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A Toxicogenomic Approach for the Prediction of Murine Hepatocarcinogenesis Using Ensemble Feature Selection
Johannes Eichner ,
* E-mail: johannes.eichner@uni-tuebingen.de
Affiliation Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
Nadine Kossler,
Affiliation Nonclinical Drug Safety, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riss, Germany
Clemens Wrzodek,
Arno Kalkuhl,
Dorthe Bach Toft,
Affiliation Nonclinical Drug Safety, H. Lundbeck A/S, Valby, Denmark
Nina Ostenfeldt,
Virgile Richard,
Affiliation Non-Clinical Development, UCB Pharma S.A., Brussels, Belgium
Andreas Zell
Johannes Eichner,
Johannes Eichner Nadine Kossler ... Andreas Zell
The current strategy for identifying the carcinogenicity of drugs involves the 2-year bioassay in male and female rats and mice. As this assay is cost-intensive and time-consuming there is a high interest in developing approaches for the screening and prioritization of drug candidates in preclinical safety evaluations. Predictive models based on toxicogenomics investigations after short-term exposure have shown their potential for assessing the carcinogenic risk. In this study, we investigated a novel method for the evaluation of toxicogenomics data based on ensemble feature selection in conjunction with bootstrapping for the purpose to derive reproducible and characteristic multi-gene signatures. This method was evaluated on a microarray dataset containing global gene expression data from liver samples of both male and female mice. The dataset was generated by the IMI MARCAR consortium and included gene expression profiles of genotoxic and nongenotoxic hepatocarcinogens obtained after treatment of CD-1 mice for 3 or 14 days. We developed predictive models based on gene expression data of both sexes and the models were employed for predicting the carcinogenic class of diverse compounds. Comparing the predictivity of our multi-gene signatures against signatures from literature, we demonstrated that by incorporating our gene sets as features slightly higher accuracy is on average achieved by a representative set of state-of-the art supervised learning methods. The constructed models were also used for the classification of Cyproterone acetate (CPA), Wy-14643 (WY) and Thioacetamid (TAA), whose primary mechanism of carcinogenicity is controversially discussed. Based on the extracted mouse liver gene expression patterns, CPA would be predicted as a nongenotoxic compound. In contrast, both WY and TAA would be classified as genotoxic mouse hepatocarcinogens.
Citation: Eichner J, Kossler N, Wrzodek C, Kalkuhl A, Bach Toft D, Ostenfeldt N, et al. (2013) A Toxicogenomic Approach for the Prediction of Murine Hepatocarcinogenesis Using Ensemble Feature Selection. PLoS ONE 8(9): e73938. https://doi.org/10.1371/journal.pone.0073938
Editor: Enrique Hernandez-Lemus, National Institute of Genomic Medicine, Mexico
Received: April 18, 2013; Accepted: July 24, 2013; Published: September 10, 2013
Copyright: © 2013 Eichner et al. 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 research leading to these results has received funding from the Innovative Medicine Initiative Joint Undertaking (IMI JU) under grant agreement nr. 115001 (MARCAR project). We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Tuebingen University. 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 the following interests: Co-authors Nadine Kossler and Arno Kalkuhl are employed by Boehringer Ingelheim Pharma GmbH & Co. KG. Co-authors Dorthe Bach Toft and Nina Ostenfeldt are employed by H. Lundbeck A/S. Co-author Virgile Richard is employed by UCB Pharma S.A. There are no patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
A crucial part of the drug development pipeline is the assessment of the carcinogenic potential of compounds, which is currently performed on the basis of the 2-year rodent bioassay. However, as these assays are associated with high costs as well as long study times, and require a high number of animals (more than 800 mice and rats) [1], there is a strong interest to develop supplementary approaches which facilitate drawing hypotheses about the carcinogenic risk of a compound at an earlier stage of the development process. Reliable predictive models of hepatocarcinogenesis could then be employed to increase the efficiency of the screening process by prioritization of drug candidates in the preclinical phase [2].
In general, carcinogens (C) can be subdivided into two groups depending on their mechanism. Genotoxic carcinogens (GC) are characterized by reactivity with DNA or by the formation of DNA-reactive metabolites which may lead to tumor initiation. Nongenotoxic carcinogens (NGC) do not directly cause DNA modifications, but mediate tumor initiation or promotion by secondary mechanisms, which are not completely discovered so far. In recent years, a wide variety of mechanisms causing nongenotoxic carcinogenesis in liver were proposed, such as oxidative stress, chronic cell injury, immunosuppression, induction of peroxisome proliferation, modulation of specific cytochrome P450 enzymes, increased secretion of growth-stimulating hormones, or the perturbance of specific signaling pathways [3], [4]. Ultimately, these mechanisms cause increased induction of mitosis, which is typically accompanied by decreased susceptibility to cellular mechanisms triggering apoptosis in degenerated cells [3], [4].
For the detection of GCs a battery of three in vitro genotoxicity tests (Ames test, mouse lymphoma assay, in vitro micronucleus or chromosomal aberrations test) is routinely applied during the early phase of drug development [5]. However, these short-term bioassays are insufficient in terms of specificity for the detection of NGCs, which are characterized by more complex mechanisms and thus require the performance of long-term tests.
Compounds such as TAA and WY which show a negative Ames test but a positive mouse lymphoma test are controversially classified by different authors as nongenotoxic or genotoxic carcinogens (reviewed by Waters et al.) [1]. Thus, it was suggested to classify these compounds as Ames-negative genotoxic compounds [1]. Especially for such compounds, which cannot definitely be assigned to either GCs or NGCs, approaches providing additional mechanistic insights would be beneficial during cancer risk assessment.
In more recently developed toxicogenomics approaches molecular events preceding neoplasia are considered to allow for the prediction of carcinogenicity based on characteristic gene expression patterns emerging after short-term exposure. These approaches typically employ microarray technology in conjunction with machine learning techniques for the early prediction of drug-induced carcinogenesis based on characteristic gene expression profiles [6]–[8]. A microarray-based approach also allows for drawing hypotheses about the mechanisms of genotoxic (GC) and nongenotoxic carcinogens (NGC) [9], [10]. However, in contrast to animal studies, toxicogenomics-based examinations of cancer risk are typically focused to specific target organs, such as, most notably, the liver.
In a pioneering study Nie et al. employed cDNA microarrays to profile mRNA expression in rat livers treated with 24 NGCs and 28 non-carcinogens (NCs) [11]. Combining the outcomes of diverse statistical and heuristic feature selection methods (e.g., t-test, genetic algorithms, etc.), a pooled gene list was generated, from which a short-list of 6 signature genes was extracted using an exhaustive enumeration method. More recently, Uehara et al. published a rat liver signature comprising 112 genes which was inferred using prediction analysis for microarrays (PAM) [12]. The signature was shown to facilitate the highly sensitive detection of carcinogenesis after single exposure, owing to a relatively high false positive rate. Inspired by the work of Auerbach et al., who recommended longer exposure periods, another signature comprising 82 probesets was published by Uehara et al. [8]. Using this signature, a considerably lower fraction of false positives was observed at comparable sensitivity when performing Support Vector Machines-based NGC/NC classification of expression profiles resulting from 28 days of repeated dosing. As an aside, the informative probesets have also proven useful for the detection of GCs. For more detailed information about the designs of preceding studies, the profiled compounds and the estimated classification accuracies, the reader is referred to a comprehensive review by Waters et al. [1].
In contrast to the majority of recent toxicogenomics studies, which focused on elucidating NGC mechanisms in rat liver, Jonker et al. investigated the presence of characteristic, reproducible transcript expression patterns in diverse murine target organs [13]. The authors inferred organ-specific multi-gene biomarkers using supervised machine learning methods (e.g., Nearest Shrunken Centroids, Recursive Feature Elimination), which were also employed for the discrimination between GC, NGC and NC, either by solving one 3-class or two 2-class problems [13].
Here, we propose a new methodology, which employs ensemble feature selection in conjunction with bootstrapping for the extraction of mRNA signatures from transcriptomic profiles of GC-, NGC-, or NC-treated mice. Besides evaluating the average classification performance achieved by different algorithms for probeset selection, we also assess their robustness against small variations of the training data. When compared to recently published signatures for the prediction of hepatocarcinogenicity in mice, we found that our signatures are equally good or slightly better, depending on the employed classifier and evaluated prediction task.
In contrast to previous toxicogenomics studies, we profiled global gene expression in both male and female mice, which is also done on a routinely basis in chronic toxicity and carcinogenicity studies. As some of the general pathways and biological processes leading to cancer (e.g., cell proliferation) may be influenced by androgens and estrogens, we consider it relevant to include both sexes in the development of biomarkers for hepatocarcinogenesis. Ultimately, this study design facilitated capturing characteristic changes in gene expression, which are present in both sexes upon treatment with GCs and NGCs, respectively.
The animal experimental work under this study was subject to the Danish Executive Order No. 1306 of 23 November 2007. The protocol was reviewed by the veterinarians responsible for animal welfare in the testing facility and conducted under animal license number 2009.561.1593 issued by the Danish Animal Inspectorate. For necropsy the animals were anaesthetized in isoflurane and exsanguinated from the heart. All animals were checked for mortality/ill health twice daily and animals in extremis were sacrificed after consultation with the veterinarian in charge of animal health and welfare. All animal procedures described in the present paper have been approved by the ethical committee for animal experimentation of UCB Pharma S.A. and were in accordance with the most recent European and Belgian legislation on the use of laboratory animals (Directive 2010/63/EU, Belgian Royal Decree of 29 May 2013) as well as with the Guide for the Care and Use of Laboratory Animals (NRC, 2010).
Animal Study
In this study, we employed the Affymetrix GeneChip Mouse Genome 430 2.0 Array to monitor the changes in the gene expression pattern induced by diverse compounds in mouse liver samples. The generated microarray data were deposited at Gene Expression Omnibus (GEO accession: GSE44783). Treatment groups of 6 male and female CD-1® mice, respectively, were dosed by oral gavage with a heterogeneous set of compounds over a time period of 3 or 14 days. Time matched control groups were treated with the corresponding vehicles, carboxymethyl cellulose or corn oil. The compounds, their CAS numbers, vehicles, doses and the group ID are all listed in Table 1. Dose levels were chosen on the basis of literature data, toxicological databases (CPDB database: http://potency.berkeley.edu/) [14], or dose range-finding studies. For the non-carcinogenic compounds a high pharmacological dose was selected. 24 hours after the last dose, the mice were anaesthetized in isoflurane, exsanguinated from the heart and subjected to necropsy. The left lateral liver lobe was cut in cubes with about 4–5 mm side length, placed in Wheaton Cryovials, snap frozen in liquid nitrogen and kept at −80°C until extraction. A histopathological examination was conducted for all liver samples from this study (data not shown).
Table 1. Overview of compounds used for training and evaluation of classifiers.
Tissue Sampling and Microarray Hybridization
Samples from 5–6 animals per treatment group were subjected to mRNA expression analysis. The snap-frozen liver samples were homogenized with Qiazol (Qiagen, Hilden, Germany). The cRNA synthesis, target hybridization, probe array washing, staining and subsequent probe array scanning were done according to the standard protocol 3′IVT Express Kit User Manual (Affymetrix). Microarray analysis was performed by using GeneChip Mouse Genome 430 2.0 Arrays from Affymetrix.
Preprocessing of Microarray Data
After importing the Affymetrix raw data (i.e., CEL files) into the R programming language and environment for statistical computing, different metrics and statistics implemented in the package arrayQualityMetrics were used to assess the quality of the raw data [15]. As no major experimental problems could be detected, all arrays were included in further analysis steps. The raw data were then normalized using the RMA method and probesets were mapped to gene symbols and Entrez IDs using the appropriate metadata packages deposited at Bioconductor [16], [17].
Analysis Pipeline for Signature Inference and Compound Classification
One of the main research objectives of this study was the selection of mRNA signatures, i.e., sets of informative marker genes, which are consistently differentially expressed between compound classes, and thus can be utilized to reliably predict the type of carcinogenicity of a given compound. For this purpose, we implemented an analysis pipeline for the inference and evaluation of gene signatures from mRNA expression data. The central element of this pipeline is a two-stage method, encompassing statistical and supervised learning methods for marker gene selection (Stage 1) and expression profile-based compound classification (Stage 2).
Stage 1: Extraction of gene signatures.
In the first stage, we employed linear Support Vector Machines (SVM), SVM-based Recursive Feature Elimination (SVM-RFE), Prediction Analysis for Microarrays (PAM) and a signal-to-noise ratio (Golub-Ratio) proposed by Golub et al., which measures the correlation between gene expression states and compound class labels [18]–[21]. All probesets represented on the microarray were considered as candidate markers and no filtering was performed based on differential gene expression. The marker genes selected should fulfill two requirements. Firstly, the signature probesets should be reliable, i.e., facilitate the accurate discrimination of the compound classes. Secondly, the signature should be robust, i.e., be largely independent of the employed selection algorithm and the set of samples used for training.
For optimization of the classification performance, we used a grid search approach to tune the individual parameters of the machine learning methods. For this purpose, we employed a 3×3 nested cross-validation procedure, which ensures unbiased parameter tuning, while maintaining independent test sets. The robustness of the signature was increased by performing ensemble feature selection with m = 4 methods on n = 25 randomly drawn bootstraps, each containing 90% of the training samples. For each bootstrap the remaining 10% of the training samples (out-of-bag samples) were used to assess the classification performance, achieved with the selected marker genes, in terms of area under the Receiver Operating Characteristic (ROC) curve.
In order to determine the optimal number of informative genes, the ROC scores were computed for k = 10 different probeset numbers between 2 and 100. Subsequently, the optimal number of features corresponding to the analytical maximum area under the ROC was estimated by fitting splines (implementation from package splines for R).
Next, for each feature selection method the robustness of the selected signatures, i.e., the correspondence of the informative gene sets inferred on different bootstraps, was measured based on the Kuncheva stability index [22]. The Kuncheva index (KI) is a measure for the consistency between multiple subsets of features, typically extracted from different bootstraps or by using different methods. In contrast to other stability indices, the KI also accounts for common selection of features purely by chance. It monotonically increases with the size of the intersection of the two compared subsets and is defined on a scale between −1 and 1. The value −1 is returned for disjoint subsets, each containing 50% of the features. 0 is expected for independently drawn subsets and 1 for identical subsets. Given a feature set X with |X| = n and two feature subsets A, B with 0<|A| = |B| = k<n and |A ∩ B| = r the Kuncheva index is defined as [22]. KI can also be generalized to a score for measuring the stability of more than two selected subsets, simply by computing the mean of the KIs that have been computed for all pairs of subsets.
On the basis of the signatures inferred on different bootstraps using different methods, we generated consensus signatures by computing an average rank for each gene. For this purpose a consensus ranking was generated by computing the rank sums across all methods and bootstraps. Subsequently, the genes were sorted in ascending order by the rank sums.
Stage 2: Prediction of compound classes.
In the second stage, the signatures inferred on the training data were incorporated as predictive features for compound class prediction by diverse classifiers, namely SVM, k-Nearest Neighbor (KNN), PAM, Naïve Bayes, Random Forest, and Weighted Voting [18], [20], [21], [23]. To this end, a stratified and nested 3×3 cross-validation was performed. While the algorithm-specific parameters were tuned in the inner 3-fold cross-validation, the performance was assessed based on the area under the ROC curve in the outer 3-fold cross-validation. The fold-changes of the signature genes obtained from Stage 1 were standardized by computing z-scores and then incorporated as features. In order to ease the interpretability of the classification outcomes, all prediction scores were transformed to a scale ranging from 0 to 1. SVM outputs (ranging between approx. −10 and 10) were rescaled using the function . Weighted Voting scores (originally between −1 and 1) were transformed using as scaling function. In order to obtain confidence values for KNN, the fraction of positively labeled neighbors among all nearest neighbors was calculated. All other methods inherently produce prediction scores between 0 and 1. The employed classification methods were either implemented from scratch in R or imported from the existing R packages knnflex, pamr, klaR and randomForest [20], [23]. For SVM classifiers we used implementations from the SHOGUN toolbox [18].
Inference of Signatures for Early Prediction of Hepatocarcinogenesis in Mouse
Using an ensemble of statistical and machine learning-based feature selection techniques in conjunction with state-of-the-art methods for supervised classification, we constructed accurate and robust models for carcinogenic compound class prediction based on characteristic gene expression profiles. Our analysis workflow (Figure 1) was employed for the identification of multi-gene mRNA signatures for the discrimination of different compound classes (C: GC+NGC vs. NC, GC vs. NGC, GC vs. NC, and NGC vs. NC), which were profiled in CD-1 mice at two dosing times (3 days and 14 days) using an Affymetrix platform. The corresponding gene lists which were used to predict the carcinogenic outcome in both sexes are attached in Table S1.
Figure 1. Overview of analysis workflow used for signature extraction and class prediction.
The methodology used for the extraction of gene expression signatures is illustrated using our analysis of the CD-1 mouse dataset as example. This dataset contains mRNA expression data from male and female mice treated with GC, NGC, and NC. In the example a signature for the discrimination of C from NC is predicted using the analysis workflow shown above. (1) A stratified cross-validation is performed on the whole dataset. In each cross-validation fold the dataset is split into a training set and a test set. (2) n = 25 bootstraps, each containing 90% of the training samples, are randomly chosen from the training data. (3) A signature, i.e., a subset of informative genes for compound class discrimination is selected on each bootstrap by using m = 4 different methods. (4) The mean classification performance achieved by each gene selection method is computed on the 10% out-of-bag samples, which are left out for each bootstrap. The stability of the consensus signature is assessed by computing the Kuncheva index from the list of signatures extracted from different bootstraps. (5) The n×m signatures selected by m methods on n bootstraps are merged, based on a joint ranking computed from the sums of the ranks calculated for individual signatures. (6) Various machine learning algorithms are employed to predict the carcinogenic compound class, based on the inferred consensus signature. The performance is evaluated on the independent test set and assessed in terms of area under the ROC curve.
The ROC scores plotted for varying signature sizes in Figure 2A demonstrate that all tested feature selection methods are well suited to produce signatures for the early discrimination of C from NC, which are both accurate (ROC scores >0.81) and stable (max. KI ≈ 0.7), when compared to a random guesser (ROC = 0.5 and KI = 0). In comparison to the 3-day signature (Figure 2A), a slightly higher mean classification accuracy (ROC scores >0.84) and comparable stability was achieved by using the 14-day signatures (Figure 2B). This gain on performance is even more striking when comparing 3-day signatures and 14-day signatures for GC vs. NGC classification (Figure 2E–F). In summary, the performance of the individual methods heavily depends on the classification task, but only the SVM and SVM-RFE method consistently achieved classification accuracies >0.8 on all four datasets (Figure 2A,B,E,F). Furthermore, the two SVM-based methods were found to provide by far the most robust signatures, when assessing the correspondence of informative gene lists selected on different bootstraps, based on the stability index proposed by Kuncheva et al. (Figure 2C,D,G,H) [22].
Figure 2. Accuracy and stability of signatures for compound classification.
(A) The line plots depict the mean performance of C vs. NC classification after 3 days of repeated dosing, which was achieved based on gene sets extracted with different feature selection methods. Each curve corresponds to a feature selection method and the performance was assessed depending on the number of genes selected as informative features. The prediction accuracy was assessed on the samples left out from 25 random subsamplings of the dataset (bootstraps), each containing 90% of the data, and measured in terms of area under the ROC curve. The inset bar plot depicts the ROC scores averaged across bootstraps and signature sizes. (B) Performance of C vs. NC classification after 14 days of treatment illustrated as in (A). (C) The correspondence of the extracted C vs. NC gene sets across 25 bootstraps was assessed based on the Kuncheva stability index (KI) for each of the 4 employed feature selection methods. The KI was then for each method plotted against the number of selected signature genes. (D) Robustness of signatures for C vs. NC classification after 14 days of treatment illustrated as in (C). (E, F) Prediction accuracy achieved with signatures for GC vs. NGC classification after (E) 3 days and (F) 14 days of repeated dosing, respectively, depicted as in (A). (G, H) Similar illustration as in (C) showing robustness of signatures for GC vs. NGC classification after (G) 3 days and (H) 14 days of administration, respectively.
The corresponding ROC and KI plots which resulted from the evaluation of signatures for GC vs. NC and NGC vs. NC classification, respectively, are shown in Figure S1.
On the basis of the signatures inferred from multiple bootstraps using different methods, we generated a consensus signature for each classification task as described in more detail in the methods section (Figure 1). For each classification task we trained six different classifiers (SVM, KNN, PAM, Random Forest, Weighted Voting, and Naïve Bayes) and evaluated their classification accuracy based on cross-validation. In order to ease the interpretation of the classification results, the outputs were transformed into confidence scores between 0 and 1, which serve as an estimate of the probability of the positive class. These confidence scores are illustrated as heatmaps in Figure 3. For most classifiers a high agreement was found between the prediction scores and the true class labels. This observation is consistent with the outcomes of ROC evaluation, where the average AUC ranged between 0.92 and 1.0 for C vs. NC classification and between 0.83 and 1.0 for GC vs. NGC classification. From Figure 3 it becomes apparent that almost perfect compound classification could have been achieved by a majority vote across all methods. Only for 1,4-dichlorobenzene (DCB) a reliable classification as either GC or NGC was not possible after 3 days of repeated dosing. However, after 14 days the compound was correctly classified as NGC by the majority of classifiers. The heatmaps showing the correspondence between the predicted and true classes obtained for GC vs. NC and NGC vs. NC classification are depicted in Figure S2.
Figure 3. Classification results obtained for different signatures.
(A) The four heatmaps show the predictions resulting from diverse binary classifiers for the discrimination of C from NC after 3 days (left two heatmaps) or 14 days (right two heatmaps) of repeated dosing. For each dosing time two heatmaps are depicted which correspond to male and female mice, respectively. The rows correspond to different classifiers and the columns to different treatment groups. The continuous prediction scores, returned from the classifiers, were transformed to confidence scores between 0 and 1, which provide an estimate of the probability of class C. The colorbar on top shows the true class annotation. The black vertical lines separate the test samples from the three folds of cross-validation. (B) Heatmaps illustrating the classification outcome of diverse predictors to distinguish GC from NGC based on characteristic gene expression profiles observed in male and female mouse liver samples after 3 or 14 days of administration. KNN, K-Nearest Neighbor; SVM, Support Vector Machine; PAM, Prediction Analysis for Microarrays.
We also evaluated the performance of two variants of our methodology for signature extraction. The first one exclusively adopts the two feature selection methods SVM and SVM-RFE, as for the corresponding signatures a consistently high performance and increased robustness across bootstraps was observed (Figure 2). Since a good separation of the compound classes could also be achieved by Principal Component Analysis (PCA), we employed PCA and the algorithmically related Partial Least Squares Discriminant Analysis (PLS-DA) for feature selection in a second approach. The gene rankings were derived from the absolute values of the loadings of the first principal and PLS component, respectively. Consistent with our expectations the SVM- and SVM-RFE-based signatures performed particularly well in conjunction with SVM classifiers in all four evaluated settings, i.e., C vs. NC and GC vs. NGC classification after 3 or 14 days (Figure S3). For the informative gene sets derived from PCA and PLS-DA slightly weaker performance was observed for C vs. NC classification, while GCs could be discriminated from NGCs with comparable accuracy. A comparison of the informative gene sets obtained from the different approaches showed that many genes were selected in common, which was especially the case for mRNA signatures used to discriminate GCs from NGCs (Figure S3). The mRNA signatures, which were compiled based on the two variants of our methodology, are available from Table S2 and S3.
Classification of Compounds with Undefined Class Assignment
The classification of the test compounds TAA, WY and CPA with respect to their mechanism of rat liver cancer induction are controversially discussed [1]. In this study, these compounds were regarded as undefined chemicals, due to ambiguous results in genotoxicity testing. Consequently, these compounds were not used in the training set. Aiming at a classification of these compounds based on their gene expression profiles, we adopted different supervised classification methods, incorporating the informative genes from the analysis of the other compounds as features (see Figure 4D–E). Furthermore, we performed principal component analyses in order to visually inspect if the undefined compounds cluster in the vicinity of confidently annotated GCs or NGCs. For this purpose, each treatment group was represented by a vector containing the fold-changes of a certain multi-gene signature and then transformed to a two-dimensional space spanned by the two principal components explaining the highest fraction of the variance in the data.
Figure 4. Reclassification of the compounds CPA, TAA, and WY.
(A) Points indicate treatment groups which were originally represented by a vector containing the fold-changes of all signature genes and then transformed to its two principal components. Here, the informative genes for discriminating GC vs. NGC after 3 days of repeated dosing were used. Groups of male animals are drawn as squares and female ones as circles. The fill color of the points indicates the compound class. Polygons indicate the convex hulls of clusters corresponding to either male or female mice treated with a certain class of compounds. (B) Similar plot as in (A), but the multi-gene signature for GC vs. NGC classification after 14 days was used here. (C) The heatmap provides a graphical representation of the fold-changes of 15 selected signature genes from the 14-day signature for GC vs. NGC classification. Rows correspond to genes and columns to treatment groups. Upregulated genes are colored in red and downregulated ones in green. The colorbar on top indicates the corresponding compound classes. (D) Heatmaps showing confidence of predictions made by diverse C vs. NC classifiers for male (M) and female (F) mice treated with CPA, TAA and WY for 3 days (left heatmap) or 14 days (right heatmap). (E) Similar illustration as in (D) showing prediction outcomes of GC vs. NGC classifiers.
It becomes obvious from the PCA plot of the 3-day GC vs. NGC signature that CPA clusters near the NGC compounds, whereas the points corresponding to TAA and WY are located in the vicinity of the GC cluster (Figure 4A). Strikingly, a clear separation of the compounds can be observed for both treatment durations and both sexes in the PCA plot of the 3-day as well as of the 14-day signature (Figure 4A–B). The differences in the gene expression patterns resulting from CPA vs. TAA or WY treatment also become apparent from the heatmap plot shown in Figure 4C. These findings are also consistent with the classification results obtained from diverse supervised learning algorithms (Figure 4D–E). Specifically, we found that the carcinogenic potential of the three "undefined" compounds was recognized by all six classifiers and except for Naïve Bayes, all methods classified TAA and WY as a GC and CPA as an NGC.
Furthermore, it is remarkable that the treatment-specific differences in the gene expression patterns observed between GC and NGC are considerably higher than the sex-specific differences observed between male and female mice (Figure 4A–C). This finding indicates that the selected informative genes are more strongly related to different mechanisms of hepatocarcinogenesis than to sex-specific physiological differences.
Comparison with Signatures from Related Studies
In order to compare our signatures to those reported from previous studies, we assessed the prediction accuracy of diverse classifiers, incorporating either our novel signatures or known ones, based on ROC evaluation. To this end, we extracted three signatures for the early detection of hepatocarcinogenicity in mice from the literature (Table S4). Two of them were reported by Jonker et al., who inferred one signature for separating C and NC and one for discriminating GC from NGC using a two-step strategy similar to our approach [13]. Another hepatocarcinogen-specific signature was more recently reported by Park et al., who employed the Ingenuity Pathway Analysis software and demonstrated by hierarchical clustering analysis that their informative genes differ between GC and NGC [24].
The results shown in Figure 5 indicate that all signatures achieve a considerably higher performance than a random guessing approach. However, only for our signatures, we found that the error-free prediction of compound classes (ROC score = 1) can be achieved in all classification tasks if either Random Forest or Weighted Voting is used. Except for the GC vs. NGC classification after 3 days of exposure, which appears to be the most challenging classification task, an error-free classification outcome could also be obtained from the SVM or PAM approach. Independent of the classification task a comparatively low average accuracy was observed for the Naïve Bayes method. In summary, the evaluation results suggest that our proposed mRNA signatures provide a valuable alternative to previously published gene sets, as in all four different settings equal or higher classification performance could be achieved (Figure 5A–D).
Figure 5. Performance comparison with signatures known from the literature.
(A) The grouped bar plots depict the area under the ROC curves obtained for novel and known signatures for the separation of C and NC after 3 days of treatment. For this purpose, the performance of diverse classifiers was evaluated by a 3-fold cross-validation. Each bar corresponds to a certain classifier (see legend) and each group of bars refers to a certain signature. The horizontal dashed line indicates the performance that would have been achieved by random guessing. The adjacent Venn diagrams illustrate informative genes common between signatures. (B) Same plots as in (A), but for C vs. NC classification after 14 days of repeated dosing. (C) Mean ROC scores and signature overlaps for GC vs. NGC classification after 3 days of treatment. (D) Same plots as in (C), but for GC vs. NGC classification after 14 days of repeated dosing.
Comparing our sets of informative genes to previously compiled sets of potential marker genes, we found small overlaps between our signatures and those proposed by Park et al. (see Venn diagrams in Figure 5). The two signatures selected by Jonker et al. were disjoint with both our gene sets and the one by Park et al. [24]. The complementarity of the signatures proposed by Jonker et al. can in part be explained by the fact that the authors used a custom oligonucleotide array limited to 8205 probes, while a standard Affymetrix platform was used in both our study and the one conducted by Park et al. [24].
In this study we conceived, implemented and applied a novel ensemble feature selection approach for the extraction of robust multi-gene signatures from mRNA transcript expression data, which facilitates reliable compound classification for hepatocarcinogens. We analyzed an Affymetrix dataset obtained from male and female CD-1 mouse liver samples taken after treatment of mice for 3 or 14 days with diverse compounds (3 GC, 3 NGC and 4 NC).
In Figure 4C our most informative genes showing characteristic profiles upon treatment with genotoxic or nongenotoxic compounds are depicted. Within the European IMI MARCAR project (www.imi-marcar.eu), we are currently assessing the mechanistic relevance of such genes, which are specifically deregulated upon treatment with GCs and NGCs in order to gain a more profound understanding of the induced cancer types. Among the probesets which were specifically deregulated upon treatment with GCs and showed either inverse or unaltered patterns for NGCs were, for instance, Vim, Armcx4, Raet1e, Marco, SerpinA7, Egr1 and Gpr98. In response to nongenotoxic carcinogenic exposure an upregulation of Dio1, Ces2a, Akr1b7, S100a8, Orm3, Cyp2b10 and Cyp2c55 was measured (Figure 4C).
Among others, the aforementioned probesets were used for the toxicogenomics-based examination of the carcinogenic class of the compounds TAA, WY and CPA. We considered these three substances as "undefined", as it cannot be reliably determined, based on mutagenicity testing results, whether rodent liver tumors are induced by these compounds via a genotoxic or nongenotoxic mechanism. Detailed literature search concerning mutagenic effects of TAA revealed ambiguous results. While no genotoxicity was observed in the Ames test, commonly used genotoxicity tests such as the mouse lymphoma assay and the in vivo micronucleus test show both positive as well as negative results [25]–[27]. Another example for such an undefined compound is WY, which is in general classified as a peroxisome proliferator belonging to nongenotoxic carcinogens, whereas a single cell gel electrophoresis assay indicated induction of DNA damage in WY treated cells [28]–[30]. A third compound we classified as an undefined agent is CPA, which was negative in the Ames test [31], too, but showed positive results concerning mutagenicity in an in vivo liver micronucleus test in female rats [32].
A central aim of the here proposed ensemble feature selection method was the identification of class-specific mRNA signatures which allow for a proper compound classification. The informative probesets were then used to classify the three "undefined" substances based on their gene expression patterns. Concerning the signature genes, the expression patterns of TAA and WY resembled those of GCs, whereas the pattern observed for CPA was similar to that of NGCs (Figure 4E). Consistent with these findings, it was also suggested by Waters et al. to classify TAA and WY as Ames test negative genotoxic compounds [1].
In contrast to previous approaches, we employed ensemble feature selection which is uncommon in toxicogenomics, but has already been successfully used for other applications, such as cancer diagnostics and biomolecular text mining [33], [34]. We chose this emerging method to increase the stability and generalizability of the extracted mRNA signatures. Ideally, the extracted signatures should be robust against changes in the training data and independent of the applied feature selection method. Thus, we propose to generate a consensus signature derived from multiple bootstraps and an ensemble of algorithms for the selection of informative genes. To this end, we employed statistical filter methods as well as supervised wrapper methods for feature selection. While the latter ones involve the use of machine learning-based classifiers to assess the prediction accuracy for a selected subset of features, the former ones assess the relevance of features based on intrinsic properties of the data [35]. Especially wrapper methods which involve learning an abstract model from a set of training compounds may suffer from overfitting as the number of features (i.e., all probesets represented on the microarray) is typically much larger than the number of profiled compounds in toxicogenomics datasets. However, in this study all constructed models were found to be well generalizable to the unknown test compounds which is supported by a high performance in cross-validation.
Evaluating our predictive multi-gene biomarkers against signatures known from the literature based on cross-validation, we found that our mRNA signatures facilitate compound classification with comparable or higher accuracy depending on the classification task (Figure 5). Arguably, the literature-based signatures were not perfectly suited for all evaluated settings, as different mouse strains, treatment durations, compounds, and doses were used. Furthermore, while all published informative gene sets were derived from male mice exclusively, animals of both sexes were considered in our study for signature inference and evaluation. However, despite the different study designs, the performance comparison showed that the previously proposed signatures are fairly well generalizable.
In this study we demonstrated that despite of the additional variation in gene expression profiles caused by the inclusion of both sexes, we were able to extract robust and predictive gene sets which show characteristic patterns indicative of carcinogenesis irrespective of the sex of the sample material. We consider this as an advantage over currently available mRNA signatures for rodent hepatocarcinogenesis, which were derived solely from male animals, and hence may have an increased risk to fail in predicting female-specific carcinogens.
The choice of the right dose is crucial for the selection of signature genes which are specifically deregulated in response to the application of carcinogenic substances. Overdosing compounds may mask the specific changes in gene expression which are related to cancer initiation or progression, due to a huge amount of deregulated genes orchestrating an unspecific stress response [13]. On the other hand, low doses may be not tumorigenic, and hence not cause differential expression of potential biomarker genes. Jonker et al. proposed to use the maximum tolerated dose (MTD) for dosing, which can be determined in a subchronic study [13]. As for the compounds analyzed here chronic mouse studies have already been performed, we considered the cancerogenic dose from published animal long-term studies and a multiple of the TD50 rate, which induced tumors in half of the tested animals.
In a recent study, Auerbach et al. reported that the treatment time is also a critical factor, which may highly impact the success of a toxicogenomics approach [36]. The authors observed the lowest error and a considerable increase of SVM margin scores for their 90-day data, which points to a higher prediction confidence for long treatment durations [36]. Uehara et al. also reported that a higher accuracy of the prediction models could be achieved for longer exposure durations [8], [36]. Consistent with these findings our results indicate that after 14 days of repeated dosing a more reliable discrimination of GCs from NGCs is possible than after 3 days (Figure 3B). This applies in particular to the compound DCB, which was not correctly classified by the majority of classifiers after 3 days. The misclassification of DCB may be in part explained by deviations in its expression profile from the ones observed for other NGCs (Figure 4C). However, characteristic changes in gene expression, facilitating accurate detection of carcinogens in general could already be extracted after 3 days of treatment (Figure 3A). As the time of emergence of characteristic profiles may vary in a compound-specific manner, Ellinger-Ziegelbauer et al. proposed to represent each compound by expression data from multiple time points, at which a sufficient number of deregulated genes could be observed [6]. Nevertheless, we chose to focus on single time points, as the premature determination of the optimal time points may not be possible in the real use case, which is the classification of completely unknown compounds. Furthermore, as early cellular defense response patterns (e.g., enzyme induction) are expected to become apparent after 3 days of carcinogenic treatment, whereas adaptive changes in gene expression may be present in response to oxidative damage, cell death, persistent toxicity or inflammation after 14 days, we did not combine data from the two time points.
When assessing prediction accuracy depending on the signature size, we found that predictions with tolerable reliability are possible, based on the expression status of ten informative genes (Figure 2A–B, E-F). This finding is also supported by recent work from Uehara et al., who published a signature consisting of 9 Affymetrix probesets (7 different genes), which was shown to discriminate NGC from NC in rat liver with reasonable accuracy [8].
Pathway enrichment analysis of the 66 genes from the 14-day C vs. NC signature (Table S1) against the KEGG database resulted in significant enrichments in the PPAR signaling pathway (p = 0.039) and the TGF-beta signaling pathway (p = 4.27E-4), which are both involved in known mechanisms of hepatocarcinogenesis [37], [38]. For both GC vs. NGC signatures, we observed a significant overrepresentation among genes related to metabolism of drugs and xenobiotics by cytochrome P450 (p<0.05).
In order to identify informative genes which are relevant for several classification tasks (C vs. NC, GC vs. NGC, GC vs. NC, NGC vs. NC) and show class-specific expression patterns at multiple time points (3 days, 14 days), we determined for each gene in how many of the 8 evaluated settings it was selected as an informative gene (Figure S4). The most frequently selected probesets present in more than 50% of the predicted signatures were Cyp2c50, Cyp2c55, Cyp2b10, S100a8, Gsta1, and Akr1b7 (Figure S5). However, our results indicate that a more reliable classification can be achieved, if additional informative genes are taken into account.
As the susceptibility to carcinogenic compounds as well as the triggered changes in gene expression may vary between mouse strains, future analysis should be conducted in order to validate to what extent the inferred gene expression signatures can be reproduced in other strains. Especially, the applicability of our proposed signatures to transgenic mouse strains should be assessed in future studies, as for instance Xpa/p53+/− knockout mice were shown to improve the prediction accuracy of bioassays while at the same time reducing the required treatment duration [39]. Furthermore, we are currently assessing the correspondence between signatures inferred for mice and rat, respectively, in a closely related toxicogenomics study.
Recent toxicogenomics studies are mostly focused on profiling the RNA expression of rodents after single or repeated exposure to carcinogens [1]. Besides profiling the transcriptome, future studies should also investigate compound-induced changes in the DNA methylome, proteome or phosphoproteome, which could reveal novel biomarker candidates facilitating reliable classification of compounds with respect to their carcinogenic potential and mechanism.
As a wide variety of mechanisms was proposed for NGC, it is hardly feasible to extract a highly specific and at the same time universal signature capturing the gene expression changes caused by NGC treatment [3], [4]. However, public toxicogenomics databases (e.g., TG-GATEs and Drug Matrix), covering a rich set of compounds and multiple representatives of each NGC subclass, may facilitate the inference of mechanism-specific signatures which would firstly allow for a more fine-grained classification and secondly provide in-depth insights into individual mechanisms [40].
In conclusion, the here proposed methodology was shown to facilitate the extraction of robust gene expression signatures which were incorporated into generalizable models for the prediction of the carcinogenic class of compounds. These prediction models may accelerate the screening for promising candidates, and hence increase the efficiency and cost-effectiveness of preclinical drug development.
Accuracy and stability of signatures for compound classification. (A) The line plots depict the mean performance of GC vs. NC classification after 3 days of repeated dosing, which was achieved based on gene sets extracted with different feature selection methods. Each curve corresponds to a feature selection method and the performance was assessed depending on the number of genes selected as informative features. The prediction accuracy was assessed on the samples left out from 25 random subsamplings of the dataset (bootstraps), each containing 90% of the data, and measured in terms of area under the ROC curve. The inset bar plot depicts the ROC scores averaged across bootstraps and signature sizes. (B) Performance of GC vs. NC classification after 14 days of treatment illustrated as in (A). (C) The correspondence of the extracted GC vs. NC gene sets across 25 bootstraps was assessed based on the Kuncheva stability index (KI) for each of the 4 employed feature selection methods. The KI was then for each method plotted against the number of selected signature genes. (D) Robustness of signatures for GC vs. NC classification after 14 days of treatment illustrated as in (C). (E, F) Prediction accuracy achieved with signatures for NGC vs. NC classification after (E) 3 days and (F) 14 days of repeated dosing, respectively, depicted as in (A). (G, H) Similar illustration as in (C) showing robustness of signatures for NGC vs. NC classification after (G) 3 days and (H) 14 days of administration, respectively.
Classification results obtained for different signatures. (A) The four heatmaps show the predictions resulting from diverse binary classifiers for the discrimination of GC from NC after 3 days (left two heatmaps) or 14 days (right two heatmaps) of repeated dosing. For each dosing time two heatmaps are depicted which correspond to male and female mice, respectively. The rows correspond to different classifiers and the columns to different treatment groups. The continuous prediction scores, returned from the classifiers, were transformed to confidence scores between 0 and 1, which provide an estimate of the probability of class GC. The colorbar on top shows the true class annotation. The black vertical lines separate the test samples from the three folds of cross-validation. (B) Heatmaps illustrating the classification outcome of diverse predictors to distinguish NGC from NC based on characteristic gene expression profiles observed in male and female mouse liver samples after 3 or 14 days of administration. KNN, K-Nearest Neighbor; SVM, Support Vector Machine; PAM, Prediction Analysis for Microarrays.
Classification results obtained for different ensembles of feature selection methods. (A) The grouped bar plots depict the area under the ROC curves achieved with signatures which were inferred based on three different ensembles of feature selection methods and applied for the separation of C and NC after 3 days of treatment. The first ensemble contains the four methods SVM, SVM-RFE, Golub-Ratio and PAM, the second one is limited to SVM and SVM-RFE and the third one comprises PCA and PLS-DA. The performance of the signatures was determined based on the prediction outcomes obtained from six different classifiers which were evaluated in a 3-fold cross-validation. In the plot depicted above each bar corresponds to a certain classifier (see legend) and each group of bars refers to a certain ensemble of feature selection methods. The horizontal dashed line indicates the performance that would have been achieved by random guessing. The adjacent Venn diagrams illustrate informative genes common between signatures. (B) Same plots as in (A), but for C vs. NC classification after 14 days of repeated dosing. (C) Mean ROC scores and signature overlaps for GC vs. NGC classification after 3 days of treatment. (D) Same plots as in (C), but for GC vs. NGC classification after 14 days of repeated dosing.
Selection frequency of signature genes. The figure depicts a histogram showing the number of genes selected twice or more times as informative genes for compound class prediction. For the generation of this figure, we considered 8 predicted signatures for solving 4 binary classification tasks (C vs. NC, GC vs. NGC, GC vs. NC, NGC vs. NC) after 3 days and 14 days of repeated dosing, respectively. The genes selected 4 and 5 times, respectively, are listed next to the histogram.
Expression profiles of most frequently selected signature genes. The heatmap shows the expression profiles of all candidate biomarker genes which are contained in at least 3 of the 8 predicted signatures. These signatures were proposed for 4 different classification tasks (C vs. NC, GC vs. NGC, GC vs. NC, NGC vs. NC). For each classification task two treatment durations (3 days and 14 days) were considered, independently.
mRNA signatures extracted with SVM, SVM-RFE, PAM and Golub-Ratio. The Excel sheets contain the Affymetrix IDs of the probesets selected by the following ensemble of feature selection methods: Support Vector Machines (SVM), SVM-based Recursive Feature Elimination (SVM-RFE), Prediction Analysis for Microarrays (PAM) and the statistical ratio proposed by Golub et al. (Golub-Ratio). If possible the corresponding gene symbol, EntrezGene ID and description are provided for each probeset.
(XLS)
mRNA signatures extracted with PCA and PLS-DA. The Excel sheets contain the Affymetrix IDs of the probesets selected by the following ensemble of feature selection methods: Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA). If possible the corresponding gene symbol, EntrezGene ID and description are provided for each probeset.
mRNA signatures extracted with SVM and SVM-RFE. The Excel sheets contain the Affymetrix IDs of the probesets selected by the following ensemble of feature selection methods: Support Vector Machines (SVM) and SVM-based Recursive Feature Elimination (SVM-RFE). If possible the corresponding gene symbol, EntrezGene ID and description are provided for each probeset.
Published mRNA signatures for the detection of hepatocarcinogenicity in mice. The Excel sheets contain the Affymetrix IDs of published signatures for the toxicogenomics-based detection of carcinogens in mouse liver. If possible the corresponding gene symbol, EntrezGene ID and description are provided for each probeset. For signatures which were not derived from an Affymetrix platform, the original probeset IDs are listed in an additional column.
We would like to thank Heidrun Ellinger-Ziegelbauer, Michael Schwarz and Jonathan Moggs for critically reading the manuscript and for helpful suggestions. Furthermore, we are grateful to all other MARCAR consortium members who supported this work.
Conceived and designed the experiments: NK AK DBT NO VR. Performed the experiments: NK AK. Analyzed the data: JE CW AZ. Contributed reagents/materials/analysis tools: DBT NO VR. Wrote the paper: JE NK AK.
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Support vector machines
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Pumpkin Crunch Cake | Buen provecho...2018!
Grease bottom of 9X13 pan.
Combine pumpkin, evaporated milk, eggs, sugar, pumpkin pie spice and salt in bowl then pour it into your pan.
Sprinkle your dry yellow cake mix evenly over pumpkin mixture.
Sprinkle chopped pecans and walnuts over the cake mix.
Drizzle melted butter evenly over everything.
Bake your pumpkin crunch cake for 55 minutes or until top is turning golden brown. Cool completely, cut and serve with whipped topping or French vanilla ice cream. Refrigerate leftovers.
Posted on November 10, 2016, in Brunch, Cakes, KISS - Keep It Simple Stupid, Pies, Pinterest and tagged butter, Carnation Evaporated milk, chopped pecans, chopped walnuts, eggs, pumpkin, pumpkin pie spice, sugar, whipped topping, yellow cake mix. Bookmark the permalink.	Leave a comment.


Real estate websites in Australia are SO boring at the moment. The economic downturn has curtailed my stalking. Thank God for those Swedes. This week it's something definitely Scandi but low on the boho. More grown up and elegant but still gorgeous! We all know it's in the styling but doesn't it help if you start with fabulous bones! The architecture is stunning. Sadly I could fit my entire apartment in the salon areas alone. Dream on. Maybe I could cheer myself up with some Josef Frank fabric. The apartment is sold but you'll find it here.
love love love the fabric used to make the kitchen blind.
also love the plate rack too!!
aside from the artwork (gorgeous pops of color), the kitchen is my favorite. love that clean white starkness!
The wallpaper in the child's room looks pretty cute. I have a soft spot for single beds anyway. And dolphins!!!
So elegant so me. I love the Scandinavian look but find Bohemian just a bit too messy. Give me "grown up" everyday. The mix of Gustavian and Midcentury classics is perfect.
Hey Jo – do you know if a home has ever sold as a result of your "stalking?" Seems to me you deserve a commission. I love these posts.
Please let me know where I can find a plate rack like the one in the kitchen in the USA.


Universal has revealed more details about the film adaptation of Andrew Lloyd Webber's musical Cats following the broadcast of a behind-the-scenes featurette at ComicCon.
Following the news that stars including Taylor Swift, Idris Elba, Dame Judi Dench, Sir Ian McKellen and Jennifer Hudson were on board for the film, it has now been confirmed that they will be transformed into actual cats.
To make them look even more realistic, digital fur technology will be used "to create the most perfect covering of fur".
The news may come as a surprise as the stage version simply featured cast members dressed up as cats.
Another revelation that was made at ComicCon featured the unusual size of the set.
According to reporters in attendance at the event, the set was three to four times larger than a standard set in order to allow the cast to appear cat-sized.
Meanwhile, Dench and Elba recently revealed that they took their new roles very seriously.
"We spent a very very long time preparing how to be a cat," Elba commented.
The big screen adaptation will also feature the musical's iconic songs including Memory, which was performed by Hudson when she appeared on stage.


People would rather spend more of their salary on additional holiday entitlement and private healthcare benefits for themselves rather than contributing towards costs for care of children or elderly relatives, a new survey shows.
The research, carried out by employee benefits provider The Total Remuneration Package Company, found that when asked what benefits they though important, 77 per cent of respondents mentioned private dental cover, 73 per cent mentioned variable holiday entitlement, 68 per cent mentioned private medical cover and 62 per cent mentioned life cover.
Only 31 per cent saw paying for childcare vouchers as important and only 16 per cent valued contributions to the cost of care for elderly relatives.


As my recovery from a fall on an icy sidewalk drags on, I've learned a few things. At the risk of being too clever by half, let's call them the Five A's.
Alone. As much as some of us (okay, it's me) value time spent alone, it is absolutely impossible to get through this life alone. For starters, there are some real safety issues. Having a health emergency without the ability to summon help from an ambulance or anyone else can be deadly. Moving through the house and the world, doing things one is physically unable to do, and having a sounding board for questions and concerns is best done with a cadre of others.
Accessibility. The issue of access to health care becomes real personal real fast when it's you or your loved one needing help. All of us understand (and accept) the concept of triage in an emergency department because it's a golden rule kind of thing. I'd expect to be treated first if my need was greatest, so it's okay if you're treated first when your need is greatest. The other part of accessibility is built structure. Our house would not accommodate a wheelchair. Even using a walker wasn't exactly a picnic. Toilet and countertop heights are all wrong.
Awareness. Am I alone in having sensitivities to drugs? I hate taking meds. But long ago I learned no medals are awarded for trying to power through without medication, that you can't let the pain get away from you, and that pain management is part of the recovery process. As headache and nausea rivaled the pain and discomfort from the actual injury I got scared. Paying attention to what our body tells us requires awareness. And a willingness to trust ourselves despite what experts may say.
Accomplished. When I decided I couldn't stand the nausea and headache another moment, I got Roy to take me back to the hospital. This time, through sheer dumb luck, I got a remarkable doctor. Her thoroughness astounded me. A sad commentary when a doctor being thorough is noteworthy. This doctor asked probing and follow up questions; listened; actually examined the injured leg; ordered an ECG, blood work, and urinalysis; got me on an IV to relieve symptoms; offered advice and reassurance that I would get better. That is an accomplished professional.
Attitude. Through this whole bloody mess, I've had to monitor my thinking. That meant accepting missing a planned writer's retreat, asking for and accepting help, managing pain, questioning treatment, and believing I will get through this. It's meant breathing and calming myself during what could have become panic attacks. It's meant making the best of this enforced inactivity by reading as much as my headaches allowed.
Because no one can escape challenge or misfortune forever, it's wise to spend some time formulating a plan. Keep caring people in your life, minimize risks for falls or injuries in and around your environment, don't disregard what your gut tells you, seek out accomplished experts, and think straight. It improves your odds, from where I sit.


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Camera Corps Q-Ball Achieves Iconic Status During a Great Summer of Sport
Camera Corps (a Vitec Group company) reports a highly successful summer of sport both in its role as a broadcast production service-provider and as manufacturer of robotic camera systems.
"One of the high points of our year to date was seeing a number of Camera Corps Q-Balls achieving global publicity when the winners of a major motor racing event celebrated their UK-leg triumph in June," states Shaun Glanville, Business Development Director. "The Q-Balls were so close to the action that they were showered with champagne on the winners' podium. Images of this event, and of two Q-Balls looking no bigger than a microphone, were broadcast live around the world and published throughout the international press. Earlier in June, Q-Ball captured close-up high definition images of Andy Murray at the climax of his success in the 2013 Wimbledon Lawn Tennis Championships. This was a moment the nation had been waiting for since Fred Perry's win in 1936, in particular the moment Murray went to hug his mother."
"We have provided cameras and support services to Wimbledon for over 10 years," adds Neil Ashworth, Equipment Manager. "These have been used in a variety of roles, giving television directors the opportunity to capture close-up images of the games in progress and of the commentators, as well as cutaways to long-range 'beauty' shots from cameras mounted high overhead. Our recently-introduced single-mode optical and Simply SMPTE dual-mode electro-optical camera links were used at Wimbledon wherever we needed to deliver live high-definition video over long distances."
During April, May and June, Camera Corps provided a wide range of support equipment for a fast-action sponsored television games series shot in Europe and South America. "The entire Camera Corp team were great," comments David O'Carroll, Technical Projects Manager, NEP Visions. "From our earliest conversations they provided the experience, flexibility and support necessary to help us achieve the best possible coverage for ESPN."
"We were active at a total of three X Games extreme-sports events in Brazil, Germany and Spain," adds Camera Corps' Senior Technician David Sisson. "Four Camera Corps technicians attended each event, working with David O'Carroll and his colleagues to capture dynamic close-up images of the action as well as long-range wide-angle views. Equipment used at each event included Camera Corps Q-Ball remotely controlled pan/tilt/zoom/focus cameras, Camera Corps VHR pan/tilt heads, point-of-view cameras and long-range optical fibre links. The Q-Ball heads were operated as com-cams, providing images of the presenters as they described the action. All the cameras were controlled using Camera Corps PTZF joysticks which provide the tactile feedback essential when covering fast-moving live content. Everything worked well at all three venues, including outdoor cameras operating under protective rain-covers. The resultant footage showed some of the most exciting events ever captured during outside broadcast productions and really was nothing short of extreme."
A Vitec Group company, Camera Corps specialises in providing high-volume support services for reality television programmes as well as very large-scale sports and stage events worldwide. Based on the site of the former Brooklands motor-racing circuit near London's Heathrow airport, Camera Corps has achieved a pre-eminent reputation for its ability to acquire exciting and unusual television content from exceptionally challenging environments.
The Camera Corps equipment fleet encompasses HotShots, MiniShots and array of remote specialist cameras and lenses, remote underwater systems, cranes, platforms and dollies, Equipment of Camera Corps' own design includes the iconic Q-Ball, Q-Ball Preset and RF Q-Ball, as well as bespoke builds such as Pop-Up Ice-Cam. Baseball Base Cam, Archery Cam, Underwater Remote Cam, Slalom Pole Cam, Helmet Cam and Body Cam.
Camera Corps Q-Ball Captu...on to Birth of Royal Baby
Q-Ball for Leading Media Networks in China

redpajama_set_name: RedPajamaCommonCrawl


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After 22 years of marriage, Actor Forest Whitaker files divorce
Mazech Media - December 28, 2018
Pakistan doctor arrested for allegedly infecting more than 400 babies and children with HIV
Michigan teacher arrested after driving to school drunk, blowing 4 times over legal limit
George Bush's Daughter Barbara marries in a secret wedding ceremony (Photos)
Zoom now worth more than 7 of the world's largest airlines combined
Poverty, economic crisis deepening daily, Nigeria's VP Osinbajo cries out
Mazech Media - September 9, 2020
Nigeria's Vice president Yemi Osinbajo on Monday lamented that the poverty situation in the country, as well the economic distortions and hardship caused by...
Google maps now lets you search for available bikes in these 24 cities
Mazech Media - July 21, 2019
Google Maps on Wednesday added a search feature that will let you see bike-share stations and availability in 24 cities around the world. This is...
VIDEO: Zimbabwean pastor locks lips with female church member in front of other church...
Mazech Media - April 24, 2019
A video showing Zimbabwean Church pastor locking lips with a female church member went viral online on Tuesday. In the video shown below, the pastor...
Israel to impose a new national lockdown as new cases of coronavirus soar
Israel is to impose a new national lockdown – becoming the first country in the world to take such a measure to contain the...
Barbara Bush, the daughter of former US President, George W Bush, tied the knot with her fiancé, screenwriter Craig Coyne, on Sunday in an...
China's central bank to destroy billions of cash in areas hard hit by coronavirus...
Mazech Media - February 17, 2020
China's central bank has ordered the return of paper currency in circulation at hospitals, animal markets and buses in areas hard-hit by coronavirus to...
VIDEO: Cruise ship releases video, blames grandfather in death of toddler who fell from...
In July last year, we published a tragic incident about 18-month-old child who fell to her death from the deck of a Royal Caribbean...
Prime minister Boris Johnson still has coronavirus 7 days after testing positive, remains in...
Mazech Media - April 3, 2020
Friday April 3 was supposed to be Boris Johnson's final day in isolation, after he tested positive for the deadly virus last week. But...
Two months after cheating scandal, Jordyn Woods announces she's going to Nigeria
Kylie Jenner's ex-bestfriend, Jordyn Woods, model and beauty enthusiast, is set to travel to Nigeria to get some rest two months after she was...
VIDEO: Nigerian dad flogs 18yr old daughter on butt for joining a group chat...
The video below emerged online showing the moment an unidentified Nigerian dad was flogging his 18-year-old daughter for joining a group chat he warned...
Second, more powerful earthquake hit Southern California, causing more damage
Mazech Media - July 6, 2019
Barely a day after the biggest earthquake in 20 years hit Southern California Thursday morning, a second earthquake, more powerful, struck the SoCal region...
Customer tips bartender with Powerball ticket that turns into $50,000
A bartender at Bleacher's Bar in O'Fallon in Missouri won't soon forget a tip of a lifetime from one of her regular customers. The...
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VIDEO: This video is currently trending for obvious reasons
The video below went viral on Africa's social media space and it's unclear what happened and the location. One social media user commented that...
Man sues British Airways for sitting him next to a very fat passenger during...
Mazech Media - November 17, 2018
A man who is suing British Airways for $12,000 has told a court he was injured after being forced to squeeze into a seat next to...
VIDEO: Stray bullet kills dispatch rider in Lagos and one other person
A dispatch rider and one other person were killed on Wednesday during a fight between hoodlums popularly called 'area boys' at the Fadeyi area...
VIDEO: Nigerian police officer cries out for help after he was scammed online
Mazech Media - June 15, 2019
A Nigerian Police officer has cried out for help after he was allegedly defrauded of huge amount of money online by internet fraudsters popularly...
Ghanaians are obsessed with sex, but good pretenders – Ghanaian actress Lydia Forson
Mazech Media - May 20, 2019
Curvy Ghanaian actress, Lydia Forson has said that Ghanaians are too addicted to getting laid and it's becoming a societal menace. The actress, who played...
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The Southeastern Pennsylvania Transportation Authority, or SEPTA, issued a policy on April 7 requiring all riders to wear facial coverings in order to help...
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The biggest topic on Chinese social media at the moment is a tragic story of a man who faked his own death to receive...


London ICA's Detroit: Techno City exhibition kicks off at the end of the month. Taking place in the institute's Fox Reading Room, it will look at "the evolution and subsequent dispersion of Detroit Techno music", charting a timeline from the 1970s to the early 1990s.
Inspired by the 1988 compilation Techno! The New Dance Sound Of Detroit released on 10 Records, and featuring the likes of Anthony Shakir, Kevin Saunderson, Juan Atkins and others, the series will focus on various aspects of the scene and music including analogue equipment used – like the legendary Roland TR808 – and how the sound travelled to Europe, inspiring a generation of producers such as Carl Craig, Richie Hawtin and Kenny Larkin. The exhibition takes a special focus on Underground Resistance's Mike Banks, John Collins, Robert Hood and Jeff Mills.
NTS Radio will be presenting a series of online programmes to accompany the exhibition. Detroit: Techno City runs from 27 July–25 September.


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Home News Philip Morris Japan: Transition to HNB Ongoing
Philip Morris Japan: Transition to HNB Ongoing
More and more people in Japan are turning to heat-not-burn products over conventional cigarettes.
Philip Morris Japan's president, Shea Lih Goh, attributes the company's success in this market with its IQOS device to certain aspects of Japanese culture, saying, "Every market is unique, but what makes this especially true with regards to Japan is that the culture is very concerned about not bothering others, which makes no combustion and similar features (of the products) resonate well with Japanese customers."
"For over a decade, we have been conducting research and development on reduced-risk products, and our immediate goal is to encourage consumers to convert from cigarettes to our IQOS lineup," Goh said. "It's a very exciting time. We are transforming from a traditional tobacco company to one focused on science and technology and realizing a smoke-free vision. This requires a lot of things to change within the company, including how we go about business and how we organize ourselves internally."
In addition, Goh believes the company's inclusive working environment and narrowing the gender gap, which distinguishes it from other companies in Japan, also play a pivotal role. Philip Morris Japan's workforce comprises employees of over 20 nationalities, 40% of which are young, talented millennials, and a gender gap of 0.4%, compared to Japan's 24.5% gap as of 2017.
Philip Morris cigarettes Japan heat-not-burn


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UPDATE 1-Peru copper and gold output nosedive in first half of 2020, ravaged by coronavirus -govt
By Marco Aquino
(Updates with additional details on June production, context)
LIMA, Aug 4 (Reuters) - Peru, a top global metals exporter, saw its output of copper, gold and zinc nosedive in the first half of 2020, ravaged by restrictions put in place to curb the coronavirus outbreak, the government said on Tuesday.
The world's No. 2 copper producer said production of the red metal plunged 20.4% in the first half of 2020 versus the same period in 2019.
Gold production also fell 34.7% in the first six months of the year, while zinc output dropped off 23.7%, the country's Energy and Mines ministry said in a statement.
The Peruvian government decreed a quarantine in mid-March with special restrictions on mining activity. The industry, a key economic engine for the Andean nation, began to gradually normalize operations in May.
The mines ministry reported copper production of 180,792 tonnes in June, 40.8% higher than the previous month as mines resumed production.
In June, gold production recovered 49.7% to 5,877,891 fine grams compared to May; while that of zinc jumped 313.6% to 119,174 tonnes.
Mine products represent 60% of Peru's exports. (Reporting by Marco Aquino; writing by Dave Sherwood; editing by Chris Reese and Sandra Maler)


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Home>All Posts>Reviews>Blu-Ray / DVD Reviews>'Halloween,' 'After...
'Halloween,' 'After Darkness,' 'Family Guy's 20 Greatest Hits,' More Available on Home Entertainment
Blu-Ray / DVD ReviewsNewsReviews Angela Dawson|January 14, 2019
SPEED KILLS. (DVD Artwork). ©Lionsgate Entertainment.
By ANGELA DAWSON
Front Row Features
The infamous killer Michael Myers strikes again in "Halloween," arriving on 4K Ultra HD, Blu-ray, DVD and On Demand Tuesday Jan. 15. The horror sequel is now available on Digital and via the digital movie app Movies Anywhere.
"Halloween" takes place four decades after Laurie Strode (Jamie Lee Curtis, "Halloween" franchise, "Scream Queens") narrowly escaped the masked Michael Myers' brutal killing spree.
After years of psychological scrutiny, Myers is set to be transported from a mental institution to a prison on Halloween, of course. Haunted by what happened to her years ago, Laurie has rigged her house with cameras and a sizable cache of weapons. She is estranged from her adult daughter Karen (played by Judy Greer) but surprisingly close with her granddaughter Allyson (played by Andi Matichak making her feature film debut).
"Halloween" also stars Will Patton as Officer Hawkins who was just a rookie when Myers went on his killing spree and Nick Castle, reprising his role as the masked, mute and menacing Myers. Though secured and guarded in the transport bus, Myers manages to escape, leaving a trail of carnage in his wake. Audiences can guess who he's coming for but his prey has been preparing for this faceoff all her life.
Packed with bonus features including chilling deleted and extended scenes as well as special featurettes showing behind the scenes looks at creating the film, Halloween delivers spine-chilling, hair-raising intensity and thrills to both new and repeat viewers.
Master of horror John Carpenter (1978's "Halloween," "The Thing") joins forces with director David Gordon Green ("Joe," "Pineapple Express") and producers Jason Blum (Blumhouse), Malek Akkad (Trancas International Films) and Bill Block (Miramax) for this follow up to Carpenter's classic horror film.
After Darkness
Hope begins at the end of the world in "After Darkness," available on DVD, Digital, and On Demand Tuesday Jan. 15 from Lionsgate. This futuristic thriller stars John Patrick Amedori ("Dear White People," "The Vatican Tapes"), Valerie Curry ("Blair Witch," "The Tick"), Tim Daly ("Wings," "The Fugitive"), Natalia Dyer ("Stranger Things") and Kyra Sedgwick ("The Closer").
This sci-fi thriller centers on a family's fight to survive a solar disaster. As skies darken and hopes grow dim, the Beatys try to stay sane in a lavish home with every possible comfort—except sunshine. Raymond (Daly) plans to have his family transported to a secure underground bunker. But as he makes a gruesome discovery, the greatest threat to the Beatys' survival may be near—or inside—their home.
Directed by Batan Silva ("Mayans M.C.") the "After Darkness" DVD will be available for the suggested retail price (SRP) of $19.98.
Two-time Academy Award nominee John Travolta ("Pulp Fiction," "Saturday Night Fever") leads the way when "Speed Kills" arrives on Blu-ray Combo Pack (plus DVD and Digital), DVD, and Digital Tuesday Jan. 15 from Lionsgate.
Travolta plays speedboat racing champ and multimillionaire Ben Aronoff, who is hiding a dangerous secret life: His boats are being used to move cocaine through Miami. Suddenly caught in a crossfire between DEA agents and the mob, Ben makes a desperate stand to save his family and his empire in this action-filled thriller based on an incredible true story.
"Speed Kills" is currently available On Demand. Based upon the true-crime book of the same name, the action thriller also stars Katheryn Winnick ("50 First Dates," "The Dark Tower") and Jennifer Esposito ("Crash," "Summer of Sam") and Matthew Modine ("47 Meters Down," "The Dark Knight Rises")
The "Speed Kills" Blu-ray and DVD will be available for $22.99 (SRP) and $19.98 (SRP), respectively.
Startling tales of sex, betrayal and corruption unravel in a Copenhagen hotel, where nine disparate lives intersect by chance or fate in "Room 304," which is available starting Tuesday Jan. 15 on DVD and Digital.
In this Scandinavian psychological thriller, a hotel manager peers into the abyss of his empty life, leading to devastating consequences for himself, his wife and his mistress. A Spanish stewardess reaches out for intimacy and finds it in a most unexpected way. A reserved concierge is forced out of his shell by a shocking event, and an Albanian refugee gets a chance to avenge his wife, but ends up discovering something surprising instead.
An Official Selection in Competition at the Karlovy Vary International Film Festival, "Room 304," is director Birgitte Staermose's debut feature.
Family Guy's 20 Greatest Hits
Celebrate the show's 20th anniversary with this marvelously musical collection of 20 song-and-dance-filled episodes, along with a symphony of extras created exclusively for this release available now on DVD. So, grab "A Bag o' Weed," roast a "Candy Quahog Marshmallow!" and enjoy "The FCC Song" with your favorite "Shipoopi," because "You've Got a Lot to See" (and hear) when you watch "Family Guy's 20 Greatest Hits."
Tags:Andi Matichak, David Gordon Green, Halloween, horror, Jamie Lee Curtis, John Carpenter, Judy Greer
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Angela Dawson is the editor and co-owner of frontrowfeatures.com. An award-winning journalist, she has covered Hollywood since 2000. Her interviews with actors, directors, writers, authors and musicians have run in numerous outlets. She is the author of the novel "Generations: A Greek Family Odyssey" available on Amazon.com.
Photos: 'Blindspot,' 'Burden,' 'Cezanne' on Home Entertainment
Blu-Ray / DVD ReviewsDVD Review PhotosNews PhotosReview Photos Angela Dawson|August 7, 2017
Marvel Goes Street-Level With 'Ant-Man'
Film ReviewsReviews James Dawson|July 8, 2015
Heidi Klum Talks on 'America's Got Talent'
FeaturesNewsTV Features Angela Dawson|May 24, 2014
'Monroe' Offers Cure for 'House' Withdrawal
Film ReviewsReviews Angela Dawson|May 28, 2012
Angela Dawson is the editor and co-owner of frontrowfeatures.com. An award-winning journalist, she has covered Hollywood since 2000. Her interviews with actors, directors, writers, authors and musicians have run in numerous outlets. She is the author of the novel "Generations: A Greek Family Odyssey" available on Amazon.com. Read Full
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Photos: REVIEW: 'The Lion King' is Dazzling Yet Disappointing
REVIEW: 'The Lion King' is Dazzling Yet Disappointing
Photos: Cub Actors Roar to Life as Simba and Nala in New 'The Lion King'
Cub Actors Roar to Life as Simba and Nala in New 'The Lion King'
Photos: 'Breakthrough,' 'Dogman,' More Arrive on Home Entertainment
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Q: How to get Eclipse Variables project specific I manage to define variables to be used as part of a classpath.
But they are always containing absolute path information.
Now I read, that such variables can be project or workspace specific.
(see here)
(But following the How-To, I always produce general variables, valid for the whole workspace)

edit:
My projects have a couple of links like REPOSITORY/org/springframework/....jar
REPOSITORY is a variable referring to an absolute path.
I have different versions of these paths and want to easily switch a project from one version to another. If resolving the variable were project specific, it were super easy.

Questions:

*

*how do I define project specific variables?

*is there a difference between classpath variables and others?

*is there a way to define Variables relative to a project root path? (in that case, the variable could eventually even be workspace wide, resulting in the same subfolder relative to the project)

*Is defining two Variables REPOSITORY_A and REPOSITORY_B and editing the project classpaths to use one of them, the only way?

(BTW: I'm using Eclipse Oxygen3 4.7.3 and want to link to different repository version, depending on the project version)

redpajama_set_name: RedPajamaStackExchange


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Swiss chief Kluhr to step down at year-end
By David Kaminski-Morrow2020-09-29T09:14:00+01:00
Lufthansa Group carrier Swiss's chief executive, Thomas Kluhr, is to resign from his post at the end of this year, having delayed his departure from March.
Swiss states that Kluhr, who has headed the airline for nearly five years, has requested that the board "release him from his duties", and that the board has agreed "with the greatest regret".
It has not disclosed the reasons for his decision, simply describing them as "personal". The board will select a successor to Kluhr in the fourth quarter, it adds.
Kluhr will also step down from his chairmanship of Edelweiss Air.
Source: Swiss
Swiss credits him with having overseen the airline's largest fleet modernisation, and for "stabilising" the carrier – operationally and financially – in the aftermath of the pandemic.
Kluhr will serve on the newly-established Swiss Aviation Foundation, which is being created as part of the conditions for providing financial support to the airline.
The foundation is to monitor an agreement between the Swiss state and Lufthansa Group on the development of Zurich airport.
"Thanks to the provision of bank credit facilities largely backed by the Swiss Confederation, Swiss has been able to secure its liquidity and begin to gradually ramp-up its flight operations," the carrier says.
Swiss to cut fleet as crisis drags carrier to Q1 loss
Lufthansa subsidiary Swiss is planning to reduce its fleet, after incurring a Swfr84.1 million ($86.1 million) operating loss in the first quarter.
Swiss ponders full operational stop amid 'draconian' restrictions
Swiss International Air Lines is considering a full suspension of flights, following the lead of other carriers within Lufthansa Group, which as a whole has shed 95% of its planned capacity.
More Airlines
Stalled recovery prompts KLM to deepen job cuts
Dutch carrier KLM is to cut up to 1,000 more jobs as a result of the later then expected recovery in travel demand from the pandemic.
Stronger Norwegian business plan convinces government to offer support
Norway's government has signalled that it is prepared to offer financial support to budget carrier Norwegian, to assist with the restructuring airline's recovery, but has laid out a number of conditions. The government is stressing that it has no intention of becoming an owner of the carrier. Trade ...