype matrix and applied PCA to it. We generated delegated phenotype by rotating coordinates in the PC space to best capture the phenotype change in disease networks from healthy networks. We extracted three subnetworks for PCC experiment and three subnetworks for MMI according to the correlation between eigengenes and delegated phenotype and the corresponding p-value (see Fig. 2). The p-value for correlation of each subnetwork was calculated by permuting the same number of genes from the dataset and calculating the correlation between the permuted eigengene and the delegated phenotype. After repeating 1000 times, the rank of the real correlation for the subnetwork is regarded as p-value.
Experiment results for type 2 diabetic. a The delegated phenotype. b, c Distribution for correlation between subnetworks and delegated phenotype. Every point refers to a subnetwork. The x-axis of the point refers to the correlation between this subnetwork and the delegated phenotype. The y-axis of the point refers to the 1 −p-value for the correlation. The y-axis on the right is for the histogram of these correlation. b is for PCC experiment and c is for MMI experiment. d, e Plots for eigengene and delegated phenotype. Blue line refers to the z-score of eigengene for that subnetwork. X-axis refers to samples. d is for PCC experiment and E is for MMI experiment
PCC experiment and MMI experiment detected 11 consensus IPRs which scattered in three subnetworks for MMI and two subnetworks for PCC. Consequently, the interaction generated from two types of inference connects these five extracted subnetworks together and merges them into one large community (see Fig. 3). Most of the consensus IPRs in the merged community are associated with the metabolic process and the catalytic activity which implies that the process is relevant to the disease. More specifically, two experiments both identified IPR018485 which participates in the carbohydrate metabolic process with the phosphotransferase activity and is active in carrying out ATP-dependent phosphorylation [38]. The extracted disease-relevant subnetworks in T2D are about the carbohydrate metabolic process and phosphorelay.
Extracted networks for T2D. Detected T2D-relevant subnetworks for PCC and MMI experiment. Blue and yellow nodes refer to IPRs identified by PCC and MMI, while green ones refer to IPRs identified by both methods. The weight calculated by PCC and MMI was normalized to the same scale. The corresponding subnetworks are labeled. Nodes with top PageRank in each subnetwork are enlarged
In addition, for PCC experiment, key drivers in subnetwork 89 and subnetwork 208 are related to the carbohydrate metabolic process, including IPR006047, IPR018485, and IPR003385. The key driver in subnetwork 166 is IPR001789, which plays a role in phosphorelay signal transduction system. MMI also detected IPR018211, IPR005538, IPR003501, and IPR001790 which are related to phosphorelay. PCC and MMI both identified IPRs related to the carbohydrate metabolic process and phosphorelay.
Oral microbiome
We applied our method to oral microbiome to detect the key drivers in microbial community related to dysbiosis in Rheumatoid Arthritis (RA). The abundance data was downloaded from EBI (ERP006678). Information of phenotype variables for different individuals was acquired from published paper [2]. We mapped the samples downloaded from EBI with the individual ID and got 49 oral microbial samples in total. Among them, 27 samples were collected from patients with RA in different disease states, 22 samples, used as the control, were collected from people without RA. 21 of them are saliva samples and 28 are dental samples.
We first conducted filtering and then applied normalization to avoid noises. After that, we constructed the MEN by computing similarities between all pairs of IPRs. Then we partition the MEN into multiple subnetworks by clustering.
Similar to the analysis for T2D, we processed the phenotype matrix and detected subnetworks most related to RA. First, we removed phenotype variables with more than 1/3 missing values. Then, for remaining phenotype variables, we conducted imputation using R package MICE [39]. By computing correlation between delegated phenotype and eigengenes of subnetworks, we extracted six subnetworks most related to RA using PCC and three subnetworks using MMI. Finally, we identified key drivers for detected disease associated subnetworks correspondingly.
We applied key drivers analysis for RA using PCC and MMI as two different inference methods respectively. Both experiments show IPRs, in extracted associated subnetworks, have higher abundance in disease state than in normal state (see Fig. 4). For PCC experiment, annotation shows that most IPRs in subnetwork 335 and 63 are about cell membrane while most IPRs in subnetwork 676, 128, 679 and 680 are about replication and cell growth. Functions for IPRs were inferred according to keywords and Gene Ontology (GO) mentioned in InterPro [35]. Moreover, subnetwork 335 also contains IPR014879(Sporulation initiation factor Spo0A, C-terminal) and IPR013783 (Immunoglobulin-like fold). IPR013783 is about immunoglobulin molecules and T-cell receptor antigen [40, 41], while RA is a disease caused by compromised immune systems [42].
Experiment results for Oral experiment. a The delegated phenotype. b, c Distribution for correlation between subnetworks and delegated phenotype. Every point refers to a subnetwork. The x-axis of the point refers to the correlation between this subnetwork and the delegated phenotype. The y-axis of the point refers to the 1 −p-value for the correlation. The y-axis on the right is for the histogram of these correlation. b is for PCC experiment and c is for MMI experiment. d, e Bar plot for the average abundance of extracted subnetworks. The relative abundance is different in different disease states
For MMI experiment, subnetwork 1642, which has the largest correlation with delegated phenotype, contains multiple IPRs about biofilm: IPR024487, IPR019669, and IPR010344. There are totally 24 IPRs in this subnetwork and top 5 of them are IPR003496, IPR024205, IPR008542, IPR010344, and IPR019669. Specifically, IPR010344 plays a role in biofilm formation and IPR019669 participates in single-species biofilm formation on the inanimate substrate. The development of biofilms is one of the drivers of persistent infections [27]. Some bacteria, when growing in the biofilm, e.g., Porphyromonas gingivalis in dental plaque, can become destructive and may contribute to RA [28]. Besides, subnetwork 1642 also contains IPR013756, associated with Flaviviruses, and IPR009774, related to hypothetical Streptococcus thermophilus bacteriophage, which hints the infection process in this subnetwork.
Noise tolerance of delegated phenotype
To test whether our delegated phenotype is robust when phenotypes are deficient, we tried every combination of phenotypes with removing 1,2,3,6,10 of them from the phenotype variables matrix of RA, and generated delegated phenotype for each of them. Then we calculated the correlations between those generated delegated phenotypes and extracted subnetworks. The result is promising and these extracted subnetworks have high correlation values in most cases (see Fig. 5).
Noise tolerance of delegated phenotype. a The first box plot refers to combination with removal of one, two, or three phenotype variables. b The second refers to the removal of six phenotype variables. c The third refers to the removal of ten phenotype variables. d The last one refers to the overall performance with all these combinations
Performance of PageRank in searching the key driver
We tested the performance of PageRank on a simulated dataset. At the beginning, we named the driven relationship as sub-gene relationship. We simplified the network by assuming that one gene could only be driven by one gene. Linear function is used to represent the driven relationship. i.e. y=Ax+n, where x and y denotes expression levels for gene and its sub-gene and n is the noise following the normal distribution with 0 mean. There are three parameters for the simulation algorithm: the number of sub-genes for each gene, the depth of the network and the noise level. Here, we used the variance to define the noise level and the variance of noise is βx. The structure of the simulated network and three parameters are shown in Fig. 6.
The structure of simulated network. The simplified network can be transferred into a tree
We generated the simulated data with various parameters. For each parameter group, 100 samples were produced. We compared the performance of PageRank with the degree algorithm that locates the key driver with the highest degree. As shown in Fig. 7, the noise level has little effect on prediction precision. The result of the degree algorithm also follows this pattern. To compare these two algorithms, we collected the cases where only one algorithm correctly found the key driver and the result is shown in C. Since when the number of sub-genes is large, both algorithms have high prediction precision, we focus more on cases where the sub-gene number is relatively small. In this situation, the PageRank has better performance.
Testing results for PageRank algorithm to find the key driver. The first two figures show the prediction precision of PageRank under different simulation parameters. a is related to the network depth and the number of sub-genes. b is for the noise level and the number of sub-genes. The third figure compares the performances of PageRank and degree algorithm. We counted cases where only one algorithm found the key driver correctly
Application to Alzheimer's disease
To further validate our method is capable of detecting key drivers of disease, we applied KDiamend to Alzheimer's Disease (AD) with analyzing RNA expression profiles, which were downloaded from GEO(GSE44770) [17]. Both of PCC and MMI experiments identified FBXL16 and OLFM1. FBXL16 related pathways are Innate Immune System and Class I MHC mediated antigen processing and presentation, while researches have shown that the activation of the Innate Immune System plays a crucial role in promoting AD [43]. OLFM1 is related to nervous system development and Neuroblastoma [44]. Besides, PCC experiment also identified RPS4Y1 and PITPNB as key drivers for extracted disease associated subnetworks. MMI experiment also identified KAZALD1, OR4A47, RNASE11, TXNDC2, 7-Mar, RTN4, TSPAN9, PCNP and PPP2R2C. More specifically, RTN4 is related to Demyelinating Disease [45] and KAZALD1 is related to Lobar Holoprosencephaly [46]. These experiments show a possible application of our method. It is capable of detecting key drivers in the network inferred from not only the microbial abundance profile but also other kinds of abundance data, like RNA expression or proteomics.
We proposed a novel method to detect key actors who drive the disease concerned MENs, which helps to understand microbial factors relevant to the certain disease. We divided the MENs into multiple subnetworks and then, instead of detecting important genes based on pathways or gene annotations, we extracted subnetworks which are most relevant to disease by utilizing the correlation between the patterns of abundance profiles and the delegated phenotype. Lastly, we identified key drivers based on PageRank.
We tested our method with two real microbial datasets. We detected that the disease-relevant subnetworks in T2D are related to the carbohydrate metabolic process and phosphorelay, while RA-relevant subnetworks are related to membrane, cell growth, and infection. The extracted subnetworks for RA include IPRs concerned with immunoglobulin, Sporulation, biofilm, Flaviviruses, bacteriophage, etc. Then we located corresponding key drivers for extracted disease-relevant subnetworks. Besides microbial data, we also tested our method with gene expression profiles to identify key drivers for AD and the outcome was inspiring. Experiments show our method is capable of detecting key drivers and providing hints to understand the mechanisms of diseases.
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Macia M, Rojo-Molinero E, Oliver A. Antimicrobial susceptibility testing in biofilm-growing bacteria. Clin Microbiol Infect. 2014; 20(10):981–90.
Marcinkiewicz J, Strus M, Pasich E. Antibiotic resistance: a "dark side" of biofilmassociated chronic infections. Pol Arch Med Wewn. 2013; 123(6):309–13.
Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007; 5(1):8.
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We would like to express sincere gratitude to our colleagues who greatly assisted the research. We thank Tan Bowen for offering assistance during designing the algorithm and Zhao Zicheng, Xu Chang for comments that greatly improved the manuscript.
This work and the publication of this article were sponsored by the GRF Project from RGC General Research Fund at City University of Hong Kong, Kowloon, HKSAR under Grant 9042181 (CityU 11203115).
Package is available at http://www.deepomics.org/pipelines/3DCD6955FEF2E64A/.
About this supplement
This article has been published as part of BMC Systems Biology Volume 12 Supplement 1, 2018: Selected articles from the 16th Asia Pacific Bioinformatics Conference (APBC 2018): systems biology. The full contents of the supplement are available online at https://bmcsystbiol.biomedcentral.com/articles/supplements/volume-12-supplement-1.
Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
Mengxuan Lyu, Jiaxing Chen, Yiqi Jiang, Wei Dong, Zhou Fang & Shuaicheng Li
Mengxuan Lyu
Jiaxing Chen
Yiqi Jiang
Wei Dong
Zhou Fang
Shuaicheng Li
LYU was responsible for the implementation of the package. Chen and LYU together designed the algorithm, conducted experiments and completed the manuscript. JIANG provided expertise on metagenomics and provided the data. DONG helped to improve the algorithm, conduct experiments and complete the manuscript. Fang provided assistance for testing and deploying the pipeline on the platform. LI provided insights and also polished the manuscript. All of the authors have read and approved of the final manuscript.
Correspondence to Shuaicheng Li.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Lyu, M., Chen, J., Jiang, Y. et al. KDiamend: a package for detecting key drivers in a molecular ecological network of disease. BMC Syst Biol 12 (Suppl 1), 5 (2018). https://doi.org/10.1186/s12918-018-0531-8
Molecular ecological network
Key driver


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Lilly Singh – Height – Weight – Body Measurements – Eye Color
Full Name: Lilly Singh
Profession: YouTuber
Lilly Singh was born on September 26, 1988 in Canada. Her parents, Malwinder Kaur and Sukhwinder Singh, immigrated from Hoshiarpur, Punjab, India and raised him as a Sikh. Her older sister Tina (born in 1982) is also a YouTuber; she makes videos about her family life with her husband and three sons. Singh said she was a tomboy as a child.
In October 2010, Singh started a YouTube channel under the pseudonym "IISuperwomanII". Originally Singh had planned to attend college as her parents wanted, but she chose to make YouTube videos and decided that she would return to graduate school if her YouTube career did not advance. In December 2011, Singh created a second channel called "SuperwomanVlogs". Its current name is "Lilly Singh Vlogs". Here she describes her daily activities and includes behind-the-scenes footage from her videos. She used it as a second channel to upload videos until August 2014, when Vlogs started uploading. Singh appeared as a background dancer in Speedy Singhs and Thank You movies in 2011.
In 2014, her channel was ranked 39th on the New Media Rockstars Top 100 Channels list. She was nominated for the Shorty Award and Streamy Award that same year. On May 28, Singh partnered with video sharing app TikTok to start creating videos on the app. As of November 2020, it has more than 800,000 followers and a total of more than 10 million likes on the app.
Singh established a strong connection with Punjabi heritage during her India visits. She struggled with depression and started making YouTube videos as a way to deal with her emotions. As a young adult, she lived with her parents in Markham, Ontario. In February 2019, Singh announced through social media that she was bisexual.
Height: 5ft 5inc (165 Cm)
Weight: 132lbs (60 Kg)

redpajama_set_name: RedPajamaCommonCrawl


To run this institution smoothly we want your cooperation at every juncture. A LITTLE BIT OF CASUAL APPROACH FROM YOUR SIDE BRINGS INSTITUTION TO SUCH A SITUATION WHERE SMOOTH WORKING GETS HAMPERED So not to face such situation we are requesting each and every one of you to be very prompt in fee payment and to follow all the norms. So we would like to share following points for the same.
1. We are not interested to charge fine with monthly tuition fee, so without fail tuition fee must be deposited every month by 15th other wise we are bound to enforce the fine of Rs. 200/- (Two hundred) only per month.
2. 75% attendance is a must for all the students. Std. IX to XII student's attendance will be sent to CBSE Board.
3. Bringing Mobile and coming to school by two wheeler (bike, scooter etc.) are banned. Students found guilty for above offence are liable of termination from this institution.
4. Guardians /Parents are requested to check the school Diary of their ward daily and sign it to ensure that they have attended their classes.
5. Once you have taken admission, re-admission will not be charged in our school, but the students those who are continuously absent (a month onward) without information they will be treated as drop – out / struck off and will be allowed for class on the basis of re-admission only.
6. After every term result declaration in the last week of that particular month (The month result is being declared) there is Parents / Teacher meet – 3 p.m. to 4 p.m.
7. Parents /guardians must see that their wards come to school in time, proper uniform & haircut, Low waist trouser, fancy shoe are not allowed.
8. If any student is absent from the institution for more than 3 days he/she will have to produce his/her guardians letter and will have to meet the Principal.
9. Conveyance will be withdrawn from those students who create nuisance inside the vehicles or tries to damage it.


Hi we have c7000 enclosure, we are using bl460c servers and in the interconnect bays we are using 4*cisco 3020 switches, on the servers we are running vmware vSphere 5.x, we are having problem with nic teaming on the servers each servers has 4 nics each of this nics terminate on different cisco 3020 switches how can we team this physical cards?. The cisco 3020 are connected to our core switches cisco 4510 we are trying to make multiple uplinks to the core switches using ether-Chanel but the minute we add uplinks to the channel other switches within the enclosure act funny and go down. Please find attached a diagram depicting our setup.
EtherChannel and pretty much any LACP based switch only allows you to Channel/Aggregate links when you have 1 source and 1 destination switch.
If you are trying to channel 2 links from a snigle 4510 to a single 3020, then this should work and if it doesn't I would either post your config of the ports and the Po## interface here or ping a Cisco guy.
If you are trying to channel links from a single 4510 to 2 different 3020s, its not going to work.
Note that if you had 3120s instead of 3020s, you could have used the special StackWise port to make 2 of your 4 3120s look like a single switch with 2 line cards. Then you could Channel ports from those 2 switches to a single 4510 because those 2 switches now act like 1.
But the 3020 doesn't have those StackWise ports.


Joining ACER India means becoming a part of the ACER family. It is an opportunity for you to work with one of the world's leading educational research organisations. You will become part of a team of committed and outstanding professionals who work in collaboration with ACER offices across the globe to deliver education projects.
At ACER India we offer challenging and rewarding career opportunities, have a friendly and flexible work environment and have a strong focus on professional development.
If you are passionate about improving learning, send your CVs at india@acer.org.


In this student-oriented community just minutes from East Carolina University, as well as Pitt Community College, you'll enjoy a spacious 4-bedroom, 4-bath apartment with hardwood-style floors, private bathrooms and a full furniture package.
Want less roommates? Sign for a Super Two or Three Bedroom and we'll lock the bedroom(s) not in use, giving you the privacy you desire.


Leading into a convention there is usually much going on and this year seems no different. As a collection of churches, we should all be praying that the unity and humility demonstrated by President Ronnie Floyd, and candidates Steve Gaines and J.D. Greear, would be on display in 2018.
The Southern Baptist Convention exists "for the purpose of carrying into effect the benevolent intention of our constituents by organizing a plan for eliciting, combining, and directing the energies of the denomination for the propagation of the gospel…It is the purpose of the Convention to provide a general organization for Baptists in the United States and its territories for the promotion of Christian missions at home and abroad and any other objects such as Christian education, benevolent enterprises, and social services which it may deem proper and advisable for the furtherance of the Kingdom of God." The Southern Baptist Convention exists is to cooperate together for the propagation of the gospel through domestic and foreign missions, Christian education (such as seminaries), and benevolent/social services. So when you participate in the annual convention you get to hear from the agencies that are carrying out these tasks as well as vote on various motions that direct how we will cooperate for this above task through those agencies! In addition, messengers vote on Resolutions that are pronouncements from the messengers of the convention about things that are important to Southern Baptists in 2018. In reality, the SBC only exists as a convention two days a year as we do business together to vote on motions that will direct our agencies in the 363 days we are not together. One of the most important votes this year will be the Presidential election. The President appoints the committee on committee's that appoints the committee on nominations that then appoints trustees to all our agencies. The trustees then provide ongoing oversight and accountability for these entities. In addition, they are responsible to ensure that SBC entities are faithfully carrying out their vision, mission, and goals.
The annual convention is made up of messengers sent from cooperating SBC Churches. SBC This Week has an easy breakdown section of how to register as a messenger, as well as instructions that will help you determine how many messengers your church is eligible to send to this year's convention. There is still time to sign up and there are still ways to get flights from most cities into Dallas to be there Tuesday and out by that evening. If you have further questions on this process email us at baptist21@gmail.com, but we hope that you will plan to be there and be in the room because that's when the decisions are made!
GuideStone Financial Resources Report, O. S. Hawkins.
Notes: During an SBC Entities report they will give an update and then there will be a time for questions from the messengers. If you would like to ask a question of an entity President proceed to one of the Microphones on the Convention floor.
Notes: At this point, any messenger can bring a motion to the convention floor. These motions will then be presented to the Committee on Order of Business to determine if they will be deferred or brought back to the convention later in the program for a vote.
The afternoon session is packed with business, messengers should plan to be in the room from about 1:35pm until the end of the session.
Election of 2019 Convention Preacher, Alternate Preacher and Music Director.
International Mission Board Commissioning Service, David Platt.
Notes: This is the highlight of the convention where we will commission and pray over many IMB missionaries who are getting ready to leave for the ends of the earth!
Woman's Missionary Union Report, Sandra Wisdom-Martin.
North American Mission Board Report, Kevin Ezell.
International Mission Board Report, David Platt.
LifeWay Christian Resources Report, Thom Rainer.
The Ethics & Religious Liberty Commission Report, Russell Moore.
Baptist21 Lunch – Our lunch panel will focus on issues facing the SBC and will take place immediately following the close of the June 12th morning session (approx. 11:45am). The panel will feature Danny Akin, Matt Chandler, D.A. Horton, Albert Mohler, Russell Moore, Trillia Newbill, and Kevin Smith.
NAMB Send Luncheon – This lunch takes place on June 11th but is already sold out. You can register to watch the simulcast of it here.
ERLC Gathering – The ERLC will discuss "Gospel Sexuality in a Me Too Culture" at 9pm on Monday June 11th. You can register for that free event here.
9Marks@9 – 9Marks will host a conversation on "The State of the SBC" on Tuesday June 12th at 9pm. The panelists will include Danny Akin, H.B. Charles, Mark Dever, and Albert Mohler. You can register for that event here.
The SBC business meeting runs procedurally based off Roberts Rules of Order. If you are not used to Roberts Rules that can at times seem complicated. Here is a cheat sheet on Roberts Rules – We would recommend sitting near someone you know that knows these rules well and they can best help you navigate the two days of business.
The SBC annual meeting is a great time to catch up with old friends, make new friends, and connect with like-minded brothers and sisters. One of the most enjoyable things about the SBC is hearing testimonies of what the Lord is doing through brothers and sisters we may have never heard of or met in our cooperative mission efforts. And since we are baptist, don't forget to have some of these conversations around meals.The ERLC has provided a good guide to the SBC that highlights some of the better food spots in the DFW area. It is a wonderful time to be encouraged by what the Lord is doing in our midst in just the everyday grind of serving our King.
As we do business and as we fellowship, let's remember that our faithful ministry in our local contexts and our cooperative efforts together are being used so that King Jesus is praised among all nations through our cooperative efforts.


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Home/Featured/Manaea's gem and Olson's bat smoke Astros, extend lead to 6.5
Manaea's gem and Olson's bat smoke Astros, extend lead to 6.5
Daniel Gluskoter Sep 10, 2020	Featured, Local Sports, Sports Comments Off on Manaea's gem and Olson's bat smoke Astros, extend lead to 6.5
In one of the best pitching duels of the season, the A's closed out their most important series of 2020 with a 3-1 win over the Houston Astros, firmly putting themselves in the drivers seat in the race for the AL West championship.
Oakland's Sean Manaea delivers a pitch during Thursday's 3-1 win over the Astros. He was masterful, retiring the first 15 Houston batters he faced and only allowing two hits in seven innings without walking a batter.
The win, their fourth in the five game series, gives Oakland a 6 1/2 game lead over the Astros. It's the largest division lead for the A's since the 2013 season and reduces their magic number to just 11 with 18 games remaining on the schedule.
Sean Manaea (4-2, 4.46) carried a perfect game into the sixth inning of the fast-paced game, played under ominous skies with an AQI approaching 200 caused by the numerous wildfires that continue to ravage the West Coast.
It was Manaea's second straight solid start on the home stand, following Saturday's outing against the Padres where he only allowed one run in five innings. Thursday he was even better, quickly dispatching the Astros first 15 batters on the way to a seven inning effort that he completed while only allowing one run on two hits. He only needed 61 pitches and didn't walk a batter.
Matt Olson follows the flight of his sixth inning home run against Houston Thursday afternoon at the Coliseum. The two-run blast gave the A's all the offense they would need in a 3-1 win over the Astros.
He was nearly matched by Houston's Jose Urquidy (0-1, 3.72), making just his second appearance of the season. Urquidy kept pace with Manaea for the first four innings, not allowing a hit. But the A's finally got to him in the sixth when Matt Olson followed a Ramon Laureano walk with a two run homer, his 12th, giving Oakland all the offense they would need.
After a scoreless eighth from Jake Diekman, Liam Hendriks came on in the ninth to pitch a perfect inning for his 12th save. It also improved Oakland's record in games that Hendriks has appeared this season to a perfect 19-0.
After the game the A's left for Arlington and a four-game series against Texas that includes another doubleheader on Saturday. Mike Fiers (4-2, 4.93) is set to face the Rangers Luis Garcia (0-1, 3.68) in tomorrow nights opener at 5:05pm.
Previous Padres, Tatis, shut down A's as Houston arrives for five games
Next Fresh Pears highlight end of season treats at Farmers Market


HSC Innovations was part of a Northern Ireland delegation which recently attended a major innovation event at the GlaxoSmithKline (GSK) R&D site in Stevenage.
The PraxisUnico "What Industry wants from Academia" event on the 7th and 8th May focused largely on bioscience and healthcare and was chaired by Malcolm Skingle CBE, Director, Academic Liaison, GSK Pharma. It was the largest UK networking event of its type, with more than 70 universities and research institutes represented, along with 30 major technology driven companies from a range of sectors presenting.
The keynote talks included Professor Sir John O'Reilly, Director General, Knowledge and Innovation at Department for Business, Innovation and Skills, setting out a vision for the future keyed to the importance of UK research and Iain Gray, CEO of the Technology Strategy Board, who explained the importance of UK research to business innovation. The Intellectual Property Office also launched the report on the evaluation of the impacts of the Lambert Toolkit in relation to the Lambert model agreements and Dr David Docherty, CEO of the National Centre for Universities and Business (NCUB), described the priorities for this newly created Centre.
Presentations from the event are available for download as is the full report from PraxisUnico.
The delegation also had the opportunity to visit the Stevenage Bioscience Catalyst (SBC), the UK's first open innovation bioscience campus pioneering a unique culture to drive early stage biotech, pharma and medtech developments.
Here they were hosted by Martino Picardo, CEO SBC, who indicated that the convergence of multiple innovative technologies is one of the futures most important healthcare trends.
Further information on the SBC and details on SBC's DiscoverAssist – helping you work in early stage drug discovery is available at http://www.stevenagecatalyst.com/openinnovation_at_sbc/discoverassist/.


Join the St. Louis NORC for a special coach bus trip to tour the Underground Railroad's "Alton Route." Click here to view a flyer for more information.
B'nai B'rith St. Louis, St. Louis NORC, and St. Louis Senior Olympics invite you to enjoy a Spring Community Concert featuring the Oasis Jazz Ensemble. Click here to view a flyer for more information.


Since 2003, the trial attorneys at Schimmel & Parks, APLC, have aggressively represented plaintiffs in high profile and serious injury cases involving construction defects, employment abuse, complex business disputes, large mass torts and class actions. The firm's partners have over six decades of combined experience. The founding partners have been selected for inclusion in Southern California Super Lawyers for multiple years and are each peer-review rated AV Preeminent* through Martindale-Hubbell for their legal skills and ethical representation of clients in state and federal court.
Based in Sherman Oaks, we represent clients throughout California. Our high end construction defect litigation practice focuses on clientele experiencing defects and property losses to mansions, estates, large-scale residential developments, and significant commercial properties. Schimmel & Parks, APLC, also has vast experience representing employees and whistleblowers in employment harassment, discrimination and retaliation cases. We have been acknowledged on ABC's 20/20 and in other media reports for our work taking on the drug "rehab" industry in cases of sexual abuse or wrongful death. Schimmel & Parks, APLC, serves on the plaintiff leadership group through its representation of thousands of victims of the Porter Ranch gas blowout case, and it has represented large groups of individuals affected by catastrophic landslide, flooding or wildfire events.
Schimmel & Parks, APLC, understands that hard work, creativity and resources are needed to take on the powerful insurers, corporations and other interests who cause suffering and harm to others. We bring these litigation strengths to bear for our clients. Schimmel & Parks, APLC, represents clients who seek high-quality, skilled, aggressive legal representation. Through their litigation efforts, the firm's partners have helped secure more than $250 million in settlements and judgments on behalf of clients throughout the years. Schimmel & Parks, APLC, offers a free consultation to review each potential case.


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Designer History
Designer History » Uncategorized
Metal In Interior Design
For many years, metal was used for roof and exterior siding. But new materials, attitudes, and applications have moved it indoors buildings creating innovative, intricate interior layouts. Metal's flexibility opens up it to almost unlimited software while providing durability and durability which surpasses a number of other inner materials. It combines function, shape, beauty, and fashion.
The past technological improvements have turned metals into a practical solution for interior designers. New metal finishes, designs, and coatings are available in the market to match varied architectural styles. The invention of new digital tools has let us create a dramatic improvement to high profile architecture jobs and public locations. Brick aluminum provides excellent design flexibility and long-lasting durability. Metal surfacing has become more and more popular to be used as wall coverings, column covers, space dividers, and stairways. More architects will also be specifying alloy for ceiling therapies in public locations. Folks have always understood metal to be lasting, but loathe to use it for inside applications, such as ceilings.
Building owners are currently discovering a ceiling made of metal may be used to give added security in a room and create an open, spacious look. It's been stated that these systems may be configured to present restricted, limited or no access to the plenum. It has been said that this information is very significant for public places specifically schools, courthouses, and airports. An important security feature to get a metallic cloth ceiling is the sprinklers can nevertheless function.
Along with ceilings, Crismond notes other popular applications for the metal as a building material are walls, flooring, walls and inside railings. It is also common to see a wire furniture being used to give a place an industrial look. Also to be noted is that metals used for the interiors are engineered with fire immunity and for the cinema or similar places, using metal helps with the acoustics.
Metal has come to be a creative inside element for configurations with high-traffic places and numerous degrees, along with its own use an accent fabric. Lately, there's been a growth in schools using flush-face metallic panels for interior design. Galvanised steel mesh panels are a sustainable and flexible option compared to conventional materials. There's an extensive selection of available colors, finishes, and profiles that provide building teams infinite chances to fulfill their demands. Metal panels possess a warm, powerful, interesting look. With the right use of color, forming, stamping and feel, metal panels offer a look that no other substance can attain."
Why steel mesh is important
One of metal's most frequent inside software is a metal net. It is extremely versatile, almost indestructible and eco-friendly.
Since the net is woven, you will find crimps in which the wires cross each other. At these crossing points, there'll be a mild reflection point. How closely woven or just how many crimps you will find will be based on the total amount of reflection you'll have. Furthermore, should you have to conceal any HVAC units, then you can just install mesh panels beneath the components, add a light component and the net will turn out to be totally opaque while allowing air flow.
Metal wire mesh's increase in popularity may be due to a greater appetite for flexible, modular walls without any drywall, wallpaper or paint. Mesh may be rigid/fixed or elastic with motion. It's quite flexible and its walls may be used for space or area alteration, light filter, walls permitting HVAC flow, safety or just as an accent.
Materials such as metal mesh provide unique textures, visual and dimensions interplay with other inside layout elements. Mesh may be utilized to reestablish classic looks—like this art hotel in Hobart—or to make fresh, contemporary looks just like floor-to-ceiling walls, layout accents, walls, and illuminated backdrops and walls. Mesh is also scalable. Mesh may be treated with different substances to radically alter the overall look.
Interior setup
It has been said that the interior installation for wire mesh is easy. Mounting hardware, like c-hooks, attachments, station systems, would be the most frequent installations. A number of the setups are fully custom made and made so as to guarantee security when serving its intended function. The ease of use and added benefits has made wire mesh materials a popular choice for some of the best hotels in Hobart.
If you're seeking to invoke a sense of motion, there'll have to be a substructure made to maintain the net in shape and strain. In conventional applications, metal ceiling installations would demand no longer than that of a normal lay-in ceiling. Due to the selection of interior metallic programs, there are always particular considerations for setup. As a result of this, Horan highlights the requirement to operate with a producer who's flexible and can offer material fast and faithfully. Most new inside metal programs are custom designed, requiring the whole project group to work collectively, and requiring producers to react immediately and consult on the plan, he states. 1 characteristic of inside metal that facilitates installation is the fact that it often comes preassembled before being sent to job websites. In the event of any extra assembly required onsite, most providers give easy-to-follow directions.
Insiders predict new photovoltaic coatings for metal which can keep sunshine and other energies which may be diverted through the night to light rooms, even whole buildings. New finishes like powder coatings will create realistic wood grain finishes making panoramic ceilings with pictures of clouds and shrub branches. Future interior metal systems may also help buildings withstand earthquake movements.
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A movement, more powerful than a chain of drake low loaders, with roots in Russia, Constructivism, was initially an architectural...


A track is the basis of Ooyala Schedule.
A track is composed of one or more linearly scheduled track segments.
Tracks have the following structure.
Note: The live_stream_asset_id property is optional.
The ID of the track.
The following table describes all properties that can be associated with a track.
Identifier of a live stream or remote live stream.
A descriptive name for this track.
Valid Values: Limit of 256 characters. Must not contain control characters, except for newline (char 10) or carriage return (char 13).


I attend church because it is a place of encouragement. It is more than a positivity in the meeting. The Presence of God creates a spiritual environment that puts peace in the place of turmoil and confusion; forgiveness that overrules any guilt; engenders faith that quiets the storm of fear. I leave happier than when I entered. Obviously, it is not coincidental that the Holy Spirit is called the "Comforter". In fact that title comes from the same root word as encouragement.
While there is "encouragement in Christ" (Philippians 2:1) even when we are on our own, we have the privilege of working in partnership through the Comforter to "encourage one another" (1 Thessalonians 5:11). That makes it supernatural encouragement since it originates in God. The foundation of this encouragement is love and its expression is grace. God so loved the world that He sent His Son, Who brought grace and truth into this broken place. Jesus is the ultimate expression of love and grace.
The Holy Spirit engages us in five ways to convey the grace of encouragement. They can be thought of as God's five love languages that we can learn to express. (An interesting foot note, five is the number associated with grace, and there are five ingredients in the anointing oil in the Old Testament symbol of the Holy Spirit).
Serving is one of the love languages, and is certainly in the character of God. Jesus became "The Servant" Who out of love lay down His life for us. The Holy Spirit is also called "The Helper" Who gives help from the throne of grace in time of need (Hebrews 4:16). Church is a place where you find genuine care and concern and where we too get to be Holy Spirit directed helpers for each other. Such solidarity is great encouragement.
Affirming someone personally gives courage to carry on when the going gets tough. Encouragement sponsored by the Holy Spirit is more than your words, it is a form of prophetic expression that strengthens the soul. A word in a time of doubt is a word in season that has power to accomplish the impossible (1 Thessalonians 2:13). Like Paul (Acts 14:22) we can instil courage and faith by our life stories of God's faithfulness.
Barnabas encouraged all the believers by selling some of his property and giving it to the apostles to distribute. Did you know his real name was Joseph? Barnabas was his nickname, or a new given name, or perhaps more accurately his giving name. The name means literally "Son of Encouragement". Giving was his expression of Christ. He gave more than money, he brought Paul to prominence at his own risk. We need to practice "grace giving". That is different than giving to a need, or because of entitlement, or for ulterior motives. God is a surprise Giver. He does not just give because you need something, or as a reward for good behaviour. He just has a generous spirit!
God can relate to our sin problems. God has a human side. It is Jesus. Jesus is our High Priest who can be "touched" by our weaknesses.. He has tenderness and compassion toward us (Hebrews 4:15). Empathy is the sensitivity that brings encouragement. Paul points out that we are one body, so that when one part suffers, the whole body suffers, and when one part rejoices, everyone experiences joy. The Holy Spirit is the supernatural source of that sense of oneness. Our part in the process means choosing to get in the boat with the one going through rough seas. Jesus did that!
What makes the time spent at church worthwhile? Everyone could be somewhere else, doing something pressing. God intends it to be a party, a celebration feast with invited guests eager to come. You leave better than when you came in. It is interesting, fun, a learning experience, with good tips for practical living. Most important, however, is the opportunity for a God encounter that will be transformational. That gives the time spent a supernatural quality. Nothing compares with His Presence. We strive to make church a place that Jesus enjoys attending.


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HomeOverwatch: In Defence of Bastion.
Overwatch: In Defence of Bastion.
May 27, 2016 May 27, 2016 Matthew Roys OpinionsBastion, Blizzard, Games, Opinion, Overpowered, Overwatch
Anyone with even the faintest connection to gaming has by now been exposed to the fleet of anger and bitterness towards Blizzard's Overwatch character Bastion. This little robot has raised the ire of certain players who claim that he is overpowered or boring. They complain that he always gets the play of the game and remark that he needs no skill to use.
When I got the chance to play Overwatch in the open beta I was instantly drawn to Bastion. He is a cute, nature loving robot who doubles as a Gatling gun and communicates using Walle like beeps. And it is true that he is a very straight forward character that anybody can play. He can heal himself and can rip through characters in seconds in his turret form. I really enjoyed using him.
But to anybody who thinks that he is overpowered, you clearly don't understand how to play Overwatch. When set up in the right place against the right people, sure, Bastion can wipe out enemies left and right and this will be what is shown in the play of the game. What you don't see is the excruciatingly slow movement speed that leaves you watching the rest of your team run off without you at the start of the game or the lonely times sat waiting in a chosen location for enemies to show up. It doesn't show the massive amounts of 1v1 battles where you are outmatched or all of the (pretty easy) counters for the turret form. For Bastion players it is very much about a game's worth of patience for a few seconds of slaughter.
Genji is the devil incarnate for Bastion players with his ability to deflect bullets back at the shooter. Widowmaker can snipe him from a safe distance and anybody can deal critical damage if they come up behind him in turret form. Hell, my biggest moments of frustraition playing the game have come from times where I have played Bastion against players who know how to deal with him. I learned from that too. Watching how players messed my Bastion up so easily made it so I knew how to deal with enemy Bastions. And that is how Overwatch should be played. Every character has other characters they are weak to. Adaptability is key.
But the point of my writing this is that I almost am made to feel shamed for choosing to play as my favourite character because of his reputation. That when I do well it is because I am a scrub using an OP character rather than just having a good round. I played twelve games in a row yesterday and a Bastion got play of the game in only one of them. All of the chat comments were still "Who saw that coming, Bastion PotG", "F*****g Bastion" and the like.
I'm going to end this piece with some advice from my time playing Dark Souls and anybody who hates Bastion should consider it. Don't get angry, git gud.
For more news and opinions, follow GamerWolves or look me up on Twitter @matthewroys (https://twitter.com/MatthewRoys).
← Early help for Dark Souls 3
Review: Overwatch →


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Home Hillary Clinton 2016 O'Keefe Fires Shot Across the Bow at Hillary Campaign, Warning That "This is Just the Tip of the Iceberg"
O'Keefe Fires Shot Across the Bow at Hillary Campaign, Warning That "This is Just the Tip of the Iceberg"
in Hillary Clinton 2016 / Top Stories / Press Releases
Award-winning journalist and New York Times' best-selling author James O'Keefe released a powerful new undercover video today exposing a concerted effort by Hillary Clinton's campaign to circumvent Iowa election laws. This is in direct contradiction with what the Clinton campaign told veteran presidential reporter, Zeke Miller of TIME Magazine. According to a recent report by Miller "A Clinton official said the campaign is confident it upheld the law."
Despite vigorous assertions to the contrary from the Clinton campaign, this latest video from O'Keefe's Project Veritas Action clearly demonstrates that paid Clinton staffers were quite comfortable in their maneuvering to violate the spirit, if not the letter, of the law. Making matters worse, not only were these paid staffers engaging in such questionable behavior, they were actually instructing volunteers and interns to engage in the same sort of behavior.
Sterner, a paid Clinton staffer was captured on Project Veritas Action's undercover cameras stating "we don't want to make our focus voter registration because then we have to like register everyone regardless of whether they're supporters or not… And so that's why I want to keep like our primary focus on, "Hey are you a Hillary supporter? And then if not, then like great move on, you know?"
Conversely, according to a Hillary spokesperson, as reported by TIME Magazine, the campaign "maintains that its policy is to register all voters, regardless of their preference in candidates."
Shortly after Sterner made these comments she banished our journalist from campaign offices.
"Hillary Clinton and her top advisers have some serious explaining to do as it pertains to their campaign's obligation to honor both the spirit and the letter of the law" said Project Veritas Action President James O'Keefe. "There are two lessons in this story," continued O'Keefe, "first, if you are not doing anything wrong you can't get caught and second, as Hillary is soon to learn the hard way, if you do something wrong, make sure you don't get caught on camera."
Multiple members of the Project Veritas Action staff contributed to this report
project veritas action


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Pearland run rules Strake Jesuit 15-5
PEARLAND — The Pearland diamondmen closed out the Strake Jesuit Crusaders 15-5 in five innings to improve to 4-0 in District 23-6A.
The Oilers opened up district with a 16-0 win over Alief Taylor, a 12-7 win over Dawson, and a 19-0 win over Alief Elsik to remain tied with George Ranch for the top spot in the league standings.
Entering the fourth inning with game tied 4-4, the Oilers broke the game wide open with 11 runs to make the score 15-4.
Strake scored a run in the top of the fifth, but the 10-run mercy went into effect ending the game at the end of five frames.
Matt Adams went the distance for Pearland on the hill, striking out five while allowing five hits and four earned runs.
Shawn Huber went 2-for-4 with four runs batted in; Cole Smajstrla went 2-for-2 with three RBIs; and Adam Vackar went 2-for-3 with 3 RBIs. Smajstrla and Vackar each had two doubles.
The win improved Pearland (11-7-1) to 4-0 in District 23-6A.


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Q: How Can Linux Top Show 2% Idle, but Load Say 1.20? On CentOS5, I'm seeing the 'top' command showing a hover between 20% and 2% idle, but then the load says 1.20. How can that be possible? When I type 'uptime', as well, I see 3 load numbers that are generally around 1.20 - 1.35.

A: Things like waiting on I/O don't take any CPU time but can block other processes from running.

A: Maybe you have a process in uninterruptible sleep (process state "D")?
They don't really contribute to load per se while they're sleeping but they are a constant process on the run queue thus the load is artificially inflated.
Cheers

redpajama_set_name: RedPajamaStackExchange


Via a comic book from the Explora collection, you will be able to find out about the amazing destiny of "Sailor Prince" Albert I, pioneer of modern oceanography and great-great-grandfather of the current Prince of Monaco.
In this album, science and adventure go hand in hand. It was produced in partnership with the Oceanographic Museum of Monaco, which was founded by Albert I.
It's the late 19th century … An intrepid young aristocrat, Crown Prince Albert I of Monaco, dreams of only one thing – going to sea. It would only take him a few years to learn the craft of sailing and own his own small boat, L'Hirondelle.