Jonas Salk Research Paper Example | Topics and Well Written Essays - 750 words

Jonas Salk - Research Paper Example Salk was the oldest of three children, having two younger brothers by the names of Herman and Lee. Despite the fact that Salk’s parents, being Russian-Jewish immigrants, had not been able to receive substantial and formal education, Salk was raised to be an intellectual, brilliant young man. Salk attended the local public schools of New York, but when it was time for him to begin high school at the young age of thirteen, he was sent to Townsend Harris High School. This high school was a free alternative to the expensive private schools for intellectually gifted students, catering to intellectually talented males of immigrant parents, just like Salk (McPherson 11). While in high school, Salk quickly became known for his intelligence and his desperate want to learn; he was constantly reading and he was one of the few students at the school who completed his four-year education in the required three, whereas most of his classmates dropped out before the three years were up. This success enabled Salk to attend City College of New York, which is one of the most competitive colleges in the United States. While Salk was in college, he worked for and obtained his Bachelor of Science degree. Salk originally attended the college with the hopes of one day becoming a lawyer, but his mother encouraged him to take an interest in the medical field instead. After his years at CCNY, Salk was accepted into New York University School of Medicine. Although Salk remained strong in his dislike of studying medicine, he found an interest in the research and scientific aspects of the medical field. He studied biochemistry and then eventually made his primary focus bacteriology, claiming that his â€Å"desire was to help humankind in general rather than single patients (Bookchin & Schumacher 72).† When Salk was in his final year at the medical school, he did a work study program in the laboratory of Doctor Thomas Francis, who was noted for having discovered the Type B infl uenza virus. Francis’ influence was great over Salk, and Salk became addicted to the field of virology. After medical school, Salk obtained an internship at New York’s Mount Sinai Hospital and continued to work in Francis’ laboratory whenever he got the chance. After his time at Mount Sinai, Salk sought for a more permanent research job, but had difficulty in doing so because of his Jewish heritage. He was unable to be hired at Mount Sinai, as this went against their rules, and Francis had moved and could not help Salk in his job-seeking endeavors. However, Francis had extra grant money and was able to give Salk a job, enabling him to work on an army-commissioned project to develop an influenza vaccine. It was during this time that Salk â€Å"discovered and isolated one of the flu strains that was included in the final vaccine (Sherrow 31).† In 1947, Salk set out to find an institution that would allow him to take charge of his own laboratory. He was offe red space at the University of Pittsburg School of Medicine. After obtaining numerous grants, he was able to create the laboratory he required to continue his research on flu vaccines. Not too long after, Salk was offered a job to work with the National Foundation for Infantile Paralysis and aid the other researchers in creating a polio vaccination, a position that Salk was only too eager to accept. Polio had been a disease that stalked the human species since 1835 and Salk was desperate to rid the world of the devastation it caused. In the years leading up to 1955, Salk worked relentlessly to discover a safe and effective vaccination to treat the polio disease. People were so optimistic about the science that Salk was doing that, six months prior to the completion and approval of

Rewrite Essay Example | Topics and Well Written Essays - 1250 words - 1

Rewrite - Essay Example WEKA enables the one of two options such as pruned tree or not pruned tree as shown in the figure. Figure 1: Properties of the Decision tree in the WEKA (J48) In addition to above features, the WEKA also performs the test options for data use and data classification. Usage of the Training set: Evaluation of the classifier is based on the prediction of the instances of a class, which is trained on. Supplied Test: Evaluation of the classifier is also performed on the prediction of the instances of a class, which is loaded from the file. Cross Validation: By entering the number of fold into the text field of the Fold in the WEKA explorer the classifier is evaluated. Percentage Split: Data percentage is predicted by the evaluation of a classifier that takes the data out for the testing. The percentage field determines the specification of data held. During the training, data is used and provided the value of percentage field that makes the important part. Value of the reminder is reserve d for the testing purposes. By the default, value of percentage split is stated as the 66%. Data about 34% is used for testing and remaining 66% is trained. Figure 2: WEKA with testing options Decision tree performance is determined by examining the cross validation and percentage split in the provided medical dataset. Usage of Cross Validation for generation of decision tree: In order to control the factors such as training’s set size and confidence by the process of cross validation, the flexibility is found in the decision tree of J48. Confidence factor is used to minimise or reduce the error rate of the classification. It is said that confidence factor is used to settle the problem of tree pruning. In order to classify the instances in a more accurate way, the classifier is given an opportunity by increasing the confidence factor and removing the noise of the training. The value of the confidence factor is 95% used for the dataset and leads to an outstanding outcome of 89 .2% for the correct and classified instances and only 10.7% is the classified incorrectly as shown in the following figure. Figure 3: Use of cross validation based on the option J-48 decision tree to generate the results by WEKA. In the above figure, the calculation of J48 decision tree has been shown which includes correct values in details. Confusion Matrix is the important point in the given figure, which describes the ways in which a classifier makes an error in the prediction of a class type. According to Dunham (2003) the confusion matrix provides the correctness of the solution for the given classification problem. Another term used as an alternative to the confusion matrix is the contingency table. Two classes having a single dataset contain a column and two rows for the confusion matrix as shown in the figure 4. Predicted Actual Figure 4: Confusion Matrix Here FP represents the incorrectly classified number of negatives as positives and called as the commission errors. TP r epresents correctly classified number of positives. TN represents the correct classification of negative numbers, and FN shows the incorrect classification of positive numbers as negative. These are called as the omission errors. Predictive accuracy becomes the way for measuring the performance of a classifier. Predictive accuracy is known as the calculated success rate determined by the use of predictive accuracy as the confusion mat