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Two major problems in data mining are 1) Dealing with missing values in the datasets used for knowledge discovery, and 2) using one data set as a predictor of other datasets. We explore this problem using four different datasets from the UCI Machine learning repository, from four different sources with different missing values. Each dataset contains 13 attributes and one class attribute which denotes the presence of heart disease and the absence of heart disease. Missing values were replaced in a number of ways; first by using normal mean and mode method, secondly by removing the attributes that contains missing values, thirdly by removing the records that contains more than 50 percent of values missing and filling the remaining missing values. We also experimented with different classification techniques, including Decision tree, Naive Bayes, and MultilayerPerceptron, using Medical Datasets. Rapid Miner and Weka tools. The consistency of the datasets was found by combining the datasets together and comparing the results of this datasets with the classification error of different datasets. It can be seen from the results that if fewer number of missing values are present, the normal mean and mode method is good. If larger amount of missing values are present than the third method of removing records along with different preprocessing steps works better, and using one dataset as a predictor of other dataset produced moderate accuracy
Data Sets
2.1 Rapid Miner
2.2 Decision Trees
2.3 Naive Bayes
2.4 Neural Networks
3.1 Preprocessing the data
3.2 Building the model
Table 1.All the four data sets
Table 2.Percentage of missing values in each data set
Table 3.Accuracy of Cleveland data set using classification algorithms
Table 4.Accuracy obtained after the model was tested
Table 5.Information gain weights of attributes
Table 6.Accuracy obtained after information gain weighting of attributes
Table 7.Accuracy of Hungary Data set using Wrapper method
Table 8. Feature Selection accuracy of VA Long Beach
1 Introduction
1.1 Background
Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.[1] Various steps involved in mining the data are data integration, data selection, data cleansing, data transformation, data mining .The first step in data mining is Data selection; that is, to only select the data which is useful for data mining. The second step is data cleansing; the data we collected may have errors, missing values; inconsistent data which must be corrected. Even after data cleansing data is not ready for data mining, so the next step is data transformation; that is, aggregation, smoothing, normalization, discretization etc. The final step is the data mining itself; that is, to find interesting patterns within the data. Data mining techniques include classification, regression, clustering, association etc. [1]
The main goal of data mining is classification. Given a collection of records containing a set of attributes, where one of the attribute is class attribute, our goal is to find the model for class attribute as a function of values of other attributes. A training set is used to build the model and a test set is used to classify the data according to that model. To predict the performance of a classifier on new data, we need to assess its error rate on a dataset that played no part in the information of classifier. This independent dataset is called a test set [2]
1.2 Problems
The main focus of this paper is to find the patterns in the data set related to coronary artery disease from UCI heart data sets. According to a 2007 report, nearly 16 million Americans have coronary artery disease (CAD). In U.S., coronary artery disease is the leading killer of both men and women. Each year, nearly 500,000 people die because of CAD.
Usually a medical dataset consists of a number of tests to be conducted to diagnose a disease. However, most medical datasets have large numbers of missing values because of the tests that are not conducted; many useful attribute values will be missing in medical data set due to the expense of performing tests, attributes that could not be recorded when the data was collected, or attributes ignored by users because of privacy concerns. Further complicating the problem from a data mining standpoint is that different groups of physicians collect different data; that is, different medical datasets often contain different attributes, making it difficult to use one dataset as a predictor of another.
This paper mainly focuses on two different issues. The first focus of this paper is preprocessing the data mainly dealing with missing attributes, and to find the improved accuracy of the data set after preprocessing steps and to compare the accuracy using different classification algorithms such as decision tree, Naive Bayes and Neural Networks.
The second focus is to train the data using classification techniques and to test the data using different datasets, to verify the results obtained from the trained data. The Cleveland database, collected from Cleveland Clinic Foundation, was used as the training set. The Switzerland, Hungary, and VA Long Beach data sets were used as test sets. All the datasets contain 13 attributes and one class attribute. The Cleveland dataset used for training contains only 6 missing values, and the three test datasets contain almost 90% missing values.
1.3 Datasets
The heart data set is collected from UCI repository. Each data set consists of 13 attributes among that 6 are numerical and 8 are categorical attributes and one special attribute.
The following are the four data sets for heart disease:
1. Cleveland
2. Hungary
3. Switzerland
4. VA long beach.
The source and the creator of the datasets:
1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D
This datasets contains 76 attributes but only 14 attributes are mostly used in most of the research. The presence value of heart disease is a value in the range of 1,2,3,4, with an absence value 0.
Table 1. All the four Datasets
Data Sets
Number of Instances
VA Long Beach
The 14 attributes that are used are:
1. Age in years
2. Sex - (1=Male; 0=Female)
3. Cp -Chest pain type
Value 1: typical angina
Value 2: atypical angina
Value 3: non-anginal pain
Value 4: asymptomatic
4. Resting blood pressure (in mm Hg on admission to the hospital)
5. Chol-Serum cholesterol in mg/dl
6. Fbs-(Fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
7. Resting electrocardiographic results
Value 0: normal
Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
8. Maximum heart rate achieved
9. Exercise induced angina (1 = yes; 0 = no)
10. ST depression induced by exercise relative to rest
11. The slope of the peak exercise ST segment
-- Value 1: up sloping
-- Value 2: flat
-- Value 3: down sloping
12. Number of major vessels (0-3) colored by flourosopy
13. Thal-3 = normal; 6 = fixed defect; 7 = reversable defect
14. The predicted attribute
Diagnosis of heart disease (angiographic disease status)
Value 0: < 50% diameter narrowing
Value 1, 2, 3, 4: > 50% diameter narrowing
The attributes with the largest amount of missing values are slope of the peak exercise, ST segment represented as slope, number of major vessels (0-3) colored by flourosopy represented by ca , normal; fixed defect; reversable defect represented by thal, and serum cholesterol in mg/dl represented by chol.
2 Literature Review
2.1 Rapid miner
RapidMiner, formerly known YALE (Yet Another Learning Environment), is software widely used for machine learning, knowledge discovery and data mining. RapidMiner is being used in both research and also in practical data mining fields.
The Java programming language is used in Rapid Miner, which means it can run in any operating system. RapidMiner can handle many formats of input such as CSV, Arff, SPSS, Xrff, Database example sources, and attributes that are described in XML file format. Different types of attributes that are present are Input, Output, data preprocessing and visualization.
RapidMiner contains more than 500 operators. The nested operator can be described through graphical user interface XML files which are created with RapidMiner. Individual RapidMiner functions can also be called directly from command line. It is used easily to define analytical steps and to generate graphs more effectively. It provides a large collection of data mining algorithms for performing classification. Many visualization tools such as overlapping histogram, 3D scatter plot and tree charts are present.
RapidMiner can handle any type of tasks like classification, clustering, validation, visualization, preprocessing, post processing etc. It also supports many kinds of preprocessing steps such as discretization, Outlier (Detection and removal), Filter, Selection, weighting, Normalization etc are available. All modeling and attribute evaluation methods from Weka are available within RapidMiner.
RapidMiner consists of two views, Design view and Result view. The design view is used to generate the process and run the process. The result view is used to generate the results.
Figure 1. RapidMiner Interface
Figure 2. RapidMiner Design View
2.2 Decision Trees
Decision trees are a supervised learning technique commonly used for tasks like classification, clustering and regression. Decision trees are mainly used in the field of finance, engineering, marketing and medicine. Decision tress can handle any type of data that is nominal, numeric and text. They mainly focus on the relationships of the attributes. Input to a decision tree is the set of objects described by the set of properties, and creates output as yes/no decision, or as one of several different classifications.
Figure 3. Decision tree created by RapidMiner
Since decision trees can be represented graphically as tree-like structures they are easier to understand by humans. The root node is the beginning of the tree, and each node is used to evaluate the attributes. At each node, the value of the attribute for the given instance is used to determine which branch to follow to a child node. Classification of instances can be done using a decision tree starting from the root node and continuing until a leaf node is reached.
Decision tree creation involves dividing the training data into root node and leaf node divisions until the entire data set has been analyzed. [3]. The data is split until they have the same set of classification or the splitting cannot be done anymore due to lack of further attributes.
An efficient decision tree is one in which the root node divides the data effectively, and therefore requires fewer nodes. One of the important things is to select an attribute that best splits the data into individual classes. The splitting is done based on the information gain of each attribute. Information gain is based on the concept of entropy, which gives the information required for a decision in bits. Entropy is calculated from
Entropy (P1, P2… Pn) = -Σi Pi log2Pi
Information of data is calculated based on each attribute. Entropy gives how important an attribute is by the information that is given. First the entropy of whole data set is calculated. The split is done based on this attribute. The attribute that can best split the data can be found from this. In the same way this procedure is used until the leaf nodes are reached.
A decision tree is built using a training dataset and these trees can then be used to classify examples in test dataset. Decision trees can also be used to explicitly describe data and also used for decision making, as they produce rules which can be easy to understand and can be read by any user.
Sometimes decision tree learning produces a tree that is too large. If the tree is too large the new samples are poorly generated. Pruning is one of the important steps in decision tree learning that addresses this problem. The size of decision tree can be reduced by pruning (that is, removing) the irrelevant attributes for which the accuracy of decision tree does not get reduced by pruning. Pruning improves the accuracy of the tree for future instances. The problem of over fitting and also noisy data can be reduced by pruning since the irrelevant attributes created by them are ignored.
2.3 Naive Bayes
Another classifier is Naive Bayes. Naive Bayes operates in two phases, training set and testing set. Naive Bayes is cheap and it can handle around 10,000 attributes. It is also fast and highly scalable model.
Naive Bayes considers attributes as independent of each others in terms of contributing to the class attribute. For example a fruit may be considered as apple if it round, red, and 4'' in diameter. Although these features depend on each other, Naive Bayes considers these features independently to consider it as apple.
One of the problems with Naive Bayes is that Naive Bayes does not require lot of instances for the possible combination of attributes. Naive Bayes can be used for both binary and multiclass classification problems. Naive Bayes can only handle discrete or discretized attributes. Naive Bayes requires binning. Several discretization methods are present they are Supervised and unsupervised discretization. Supervised discretization method uses class information of training set to select discretization cut point. Unsupervised discretization does not use the class information. [4]
Entire training data set is used for classification and discretization. Unsupervised discretization methods are equal width, Equal frequency and fixed frequency discretization. Error based, Entropy based are supervised discretization methods. [5]. Entropy based discretization uses class information, the entropy is calculated based on the class label then it finds the best split so that the bins are as pure as possible that is the majority of values in a bin correspond to having the same class label. The split is done based on the maximal information gain. [6]
2.4 Neural Networks
The human brain serves as a model for Neural Networks. Artificial neurons were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, an MIT logician. [7]. Neural Networks are useful for data mining and decision support applications. They are also useful for pattern recognition or data classification through the learning process. [8].
A neural network contains the neurons and weight building blocks. The strength of the network depends on the interaction between the building blocks. The MultilayerPerceptron (MLP) Neural Network Model is mostly used, with networks that consist of three layers Input, Hidden and Output. The values of the input layer come from values in a data set. The input neurons send data via synapses to the hidden layer and through output layer through synapses.
The MLP uses supervised technique called back propagation for training. Each layer is fully connected to the succeeding layer. The signal for each neuron is received from the previous layer; each signal is multiplied by a different weight value. Then the inputs that are weighted are summed and these are passed through the limiting function through this the outputs are scaled through the fixed range of values. Then the output is send to the all the neurons in the next layer. Error at each output is then "back propagated" to the hidden and the inputs, changing the weights based on the derivative of the error with respect to the weights.
MLP training involves adjusting parameters such as the number of neurons to be used in hidden layer. If insufficient number of neurons is used the complex data cannot be modeled and the result would be poor. If more number of neurons are used, it may take long time, it may over fit the data. The network may perform well on the training set but the test set would give poor results on future instances.
3. Methodology
The Cleveland, Switzerland, Hungary and VA Long Beach are the four datasets collected from the UCI Machine Learning Repository for this project. The Cleveland dataset is used for training and Switzerland, Hungary and VA datasets are used for testing. One important problem will be to use the training Cleveland dataset for testing the three datasets. The other important problem is to deal with the missing values in each dataset. All four datasets contains about 13 attributes and one class attribute.
First, all the four datasets are collected in .txt format. The datasets are loaded into RapidMiner using the IO Example Source operator.
3.1. Preprocessing the Data
The first step is to preprocess the Cleveland dataset used for training. This primarily involves dealing with missing values, outliers and feature selection. Different preprocessing steps that are used are
To fill the missing values
To deal with outliers
Attribute selection ,numeric data discretization, Normalization etc
Once preprocessing of Cleveland data is done, the data set is used for training using different algorithms. Then different preprocessing steps are done on the three test data sets.
Missing values in the dataset represent a lot of different things. They may be due to a test that is not conducted or the data that is not available. Missing values in RapidMiner and weka are usually represented by "?". The quality of the classification of the data could be reduced by the missing values, so filling in the missing values plays an important role in data mining. Different methods are used for dealing with missing values. The most frequently used method is replacing the categorical values with the mode and numerical values with mean. The second method is removing the attributes that contain around 90% of missing data. Attributes that are removed are ca, thal, slope, chol. No change was observed in the classification error when these attributes were removed. The third method that is used is to remove the instances in a data set if it contains 6 or more missing values out of the 13 values that are present, since instances missing too many values would not be good to use due to the consistency of the data. Then the remaining missing values are filled based on the frequency of class attributes.
Outliers are observations that deviate from the original dataset. That is, the instances that are abnormal distances from the other instances in the data. Sometimes they may occur due to some common errors that occur due to data transmission. In some cases outliers plays a significant role in acquisition of the data. Common methods used to identify outliers are Density based outlier detection, Distance based outlier detection and LOF detection methods.
Distance based outlier detection using a k-nearest neighbor algorithm is used to identify outliers. A density based outlier detection uses density functions like Square distance, Euclidean distance, angle, cosine distance, inverted cosine distance, and the LOFoutlierdetection identifies outliers using minimal upper and lower bounds with a density function.
Feature Selection is mainly used to find features that play an important role in classification, and to remove features with little or no predictive information. This method is mainly used in data sets with many features .Types of feature selection methods used are Filter method and Wrapper methods. The Filter method selects features independent of the classifier, and Wrapper methods makes use of classifier for feature selection. The filter method select features based on general characteristics of data so the filter methods are much faster than the wrapper method. The wrapper method uses an induction algorithm as a evolution function to select feature subsets.
Four data sets are collected in .txt format. First the Cleveland dataset is read into Rapid miner by using the attribute editor. The (.dat and .aml) files are created for the Cleveland datasets. The average and mode of each attribute is obtained. Then the missing values are identified. The Cleveland dataset contains only 6 missing values. Removing the missing values from the Cleveland dataset would not affect the dataset since less than 2% of the data is missing. So the missing values are removed from the dataset.
The next step would be to deal with the outliers. Distance based outliers methods are used to detect the outliers using the K Nearest Neighbor and Euclidean distance. It has been identified that depending on the significance of outlier, outlier role is determiner in medical data set, depending on role of the outlier they are removed or not removed. (Reference imp)
The next step would be to select features that play important roles in the data. Feature selection generally improves the accuracy of the classification by basing it on the most relevant features. The Infogainweighting using Filter method and Wrapper method using Forward and Backward selection method are used. First the Information gain of the attributes is obtained. The attributes are selected first by choosing 4 attributes, 5 attributes and so on and also by using 50% of the attributes, 70% of the attributes. By using these different methods we identify the top 10 features that play important roles in the classification, which are selected to build the model. Wrapper method selects features depending on the learning algorithm and the features selected by one algorithm may differ for another algorithm. Forward and backward selections are the two methods used to build a set of features. Forward selection starts only with one subset of attribute and additional attributes are added until there is no performance gain. Backward selection is the opposite of forward selection, as it starts with complete attribute set and attribute are removed from that subset until there is gain in the performance. Decision Tree and MultiLayerPerceptron are used as algorithms to select features using Forward and Backward selection. Then the preprocessed Cleveland data set is saved as a new file.
Once the Cleveland data set used for training is preprocessed it is ready to test with the three testing sets. Before testing the data sets, preprocessing of the three datasets is also done. Three classification algorithms Decision tree, Naive Bayes and MultiLayer Perceptron are used to build a classification model using the Cleveland dataset. Then the model is build using the classification algorithms.
The next important step is preprocessing of test datasets. One important step in preprocessing this datasets is dealing with the missing values, which were much more prevalent in the test datasets. The Switzerland, Hungarian and VA long Beach datasets consist of 50% to 90% of instances with missing values. Because of this, it is not possible to simply remove instances with missing values.
The following missing values methods are used to fill the missing data. We first replaced the categorical values with the mode and numerical values with mean. As the cholesterol attribute has about 99% of missing values in one dataset, it was replaced by normal value based on age and gender. The cholesterol value is replaced by normal level of values that is for Females below age 40 years chol level is 183 mg/dL, from age 40 to 49 years chol level is 119 mg/dL, from 50 years or above chol level is 219 mg/dL. For male below 40 years chol level is 185 mg/dL, age 40 to 49 years chol level is 205 mg/dL, age above 50 years chol level is 208 mg/dL.
By replacing the missing values in all the data sets with this method the Hungarian gave less classification error while the other two datasets still produced high classification error.
Different methods are used to deal with missing in the two data sets that produced the highest classification error. We tested removing the missing attributes that contain around 90% of missing data, ca, thal and slope, chol. Using this method did not affect improve the number of correct predictions, so the third method is used to deal with missing values.
As with the Cleveland dataset, instances in a data set were removed if they contain 6 or more than 6 missing values out of 13 values that are present. Since the ca attribute contains about 99% of missing values the ca attribute is removed from the datasets. Since ca is a redundant attribute, removing this attributes does not affect the data set. Then the remaining missing values are filled based on the frequency of class attribute. This method, however, did not produce any change in results on the Switzerland data set. There was an increase in number of correct predictions for Hungary data set.
3.2. Building the Model
In order to compare the effectiveness of different classification algorithms, decision tree classification, Naive Bayes, and Multilayer Perceptron are used. First the Cleveland data set is used to build the model using the Decision tree classification algorithm in Rapid miner. Different criteria are used to build the decision tree, the criteria's used are gini index, gain ratio and information gain. Of these, the information gain produced a better decision tree, so the criteria used for attribute selection and also for numerical split for building decision tree is information gain. Simple accuracy is not the best to determine the classifier, so sensitivity and specificity are used instead.
The accuracy on the positive instances is Sensitivity:
Sensitivity = True Positive/ (True Positive + False Negative)
The accuracy on the negative instances is Specificity:
Specificity = True Negative/ (True Negative + False Positive)
Accuracy = True Positive + True Negative/ (True Positive + False Negative+ True Negative + False Positive)
The MultilayerPerceptron method is used with more than one hidden layer to find the accuracy. One important step in MLP is choosing the number of hidden layers, as RapidMiner allows a choice of the number of hidden layers. First the numbers of hidden layers are chosen as 0, 1, 2 and so on and minimum of one hidden layer is used. The number of hidden layers that are used are 3 hidden layers. In this way, the training set is used to classify the other three datasets. The datasets are tested by replacing the missing values by three different methods.
Different experiments are conducted to test how the data sets collected from one source act as a predictor of another data sets collected from different sources, and to compare the accuracy using different algorithm. Since the data set contains missing values three different methods are used to fill the missing values. Then the accuracy is obtained to identify the method that worked better to fill the missing values. Different preprocessing steps are also conducted. The features that play an important role are identified.
The first step is to find percentage of missing values in each data set. It can be noticed from Table 2. Below show that the attribute ca contains 90% to 99% of missing values in all the data sets where the ca value is replaced by using the normal value and also one attribute in the Switzerland data set is missing 100%.
Table 2. Percentage of missing values in each dataset
VA Long Beach
The main goal is to use one data set as a predictor of another data set. Since the Cleveland data set contains less than 2%, for our experiments, we build a model using the Cleveland data set. Models were built from the preprocessed data set using Decision tree, Naive Bayes, and Multilayerperceptron (MLP) algorithms. Table 3 gives the accuracy obtained using three algorithms after building the model in terms of the percentage of correct predictions. It can be observed from the table that the MultiLayerPerceptron worked better for building the model with an accuracy of 91.75%, and Decision Tree was second with an accuracy of 81.19%.
Table 3. Accuracy of the Cleveland dataset using classification Algorithms
Decision Tree
Naïve Bayes
Each models built using the Cleveland Data set is used to test the preprocessed datasets Hungary, Switzerland and VA Long Beach. The data from the Table 4 below shows that the highest amount of accuracy is obtained from the Hungary data set. As seen from the Table 2 we know that the numbers of missing values in the Hungary data set are less compared to the Switzerland and VA Long Beach. This indicates that the normal method works better for Hungary data set.
Table 4. Accuracy obtained after the model was tested
Training and Test datasets
Decision Tree
Naïve Bayes
Cleveland and Switzerland
Cleveland and VA Long
Cleveland and Hungary
The next important step is using Feature Selection for selecting the features that plays an important role in classification. Information gain weighting is used to select the features that plays appropriate role in the classification of data set. The top 10 attributes are selected to build the model. The following Table 5. gives the Information gain weighting of the attributes.
Table 5. Information gain weights of attributes
After selecting the attributes using the Information gain weighting, the top 10 attributes are used to build the model and the model build is used to test the three data sets to see if the number of correct predictions is improved. Table 6 shows the increase and decrease in the number of correct predictions.
Table 6. Accuracy obtained after information gain weighting of attributes
Data sets
Decision tree
Naïve Bayes
VA Long Beach
Feature selection using the Wrapper method was tested with different algorithms. The Decision tree and MLP algorithms are used to select the attributes, using both forward selection and backward selection methods. Table 7 gives the accuracy obtained by using forward selection method and backward selection methods for all three algorithms.
Table 7. Accuracy of Hungary Data set using Wrapper method
Data sets
Decision tree
Naive Bayes
Forward Selection
Backward Selection
From Table 4 and Table 6 it can be noticed that the model build by the Cleveland data set was better able to classify the Hungary data set than the Switzerland and VA Long Beach data sets. The Switzerland and VA Long Beach algorithms were far worse, with less than 30% of attributes are only correctly predicted.
In order to improve this, removing instances with at least 6 missing attributes and filling the remaining instances with the instances that are present in the Switzerland and VA Long Beach, and the same algorithms and the preprocessing steps used for learning. Table 7 shows the accuracy of the dataset is improved than compared to the first method that is used to fill the missing values. The numbers of correct predictions are improved.
Table 7. Accuracy of the VA Long Beach after replacing the missing values by remaining values
Decision Tree
Naive Bayes
The next step is to select the features that plays important role in the data set, based on different algorithms. Table 8 shows that the numbers of correct predictions are improved in some cases while the number of correct predictions in some cases remains the same.
Table 8. Feature Selection accuracy of VA Long Beach
Data sets
Decision tree
Naive Bayes
Information gain weighting
Forward Selection
Backward Selection
Study has shown that selecting the appropriate features in the data set plays important role in data. If the appropriate features are removed from the data set however it can be seen that the accuracy of the data is reduced. The normal method worked better for less number of missing values. However if large number of non random missing values are present in the data removing the instances that contains 60% or more than 60% of missing values for each patient record are removed and replacing the remaining values based on the remaining instance worked better compared to the Normal method .
The datasets collected from different sources gave moderate accuracy if less number of missing values are present in the data. If large number of missing values are present in the data the data collected from one source gave very inefficient result in classification of the data from other source.


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A couple things to share today!
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- Jennifer said...
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- June 3, 2011 at 4:16 PM
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Mechanism Of Action
Somatropin (as well as endogenous GH) binds to a dimeric GH receptor in the cell membrane of target cells resulting in intracellular signal transduction and a host of pharmacodynamic effects. Some of these pharmacodynamic effects are primarily mediated by IGF-I produced in the liver and also locally (e.g., skeletal growth, protein synthesis), while others are primarily a consequence of the direct effects of somatropin (e.g., lipolysis) [see Pharmacodynamics].
Tissue Growth
The primary and most intensively studied action of somatropin is the stimulation of linear growth. This effect is demonstrated in children with GHD.
Skeletal Growth
The measurable increase in bone length after administration of somatropin results from its effect on the cartilaginous growth areas of long bones. Studies in vitro have shown that the incorporation of sulfate into proteoglycans is not due to a direct effect of somatropin, but rather is mediated by the somatomedins or insulin-like growth factors (IGFs). The somatomedins, among them IGF-I, are polypeptide hormones which are synthesized in the liver, kidney, and various other tissues. IGF-I levels are low in the serum of hypopituitary dwarfs and hypophysectomized humans or animals, and increase after treatment with somatropin.
Cell Growth
It has been shown that the total number of skeletal muscle cells is markedly decreased in children with short stature lacking endogenous GH compared with normal children, and that treatment with somatropin results in an increase in both the number and size of muscle cells.
Organ Growth
Somatropin influences the size of internal organs, and it also increases red cell mass.
Protein Metabolism
Linear growth is facilitated in part by increased cellular protein synthesis. This synthesis and growth are reflected by nitrogen retention which can be quantitated by observing the decline in urinary nitrogen excretion and blood urea nitrogen following the initiation of somatropin therapy.
Carbohydrate Metabolism
Hypopituitary children sometimes experience fasting hypoglycemia that may be improved by treatment with somatropin. In healthy subjects, large doses of somatropin may impair glucose tolerance. Although the precise mechanism of the diabetogenic effect of somatropin is not known, it is attributed to blocking the action of insulin rather than blocking insulin secretion. Insulin levels in serum actually increase as somatropin levels increase. Administration of human growth hormone to normal adults and patients with growth hormone deficiency results in increases in mean serum fasting and postprandial insulin levels, although mean values remain in the normal range. In addition, mean fasting and postprandial glucose and hemoglobin A1c levels remain in the normal range.
Lipid Metabolism
Somatropin stimulates intracellular lipolysis, and administration of somatropin leads to an increase in plasma free fatty acids and triglycerides. Untreated GHD is associated with increased body fat stores, including increased abdominal visceral and subcutaneous adipose tissue. Treatment of growth hormone deficient patients with somatropin results in a general reduction of fat stores, and decreased serum levels of low density lipoprotein (LDL) cholesterol.
Mineral Metabolism
Administration of somatropin results in an increase in total body potassium and phosphorus and to a lesser extent sodium. This retention is thought to be the result of cell growth. Serum levels of phosphate increase in children with GHD after somatropin therapy due to metabolic activity associated with bone growth. Serum calcium levels are not altered. Although calcium excretion in the urine is increased, there is a simultaneous increase in calcium absorption from the intestine. Negative calcium balance, however, may occasionally occur during somatropin treatment.
Connective Tissue Metabolism
Somatropin stimulates the synthesis of chondroitin sulfate and collagen, and increases the urinary excretion of hydroxyproline.
A 180-min IV infusion of Norditropin (33 ng/kg/min) was administered to 9 GHD patients. A mean (±SD) hGH steady state serum level of approximately 23.1 (±15.0) ng/mL was reached at 150 min and a mean clearance rate of approximately 2.3 (±1.8) mL/min/kg or 139 (±105) mL/min for hGH was observed. Following infusion, serum hGH levels had a biexponential decay with a terminal elimination half-life (T½) of approximately 21.1 (±5.1) min.
In a study conducted in 18 GHD adult patients, where a SC dose of 0.024 mg/kg or 3 IU/m² was given in the thigh, mean (±SD) Cmax values of 13.8 (±5.8) and 17.1 (±10.0) ng/mL were observed for the 4 and 8 mg Norditropin vials, respectively, at approximately 4 to 5 hr. post dose. The mean apparent terminal T½ values were estimated to be approximately 7 to 10 hr. However, the absolute bioavailability for Norditropin after the SC route of administration is currently not known.
Clinical Studies
Short Stature In Children With Noonan Syndrome
A prospective, open label, randomized, parallel group trial with 21 children was conducted for 2 years to evaluate the efficacy and safety of Norditropin treatment for short stature in children with Noonan syndrome. An additional 6 children were not randomized, but did follow the protocol. After the initial two-year trial, children continued on Norditropin until final height. Retrospective final height and adverse event data were collected from 18 of the 21 subjects who were originally enrolled in the trial and the 6 who had followed the protocol without randomization. Historical reference materials of height velocity and adult height analyses of Noonan patients served as the controls.
The twenty-four (24) (12 female, 12 male) children 3 – 14 years of age received either 0.033 mg/kg/day or 0.066 mg/kg/day of
Norditropin subcutaneously which, after the first 2 years, was adjusted based on growth response. In addition to a diagnosis of Noonan syndrome, key inclusion criteria included bone age determination showing no significant acceleration, prepubertal status, height SDS < -2, and HV SDS < 1 during the 12 months pre-treatment. Exclusion criteria were previous or ongoing treatment with growth hormone, anabolic steroids or corticosteroids, congenital heart disease or other serious disease perceived to possibly have major impact on growth, FPG > 6.7 mmol/L ( > 120 mg/dL), or growth hormone deficiency (peak GH levels < 10 ng/mL).
Patients obtained a final height (FH) gain from baseline of 1.5 and 1.6 SDS estimated according to the national and the Noonan reference, respectively. A height gain of 1.5 SDS (national) corresponds to a mean height gain of 9.9 cm in boys and 9.1 cm in girls at 18 years of age, while a height gain of 1.6 SDS (Noonan) corresponds to a mean height gain of 11.5 cm in boys and 11.0 cm in girls at 18 years of age.
A comparison of HV between the two treatment groups during the first two years of treatment for the randomized subjects was 10.1 and 7.6 cm/year with 0.066 mg/kg/day versus 8.55 and 6.7 cm/year with 0.033 mg/kg/day, for Year 1 and Year 2, respectively. Age at start of treatment was a factor for change in height SDS (national reference). The younger the age at start of treatment, the larger the change in height SDS.
Examination of gender subgroups did not identify differences in response to Norditropin. Not all patients with Noonan syndrome have short stature; some will achieve a normal adult height without treatment. Therefore, prior to initiating Norditropin for a patient with Noonan syndrome, establish that the patient does have short stature.
Short Stature In Children With Turner Syndrome
Two randomized, parallel group, open label, multicenter studies were conducted in the Netherlands to evaluate the efficacy and safety of Norditropin for the treatment of children with short stature associated with Turner syndrome. Patients were treated to final height in both studies [height velocity (HV) < 2 cm/year]. Changes in height were expressed as standard deviation scores (SDS) utilizing reference data for untreated Turner syndrome patients as well as the national Dutch population.
In Study 1 (the primary study), 68 euthyroid Caucasian patients stratified based on age and baseline height SDS were randomized in a 1:1:1 ratio to three different Norditropin treatment regimens: 0.045 mg/kg/day (Dose A) for the entire study; 0.045 mg/kg/day for the first year and 0.067 mg/kg/day thereafter (Dose B); or 0.045 mg/kg/day for the first year, 0.067 for the second year, and 0.089 mg/kg/day thereafter (Dose C). Overall, at baseline, mean age was 6.5 years, mean height SDS (National standard) was -2.7, and mean HV during the previous year was 6.5 cm/year. Patients also received estrogen therapy after age 12 and following four years of Norditropin treatment if they did not have spontaneous puberty.
Patients were treated for a mean of 8.4 years. As seen in Table 3, overall mean final height was 161 cm in the 46 children who attained final height. Seventy percent of these children reached a final height within the normal range (height SDS > -2 using the National standard). A greater percentage of children in the two escalated dose groups reached normal final height. The mean changes from baseline to final height in height SDS after treatment with Dose B and Dose C were significantly greater than the mean changes observed after treatment with Dose A (utilizing both the National and Turner standards). The mean changes from baseline to final height in height SDS (Turner standard) in Table 3 correspond to mean height gains of 9.4, 14.1 and 14.4 cm after treatment with Doses A, B and C, respectively. The mean changes from baseline to final height in height SDS (National standard) in Table 3 correspond to mean height gains of 4.5, 9.1 and 9.4 cm after treatment with Doses A, B and C, respectively. In each treatment group, peak HV was observed during treatment Year 1, and then gradually decreased each year; during Year 4, HV was less than the pre-treatment HV. However, between Year 2 and Year 6, a greater HV was observed in the two dose escalation groups compared to the 0.045 mg/kg/day group.
Table 3 : Final Height-Related Results After Treatment
of Patients with Turner Syndrome with Norditropin in a Randomized, Dose
Escalating Study
Dose A 0.045 mg/kg/day (n = 19) |
Dose B up to 0.067 mg/kg/day (n = 15) |
Dose C up to 0.089 mg/kg/day (n = 12) |
Total (n = 46) |
Baseline height (cm)1 | 105 (12) | 108 (12.7) | 107 (11.7) | 106 (11.9) |
Final height (cm)1 | 157 (6.7) | 163 (6.0) | 163 (4.9) | 161 (6.5) |
Number (%) of patients reaching normal height (height SDS > -2 using National standard) | 10 (53%) | 12 (80%) | 10 (83%) | 32 (70%) |
Height SDS (Turner standard)2 | ||||
Final [95% CI] | 1.7 [1.4, 2.0] | 2.5 [2.1, 2.8]3 | 2.5 [2.1, 2.9]4 | NA |
Change from baseline [95% CI] | 1.5 [1.2, 1.8] | 2.2 [1.9, 2.5]3 | 2.2 [1.9, 2.6]4 | NA |
Height SDS (National standard)2 | ||||
Final [95% CI] | -1.9 [-2.2, -1.6] | -1.2 [-1.5, -0.9]4 | -1.2 [-1.6, -0.8]5 | NA |
Change from baseline [95% CI] | 0.7 [0.4, 1.0] | 1.4 [1.1, 1.7]4 | 1.4 [1.1, 1.8]5 | NA |
Values are expressed as mean
(SD) unless otherwise indicated. SDS: Standard deviation score. 1Unadjusted (raw) means; 2Adjusted (least squares) means based on an ANCOVA model including terms for treatment, duration of treatment, age at baseline, bone age at baseline, height SDS at baseline, age at onset of puberty and mid-parental target height SDS; 3p=0.005 vs. Dose A; 4p=0.006 vs. Dose A; 5p=0.008 vs. Dose A |
In Study 2 (a supportive study), 19 euthyroid Caucasian patients (with bone age ≤ 13.9 years) were randomized to treatment with 0.067 mg/kg/day of Norditropin as a single subcutaneous dose in the evening, or divided into two doses (1/3 morning and 2/3 evening). All subjects were treated with concomitant ethinyl estradiol. Overall, at baseline, mean age was 13.6 years, mean height SDS (National standard) was -3.5 and mean HV during the previous year was 4.3 cm/year. Patients were treated for a mean of 3.6 years. In that there were no significant differences between the two treatment groups for any linear growth variables, the data from all patients were pooled. Overall mean final height was 155 cm in the 17 children who attained final height. Height SDS changed significantly from -3.5 at baseline to -2.4 at final height (National standard), and from 0.7 to 1.3 at final height (Turner standard).
Short Stature In Children Born Small For Gestational Age (SGA) With No Catch-up Growth By Age 2-4 Years
A multi-center, randomized, double-blind, two-arm study to final height (Study 1) and a 2-year, multi-center, randomized, double-blind, parallel-group study (Study 2) were conducted to assess the efficacy and safety of Norditropin in children with short stature born SGA with no catch-up growth. Changes in height and height velocity were compared to a national reference population in both studies.
Study 1
The pivotal study included 53 (38 male, 15 female) non-GHD, Dutch children 3-11 years of age with short stature born SGA with no catch-up growth. Catch-up growth was defined as obtaining a height of ≥ 3rd percentile within the first 2 years of life or at a later stage. These prepubertal children needed to meet the following additional inclusion criteria: birth length < 3rd percentile for gestational age, and height velocity (cm/year) for chronological age < 50th percentile. Exclusion criteria included chromosomal abnormalities, signs of a syndrome (except for Silver-Russell syndrome), serious/chronic co-morbid disease, malignancy, and previous rhGH therapy. Norditropin was administered subcutaneously daily at bedtime at a dose of approximately 0.033 (Dose A) or 0.067 mg/kg/day (Dose B) for the entire treatment period. Final height was defined as a height velocity below 2 cm/year. Treatment with Norditropin was continued to final height for up to 13 years. Mean duration of treatment was 9.5 years (boys) and 7.9 years (girls).
38 out of 53 children (72%) reached final height. Sixty-three percent (24 out of 38) of the children who reached final height were within the normal range of their healthy peers (Dutch national reference). For both doses combined, actual mean final height was 171 (SD 6.1) cm in boys and 159 (SD 4.3) cm in girls.
As seen in Table 4, for boys and girls combined, both mean final height SDS (Dose A, -1.8 vs. Dose B, -1.3), and increase in height SDS from baseline to final height (Dose A, 1.4 vs. Dose B, 1.8), were significantly greater after treatment with Dose B (0.067 mg/kg/day). A similar dose response was observed for the increase in height SDS from baseline to Year 2 (Table 4).
Overall mean height velocity at baseline was 5.4 cm/y (SD 1.2; n=29). Height velocity was greatest during the first year of Norditropin treatment and was significantly greater after treatment with Dose B (mean 11.1 cm/y [SD 1.9; n=19]) compared with Dose A (mean 9.7 cm/y [SD 1.3; n=10]).
Table 4: Study 1: Results for Final Height SDS and Change from Baseline to Final Height in Height SDS Using National Standard After Long-Term Treatment of SGA Children with Norditropin
Raw Mean ± SD (N) | |||
Dose A 0.033 mg/kg/day | Dose B 0.067 mg/kg/day | Total | |
Baseline Height SDS | -3.2 ± 0.7 (26) | -3.2 ± 0.7 (27) | -3.2 ± 0.7 (53) |
Adjusted least-squares mean ± standard error (N) and [95% confidence intervals] | |||
Height SDS: Change from Baseline at Year 22 | 1.4 ± 0.1 (26) [1.1, 1.6] |
1.8 ± 0.1 (26) [1.5, 2.0] |
Treatment Diff = 0.4 [0.2, 0.7] p-value = 0.002 |
Height SDS: Change from Baseline at Final Height1 | 1.4 ± 0.2 (19) [0.9, 1.8] | 1.8 ± 0.2 (19) [14, 2.2] | Treatment Diff = 0.5 [0.0, 0.9] p-value = 0.045 |
Final Height SDS1 | -1.8 ± 0.2 (19) [-2.2, -1.4] |
-1.3 ± 0.2 (19) [-1.7, -0.9] |
Final Height SDS > -2 | 13/19 (68%) | 11/19 (58%) | 24/38 (63%) |
SDS: Standard deviation score. 1Adjusted (least-squares) means based on an ANCOVA model including terms for treatment, gender, age at baseline, bone age at baseline, height SDS at baseline, duration of treatment, peak GH after stimulation and baseline IGF-1. 2 Adjusted (least-squares) means based on an ANCOVA model including terms for treatment, gender, age at baseline, height SDS at baseline, and pubertal status. |
Study 2
In this study, 84 randomized, prepubertal, non-GHD, Japanese children (age 3-8) with short stature born SGA with no catch-up growth were treated for 2 years with 0.033 or 0.067 mg/kg/day of Norditropin subcutaneously daily at bedtime or received no treatment for 1 year. Additional inclusion criteria included birth length or weight SDS ≤ -2 or < 10th percentile for gestational age, height SDS for chronological age ≤ -2, and height velocity SDS for chronological age < 0 within one year prior to Visit 1. Exclusion criteria included diabetes mellitus, history or presence of active malignancy, and serious co-morbid conditions.
As seen in Table 5, for boys and girls combined, there was a dose-dependent increase in height SDS at Year 1 and Year 2. The increase in height SDS from baseline to Year 2 (0.033 mg/kg/day, 0.8 vs. 0.067 mg/kg/day, 1.4) was significantly greater after treatment with 0.067 mg/kg/day. In addition, the increase in height SDS at Year 1 was significantly greater in both active treatment groups compared to the untreated control group.
Table 5 : Study 2: Results for Change from Baseline in
Height SDS At Year 1 and Year 2 Using National Standard After Short-Term
Treatment of SGA Children with Norditropin
Raw Mean ± SD (N) | ||||
No Treatment | 0.033 mg/kg/day | 0.067 mg/kg/day | Total | |
Height SDS: Baseline | -2.9 ± 0.5 (15) | -3.0 ± 0.6 (35) | -2.9 ± 0.7 (34) | -2.9 ± 0.6 (84) |
Height SDS: Year 1 | -2.8 ± 0.5 (15) | -2.4 ± 0.6 (33) | -2.0 ± 0.8 (34) | -2.3 ± 0.7 (82) |
Height SDS: Year 2 | NA | -2.2 ± 0.7 (33) | -1.4 ± 0.7 (32) | -1.8 ± 0.8 (65) |
Adjusted least-squares mean ± standard error (N) and [95% confidence intervals] | ||||
Height SDS: Change from Baseline at Year 11 | 0.1 ± 0.1 (15) [-0.1, 0.2] | 0.6 ± 0.1 (33) [0.5, 0.7] | 0.9 ± 0.1 (34) [0.8, 1.0] | |
0.033 vs. No Treatment: Treatment Diff = 0.5, [0.3, 0.7], p < 0.0001 0.067 vs. No Treatment: Treatment Diff = 0.8, [0.6, 1.0], p < 0.0001 0.067 vs. 0.033: Treatment Diff = 0.3, [0.2, 0.5], p-value < 0.0001 |
Height SDS: Change from Baseline at Year 21 | NA | 0.8 ± 0.1 (33) [0.7, 0.9] | 1.4 ± 0.1 (32) [1.3, 1.6] | |
0.067 vs. 0.033: Treatment Diff = 0.6, [0.5, 0.8], p-value < 0.0001 | ||||
SDS: Standard deviation score. 1Adjusted (least-squares) means based on an ANCOVA model including terms for treatment, gender, age at baseline, and height SDS at baseline. All children remained prepubertal during the study. |
Adult Growth Hormone Deficiency (GHD)
A total of six randomized, double-blind, placebo-controlled studies were performed. Two representative studies, one in adult onset (AO) GHD patients and a second in childhood onset (CO) GHD patients, are described below.
Study 1
A single center, randomized, double-blind, placebo-controlled, parallel-group, six month clinical trial was conducted in 31 adults with AO GHD comparing the effects of Norditropin [somatropin (rDNA origin) for injection] and placebo on body composition. Patients in the active treatment arm were treated with Norditropin 0.017 mg/kg/day (not to exceed 1.33 mg/day). The changes from baseline in lean body mass (LBM) and percent total body fat (TBF) were measured by total body potassium (TBP) after 6 months.
Treatment with Norditropin produced a significant (p=0.0028) increase from baseline in LBM compared to placebo (Table 6).
Table 6 : Lean Body Mass
(kg) by TBP
Norditropin (n=15) |
Placebo (n=16) |
Baseline (mean) | 50.27 | 51.72 |
Change from baseline at 6 months (mean) | 1.12 | -0.63 |
Treatment difference (mean) | 1.74 | |
95% confidence interval | (0.65, 2.83) | |
p-value | p=0.0028 |
Analysis of the treatment difference on the change from baseline in percent TBF revealed a significant decrease (p=0.0004) in the Norditropin-treated group compared to the placebo group (Table 7).
Table 7 : Total Body Fat (%) by TBP
Norditropin (n=15) |
Placebo (n=16) |
Baseline (mean) | 44.74 | 42.26 |
Change from baseline at 6 months (mean) | -2.83 | 1.92 |
Treatment difference (mean) | -4.74 | |
95% confidence interval | (-7.18, -2.30) | |
p-value | p=0.0004 |
Fifteen (48.4%) of the 31 randomized patients were male. The adjusted mean treatment differences on the increase in LBM and decrease in percent TBF from baseline were larger in males compared to females. Norditropin also significantly increased serum osteocalcin (a marker of osteoblastic activity).
Study 2
A single center, randomized, double-blind, placebo-controlled, parallel-group, dose-finding, six month clinical trial was conducted in 49 men with CO GHD comparing the effects of Norditropin and placebo on body composition. Patients were randomized to placebo or one of three active treatment groups (0.008, 0.016, and 0.024 mg/kg/day). Thirty three percent of the total dose to which each patient was randomized was administered during weeks 1-4, 67% during weeks 5-8, and 100% for the remainder of the study. The changes from baseline in LBM and percent TBF were measured by TBP after 6 months.
Treatment with Norditropin produced a significant (p=0.0079) increase from baseline in LBM compared to placebo (pooled data) (Table 8).
Table 8 : Lean Body Mass
(kg) by TBP
Norditropin (n=36) | Placebo (n=13) | |
Baseline (mean) | 48.18 | 48.90 |
Change from baseline at 6 months (mean) | 2.06 | 0.70 |
Treatment difference (mean) | 1.40 | |
95% confidence interval | (0.39, 2.41) | |
p-value | p=0.0079 |
Analysis of the treatment difference on the change from baseline in percent TBF revealed a significant decrease (p=0.0048) in the Norditropin-treated groups (pooled data) compared to the placebo group (Table 9).
Table 9 : Total Body Fat (%)
by TBP
Norditropin (n=36) | Placebo (n=13) | |
Baseline (mean) | 34.55 | 34.07 |
Change from baseline at 6 months (mean) | -6.00 | -1.78 |
Treatment difference (mean) | -4.24 | |
95% confidence interval | (-7.11,-1.37) | |
p-value | p=0.0048 |
Norditropin also significantly reduced intraabdominal, extraperitoneal and total abdominal fat volume, waist/hip ratio and LDL cholesterol, and significantly increased serum osteocalcin. Forty four men were enrolled in an open label follow up study and treated with Norditropin for as long as 30 additional months. During this period, the reduction in waist/hip ratio achieved during the initial six months of treatment was maintained.
Last reviewed on RxList: 12/15/2016
This monograph has been modified to include the generic and brand name in many instances.
Norditropin FDA Prescribing Information
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We continue our monthly look at the most useful new features in Mac OS X Yosemite with how to mirror and record your iPad screen on your Mac.
This is fantastic if you want to run through a presentation or process you have on your iPad with colleagues, as you can record everything you're doing on screen, as well as your voiceover explaining any details, then package it up and share it as one file. here's how to go about it:
1. To start, simply connect your iPad to your Mac with the USB/Lightning adaptor.
2. Now, open up QuickTime Player from your Mac's Applications folder, and start a 'New Movie Recording' from under the File tab.
3. QuickTime will detect your iPad and should automatically start mirroring your screen. If it's still showing your Mac's iSight camera view, however, all you have to do to switch to iPad is click on the little drop down menu next to QuickTime's record button and click on 'iPad' as the input source, instead of 'FaceTime HD Camera'.
4. Now you can record anything you do on iPad as normal using QuickTime. Just click the record button to start recording.
5. When you're finished, hit the stop button, and your recording will be saved. You can now export the resulting .mov file by clicking on File > Export, or immediately share the file with clients and colleagues over Email, Messages, AirDrop or a range of social media platforms. Easy!
You'll also notice that the battery life of your iPad is shown at 100% in the recording, and the carrier (eg EE) is not displayed. This is a clever touch from Apple, especially for business users who may be presenting externally and would prefer not to show those details.
Want to know more about Mac? Give our team a call on 03332 409 306 or email email@example.com. For all the latest news and tips, follow @WeAreJigsaw24 on Twitter or 'Like' us on Facebook.


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Minions do your bidding. The Maxxis Minion DHF 3C 2.5 tire will do your bidding when it comes to loose and muddy downhills. You want to go straight? It'll keep you straight. You want to gently turn? It'll do that. You want to lean hard into an impossibly tight radius turn because victory is on the line? It'll sing the happy song as it corners like your bike is on rails! As with many tires this one needs jargon explained. DHF means "downhill front." Its a downhill tire first and it can be used as a front with a Minion DHR or depending on conditions as a rear with another DHF in front. 3C is Triple Compound Technology -- three different rubber compounds comprise the tread. The tire has a 70a durometer rubber base layer which reduces knob flex and increases tire life. 40a durometer shoulder knobs grip without rebounding fast. 42a durometer rubber crown knobs help improve braking performance as part of going fast is controlling speed.The Maxxis Minion DHF 3C 2.5 tire measures 26 x 2.5. The wire bead tire is designed to be ridden with tubes. The thread count is 60 tpi. The maximum recommended tire pressure is 65 psi. Black. 1330g.An extremely cut-resistant and abrasion-resistant material added to the sidewalls of selected mountain bike tyre. The densely woven fabric is also lightweight and highly flexible ensuring that the performance of the tire remains unaffected. Choose Exo-protection for exceptionally rocky treacherous trails where the chance of the sidewall cuts and abrasion is high.
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From | Stefano Mazzocchi <stef...@apache.org> |
Subject | Re: Possible points of scalibility issues |
Date | Thu, 14 Feb 2002 12:36:36 GMT |
Berin Loritsch wrote: > > I have been performing some performance analysis of the Avalon Excalibur code, and I discovered > some serious points of thread contention. In a web environment, there can be as many as 150 > threads (or more) if the web server uses simple thread per connection technology > (most common aproach for Java based servers like Tomcat). I expanded the Profile tests to work > using 100 threads. The default pooling implementation has some serious slowdown due to thread > contention. Berin, I spent several *months* finetuning the thread-pool code that was used in Apache JServ 1.0 (and is still there, used in both IBM WebSphere and Oracle Application Server). I remember that I got a report from Alcatel that they were using it with loads up to 12000 req/sec. Guess that if you need something clean and scalable, that's the way to go :) [I proposed this a while back but you and Peter tought it was too complex.... but I still like mine more :) but I'm biased] -- Stefano Mazzocchi One must still have chaos in oneself to be able to give birth to a dancing star. <email@example.com> Friedrich Nietzsche -------------------------------------------------------------------- --------------------------------------------------------------------- To unsubscribe, e-mail: firstname.lastname@example.com For additional commands, email: email@example.com | |
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white collar
8 white collar Articles
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Literacy in the Classroom: Read Alouds, World Read Aloud Day, and more. . .
I can't really remember when I couldn't read. How fortunate I am! My earliest memories are of my mother reading to my brother and me, one of us on each side of her. Ours were the Little Golden Books. My favorite was The Little Red Caboose by Marian Potter. I especially loved the illustrations. That train took my imagination to places I had never been. Thank you Mom, for taking the time to give me the love of reading!
I remember 5th grade and how my teacher read aloud to us every day after lunch recess. Here I first heard the words of Mark Twain and discovered Tom Sawyer and Huckleberry Finn. I still recommend those after recess read alouds as an opportunity to engage students and provide a quiet time to settle and focus on classroom tasks. My reflections here are to remind all of us of the importance of sharing the love of reading. We do this by reading to and with our students. Taking time each day for read alouds or silent reading like DEAR (Drop Everything And Read) gives us an opportunity to model and cultivate a love for reading in our students. Using literature in the classroom provides a vehicle for teaching not only literacy but many valuable lessons that connect to real life and content areas like social studies.
This week I came across three articles/posts that really speak to the importance of literacy for all our children. If you find these useful, please let us know how you use them.
Have a great week.
Read Aloud and Literacy Resources
- Make Every Day Read Aloud Day blog post by Donalyn Miller on The Book Whisperer Blog. Donalyn provides valuable reasons for reading aloud, gives suggestions for books to read to 4th-6th grades, and invites readers to share their favorite read alouds. I thoroughly enjoyed this post and highly recommend it. She also shares World Read Aloud Day.
- World Read Aloud Day: March 7, 2012
- Celebrate the Power of Words and Stories and Take Action for Global Literacy with LitWorld
Worldwide at least 793 million people remain illiterate. Imagine a world where everyone can read…On March 7, 2012, LitWorld, a global literacy organization based in New York City, will be celebrating World Read Aloud Day. World Read Aloud Day is about taking action to show the world that the right to read and write belongs to all people. World Read Aloud Day motivates children, teens, and adults worldwide to celebrate the power of words, especially those words that are shared from one person to another, and creates a community of readers advocating for every child's right to a safe education and access to books and technology. By raising our voices together on this day we show the world's children that we support their future: that they have the right to read, to write, and to share their words to change the world.
To learn more about LitWorld and to register to participate in World Read Aloud Day, please visit: http://litworld.org/worldreadaloudday
- Recently I blogged that Walter Dean Myers was named National Ambassador for Young People's Literature 2012-2013
- An Education in Books blog has an excellent review of The Cruisers by Myers, Grades 4-7. Click on the links in the post for resources for teaching this book. I am currently reading it on my iPad. It looks like a great book for teaching both the civil war and current social issues. I'm adding it to my social studies lit set.
- Resources for Read Alouds
- World Read Aloud Day 2015 | From Surviving to Thriving
- World Read Aloud Day Blog Challenge Week 3 | From Surviving to Thriving
- March 2 – Happy Birthday Dr. Seuss – Read Across America | From Surviving to Thriving
- World Read Aloud Day Blog Challenge Week 2 | From Surviving to Thriving
- World Read Aloud Day 2013 « From Surviving to Thriving
- Read-Alouds in the Classroom « From Surviving to Thriving
- World Read Aloud Day Wrap Up « Culture Bean
- Chapter Book Read Alouds: My Top Five List | The Lit Witch: A Book Blog


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Dang , i can't believe summers coming to an end . It's getting cold here , no more going topless , at least in the morning . So i need to dig out the soft top from the basement , it's been off since the end of March , anyway pretty much all of the zippers are really stiff , is there a trick to freeing them up ? I also need to have the shop replace the plastic in the rear window . Any hints on the zippers?
Thanks ........................................ 409 :D |
There are a variety of lubricants you can use but there is always the danger of staining the top and some will acutally attract dust and dirt. I had the same problem with mine as I live on the coast and there is a lot of salt in the air and it drys into a crust. As a comercial diver, I have to take special care of my equipment as it expencive and my life and work depend on it.
The all around best lubricant I have found for plastic and mettle zippers is bee's wax. I use it on the zippers of my dry suits. I tryed it on my Jeep top and it works great. You can get the Bee's Wax in a large chap stick tube and has the similar consistancey. Because it's in a stick it makes it easey to apply and doesn't get everywhere. I'm sure there are a lot of silicone and teflon and other high tech lubes but this has worked well for me. |
I love it...you guys are putting your tops on, in about two weeks, i plan to take my top off. Been about 95 degrees for about 4 months, cant wait for it to cool down.
Later ....................................... 409 :) |
ive been lazy a couple nights in a row, and drove home in the morning in 38* cold.. heater blasting and windows up.. no top on though... BRR!!!
Buffalo weather... Today (Sunday) High of 81 :) Wednesday High of 52 :mad: |
I bought some zipper lube the the dealership sells, and have good luck with it...
The guy at the dealership (apparently his entire family owns some form of jeep) said to use bee's wax if my zippers started sticking.
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Accelerating data backups when the economy slows
Accelerating data backups when the economy slows
Accelerating data backups when the economy slows
By Bud Stoddard
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To say we've seen a significant downturn in the economy in the past 12 months would be hyperbole at its best. As business owners and executives, it is incumbent on each of us to go through the necessary belt-tightening and reassessment of the budget process whenever the economic conditions and the stock market make a turn in a southerly direction.
There are the obvious areas we turn to first - with scalpel in hand - that include head count, marketing, accounting, human resources, legal, customer support and ultimately information technology (IT). Certainly with IT investment that have risen upwards by 15-20%, this is an area that could stand to go on a fiscal version of the slim fast program, at least until we climb out of this economic malaise. It's standard to cut infrastructure costs, defer the Web initiative or stretch out the additional hires for a few months but I beg you, SPARE THE BACKUP AND RECOVERY LINE ITEM. In fact, when things turn down, I would even advocate that you increase the backup and recovery expense. Yes, I know that might sound a little self-serving since my company provides backup services to companies like yours, but I will explain.
The central premise
Information and data are two of the most critical assets in your business. Lost data is considered irreplaceable - period. Here are a few examples to make my point. Would you or anybody on your management team (following a data loss or disaster) tell you the following?
- What are our outstanding account receivables and who owes us what?
- What are our outstanding account payables and whom do we owe what?
Yeah, you're thinking this is not really a problem. But in fact, does anybody really know who their customers and suppliers are and can they recite the list without a computer printout? Not likely.
- What do all of our employees make and what are their deductions? You outsource payroll, does your payroll company have offsite backup? I know two that don't.
- And what about all those customer names, phone numbers, addresses and detailed purchasing information that are in your CRM system or at least in Act or Outlook...GONE. Was it backed up?
Enough said on this...I'm sure you get the point. You can replace desks, computers, supplies and inventory, even people but data is irreplaceable and its loss could put you out of business. Think about it.
The only viable insurance program for data is to have a bulletproof, fully automated backup and recovery service in place that removes data nightly over a secure Internet connection to a remote vault for instant retrieval in the event of a loss. It's called evaulting or online backup for business.
About the author: Bud Stoddard is president and CEO of amerivault, a provider of online backup services to computer dependent companies. Mr. Stoddard draws from twenty years experience in data backup and recovery services and his companies have helped support hundreds of disaster recovery tests and events during that period.
-Dig Deeper on Storage Resources
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