text:
- Research article
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Entropy-based gene ranking without selection bias for the predictive classification of microarray data
BMC Bioinformatics volume 4, Article number: 54 (2003)
We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process).
With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles.
Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.
The study of gene expression patterns is expected to enable significant advances indisease diagnosis and prognosis. The main objectives of a discovery process based on microarray data are the understanding of the molecular pathways of diseases, their early detection, and the development of measures of individual responsiveness to existing or new therapies. In particular, the perspective of providing new targets for therapy and of developing clinical biomarkers has given a strong impulse to methods for ranking genes in terms of their importance as predictor variables in the construction of classification models from arrays [1–6].
In this paper, we address the problem of developing a practical methodology for gene ranking based on the support vector machine classifier (SVM), a machine learning method that is considered particularly suitable in the classification of microarray data [7–9]. A typical prediction task for the methodology would be the identification of patients resistant to a therapy or the definition of a 'terminal signature', a set of genes and a decision rule identifying short-term survivors who might benefit from specific therapies [10, 11]. For example, recent results have shown that the clinical outcomes of high grade gliomas [12] and of cutaneous T cell lymphoma [11] may be better identified by gene expression-based classification than by histological classification or measures of tumor burden.
The methodology described in this paper is designed to obtain a list of candidate genes, ranked for importance in discriminating between classes, and the corresponding SVM classification model. The method also provides an honest estimate of the model accuracy on novel cases (predictive accuracy).
Feature elimination for SVM
We have developed the entropy-based recursive feature elimination (E-RFE) as a non-parametric procedure for gene ranking, which accelerates – without reducing accuracy – the standard recursive feature elimination (RFE) method for SVMs [6]. The RFE procedure for SVM has been evaluated in experimental analyses [13] and it is considered a relevant method for gene selection and classification on microarrays. However, RFE for SVM has high computational costs. At each model building step, a pair (classifier, ranked gene set) is constructed from samples in a training set and evaluated on a test set, where training and test are subsets of the data available for development at this step. The contribution of each variable is defined through a function of the corresponding weight coefficient that appears in the formula defining the SVM model. The elimination of a single variable at each step (as in the basic RFE procedure) is, however, inefficient. In a typical microarray study, thousands of genes have very low SVM weights in the initial steps. An alternative is the simultaneous removal of a fixed fraction of the genes (decimation) or according to a parametric rule (e.g. the square root function). These basic, parametric, acceleration techniques or gradient based methods have been proposed in machine learning studies [6, 14, 15], showing that accuracy close to basic RFE may be obtained.
The aim of our E-RFE procedure is to provide a more flexible feature elimination mechanism in which the ranking is obtained by adaptively discarding chunks of genes which contribute least to the SVM classifier. In our E-RFE method, we cautiously discard, according to the entropy of the weight distribution, several (possibly many) genes at each step to drive the weight distribution in a high entropy structure of few equally important variables (see Methods for details). The procedure should accommodate for the different SVM weight distributions arising from supervised classification tasks on different microarray data.
The selection bias problem
As shown in the Results section, the E-RFE method achieves a speed-up factor of 100 with respect to RFE. It also produces a faster and more flexible gene elimination curve than parametric versions of RFE. Finally, feature elimination with E-RFE does not significantly degrade accuracy with respect to the slower, one-step RFE.
These results have allowed us to adopt E-RFE for SVM as the basis for a complete methodology scheme for gene selection designed to control the "selection bias". This bias causes a methodology flaw which is easily introduced within gene selection procedures that depend on the optimization of a classification rule ("wrapper" algorithms). While this flaw can be reproduced with any wrapper algorithm, the selection bias is a specific risk for RFE-SVM gene selection procedures.
To separate the feature-selection process from the performance assessment, the bias has to be corrected in the estimates of prediction error whenever the selected model is tested on data previously used to find the best features [16]. This occurred in several early studies on microarrays that discovered very few genes yielding classification models with negligible or zero error rates ("perfect" or "near-perfect" classification with very few genes on arrays of dozens of subjects and up to 20 000 genes). The flaw unfortunately leaked into the original work on RFE, and it is still being replicated in different supervised machine learning approaches [17]. A typical contamination pattern is the following. Consider a data set S and its three subsets S0, S1, and S2. Suppose that the best feature set is obtained on S0, e.g. composed by the features of a classification model with minimum cross-validation (CV) error estimated on S0. The correct methodology requires that a model with the optimal features is then trained on S1 and tested on S2 to obtain an error estimate for new cases. But if the test data set S2 overlaps with the S0 used in the selection process, even for disjoint S1 and S2, an over-optimistic error rate will be estimated on S2, possibly leading to the conclusion that a panel of very few genes is adequate to differentiate between classes. In particular, over-optimistic error rates are being produced when the S2 hold-out set is not available and the CV error is computed on a part of S0 ∪ S1.
A flawed scheme can be assessed by developing models on no-information data, i.e., by a random permutation of class labels. Errors on a disjoint test set should result close to the no-information error rate – approximately 50% on a balanced data set in a binary classification task. Instead, whenever the error is estimated on data previously used in the feature selection process, a CV error close to zero can be obtained with a small subset of genes even if a classifier is trained on data with class labels randomly permuted, (see [16, 17] and the synthetic and real examples in the Results section in this paper).
Validation scheme
A more sophisticated experimental design is thus needed to validate the performance of diagnostic methods based on supervised class prediction rules on gene expression data. An unbiased error estimation for models developed on a reduced number of biomarkers must be obtained from an external process operating out-of-sample from the data involved in the selection process [18, 17]. Two strata of processes have to be considered, an external one for performance assessment and an internal one for feature selection. At each level, a resampling and partition method has to be applied, to smooth data variability and balance class cardinalities.
The methodology scheme we propose for the validation of supervised methods belongs to this family of experimental designs and it is summarized in Fig. 1. The scheme can be applied to alternative feature ranking procedures, such as the penalized discriminant analysis (PDA) [19, 11]. It can also be used with SVM in conjunction with other gene filters based on statistical tests (the correlation coefficients [20], and the T-score filters [8, 16, 21, 6, 5, 14]; see implementation in Results).
A crucial problem is that this kind of designs requires an intensive replication of classifier builds, leading to a tradeoff between feasible computation times and the level of sophistication in the development of the overall gene selection process.
The availability of such a bias-correcting scheme also has an important implication for gene panel sizes. A monotonic and exponential-like decrease of predictor accuracy is typically found for increasing numbers of genes when the complete method is used, and perfect classification is rarely achieved with very small sets of genes. No intrinsic cutoff for gene selection is thus automatically provided by the classification models. The observation led us to adopt a Zipf's power-law hypothesis for gene selection [22]. We have thus used a strategy for gene selection that is based on a saturation profile derived from an exponential approximation of the classification error, as estimated by the two-strata modeling scheme.
The effect of selection bias on synthetic data
We set up two experiments on synthetic data in order to elucidate the need for a complex methodology scheme, as indicated in [17]. We considered first the dataset f1000–5000, structured as follows: 100 samples described by 5000 features, in which 1000 of them are significant (i.e. generated by 1000 Gaussian distribution centered in 1 and -1, with standard deviation uniformly ranging between 1 and 5), and the remaining are uniform noise in the range [-2,2]. We first applied the RFE feature ranking procedure to the whole dataset. The importance of features for discrimination was then evaluated by creating a sequence of SVM models based on an increasing number of features (every single step at 1–15 genes, every 5 at 15–50, every 10 at 50–200, every 100 at 200–1000; further details are given in Methods). The performance was evaluated by a 10-fold CV procedure (one tenth of the data held out and used for testing in turn, test results averaged).
In Figure 2(a) we represent the average CV error over the ten experiments: the selection bias effect is shown by the f1000–5000 curve (solid line), which reaches a zero CV error with only 9 features. Note that the error raises using 11 variables, and it is definitely zero for 12 or more variables.
We set up a second data set of 100 samples described by 5000 uniform noise features in the range [-2,2]. We applied the RFE feature ranking procedure to the whole dataset and then we performed a 10-fold CV with different feature subsets. In Figure 2(a), the f0–5000 curve (dashed-dotted line) displays the average CV error over the 10 experiments. The selection bias effect can be read on the obtained curve which shows a zero CV error with only 20 features. This is an even clearer example of the selection bias effect: the features consist of pure noisy data (and thus not separable at all), nevertheless the classifier indicates some of them as relevant, reaching a 100% accuracy.
The effect of selection bias on real data
The potential for inducing the bias in a class prediction study can be shown for the the colon cancer microarray data set from [23]; the set consists of expression levels of 2000 genes from 62 tissues (22 normal and 40 tumor cases, Affimetrix oligonucleotide arrays). The RFE error curves shown in the right plot of Figure 2(b) were estimated by leave-one-out cross-validation for models trained on feature subsets of increasing size, after a feature ranking performed on all the available data. The same data were used for development and test: as a consequence a zero error was obtained with only 8 genes (solid curve). Surprisingly, when the procedure was applied to the same data after a label randomization, a very similar result was obtained without any class information: 14 genes were sufficient for a zero leave-one-out error estimate (dashed line). This behavior was replicated by using the other ranking methods we describe below.
Entropy-based ranking of microarray data
We tested the E-RFE ranking approach (see below) on three well known data sets: (i) a colon cancer data set [23], (ii) a lymphoma data set of 96 samples (72 cancer and 24 non cancer, cDNA) described by 4026 genes [24], and (iii) a tumor vs. metastases data set, consisting of expression levels of 16063 genes describing 76 samples (64 primary adeno-carcinomas and 12 metastatic adeno-carcinomas, Affimetrix oligonucleotide arrays) [13, 10]. The original public data were subjected to the following preprocessing across genes: the vector of the expression values of every gene was linearly rescaled to mean zero and standard deviation one. We analyzed the properties of E-RFE by considering the effect of recursive feature elimination on the distribution of the weights in the SVM model.
In Figures 3(a)–3(c), referring to the tumor vs. metastases data, we show how the distribution of the SVM weights is modified by the entropy-based selective gene elimination. At the beginning of the feature elimination process, the construction of a SVM on the complete set of features on the tumor vs. metastases data produces a large amount of weights w whose cost function J (α) is concentrated nearby zero (see Methods for the definition of J (α)). The result is displayed in Figure 3(a): the n = 16063 J (α) values, normalized in the unit interval, are plotted in the left panel, and their frequencies are stratified at 1/ bin width in the right panel.
With the one-step RFE algorithm, just one of the features with negligible weight would be removed, while it would be more efficient to eliminate a chunk of the lowest ranking genes. On the other hand, eliminating genes at fixed steps, or according to a specific parameterization introduces the problem of making assumptions on the SVM weight distribution, which may be specific of the microarray data set. A process based on the entropy measure (defined by Eq. 1 in Methods) allows to eliminate chunks of uninteresting genes until the remaining distribution stabilizes in a higher entropy regime of the weight distribution, adapting to the characteristics of the microarray data at hand.
Speedup of E-RFE
As described below, in E-RFE procedure we apply different strategies for weight removal, according to a comparison of the current entropy H and of the mean M of the weight distribution with two thresholds H t and M t , respectively.
For the low entropy structure with n = 16063 genes in Figure 3(a), 5678 genes are eliminated at Step 1. In 10 reduction steps we obtain a classification model based on 1293 features (Figure 3(b)). The distribution is still characterized by many low weight values, which are selectively removed by E-RFE: at Step 17 (Figure 3(c)) a subset of 315 genes is obtained, with a high entropy and less concentrated distribution. Only 15 genes will be eliminated.
Let us consider also a parametric feature elimination in which the leftmost bin of
features is removed at each step (SQRT-RFE method). In Figure 4 the number of steps required by the E-RFE and SQRT-RFE methods on the tumor vs metastases data set are compared along with the number of undiscarded features at each step. The E-RFE procedure is much faster in this task, as detailed in Table 1 for the three microarray data sets. Similar results were found for the other datasets.
Accuracy of E-RFE
We analyze now the accuracy of the E-RFE and SQRT-RFE methods as compared to the basic RFE procedure on the colon cancer and lymphoma data sets. We also considered two filter ranking methods, the Correlation Coefficients (CC) in the version of [20], and the T-score method (TT), previously applied to these data by different authors [8, 16, 21, 6, 5, 14]. In the tumor vs. metastases data set only the E-RFE and SQRT-RFE methods were employed due to computational feasibility reasons. Classification errors were estimated by a random partition resampling scheme over 50 repeated experiments, with a development-test splitting, in proportion of 3/4–1/4, and preserving the proportion of positive and negative cases for each set. For each resampling, the five ranking methods were applied to each development set, and linear SVMs were trained using the ranked subsets of features. The performance was then estimated on the independent test set, and finally averaged over the runs. A full description of the experimental scheme, designed to correct the effect of the selection bias, is given in Methods.
In Tables 2, 3 and 4 we report the average test errors of SVMs for gene subsets of increasing car-dinality for each data set. Very close errors and standard deviations are estimated for E-RFE and RFE: accuracy is thus preserved even though the E-RFE algorithm is much faster (about 100 steps instead of 2000 on colon cancer, about 120 instead of 4026 on lymphoma and 116 instead of 16063 for the tumor vs. metastases dataset). In general, for gene subsets of fixed size, no significant difference is detected among the RFE-based methods on the three datasets (t-test at 95% confidence level, df = 49). A significant difference is found between the RFE-based methods and the CC or TT methods for less than 300 genes.
An error estimate with a correction of the selection bias is shown in Figure 5(b). Given a development/test split of the data, we considered an internal K-fold cross-validation experiment on the development data only. For K = 3, two thirds of the development data were used at each cross-validation step to build SVM models on feature sets of increasing size ranked by the selected method. Models were then tested on the remaining third of the development data. The error curve in Figure 5(b) is estimated by averaging over the CV test sets. The error does not reach zero, but it exhibits a rapidly decreasing pattern which suggests an exponential behavior (fit shown by a solid line).
Such a pattern can be used to implement a feature selection procedure convenient for microarray studies. We have analyzed the results obtained by applying an optimal number of features (ONF) procedure designed to compute an approximate estimate of the optimal number of features n* for microarray data sets. Based on a K-fold experimental structure, the cross-validation error curve is fitted by an exponential map and n* is chosen as the point where the error difference with the next point along the fit is less than 1/1000 of the error range (see dotted line in Figure 5(b); details are given below).
In Table 5 we show the results of the OFS-M procedure application for a feature selection process where the SVM models were developed for E-RFE and RFE on the complete colon cancer, lymphoma and tumor vs. metastases data sets, respectively. For comparison, the OFS-M procedure was also applied with CC, TT and SQRT-RFE feature rankers and SVM classifiers. For the colon cancer data set, the optimal numbers of features are similar for the RFE-based methods (E-RFE, RFE, SQRT-RFE) and greater than those for CC and TT. On the other two data sets, no significant differences were found.
The complete methodology scheme introduced in Fig. 1 was applied to evaluate the predictive accuracy of the model obtained by the application of the OFS-M procedure. We plugged the previous experiment within the more complex validation (VAL) procedure, detailed below. For each of the three microarray data sets, 50 development/test splits were considered, and the ONF procedure was replicated: on each of the b = 1,...,50 development sets, we used an internal 3-fold cross-validation for feature ranking and estimation of the error curve and of the n*boptimal number of features. In Table 6, we report the average error on the independent test sets, and the average , the average of the 50 n*b. On the colon cancer data, the optimal number of features was higher for E-RFE, RFE and SQRT-RFE (about 70 against 50 for CC and TT), but more accurate models were obtained. The test error was close to 18% for the three wrapper methods and greater than 20% for the two filter ones. On the lymphoma data, the mean expected number of features resulted 80 for all methods, but with a lower test error for the RFE-based methods. For the tumor vs. metastases data set all the obtained results are similar regardless of the method employed.
Diagnostics of E-RFE
Providing a measure of relative importance of genes in the classification problem is a central product of gene ranking procedures. For each of the genes selected in the optimal feature sets over the total data as produced by E-RFE (80 features for colon cancer and 70 for the other two datasets), we compared the SVM weights with the number of extractions in the optimal gene subsets (gene multiplicity) as resulting from the replicated experiments. Figure 6 shows the number of extractions of the i-th feature in replicated experiments vs the weight function f w (i) = max i (|w i |) - |w i | obtained by the SVM model with optimal number of features. The points in the upper part of the figure indicate variables not chosen in the global model, but extracted in at least one run; the features labeled by a capital Latin letter are those extracted at least 25 times (threshold indicated by the dotted line) in the replicated experiments.
We performed a simulation to examine the stability of the gene multiplicity rank for E-RFE. We considered two synthetic data sets, each of 100 cases (50 labeled 1 and 50 labeled -1) described by 1000 features: the 1000 features in U1 were all uniformly distributed in the interval [-2,2] and thus not discriminating the classes. The second data set U2 was derived from U1 by keeping unvaried 995 features and introducing 5 features normally distributed with mean 1 or -1 according to class, and variance 1.5.
In Figure 7(A),7(B),7(C) we display the distribution of the feature normalized position for the first 5 variables according to the multiplicity rank computed over 50 replicated runs for datasets U1, U2 and colon cancer, respectively. Let r(v,b) be defined as the rank of v at run b, and n*bthe optimal estimated number of features for the colon cancer microarray at run b. The feature normalized position of feature v at run b is then defined as ν (v, b) = 1 - ((r(v, b) - 1) / n*b), if r ≤ n*b, and ν (v, b) = 0, otherwise. In practice, at run b, ν(v, b) is maximal when v has rank 1, and minimal when v is the last of the features considered.
The first 5 features for the no-information synthetic data set U1 in Figure 7(A) are in the lowest half of the range of ν. High scores may be produced by randomization at single b runs, but the rank is not steadily maintained throughout the 50 replicates. On the contrary, exactly the 5 relevant features of the synthetic data set U2 obtain the first multiplicity E-RFE ranks, with a limited variability, shown in Figure 7(B). Thus the highest ranks are constantly confirmed in the synthetic data set. The top 5 genes for the real data set displayed in Figure 7(C) have a behavior much similar to the 5 relevant variables from U2, with more variability.
In Tables 7 and 8 (respectively for colon cancer and lymphoma data), for each of the labeled points of the Figure 6, we report the gene name (Gene Accession Number for colon cancer and Clone number for lymphoma) and the weight ranking. We also report the gene multiplicity, i.e. the number of replicated experiments in which the gene was in the optimal feature set. In Table 8, the E-RFE ranking instead of the w score function is reported for variables not selected in the global model and indicated by a letter in Figure 6.
Finally, random permutations of class labels were produced as described in Methods to detect additional overfitting effects. The E-RFE procedure was then applied and the mean error was computed for subsets of increasing size. In Figure 8 we report the comparison between test error curves on true and random labels for colon cancer and lymphoma. The error on randomized classes is close to 40% or above; a paired t-test confirmed significant difference (p = 0 for all experiments) between mean accuracy over random and true labels data. It is worth noting that the error is less than 50% because of the unbalanced proportion of classes.
The best prediction accuracy on the colon cancer and lymphoma microarray data sets is obtained with more than 50 genes. The estimated error is less than 20% for the colon cancer data, and less than 5% for lymphoma. For the tumor vs. metastases dataset, we obtain an error lower than 14% by using more than 300 variables. These results are consistent with recently published work using a similar experimental schemes [16], while they differ from results of perfect or near-perfect classification with very few genes. Also considering the results of the experiments with no-information data, we may conclude that several promising results on microarray data may be descriptive of the shattering properties of classifiers on the given microarray data sets [18, 16, 17].
The exponentially decreasing behavior we observed after correcting for selection bias is consistent with recent literature proposing that the relationship between cancer classification accuracy and gene ranking from microarray data may be modeled by a power-law function, also called a Zipf's law [22, 25]. The exponential fit in the ONF procedure and the power-law functions are rough but working approximations for microarray data. In our experience, the choice of a cutoff based on a Zipf's law fit was less accurate than applying ONF with the exponential fit.
The ONF procedure defined by the exponential fit allows the identification of a saturating number of genes. The gene-ranking procedure can also be used to select larger or smaller subsets of genes as biomarkers, according to practical considerations. The validation procedure provides an error estimate for the selected subset in these cases. The ONF procedure was developed for the recursive feature elimination with SVM classifiers, and it results less accurate with the basic CC and TT filter approaches. A similar strategy similar to ONF is described in [26].
The VAL procedure allowed the estimation of model accuracy, optimal number of features, and a ranking of the most important features on the three microarray data sets. The entropy-based method E-RFE for the recursive feature elimination process showed results comparable with the more time consuming RFE and SQRT-RFE. The running times for a complete feature ranking process on a Pentium III 1 GHz processor are reported in Table 1 for all data sets. Proportional results were found for computations run on an Open-Mosix cluster. The accelerated E-RFE method therefore allows the standard use of complex model-evaluation and model-selection schemes.
On colon cancer data, all the variables extracted more than 25 times in the replicated experiments are contained in the list obtained by the global model. For the lymphoma data, all genes with multiplicity greater than 25 ranked within the first 165 genes of the global model, and only 8 were not in the selected set of 70 genes. Finally, in the tumor vs. metastases data set, only two features extracted more than 25 times are not included in the 70 genes optimal feature set, but they rank 84th and 89th in the reordered list.
Together with the rank based on SVM weights, gene multiplicity provides an additional measure of importance for the extracted genes. A good correspondence between the two indicators was found for the highest ranked genes. In a simulation study, gene multiplicity allowed the identification of the only relevant genes; moreover, the best ranked genes in simulation and on real data seem to maintain stability in the estimated rank. This property might be exploited in future comparisons of ranked lists produced by different ranking and classification methods.
Comparing Table 7 with other published work on the colon cancer data, two genes (numbered as 20 and 42 in the table) were also cited in the original study [23]. Moreover, twelve genes (5, 9, 10, 21, 25, 34, 37, 44, 46, 56, 71, 73) are also listed as important in [21], four genes (9, 10, 22, 26) in [6], three genes (5, 9, 44) in [2], and 9 genes (5, 9, 21, 22, 28, 34, 44, 57, 71) in [4].
In all our experiments, the use of linear SVM was accurate enough and provided faster performance than kernel-based SVM. Thus we did not introduce methods for kernel-based selection. However the scheme can be used to test models defined by different SVM architectures.
As alternatives to the ranking methods discussed in this paper in association to SVM supervised models, several other procedures are available to researchers. In a similar framework, the PDA based software CLEAVER [27] can be used in a combination of supervised classification and gene ranking. In [11], the authors based a first reduction of the number of genes on the t-test, one of the most used strategies for gene filtering. Variation filters and signal-to-noise ratio, also used together with random permutation testing, are other valid alternatives [12].
While attempting to reproduce results from other authors, we noticed the existence of a "preprocessing bias", also mentioned in [16]. The bias appears to originated with the application of ad-hoc strategies for reducing the effect of outliers. We decided to adopt the standard normalization for all data sets in our experiments. For instance, in the colon cancer example, due to the lack of additional independent data, we choose not to apply any squashing function optimized for outlier control. In developing diagnostic methods for microarray data, an additional adaptation to test data may be hidden in the choice of the normalization procedures and parameters. The methodology scheme implemented for E-RFE might be a candidate system for an unbiased estimate of the optimal preprocessing metaparameter related with the predictive model structure.
The new E-RFE algorithm was designed to estimate the relative importance of genes, with applications for predictive classification on array data. The algorithm is shown to preserve the accuracy achieved by the SVM classifier by using other ranking methods. At the same time it achieves a significant reduction of the computational workload. This result is crucial because high-throughput data analysis must also include the resampling procedures needed to ensure an honest estimate of accuracy and thus to avoid a gene selection process mainly driven by overfitting.
In order to correctly deal with the problem, we have developed an experimental set-up for analysis and prediction on microarray data. This set-up has allowed us to correctly identify the impact of the selection bias on synthetic and real data sets.
By controlling the risk of overoptimistic predictions, which have affected a number of recently published works, this set-up has provided a support for the identification of the function which associates prediction accuracy to the number of genes. On this basis, we have also pointed out a strategy for the individuation of a saturating subset of genes.
A basic advantage of the E-RFE method, within the experimental set-up we have adopted, is the automatic adaptation to the different weight distributions coming from prediction models developed from different DNA chips. Automatic model selection based on an extension of E-RFE may become of further interest for the integrated treatment of all the phases of the array analysis, including the selection of parameters for data normalization purposes.
Finally, it is important to start investigating new diagnostic criteria for the comparison and possibly the combination of ranked lists of genes computed from different supervised methods on the same array data.
Model selection and assessment
The experimental set-up proposed in this paper is partially similar to those described in [5, 16], and it may be summarized as an external stratified partition resampling scheme coupled with an internal K-fold cross-validation, to be applied to E-RFE or to other feature ranking methods at each run. This intensive double model selection and error estimation process is graphically outlined in Figure 9. The method is composed by three main blocks:
OFS-M procedure (Figure 9(a)): given a training set TR, a feature ranking method produces a list of ranked features RF, from which an optimal feature set OFS of size n* is selected. Based on OFS, a model M is developed by a suitable learning method. The optimal number of features n* is computed by the ONF procedure, while the accuracy of OFS-M is to be validated by the VAL procedure.
ONF procedure (Figure 9(b)): given a training set TR, this procedure is applied to select the optimal number of features based on a ranking method. A resampling procedure is iterated K times, each time producing a (TR k , TS k ) split of TR. A feature ranking method is applied to TR k producing a ranked list RF k ; a family (M ki , F ki ) of models M ki is produced, one for each increasing F ki feature subsets. The M ki models are evaluated on the TS k test data, computing test errors TE ki , and the average error curve is obtained. An exponential fit procedure is applied, and the n* value leading to saturation in terms of the exponential curve is returned as the ONF result.
VAL procedure (Figure 9(c)): the OFS-M procedure is validated over B replicated experiments (runs) using a resampling scheme. The model with n* features is operated on the test set, in order to minimize risk of data overfitting, obtaining a TEberror. The procedure returns the expected test error and a resulting feature ranking score RF.
The first step is to build the (SVM) model through the OFS-M procedure on the whole dataset. As a resampling scheme for the ONF procedure, for compatibility with class cardinalities in the microarray data sets, we use a three-fold cross-validation and we obtain the optimal feature set for the data set, as in Figure 5(a). The feature ranking criteria considered are E-RFE, RFE, SQRT-RFE, Correlation Coefficients (CC) and T-score (TT): for proper comparisons, the same datasets (from resampling as well as from the training/test splits) are used for all criteria. For the model validation, we set up B = 50 experiments according to the following guidelines:
the resampling scheme used in the VAL procedure consists in splitting the dataset into training and test set with proportion 3/4-1/4; class priors are preserved within the split.
the resampling procedure used in ONF is a three-fold cross-validation; class priors are preserved within the folds.
the curve representing the cross-validation error versus the number of mostly relevant features given in the previous step is fitted by an exponential map
(a, b estimated by least-squares);
the optimal number n* of mostly relevant features for the running experiment is chosen as the point where the error difference with respect to the next point is less than 1/1000 of the cross-validation error range (i.e. the difference between the maximum and the minimum cross-validation error); two examples of this fitting procedure are reported in Figures 5(b) and 5(a).
The total number of extractions (multiplicity) of the n selected features from the optimal feature sets over all the B experiments of the VAL procedure additionally provides a measure of relative importance for the selected features.
Finally, a randomization procedure was used to detect design problems. First we built 50 new data sets starting from the colon cancer dataset by randomizing the labels via permutation and then we applied B = 20 times the VAL procedure on each of those no-information data sets. Since a statistical analysis (t-test) revealed that fewer randomizations were sufficient to reach a significant result, a procedure involving only 25 new data sets (built as in the colon cancer case), each undergoing B = 10 VAL runs, was set up for the lymphoma data set. No randomization procedure was carried out for the tumor vs. metastases data set due the heavy workload of the involved computation.
The Recursive Feature Elimination (RFE) is a well-known feature selection method for support vector machines (SVM), firstly introduced in [6]. In brief, a SVM realizes a classification function , where the coefficients α = (α i ) and b are obtained by training over a set of examples S = {(x i , y i }i=1,...,N, x i ∈ , y i ∈ {-1, 1} and K(·, ·) is the chosen kernel. In the linear case, the SVM expansion defines the hyperplane f(x) = <w, x> + b, with . The idea is to define the importance of a feature for a SVM in terms of its contribution to a cost function J(α). At each step of the RFE procedure, a SVM is trained on the given data set, J is computed and the feature less contributing to J is discarded. In the case of linear SVM, the variation due to the elimination of the i-th feature is ; in the non linear case, , where Z i,j = y i y j K (x i , x j ). The heavy computational cost of RFE is a function of the number of variables, because a SVM must be trained each time a variable is removed. The removal of chunks of variables at every loop represents a feasible approach, and it was suggested in [6]. However, at the first loops of the RFE algorithm, many weights are generally similar and concentrated nearby zero, as shown in Figure 3(a). In the standard RFE algorithm we would eliminate just one of the many features corresponding to a minimum weight, while it would be convenient to remove all of them at once. Another possible choice is to remove ; features at each step, where R is the set of the remaining features, thus obtaining the SQRT-RFE procedure. We developed an ad hoc strategy for an elimination process based on the structure of the weight distribution. This strategy was first described in [28]. We introduce an entropy function H as a measure of the weight distribution. To compute the entropy, we split the range of the weights, normalized in the unit interval, into n int intervals (with n int = ), and we compute for each interval the relative frequencies
Entropy is then defined as the following function:
The following inequality immediately descends from the definition of entropy:
≤ H ≤ log2 n int .
The two bounds corresponds to the situations:
H = 0: all the weights lie in one interval;
H = log2n int : all the intervals contain the same number of weights.
The new entropy-based RFE (E-RFE) algorithm eliminates chunks of genes at every loop, with two different procedures applied for lower or higher values of H. A scheme is detailed in Figure 10. The distinction is needed to remove many genes that have a similar (low) weight while preserving the residual distribution structure, and also allowing for differences between microarray classification problems. Let {pw i }i∈Rbe the projected weights, i.e. the weights linearly projected in the interval [0,1] ; let H be their entropy and H t a threshold to discriminate feature importance. We set to equally split the entropy values range. When H >H t the weights are not concentrated: nevertheless, in some cases, many of them have approximately the same low value, as shown in Figure 3(b).
To take care of the situation where many weights are close to 0, it is necessary to introduce a further discriminating measure. Let M be the mean of the projected weights and M t a suitable threshold for such a measure. This threshold must be chosen to decide which projected weights should be eliminated: in fact, the situations where M ≤ M t are precisely those when many features should be discarded. A meaningful value for the considered datasets is M t = 0.2.
When H >H t and M >M t (as in Figure 3(c)), the features whose weight lies in the interval are discarded. In the remaining cases (H >H t and M ≤ M t , as in Figure 3(b), or H ≤ H t , Figure 3(a)), we cautiously discard the features whose weight is in the leftmost quantile through a bisection procedure. The stopping condition is that no more than half of the features with weights in should be discarded. We take a conservative approach by reverting to one-step RFE when the number of variables has been reduced below a threshold value R t , which has to be chosen as a suitable compromise between the computational cost and the estimated size of supposed optimal features subset. For the three microarray data sets R t = 100 was used. As a further caution, the classification methods were compared at a finer resolution for smaller subsets of genes: every single step at 1–15 genes, every 5 at 15–50, every 10 at 50–200, every 100 at 200–1000. The extensive comparisons among methods with the complete validation scheme were run on a Open-Mosix cluster of 38 processing units (1 GHz Pentium).
Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V: Feature selection for SVMs. In Advances in Neural Information Processing Systems (NIPS) 13, 2000 (Edited by: TK Leen, TG Dietterich, V Tresp). MIT Press 2001, 668–674.
Li Y, Campbell C, Tipping M: Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics 2002, 18: 1332–1339. 10.1093/bioinformatics/18.10.1332
Zhang X, Wong W: Recursive sample classification and gene selection based on SVM: method and software description. Technical report, Department of Biostatistics, Harvard School of Public Health 2001.
Xiong M, Fang X, Zhao J: Biomarker identification by feature wrappers. Genome Research 2001, 11: 1878–1887.
Nguyen DV, Rocke DM: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002, 18: 39–50. 10.1093/bioinformatics/18.1.39
Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Machine Learning 2002, 46: 389–422. 10.1023/A:1012487302797
Golub T, Mukherjee S, Tamayo P, Slonim D, Verri A, Poggio T, Mesirov J: Support vector machine classification of microarray data. Technical Report 182/AI Memo CBCL Paper 1676.
Furey T, Cristianini N, Duffy N, Bednarski D, Schummer M, Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16: 906–914. 10.1093/bioinformatics/16.10.906
Slonim D: From patterns to pathways: gene expression data analysis comes of age. Nature genetics supplement 2002, 32: 502–507. 10.1038/ng1033
Ramaswamy S, Ross K, Lander E, Golub T: A molecular signature of metastasis in primary solid tumors. Nature Genetics 2003, 33: 1–6. 10.1038/ng1060
Kari L, Loboda A, Nebozhyn M, Rook A, Vonderheid E, Nichols C, Virok D, Chang C, Horng WH, Johnston J, et al.: Classification and prediction of survival in patients with the leukemic phase of Cutaneous T Cell Lymphoma. J Exp Med 2003, 1477–1488. 10.1084/jem.20021726
Nutt CL, Mani D, Betensky RA, Tamayo P, Cairncross JG, Ladd C, Ute Pohl CH, McLaughlin ME, Batchelor TT, Black PM, et al.: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res 2003, 63: 1602–1607.
Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C, Angelo M, Ladd C, Reich M, Latulippe E, Mesirov J, et al.: Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci 2001, 98: 15149–15154. 10.1073/pnas.211566398
Weston J, Elisseeff A, Tipping M, Schölkopf B: Use of the zero norm with linear models and kernel methods. Journal of Machine Learning Research 2002, 3: 1439–1461. 10.1162/153244303322753751
Rakotomamonjy : Variable selection using SVM-based criteria. Journal of Machine Learning Research 2002, 1357–1370.
Ambroise C, McLachlan G: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci USA 2002, 99: 6562–6566. 10.1073/pnas.102102699
Simon R, Radmacher M, Dobbin K, McShane L: Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 2003, 95: 14–18. 10.1093/jnci/95.1.14
Spang R, Blanchette C, Zuzan H, Marks J, Nevins J, West M: Prediction and uncertainty in the analysis of gene expression profiles. In Silico Biology 2002, 2: 0033. [http://www.bioinfo.de/isb/2002/02/0033/]
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning. New York: Springer-Verlag 2001.
Weston J, Pérez-Cruz F, Bousquet O, Chapelle O, Elisseeff A, Schölkopf B: Feature Selection and Transduction for Prediction of Molecular Bioactivity for Drug Design. Bioinformatics 2002, 1: 1–8.
Bø T, Jonassen I: New feature subset selection procedures for classification of expression profiles. Genome Biology 2002, 3: research0017.1–0017.11. 10.1186/gb-2002-3-4-research0017
Li W, Yang Y: Zipf's law in importance of genes for cancer classification using microarray data. Journal of Theoretical Biology 2002, 219: 539–551. 10.1006/jtbi.2002.3145
Alon U, Barkai N, Notterman D, Gish K, Ybarra S, Mack D, Levine A: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 1999, 96: 6745–6750. 10.1073/pnas.96.12.6745
Alizadeh A, Eisen M, Davis E, Ma C, Lossos I, Rosenwald A, Boldrick J, Sabet H, Tran T, Yu X, et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403: 503–511. 10.1038/35000501
Furusawa C, Kaneko K: Zipf's law in gene expression. Phys Rev Lett 2003, 90: 088102. 10.1103/PhysRevLett.90.088102
Xing EP: Feature selection in microarray analysis. In Understanding and using Microarray Analysis Techniques: a Practical Guide (Edited by: D Berrar, W Dubitzky, M Granzow). Kluwer Academic Publishers 2003.
Raychaudhuri S, Sutphin P, Chang J, Altman R: Basic microarray analysis: grouping and feature reduction. Trends in Biotechnology 2001, 19: 189–193. 10.1016/S0167-7799(01)01599-2
Furlanello C, Serafini M, Merler S, Jurman G: An accelerated procedure for recursive feature ranking on microarray data. Neural Networks 2003, 16: 641–648. 10.1016/S0893-6080(03)00103-5
MS is supported by the FUPAT 'WebFAQ grant'. BJ is supported by the FUPAT post-graduate project 'Algorithms and software environments for microarray gene expression experiments'. We thank T. Poggio, G. Anzellotti and B. Caprile for helpful discussions and comments, and E. Manduchi and J. Reid for reading earlier versions of the manuscript. We are grateful to R. Flor and A. Soraruf for assistance in developing and administrating the computing facility.
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CF devised the study and drafted the manuscript. MS designed the study methodology and performed the data analysis. SM devised the machine learning methods and contributed to the validation methodology. GJ organized the experimental results and contributed to the study methodology. All authors equally contributed to, read and approved the final manuscript.
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Furlanello, C., Serafini, M., Merler, S. et al. Entropy-based gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics 4, 54 (2003). https://doi.org/10.1186/1471-2105-4-54
DOI: https://doi.org/10.1186/1471-2105-4-54


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Certified Tutor
Undergraduate Degree: New York University - Current Undergrad, Economics-Mathematics
ACT Composite: 33
ACT English: 35
ACT Math: 32
ACT Reading: 33
ACT Science: 31
Soccer, music and skiing
What is your teaching philosophy?
Patience is essential to working with a student. Additionally, I like to begin with the fundamentals. With those two elements combined, we can accomplish a lot together.
If a student has difficulty learning a skill or concept, what would you do?
Derive it or demonstrate it conceptually. You must be patient, and you must dissect the problem into his most fundamental terms. I would use what we know then to answer the question.
How do you help students who are struggling with reading comprehension?
I want them to annotate and constantly summarize what they are reading along the margins of the text.
What strategies have you found to be most successful when you start to work with a student?
Patience is essential to working with a student. Additionally, I like to begin with the fundamentals. With those two elements combined, we can accomplish a lot together.
How would you help a student get excited/engaged with a subject that they are struggling in?
As I previously mentioned, I must encourage the student. In other words, I must provoke their curiosity. When I took physics, for instance, my teacher derived each equation using a series of labs and experiments. I think I can accomplish a similar task, but with math or economics.
What techniques would you use to be sure that a student understands the material?
I would offer them challenge problems. Problems that are intended to apply what they have learned. It is not enough to memorize a formula or a pattern, but it has proven effective if the student can apply his/her knowledge to the question.
How do you build a student's confidence in a subject?
Again, encouragement. When the student succeeds, I succeed. As a result, I want to compliment the student on a job well done when the student accomplishes a task or a tricky problem.

subset: commoncrawl


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« Chicagoan? Or Chicagoish? | Confession Time » |
Profiles Tue Mar 06 2007
Beasts! Found in Chicago!
by Kara Luger
There are many things to be afraid of in this world: sickness, global warming, the next Britney Spears freakout. Although Beasts! is a monster of a compendium, featuring nearly 100 of the frightening and lamentable critters, it is luckily the least of your worries.
Lovingly curated by Jacob Covey, the book features artists from various fields — graphic novelists, children's books illustrators, fine artists, rock poster-makers, skate graphics guys, etc. — who were assembled to give lend their visage. A brief bio accompanies each beast, so the book is informational as well as, you know, cool.
In celebration of the book's release, a whole slew of Chicago-area contributors will be on-hand to sign copies of Beasts!, including Dan Grzeca, Jason Robards, The Little Friends of Printmaking, Anders Nilsen, Julie Murphy and Justin B. Williams. The shindig goes down at 7pm on Friday, March 9, at Quimby's, 1854 W. North Ave.
I spoke with JW and Melissa Buchanan of The Little Friends of Printmaking about the travails of their adopted beast, the unfortunate Hundred-Handed Giant.
Kara Luger: How did you choose which beast to do?
The Little Friends of Printmaking: We picked it from a big long list. It was like a nine-page document with little descriptions. We tried to read it and respond as fast as possible because we felt like it was a race. Stephan Britt had already taken the troll, so we were like, "Oh, snap."
I think it's a good way to get artists to participate in a book: to create competitive pandemonium, and to have them fight to the death over who gets to doodle a Bigfoot.
KL: What did you find attractive or interesting about the Hundred-Handed Giant?
LFOP: We narrowed it down to about five different monsters, but we ended up choosing the Hundred-Handed Giant because it would be the most fun to draw. We read somewhere that they were kind of stupid, which is endearing.
KL: Your beast doesn't seem as fearsome as he is misunderstood.
LFOP: I think that he's a functional illiterate and he has a hard time controlling his rage. The existence of the Internet is an enormous insult to him because he can't read it. Also, he can't go to the movies because he is always naked. So definitely, he's misunderstood.
KL: The beasts in the book are typically mythological/folkloric in nature (i.e. non-modern). What is a modern beast you'd like to portray?
LFOP: We really want to do a drawing of a labradoodle, which is half-lion, half-suicide bomber and covered in sh*t.
KL: What's on the horizon for Little Friends?
LFOP: The sweet embrace of death. For you Chicago people, we're going to be in We're Rollin',They're Hatin', a show during VersionFest7 that also features the likes of Cody Hudson and Paper Rad. Otherwise, some toys and the usual.


text: It also needs the implementation of a right setup of invoicing. That is one of the setups which containe software SFA. considerably more optimal alternative seems to be a network Ferrodo. It lets the integration of data on stock levels, which would be a very attractive alternative for both managers online shop, as well as for consumers. Clients certainly will be surprised strap availability of products located on the website. He likewise has a software SFA . The usefulness of that network is too connected with two further chances that it offers. The first is the possibility of conversion cycle orders, so that was the most reasonable. In the previous section I have revealed that in the tense months, even the best system will not help to optimize work if undue amount of orders appers in the middle of the year. Work on the remaining months is likely to be calculated automatically. The second advantage is control the setting logistics flows in the company and cooperate with appliances indicating the storage location of the goods. And the problem was constatly on.


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Introduction: The modern care of suspected and confirmed acute coronary syndrome (ACS) is informed by an extensive and evolving evidence base. This clinical practice guideline focuses on key components of management associated with improved clinical outcomes for patients with chest pain or ACS. These are presented as recommendations that have been graded on both the strength of evidence and the likely absolute benefit versus harm. Additional considerations influencing the delivery of specific therapies and management strategies are presented as practice points. Main recommendations: This guideline provides advice on the standardised assessment and management of patients with suspected ACS, including the implementation of clinical assessment pathways and subsequent functional and anatomical testing. It provides guidance on the: • diagnosis and risk stratification of ACS; • provision of acute reperfusion therapy and immediate post-fibrinolysis care for patients with ST segment elevation myocardial infarction; • risk stratification informing the use of routine versus selective invasive management for patients with non-ST segment elevation ACS; • administration of antithrombotic therapies in the acute setting and considerations affecting their long term use; and • implementation of an individualised secondary prevention plan that includes both pharmacotherapies and cardiac rehabilitation. Changes in management as a result of the guideline: This guideline has been designed to facilitate the systematic integration of the recommendations into a standardised approach to ACS care, while also allowing for contextual adaptation of the recommendations in response to the individual's needs and preferences. The provision of ACS care should be subject to continuous monitoring, feedback and improvement of quality and patient outcomes.
Original language | English |
Pages (from-to) | 128-133 |
Number of pages | 6 |
Journal | Medical Journal of Australia |
Volume | 205 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Aug 2016 |


text:
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NAPO-NY Newsletter, Winter 2008 Dorothy Madden
My company, ORGANIZE IT!, was formed in 1997 and I joined Golden Circle in 2003.
Like many of you, my organizing business was created because a colleague pointed out my organizing strengths and said I should put them to greater use! She recommended Michael Gerber's book, The E-Myth Revisited. Reading it confirmed that organizing was my strength and perhaps I should pursue it - but how?
Unaware of professional organizing, I explored the Internet and found NAPO. Wow - an organization of like-minded folks who charged for their services and made a career out of it - what a concept and how right it felt for me!
After completing legal documents, my company was formed and I joined NAPO in February 1998. While awaiting membership information, I contacted NAPO to inquire about networking with organizers around the country. I was full of questions and eager to learn.
They directed me to Stephanie Denton, Membership Chair, who suggested I attend the upcoming national conference because, as she assured me, it would shorten my learning curve by two to three years. I was convinced and off I went to the 1998 conference in Portland, Oregon. Stephanie was right. I learned a lot, gained confidence, and connected with organizers around the country who became friends and advisors.
I'm a former teacher and have a Master's Degree in Education. Now, instead of teaching students in a school setting, I'm teaching people of all ages who have found that what they've been doing is no longer working for them and they are ready to change. I feel fortunate to do what I love and I love what I do.
I work with residential, small office/home office, and corporate clients and thoroughly enjoy the mix. I've been published in local business journals and newspapers and have had numerous local TV and radio appearances.
I received the "Home-Based Business of the Year Award" from the U.S. Small Business Administration's Buffalo District, an area covering 14 counties in 2004. That same year I was also recognized as "Inspiring Businesswoman of the Year" by the National Association of Women Business Owners' Rochester Chapter.
Based in Rochester, I don't attend Golden Circle meetings as often as I would like. However, the meetings I've attended I've truly enjoyed. The camaraderie of professional organizers is unparalleled and refreshing. I just wish I lived closer!
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text: Statesman Journal, November 21, 2006, Marvin Clatterbuck, former director of School for the Deaf, dies at 95 A former longtime director of the Oregon School for the Deaf, who also served as a school administrator in Ivory Coast, died Sunday. Marvin B. Clatterbuck, 95, served as an educator and a director for the Oregon School for the Deaf in Salem for nearly 40 years. He retired in 1973. Clatterbuck then moved to Ivory Coast in Africa to serve as a director for Ivory Coast Academy, a school for the children of missionaries. He and his wife, Margaret, served twice in Africa for two-year stretches, said his son Gary. Under Clatterbuck's time as director, 19 buildings opened on campus, said Jane Mulholland, the school's current director. The Clatterbuck Central Services Building on campus was dedicated in his honor, Mulholland said. Oregon School for the Deaf dorm manager Ricky Wright attended the school while Clatterbuck was the director. Wright said through a video-phone interpreter that Clatterbuck was attentive to students' needs and hardly missed a school sporting event. "He was a very highly respected gentleman," Wright said. "He was friendly with the students always." Calvin Johanson was one of Clatterbuck's students at the school in the 1930s. Johanson said in an e-mail that Clatterbuck was a stern social-studies teacher but picked Johanson to help him grade papers during lunch recess. "We always had good rapport. Whatever he taught, I listened and learned," Johanson wrote. Clatterbuck was known nationally among deaf educators and recruited many into the field of teaching deaf students. Salem resident Judy Weathers said Clatterbuck inspired her to pursue a master's degree in deaf education. Weathers worked under Clatterbuck as a counselor at the Oregon School for the Deaf while she was in college. "It was because of him that I went into this area," Weathers said. "I owe this man a great deal in how my life went in the direction it took, even to this day." Weathers' husband, Keith, who grew up with the Clatterbuck family, described Marvin Clatterbuck as a kind, disciplined and loving father who provided a model of integrity for both his public and private lives. Clatterbuck's interest in deaf education stemmed from his childhood spent in Fulton, Mo., the hometown of the Missouri School for the Deaf, Gary Clatterbuck said. Marvin Clatterbuck received an honorary doctorate signed by Lyndon B. Johnson from Gallaudet University, a national college for the deaf and hard of hearing, in Washington, D.C. While teaching at the Oregon School for the Deaf, Clatterbuck met his wife, Margaret, a deaf educator. She died in 2003. "He loved and enjoyed the school," Gary said. "We lived on campus. In those days, it was basically a 24-hour day job, in terms of his accessibility." Gary Clatterbuck said his father came from the era that lacked unions in public education and that his father helped fill the void on his own. "In this sort of Thanksgiving season, if there was an employee who didn't have a place to go, they'd have a place at our table," he said. Besides serving as a missionary administrator in Ivory Coast, Marvin Clatterbuck was an active member of First Baptist Church of Salem for about 40 years. After moving to California, he also served as the business director of the Conservative Baptist Association of Northern California and served on the staff of Quail Lakes Baptist church in Stockton, Calif. As a hobby, Clatterbuck enjoyed woodworking and crafted coffee tables for all of his children out of myrtle wood from the Oregon Coast, Gary Clatterbuck said. Clatterbuck was living in Arroyo Grande, Calif., with his family at the time of his death. Clatterbuck is survived by three sons, one daughter, six grandchildren and nine great-grandchildren. A graveside service for Marvin B. Clatterbuck is planned for Friday in Arroyo Grande, Calif. The family asks for memorials to go to Grace Bible Church in Arroyo Grande. |


text:
The direction of Zayn Malik and his former band mates ultimately lies apart. The group's most talented vocalists moves on.
Dance Ensemble Struts Its Stuff In 'Vogue' Showcase
BC's Dance Ensemble brought stylish flair to Robsham this weekend in its 'Vogue' Showcase.
Finding Our 'Definitions' At Boston College
While we're at BC, what we do tends to define who we are, making it difficult to think of ourselves independent of our involvement, particularly when our commitments end.
This Weekend In Arts: Fall Band Showcase, Faneuil Hall Tree Lighting, And More
CAB and the Music Guild present a fall band showcase, Christmas comes to Faneuil Hall with a tree lighting ceremony, and more Boston events this weekend.
One Direction's New Rock Record 'Four' Adds Up To A Hit
The boys of 'The X-Factor' band One Direction continue to push their sound in a mature, edgy direction with their rock-inspired record, 'Four.'
Why My Mother's Fleabag Belongs In The O'Connell House
Is accommodating growing crowds or retaining old traditions more important when it comes to My Mother's Fleabag hosting its Big Shows in The O'Connell House?
This Weekend In Arts: Fleabag's Big Show, Career Night In The McMullen, And More
Career Night for the Arts is held in the McMullen, My Mother's Fleabag takes over The O'Connell House, and more events this weekend.
Keira Knightley And The Naked Truth Of Body Image
Actress Keira Knightley joins the discussion of body image, appearing unedited, topless in a photoshoot.
This Weekend In Arts: Seaver's Express, Andrew McMahon, And More
Andrew McMahon performs at House of Blues, Seavers Express plays at the Superfan Zone concert, plus more events this weekend.
Masking Your 'Authentic' At BC
Authenticity should be intrinsic and natural, not something we try out for size every once in a while.


text:
Can you paint railroad ties that have cresol in them?
How do you learn to paint edges of something?
What are the steps to create a crackle paint look?
What are two colors that would be good to use for a girls room, I would to paint stripes?
What grit sanding paper and which primer should I use for auto painting?
What is the best glaze to use for flat paint when doing a faux paint job?
What paint color should I use to match blue and pink furnishings?
home improvement walls
Question by mkoepp (37)
I would like to update knotty pine but do not want to paint.
Answer by RickH (29)
A very cool technique is to beat the wood with a big linked chain, putting interesting distressed markings in the wood. When adding stain or varnish to it the effect is quite unique.
Answer by JD (129)
One way to update knotty pine without paint would be to stain the wood. First, sand the wood using a fine grit sand paper. Make sure the wood is clean and smooth before applying a stain. You may need to apply more than one coat of stain. Once the wood has been stained you can add polyurithane to finish.
Answer by fire157 (21)
You can simply sand it down to its natural appearence. purchase a cheap orbital palm sander and get to work. Be careful, never allow the sander to stay in one spot or you will create dips in the wood. stain can be applied via rag instead of painting.
Answer by Jonathan62 (5)
Use sandpaper to remove whatever you have on it now. Then apply a light natural looking stain. This will give it that update you desire
Answer by MikeM (11)
The easiest way to do this is to use a sander with 80 grit sand paper to lightly sand the finish off of the pine. Next, use 120 or 140 grit sand paper and smooth it out. After the wood is smooth, Use a clear-coat to seal the wood.
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This is the sequel to the road trip back in the June edition of MSCC Fallen Stars.
The pictures were taken in Utah and Colorado in late spring and these are examples from the second batch of pictures.
Ryan Sardachuk is a typical car guy so he sees the inner beauty in these project cars.
This is definitely one of those "if you have to explain it, you'll never get it" scenarios so if you don't get how cool these pictures are–feel free to browse the latest online edition of Elle.
For past MSCC Fallen Stars please click here.
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Adult students in the School of Cosmetology competed recently in the regional SkillsUSA competition. Imani Marshall and Alyssa McKenna practiced the skills they are learning in the Western Suffolk BOCES program.
Adults can begin this 1,000-hour program, located at Wilson Tech's Northport campus, on the first school day of every module with approval of instructor and counselor. Financial aid is available to those who qualify.
For more information about enrolling, call (631) 261-3600 x204 or email a counselor.


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Pathology Testing for Breast Cancer
- Introduction to pathology testing for breast cancer
- What is involved in pathology testing for breast cancer?
- Interpretation of pathology results for breast cancer
Introduction to pathology testing for breast cancer
Breast cancer is the most common cause of cancer-related death in women in Australia. The initial evaluation of a breast cancer usually involves a physical examination, mammogram, chest x-ray, and sometimes CT (specialized x-rays) or a bone scan. However, the final evaluation frequently depends upon the findings on pathology testing, which is the most accurate way to determine how far the tumour has spread.
Treatment and prognosis (disease outcome) depend on the stage of the cancer, which is based upon the size of the tumour, involvement of the skin, chest wall or local lymph nodes, and whether the cancer has spread to other parts of the body (also called metastasis).
What is involved in pathology testing for breast cancer?
In the patient with a suspicious abnormality on physical examination or mammogram, the best way to arrive at a diagnosis is through a biopsy. A biopsy can be performed in the form of percutaneous (through the skin) fine needle aspiration, percutaneous core needle biopsy, or excisional biopsy.
Fine needle aspiration is a simple and inexpensive procedure which uses a small needle to obtain sample cells or fluid from a mass.
Core needle biopsy, on the other hand, needs a larger needle, but is useful in differentiating non-invasive lesions from invasive lesions.
Excisional biopsy involves surgery and is carried out by the radiologist and the surgeon.
Interpretation of pathology results for breast cancer
When samples obtained from the procedure are sent to the laboratory, they are examined under the microscope and are placed through special tests in order to ascertain particular features.
The image below is an example of the appearance of cells under the microscope.
Grading of pathology results for breast cancer
Cancer cells are categorised using a grading system ranging from Grade 1 to Grade 3.
- Grade 1: In Grade 1, the cancer cells look like normal cells and are usually slow growing. This is referred to as "Low Grade" or "Well Differentiated".
- Grade 2: In Grade 2, cancer cells do not resemble normal cells but instead appear to stick together and grow at a faster rate. This is called Intermediate/Moderate Grade or Moderately Differentiated.
- Grade 3: Grade 3 cancer cells have irregular shapes, stick together and are fast growing. This is called High Grade or Poorly Differentiated.
Invasive vs non-invasive (in-situ) cancer
Another important piece of information provided by pathology testing is whether the breast cancer is invasive or non-invasive. This affects the approach to treatment.
In invasive cancer, the cancer cells have spread beyond the original area of growth, i.e. beyond the milk ducts or lobules of the breast where it first started.
In non-invasive cancer, the cancer cells are contained within the original area of growth. This is sometimes called carcinoma in situ ('in place'). The cancer cells stay within the milk ducts or milk lobules in the breast. They do not grow into or invade normal tissues within or beyond the breast.
- Ductal carcinoma in situ (DCIS): This is a type of very early breast cancer. The cells lining the milk ducts of the breast have overgrown and become cancerous, but do not have the ability to spread beyond the ducts.
- Lobular carcinoma in situ (LCIS): This is a tumour that is an overgrowth of cells that stay inside the lobules, the milk-making part of the breast. Again, LCIS is a very early form of cancer, sometimes called a pre-cancer.
- Invasive ductal carcinoma (IDC): This is a cancer that begins in the milk duct but grows into the surrounding normal tissue inside the breast. This is the most common kind of breast cancer.
- Invasive lobular carcinoma (ILC): This is a cancer that starts inside the milk-making glands (lobules), but grows into the surrounding normal tissue inside the breast.
The surgical treatment of non-invasive cancers is similar to that of invasive cancers, but systemic (bodywide) therapy after surgery (adjuvant therapy) is generally not recommended for in situ cancer. This is because the chance of recurrence for in situ cancer is much lower than that for cancer which has spread beyond the site of origin.
Factors involved in pathology testing for breast cancer
The size of the tumour is measured in centimetres. Tumour size is one of the factors to determine the stage of the cancer. The larger the cancer is, the higher the stage.
The margin of the cancer cells, which means how close to the outer edges of the tissue the cancer cells are found, can be described in three ways:
- Positive: This means that cancer cells come to the edge of the tissue. Further surgery is required.
- Negative/clean: No cancer cells are seen at the outer edge.
- Close margins: This is between positive and negative margins. Cancer cells are close to the edge of the tissue, but not right at the edge.
The breast has lymph nodes that filter along the lymph fluid channels that connect breast tissue to other parts of the body to remove waste products. Breast cancer cells, like the cells of other cancers, may have the potential to spread beyond their site of origin via the lymphatic channels. If this happens, cancer cells can be found in the 'draining' lymph nodes. The higher the number of cancerous lymph nodes, the greater the chance the cancer will return (recurrence) after treatment. In these cases, your doctor may recommend treatment to your whole body (systemic treatment), not just the breast area.
Lymph node involvement is either:
- Node negative (lymph nodes clear of cancer); or
- Node positive (lymph nodes contain cancer cells).
Hormone receptors on breast cells receive signals from hormones such as oestrogen and progesterone. These signals switch on growth in breast cells that have receptors. A cancer is called "oestrogen receptor-positive" if it has receptors for the hormone oestrogen. It is called "progesterone receptor-positive" if receptors for the hormone progesterone are present.
Breast cancers that are either oestrogen receptor-positive or progesterone receptor-positive, or both, tend to respond well to hormone therapy. In addition, patients with oestrogen- or progesterone-receptor positive breast cancers tend to do better than patients with receptor negative cancers.
HER-2 is a gene that helps control how cells grow, divide, and repair themselves. About 15-20% of breast cancers have too many copies of the HER-2 gene. The HER-2 gene directs the production of special proteins, called HER-2 receptors, in cancer cells. HER2-positive breast cancer is usually associated with more aggressive behaviour, with higher growth rate and proliferation rate and increased risk of invasion and spread (metastasis). But they do respond very well to treatment that works against HER-2.
Overall, the size and extent of the breast tumour, involvement of adjacent lymph nodes, and the presence or absence of spread to other organs are grouped together to form the "stage grouping" of a breast cancer, which ranges from stage I to IV. This staging system can help guide treatment decisions, such as whether adjuvant therapy with radiotherapy, chemotherapy or hormone therapy may be appropriate.
More information
For more information on breast cancer, types of breast cancer and its investigations and treatments, as well as some useful videos, see Breast Cancer. |
- Ciatto, S, Cariaggi, P, Bulgaresi, P. The value of routine cytologic examination of breast cyst fluids. Acta Cytol 1987; 31:301.
- Kerlikowske, K, Smith-Bindman, R, Ljung, BM, Grady, D. Evaluation of abnormal mammography results and palpable breast abnormalities. Ann Intern Med 2003; 139:274.
- National Breast Cancer Centre. Key facts about breast cancer in Australia[online]. 2005 [cited 2005 December 5th]. Available from: URL: http://www.nbcc.org.au/bestpractice/statistics/index.html
- Understanding Your Breast Cancer Pathology Results [online]. 2005 [cited 2007 January 1st]. Available from: URL: http://www.xeloda.com/learn-about-diagnosis/breast-cancer/breast-cancer-pathology-reports.aspx
- Murtagh J. General Practice (3rd edition). 2003, McGraw-Hill: Sydney.
- National Breast Cancer Centre. Breast fine needle aspiration cytology and core biopsy: a guide for practice [online]. 2004 [cited 2007 January 1st]. Available from: http://www.nbcc.org.au/bestpractice/resources/FNA_fineneedleaspir.pdf
- National Breast Cancer Centre. Clinical practice guidelines for the management of early breast cancer (second edition) [online]. 2001 [cited 2007 January 1st]. Available from: http://www.nhmrc.gov.au/publications/_files/cp74.pdf
- Kopans, DB, Lindfors, K, McCarthy, KA, Meyer, JE. 'Spring hookwire breast lesion localizer: use with rigid-compression mammographic systems'. Radiology 1985; 157:537.
- National Breast Cancer Centre. The pathology reporting of breast cancer; a guide of Pathologists, Surgeons, Radiologists and Oncologists [online]. 2001 [cited 2007 January 1st]. Available from: http://www.nbcc.org.au/resources/documents/PAT_ThePathology_reporting.pdf
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For an experience to be memorable let alone transformative, the human brain has to be pushed out of default auto-pilot mode into conscious thought. And that push necessarily involves some level of discomfort.
The unrelenting pace of technologies is deeply ironic, given the original intent of them to make our lives more efficient and give us more time. But we can all attest that the actual effect of this escalation of efficiency has been to increase the pace of work and play in our worlds.
If the point of making a 10,000-year clock is to get people to think longer term how do you design that experience so that it really does that? And one of the things that we we realized is that people really need to be able to interact with it. That they need to be able to make the moment they visit it their own. So while the clock does keep time all by itself with the temperature difference from day to night, it doesn't actually update any of the dials, none of the chimes chime, unless someone's there to wind it.


text: Categories Instagram Until next time, lovely #hoteldelachauxdabel! Post author By admin Post date April 22, 2018 No Comments on Until next time, lovely #hoteldelachauxdabel! First posted on Instagram. Tags ifttt, instagram, pics ← Popeye's got his own mind → Hello Warsaw Leave a Reply Cancel replyYour email address will not be published. Required fields are marked *Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Δ


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Dressing, Tielle Essential? Silicone Border, 5" x 5", 10 ea/ct, 10 ct/cs (Continental US+HI Only) (To Be DISCONTINUED)
Product Description: TIELLE ESSENTIAL? dressings provide a range of cost effective foam wound dressings.They are designed to absorb exudate and maintain a moist wound healing environment to aid in the healing process of moderate to heavily exudating chronic and acute wounds.
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A high-performance carbon electrode is desirable for promoting electrochemical desalination efficiency in the membrane capacitive deionization (MCDI). Sugarcane bagasse (food waste) was employed in this study to prepare hierarchically porous biochars by microwave-assisted carbonization and activation with potassium hydroxide in N2 or CO2 atmosphere under varying flow rates (100-600 cm3 min−1). The sugarcane bagasse-derived biochars activated under CO2 flow of 300 cm3 min−1 (denoted as SBB-CO2-300) possessed the ratio of mesopores to total pore volume (Vmeso/Vtotal) of 56.7% with a specific surface area of 1019 m2 g−1. The electrochemical behavior of SBB-CO2-300 was demonstrated by a surpassing specific capacitance of 208 F g−1 at 5 mV s−1 by means of cyclic voltammetry. The desalination tests using a batch-mode MCDI at 1.2 V in a 5 mM NaCl solution indicated that the SBB-CO2-300 electrode exhibited an excellent electrosorption capacity of 28.9 mg g-1. The improvement in the electrochemical deionization performance of SBB-CO2-300 was attributed to the superior Vmeso/Vtotal ratio, high surface area, excellent capacitance behavior, and hierarchical pore structure. The biowaste-derived biochars prepared via facile microwave-assisted carbonization and CO2 activation route can provide a sustainable and high-efficiency carbon electrode for electrochemical deionization of brackish water.
原文 | English |
文章編號 | 121192 |
期刊 | Journal of Hazardous Materials |
卷 | 383 |
DOIs | |
出版狀態 | Published - 2020 2月 5 |
All Science Journal Classification (ASJC) codes
- 環境工程
- 環境化學
- 廢物管理和處置
- 污染
- 健康、毒理學和誘變


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Rex Energy Corp. said it has seen "impressive" results so far from its first three wells in the Warrior South prospect, an area in Ohio's portion of the Utica Shale.
Articles from Call
NRG, Tenaska Dumping Coal for Natural Gas
The news preceded President Obama's call last week for more action to address climate change, but executives at NRG Energy Inc. and Tenaska Inc. may have been reading between the lines after announcing over the past two weeks that because of abundant U.S. natural gas, coal is going to play a much smaller role in their generating schemes going forward.
NRG, Tenaska Dumping Coal for Natural Gas
The news preceded President Obama's call on Tuesday for more action to address climate change (see Daily GPI, June 26), but executives at NRG Energy Inc. and Tenaska Inc. may have been reading between the lines after announcing within the past few days that because of abundant U.S. natural gas, coal is going to play a much smaller role in their generating schemes going forward.
First of Several Fracking Bills Passes California Senate
Even as an administrative process is ongoing to hammer out new state rules on hydraulic fracturing (fracking) by oil/gas regulators, the California Senate on Thursday passed on a 27-11 vote a measure (SB 4) to cover chemical disclosure requirements and public notice before fracking can take place. It drew short, at the industry's urging, of placing any moratorium on the practice for an interim period.
Consol-Noble JV to Build Marcellus Dry Gas Pipeline
With natural gas prices increasing, a joint venture (JV) in the Marcellus Shale has announced plans to build a dry gas pipeline across two neighboring counties in north-central West Virginia.
Colorado Governor Threatens to Sue Cities That Ban Fracking
Colorado Gov. John Hickenlooper said Wednesday he will continue to take legal action against cities and counties that insist on passing bans on the use of hydraulic fracturing (fracking).
Midwest Cold, Wind Firm Quotes; Futures Balk
Cash prices overall lurched higher Wednesday as weather forecasts continued to call for bone-chilling winds across major Midwest and eastern market centers. Without the multi-dollar gains posted at a handful of Northeast points, the average national gain was 8 cents, but with those gains factored in, the average rose to 21 cents.
Gulfport Gas Output Up, Oily Production Falls
Gulfport Energy Corp., which released its 4Q2012 and full-year operations results on Wednesday, reported strong natural gas growth in the onshore, with falling oil and natural gas liquids (NGL) output.
Pioneer Strikes Wolfcamp JV, Abandons Barnett Sale
Pioneer Natural Resources Co. has signed an agreement with Sinochem Petroleum USA LLC, a U.S. subsidiary of China's Sinochem Group, to sell 40% of its interest in 207,000 net acres leased by the Pioneer in the horizontal Wolfcamp Shale in the southern portion of the Spraberry Trend Area Field for $1.7 billion.
Seneca Boasts Six 'Most Productive Wells' in the Marcellus
Seneca Resources Corp., the exploration and production subsidiary of National Fuel Gas Co., said Tuesday that six recently completed wells targeting the Marcellus Shale in Pennsylvania are among the most productive ever drilled in the play, according to initial test results.


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Winter 2021 Book Club
The book club will be held virtually on February 26, 2021, from 10:00-11:30am and is open to everyone.
We will read selections of Erik O. Wright's "Envisioning Real Utopias" and critically assess this work's relationship to emancipatory visions of racial equality, reading a direct critique offered by Eduardo Bonilla-Silva alongside the visions for racial equality as formulated by the Movement for Black Lives. Further background readings are available but not required.
Core Readings
- Envisioning Real Utopias by Erik O. Wright (selected chapters)
- Race, Racism & Utopias for Real by Eduardo Bonilla-Silva
- Movement for Black Lives: Vision for Black Lives (2020) and Platform (2016)
Additional Background / Further Readings (not required)
- Envisioning Real Utopias, full book: https://www.ssc.wisc.edu/~wright/ERU.htm
- Ugly Progress: W. E. B. Du Bois's sociology of the future by Joe P. L. Davidson
- The Alternative to Utopia is Myopia by Boaventura de Sousa Santos
- African Americans and Utopia: Visions of a better life by Lyman Tower Sargent
- M4BL website
When and Where?
- The Zoom session will be held on February 26, 2021, from 10am-11:30am
- Zoom invitations will be sent to registered participants
How Does It Work?
- The book club will follow an open structure. There will be a faculty and graduate student moderator to help structure the discussion where needed. Break-out groups focused on particular topics/sections may be integrated
Who Can Join?
- This event is open to anyone interested


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THE QUEEN COULD be set to lose over a quarter of a million of her subjects as the Caribbean island nation of Barbados looks set to become a republic.
Barbados has been an independent nation since 1966 but will now look at having an elected president to coincide with the 50th anniversary of independence in 2016.
Prime Minister Freundel Stuart told supporters in the capital Bridgetown that Barbados already acts like a 'functioning republic' and their constitution must reflect that.
"We respect (the Queen) very highly as head of the Commonwealth and accept that she and all of her successors will continue to be at the apex of our political understanding," Prime Minister Stuart said according to the Jamaica Observer.
But in terms of Barbados' constitutional status we have to move from a monarchical system to a republican form of government in the very near future.
Stuart notes that that a republican form of government means that "those who run the people's affairs should be chosen directly or indirectly by the people themselves".
"In other words, they should only be able to stay as long as you want them to stay," he added.
Although the Commonwealth of Nations is made up of 53 member states, only 16 have Queen Elizabeth II as their head of state. Barbados is one of these nations.
Read: This British MP wants to let us join the Commonwealth >
Read: Kenny asked if Ireland will rejoin Commonwealth >


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Money Not There For Sidewalk
For those residents who travel by foot or wheelchair from Hammock Homes just off the Douglas Highway to Jeff Davis Hospital, cheers went up when the City of Hazlehurst announced its new sidewalk projects that would link the two. At the same time, the city also said it planned to build other sidewalks in the school district, a project that is now nearing completion.
But in last week's workshop and regular monthly meeting of the Hazlehurst City Council, Mayor Bayne Stone said that because of an "error in design" by Hofstadter and Associates, one of the city's engineering firms headquartered in Macon, the cost of the hospital segment of the project jumped from $8,000 to $40,000, which squarely placed the plan in jeopardy because only the lesser amount had been budgeted.
Stone said that Hofstadter designed the sidewalk that is to link Hammock Homes with the hospital as a street project, but because it will instead run along the state route of Highway 221, also known as the Douglas Highway, the budgeted amount of $8,000 leaped skyward to $40,000 because of, among myriad reasons, increased construction and utility costs.
"We have $8,000 but $40,000 worth of work to be done," said Stone. "I don't know how we're going to do it. It's been in the works for a long, long time, and that project needs to be done."
Stone pointed out that if any money is left over from a Local Maintenance and Improvement Grant (LMIG) street paving project, the funds could go toward the now, in Stone's words, "much more complex" sidewalk project.
[EDITOR'S NOTE: We contacted Carl Hofstadter of Hofstadter and Associates about Mayor Stone's saying the increase in the cost of the hospital sidewalk project from $8,000 to $40,000 was due to an error in design by Hofstadter and Associates. Carl Hofstadter said that was "absolutely false." He added that the firm did bid the project, but after designing it, the Department of Transportation decided to move the location of the sidewalk which ran the cost up because of having to relocate utilities. "The problem is, it is a $45,000 project and DOT put an estimated cost of $8,000 on the project," he said. "The money wasn't there to do that project."]
The resurfacing project, which the counsel okayed during Monday's work session, is for all or portions of Gill, East Plum, Railroad, James, Wilson, and Hughes streets; Carey Drive; Burketts Ferry Road; and Pine Forest Avenue. The LMIG is providing about $124,000 for repaving, with an additional $24,000 to come from SPLOST funds.
Public Works Director Carl Leggett offered that his department could help with in-kind services such as providing fill-dirt for the Hammock Homes-to-hospital sidewalk.
Both Stone and Ward One Councilman Dywane Johnson suggested that funds may be available from Governor Brian Kemp's emergency fund, with Stone saying that he would be in touch with the governor's office to determine if the project is eligible, and with Ward Three Councilman Eric Griffin proposing that he would speak with officials from the Georgia Department of Transportation to seek additional solutions.
In other action, the council ….
…. after hearing a request by Ward Four Councilman John Bloodworth to approve outside employment for a police officer, agreed that all requests for part-time employment must be approved by the department head and the council member assigned to that department only and not the approval of the entire council as had been previously required.
…. after a lengthy discussion regarding Ryland Environmental of Dublin, the waste management company with whom the city commissioned several months ago for trash pickup services, came to the conclusion that the contract is not, in Bloodworth's words, "in our best interests," and then passed a motion for Stone and his designees, to include city council members, to meet with Ryland representatives and discuss the best way to break the agreement.
…. after yet another very lengthy discussion with Airport Manager Cody White and City Attorney Ken W. Smith about the legalities of purchasing a new and much-needed credit card machine at Hazlehurst Municipal Airport and possibly contracting with and splitting the cost of the equipment with World Fuel Services, the company that supplies fuel to the airport, the council made no firm decisions on the matter.
…. heard an update from Griffin on recent cleaning, painting, and repairing work at Bully Creek Wastewater Treatment Plant. He said the projects are "moving along and looking good," that previous violations cited by the Environmental Protection Division (EPD) have been taken care of, and that the costs were much lower by several thousand dollars than had been anticipated.
…. authorized Stone to sign an agreement with the Department of Community Supervision to allow probationers to work on city buildings at no cost to the city, with the specific request that no sex offenders would be allowed in the local program.
…. agreed to vote for the slate of officer nominees for District 9 at the Georgia Municipal Association's upcoming 2019 convention. The council also directed that Smith draw a resolution to approve the recently-designed city flag, and then passed another motion to have Griffin serve as the flag bearer and Stone as the delegate for voting credentials at the convention.
…. approved training for several employees including Hazlehurst Police Department's David Moore, Randy Roberson, and Jose Zamora to travel in July to Socorro, N.M., to take the Incident Response to Terrorist Bombings course at New Mexico Tech; for Macy Deen, also of the police department, to travel to Alpharetta May 20-21 for the Georgia Tactical Officers Association's Criminal Interdiction Training class; for City Clerk Vernice Thompson to travel to Moultrie in June for the GMA's Election and Ethics Training class; and for Rory Chaney of Hazlehurst Main Street to travel to Monroe for the Heart and Soul Downtown Workshop on May 23.
…. appointed Johnson as the city's representative on the Jeff Davis County Board of Health.
…. gave the okay for naming the city-owned splash pad. The four finalist names, chosen by each member of the council, are Hazlehurst Waterworks, Hydrohurst, Yellow Jacket Chill Zone, and Hazlehurst Yellow Jacket Splash Pad, with the winner to be named on May 22 (today).
…. approved water and sewer adjustments and departmental reports for the period ending April 30 and the check register for bills already paid in April.
…. voted to purchase an advertisement for the Hazlehurst-Jeff Davis County Chamber of Commerce's 2019 city and county map.
…. in Citizens Comments, citizen advocate Rhonda Walsh asked the council if boaters could purchase high octane aviation or jet fuel from Hazlehurst Municipal Airport since other surrounding counties were allowing it. Stone explained that in the past it had been done but because of liability issues it is now a violation of recently-enacted laws.


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The Justice Unit affirms Google's acquisition of the ticketing software firm ITA Software over assurance of availability of the ITA's products for Google's opponents.
The unit revealed that Google would reduce opponents' entry to ITA's information. Google may employ its authority of web search to send consumers to its personal tourism provisions, it revealed further.
Opponents comprise Expedia, Kayak and TripAdvisor, which possesses the globe's major scheme that unites fare subscriptions from several airlines to split among tourism organizations.
The Justice Unit stated on Friday that Google would be obliged to certify the software, to carry on improving it and to set up firewalls to conceal ITA consumers' registered software — which ITA can view — to defend their intellectual possessions.
The Justice Unit stated the contract as formerly offered by Google would have considerably diminished battle between flight-search sites and lessened improvement and customer pick.
Google, the globe's No. 1 Internet search engine, stated in July that it would purchase ITA Software for $700 million in ready money. The statement flashed bothers that tourism websites, for instance, Kayak and TripAdvisor might be dispossessed of ITA's software.
The TX legal representative and the EU are inspecting Google's supremacy in Internet search and broadcasting reports have recommended that the FTC may track match