Alexander Fleming Discovers Penicillin

Alexander Fleming Discovers Penicillin In 1928, bacteriologist Alexander Fleming made a chance discovery from an already discarded, contaminated Petri dish. The mold that had contaminated the experiment turned out to contain a powerful antibiotic, penicillin. However, though Fleming was credited with the discovery, it was over a decade before someone else turned penicillin into the miracle drug that has helped save millions of lives. Dirty Petri Dishes On a September morning in 1928, Alexander Fleming sat at his workbench at St. Marys Hospital after having just returned from a vacation at the Dhoon (his country house) with his family. Before he had left on vacation, Fleming had piled a number of his Petri dishes to the side of the bench so that Stuart R. Craddock could use his workbench while he was away. Back from vacation, Fleming was sorting through the long unattended stacks to determine which ones could be salvaged. Many of the dishes had been contaminated. Fleming placed each of these in an ever-growing pile in a tray of Lysol. Looking for a Wonder Drug Much of Flemings work focused on the search for a wonder drug. Though the concept of bacteria had been around since Antonie van Leeuwenhoek first described it in 1683, it wasnt until the late nineteenth century that Louis Pasteur confirmed that bacteria caused diseases. However, though they had this knowledge, no one had yet been able to find a chemical that would kill harmful bacteria but also not harm the human body. In 1922, Fleming made an important discovery, lysozyme. While working with some bacteria, Flemings nose leaked, dropping some mucus onto the dish. The bacteria disappeared. Fleming had discovered a natural substance found in tears and nasal mucus that helps the body fight germs. Fleming now realized the possibility of finding a substance that could kill bacteria but not adversely affect the human body. Finding the Mold In 1928, while sorting through his pile of dishes, Flemings former lab assistant, D. Merlin Pryce stopped by to visit with Fleming. Fleming took this opportunity to gripe about the amount of extra work he had to do since Pryce had transferred from his lab. To demonstrate, Fleming rummaged through the large pile of plates he had placed in the Lysol tray and pulled out several that had remained safely above the Lysol. Had there not been so many, each would have been submerged in Lysol, killing the bacteria to make the plates safe to clean and then reuse. While picking up one particular dish to show Pryce, Fleming noticed something strange about it. While he had been away, a mold had grown on the dish. That in itself was not strange. However, this particular mold seemed to have killed the Staphylococcus aureus that had been growing in the dish. Fleming realized that this mold had potential. What Was That Mold? Fleming spent several weeks growing more mold and trying to determine the particular substance in the mold that killed the bacteria. After discussing the mold with mycologist (mold expert) C. J. La Touche who had his office below Flemings, they determined the mold to be a Penicillium mold. Fleming then called the active antibacterial agent in the mold, penicillin. But where did the mold come from? Most likely, the mold came from La Touches room downstairs. La Touche had been collecting a large sampling of molds for John Freeman, who was researching asthma, and it is likely that some floated up to Flemings lab. Fleming continued to run numerous experiments to determine the effect of the mold on other harmful bacteria. Surprisingly, the mold killed a large number of them. Fleming then ran further tests and found the mold to be non-toxic. Could this be the wonder drug? To Fleming, it was not. Though he saw its potential, Fleming was not a chemist and thus was unable to isolate the active antibacterial element, penicillin, and could not keep the element active long enough to be used in humans. In 1929, Fleming wrote a paper on his findings, which did not garner any scientific interest. 12 Years Later In 1940, the second year of World War II, two scientists at Oxford University were researching promising projects in bacteriology that could possibly be enhanced or continued with chemistry. Australian Howard Florey and German refugee Ernst Chain began working with penicillin. Using new chemical techniques, they were able to produce a brown powder that kept its antibacterial power for longer than a few days. They experimented with the powder and found it to be safe. Needing the new drug immediately for the war front, mass production started quickly. The availability of penicillin during World War II saved many lives that otherwise would have been lost due to bacterial infections in even minor wounds. Penicillin also treated diphtheria, gangrene, pneumonia, syphilis, and tuberculosis. Recognition Though Fleming discovered penicillin, it took Florey and Chain to make it a usable product. Though both Fleming and Florey were knighted in 1944 and all three of them (Fleming, Florey, and Chain) were awarded the 1945 Nobel Prize in Physiology or Medicine, Fleming is still credited for discovering penicillin.

Critical analysis Journal opinion article Essay Example | Topics and Well Written Essays - 500 words

Critical analysis Journal opinion article - Essay Example The economies will be fully employed, allowing them to attain their potential in offering jobs and raise the income of their inhabitants. These will play a key role in helping generate high growth levels in cities, contributing to overall advancement of the entire economy (Sparshott). The latest outlooks evident in the case of American cities are optimistic, although some urban areas as well as their suburbs have populations representing about 86 percent of the regions’ population, while 88 percent are employed in nonfarm sectors. Even though more than half of the cities have attained record levels of employment, about one-third are anticipated to fail reaching this level when 2016 ends. Moreover, approximately 130 metropolitan areas are anticipated to penetrate the market in 2017, although they will be supporting few jobs unlike the case of the past decades. The areas that are recovering at the slowest rates are the older Midwestern regions, which were negatively influenced when the region lost a large number of jobs offered by the manufacturing sector. The deteriorating infrastructure and aging population will also play a role in preventing these areas from recovering, making them rank behind the top-advancing cities in the U.S. (Sparshott). In a different perspective, the boom witnessed in the oil sector, which played a key role in allowing certain cities in the U.S. boost their performance, is currently reversing progress. For instance, Midland, Texas portrayed fastest growth in 2014, but this is not the case presently. However, in 2015, the employment and economy of the metropolitan are anticipated to slow down and contract at the start of 2016. Overall, however, the cities in the U.S. are playing a key role in driving the growth of the economy, thus paving the way towards the recovery process. Moreover, even though some

Industrial Safety Jobs in Oklahoma Research Paper

Industrial Safety Jobs in Oklahoma - Research Paper Example The legal environment is a factor because there are strict laws in place which compels the organizations to ensure that the workers are safe and that al the measures of industrial safety are in place. These laws are in place not only in the developed countries but also in the developing countries. This shows the level of importance which governments have for the industrial safety. The humanitarian argument has its roots is humanities. It suggests that it is the responsibility of the organization to ensure that the fellow humans are not treated in any inhumane way. The proponents of this concept suggest that if an organization does not focus on industrial safety, it can have a severe implication on its image and eventually profits. The economic argument, as discussed earlier, is based on simply the costs of industrial hazards. Moreover, if the legal aspect is also held into consideration, then an organization should also keep in mind the legal penalties and fines which the government can impose because of safety breaches. The situation in Oklahoma in particular and the US in general is such that the Chemical Safety Board is seriously working to ensure that a proper system is in place for industrial safety. This has led to an increase in opportunities for jobs in this area. For instance, CSB is now considering implementing a program in which a greater emphasis is placed on the employee involvement in the safety ensuring process. In this way the workers can equally participate in monitoring, controlling and more importantly owning the safety process. If, for instance, an accident occurs, the employees can now directly report the issue to the CSB and thus CSB can investigate on quick and accurate information (Rick, 2012). The prospect of industrial safety in Oklahoma is promising. The state is included in one of those places where this concept is being taken seriously. There are many universities which are offering programs for

Support Vector Machine Based Model

Support Vector Machine Based Model Support Vector Machine based model for Host Overload Detection in Clouds Abstract. Recently increased demand in computational power resulted in establishing large-scale data centers. The developments in virtualization tech-nology have resulted in increased resources utilization across data centers, but energy efficient resource utilization becomes a challenge. It has been predicted that by 2015 data center facilities costs would contribute about 75%, whereas IT would contribute the remaining 25% to the overall operating cost of the data center. The Server consolidation concept has been evolved for improving the energy efficiency of the data centers. The paper focuses on support vector machine based novel approach to predict the overload and underload pattern of the servers for better data center reconfiguration. Keywords: Support vector machine, energy efficiency. 1 Introduction Virtualization plays an important role in cloud computing, since it permits appropriate degree of customization, security, isolation, and manageability that are fundamental for delivering IT services on demand. One of its striking features is the ability to utilize compute power more proficiently. Particularly, virtualization provides an opportunity to consolidate multiple virtual machine (VM) instances on fewer hosts depending on the host utilization, enabling many of computers to be turned-off, and thereby resulting in substantial energy savings. In fact, commercial products such as the VMware vSphere Distributed Resource Scheduler (DRS), Microsoft System Center Virtual Machine Manager (VMM), and Citirix XenServer offer VM consolidation as their chief functionality[1]. But with the rapid growth in computing demand, the number of datacenters grows with the need which leads to more number of servers active at a time. The high active servers’ ratio leads to more energy emission and production of Carbon dioxide (CO2). According to data centers’ study, the data centers are not utilized up to their maximum utilization level which leads to more active servers, everyone utilized to less than their total capacity. With this in mind, it is worthwhile to attempt to minimize energy consumption through any means available. Various research agencies and universities have contributed into the research and design of heat dissipation and control in the data center. Virtualization is a technology that contributes to the maximum u tilization of the servers by virtual machine (VM) consolidation and VM Migration. The decision of reallocation of virtual machine for VM consolidation depends on the host utilization behavior. The VMs from the under-utilized and over-utilized hosts are relocated to other hosts by packing the VMs on minimum number of hosts. The hosts having no virtual machine are shifted to the passive mode so that the total energy consumption can be reduced. Statistical methods played a great role in predicting the behavior of the host in dynamic manner. The author [3] has proposed various statistical methods for host overload and underload behavior of the hosts in his thesis. These algorithms take input as the previous or current utilization of the hosts and predict the future based on the previous or current state of the system. He has proposed Local Regression, Median Absolute Deviation, Robust Local Regression and Markov Chain model for predicting the overloaded hosts [3]. All statistical models cannot be applied to all the environments. The choice of the statistical methods d epends on the input data, because every statistical model is based on some assumptions. Markov chain model assumes that the data will be stationary but complex and dynamic environment like cloud, experience highly variable non-stationary workload. The author [3] in his thesis modified his model by using multisize sliding window workload estimation method so that it can be suitable for the cloud environment. In this paper we have proposed a prediction based model i.e. Support Vector Machine (SVM) to predict the host utilization to forecast the host overload and underload behavior of the host. The rest of the paper is organized as follows. Section 2 explains the basic concepts and modeling approaches of the Support Vector Machine. In section 3, the literature review related to Support Vector Machine is presented. In section 4 the model is applied to time series forecasting and its performance is compared with those of other forecasting models. Section 5 contains the concluding remarks. 2 Support Vector Machine Support vector machine is a novel technique based on neural network invented by Vapnik and his co-workers at AT T Bell Laboratories in 1995. The objective of SVM is to find a generalized decision rule through selecting some particular subset of training data, called support vectors. Training SVMs is equivalent to solving a linearly constrained quadratic programming problem. The quadratic equation is solved such that the solution of SVM is globally optimal and the quality complexity of the solution does not directly depend on the input space. Another key advantage of SVM is that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations. According to Vapnik there are three main problems in machine learning, e.g. Density Estimation Classification and Regression. In every case the main goal is to learn a function (or hypothesis) from the training data using a learning machine and then conclude general results base d on this knowledge. Time series is a series of data points S t à ¯Ã†â€™Ã… ½ R usually ordered in time. Time series analysis comprises the methods for analyzing the time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting models predicts future values based on the previously observed values. The main focus of this paper is to predict the overload and underload behavior of the hosts in cloud data centers based on the previous load pattern of the hosts in the datacenter. The time series prediction is affected by various factors like data is linearly separable or follows non-linear patterns, the learning is supervised learning or unsupervised learning and on support vector kernels. In Euclidean geometry linear separability is a geometric property of a pair of sets of points. The points are linearly separable or not are decided by visualizing the points in two dimensions plane by taking one set of points as being colored green and the other set of points as red. These two sets are linearly separable if there exists at least one line in the plane with all of the green points on one side of the line and all the red points on the other side. Usually in practical problems the data points are mapped to the high dimensional plane and the optimal separating hyper plane is constructed with the help of some special functions known as support vector Kernels in this new feature space. This method also resolves the problem where the training points are not separab le by a linear decision boundary. Because by using an appropriate transformation the training data points can be made linearly separable in the feature space. Figure1 (a) Linearly separable data1(b) non-linear patterns of data In supervisory learning, the training data is composed of input as well as the output vector (also called supervisory signals) whereas in un-supervisory learning the training data is composed of only input vectors. Supervisory learning produces better results because the output vector is already known and the predicted values by the SVM are compared with the output to learn better for the next step. In un-supervisory learning the output data points are not known and the training depends on the probability to drive better results out of it. SVM comes in the supervisory learning category and the kernel function makes the technique applicable for the linear as well as non-linear approximation. 3 Related Works In various practical domains time series modeling and forecasting has essential importance. A lot of research works is going on in this subject during several years. Many models have been proposed in literature for improving the accuracy and efficiency of time series modeling and forecasting. The author [1] has compared various time series prediction methods widely used these days. This paper investigated the application of SVM in financial forecasting. The autoregressive integrated moving average model(ARIMA), ANN, and SVM models were fitted to Al-Quds Index of the Palestinian Stock Exchange Market time series data and two-month future points were forecast. The results of applying SVM methods and the accuracy of forecasting were assessed and compared to those of the ARIMA and ANN methods through the minimum root-mean-square error of the natural logarithms of the data. Results proved that svm is better method of modeling and outperformed ARIMA and ANN. The author of [2] explains the time series concept and the various methods of predicting the future values based on ARIMA model, Seasonal ARIMA model, ANN model, time lagged ANN, seasonal ANN, SVM for regression, SVM for forecasting etc. they have also explained the forecast performance measure MFE (Mean Forecast Error), MAE (Mean Absolute error), MAPE (Mean absolute percentage error), MPE (Mean percentage error), MSE (Means squared error) etc. In paper [5], a model based on least squares support vector machine is proposed to forecast the daily peak loads of electricity in a month. In [5] the time series prediction was first used to forecast electricity load .In paper [4] the author has improved the method presented in [5] to derive more accurate results. The author has proposed dynamic least square support vector machine (DLS-SVM) to track the dynamics of nonlinear time-varying systems. The dynamic least square method works dynamically by replacing the first vector by the new input vector to obtain more accurate result.. The author in paper [9] has proposed the modified version on svm for time series forecasting. The algorithm performs the forecasting in phases. In the first phase, self-organizing map (SOM) is used to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second phase, multiple SVMs, also called SVM experts, are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. 4 Support Vector Machine Regression Formulations for Forecasting Host Overload Detection Host overload and underload detection is based on current utilization patterns of the host. The host utilization is a univariate time series. In univariate time series the future values are entirely based on past observations. The goal of the SVM regression is to find a function that presents the most deviation from the target values so the maximum allowed error is. The future values are predicted by splitting the time series data into training inputs and the training outputs. Given training data sets of N points, with input data and output data . Assume a non-linear function as given below: (1) w = weight vector, b=bias and is a non-linear mapping to a higher dimensional space. The optimization problem can be defined as: : (2) is a user defined maximum error allowed. The above equation (2) can be rewrite as: : (3) To solve the above equation slack variables needs to be introduced to handle the infeasible optimization problem. After introducing the slack variables the above equations takes the form as given below: : (4) The slack variables defines the size of the upper and the lower deviation as shown in the figure 2(a). Figure 2 (a) The Accurate points inside Tube 2(b) Slope decided by C For simplicity and for avoiding the case of infinite dimensionality of the weight vector w the optimization operation are performed in the dual space[4] the Lagrangian for the problem(a) is given by [2] (3) Here, where are the Lagrange multiples. Applying the conditions of the optimality, one can compute the partial derivatives of L with respect to equate them to zero and finally eliminating w and obtain the following linear system of equations (4) Here, and with is the kernel matrix. The LS-SVM decision function is thus given by [4] (5) The dynamic least square support vector machine is modified so that it is best suitable for the real world problems. The key feature of DLS-SVM is that it can track the dynamics of the non-linear time varying system by deleting one existing data point whenever a new observation is added, thus maintaining the constant window size. 4 Experiments We have used CloudSim for retrieving the utilization of the host based on the workload defined in the PlanetLab folder in CloudSim. It contains the daily virtual machine requirement and the utilization of the host is calculated based on the daily requirement of the virtual machines. After retrieving the utilization of the hosts LSSVMLabv1 toolbox is used for support vector regression and the results are compared with [10] and [5]. The comparison is based on MAPE (mean absolute percentage error) and Maximal error (ME). The chart shows that DLS-SVM produce better forecast for the load pattern of the hosts in the data centers. Figure2: Comparison of errors References Okasha, M. K.,Using Support Vector Machines in Financial Time Series Forecasting.International Journal of Statistics and Applications 2014, 4(1): 28-392. Adhikari, R., Agrawal, R. K. (2013). An Introductory Study on Time Series Modeling and Forecasting.arXiv preprint arXiv:1302.6613. Beloglazov, Anton. Energy-efficient management of virtual machines in data centers for cloud computing. (2013). Niu, D. X., Li, W., Cheng, L. M., Gu, X. H. (2008, July). Mid-term load forecasting based on dynamic least squares SVMs. InMachine Learning and Cybernetics, 2008 International Conference on(Vol. 2, pp. 800-804). IEEE. Bo-Jeun Chen, Ming-Wei Chang, and Chih-Jen LIN, â€Å"Load forecasting using support vector machines: A study on EUNITE competition 2001†, IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1821-1830, Nov. 2004. Fan, Y., Li, P., Song, Z. (2006, June). Dynamic least squares support vector machine. InIntelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on(Vol. 1, pp. 4886-4889). IEEE. Kim, K. J. (2003). Financial time series forecasting using support vector machines.Neurocomputing,55(1), 307-319. Gui, B., Wei, X., Shen, Q., Qi, J., Guo, L. (2014, November). Financial Time Series Forecasting Using Support Vector Machine. InComputational Intelligence and Security (CIS), 2014 Tenth International Conference on(pp. 39-43). IEEE. Cao, L. (2003). Support vector machines experts for time series forecasting.Neurocomputing,51, 321-339. Haishan Wu, Xiaoling Chang. â€Å"Power load forecasting with least square support vector machines and Chaos Theory†, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006. Rà ¼ping, S. (2001).SVM kernels for time series analysis(No. 2001, 43). Technical Report, SFB 475: Komplexità ¤tsreduktion in Multivariaten Datenstrukturen, Università ¤t Dortmund.

Laws and Rules of the Road Essay

Create a car saying (Bumper Sticker) or a Road Sign (Billboard) that would describe one main point you learned in Module 5. This is an example of a bumper sticker from a former student: â€Å"â€Å"Driving the right speed is always a good deed. Enjoy your ride and don’t collide!† 1. What would yours say? When you speed it causes more collisions so remember always be safe and wear a seat belt. 2. How would it look? It would be a billboard and it would have a picture of a had collision that happened because of speeding 3. Now, write at least one paragraph (5 sentences or more) which explains why you thought this would make a great bumper sticker or billboard, and how it summarizes the information you learned in Module Five. Remember to use complete sentence answers and proper spelling and grammar. I thought this would make a good bumper sticker because most collisions are caused because of speeding and people should not take advantage of the roads. This bumper sticker summarizes what I learned in module 5. That is because In this module I learned about driver licenses and what you need to do if you are new to the state or if you are a new comer. Also in this module I learned that excessive speeding is the cause of many collisions. Module 6 Effects of Alcohol and Drugs Some day you might find yourself in a dangerous driving situation because of drugs, alcohol, or extreme drowsiness due to medication. Talk to a parent or guardian about what they would like for you to do if you find yourself in this situation. Answer the following questions in one or more complete sentences. 1. Explain three ways you can get home safely, without getting behind the wheel, if there are drugs or alcohol in your system. A. Call a friend B. Call a taxi C. Call a parent or relative 2. Explain three ways you can get home safely if the friend you rode with has drugs or alcohol in his system and you prevent him from getting behind the wheel. A. You can drive B. Call a taxi C. Tell your parents to pick you up 3. What would your parent/guardian want you to do? They would want me to contact them and tell them I need a ride home. 4. Look up and list the number of a local taxi or car service in your community. Include the company name and telephone number.

Eating Healthily and Advantages Disadvantages of Foods Essay

Today, every people and every country were all developing and moving forward, by then shall we keep in mind, what make us live until today and keeping us healthy.â€Å"Eating Healthily With A Busy Lifestyle†, is the topic that I chose. By reading the topic, the main point that I chose, straight away in people’s mind they will think of delicious food, delicacy that bring up the appetite, but do they have the time to eat what they want, to enjoy such appetizing meals? Does it suit our healthy life since nowadays we usually eat what we, just like the often phrase we usually heard, saw in the advertisements, ‘eat all you can’ or ‘eat while you can’. Some people neglect the healthy food thing, because they thought that healthy food is boring, not delicious and many more. I have seen people shall I say my friends, colleagues which they don’t consume any type of vegetable. I have few colleagues of mine, whenever we ate together sitting on the same table during dinner night especially, when the waiter brought the meal, and it vegetables, their first thought was they will not take those vegetables, they won’t eat it. Vegetable which contain a lot of vitamins and minerals, helps to protect our immune system, to beautify our skins and many more. It is very, very easy to eat the greens (vegetables), if they doesn’t look tasty, make them look tasty, use our imagination to think how to decorate, form the vegetables to look yummy. Here in this speech I will show how to eat healthily during working hours especially, how we divide our time to unleash our appetite towards healthy and scrumptious food. In this speech I will share what I have learnt and analysed for the healthy food, which is simple to make, and I will also points out of what are the advantages are and also disadvantages of foods especially in our country, Malaysia which most of them were highly contain of cholesterols and calories. And I will be talking on how to keep nutritious snacks on hand, packing your lunch and choosing healthy food when you are at a restaurant.

Today s Criminal Justice Over The Past Few Decades

Modern trends in criminal justice over the past few decades exhibit the need for some criminal penalties amid the extremes of imprisonment and regular probation. Usually, increases in crime have been retorted with increases in imprisonment. This has developed a counterproductive model that often lead to overcrowded prisons and jails, early release of potentially dangerous criminals, and corrections budgets that eat away state funds. In an effort to be hard on crime, many jurisdictions are making their incarceration standards harsher. Regular probation isn’t the answer either. The security of the public can be seriously endangered by sentencing to probation. Unfortunately, probation often serves as a delta in sentencing for congested prisons. This causes sentencing to become much undefined. Because of this ambiguity, potential offenders cannot be sure how they will be disciplined if caught lawbreaking. In corrections, would be crooks need to know that at any time they commit a crime, a just punishment will follow. Intermediate sanctions give judicial and corrections personnel new ways to deal with trends in crime that’s more tailored to the crime committed. Intermediate sanctions are meant to be used as a means that is not as harsh as incarceration, but more severe than regular probation. The goal of these sanctions are to allow courts to have more options, have punishments better fit the crime, and to be more cost effective for the criminal justice system. In addition, theyShow MoreRelatedPrison Terms Ineffective as Deterrent to Crime Essay1122 Words   |  5 Pagesthe world have adopted the criminal justice system. Criminal justice consists of two tools: Law and Order. On the road to maintain Law and Order, penalty like Prison Term has been espoused. 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Evidence suggests the mass imprisonment policy from the last 40 years was a horrible catastrophe. Putting more people in prison not only ruined lives, it disrupted families, prevented ex-prisoners to find housing, to get an education, or even a good job. Regrettably, the United States has a higher percent of its population incarceratedRead MoreThe History of Capital Punishment1239 Words   |  5 Pagesgurney. If he is scared, he does not show it; he appears strong and resolute in what is undoubtedly a very daunting situation. â€Å"For those about to take my life,† he says, â€Å"may God have mercy on your souls† (â€Å"Georgia†). Davis has been on death row for over twenty years for killing a police officer. After every request and appeal has failed, the time has come for Davis to be executed by the state of Georgia. What is special about this case is that thousands around the globe refuse to believe that he is