text:
Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.
Citation: Gobbi A, Jurman G (2015) A Null Model for Pearson Coexpression Networks. PLoS ONE 10(6): e0128115. https://doi.org/10.1371/journal.pone.0128115
Academic Editor: Francisco J. Esteban, University of Jaén, SPAIN
Received: February 11, 2014; Accepted: April 23, 2015; Published: June 1, 2015
Copyright: © 2015 Gobbi, Jurman. 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: The work has been funded by Fondazione Bruno Kessler. 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.
Universally acknowledged by the scientific community as the basic task of systems biology, network inference is the prototypical procedure for moving from the classical reductionist approach to the novel paradigm of data-driven complex systems in the interpretation of biological processes .
The goal of all network inference (or network reconstruction) procedures is the detection of the topology (i.e., the wiring diagram) of a graph whose nodes belong to a given set of biological entities, starting from measurements of the entities themselves. In the last fifteen years, the reconstruction of the regulation mechanism of a gene network and of the interactions among proteins from high-throughput data such as expression microarray or, more recently, from Next Generation Sequencing data has become a major line of research for laboratories worldwide. The proposed approaches rely on techniques ranging from deterministic to stochastic, and their number is constantly growing. Nonetheless, network inference is still considered an open, unsolved problem . In fact, in many practical cases, the performance of the reconstruction algorithms are poor, due to several factors limiting inference accuracy [3, 4] to the point of making it no better than a coin toss in some situations . The major problem is the under-determinacy of the task , due to the overwhelming number of interactions to be predicted starting from a (usually) small number of available measurements. In general, size and quality of available data are critical factors for all inference algorithms.
In what follows the impact of data size is discussed for one of the simplest inference techniques, i.e., the gene coexpression network, where interaction strength between two genes is a function of the correlation between the corresponding expression levels across the available tissue samples. Despite its simplicity, the number of studies in the literature based on coexpression networks is still a large fraction of all manuscripts in systems biology [7–13], including Next Generation Sequencing data . The underlying biological hypothesis is that functionally related genes have similar expression patterns , and thus that coexpression is correlated with functional relationships, although this does not imply causality. This implies for instance that genes that are closer in a biological network are likely to have more similar expression [16, 17]: this hypothesis has been validated to some extent, for example by Jensen et al. in . However, a caveat is mandatory here: since correlation is a univariate method, it is unable to capture the relations occurring among genes when the independence hypothesis does not hold, which is a common situation in the -omics datasets [19, 20]. To overcome this problem, characterized by small correlation values between functionally related genes, multivariate approaches are required , also in the network case .
Notwithstanding this limitation, as highlighted in , correlation can help unveil the underlying cellular processes, since coordinated coexpression of genes encode interacting proteins. The Pearson Correlation Coefficient (PCC for short) is used as the standard measure, although alternative correlation measures can be also employed: see for instance  for a comparative review of eight well known association metrics. Furthermore, coexpression analysis has been intensively used as an effective algorithm to explore the system-level functionality of genes, sometimes outperforming much more refined approaches [25, 26]. The observation that simpler approaches such as correlation can be superior even on synthetic data has been explained by some authors [27, 28] with the difficulties of a complex algorithm in detecting the subtleties of combinatorial regulation. Coexpression networks can also capture more important features than the conventional differential expression approach , and their use has been extended to other tasks, for instance the investigation of complex biological traits . Finally, these networks can be crucial for understanding regulatory mechanisms , for the development of personalised medicine  or, more recently, in metagenomics .
However, as noted in , correlation between genes may sometimes be due to unobserved factors affecting expression levels. Moreover, deciding when a given correlation value between two nodes can be deemed statistically significant and thus worthwhile for assigning a link connecting them is a major issue affecting coexpression networks . This translates mathematically into choosing (a function of) a suitable threshold, as in the case of mutual information and relevance networks . As reported in , in the literature statistical methods for testing the correlations are underdeveloped, and thresholding is often overlooked even in important studies . The two main approaches known in the literature to solve this problem can be classified as soft or hard thresholding. Soft thresholding is adopted in a well-known framework called Weighted Gene Coexpression Network Analysis (WGCNA) , recently used also for other network types [40, 41]. All genes are mutually connected, and the weight of a link is a positive power of the absolute value of PCC, where the exponent is chosen as the best fit of the resulting network according to a scale-free model [42, 43]. This approach, without discarding any correlations, promotes high correlation values and penalises low values. In the hard thresholding approach, instead, only correlation values larger than the threshold are taken into account, and an unweighted link is set for each of these values, so that a binary network is generated (see  for one of the earliest references). Clearly, an incorrectly chosen threshold value can jeopardise the obtained results with false negative links (for too strict a threshold) or false positive links (for too loose a threshold). Many heuristics have been proposed for setting the threshold values, such as using the False Discovery Rate [45–47], using a cross-validation strategy , the p-value of the correlation test [15, 32], employing partial correlation [49, 50], using rank-based techniques [51–53], more complex randomisation techniques , or keeping only values exceeding a minimum acceptable strength (MAS) level specified by the threshold . Alternative approaches have been recently studied, evaluating the correlation distribution, both experimentally  and theoretically . However, these studies focus on the level of single interaction rather than considering the whole network.
Moreover, in many studies in the literature, the threshold is not chosen according to an algorithmic procedure, but referring to standard choices [58–61], or to heuristics not directly related to the correlation values, but rather with the resulting network topology [62–71]. In  a comparison of some coexpression thresholds is shown on a few microarray datasets. Furthermore, each threshold choice yields a compromise between detecting artefacts (false positives) and neglecting existing connections (false negatives) , which can be driven by the cost attached to the task studied. For instance, the optimized threshold selection procedure in  attains a very low false positive rate by using a permutation-based strategy .
Here we propose a new a priori model for the computation of a hard threshold based on the assumption that a random coexpression graph should not have any edges. This threshold follows from the work of Fisher  and Bevington , and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data [77, 78]. Hence, the procedure to obtain the threshold is not stochastic and thus deeply different from approaches based on permutation tests . Further, this model is non-parametric, because its only input is the data themselves and no additional quantity needs to be tuned. By definition, this threshold is aimed at minimising the possible false positive links, paying a price in terms of false negative detected edges. This characterising property makes this method especially useful when the intrinsic cost function associated with the studied task is biased towards penalising false positives. We conclude by demonstrating the procedure first on a synthetic dataset and then on an ovarian epithelial carcinoma dataset on a large cohort of 285 cases [79, 80].
A Motivating Example
We show hereafter an example of a common situation arising in the small sample size setting, where PCC can reach extremely high values possibly leading to the detection of false positives.
Consider the HepatoCellular Carcinoma (HCC) dataset, first introduced in  and later used in  and publicly available at the Gene Expression Omnibus (GEO) http://www.ncbi.nlm.nih.gov/geo with accession number GSE6857. The dataset collects 482 tissue samples from 241 patients affected by HCC. For each patient, a sample from cancerous hepatic tissue and a sample from surrounding non-cancerous hepatic tissue were extracted, hybridised on the Ohio State University CCC MicroRNA Microarray Version 2.0 platform consisting of 11,520 probes collecting expressions of 250 non-redundant human and 200 mouse miRNA. After a preprocessing phase including imputation of missing values  and discarding probes corresponding to non-human (mouse and controls) miRNA, the resulting dataset HCC includes 240 + 240 paired samples described by 210 human miRNA, with the cohort consisting of 210 male and 30 female patients. In particular, consider now the three microRNA identified as hsa.mir.010b.precNo1, hsa.mir.016a.chr13 and hsa.mir.016b.chr3. A search by sequence with the miRBase web-service  shows that the two probes hsa.mir.016a.chr13, and hsa.mir.016b.chr3 share a high alignment score (e-value: 5 · 10−4), while no link due to coherent coexpression is known between each of these probes and hsa.mir.010b.precNo1. Consistent with this observation, the absolute PCC |ρ| on the dataset H consisting of the 210 tumoral samples from male patients is When we restrict the analysis to a suitable small subset of patients, different situations can occur. Consider in fact the following sub-cohort of 10 patients On S, PCC reads as follows: that is, the correlation between the two unrelated miRNA on S is very high and even higher than the correlation of the two aligned probes. The curves (averaged on 1000 runs) of PCC versus the sample size when the remaining samples are increasingly and randomly added (while S remains the same across the 1000 runs) are shown in Fig 1. When sample size increases, |ρ(hsa.mir.010b.precNo1,hsa.mir.016b.chr3)| quickly drops down to 0.536, while the correlation of the two aligned probes remains almost constant.
When the correlation is computed on the 10-sample set S the correlation between the two uncoexpressed probes is even higher than the correlation between the two probes sharing an almost identical alignment. When more samples are randomly added, the blue line slightly increases, while the red line quickly drops to the final value 0.536 on the whole dataset H. Both curves are averaged over 1000 randomisations of the added samples, keeping the sub-cohort S constant.
Clearly, if the task were to build a PCC coexpression network NS on S, whatever the chosen hard threshold t, the network NS would include either a false positive or a false negative link (or even both, for 0.907 ≤ t ≤ 0.941). Note that the probability of extracting 10-sample sub-cohorts S′ satisfying is about 0.03%, but when the constraints are relaxed the probability of detecting artificially high correlation values |ρS′(hsa.mir.010b.precNo1,hsa.mir.016b.chr3)| > 0.75 raises to about 6.2% for this dataset. Situations like those shown in this example regularly occur when building coexpression networks, supporting the need for an algorithmic solution to the problem of hard thresholding in the small sample size setup.
Distribution of PCC
The results in [75, 86–92] and the symmetry of ρ yield that F(n, p) has the following close form: (2) where Γ(x) is the Gamma function . In Text A in S1 Text we also propose a novel proof of Eq (2) by means of an analytic argument stemming from the work of Bevington in [76, Ch. 7]. Moreover, Eq (2) is also a good approximation in the case of a non-normal distribution of x and y when data skewness can be bounded , because of the generalization shown in [89, 94–96]. Non-Gaussian asymmetric distributions can occasionally be detected in some array studies : however, techniques for reducing the skewness are routinely applied during preprocessing , and thus the aforementioned results can be safely used in the microarray framework.
Coexpression network and threshold selection
The results derived in the previous section are used here to construct a null model for the correlation network, thus yielding a threshold for the inference of a coexpression network from the nodes’ data. As mentioned in the Introduction, these correlation networks are subject to the hypothesis of independence between genes, so they are not detecting higher-order relations for which a multivariate method is needed. In Text E in S1 Text we show a few synthetic and -omics examples of relations among genes that are not captured by coexpression networks.
In the general situation, we are measuring the signal (expression) of m probes (genes), in n different instances/conditions (samples), to infer the corresponding correlation networks on m nodes.
The first result characterizes the coexpression graphs in terms of null models:
The proof follows immediately from the definition of 𝒢p and Eq (2).
Definition Using the results in the previous section, the secure threshold is defined as follows, for an arbitrarily small ɛ > 0: (3) for m nodes measured on n samples. As a consequence of , stating that the median of the binomial distribution is the integer part of the mean when F(n, p) < 1 − log 2, the secure threshold is the minimum value of p such that the corresponding random coexpression network is on average an empty graph Em, i.e., .
The underlying hypothesis for Eq (3) is the assumption that in a random dataset no edge is expected, since no relation should occur between nodes. Due to its definition, the secure threshold is biased towards avoiding the false positive links, paying a price in terms of false negatives. In Table 1 a collection of values of is listed for different m and n, while in Fig 2 the contour plot of the function is shown first on a large range of values and then zooming on the small sample size area.
As a first example, we compare the secure threshold with the threshold derived by applying the Bonferroni correction procedure [99, 100] to the p-value of PCC. Let now ΦF and Φt, n − 2 be the cumulative distribution functions for the Fisher transformation and the Student’s t-distribution with n − 2 degrees of freedom, respectively. For a given PCC ρ computed between vectors in ℝn, let (4) be the functions computing the p-value associated to ρ following either the Fisher approximation FTn [75, 89, 101] or the Student’s t-distribution tTn [102–106]. Set now the global familywise error rate to 5% significance level (FWER < 0.05): for a network with m nodes, by Bonferroni correction, this yields a p-value at most for each link in the graph. By using the inverse functions and we obtain the corresponding thresholds (Fisher and Student’s t, respectively) attaining the desired significance level. In Table 2 the values of B5, F and B5, t are reported for a selection of pairs (n, m), together with the corresponding secure threshold , while in Fig 3 the surface plot of is compared to B5, t.
In all cases, the relation holds, and when n < 10 both B5, F and B5, t cannot be defined, since the 5% significance level is not reached by any threshold. Both these results do not come unexpected, since the Bonferroni correction is known to be a very conservative procedure, especially when the number of tests is large (see for instance [107–109]) because it ignores existing relations between measurements. This results in a large False Negative Rate. Thus, in many situations in general and in -omics studies in particular, the Bonferroni correction should be substituted by more refined multiple testing approaches .
We show now in Table 3 the comparison on a set of synthetic and array datasets of the secure threshold with other well known hard thresholding methods, the clustering coefficient-based threshold C*  and the statistical thresholds based on the adjusted p-values of 0.01, 0.05 or 0.1, while in Table 4 we compare the secure threshold with the optimal threshold O  on a collection of array datasets on brain tissues. The threshold O is optimized for each dataset (i.e., sample size, data quality and biological structure), considering also the effects induced by the influence of subpopulations on network generation, and comparing to permutated data. Note that for a given pair (n, m), the threshold is constant, while the threshold O depends on the particular dataset. In almost all cases, the threshold is the strictest.
Note that for a given pair (n, m), the threshold p is constant, while the threshold O depends on the particular dataset.
As shown in the previous section, for a not very skewed distribution, the good approximation provided by the exact formula for F(n, p) given in Eq (2) guarantees the effectiveness of the secure threshold in detecting actual links between nodes. Nonetheless, whenever a stricter threshold is needed, it is still possible to follow the construction proposed, with the following refinement: the edge-creation process in the Erdös-Rényi model follows a binomial distribution, where n is the number of trials and p the probability associated with the success of a trial. The mean np of this distribution is one of the contributing terms in the definition Eq (3) of the secure threshold. To further restrict the number of falsely detected links, the variance term (np(1 − p) for the binomial distribution) can be added to the original formula through Chebyshev’s inequality: where μ and σ are the mean and the standard deviation of X. Thus, the definition of secure threshold can be sharpened to as follows, for an arbitrarily small ɛ > 0:
For instance, the binomial distribution, for large values of n, can be approximated as a normal distribution for which 95.45% of the values lie in the interval (μ − 2σ, μ + 2σ). Moreover, the Chebyshev’s inequality implies that at least 96% of the values lie in the interval (μ − 5σ, μ + 5σ). Finally, in Text D of S1 Text we show the analogue of Table 1 for and , respectively.
Results and Discussion
To conclude, we show the application of the secure threshold in two datasets—synthetic and array—demonstrating its behaviour as a function of data subsampling.
In order to construct a simulated microarray dataset 𝒢, we first created a correlation matrix M𝒢 on 20 genes G1, …G20, together with a dataset 𝒢 of the corresponding expression across 1000 synthetic samples, so that is the absolute PCC between the expression of the genes Gi and Gj from 𝒢. In particular, M𝒢 has two 10 × 10 blocks highly correlated on the main diagonal, and two 10 × 10 poorly correlated blocks on the minor diagonal, as shown in Fig 4(a).
The generating gene expression vectors and are marked with *.
These blocks are derived from the following generating rule, given uncorrelated starting element and : Outside the two main blocks, all correlation values range between 0.002 and 0.074. In order to use M𝒢 as the ground truth in what follows, all values outside the two blocks on the diagonal are thresholded to zero (M𝒢)i 10 + j = (M𝒢)i 10 + j = 0 for 1 ≤ i, j ≤ 10, while entries in the two main blocks are considered as real numbers when evaluating HIM distance and binarized to one when evaluating True/False Positive/Negative links.
In Fig 4(b) we also show the heatmap of the gene expression dataset 𝒢. Then a subset of ns samples is selected from the starting 1000, and the corresponding coexpression networks is built, for the 100 hard threshold values 0.01j, for 1 ≤ j ≤ 100. The secure threshold for these cases are respectively 0.799, 0.596 and 0.389. This procedure is repeated 500 times for each value ns = 10,20,50. The same experiment is then repeated adding a 20% and a 40% level of Gaussian noise to the original data: for a given signal s, we build s + ɛ with ɛ ∈ 𝒩(0, α ⋅ (max(s) − min(s))) for α = 0.2,0.4 respectively.
Using M𝒢 as the ground truth, for each hard threshold 0.01j we evaluate the ratio of False Positive links (i.e., the quotient between the number of False Positive links and 190, the number of all possible links in a complete undirected network on 20 nodes), the ratio of False Negative links and the Hamming-Ipsen-Mikhailov (HIM) distance from the gold standard. The HIM distance [111, 112] is a metric between networks having the same nodes, ranging from 0 (distance between two identical networks) to 1 (comparison between the complete and the empty graph). The graphs summarizing the experiments are displayed in Fig 5.
Coexpression inference of the network from random subsampling of the dataset, without noise (a,b,c), with 20% Gaussian noise (d,e,f) and with 40% Gaussian noise (g,h,i), on 10 (a,d,g), 20 (b,e,h) and 50 (c,f,i) samples. Solid lines indicate the mean over 500 replicated instances of HIM distance (black), ratio of False Positive (blue) and ratio of False Negative (red); dotted lines of the same color indicate confidence bars (+/-σ), while grey vertical dashed lines correspond to the secure threshold .
In all cases, the secure threshold corresponds to the strictest value yielding a coexpression network with no false positive links included, which is its characterizing property. Moreover, in almost all displayed situations, thresholding at still guarantees an acceptable HIM distance from the ground truth, and a false negative ratio that is always less than 0.4.
The aforementioned results obtained in the synthetic case are then tested here in a large array study on 285 patients of ovarian cancer at different stages , recently used in a comparative study on conservation of coexpressed modules across different pathologies . In detail, the authors profiled the gene expression of 285 predominately high-grade and advanced stage serous cancers of the ovary, fallopian tube, and peritoneum; the samples were hybridized on the Affymetrix Human Genome HG-U133 Plus 2.0 Array, including 54,621 probes. The goal of the original study was to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. As a major result, the authors presented two ranked gene lists supporting their claim that molecular subtypes show distinct survival characteristics. The two gene lists characterize the Progression Free Survival (PSF) and the Overall Survival (OS) patients, respectively. In each list genes are ranked according to a score weighting their association to target phenotype (PSF or OS): association is stronger for larger absolute values of the score, while the score’s sign is negative for associations with good outcome and positive for associations with poor outcome.
Following the procedure of the previous, synthetic example, first we individuate the sample subset corresponding to the homogeneous cohort of 161 patients with grade three cancer and a set T of 20 genes, with 11 genes strongly associated with good PSF or good OS (EDG7, LOC649242, SCGB1D2, CYP4B1, NQO1, MYCL1, PRSS21, MGC13057, PPP1R1B, KIAA1324, LOC646769) and 9 genes strongly associated to poor PFS or poor OS (THBS2, SFRP2, DPSG3, COL11A1, COL10A1, COL8A1, FAP, FABP4, POSTN), thus generating a dataset 𝒪T of dimension 161 samples and 20 features. The corresponding absolute PCC matrix OT is then used as the ground truth for the subsampling experiments, thresholding to zero all values smaller than 0.1: the levelplot of OT and the heatmap of 𝒪T are displayed in Fig 6.
Level plot of the structure of the correlation matrix OT (a) and heatmap of the Ovarian dataset restricted to the set of 20 selected genes T (b). Solid lines separate the group of good and poor PFS/OS top genes.
In these experiments, a random subdataset of ns samples is extracted from 𝒪T, and the corresponding absolute PCC coexpression network on the nodes T is built, for increasing threshold values. In Fig 7 we report the HIM and the ratio of False Positive and False Negative links for 500 runs of the experiments, separately for ns = 5,10,20 and 50. Again, the secure threshold corresponds to the smallest PCC value warranting that no false positive links are included. Finally, on average, for threshold values greater than , the derivative of HIM distance is larger than before , while the false negative rate remains under 0.8.
Coexpression inference of the coexpression network from subsampling of , on 5 (a), 10 (b), 20 (c) and 50 (d) samples. Solid lines indicate the mean over 500 replicated instances of HIM distance (black), ratio of False Positive (blue) and ratio of False Negative (red); dotted lines of the same color indicate confidence bars (+/-σ), while grey vertical dashed lines correspond to the secure threshold .
We have proposed a simple a priori, theoretical and non-parametric method for the selection of a hard threshold for the construction of correlation networks. This model is based on the requirements of filtering random data due to noise and reducing the number of false positives, and it is implemented by means of analytic properties of PCC. This new approach can be especially useful where there is a small sample size and when the task requires minimising the number of false positive links, probably the most common situation in profiling studies in functional genomics. Finally, when the number of samples increases, coupling this method with soft thresholding approaches, can help recovering false negative links neglected by overly strict thresholds.
Conceived and designed the experiments: AG GJ. Performed the experiments: AG GJ. Analyzed the data: AG GJ. Wrote the paper: AG GJ.
- 1. Barabási AL. The network takeover. Nature Physics. 2012;8:14–16.
- 2. Szederkenyi G, Banga J, Alonso A. Inference of complex biological networks: distinguishability issues and optimization-based solutions. BMC Systems Biology. 2011;5(1):177. pmid:22034917
- 3. He F, Balling R, Zeng AP. Reverse engineering and verification of gene networks: Principles, assumptions, and limitations of present methods and future perspectives. Journal of Biotechnology. 2009;144:190–203. pmid:19631244
- 4. Meyer P, Alexopoulos LG, Bonk T, Califano A, Cho CR, de la Fuente A, et al. Verification of systems biology research in the age of collaborative competition. Nature Biotechnology. 2011;29(9):811–815. pmid:21904331
- 5. Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, et al. Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PLoS ONE. 2010 02;5(2):e9202. pmid:20186320
- 6. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nature Reviews Microbiology. 2010;8(10):717–729. pmid:20805835
- 7. Liang M, Zhang F, Jin G, Zhu J. FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks. PLoS ONE. 2015;10(1):e0116776. pmid:25602758
- 8. Song Q, Zhao C, Ou S, Meng Z, Kang P, Fan L, et al. Co-expression analysis of differentially expressed genes in hepatitis C virus-induced hepatocellular carcinoma. Molecular Medicine Reports. 2015;11(1):21–28. pmid:25339452
- 9. Wang S, Pandis I, Johnson D, Emam I, Guitton F, Oehmichen A, et al. Optimising parallel R correlation matrix calculations on gene expression data using MapReduce. BMC Bioinformatics. 2014;15:35.
- 10. Rotival M, Petretto E. Leveraging gene co-expression networks to pinpoint the regulation of complex traits and disease, with a focus on cardiovascular traits. Briefings in Functional Genomics. 2014;13(1):66–78. pmid:23960099
- 11. Okamura Y, Aoki Y, Obayashi T, Tadaka S, Ito S, Narise T, et al. COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic Acids Research. 2015;43(D1):D82–D86. pmid:25392420
- 12. Yang Y, Han L, Yuan Y, Li J, Hei N, Liang H. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nature Communications. 2014;5:Article 3231.
- 13. López-Kleine L, Leal L, López C. Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data. Briefings in Functional Genomics. 2013;12(5):457–467. pmid:23407269
- 14. Rau A, Maugis-Rabusseau C, Martin-Magniette ML, Celeux G. Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics. 2015;2015 Jan 5 [Epub ahead of print]:btu845.
- 15. Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P. Coexpression Analysis of Human Genes Across Many Microarray Data Sets. Genome Research. 2004;14(6):1085–1094. pmid:15173114
- 16. Lavi O, Dror G, Shamir R. Network-Induced Classification Kernels for Gene Expression Profile Analysis. Journal of Computational Biology. 2012;19(6):694–709. pmid:22697242
- 17. Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert JP. Classification of microarray data using gene networks. BMC Bioinformatics. 2007;p. 35. pmid:17270037
- 18. Jansen R, Greenbaum D, Gerstein M. Relating Whole-Genome Expression Data with Protein-Protein Interactions. Genome Research. 2002;12(1):376.
- 19. Zucknick M, Richardson S, Stronach EA. Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods. Statistical Applications in Genetics and Molecular Biology. 2008;7(1):Article 7. pmid:18312212
- 20. Khodakarim S, AlaviMajd H, Zayeri F, Rezaei-Tavirani M, Dehghan-Nayeri N, Tabatabaee SM, et al. Comparison of Univariate and Multivariate Gene Set Analysis in Acute Lymphoblastic Leukemia. Asian Pacific Journal of Cancer Prevention. 2013;14(3):1629–1633.
- 21. Rahmatallah Y, Emmert-Streib F, Glazko G. Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets. Bioinformatics. 2014;30(3):360–368. pmid:24292935
- 22. Zhi W, Minturn J, Rappaport E, Brodeur G, Li H. Network-based Analysis of Multivariate Gene Expression Data. In: Statistical Methods for Microarray Data Analysis. vol. 972 of Methods in molecular biology. Springer; 2013. p. 121–139.
- 23. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences. 1998;95(25):14863–14868.
- 24. Kumari S, Nie J, Chen HS, Ma H, Stewart R, Li X, et al. Evaluation of Gene Association Methods for Coexpression Network Construction and Biological Knowledge Discovery. PLoS ONE. 2012;7(11):e50411. pmid:23226279
- 25. Allen JD, Xie Y, Chen M, Girard L, Xiao G. Comparing Statistical Methods for Constructing Large Scale Gene Networks. PLoS ONE. 2012;7(1):e29348. pmid:22272232
- 26. Song L, Langfelder P, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics. 2012;13:328. pmid:23217028
- 27. Madhamshettiwar P, Maetschke S, Davis M, Reverter A, Ragan M. Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Medicine. 2012;4(5):41. pmid:22548828
- 28. Baralla A, Mentzen WI, de la Fuente A. Inferring Gene Networks: Dream or Nightmare? Annals of the New York Academy of Science. 2009;1158:246–256. pmid:19348646
- 29. Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242–2250. pmid:15130938
- 30. Wood LD, Parsons DW, Jones S, Lin J, Sjöblom T, Leary RJ, et al. The Genomic Landscapes of Human Breast and Colorectal Cancers. Science. 2007;318(5853):1108–1113. pmid:17932254
- 31. Carlson M, Zhang B, Fang Z, Mischel P, Horvath S, Nelson S. Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks. BMC Genomics. 2006;7(1):40. pmid:16515682
- 32. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, Chen R, et al. Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes. Cell. 2012;148(6):1293–1307. pmid:22424236
- 33. Friedman J, Alm EJ. Inferring Correlation Networks from Genomic Survey Data. PLoS Computational Biology. 2012;8(9):e1002687. pmid:23028285
- 34. Furlotte NA, Kang HM, Ye C, Eskin E. Mixed-model coexpression: calculating gene coexpression while accounting for expression heterogeneity. Bioinformatics. 2011;27(13):i288–i294. pmid:21685083
- 35. Rider AK, Milenković T, Siwo GH, Pinapati RS, Emrich SJ, Ferdig MT, et al. Networks’ characteristics are important for systems biology. Network Science. 2014;2(02):139–161.
- 36. Butte AJ, Kohane IS. Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements. Pacific Symposium on Biocomputing. 2000;5:415–426.
- 37. Wang HQ, Tsai CJ. CorSig: A General Framework for Estimating Statistical Significance of Correlation and Its Application to Gene Co-Expression Analysis. PLoS ONE. 2013;8(10):e77429. pmid:24194884
- 38. Cho DY, Kim YA, Przytycka TM. Chapter 5: Network Biology Approach to Complex Diseases. PLoS Computational Biology. 2012;8(12):e1002820. pmid:23300411
- 39. Zhang B, Horvath S. A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology. 2005;4(1):Article 17. pmid:16646834
- 40. Zhang J, Lu K, Xiang Y, Islam M, Kotian S, Kais Z, et al. Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability. PLoS Computational Biology. 2012;8(8):e1002656. pmid:22956898
- 41. Gibbs D, Baratt A, Baric R, Kawaoka Y, Smith R, Orwoll E, et al. Protein co-expression network analysis (ProCoNA). Journal of Clinical Bioinformatics. 2013;3(1):11. pmid:23724967
- 42. de Solla Price DJ. Networks of Scientific Papers. Science. 1965;149(3683):510–515.
- 43. Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999;286:509–512. pmid:10521342
- 44. Davidson GS, Wylie BN, Boyack KW. Cluster Stability and the Use of Noise in Interpretation of Clustering. In: Proceedings of the IEEE Symposium on Information Visualization 2001 INFOVIS’01. IEEE Computer Society; 2001. p. 23.
- 45. Chen H. Clustering and Network Analysis with Single Nucleotide Polymorphism (SNP) [Ph.D. Thesis]. Stony Brook University; 2011.
- 46. Numata J, Ebenhöh O, Knapp EW. Measuring correlations in metabolomic networks with mutual information. Genome Informatics. 2008;20:112–122. pmid:19425127
- 47. Fukushima A. DiffCorr: An R package to analyze and visualize differential correlations in biological networks. Gene. 2013;518(1):209–214. pmid:23246976
- 48. Prieto C, Risueño A, Fontanillo C, De Las Rivas J. Human Gene Coexpression Landscape: Confident Network Derived from Tissue Transcriptomic Profiles. PLoS ONE. 2008;3(12):e3911. pmid:19081792
- 49. Opgen-Rhein R, Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Systems Biology. 2007;1:37. pmid:17683609
- 50. Khanin R, Wit E. Construction of Malaria Gene Expression Network Using Partial Correlations. In: McConnell P, Lin SM, Hurban P, editors. Methods of Microarray Data Analysis V. Springer US; 2007. p. 75–88.
- 51. Obayashi T, Hayashi S, Shibaoka M, Saeki M, Ohta H, Kinoshita K. COXPRESdb: a database of coexpressed gene networks in mammals. Nucleic Acids Research. 2008;36(suppl 1):D77–D82. pmid:17932064
- 52. Ruan J, Dean A, Zhang W. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Systems Biology. 2010;4(1):8. pmid:20122284
- 53. Mistry M, Gillis J, Pavlidis P. Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls. BMC Neuroscience. 2013;14(1):105. pmid:24070017
- 54. Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson D, et al. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinformatics. 2007;8(1):299. pmid:17697349
- 55. Zhu D, Hero AO, Qin ZS, Swaroop A. High throughput screening of co-expressed gene pairs with controlled false discovery rate (FDR) and minimum acceptable strength (MAS). Journal of Computational Biology. 2005;12(7):1029–1045. pmid:16201920
- 56. Scholz M. Approaches to analyse and interpret biological profile data [Ph.D. Thesis]. Potsdam University; 2006.
- 57. Ma C, Wang X. Application of the Gini Correlation Coefficient to Infer Regulatory Relationships in Transcriptome Analysis. Plant Physiology. 2012;160(1):192–203. pmid:22797655
- 58. Caraiani P. Using Complex Networks to Characterize International Business Cycles. PLoS ONE. 2013;8(3):e58109. pmid:23483979
- 59. Inouye M, Silander K, Hamalainen E, Salomaa V, Harald K, Jousilahti P, et al. An immune response network associated with blood lipid levels. PLoS Genetics. 2010;6(9):e1001113. pmid:20844574
- 60. Giorgi FM. Expression-based Reverse Engineering of Plant Transcriptional Networks [Ph.D. Thesis]. Potsdam University; 2011.
- 61. Usadel B, Obayashi T, Mutwil M, Giorgi FM, Bassel GW, Tanimoto M, et al. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant, Cell & Environment. 2009;32(12):1633–1651.
- 62. Yuan A, Yue Q, Apprey V, Bonney GE. Global pattern of pairwise relationship in genetic network. Journal of Biomedical Science and Engineering. 2010;3:977–985. pmid:21804923
- 63. Bassel GW, Lan H, Glaab E, Gibbs DJ, Gerjets T, Krasnogor N, et al. Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. Proceedings of the National Academy of Sciences. 2011;108(23):9709–9714.
- 64. Zheng ZL, Zhao Y. Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to “Candidatus Liberibacter asiaticus” infection. BMC Genomics. 2013;14:27. pmid:23324561
- 65. Stöckel J, Welsh EA, Liberton M, Kunnvakkam R, Aurora R, Pakrasi HB. Global transcriptomic analysis of Cyanothece 51142 reveals robust diurnal oscillation of central metabolic processes. Proceedings of the National Academy of Sciences. 2008;105(16):6156–6161.
- 66. Dempsey K, Bonasera S, Bastola D, Ali H. A Novel Correlation Networks Approach for the Identification of Gene Targets. In: Proceedings of the 44th Hawaii International Conference on System Sciences—HICSS 2011. IEEE; 2011. p. 1–8.
- 67. Elo LL, Järvenpää H, Orešič M, Lahesmaa R, Aittokallio T. Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics. 2007;23(16):2096–2103. pmid:17553854
- 68. Gibson SM, Ficklin SP, Isaacson S, Luo F, Feltus FA, Smith MC. Massive-Scale Gene Co-Expression Network Construction and Robustness Testing Using Random Matrix Theory. PLoS ONE. 2013;8(2):e55871. pmid:23409071
- 69. Feltus FA, Ficklin SP, Gibson SM, Smith MC. Maximizing capture of gene co-expression relationships through pre-clustering of input expression samples: an Arabidopsis case study. BMC Systems Biology. 2013;7:44. pmid:23738693
- 70. Perkins AD, Langston MA. Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC Bioinformatics. 2009;10(Suppl 11):1–11.
- 71. Stathias V, Pastori C, Griffin TZ, Komotar R, Clarke J, Zhang M, et al. Identifying Glioblastoma Gene Networks Based on Hypergeometric Test Analysis. PLoS ONE. 2014;9(12):e115842. pmid:25551752
- 72. Borate B, Chesler E, Langston M, Saxton A, Voy B. Comparison of threshold selection methods for microarray gene co-expression matrices. BMC Research Notes. 2009;2(1):240. pmid:19954523
- 73. Gaiteri C, Sibille E. Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks. Frontiers in Neuroscience. 2011;5:Article 95. pmid:21922000
- 74. Good P. Permutation Tests. Springer; 2000.
- 75. Fisher RA. Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika. 1915;10(4):507–521.
- 76. Bevington PR. Data Reduction and Error Analysis for the Physical Sciences. McGraw-Hill; 1969.
- 77. Zhang A. Advanced Analysis of Gene Expression Microarray Data. World Scientific; 2006.
- 78. Casellas J, Varona L. Modeling Skewness in Human Transcriptomes. PLoS ONE. 2012;7(6):e38919. pmid:22701729
- 79. Doig T, Hume D, Theocharidis T, Goodlad J, Gregory C, Freeman T. Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment. BMC Genomics. 2013;14(1):469. pmid:23845084
- 80. Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, et al. Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome. Clinical Cancer Research. 2008;14(16):5198–5208. pmid:18698038
- 81. Budhu A, Jia HL, Forgues M, Liu CG, Goldstein D, Lam A, et al. Identification of Metastasis-Related MicroRNAs in Hepatocellular Carcinoma. Hepatology. 2008;47(3):897–907. pmid:18176954
- 82. Ji J, Shi J, Budhu A, Yu Z, Forgues M, Roessler S, et al. MicroRNA Expression, Survival, and Response to Interferon in Liver Cancer. New England Journal of Medicine. 2009;361:1437–1447. pmid:19812400
- 83. Troyanskaya OG, Cantor M, Sherlock G, Brown PO, Hastie T, Tibshirani R, et al. Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17(6):520–525. pmid:11395428
- 84. Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Research. 2011;39(suppl 1):D152–D157. pmid:21037258
- 85. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular Biology. 1990;215(3):403–410. pmid:2231712
- 86. Olkin I, Pratt JW. Unbiased estimation of certain correlation coefficients. Annals of Mathematical Statistics. 1958;29:201–211.
- 87. Pearson ES, Adyanthāya NK. The distribution of frequency constants in small samples from non-normal symmetrical and skew populations. Biometrika. 1929;21(1/4):259–286.
- 88. Rider PR. On the distribution of the correlation coefficient in small samples. Biometrika. 1932;24(3/4):382–403.
- 89. Gayen AK. The Frequency Distribution of the Product-Moment Correlation Coefficient in Random Samples of any Size Drawn from Non-Normal Universes. Biometrika. 1951;38(1/2):219–247. pmid:14848124
- 90. Kenney JF, Keeping ES. Mathematics of Statistics, Part 2. 2nd ed. Van Nostrand; 1951.
- 91. Kenney JF, Keeping ES. Mathematics of Statistics, Part 1. 3rd ed. Van Nostrand; 1962.
- 92. Pugh EM, Winslow GH. The Analysis of Physical Measurements. Addison-Wesley; 1966.
- 93. Kendall MG, Stuart A. The Advanced Theory of Statistics: Distribution theory. Griffin; 1977.
- 94. Haldane JBS. A note on non-normal correlation. Biometrika. 1949;36:467–468. pmid:15409360
- 95. Hey GB. A new method for experimental sampling illustrated in certain non-normal populations. Biometrika. 1938;30:68–80.
- 96. Kowalski CJ. On the Effects of Non-Normality on the Distribution of the Sample Product-Moment Correlation Coefficient. Journal of the Royal Statistical Society Series C (Applied Statistics). 1972;21(1):1–12.
- 97. Erdös P, Rényi A. On Random Graphs. I. Publicationes Mathematicae. 1959;6:290–297.
- 98. Hamza K. The smallest uniform upper bound on the distance between the mean and the median of the binomial and Poisson distributions. Statistics & Probability Letters. 1995;23(1):21–25.
- 99. Bonferroni CE. Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del Regio Istituto Superiore di Scienze Economiche e Commerciali di Firenze. 1936;8:3–62.
- 100. Miller RGJ. Simultaneous Statistical Inference. Springer; 1981.
- 101. Fisher RA. On the “probable error” of a coefficient of correlation deduced from a small sample. Metron. 1921;1:3–32.
- 102. Sealy Gossetm WS. The probable error of a mean. Biometrika. 1908;6(1):1–25.
- 103. Soper HE, Young AW, Cave BM, Lee A, Pearson K. On the distribution of the correlation coefficient in small samples. Appendix II to the papers of “Student” and R. A. Fisher. A co-operative study. Biometrika. 1917;11:328–413.
- 104. Fisher RA. Applications of “Student’s” distribution. Metron. 1925;5:90–104.
- 105. Rahman NA. A Course in Theoretical Statistics. Charles Griffin and Company; 1968.
- 106. Kendall MG, Stuart A. The Advanced Theory of Statistics, Volume 2: Inference and Relationship. Charles Griffin and Company; 1973.
- 107. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. British Medical Journal. 1995;310(6973):170. pmid:7833759
- 108. Perneger TV. What’s wrong with Bonferroni adjustments. British Medical Journal. 1998;316(7139):1236–1238. pmid:9553006
- 109. Azuaje FJ. Selecting biologically informative genes in co-expression networks with a centrality score. Biology Direct. 2014;9:12. pmid:24947308
- 110. Dudoit S, van der Laan MJ. Multiple Testing Procedures with Applications to Genomics. Springer; 2008.
- 111. Jurman G, Visintainer R, Riccadonna S, Filosi M, Furlanello C. The HIM glocal metric and kernel for network comparison and classification; 2013. ArXiv:1201.2931 [math.CO].
- 112. Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C. Stability Indicators in Network Reconstruction. PLoS ONE. 2014;9(2):e89815. pmid:24587057

domain: literature


text:
Game Development and Design
Age Group: Elementary & Middle School
Aspiring game creators will use creativity and critical thinking to develop logical strategies within Construct3 to create their own appealing video games. Students code, compete, interact and will take their game home to show off to friends and family.
This camp is perfect for detailed-oriented students with strong artistic abilities. Imaginations are taken to new heights as campers test their creative flair.

domain: artistic


Parts of Lancashire could be in for a flurry of snow according to some forecasters, however Met Office experts say its more likely we'll see rain than a sprinkling of the white stuff.
Maps from WXCharts.com which uses a Met Check show a huge band of snow over much of the north of the UK on Saturday 14 at 9pm.
Tomorrow is expected to be chilly with highs of 6C.
Met Office forecast for Saturday 14:
A brighter day for most with some sunshine.
Despite this, showers will affect many areas through the day and be wintry over hills.
Maximum temperature are expected to be 6 °C.
Sunday, December 15 to Tuesday, December 17:
It'll be rather windy with sunshine and showers on Sunday and wintry again on hills.
Mainly dry and bright on Monday before cloud thickens for Tuesday.
It's expected to be rather cold with an overnight frost risk.
Long range forecast From December 17 to December 26:
A less windy interlude is likely for many at the start of next week with longer drier, brighter spells but also night frost and patchy fog.
However, there may be further rain in the southeast on Tuesday, perhaps extending further north.
From mid-week it will turn milder and unsettled again as further bands of rain and strong winds edge in from the west, after which unsettled and sometimes windy conditions will probably last for the rest of the period.
Most of the rain will probably be in the west with drier spells more likely in the east and southeast. Snow may occur at times in the north, mainly on high ground.
There remains a chance of brief drier, colder interludes, with an increased risk of frost and fog.
For the latest news and breaking news visit the LancsLive website.
Get all the big headlines, pictures, analysis, opinion and video on the stories that matter to you.
To keep updated, follow LancsLive on Facebook and @LiveLancs on Twitter.
Have you got news for us? Contact our newsdesk on email@example.com


The woman who was raped by Roman Polanski
when she was just 13 is speaking out against victims' right advocates trying to paint her as a pathetic creature. Samantha Geimer
says it was "not the end of the world" and that she doesn't like to be used by the judge or others for their agendas. Watch the video of her speaking out...
Caitlyn Jenner cheered on the UCLA Bruins this weekend.
The 67-year-old reality star sat courtside at the basketball game against the USC Trojans on Saturday (February 18) at Pauley Pavilion in Los Angeles.
PHOTOS: Check out the latest pics of Caitlyn Jenner
She chatted up a guy pal and ate a sandwich while watching her team win 102-70.
“Go Bruins! #uclabasketball” Cait captioned an Instagram photo, which you can see below.
Also sitting courtside for that game was Jessica Aba.
20+ pictures inside of Caitlyn Jenner and Jessica Alba at the same basketball game…More Here! »
Mariah Carey is a busy woman, but she always makes time for her kids.
The legendary singer shared a cute new Instagram photo with twins Monroe and Moroccan, 5, in a music studio.
PHOTOS: Check out the latest pics of Mariah Carey
“Pon de studio with #demkids 😄😍💕,” she captioned the adorable shot, which you can see below.
In case you missed it, Mariah just recently confirmed her relationship with boyfriend Bryan Tanaka after months of speculation.
Also pictured: Mariah is all smiles while doing some retail therapy on Saturday afternoon (February 18) Beverly Hills, Calif.
It looks like Miles Teller had a lot of fun for his big birthday this weekend.
The War Dogs star turned 30 years old and celebrated with friends and family at the private residence of Jonas Tahlin, CEO Absolut Elyx, on Saturday evening (February 18) in Los Angeles, California.
Miles longtime girlfriend Keleigh Sperry was there, as were some of his famous friends – Nina Dobrev, Chase Crawford (with girlfriend Rebecca Rittenhouse), Jessica Szohr, and Emile Hirsch.
PHOTOS: Check out the latest pics of Miles Teller
At one point, Miles got behind the bar and started making Elyx Martinis.
Keleigh made a special video featuring birthday shout-outs from Miles‘ parents, as well as his former-costars Zoe Kravitz and Michael B. Jordon.
Miles then gave a speech thanking everyone for coming and blew out the candles on his yummy Hansen’s cake.
25+ pictures inside from Miles Teller‘s birthday party…More Here! »
Mandy Moore is still not used to Milo Ventimiglia‘s lack of facial hair!
The 32-year-old actress was shocked when she first witnessed the new look – her This Is Us co-star shaved it all off for his role in the hit NBC series.
“We have, like, a gigantic cast text chain, and we’re like, ‘Who is that young man?!’” Mandy told People about her on-screen husband. “My first reaction was, like, ‘I don’t know that person! That is not my husband, that is not the person I fell in love with!’ I mean, it’s Milo and he’s gorgeous and he can pull off anything, but I like a man rocking a little facial hair myself – [that’s] my preference.”
“The funny thing is, they got so used to the mustache that now I walk around with no mustache and they don’t recognize me anymore,” Milo told Entertainment Tonight. “So I got a couple days of anonymity, which is nice.”
Check out pics from the This Is Us season one wrap party here!
Also pictured: Mandy stylishly sporting pink lips while leaving The Bowery Hotel on Saturday (February 18) in New York City.


|Published||February 19, 2016|
Professional Experience with Computer Added Software Engineering (CASE) tools. Professional Experience with Object Oriented Design (OOD). Ability to gather user requirements and work with users to solve user issues. Bachelor's Degree in Information Systems, Computer Science, Computer and Information Technology, or similar degree program....


As the son of a funk guitarist, Topaz Jones has been a lifelong student of the genre. And on his 2016 debut album, Arcade, the New York artist proved his scholarship by weaving West Coast G-funk with Southern rap flows. On his layered and colorful new dance single, “Toothache,” Topaz expand his funk palette even further—paying homage to all the musicians who came before him.
“Toothache” is an accumulation of allusions; within its first few seconds, Jones and co-producer Kashaka invoke Marvin Gaye, James Brown, and Earth, Wind & Fire. After being introduced by his aunt Lisa, Topaz emerges to do his best Prince impression. The song grooves on with a slamming bass line, swinging rhythm guitar, Parliament-like synths, and ascending string flourishes. Meanwhile, Jones nimbly switches between rapping and singing, name dropping the likes of Count Basie and Maceo Parker. But none of his references lead to a direct rip-off. Instead, he shows that he’s so well-versed in the foundation laid by the masters that he’s able to reinvent the genre in his own funk-rap fashion. It’s clear from the effortlessly soulful “Toothache” that his love for funk goes beyond a template; the genre is living and breathing inside of him.


Renault Trucks begins series e-truck production in France
The electric truck models Renault debuted in November 2022 are now rolling off the production line at the French plant in Bourg-en-Bresse. The battery cells and modules come from Samsung SDI and are assembled in Ghent, Belgium, at a new plant belonging to the Volvo Group, which also includes Renault Trucks. Volvo Trucks started series production of its three heavy-duty models, FH Electric, FM Electric and FMX Electric, in Ghent in September 2023.
In the case of Renault trucks, four to six battery packs with a 390 to 540 kWh capacity can be installed per vehicle, depending on customer requirements. Interesting detail: when the company presented its first technical data in spring 2022, there was still talk of offering the option of two battery packs with 180 kWh. Now Renault Trucks only mentions the four or six pack variants.
According to the manufacturer, a range of up to 300 kilometres is possible with the maximum configuration on a single charge and up to 500 km with an intermediate fast charge with up to 250 kW for an hour. Aside from fast charging, Renault Trucks specifies a charging capacity of 43 kW AC. The charging time is 9.5 hours with alternating current or 2.5 hours with direct current.
To cover various use cases, Renault Trucks offers three power take-off types for auxiliary units: purely electric, electromechanical or directly on the transmission. Customers can choose between two or three electric motors for the drive, enabling a total output of up to 490 kW.
With a gross combination weight of up to 44 tonnes, Renault Trucks E-Tech T and C are available as 4×2 and 6×2 tractor units with an extended cab and a wheelbase of 3,900 mm. In the rigid configuration, they are available as 4×2, 6×2 and 8×4 tridem, with short or long cab and a choice of eleven wheelbases from 3,900 t.
Renault Trucks has been making trucks for nearly 60 years in Bourg-en-Bresse and now assembles the new 44-t electric trucks. The manufacturer says it is drawing on four years of experience manufacturing medium-tonnage electric trucks at its Blainville-sur-Orne plant and applying the same precepts and best practices at Bourg-en-Bresse.
The Renault Trucks E-Tech T and C are assembled on the same line as their diesel equivalents, after which they are transferred to a dedicated 5,200 m2 area that the company calls the Renault Trucks E-Tech Factory. Staff there assembles the Electric Drive Unit (EDU) and installs the transmission and batteries. The electric vehicles then undergo performance tests and track trials.
With the expansion of its electric range, Renault Trucks is taking another step in its eMobility strategy and targets: By 2030, the manufacturer aims to generate 50 per cent of its sales from electric vehicles. By 2040, all vehicles shall be locally CO2-neutral.


But the optimum lift distribution is not unique. Best practice calls for this differential to be minimized at the tip, so the gradient is weakened. Is there anyone out there that could make suggestions so that Susannah could redesign, rebuild, and get this cool idea in the air.
This pipe is also the axis of the flaps rotation. Drag increases exponentially with the velocities imparted to the air, therefore designers use a variety of approaches to keep this equalization from happening quickly. Then we cut this plate according to the needful sizes of the spare width and mould a place for the joiner in the matrix, wrapping it up with carbon rowing.
When we glue ribs with carbon cap-strips to this pipe, we get extra rigid and strong flap or aileron. However, most aircraft wing designs allow something else to happen that greatly increases this cost of making lift with a finite wingspan: That action causes both a Newtonian reaction and a Bernoulli pressure differential, resulting in lift.
In the construction of a built up wing curving forces are taken up by the spar and the twisting ones by the D-box construction.
Allowing any strong gradient to form between low pressure and high pressure will cause air to move toward the low pressure at a high velocity, if it can. A patented boxtail aircraft design.
This is why they are not at all like "normal wings. Using knowledge from building military planes, Northrop Grumman puts this design together without a stabilizing tail, a first for the commercial aviation market.
Thus received plates we glue to the balsa cut in vertical direction with epoxy in vacuum. Configuration studies have shown that the least total drag for an aircraft occurs when a tail is somewhere behind the wings, pushing downward to provide longitudinal stability.
The largest commercial airliner, the Airbus Amust make efficiency trade-offs to keep the wingspan below the meter limit at most airports, but a closed wing with optimal wingspan could be shorter than that of conventional designs, potentially allowing even larger aircraft to use the current infrastructure.
Placement of Box wing configuration. This covering is produced according to the same prepeg technology as a very thin film.
D-box is a tube in a letter D form in front of the spar. Most is caused simply by applying force on air and the air, being freely movable, accelerating and taking kinetic energy with it. It has to 'fall into the descending trough of air' that the wings displaced downward.
When this form is put into the stove there appears an excess pressure of about 30Bar and at the temperature C the epoxy polymerizes. Carbon fiber is a micro pipe. For more information regarding span efficiency and non-planar configurations, Ilan Kroo has published very thorough overviews of the subject.
During the flight the wing of a model is influenced by aerodynamic forces that curve and twist it. What appears to be a structural advantage actually merely concentrates the bending forces, generated by each wing, into the corners of the box. The easier and more effective way to reduce the induced drag is simply to increase the wingspan, or reduce the vehicle weight.
The slower it happens, the less kinetic energy is imparted to the air by the airplane. We create pressure to the carbon by screws and in addition silicon expands and create pressure of Bar. They become more effective in this with greater vertical spacing.
It was my desire to utilize high span efficiency and laminar flow in a high speed aircraft design, while avoiding high speed landings and unpredictable, unstable behaviors at low speeds.
In this way we achieve high strength and inflexibility by the minimal weight of the spar. You see, not even a model airplane company should gamble their resources developing aircraft that profit from this detailed, fundamental teaching without looking into licensing of the patent, which obviously has to offer a highly desirable value, and low upfront costs, if it is to proliferate.
The momentum difference is quite literally the induced drag, although we usually teach it in ways more related to how induced drag is visualized and computed in 2-D. In the PopSci article, Lockheed projects a 40 percent savings in fuel cost.
Kitchen, Cedric Lee and G. This secondary movement causes completely unavoidable rotational movements in the "wake" zone between air directly moved by the wings and the nearby stationary air, thereby involving more air mass than the plane needed to move just to get the lift it needed.
Tilghman Richards built and flew several annular-wing aeroplanes in which the fore and aft segments were on the same level.
Drag increases exponentially with the velocities imparted to the air, therefore designers use a variety of approaches to keep this equalization from happening quickly. During the last two years we've run lots of experiments on the wings and stabilizer gluing technology. After going through a series of designs on CAD, she dry fit one to figure out final touches.
aircraft conﬁguration of interest is the box/joined wing aircraft conﬁguration, which in recent times has attracted the attention of researchers due to its claimed merits of reduced structural weight and low induced drag.
Box/joined wing aircraft. Explanation of The D-Box build technology Article cutesy of Vladimir's Model stylehairmakeupms.com D-box built up technology In order to understand why our composing models are so light and strong and what is the difference between our construction and technology differs from the traditional let's consider the forces that influence the wing in a flight and which parts of the construction take.
A box is a naturally strong shape in the same way that biplanes were very strong making them the dominant form in early aviation, also both forward and rear surfaces would produce lift, as with canard designs, unlike a tailed design which creates downforce in order to balance the plane.
Oct 10, · Is the 'box-wing' aircraft the plane of the future? European researchers are developing a 'box-wing' airplane. Such planes can carry more passengers and cargo than current commercial aircraft. DW Hobby EPP Electric Micro Flying Wing Rainbow ZAGI Plane w/mm wingspan Delta Wing Tail-pusher Flying Aircraft for Toy Plane Model Aeroplane to build (E) by Dancing Wings Hobby.
Box Office Mojo Find Movie Box Office Data: ComiXology Thousands of Digital Comics: CreateSpace Indie Print Publishing Made Easy.
The wing box of an airplane is the structural component from which the wings extend. It is usually limited to the section of the fuselage between the wing roots, although on some aircraft designs, such as the Boeingit may be considered to extend further.Box wing plane


text:
Former teacher jailed
27 October, 2015, 12:00 am
A 32-YEAR-OLD former school-teacher was sentenced to five years imprisonment last week by the High Court in Suva for money laundering.
Savita Singh had pleaded guilty to one count of money laundering.
She had admitted to laundering funds amounting to $2400 from the Fiji Revenue and Customs Authority (FRCA) in 2007.
High Court judge Justice Achala Wengappuli said the proceeds of crime in relation to the offence committed were funds of a government agency.
“Your involvement in the commission of the offence is not limited to being a mere accessory to a grand scheme of fraud, but as an active participant at its several stages,” Justice Wengappuli said.
However, the court took into account that she was a first offender, a mother, co-operated with police, she had repaid the money to the State, she had apologised to the court and she promised not to reoffend.
Singh will serve 12 months in prison before she is eligible for parole.

domain: news


Nearly five years after announcing a competition to develop a new cryptographic hash algorithm, the National Institute of Standards and Technology announces a winner that was designed by a team of European scientists.
Website outages at leading U.S. banks have garnered global attention. But how concerned should European institutions be that they could be the next targets? A London-based security expert shares insight.
Malware and cyberespionage tools like Gauss are hitting U.S. banking institutions and businesses from all corners of the globe. But why are these sometimes not-so-sophisticated attacks causing so much damage?
Online attacks aimed at major U.S. banks have helped to heighten industry cooperation and information sharing. But experts say not all attacks are equal, and understanding the motivation behind the attack is key.
The guidance discusses methods, techniques and best practices for the sanitization of target data on different media types and risk-based approaches organizations can apply to establish and maintain a media sanitization program.
Five pilot projects unveiled by the federal government, if successful, should build trust in online commerce, helping to boost the economy, says Jeremy Grant, head of the National Strategy for Trusted Identities in Cyberspace National Program office.
Banking institutions can expect more cyberattacks, including threats from nation-states, as the U.S. elections draw near. So they must take adequate security steps - and clearly explain them to their customers.
The new report aims to help access-control experts improve their evaluation of the highest security access-control systems by discussing the administration, enforcement, performance and support properties of mechanisms that are embedded in each system.
Financial institutions are now at high risk of cyberattack, according to the Financial Services Information Sharing and Analysis Center. What can institutions do to protect themselves from new threats?


text:
By; BAYO AKAMO, Ibadan
The Social Democratic Party (SDP), governorship candidate in Osun State, Senator Iyiola Omisore on Friday disowned a statement circulating in the Social media that the Presidency has allegedly wooed him to back the candidate of the African Democratic Party, Alhaji Mashood Adeoti.”
Senator Omisore in a statement issued by his Media Team emphasized that the he is still in the governorship race for the September, 22 election in Osun state contrary to the insinuation in the said Social Media message which he described as total falsehood.
According to the SDP governorship candidate in Osun state, the Social media message, entitled “PRESIDENCY ABADONED OYETOLA, WOOS OMISORE THROUGH GOVERNOR AMOSUN”
“is untrue and wishful thinking of those that are feeling uncomfortable with his increasing popularity”.
The SDP governorship candidate pointed out that there was never a time the said purported “carrot of debt buy-back or case withdrawal” was dangled before him not to talk of holding meeting with the imaginary cabals .
“It is unfortunate that as the good people of Osun state are preparing and praying for a peaceful, acceptable, free and fair governorship election in the state, come September, 22, this year, Some enemies of the state and its people are busy feeding the people, especially the electorates with total falsehood aimed at confusing the people about my governorship ambition. I wish to state categorically that the said message in its entirety is figment of the imagination of those circulating it,” he said.
Senator Omisore maintained that it was indeed laughable that some people could at this period be dreaming of making of making him drop his governorship ambition by resulting to series of falsehood, blackmail and all of sort by formulating tales such as fabricating that ” conditions for his pardon was clearly spelt out to him to give his backing to the candidate of the African Democratic Party, AlhaJI Mashood Adeoti.”
The SDP governorship candidate stated that it was unfortunate that the sponsors of the total falsehood even claimed ” that part of the resolution reached earlier this week was that Omisore should present the Deputy governor of ADP and he will be given Osun East Senatorial ticket of ADP in addition to his presenting the Deputy Governor to the party”.
” I wish to state clearly that there was never a time I Senator Iyiola Omisore attended such an imaginary meeting, nor send any representation and there was no such meeting to my knowledge not to talk of reaching or agreeing on the purported resolution or any other on my governorship ambition.
I am saying it loud and clear that I am still in the race, my governorship ambition is still very much alive and with God and the support of the good people of Osun state. We shall rescue Osun state with my election as the next governor of Osun state come September,22 this year.”
The SDP governorship candidate frowned at the plans by those behind the falsehood which he said was targeted at setting his teeming supporters and admirers against him as well as causing confusion in his camp,, saying, “one can easily deduced from their statement that those behind the concorted lies are out to confuse and mislead the good people of Osun state. Going by their words that ” questions being asked now are *** has Omisore sacrificed his gubernitorial (sic) ambition?
*** Why did he scattered and hijacked SDP from the original owners? * Will Faforiji and others still follow him to ADP and what will become of Dr. Bayo Faforiji’s ambition to be the State Chairman of a political party? * Hope Omisore remember this is his last chance to try his luck to become Osun state governor which he has been pursuing for over 20years?” It is clear that the authors of the statement are enemies of Osun state and its people “
Senator Omisore while urging his supporters,and admirers and members of the SDP to ignore the statement and its contents entirely, said with God and the people of Osun state solidly behind him on his governorship ambition, no amount of falsehood, lies and propaganda will stop him from realizing his mission of rescuing Osun state and its people from the present untold hardships and mis governance of the ruling All Progressives Congress (APC) in Osun state.
“By the Special grace of God. I am in the SDP, and the party’s standard bearer in the September, 22 governorship election. I am urging the people of Osun state to disregard the faslehood entitled “PRESIDENCY ABADONED OYETOLA, WOOS OMISORE THROUGH GOVERNOR AMOSUN” . It is part of the antics of those that are afraid of our good intention for Osun state and its people. They have failed. I am again tasking our people to be focused and forge ahead in our determination to bring back the lost glory of our dear Osun state.”


text: The technology used in landfill covers is changing, and an alternative cover called the evapotranspiration (ET) landfill cover is coming into use. Important design requirements are prescribed by Federal rules and regulations for conventional landfill covers but not for ET landfill covers. There is no accepted hydrologic model for ET landfill cover design. This paper describes ET cover requirements and design issues, and assesses the accuracy of the EPIC and HELP hydrologic models when used for hydrologic design of ET covers. We tested the models against high-quality field measurements available from lysimeters maintained by the Agricultural Research Service of the U.S. Department of Agriculture at Coshocton, Ohio, and Bushland, Texas. The HELP model produced substantial errors in estimating hydrologic variables. The EPIC model estimated ET and deep percolation with errors less than 7% and 5%, respectively, and accurately matched extreme events with an error of less than 2% of precipitation. The EPIC model is suitable for use in hydrologic design of ET landfill covers.

domain: geography


Co-operative Funeralcare have issued the following statement after it came to light that the wrong body had been cremated at a service at Birtley crematorium last month.
Two people have been sacked.
“A serious breach of our strict procedures occurred and following a thorough investigation two members of staff have been dismissed.
Once the issue came to light, we immediately notified the families concerned and apologised for any distress caused and we have liaised closely with the crematoria.
We remain in close contact with the families.
In the interests of client confidentiality we are unable to comment further.”
More top news
Two fire crews have a tackled a sinkhole in North Yorkshire.
Sunderland take on Middlesbrough at the Stadium of Light on Saturday with Chris Coleman saying it's not a derby match
Northumbria’s PCC said an interactive education workshop has already led to several disclosures by children of sexual abuse.


text:
Электронная книга: Louise Dixon «The Wiley Handbook of What Works in Child Maltreatment. An Evidence-Based Approach to Assessment and Intervention in Child Protection»
A comprehensive guide to empirically supported approaches for child protection cases The Wiley Handbook of What Works in Child Maltreatment offers clinicians, psychologists, psychiatrists and other professionals an evidence-based approach to best professional practice when working in the area of child protection proceedings and the provision of assessment and intervention services in order to maximize the well-being of young people. It brings together a wealth of knowledge from expert researchers and practitioners, who provide a comprehensive overview of contemporary work informing theory, assessment, service provision, rehabilitation and therapeutic interventions for children and families undergoing care proceedings. Coverage includes theoretical perspectives, insights on the prevalence and effects of child neglect and abuse, assessment, children’s services, and interventions with children, victims and families.
Издательство: "John Wiley&Sons Limited"
ISBN: 9781118976142 электронная книга Купить за 12659.41 руб и скачать на Litres
Купить за 12659.41 руб и скачать на Litres
Другие книги автора:
|What Works in Offender Rehabilitation. An Evidence-Based Approach to Assessment and Treatment||This comprehensive volume summarizes the contemporary evidence base for offender assessment and rehabilitation, evaluating commonly used assessment frameworks and intervention strategies in a… — John Wiley&Sons Limited, электронная книга Подробнее...||9570.75||электронная книга|
См. также в других словарях:
Eye Movement Desensitization and Reprocessing — (EMDR) is a form of psychotherapy that was developed to resolve symptoms resulting from disturbing and unresolved life experiences. It uses a structured approach to address past, present, and future aspects of disturbing memories. The approach… … Wikipedia


VW And BMW Are Determined To Keep Pace With Tesla, Whatever The Cost
With only one model and a zero emissions message that the average vehicle consumer wasn't yet ready to hear a decade ago, Tesla was an
automotive oddity. Yet, those manufacturers and consumers are taking Tesla seriously in 2023. In order to stay up with the American
upstart, two of the biggest and best-known automakers, BMW and VW, are investing billions of dollars in electric car initiatives.Of course,
when the tiny i3 was unveiled in 2013, BMW was at the forefront of the current EV revolution. But, it never truly took use of that
advantage, opting to concentrate on hybrid vehicles rather than create a family of EVs. With the e-Golf in 2014, VW also dipped a toe in the Electric waters.


text:
On Saturday, I had the opportunity to go to an event hosted by Raincoast Canada. For those of you who don’t know, Raincoast is the distributor for publishers such as Macmillan, Sourcebooks, and many others. At this event, we were introduced to over twenty titles for the upcoming Winter and Spring season. Let me tell you — there’s some great looking stuff coming. I thought it would be fun to share the top five books from the event I am most excited for.
#1: Gena/Finn by Kat Helgeson & Hannah Moskowitz (Release Date: February 23rd 2016 by Chronicle Books)
Sooooo… my most anticipated book from the event doesn’t actually have a cover yet. However, that is OK! Want to know why this my most anticipated read? Because it focuses on friendship, fandoms, fanficition and it’s going to be written in a multimedia format. I love stories about friendship, and I equally love how fandoms how the power to bring people together. Needless to say, this was the book I came out of the event wanting the most!
#2: The Art of Being Normal
by Lisa Williamson (Release Date: May 31st 2016 by Farrar, Straus and Giroux (BYR)
I’m always looking for new and interesting LGBTQIA reads, and this one sounds like something I’d completely adore. It focuses on a young boy who wants to be a girl, and the unlikely friendship it sparks when he is saved from the school bully. This book looks like it will tackle a lot of different issues, and a message of what it means to be true to yourself. Looks wonderful and heartfelt, and I cannot wait!
#3: How It Ends
by Catherine Lo (Release Date: June 7th 2016 by Houghton Mifflin Harcourt)
Another book that just sounds sad and mysterious. Written by a Canadian author, this book looks like it will focus on secrets, friendship, how secrets can affect a friendship in ways that might tear it apart. Really excited to see how this book is going to go down, because itis vague premise really has me intrigued.
#4: Riders (Riders #1)
by Veronica Rossi (Release Date: February 16th 2016 by Tor Teen)
It is so great to see a new Veronica Rossi YA novel. I was a slow convert to her Under the Never Sky series. It was one of those situations where the first book didn’t impress me and then all of a sudden I chose to kept going and it turned out to be a much more rewarding series than I initially gave it credit for. This book is about the Four Horseman of the Apocalypse, and that alone makes me want it. UNDEAD PONIES.
#5: These Vicious Masks
by Tarun Shanker & Kelly Zekas (Release Date: February 9th 2016 by Swoon Reads)
Jane Austen meets X-Men? Yes, you heard me, that alone piqued my interest during yesterday’s discussion. Not only did it confuse the heck out of me, but the book sounds utterly ridiculous and charming. Due to those two factors, I feel like my expectations might be way too high on this one, BUT JANE AUSTEN MEETS X-MEN YOU GUYS. ALL THE HYPE!
And then here’s what I got in my swag bag! (Minus the Kate Beaton book, I admit, I bought that)
Truthwitch by Susan Dennard
In Real Life by Jessica Love
Honestly, I didn’t know much about either, and ended up trading with some other bloggers at the event for these. Excited to see if I’ll like either of them (both look like something I’d enjoy).
Everyone who attended! (Photo curtsey of Vanessa Di Gregorio from Raincoast Books & ampersand inc)
Once again I want to thank to Raincoast Canada for allowing me the opportunity to see all their new upcoming titles. I want to thank all the bloggers I met for great conversation, and also Melissa Pellegrin for being a standup hostess for the events in both TO and Vancouver. I seriously cannot wait for all these upcoming titles, because even though I only focused on five, there’s a ton worth checking out.


text:
Zabrig is a beautiful handwritten script font that will give your designs a refined look with tons of style and simplicity. This font has a natural flow to it and works well in signature styled applications.
This resource comes from other websites and we cannot confirm its legitimacy. If you are the copyright owner, please submit a copyright infringement. So we will delet the resources and link to your page.

domain: design


text:
A mum has posted an incredible picture of her breast milk changing colour as her child battled a cold.
Mallory Smothers, from Arkansas, is convinced the change in colour helped to fight off the infection.
Mallory said she expressed milk as usual, before putting her baby girl to bed, and nursing regularly throughout the night.
Overnight, she noticed her daughter was ‘congested, irritable and sneezing A LOT.’
The next morning, Mallory expressed milk again, as usual but this time it looked very different.
It was only later that she noticed the striking difference. She took to Facebook to share her theory behind the dramatic change in appearance.
‘I read an article from a medical journal not too long ago about how Mom’s milk changes to tailor baby’s needs,’ she wrote.
She added that the study, published in the Clinical &Translational Immunology Journal found infections can trigger a rise in leukocytes -a type of white blood cell that helps fight off infections.
She said the second bag of milk was very like colostrum, the milk produced in the first few days after birth, which is rich in antibodies and leukocytes.
Colostrum is thicker and can range from clear to dark yellow in colour.
‘Look at how much more the milk I produced Friday resembles colostrum (the super milk full of antibodies and leukocytes you make during the first few days after birth), and this comes after nursing baby with a cold all night long,’ Mallory wrote alongside the picture which has now been shared over 70,000 times.
‘Pretty awesome huh?! The human body never ceases to amaze me,’ she concludes.


Needle Valve, Spool Type, Metric Cartridge – SDH05330
The SDH05330 is a hydraulically operated, spring-loaded, adjustable spool valve.
In the normal position, the valve is open and there is flow from port 3 to port 2. When we apply control pressure to port 1, the valve switches into the flow control position and it reduces the flow.
Furthermore, it’s possible to adjust the flow setting by using the set screw (clockwise to increase flow, counter-clockwise to decrease flow).
Caution: the control pressure must generally be greater than the pressure at port 2, otherwise the valve will not function. For the valve to switch back from the flow control position to the fully open position, we must vent port 1. At this moment, there must be no flow through the valve.
See datasheet for drawings, dimensions, model code, etc…
- Flow control is dependent on the viscosity of the oil and the system pressure
- A lock-nut lock the setting
- External surfaces zinc-plated
- Hardened and ground control piston to ensure minimal wear and extended service life
- Compact design


text:
TripAdvisor has put moves into place to safgeguard its natural search traffic in the likely event of Google pulling completely out of China.
The user review site admitted in January 2010 that around a third of its search engine traffic comes from the Google’s Chinese engine.
But with Google redirecting traffic to its Hong Kong site last week as relations between it and the Chinese authorities waned, TripAdvisor has started making alternative arrangements.
TripAdvisor general manager in China, Hao Wu, says Google decision is “as expected – it has always been a matter of when, not if”.
Read the full story at:http://www.tnooz.com/2010/03/29/news/tripadvisor-makes-plans-for-a-post-google-china/


text:
By: Noah Rue
Retailers all over the United States are filing for bankruptcy at unprecedented levels. This is, in part, due to the ways the global economy has evolved. Now, more than ever, retailers are turning to the internet for sales, and consumer habits have adapted. Bankruptcy is an alarming and confusing process whether you’re a CEO of a large corporation or an individual who is worried about the implications it might have on your credit score.
For smaller businesses, or businesses who have a more niche clientele, the idea of bankruptcy can be especially daunting, especially if they are committed to maintaining a fiduciary responsibility to their clients, community members, employees, and vendors. Not to mention, the recent bankruptcy filing from retailers like Toys ‘R’ Us, Nine West, Claire’s, and others might be another source of anxiety for smaller business owners. After all, each of these companies have been largely successful for years in a rapidly transforming, increasingly internet-based market.
It must be mentioned, however, that there are a number of businesses, both big and small, that have successfully recovered after filing for bankruptcy or coming close to it. We cover some of those below.
It’s hard to believe that one of the world’s largest and most successful tech-based retail giants was at one point at the brink of financial disaster. Though they never officially filed for bankruptcy, Apple was on the verge of financial collapse in 1997. At the last minute, however, their rival and largest competitor to date, Microsoft, invested $150 million dollars into the company, which allowed Apple to experience one of the biggest business comebacks in history. Sources speculate that Microsoft made this investment as a means to save face, so that regulators wouldn’t spectate that the company had a monopoly on the market.
Best Buy is currently the world’s largest and most recognizable brick-and-mortar electronics store. In 2010, however, sales and profits had plummeted, and it looked like the retail giant might have to close down its operation for good. A scandal with the then-CEO in 2012 only seemed to make matters more dire for the electronic goods supplier.
Under the management of the new and current CEO Hubert Joly, things have taken a turn for the better with the electronic retail giant. Joly managed to clean up once-disorganized stores, close locations that were unnecessary and costing the store profits, brought in more exclusive products, and improved customer service. Perhaps most important, however, he transformed the store’s online and in-store experience, making it easier for customers to research in-store and order online.
The Chicago Cubs
The Cubs are perhaps most well known for their 100-year losing streak, so it may come as no surprise that the Chicago-based team was at one point on the verge of financial ruin. Although the team has made headlines for their less-than-spectacular performance on the field, the Cubs do have a spectacular attendance record —one of the best in Major League baseball. After the team’s owner, the Tribune Company filed for Chapter 11 bankruptcy in 2009, the Ricketts family, prominent bankers in the Chicago area, later purchased the team for $845 million.
The fashion brand and popular storefront American Apparel has rarely strayed from headlines, whether it be criticism for their racy advertising, issues caused by their controversial founder, Dov Charney, or occasional praise for their labor practices. In October 2015 the fashion chain filed for Chapter 11 bankruptcy, emerging from it the next year as a private company. Now an LLC, the company celebrated its revival by rolling out a new, more simple ad campaign featuring what they deemed to be “luxury” T-shirts.
American Airlines is one in a long list of airline companies that have filed and emerged from bankruptcy, which also includes Delta, United, and Air Canada, to name a few. American Airlines, however, has a particularly impressive recovery.
The company filed for bankruptcy in November 2011. By 2014, American Airlines was profitable for the first time since 2007, and the company has since entered the S&P 500.
For over a century, Kodak was an essential part of the film industry. As times changed, however, Kodak was slow to adapt to the new wave of digital photography, and as a result, the company’s film sales struggled. In January 2012, the company filed for Chapter 11 bankruptcy, and after years of reorganization, rebranding, and a changing their product offerings, Kodak is once again a profitable company who seems to be around for the long haul.
Jack in the Box
Just over 20 years ago, a severe E. coli outbreak at Jack in the Box restaurants hospitalized 175 people, and killed four children, making headlines throughout the United States. After the scandal, there were a number of layoffs, and most plans for expansions of the chain were put on hold. The company recovered and became the company it is today by enforcing strict food-safety standards and revamping their marketing campaigns.
Whether you’re a big chain, a small business, or an individual struggling with finances, filing for bankruptcy is a difficult pill to swallow. It is important to note, that even in the most dire of financial circumstances, some of the most prolific, well-known, and profitable companies today were once on the brink of bankruptcy.
By Noah Rue
About the author
Noah Rue is a writer, a digital nomad, an ESL teacher, and an all around good dude, if he doesn’t say so himself.
Featured image via Unsplash.

domain: finance


Massachusetts Taxpayers Foundation Sees State's Finances Worsening
How badly has the shutdown of the economy in Massachusetts hurt the state’s finances?
A Beacon Hill watchdog projects the state is facing a $6 billion revenue shortfall in the fiscal year that starts July 1. The report from the Massachusetts Taxpayers Foundation said Massachusetts will need to find new revenue, or drastically cut spending. WAMC’s Pioneer Valley Bureau Chief Paul Tuthill spoke with Massachusetts Taxpayers Foundation president Eileen McAnneny.
We testified a month ago at a virtual economic summit that budget leaders held. And at that time, we said, we were very transparent about our assumptions and said as some of the underlying data change, then so will our forecast, and that's exactly what's happening. In the interim. There's been a lot more information that's become available.
When you testified at that virtual hearing, what was the estimate, then for the revenue loss?
It was $4.4 billion.
So why have we why have we gone from that to 6 billion?
Because in our estimate, back in April, we had thought that the unemployment would be less than it is in fact today. And we also thought that there would be a V shaped recovery so there was a precipitous drop in economic activity. We thought that there would be, you know, a steep upturn starting in July, and that looks less and less likely.
Is that because of the of the slow phased reopening of the state's economy?
Well, I mean, I think that's one of the reasons that's certainly not the only reason. And so, you know, we've just seen a number of things like the money from the PPP plan, for instance, for small businesses, was slower to get out as a result of just, you know, some issues rolling out that program. Right. So I think everything takes longer than people initially anticipate. And that's part of the issue. Right. And so but I do think that this phased economic opening is playing into that as well.
Where is most of the revenue loss coming from? Is it the income tax, sales, tax or combination Have all of them.
But it's a combination of all of them. But you know, you think about Massachusetts, how our revenue is structured, and Massachusetts relies on income tax disproportionately. So you know, about 56% or so of all tax revenue comes from income, and a big portion of that is from what people call unearned income, but I would call it investment income. So things like interest in dividends and capital gains, and certainly we're going to see, you know, a drop off in that. But as some of it is, just as the economy is slowed, fewer people are working or they're working fewer hours or maybe half time and those types of things that has a direct impact on the amount of tax revenue people collect in. And as far as sales tax, really, these are the only sales activity we're seeing or not shouldn't say the only but the primary is non-taxed goods. So people are buying groceries, they're buying medicine. But those aren't subject to tax. Things like motor vehicles or other big ticket taxable items, we're just not seeing a lot of that.
And so what options are there for the state looking to fiscal year 2021? I mean, we don't even know when we're going to get a budget proposal on Beacon Hill.
The data is changing very quickly. I mean, you think about how much our revenue forecast changed in four weeks. And so you know, I think this is unprecedented and it is tough for budget makers. So you're right, we probably will see some type of interim budget until people feel that they have solid data on which to build a full year budget. But suffice it to say, I think any and all things have to be on the table because this is a sizable revenue gap and you know, we can help. We can use a rainy day fund. That has $3.5 billion. So to the extent we use some of that for fiscal year 2020, it means there's less available for fiscal year 2021. And we anticipate this will be a multiyear problem. So you can't use it all in one year, because you're going to probably need it for a few years. So, rainy day funds can help, I think there’ll the budget cuts, I think they'll be looking for new sources of revenue, I think they're gonna have to think about the Student Opportunity Act and, you know, whether they're able to afford the current schedules for that, or whether that's going to have to be postponed or extended, you know, elongated, I mean, so I think, really, any and all solutions are going to have to be looked at.


Publication IEICE TRANSACTIONS on Information and SystemsVol.E103-DNo.5pp.1031-1038 Publication Date: 2020/05/01 Publicized: 2020/01/24 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2019EDP7264 Type of Manuscript: PAPER Category: Artificial Intelligence, Data Mining Keyword: deep image compression, ROI, quality control,
Full Text: PDF(2.2MB)>>
Summary: In this paper, we present the effectiveness of image compression based on a convolutional auto encoder (CAE) with region of interest (ROI) for quality control. We propose a method that adapts image quality for prioritized parts and non-prioritized parts for CAE-based compression. The proposed method uses annotation information for the distortion weights of the MS-SSIM-based loss function. We show experimental results using a road damage image dataset that is used to check damaged parts and an image dataset with segmentation data (ADE20K). The experimental results reveals that the proposed weighted loss function with CAE-based compression from F. Mentzer et al. learns some characteristics and preferred bit allocations of the prioritized parts by end-to-end training. In the case of using road damage image dataset, our method reduces bpp by 31% compared to the original method while meeting quality requirements that an average weighted MS-SSIM for the road damaged parts be larger than 0.97 and an average weighted MS-SSIM for the other parts be larger than 0.95.


New channel is keen to reject comparisons with Fox News despite positioning itself as ‘anti-woke’
When GB News launches at 8pm on Sunday, viewers will for the first time be able to immerse themselves in a non-stop, opinionated, US-style “anti-woke” current affairs television channel aimed at a British audience. But there’s one thing those running the outlet are desperate to make clear: please don’t compare us to Fox News.
“We’re going to embrace the mood of the nation and communities that feel their voice has not been heard in the mainstream media,” said Angelos Frangopoulos, the Australian media executive brought in to launch the channel. “We’re just being true to telling the stories that people want to talk about.”


Testing the Ideal Free Distribution Hypothesis: Moose Response to Changes in Habitat Amount
The ideal free distribution (IFD) hypothesis predicts that animal density in habitat is constant across a range of habitat amounts (gray dashed line). The influence of fragmentation (Frag.) on animal density results in a nonlinear response across a range of habitat amount (solid black line).
Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.


Brainiac: Transmissions After Zero to screen at the Little Art
Indie experimental band 3RA1NA1C (Brainiac) formed in 1992 in Dayton, Ohio. They were known for their loud, expressive indie, synth punk attitude and music and quickly rose through the ranks of stardom before the death of Tim Taylor in 1997. After his sudden passing, the band members went their own ways, ultimately disbanding the group.
Brainiac had deep roots in the Dayton area and are often praised because they “championed their midwest roots”. Filmmaker Eric Mahoney set his sights on crafting the legacy of the band 24 years later with Brainiac: Transmissions After Zero.
Mahoney visited the WYSO studio for a conversation with Excursions host Evan Miller. The pair remembered fondly the Dayton events in 2019 that showcased the documentary, even bringing up Warped Wings signature Brainiac Beer with the hopes of bringing it back. Brainiac: Transmissions After Zero, will screen at the Little Art on Thursday November 11th at 7 pm with a Q&A session with Eric after the show. For more info go to https://www.littleart.com/.