Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.
Copyright: © 2017 Huang 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: Funding was provided by the Rising Tide Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The sequencing of the human genome, genome-wide association studies (GWAS), quantitative trait loci (QTL) mapping, and similar research initiatives over the past few decades have greatly increased our understanding of the molecular pathways associated with human diseases. These efforts have significantly benefited from the liberal sharing of data and open-source scripts utilized for these efforts. Over the last few years, there has been a number of alternative machine-learning (ML) approaches employed in personalized cancer drug prediction, each associated with variable degrees of success [1–3]. For example, pRRocphetic  is a recently designed R package designed to run the entire learning and subsequent calling of patient data. Other recent contributions include the Bioconductor  package SCAN that allows for single-sample array normalization for precision medicine workflows. While a number of ML applications for precision medicine have benefited from community assessments of predicted drug response [e.g., [1,2]), such efforts have not always shared code, and for the majority of efforts only the organizers of the community assessment exercise were able to see the source code to evaluate each independent solution. This is unfortunate because the open sharing of code has been demonstrated to be a significant catalyst in the optimization of ML applications as in the Large Scale Visual Recognition Challenge (ILSVRC) where computational solutions are openly available [6,7].
We present here an open source software platform using a highly versatile support vector machine (SVM) algorithm that utilizes standard recursive feature elimination (RFE) methods to predict cancer drug response. In pilot applications, we utilized publicly available datasets from NCBI Gene Expression Omnibus (GEO)  that we formatted and the array files were partitioned into learning sets and experimental sets. Each individual array is accessible as a.CEL file with individual identifiers at a publically accessible GitHub site that outlines the learning, validation and test sets employed in our initial studies (https://github.com/chuang95/KEA_DrugResponse). Also available at this GitHub site are general procedures for open application of our software to additional datasets (https://github.com/chuang95/KEA_DrugResponseLearning). We have employed the algorithms to explore the effect of a variety of alternative learning datasets on predictive accuracy leading to several unanticipated findings. First, predictive accuracy was significantly improved when microarray probe level expression data rather than average gene expression values were employed in the model building process. Second, predictive accuracy was improved when models were built upon a diversity of cancer types. Third, the pre-filtering of learning datasets based upon preconceived biological models significantly reduces predictive accuracy. Application of our optimized models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions highly consistent with observed responses to a variety of drugs. By providing true open access to our software, we seek to encourage additional improvements in current methods, as well as, constructive comparisons with alternative approaches leading to the development of optimal ML-based strategies for personalized cancer medicine.
A variety of ML techniques and strategies have been employed in the quest for optimal accuracy, sensitivity and specificity in drug response predictions. In this work, we utilize an SVM approach paired with recursive feature elimination (RFE). SVM has been successfully applied in a variety of biological applications in recent years (e.g., ). Our SVM models were built using gene expression (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32474) (see also Table A in S2 File) and drug sensitivity profiles (https://wiki.nci.nih.gov/display/NCIDTPdata/NCI-60+Growth+Inhibition+Data)) (see also Table B in S2 File) of the NCI-60 panel of human cancer cell lines. Predictive models were built for seven drugs often employed in the treatment of ovarian cancer (carboplatin, cisplatin, paclitaxel, docetaxel, gemcitabine, doxorubicin, gefitinib). The drug sensitivities (GI50) of the NCI-60 cell lines approximate a normal distribution (Fig 1A; Fig A in S1 File). For our learning dataset, we conservatively excluded cell lines displaying GI50 values within ±0.50 SD of the mean. The test dataset, however, was selected from all cell lines. In all cases, cell lines used to build the models were distinct from those used in testing the models.
Fig 1. An SVM-RFE predictive model of carboplatin sensitivity for NCI-60 cell lines.
(A) Ranked display of -log transformed GI50 values for carboplatin for each of the NCI-60 cell lines. Blue circles = carboplatin resistant cells; red circles = carboplatin sensitive cell lines. Cell lines with GI50 values within ±0.5 SD of the mean (green circles) are less reliably classified as resistant or sensitive and were, thus, not employed in learning datasets. Test sets were selected from cell lines across the entire distribution; (B) Evolution of accuracy of predicted response to carboplatin using SVM-RFE selection for gene probe classifiers; (C) Visualization of the optimal separation between carboplatin sensitive and resistant NCI-60 cell lines. The X-axis is the optimal weight vector (prediction score) of the SVM model for carboplatin; the Y-axis is the -log transformed GI50 values for carboplatin.
SVM models built upon large datasets typically contain uninformative features, and a number of feature selection methods have been developed to identify subsets of features with optimal predictive accuracy [10–12]. We employed a previously described RFE [13, 14] method to select for features (gene probe sets) that optimally distinguish cells predicted to be sensitive to a drug from those that are not. The RFE method starts by discarding the least relevant features of the model from the bottom of the sorted feature list (Table C in S2 File). The subsequent SVM model is built on the remaining features and again, features with the lowest weights are removed. This process proceeds in a recursive manner until a minimal subset of features is identified that is essential to maintain optimal predictive accuracy. For example, Fig 1B depicts the evolution of predictive accuracy using SVM-RFE feature selection for increased sensitivity to carboplatin (see Fig B in S1 File for feature selection of the other drugs). In this case, initial removal of uninformative features increased accuracy due to the elimination of features that negatively interact with predictive accuracy. Our SVM-RFE approach compares favorably with other commonly employed methods of feature selection (see Fig F in S1 File).
The minimal number of informative features associated with optimally predicted responsiveness to the seven drugs modeled in this study ranged from 10 to 32 (Table D in S2 File). While the biological contribution of the majority of these genes to drug responsiveness is currently unknown, potentially informative trends are often apparent. For example, several of the most informative genes predictive of carboplatin sensitivity have been directly or indirectly implicated with apoptosis (Table E in S2 File), a cellular function known to be induced in response to carboplatin treatment .
The SVM models generate drug prediction scores for each cell line. Scores higher than "0" indicate a predicted sensitive response, less than "0" a predicted resistant response (e.g., see Fig 1C, X-axis). The overall accuracy, specificity and sensitivity are evaluated by leave-one-out cross-validation (LOOCV). The SVM computed predictive scores are plotted against observed GI50 values to graphically display the accuracy of each model. For example, the quadrant plot for carboplatin (Fig 1C) shows that the SVM model is 84% accurate across the NCI-60 test dataset. The predictive accuracies of each of the seven models ranged from 75% to 85% (see Fig C in S1 File for predictive accuracies of the other 6 chemotherapeutic drugs).
While feature selection methods are designed to identify the most informative features by systematically eliminating less informative ones, the predictive accuracy of ML models is heavily dependent on the presumption that the original learning dataset encompasses the full spectrum of features potentially relevant to the predicted variable . The selection of appropriate learning datasets for building predictive models of cancer drug response is especially challenging because a full understanding of the molecular processes underlying cancer onset/progression has yet to be attained . For this reason, subjective limitations in the scope of data employed in learning datasets may negatively affect predictive accuracy of derived models if informative features are inadvertently excluded. For example, it is frequently assumed that models designed to predict optimal therapies for a particular cancer type should appropriately be built using learning datasets derived exclusively from that same type of cancer. However, a growing body of evidence indicates that the molecular pathways underlying cancer onset/progression are not necessarily defined by a tumor's tissue of origin . Thus, a gene expression pattern associated with a particular cancer type may underlie cancer development in other cancer types as well.
To explore this issue, we compared the relative accuracies of two SVM-derived models designed to predict response to the commonly prescribed cancer drug carboplatin. The respective models were built using gene expression profiles and drug response profiles (i.e., learning datasets) derived from 18 of the NCI-60 cell lines. In one case, the 18 cell lines were representative of only two types of cancers (lung and melanoma) while in the other case, the 18 cell lines were randomly selected to be representative of all 9 types of cancer comprising the NCI-60 dataset (lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate and CNS). As shown in Fig 2A, the model built using data from the 9 cancer types was more accurate in predicting carboplatin sensitivity (87.5%) than the model built upon only 2 cancer types (75.0%) (Fig D in S1 File). This finding is consistent with growing evidence that the molecular basis of individual cancers may not necessarily be defined by tissue of origin . In addition, the fact that variation in gene expression levels is typically greater among multiple cancer types (see Fig G in S1 File) may be an additional relevant factor since the predictive accuracy of ML models is well known to improve with increasing diversity of the learning set data .
Fig 2. The influence of learning datasets on the predictive accuracy of SVM-RFE models.
(A) Comparison of predictive accuracy (ROC curves) for two SVM models of response to carboplatin using a learning dataset derived from 2 cancer types (lung, melanoma) vs. 9 cancer types (brain, breast, lung, leukemia, renal, colon, ovarian, prostate and melanoma). In each case, the data were derived from a total of 18 cell lines. The results indicate that the model built using learning set data from 9 cancer types generates a more accurate prediction (see also Fig D in S1 File); (B,C,D) Prediction of the sensitivity of breast cancer cell lines to doxorubicin. In one case, the model was built using a learning dataset comprised of average gene expression values. In the other case, the model was built using a learning dataset comprised of the expression values of all gene probes. The results demonstrate that the model built using probe set data is more accurate than the model built using average gene expression data; (C) prediction score accuracy using average gene expression values; (D) prediction score accuracy using expression values of all gene probes (Red circles = drug sensitive training set; Blue circles = drug resistant training set; Black diamonds = breast cancer cells test set).
Another way in which the information content of learning datasets may be compromised is by the employment of average rather than raw experimental values. For example, Affymetrix and other microarray gene expression systems typically incorporate multiple probe sets per gene, thereby providing the possibility of monitoring differences in levels of alternative splicing and other post-transcriptional expression variants (e.g., Fig E in S1 File). While the use of average gene expression values may be appropriate for many applications, the loss of information associated with the use of such average values in learning datasets could negatively affect the accuracy of drug prediction algorithms if, for example, rare splice variants turn out to be particularly informative features.
To test this possibility, we compared the relative predictive accuracies of two SVM-based algorithms developed to predict the sensitivity of the set of breast cancer cell lines recently employed in the NCI-DREAM Challenge to the drug doxorubicin . In one case, we employed the average Affymetrix gene expression dataset that was provided to the Challenge participants (https://www.synapse.org/#!Synapse:syn2785783). In the other case, we downloaded and employed the original probe data as our learning set (ArrayExpress E-MTAB-181, http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-181/). The results presented in Fig 2B, 2C and 2D demonstrate that the model built using the probe set data is substantially more accurate (89%) in predicting the sensitivity of the breast cancer cells lines to doxorubicin than the model (78%) built using the averaged gene expression values.
Some methods to assess risk of cancer progression and/or severity focus almost exclusively on genes previously identified as drivers of cancer onset/progression . The advantage of such data pre-filtering is a reduction in the complexity of downstream analyses but, as discussed above, it may also negatively impact the accuracy of derived predictive models if the truncated datasets do not encompass all genes associated with drug sensitivity.
To explore this question, we compared the predictive accuracy of two SVM-based models using the above breast cancer cell line data. In one model, the learning dataset consisted of expression patterns of 297 genes previously implicated in cancer onset/progression  (http://foundationone.com/docs/FoundationOne_tech-info-and-overview.pdf). In the second model, the learning dataset included probes of all significantly expressed genes (Table A in S2 File). The models built using the pre-filtered data from the 297 genes were substantially less accurate (59.6%) in predicting responses to doxorubicin than the model built upon unfiltered data (89.4%) (Fig 3).
Fig 3. Pre-filtering of learning datasets can reduce the accuracy of predictive models.
Shown is the predicted sensitivity of breast cancer cell lines to doxorubicin by two SVM models built using different learning datasets. In one case, the model was built using a learning dataset limited to the expression of 297 genes previously associated with cancer onset/progression . In the other case, the model was built using a learning dataset drawn from all significantly expressed genes (Table A in S2 File). The results indicate that pre-filtering of the learning dataset to only include gene expression values of previously identified cancer related genes reduces predictive accuracy. (A) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using a learning dataset pre-filtered for genes previously associated with cancer onset/progression; (B) Quadrant plot of SVM predicted sensitivity to doxorubicin vs. observed sensitivity to doxorubicin of model built using all gene expression data (Table A in S2 File); (C) ROC curves of the two models showing reduced predictive accuracy associated with the pre-filtered learning dataset (Red circles = drug sensitive training set; Blue circles = drug resistant training set; Black diamonds = breast cancer cells test set).
While our predictive models were established using gene expression and drug sensitivity data from human cell lines, we were interested in conducting preliminary evaluations of the models' ability to predict the response of human cancer patients to chemotherapeutic treatments. Toward this end, we downloaded three independently derived (Affymetrix) gene expression datasets of 273 ovarian cancer patient tumors from the Gene Expression Omnibus (GEO) repository (GSE30161, GSE18521, GSE20565; http://www.ncbi.nlm.nih.gov/gds). The expression values for each individual array were normalized back to the NCI-60 gene expression data matrix.
Using these data, we employed our models to predict the response of the 273 cancer patients to cisplatin, doxorubicin, paclitaxel, carboplatin, docetaxel, gemcitabine and gefitinib. For example, Fig 4A and 4B display the predicted response of two randomly selected patients from the GEO data set. One of the patients (Fig 4A) is predicted to respond favorably to the standard first-line therapy (carboplatin/paclitaxel) while the second patient (Fig 4B) is not. Interestingly, the patient predicted not to respond to first line therapy, is predicted to respond favorably to gemcitabine. Unfortunately, the observed response of these individual patients to therapy is not available. However, the collective response of ovarian cancer patients to the seven drugs analyzed in these studies has been previously reported (Table F in S2 File). To compare the collective predictive accuracy of our models to the collective observed response rates, we combined the predictive sensitivities of the 273 patients comprising the 3 GEO datasets and displayed the results as a distribution of the combined SVM predicted scores (Fig 4C). The results indicate that while at least some patients are predicted to respond to each of the seven drugs, the vast majority (75%) of patients are predicted to respond favorably to carboplatin (Fig 4C), followed closely by gemcitabine, cisplatin (58%) and paclitaxel (56%). Of interest is the fact that carboplatin, given concurrently with paclitaxel, is the current first-line chemotherapy for ovarian cancer patients, with approximately 75–80% of patients being responsive to this combination . Our predictions suggest that the drug primarily responsible for this favorable response is carboplatin. Gemcitabine is commonly given as a second-line chemotherapy for OC and has been found to be of moderate clinical effectiveness, in line with our predictive models . Fig 4D displays the linear regression between the predicted response rates to the seven chemotherapeutic drugs by our models and the observed response rate from clinical studies (Table F in S2 File). The overall predictive accuracy of our models in this dataset is > 80% (R2 = 0.8201).
Fig 4. Individual and aggregate prediction of response to chemotherapeutic drugs.
The SVM algorithms output binary classifications for each drug (sensitive/resistant) established through a decision function that numerically separates cancer cells predicted to respond to the drug (positive score) from those predicted to be non-responders (negative score). (A) The predicted response of an individual patient (GSM516724) to seven chemotherapeutic drugs. This patient is predicted to respond favorably to the first line therapies of carboplatin (score 2.88) and paclitaxel (score 3.20). (B)The predicted response of a second individual OC patient (GSM516801) to seven chemotherapeutic drugs. The patient is predicted NOT to respond favorably to the first line therapies of carboplatin (score -0.28) and paclitaxel (score -2.53). (C) Density plot of aggregate prediction scores for 3 GEO data sets of 273 ovarian cancer patients and the predicted group response rate for each drug. (D) Scatter plot of the predicted group response rates vs. the observed group responses of OC patients to seven chemotherapeutic drugs (Linear regression p value = 0.0031, R2 = 0.8201) (Table F in S2 File).
A primary goal of personalized cancer medicine is the accurate prediction of optimal drug therapies based upon individualized molecular profiles of patient tumors . In an ideal world, such predictions are based upon firmly established cause and effect relationships between identified molecular aberrations and specific aspects of the onset and progression of the disease. An example is the well-established relationship between constitutively active Bcr-Abl tyrosine kinase (TK) expression and the leukemic phenotype associated with CML (chronic myelogenous leukemia) . Patients identified with this molecular aberration are effectively treated with targeted TK inhibitors that work to reduce the elevated activity and restore regulatory balance to the cell. Regrettably, the underlying molecular causes of most tumors are, as yet, not as well understood as for CML. This has led to growing interest in the application of ML approaches to the prediction of optimal drug therapies . ML-based predictive models are not predicated upon knowledge of underlying cause and effect relationships but rather on the identification of significant correlations between specific components of tumor molecular profiles and the favorable response of tumors to specific drugs.
The open source availability of ML prediction algorithms provides the research community with unique opportunities for creative modifications and improvements of existing algorithms not otherwise possible. For example, open sharing of code has been critical to improvements in ML approaches to image recognition [6,7].
Despite the documented advantages of the open sharing of code, to date, the practice has been extremely limited within the field of cancer drug prediction. For example, there is a notable lack of GitHub, Sourceforge, R Bioconductor and other online repositories of cancer drug prediction applications in contrast to the resources available for other ML applications such as the Large Online Image (LOI) repository competitions where alternative computational solutions are openly deposited . We believe that making cancer drug prediction algorithms open source could result in similar benefits in the field of personalized cancer medicine.
Toward that end, we present here an open access support vector machine (SVM)-based algorithm for the predictive response of cancers to seven widely employed chemotherapeutic drugs. The algorithm combines a standard SVM approach with a "one-by one" data normalization pipeline. We have employed the algorithm to explore the effect of a variety of alternative learning datasets on predictive accuracy leading to several unanticipated findings. For example, although it may seem intuitive that drug predictive models for a specific type of cancer should optimally be built upon data from the same cancer type, our results suggest that this may not always be the case. The predictive accuracy of the drug response of a particular cancer type was significantly increased when the model was built using data from a variety of cancer types. This finding is consistent with growing evidence that molecular signatures of optimal cancer drug response are not necessarily defined by the cancer's tissue of origin .
Microarray platforms typically monitor gene expression levels using multiple probe sets. This allows discrimination between the expression patterns of alternative splice variants and/or other gene transcript isoforms. Most often, the input expression data for the building of ML predictive models utilizes average expression values across all gene probes. We found that higher accuracy is attained when all probes are incorporated in the learning dataset presumably because some isoforms are more informative than others with respect to drug response and this information is lost or diluted when individual probe data are combined in an average value.
Personal cancer drug therapy, as currently envisioned, involves the targeted inhibition of one or more "cancer driver" genes, i.e., genes that have been previously identified as playing key roles in cancer onset and progression. For this reason, the molecular profiles of putative cancer driver genes or other pre-defined subsets of genes are often considered sufficient for the accurate prediction of optimal drug therapies. We found that predictive accuracy can, in fact, be significantly reduced when expression profile datasets are pre-filtered prior to ML-based model building. This result suggests that genes involved in cancer drug response are not necessarily limited to those involved in cancer onset even when the drug in question is designed to target a specific group of driver genes.
Although our models were built using the publically available NCI-60 cancer cell line datasets, we are encouraged that predictions using publically available human patient datasets are generally consistent with clinical observations. By making our predictive models open source, we hope to encourage the testing of predictions in additional human datasets representative of a diversity of tumor types.
In summary, our findings demonstrate that significant improvements can be made in the predictive accuracy of ML-based algorithms by modulating the format and/or type of learning datasets employed in the model building process. This finding is likely to be relevant regardless of the type of ML approach employed. While our results illustrate several paths by which the predictive accuracy of our ML-based cancer drug prediction algorithm was improved, these and additional possibilities need to be tested with larger and more extensive datasets. We believe that such goals are most effectively attained by communal efforts where the research community is provided open access to the underlying code and pipelines employed so that meaningful improvements and comparisons with alternative methods can be made . Toward this end, we currently provide an open source R package and pipeline for application of our prediction methods (https://github.com/chuang95/KEA_DrugResponse). In addition, a user-friendly web server is currently under construction that will further enhance public access to our methods.
It is our hope that through community sharing of this and other open source cancer drug prediction algorithms and associated data formatting/normalization procedures that the attainment of a major goal of personalized cancer medicine will be facilitated.
Standard gene expression data analyses were conducted as previously described . Individual gene expression microarray (.CEL) files were normalized one-by-one against the original NCI-60 gene expression microarray data specific to each array (both Affymetrix U133 Plus 2 and Human Exon Array) using standard quantile normalization [26, 27] and using the mean of each probe. This approach creates distributions for each array that are as similar as possible in terms of statistical properties.
We define the negative log transformed GI50 value for each cell line. In this approach, the higher the transformed GI50 value, the more sensitive the cell line is to the drug. Three labels were collected: sensitive (marked 1), resistant (marked -1) and indeterminate (marked 0). The sensitive label indicates GI50 values above mean+0.50 SD while resistant label indicates GI50 values below mean+0.50 SD. GI50 values that lie within ±0.50 SD of the mean are marked as indeterminate.
The microarray gene expression values of the NCI-60 cell lines are formatted as a matrix, and sub-divided into training (75%) and validation (25%) datasets. Each probe of a gene is analyzed as a separate feature for each sample. We applied SVM on training data to get weights for each feature, and sort the features based on the weights (Table C in S2 File). Models are built using a learning dataset, and evaluated using a test dataset. Linear support vector machine (SVM) is employed recursively as a classification model to separate samples into two classes: sensitive and resistant. The learning function is svmtrain (Matlab R2013b version 8.2.0.701), and the kernel function is linear. The samples are represented as a vector x, and the two classes are divided in the dataspace by a hyperplane wx' + b = 0 that maximizes the margins between the learning samples of the two classes. This margin is defined such that: Binary classification is performed for the test prediction. The prediction score for test samples are calculated by using the decision function as follows: where w and b are the weight vector and bias parameters from the SVM model. The input x is the normalized test sample gene expression data with RFE selected i number of features. The classification of the patient drug response is based on this score. We call a sample a responder to the drug if this score is higher than 0, and a non-responder to the drug if the score is lower than 0.
Recursive feature elimination (RFE) was performed to find the minimum set of features that maximized accuracy in the classification on the test dataset (Fig 5). The approach starts by removing the least relevant 100 features for the model from the bottom (lowest weights) of the sorted feature list. The following SVM model is built using the remaining features, and then again removes the 100 features with lowest weights. This process proceeds recursively until the number of remaining features reaches 100. Thereafter, features are removed one at a time until the most informative set of features is obtained [28–30]. If there are multiple models with the highest accuracy, the model with the fewest number of features is selected. Each model is forced to contain a minimum of ten probes. The predictive model for each drug is based on the most informative set of features determined for that drug. Leave one out cross-validation (LOOCV) is used to evaluate the performance of each of the models as previously described .
Fig 5. Pseudo code for the RFE approach.
This approach takes the microarray expression data of NCI-60 cancer cell lines as input data, and the output is a model with the most informative features.
ROC curves were generated using the standard function as outlined below: where AUC = area under the curve, tpr = true positive, fpr = false positive, tnr = true negative and fnr = false negative. We optimize AUC by maximizing tpr—fpr or minimizing a sum of (absolute) normalized error fpr + fnr. Optimal models are associated with higher AUC values.
S1 File. Supporting information with additional figures.
Fig A. Ranked display of -log transformed GI50 values for the other six chemotherapeutic drugs for each of the NCI-60 cell lines.
Fig B. Evolution of accuracy of predicted drug responses for the other six chemotherapeutic drugs using SVM-RFE selection for gene probe classifiers.
Fig C. Visualization of the optimal separation between drug sensitive and resistant NCI-60 cell lines for the other six chemotherapeutic drugs.
Fig D. The models built using data from the 9 cancer types vs. 2 cancer types.
Fig E. Example of expression levels for probes from the same gene, NEAT1.
Fig F. Comparison of LOO-cross validation of predicted response to carboplatin using our SVM-RFE method vs. two other commonly employed methods.
Fig G. Comparison of the average gene expression for the learning datasets derived from 2 cancer types (lung, melanoma) vs. 9 cancer types (brain, breast, lung, leukemia, renal, colon, ovarian, prostate and melanoma).
S2 File. Supporting information with additional tables.
Table A. Expression values of gene probes (Affymetrix_U133_2.0_plus) for the NCI-60 cell lines.
Table B. Sensitivity (GI50) of NCI-60 cell lines to seven chemotherapeutic drugs.
Table C. Ranking of probe (feature) weights for employed in the recursive feature elimination (RFE) process for the prediction of carboplatin sensitivity.
Table D. Probes associated with optimal predictive accuracy for 7 chemotherapeutic drugs.
Table E. Several of the most informative genes predictive of carboplatin sensitivity have been directly or indirectly implicated with apoptosis.
Table F. Comparison between predicted response rates of ovarian cancer patients to 7 chemotherapeutic drugs and response rates as reported in the literature.
1. Costello JC, Heiser LM, Georgii E, Gonen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotech. 2014;32: 1202–1212.
7. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision. 2015;115: 211–252.
10. Hsu C-W, Chang C-C, Lin C-J. A practical guide to support vector classification, Technical Report. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, 2003.
13. Gaul DA, Mezencev R, Long TQ, Jones CM, Benigno BB, Gray A, et al. Highly-accurate metabolomic detection of early-stage ovarian cancer. Sci Reports. 2015;5: 16351.
18. Azuaje F. Computational models for predicting drug responses in cancer research. Brief Bioinform. 2016;pii: bbw065 (Epub ahead of print).
19. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nature Biotech. 2013;31: 1023–31.
20. Ozols RF. Challenges for chemotherapy in ovarian cancer. Ann Oncol. 2006;17: 181–187.
21. Hansen SW, Tuxen MK, Sessa C. Gemcitabine in the treatment of ovarian cancer. Ann Oncol. 1999;10: 51–54.
22. Bode AM, Dong Z, Precision oncology-the future of personalized cancer medicine? Precision Oncol. 2017;1: 2.
27. Amaratunga D, Cabrera J. Analysis of data from viral DNA microchips. J Amer Stat Assoc. 2001;96: 1161–1170.
28. Gysels E, Renevey P, Celka P. SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in brain-computer interfaces. Signal Proc. 2005;85: 2178–2189.
29. Bedo J, Sanderson C, Kowalczyk A. An efficient alternative to SVM based recursive feature elimination with applications in natural language processing and bioinformatics. In: Satter A, Kang BH, editors. AI 2006: Advances in Artificial Intelligence. Springer; 2006. pp. 170–180.


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Aloha Poke claim revives Hawaiian culture protections push
Image via Wikipedia user Openmalware.exe.
published on April 19, 2019 - 2:40 PM
Written by AUDREY McAVOY Associated Press
(AP) — Last year, much of Hawaii was shocked to learn a Chicago restaurant chain owner had trademarked the name "Aloha Poke" and wrote to cubed fish shops around the country demanding that they stop using the Hawaiian language moniker for their own eateries. The cease-and-desist letters targeted a downtown Honolulu restaurant and a Native Hawaiian-operated restaurant in Anchorage, among others.
Now, Hawaii lawmakers are considering adopting a resolution calling for the creation of legal protections for Native Hawaiian cultural intellectual property. The effort predates Aloha Poke, but that episode is lending a sense of urgency to a long-festering concern not unfamiliar to native cultures in other parts of the world.
"I was frustrated at the audacity of people from outside of our community using these legal mechanisms to basically bully people from our local community out of utilizing symbols and words that are important to our culture," said state Sen. Jarrett Keohokalole, a Native Hawaiian representing Kaneohe and Heeia.
The resolution calls on state agencies and Native Hawaiian organizations to form a task force to develop a legal system to "recognize and protect" Native Hawaiian cultural intellectual property and traditional cultural expressions. It also seeks protections for genetic resources, such as taro, a traditional crop that legend says is an ancestor of the Hawaiian people and that scientists have tried to genetically engineer in the past.
The task force would be commissioned to submit its recommendations and any proposed legislation to lawmakers in three years.
The House passed the resolution Thursday. The Senate is scheduled to vote on it Monday.
The Aloha Poke incident echoes past disputes, like when a non-Hawaiian photographer claimed copyright over an image of a woman dancing hula and Disney copyrighted a modified version of a Hawaiian chant used in a movie.
Chicago's Aloha Poke Co. chose as its battleground the word "aloha" — a term meaning love, compassion, kindness as well as hello and goodbye. It's a term central to how Native Hawaiians treat others and how many in Hawaii — Native Hawaiian or not — try to live.
"It's traumatic when things like this happen to us — when people try to take, modify or steal what's been in our people's world view for generations," said Healani Sonoda-Pale, chairwoman of the Ka Lahui Hawaii political action committee, who testified in support of the resolution.
Aloha Poke CEO Chris Birkinshaw didn't return messages seeking comment left at his West Madison store in Chicago and on the company's website. The company has stores in Illinois, Wisconsin, Minnesota, Florida and Washington, D.C.
Aloha Poke Shop in Honolulu initially ignored the Chicago company's letter, said co-founder Jeff Sampson. When the issue burst into the news, he and his partners had an attorney write their Chicago counterpart saying they wouldn't change their name. They explained there would be no confusion between their businesses because they operated far from the mainland company's stores.
But Tasha Kahele, who is Native Hawaiian, has spent nearly $10,000 so far changing her Anchorage store's name to Lei's Poke Stop after receiving one of the letters.
Native Hawaiian experts note there's a cultural clash underlying much of this. Modern European-based traditions use trademarks, copyright and patents to create economic incentives and rewards for creating knowledge and culture. Indigenous culture, on the other hand, is often passed on through generations and held collectively.
"They're never going to sit nicely together in a box," said Kuhio Lewis, the CEO of the Council for Native Hawaiian Advancement.
It will be difficult to determine who would decide who can use Native Hawaiian culture and who would be able to use it. Limits may violate the First Amendment of the U.S. Constitution. The task force will have to explore who can do what, Lewis said.
"At the least, they need to have some cultural sensitivity about how it's used. And they need to know you can't be telling Native Hawaiian businesses they can't use their own language," Lewis said.
The resolution points to potential models in New Zealand and Alaska, which both created signifiers that indigenous people may place on their art as a mark of authenticity.
Marie Texter of Anchorage said her late father Andy Makar — who drew, made carvings from tusks, cottonwood and horns, and sewed animal skins — was a strong believer in the Silver Hand seal for Alaska Natives.
"He said this is a great program because so many times the Native artwork gets commercialized or used by someone else," she said.
He had to fill out proof of his Indian blood — he was mostly Yup'ik but his mother was Athabascan — to apply.
But Rosita Worl, president of Juneau-based Sealaska Heritage Institute, said not all Alaska Native artists apply for or use the emblem. Nor does the program deter the sale of bogus Native art made overseas, she said. It also lacks enforcement and publicity, she said.
Charles E. Colman, a University of Hawaii law professor, said such programs hold up under federal law because they don't prohibit people from making work that resembles indigenous art. They merely won't allow people to say their work is produced by an indigenous person if it's not.
Colman believes the Aloha Poke situation, on the other hand, could be addressed within existing trademark law.
He believes the Chicago company's trademark could be cancelled if challenged because it's not so well-known that its name has developed a secondary meaning the way the words in the retailer name "Best Buy" have, for example.
"You can't just register a descriptive phrase unless you've achieved a certain amount of public recognition," he said.
Oil sheen 'barely visible' at site of 14-year-old Gulf leak Posted: May 16, 2019 at 12:38 pm
(AP) — A chronic sheen has become "barely visible" since
Fed Chairman Powell doesn't see elevated recession risks Posted: February 12, 2019 at 12:44 pm
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Small businesses find tight job market makes it hard to hire Posted: August 7, 2018 at 1:44 pm
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Grocer Albertsons eyes Rite Aid deal in health care push Posted: February 20, 2018 at 2:02 pm
The owner of Safeway and other grocery brands is buying
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Anyway to eliminate or rename "Archives for"?
No such option at the moment. I will be working on an update today.
Thanks Satish, appreciate the response!
Thanks Satish. I removed "Archives for %s" in option. If I leave "%s" it displays a directory path. Is that normal? I ended up deleting all from option and that works fine.
BTW I have a child theme so I'm never notified of update on admin. Only know if I check my Swift Themes account. Is this normal?
Path thing is a bug.
I delay pushing the updates to the update server by upto a week and you always want to be on the bleeding edge!!
Can you check the %s thing again.
Yep, working great now. Must be the darn cache thing, again!
I have to paste that note to my forehead, LOL!


This small family-owned company has been making artist quality oil paints since 1865. Jacques Blockx, a brilliant early chemist, was sought out by French and Belgian artists for his advice on pigments and as a response, commenced manufacture of his selection of oil paints. The brand is synonymous with the highest of quality where no compromise is made in their manufacture, either in the cost of pigments or the time for manufacture, for they are still made today on the same slow moving stone three-roll mills that have been used for a century.
Amongst the most ardent supporters of this paint have been Rene Magritte and Paul Signac, and of recent times, the technical painting master, Salvador Dali. The concept is that the pigment is not just the colour, but the way it paints irrespective of the pigment and its concentration. The colours have been ground only in cold pressed poppy oil which is around six times the cost of cold pressed linseed oil. Difficult pigments are put through the three-roll mill up to five times to ensure perfect wetting and fluidity with the maximum quantity of pure pigments without a trace of fillers. Many of the colours are unique to Blockx and are a tribute to the vision of the original Jacques Blockx.
Note that Blockx do not make any range other than professional quality. In 1918 the factory employed 14 staff, today they employ only 8, ensuring a semi-handmade quality opposed to mass production paints. Surprisingly, these paints are in the same price bracket as mass produced paints.
Many of these colours are unique and have special mass tones unseen in any other paints, ie, Brown Ochre and Brown Ochre Light, Cassel Earth, Gold Ochre, Italian Earth. Note that colours are priced by pigment and so may appear out of context in pricing, ie. the cobalts which feature in Series 6. Available in 35ml & 200ml tubes.
Reproduction set, based on the 1887 painters' field set sold to the French Impressionist painters for 'plein air' painting trips. Comprises 12 x 40ml tubes of colour, plus a large tube of white paint, turpentine, gloss varnish, palette, palette knife, brushes and double dipper.
The development of Impressionist painting is almost in parallel with the development of Sennelier oil paints. The evidence of quality and long-term consistency is visible in most of the world's major art collections with Sennelier on the canvases of Monet, Bonnard, Chagall, Kandinsky, Modigliani and Yves Klein.
In keeping with early manufacturing traditions, the paint of today is highly concentrated without fillers, employing special methods of manufacture. In its current range, Sennelier retains many unique colours, such as Chinese Vermillion, Madder Lake Pink, Pouzzoles Red and Pink Brown. Available in 140 colours in 40ml tubes.
Fine oil starter set. Cardboard-packaged set of 10 x 21ml tubes, turpentine, charcoal, brushes, palette knife and metal dipper.
Cardboard-packaged set of 14 x 34ml tubes, turpentine, charcoal, brushes, palette knife and metal dipper.
Clear organic pigments, 200ml in metal tubes. Excellent for colour mixing. All one price in a range of 36 colours.
Starter set of 5 x 34ml tubes; 3 primary colours plus black and white.
Cardboard-packaged set of 8 x 40ml tubes.
Lacquered and dovetailed, polished wooden-boxed set with brass piano hinges, hand made in France. Has leather carry handle and is firmly secured by twin leather straps. Comprises 22 x 40ml tubes, turpentine, gloss varnish, double dipper, brushes, charcoal, palette knife and wooden palette.
Wooden boxed set, comprising 15 x 21ml tubes, plus accessories.


text:
by Paul Lilly — Wednesday, October 21, 2020, 11:42 AM EDT
Acer Blitzes Gamers With Six New Predator And Nitro Displays Offering Up To 280Hz Refresh Rates
It has been a long time since the monitor market was a stale one. These days, it is one of the most frequently updated categories, with new models arriving all the time sporting faster speeds and a barrage of fancy features. So it is not surprising that among the products Acer announced today, there are half a dozen new displays, including four that fall under its Predator branding and two that carry the Nitro designation.
What you see above is the fastest of the bunch, Acer's new Predator XB253Q GW. It sports an IPS screen with a 1920x1080 resolution that users can overclock to 280GHz (there is no mention of the stock refresh rate, but I'm guessing it is 240Hz, with up to a 0.5ms gray-to-gray (GtG) response time.
Those are fast speeds, with a delightful combination of what is likely to be a high color gamut and accurate color reproduction. Acer is keeping many of the specs close to the vest, but IPS screens typically look better than cheaper TN panels. This is also a G-Sync Compatible model, to further help keep the action buttery smooth.
Acer's Predator XB323U GX is a bigger version, checking in at 32 inches with a 2560x1440 screen. There is no mention of the display type, but Acer claims it offers 99 percent coverage of the AdobeRGB color space, so my hunch is either IPS or VA.
This is also a fast display, with a 270Hz refresh rate (overclocked), up to a 0.5ms GtG response time, and a G-Sync Compatible label. The cherry on top is DisplayHDR 600 certification, which monitors can only qualify for if they are able to achieve at least a 600 nits brightness. Local dimming is part of the package as well.
The biggest and curviest of the bunch is the Predator X34 GS (shown above). It features a curved 34-inch "Agile-Spendor" IPS screen that is certainly expansive, with an ultrawide 3440x1440 resolution.
This one is not quite as speedy as the other Predator monitors that were introduced, but can still hit an overclocked 180Hz refresh rate and ultra-fast 0.5ms GtG response time. It is also another G-Sync Compatible display. Other features include DisplayHDR 400 certification, 98 percent coverage of the DCI-P3 color spectrum, and two integrated 7-watt speakers.
The last of the Predator monitors is the Predator XB273U NV. Acer says this one is easy on the eyes and has been certified by Eyesafe to effectively manage high-energy wavelengths to filter out blue light, along with some other viewing technologies (LightSense, AdaptiveLight, ColorSense, and ProxiSense).
This one is a 27-inch monitor with a 2560x1440 resolution, 170Hz refresh rate (overclocked), 1ms response time, and DisplayHDR 400 certification. Acer claims it offers 95 percent coverage of the DCI-P3 color space.
Finally, Acer announced a pair of Nitro monitors, including the Nitro XV272U KV and Nitro XV272 LV. Both are 27-inch displays and both are Eyesafe-certified. So, what's different?
the KV model offers a 2560x1440 resolution, 170Hz overclocked refresh rate, and 95 percent coverage of the DCI-P3 color space. Meanwhile, the LV model features a 1920x1080 resolution, 165Hz overclocked refresh rate, and slightly lower 90 percent coverage of the DCI-P3 color space.
Pricing and availability for all these monitors breaks down as follows...
Predator XB273U NV: $549.99
Predator XB253Q GW: $429.99
Predator XB323U GX: $899.99
Predator X34 GS: $999.99
Nitro XV272U KV: $399.99
Nitro XV272 LV: $279.99
The Predator XB273U, XB253Q GW, and XB323U GX will launch to retail in January 2021. All of the rest will be available in December of this year.
Tags: Acer, Monitors, predator x34 gs


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My last few days at GA and then on to DOT.
This week marks my last few days at General Adminstration. I have been there 7 years so moving somewhere else is a bit of an event. My new position will be essentially the same type of work, programming in powerbuilder and C#.Net but I do get a promotion and raise out of it. That is the main reason I am leaving actually, the management at GA decided that I should not get a promotion there despite all that I did there. Many people have expressed reget to me that my presence will be missed, this did not have to happen but I wanted a promotion on my merits not simply on because someone else offered me one. One of the supervisors actually said to he did want to have "a gun held to head" (figuratively, of course) regarding giving me a promotion. Well, I did not want to do that either so I took the offer and removed the pressure from him.
Ah well, I ramble on here. I am excited about moving and look forward to new challenges but I am also a bit sad about leaving. I feel that I did many good things at GA and there were opportunities to do more but I could not continue there without some kind of recognition. Time will tell whether this was a good move or not, I personally have the feeling it will work out quite well.
Next Next post: Microsoft's Future?


text:
Posted inSports
Keira D'Amato Finishes the Year With a U.S. Top 10 Marathon Performance
"It's wild and surreal, but at the same time, it's like, I have more work to do. I'm gonna move up that list."
by Kelyn Soong December 23rd, 2020 December 23rd, 2020
Keira D'Amato Credit: @kevmofoto
When Keira D'Amato crossed the finish line at the Marathon Project race in Arizona this past Sunday in second place, she wasn't thinking about how her time of two hours, 22 minutes, and 56 seconds made her the seventh-fastest American woman to run a marathon on a record-eligible course. She wasn't thinking about all the professional runners she beat.
D'Amato, an elite amateur runner and full-time realtor, had other things on her mind. She had finally earned the title of fastest marathoner in the D'Amato household, beating her husband Anthony's record of 2:31:49 from 2008. It's a goal she's had for a while.
"For years," D'Amato laughs. "I'm not making this up. This isn't something that I'm just saying to be funny. I've really trained my butt off to be able to be the fastest D'Amato."
Her second place finish at the Marathon Project in Arizona caps a streak of record-breaking runs and headline feats for the 36-year-old mother of two who lives in Midlothian, Virginia.
In January, D'Amato ran a 1:10:01 half marathon, followed in late February by a 15th place showing at the Olympic Marathon Trials in Atlanta. Over the summer, she ran multiple time trials on the track that would've been Olympic qualifying times, including a blazing 15:04 in the 5K, which made her just the seventh woman 35 and over in history to run a sub 15:05 in the distance.
D'Amato was just getting started. In October, she won the Michigan Pro Half Marathon with a personal best of 68:57 to move to No. 10 on the U.S. all-time performers list. And just last month, she ran a 10-mile road American record around Anacostia Park in 51:23.
D'Amato improved her marathon time by 11 minutes on Sunday, and joins a top ten list of best U.S. marathon times alongside Olympic champions like Joan Benoit Samuelson and legends of the sport like Deena Kastor and Shalane Flanagan. She calls the women on the list her "role models and inspirations."
"Just wild," D'Amato says after hearing the list of names. "It's funny, because there's part of me that just listening to you, I just have to like double blink and be like, 'What did he just say?' But then there's the other part of me that's like, 'Seventh? Let's try to improve that.' It's such a weird, like, I don't even know how to explain it, but you can kind of think the same things. It's wild and surreal, but at the same time, it's like, I have more work to do. I'm gonna move up that list."
With guidance from her coach, Scott Raczko, D'Amato headed into Sunday's race planning to run at a pace of under 5:30 per mile with the intention of running the second half faster than the first.
She started the race, which involved several loops around the flat, 4.26-mile course, with the second pace group alongside Emma Bates and Stephanie Bruce, both professional runners, with the goal of breaking 2:23. Olympian Nick Willis and Ben Bruce served as their pacers. In front of them, Sara Hall, the eventual winner, and third place finisher Kellyn Taylor were aiming for the American record.
Hall's time of 2:20:32 fell less than a minute short of the American record set by Kastor in 2006, and Taylor finished in 2:25:22.
"When we took off and I saw Kellyn go with Sara, I was so pumped, like, I was so proud of her for like putting it out there and going for it," D'Amato says. "And it's really inspiring to see someone being like, yep, I'm going for it. Like it got me all jazzed up."
Bates and Stephanie Bruce eventually fell off the pace, and for the second half of the marathon, it was just D'Amato and Ben Bruce. With around six miles to go, she asked Ben if he could pick up the pace.
"He was like, 'You want to go faster?' I'm like, 'Not a lot faster, just like a little bit,'" D'Amato explains. "He said, 'OK,' so we started working it down a little bit."
A few miles later, she had Taylor in sight, using the light poles next to the street as a measuring tool to see how far behind she was. Once D'Amato passed Taylor, she knew to lock in. Ease up, and experienced pros will take advantage.
"Having those women behind you is terrifying," she says. "Because they are so strong, that if I don't keep pedal to the metal, they are gonna catch me and just pass me, so it gives you so much like fuel and so much encouragement, just be like, OK, I cannot let up because these are women you do not want to mess around with."
She didn't, and created a several minute buffer between her and Taylor, and finished the race with a negative split, just like her coach wanted.
Now, D'Amato is back home in Virginia with Anthony and their two children. She plans to take it easy the next two weeks, with no scheduled workouts. That's her way of celebrating.
"We can just relax in the mornings," D'Amato says.
Next year, she plans to focus on the track and wants to make the Olympic team in either the 5K or 10K. As for sponsors, D'Amato has been in discussions with several companies but has yet to finalize a deal.
That's not really one of the goals, anyway. She wants to take her time making that decision.
"I don't want to put any pressure on that being a goal. Because my goal is to run as fast as Keira can run," D'Amato says. "It's really important to me to find the right partner with a company that I believe in and that I respect and that I'm passionate about. And so I'm not taking this decision lightly."
Whatever she decides, D'Amato can rest easy knowing that during a global pandemic, few runners trained as hard or accomplished as much as she did. Family records included.


One of my favorite writing quotes.
This is why we revise and edit.
As a writer who has a 50+ hour a week day job, I have to take advantage of every free moment I have and use it to write. During the school year that generally means writing in the evening, staying up late on the weekends, and taking advantage of my days off. Granted, some days are much more productive than others depending on what's going on, but I don't like sacrificing my writing time and make it a point to write a little bit every day.
Finding time to write, however, is not always easy. Most week nights, I don't get home from work until between 6:00 and 7:00 pm, at which time I take a moment to unwind (because I drive for 45 minutes through rush hour traffic and am grateful to walk through the door alive) before I work on making dinner for the family. During our meal, I catch up with the kids and husband on daily happenings. Down time follows. With a movie or the family's favorite TV show on in the background, I boot up the laptop and begin to write or revise or edit a manuscript I'm working on.
Since I get up at the crack of dawn and commute through big city morning traffic to get to work by 7:30 a.m., I usually only write for an hour or two before sleep beckons me. I am NOT a morning person, and if I don't get a full night's sleep, I have a difficult time functioning and effectively teaching children all day. Sleep and I are best friends.
This writing pattern changes on the weekends, however, when I have a tendency, and probably a bad habit, of staying up until 4:00 a.m. I'm most productive in my writing this time of day because the house is quiet and no one is around to demand my time or attention. Trying to write with background noise distracts me, so I prefer the stillness during the wee hours of the morning, although I usually regret this decision when I'm dragging myself out of bed the next day.
Being a teacher, I am blessed to have Thanksgiving, Christmas, spring break, and summers off. These are ideal writing times for me. I fully utilize this extended time and have what I call a writing marathon, where I spend the whole day writing. Of course I get up occasionally to stretch my legs, use the facilities, walk the dog, pet the cat, and grab a glass of tea, but the bulk of my day is used to write. I've been known to complete several chapters during such times and could probably bust out an entire book if I really pushed myself.
Writing is hard work, but it's worth it in the end. I can't imagine my life without the written word or go a day without writing.
Every writer goes through the writing process at various stages. Some follow the stages diligently step by step, others are more haphazard in their process. My writing routine is a bit more unorthodox than most because I don't have the luxury of writing all day, every day. I wish I did. With a full time 50+ hour a week job, I utilize what time I have.
I've never been the type of writer that uses an outline or a story board. I simply open a notebook and jot down character ideas, possible plot elements, and brief sketches of scenes I want to include. Once my notes are written, I start writing. Ideas generate as I write. I have a basic plan of where I want my story to go, but details develop the more I write. I never really know exactly how a book is going to end until I get to the end.
Once the draft is complete, I re-read the entire thing from beginning to end, adding details, refining dialogue, and including more character development to make my message clearer. Once the first revision is finished, it's time for round two. In this revision, I look for unnecessary information, pointless characters, redundancy, word choice, and check for factual accuracy. I cut what I need to and add more detail where appropriate. This is also the stage where I cut scenes that are not needed.
The next stage of revisions has to do with flow. Here, I'm reading for plot and story fluency. Cutting and revising to eliminate choppiness, moving scenes around to make the plot more fluid, and refining transitions. This is usually the final revising stage I go through. Then I re-read, making only minor changes as I see fit, before I begin the editing process.
Editing involves double checking grammar, spelling, and punctuation, cutting overused words, refining dialogue tags, and checking sentence structure. Once I read the manuscript through once from beginning to end, I read it again from back to front. That way I'm looking specifically for errors and storyline doesn't get in the way. Once I'm done editing, the manuscript then goes to a second set of eyes (my editor) who gives their input on content, grammar, revisions, etc… My editor and I go back and forth several times regarding revisions, cuts, additions, etc… before a complete document is created. I then read through the entire manuscript one more time to make sure nothing was missed and all typos have been corrected.
The document is now ready for publication, which is a complex process in itself.
The whole writing process is time consuming and takes many months to complete, at least for me it does. I know not every writer follows these steps exactly like I do, but that's the beauty of writing. Each writer has their own style and their own way of perfecting the craft. Creativity has no defined set of rules, which is what makes books unique and every author special. The unique road each of us travels to get our story out there is what makes writing so great.
So write on, follow your heart, and let the creative juices flow.
Inspiring cartoon. Write because you love it. Write because you have something to say. Write because you believe it. Don't stop creating.
I never thought about it like this.


Autism refers to a neurodevelopmental disorder covering a range of conditions that impair a person's social skills and verbal and non-verbal communication. It also causes a person to have restricted or repetitive behavior.
Here at Kids Neuro Clinic and Rehab Center, we understand that each case of autism is unique for every individual. As such, our specialists devise and provide complete autism treatments according to the unique needs of each patient. Preventive measures are also done to alleviate behavioural issues, as well as treat the main symptoms of autism, which include sleep complications, seizures, and gastrointestinal disorders.
Our experts analyse the condition of each child using the most advanced treatments, latest findings, and medical equipment. We provide different types of intervention measures for children, depending on their individual needs. It may include behavioural treatments, medications, or a combination of both.
Our experts examine the medical history of a child, in addition to conducting reviews and examinations, to determine the best possible treatment plan.
We have a team of paraprofessionals who provide unparalleled assistance to our experienced autism therapists.
We have designed our therapy programmes to incorporate specific activities that help ensure children progress and meet their respective goals regularly and properly. For example, children with this condition may benefit from focused social skills training to improve how they interact and perform at school.
What makes our quality of treatment different?
Our center focuses on the main areas that have been primarily affected by autism, and we seek to rehabilitate these gradually through an organised, step-by-step treatment plan. Children are encouraged to participate in programmes that will enable them to become more confident and at ease in various social situations. They are given ample opportunities to mingle with other kids, and we work closely with them to help them overcome their speech and communication problems.
If you need more information about our center and programmes for kids, you may reach us at +971 4 557 0326. One of our professional staff members will gladly answer your questions and let you know how we can help in your child's holistic growth and development.


The 130 grain #1903 bullet is a lighter weight competition bullet with some usage in Silhouette on chickens and pigs..
In handguns, this bullet has been used widely for handgun silhouettes and other target competitions. They deliver outstanding accuracy, high momentum downrange, and low sensitivity to crosswinds.


Get pension planning in St Stephen today!
Like most people, you know that you need to plan for your retirement. It's a matter of insuring for the future, not just for yourself, but for your spouse, children and the people who mean the most to you. At The Co-operators, our qualified Financial Advisors are in your area and can answer any questions you might have about pension planning in St Stephen. For more information about planning for your retirement, talk to The Co-operators today.
The biggest attraction of RRSPs in St Stephen is it allows you to defer taxes while saving for your retirement. You can start investing in RRSPs in St Stephen at most any age, but as you may know, the sooner you set up, the more you're bound to gain. But before taking the plunge, consider talking to a Financial Advisor first. At The Co-operators, we'll listen to your investing needs and lifestyle, and answer any questions you may have about investing. Get the advice you need, talk to your local Co-operators Financial Advisor.
Contact Canada's source for local information and advice on pension planning in St Stephen; The Co-operators.
As a responsible corporate citizen, we believe in balancing our economic, environmental and social priorities. In fact, The Co-operators was recently recognized in Hewitt Associate's Green 30 guide, which identified Canada's 30 most environmentally-conscious employers. Talk to The Co-operators today about pension planning in St Stephen AND about what we're doing to help the environment.
Get extended Pension Insurance in St Stephen now!
St Stephen has a public pension policy system that provides a minimum level of mandatory coverage to vehicle owners when they purchase license plates.


Luxembourg health authorities have issued a food safety warning, after tests showed that ready-made salads by the brand Dessaint were contaminated with listeria monocytogenes bacteria.
(CS) Luxembourg health authorities have issued a food safety warning, after tests showed that ready-made salads by the brand Dessaint were contaminated with listeria monocytogenes bacteria.
According to the health ministry, the salads were sold at Cora and Cactus supermarkets. While the shelves have been emptied, some of the salads were sold and customers are urged not to eat them.
Affected are three pasta salads (tomato/mozzarella, ham/cheese and chicken), as well as mixed green salads with tuna and surimi, with expiry dates between October 14 and October 26.
For a detailed list of the products affected by the recall click here.
Listeria monocytogenes bacteria can cause gastoenteritis in healthy individuals, but can pose a risk of infections of the central nervous system, as well as bacteremia – the presence of bacteria in the blood – in pregnant women, the elderly or other at risk groups.
Anyone who has eaten of the recalled salads and feels unwell, or presents symptoms, such as fever or headaches is urged to consult a doctor.


text:
Seljačka buna, buna 1573. godine
 Seljačka buna (Šenoa), povijesni roman Augusta Šenoe
 Seljačka buna (1975.), hrvatski dugometražni film
 Seljačke bune, oblik seljačkoga otpora tijekom srednjega i ranoga novoga vijeka
 Seljačka buna (sastav), heavy metal glazbeni sastav iz Srbije

Izvori

redpajama_set_name: RedPajamaWikipedia