{'fraction_non_alphanumeric': 0.028068327187899766, 'fraction_numerical': 0.006208142072390699, 'mean_word_length': 4.869920494699647, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Crowdsourced Data Indicate Widespread Multidrug Resistance in Skin Flora of Healthy Young Adults†

In a laboratory exercise for undergraduate biology majors, students plated bacteria from swabs of their facial skin under conditions that selected for coagulase-negative Staphylococcus; added disks containing the antibiotics penicillin, oxacillin, tetracycline, and erythromycin; and measured zones of inhibition. Students also recorded demographic and lifestyle variables and merged this information with similar data collected from 9,000 other students who had contributed to the database from 2003 to 2011. Minimum inhibitory concentration (MIC) testing performed at the Harborview Medical Center Microbiology Laboratory (Seattle, WA) indicated a high degree of accuracy for student-generated data; species identification with a matrix-assisted laser desorption ionization (MALDI) Biotyper revealed that over 88% of the cells analyzed by students were S. epidermidis or S. capitus. The overall frequency of resistant cells was high, ranging from 13.2% of sampled bacteria resistant to oxacillin to 61.7% resistant to penicillin. Stepwise logistic regressions suggested that recent antibiotic use was strongly associated with resistance to three of the four antibiotics tested (p = 0.0003 for penicillin, p << 0.0001 for erythromycin and tetracycline), and that age, gender, use of acne medication, use of antibacterial soaps, or makeup use were associated with resistance to at least one of the four antibiotics. Furthermore, drug resistance to one antibiotic was closely linked to resistance to the other three antibiotics in every case (all p values << 0.0001), suggesting the involvement of multidrug–resistant strains. The data reported here suggest that citizen science could not only provide an important educational experience for undergraduates, but potentially play a role in efforts to expand antibiotic resistance (ABR) surveillance.


I. Introduction
Antibiotics are molecules produced by bacteria and fungi that kill bacterial cells. Biomedical researchers have isolated hundreds of different antibiotics, and many have proven to be extremely effective in the treatment of bacterial infections in humans. The antibiotic penicillin, for example, from the fungus Penicillium chrysogenum, has been credited with saving hundreds of thousands of lives.
To be medically useful, an antibiotic must affect the bacterial invader but not harm the human host. Penicillin is toxic to bacteria because it inhibits the formation of a material called peptidoglycan, which is the main structural element in bacterial cell walls. The molecule is harmless to humans, however, because human cells do not contain peptidoglycan.
Unfortunately, bacteria can evolve resistance to antibiotics. For example, if a mutation allows a fungus or bacterium to synthesize an antibiotic that is effective against a bacterial species with which it competes, then natural selection begins to favor members of the competitor species that exhibit resistance to the compound. Over time, the percentage of individuals in the competitor population that are resistant to the antibiotic will increase. In response to the evolution of these resistant forms of bacteria, natural selection will begin to favor members of the antibioticproducing species that synthesize modified compounds or new antibiotics that are effective against the resistant competitors. When this occurs, the competitor population experiences natural selection favoring resistance to the novel antibiotic, and so on. Biologists describe a series of reciprocal interactions such as this as a coevolutionary arms race.
A similar arms race is now underway between biomedical researchers and disease-causing microorganisms or pathogens. Streptococcus pneumoniae, for example, is a common bacterial pathogen in humans and a leading cause of pneumonia and meningitis. Just 25 years ago this organism was readily controlled by penicillin. Now many countries report that over 30% of all patients infected with this pathogen do not respond to treatment with penicillin. The unresponsive patients are infected with S. pneumoniae cells that are resistant to this antibiotic. Unless alternative antibiotics are available, physicians may be able to do very little to combat the infection. The evolution of antibiotic resistance presents one of the great medical challenges of our time.

How do bacteria become resistant to a particular drug? Several mechanisms have been documented.
• Some individuals have uncommon versions of the molecule targeted by the drug. If the chemical structure of the target molecule is different enough, the drug may not bind and disrupt its function.
• Some individuals have enzymes that break down the drug before it can poison the cell.
• Some individuals have molecules called pumps that eliminate the drug from the cell interior, before the drug can affect the cell's function.
Although the genes that confer antibiotic resistance can and do arise via mutation, they are often acquired directly from another bacterium via a special form of gene flow. Transfer of genes between bacterial cells is possible because some bacterial cells contain small, chromosome-like loops of genes called plasmids. Most of the genes involved in antibiotic resistance are found on plasmids; some plasmids contain genes that confer resistance to several different antibiotics. Plasmids that contain resistance genes can be transferred from one bacterial cell to another even if the receiving bacterium belongs to another species. Gene flow also occurs when organisms (in this case, whole bacteria cells) migrate from one population to another (think "sneeze").
The goal of this laboratory is to introduce you to how biomedical researchers study the evolution of antibiotic resistance. You'll begin by investigating the frequency of antibiotic resistance in Staphylococcus epidermidis, a nonpathogenic bacterium commonly found on human skin. You will do this by collecting a sample of these cells from your own skin, and then using this sample to inoculate plates. The plates contain a medium that is conducive to the growth of S. epidermidis but detrimental to the growth of other bacteria. One week later you'll use the isolated colonies of S. epidermidis that have grown to inoculate new plates. By adding disks containing several different antibiotics to these new plates, you will be able to determine whether antibiotic resistant cells are found among the S. epidermidis on your body. In the third and final session in this lab, you will estimate the frequency of antibiotic resistant S. epidermidis in the Biology 180 student population, and then use the combined data from many quarters of Biology 180 to investigate patterns of antibiotic resistance in a healthy student population.

II. Learning objectives
At the end of this three-week lab, you should be able to:  Describe how researchers estimate the frequency of antibiotic-resistant cells in natural populations of bacteria.
 Understand how mutation, selection, drift, and gene flow affect the frequency and spread of resistant alleles in bacteria populations.
 Begin to explore the use and analysis of large data sets in Biology.
 Find and explain a pattern in the antibiotic resistance of skin bacteria in the student population, using chisquared analysis and graphing.
This lab is also designed to help you review an array of concepts introduced in lecture, including mutation, natural selection, migration (gene flow), evolutionary arms races, and host-parasite interactions. The skills you will practice include culturing bacteria and using statistical tests and graphs to analyze a data set.
Finally, the lab will allow you to practice using a series of important terms, including gene, allele, plasmid, host, parasite, and natural selection.
IMPORTANT PRECAUTIONS FOR THIS LAB! 1. No food or drink in the lab.

Do not place any contaminated material on bench tops.
4. Discard all contaminated material in the waste containers provided.

EXERCISE 1 (the "Antibiotic I" lab): Isolation of Staphylococcus epidermidis
In this exercise you will "plate out" a sample of bacteria from your skin in a way that will allow you to isolate colonies of a benign bacterium called Staphylococcus epidermidis. ("Benign" means that it does not cause disease.) Other types of bacteria in addition to S. epidermidis live on your skin, but we want to all look at the same type.
You will be provided with the following supplies to begin this part of the lab:  1 mannitol salt plate  2 sterile swabs  1 tube of sterile saline PROCEDURE 1. Label your plate (the base, not the lid) with your name and lab section.
2. Moisten a sterile swab in saline. Roll the swab against the inside of the tube to remove any excess.
3. Collect a sample of cells from the crease between your cheek and the outside of your nose.
4. Lightly roll the swab over 1/3 of the plate, rubbing all sides of the swab against the surface of the agar (as shown for "swab #1" in the diagram at right).

5.
Discard the swab in the receptacle provided.
6. Use a new sterile swab to streak material from the first area onto the adjacent area, then turn the swab over and streak material from the second area to the third area (as shown for "swab #2 and "swab #3" in the diagram to the right).
7. Discard the swab in the receptacle provided.

EXERCISE 2 (the "Antibiotic II" lab): Antibiotic Susceptibility Testing
In this exercise, you will create a plate that contains only Staphylococcus epidermis and expose the S. epidermis cells to four different antibiotics that physicians use to fight bacterial infections.
You will be provided with the following supplies for this part of the lab: 1 Mueller-Hinton agar plate 1 sterile swab 1 tube sterile saline 4 types of antibiotic disks PROCEDURE 1. Label the TSY agar plate (the base, not the lid) with your name and lab section.
2. Find the mannitol plate that you inoculated in the first lab period and look for an isolated, pink colony on your plate. Choose the colony that is the most uniform in color and texture. Use only one colony!! 3. Use a sterile swab to pick up the isolated colony. Stir this sample into a tube of sterile saline to make a solution of bacteria.
4. Use the swab to streak your new plate in 3 directions. Try to obtain an even spread across the plate.
5. Discard the used swab and your mannitol plate in the waste container provided.
6. Use the disc dispenser to place 4 discs impregnated with antibiotics on your plate.

7.
Invert the plate (the discs will stick to the moist agar) and put it in the tray for your lab section.

EXERCISE 3 (the "Antibiotic Resistance III" lab): Scoring plates for antibiotic susceptibility
In this exercise you will determine whether the Staphylococcus epidermidis growing on your skin are resistant to four different antibiotics. You will also assess the frequency of resistant forms in the class as a whole and consider whether variation in degree of resistance exists. Note that you might still have resistant staph cells growing on your face even if you have never used a topical antibiotic (one applied to your skin). Resistant staph cells can be favored by natural selection because antibiotics that are administered via pill or injection are transported to the skin surface.

PROCEDURE
1. Keeping the lid on your plate at all times, look for zones of inhibition around the antibiotic disks. Using a ruler or graph paper, measure the diameter (in millimeters) of the zone of inhibition. If there are small colonies within a zone of inhibition, measure the zone inside those resistant colonies-an antibiotic must inhibit even the most resistant bacteria.
2. Use the table below to interpret your results. The diameter of the zone of inhibition is influenced by the organism's sensitivity to the antibiotic and by the concentration and molecular weight of the antibiotic.
3. Discard your plate in the waste container provided.
Zone diameter-nearest whole mm. 6 Exercise 3 Report

Student Names_
You have just done a test that is nearly identical to those ordered by physicians and performed in hospitals and clinics. Look at the data collected for your lab section and answer the following questions.
1. In this population of bacteria, resistance to which antibiotic is most common? Least common? Generate two hypotheses to explain why bacterial populations might have a higher frequency of resistance to some antibiotics than others.
Most common: _ Least common: _  Hypothesis 1:  Hypothesis 2: 2. State a hypothesis to explain why different host individuals (your classmates) might have bacteria that differ in their resistance to certain antibiotics.
3. Predict the results that will be observed if this same lab is done with Biology 180 students 10 years from now. Explain the logic behind your prediction.

EXERCISE 4: Analyzing population-level resistance data
In this exercise, you will explore resistance data from previous quarters. First, you will enter your own resistance data for the use of future lab participants.
1) Enter your own personal resistance data on the computer-based questionnaire. You will be assigned a random "sequence code" so that your answers will be entirely anonymous and cannot be linked back to you in any way, even by the course instructors.
2) Your TA will provide an Excel file containing data from a similar questionnaire collected in previous quarters. Drug resistance is indicated by a "1" in the column corresponding to each drug; drug sensitivity is indicated by a zero. The numerical scores associated with each column reflect the answers to the other questions on the questionnaire. Use the laminated chart on your bench to decipher the numbers in the spreadsheet.
3) Your job is to explore these data to try to find interesting patterns. If resistance to oxacillin is correlated to penicillin, does that mean that oxacillin resistance also causes resistance to penicillin? What can you infer from this correlation?
Think of a question that interests you, and explore the data using chi-squared analysis (see below). Complete the Exercise 4 Report, and include your chi-squared results and a bar chart.

Introduction
Biologists often measure the frequencies of data that occur as categories, and then need to assess whether the observed frequencies are consistent with the expectations of the relevant null hypothesis. For example, in genetics you might perform an experimental cross under the hypothesis that the two traits you are interested in are autosomal and unlinked. In this case, you'd expect to observe four phenotypes (the phenotypes are categories) in a 9:3:3:1 ratio-meaning, in frequencies of 9/16, 3/16, 3/16, and 1/16. But it's extremely unlikely that the phenotypes you observe in the experiment will be in exactly these frequencies. Are the genes really autosomal and unlinked, meaning that the difference from the expected frequencies is just due to chance? Or is the difference from the expected frequency large enough to indicate that something else is going on-meaning that your hypothesis is probably not correct?
One of the standard statistical approaches to answer questions like this is called the chi-squared test. The basic approach is to calculate a test statistic that summarizes the differences between the observed and expected values, for all of the categories measured. The test statistic is called  2 ( is the Greek letter chi) and is calculated as: 2 2 (observed-expected) expected The logic here is to calculate the difference between the observed and expected frequencies for a particular category (like one of the phenotypes in a cross), and square the result so that all of the data are positive. You then divide by the expected frequency, to scale the difference you calculated. For example, if the numerator is 4 and the expected frequency is 6, then the squared difference between observed and expected is very large. But if the expected frequency is 100, then the squared difference is actually small. The summation sign (, the Greek letter sigma) means that you sum up all of these values for all of the categories in the study.
What do you do, once you have calculated the value of  2 ? A statistician named Karl Pearson, who invented the chisquared test, found that  2 values have a distribution that can be calculated, based on the assumption that the observed and expected values differ by chance. This distribution specifies the probability, or p-value, that various values of  2 occur by chance. The distribution, though, depends on something called the degrees of freedom (abbreviated df). Degrees of freedom are a measure of how constrained the data are-how much they can vary, given the information available.

Doing the Calculation -Some Examples
When you set up a chi-square test, you routinely arrange the data in rows and columns, with the rows containing the data from each treatment and the columns containing the categories that you've measured. In the dihybrid cross example above, you'd just have a single row with one column for each of the phenotypes observed. Note that the chart includes "marginal totals" and the "grand total"-the number of individuals summed across each row (

Interpreting a  2 Value
Before you can determine the probability of getting a particular value of  2 , you have to determine the number of degrees of freedom in the data. For data in this course, df is always calculated as (# rows -1) x (# columns -1). If you only have one row of data, df is simply the # columns -1. In the dihybrid cross example, the number of degrees of freedom is 3; in the long/short/purple/red example, df = 1.
Now that you have a value of  2 and the relevant degrees of freedom, you can use a chart or computer program to look up the p-value-the probability of getting the result under the null hypothesis that you are testing. Your TA can help you with this.
One final note: the chi-squared test only tells you the overall probability that the observed and expected values are the same. If there is a significant difference, it doesn't tell you which row or column is responsible.

Student Names _
To summarize your analysis of antibiotic resistance in bacteria colonizing Biology 180 students, your group should complete and hand in one copy of this report. Print and attach your chi-squared test results and graph(s).

Question:
Hypothesis: Null hypothesis: Data used (which variables; how did you group the categories; what did you do with the "intermediates"?): Prediction of your hypothesis: Prediction of null hypothesis: Results:

Conclusions:
Appendix 2: Teaching assistant laboratory manual pages. • Prelab (lab coordinator will have scores) 2 pts

Group (of 4 students):
• Scoring plates questions 3 pts (0.5 pts each hypothesis in question 1; 1 pt. each questions 2 and 3) • Graph and question from data analysis 5 pts Suggested point breakdown; and grading guidelines: 1 pt. question, hypothesis, and predictions are clear and consistent; 2 pt. results and conclusion consistent with analysis; 1 pt. graph is labeled and appropriate; 1 pt. stats are appropriate) -Question: The question should be interesting-meaning that it addresses something that is worthwhile knowing-and answerable.
-Hypothesis: The hypothesis should be logical and testable; the null should be appropriate.
-Predictions: These must follow from the hypothesis and the null.
-Results and conclusion: These should be consistent with the data (the statistical analyses and graphs); the conclusion must not go beyond the data.
-Graph: Should be appropriate to the analysis and labeled correctly (including units on the axes); the scales should be appropriate.
-Statistics: Should be appropriate to the analysis and interpreted correctly.

Today's theme: How do the four evolutionary forces affect drug resistance?
Today's questions: In the UW student population, how common is resistance to frequently used antibiotics? What patterns of antibiotic resistance do we see in the skin bacteria of Biology 180 students?
Chi-squared analysis: This is the third and final statistical test we will use in Biology 180. You might introduce it by working through the genetics example in the student lab manual, using the "chi-squared calculator" in the Excel spreadsheet. That allows you to explain the chi-squared test and the calculator with one example.
During one of the whole-class discussions (your initial introduction or later) during this lab, ask students to describe how the four evolutionary forces might affect the frequency and spread of resistance alleles: Driftsmall effect. Drift tends to be a major factor in small populations; bacterial populations tend to be very large.
Gene flow (migration) -large effect. It is crucial for students to realize that from the bacterium's point of view, host individuals are habitat. Depending on the bacterium involved, transmission from one population (host) to another is accomplished via contaminated food or water, inhalation (sneezing, coughing), or physical contact (touching with hands, sexual intercourse). Ask students how they might have acquired resistant cells on their skin. In addition, point out that gene flow in bacteria can occur between distantly related species. As a result, antibiotic resistance alleles can pass to and from Staphylococcus and other species.
Mutationthis one is trickier. Mutation by itself doesn't do much to alter gene frequencies for two reasons: One is that the population of bacteria is so large that individual mutations don't affect frequencies very much; the second is that a lot of mutations occur at about the same frequency as back mutations. However, bacteria replicate so rapidly that mutation rates are high; hence, mutation provide lots of genetic variation for selection to act upon.
Selectionlarge effect, exerted by drugs. The strength of selection depends on the dosage and duration of treatment. Pressure exerted by antibiotics results in an increase in the frequency of resistant strains of bacteria.

D) Exercise 3: Scoring Plates
One of the goals of this exercise is to elicit a "Whoa" reaction. Students should be impressed that antibiotic resistance is common.
• Point out that antibiotics that are taken orally or by injection leak out onto the skin. What antibiotics are found in topical ointments used to treat acne or wounds? What antibiotics are physicians prescribing for the types of infections experienced by the UW student population?
• Point out that Staphylococcus epidermidis is not a pathogen. Why would it contain alleles that confer resistance to antibiotics?
• Ask your students why the frequency of resistance might vary from one antibiotic to another. Also, be sure to discuss why the degree of resistance might vary within populations. (Make sure that students don't think that resistance is an all-or-none trait.) • You might mention that the test they performed is used routinely in clinics and hospitals throughout the world, and that it was developed here at the UW Medical Center. Ask your students why a physician would bother to order this test before prescribing an antibiotic.

E) Some notes on the Antibiotics:
Oxacillin is a penicillin family drug (a β-lactamase) that poisons enzymes involved in formation of bacterial cell walls. Students could ask if there is a cross resistance, meaning any correlation between penicillin resistance and oxacillin resistance.
The question of how long antibiotics stay in the bloodstream after ingestion is the subject of a field known as pharmakinetics. Penicillin, for example, has a very short half-life, about 30 min to an hour. This makes the development of once-a-day antibiotics tricky, as researchers need to find ways to slow down absorption and/or metabolism in order to maintain a relatively even concentration.
Tetracycline, penicillin, erythromycin and other antibiotics prescribed for humans are also used extensively-for nontherapeutic purposes-in many livestock populations in the U.S. Author Michael Pollen claims that 70% of the antibiotics in this country are used by the livestock industry.

F) Exercise 4: Investigating Demographic Data
This is an exercise based on collecting demographic data from students in the class. We have been very careful to make sure that anonymity is preserved-there is no way to identify an individual student.
In this exercise, students investigate the relationship between antibiotic resistance and some aspect of personal history or lifestyle. Stress that each group should work on a different question. If we have enough computers, people in a group of four can look at several specific aspects of a more general question.

G) Tips and Hints
Have each group write the question that they are going to address on the board, so you get diversity in questions and so the students can see what other people are doing. Later, they can sketch their graph and other pertinent results, e.g., the p-value.
There are a number of questions that students can ask that will not produce any differences (e.g. country of origin)-you will then need to encourage discussion of why there are no differences.
In this and other data analysis labs (and in lecture and on exams!), students may be confused by basic terminology and synonyms. Explain that observed=actual and predicted=expected.
Some TAs have reported more success with stacked bar charts with both % sensitive and % resistant than with simple bar charts with just % resistant.
Have you used acne medication: Using it now Year data were collected d. The skin of a human using acne medication that contains tetracycline.

Why is it significant that many antibiotic resistance genes are found on the loops of DNA (mini-chromosomes)
called plasmids?
a. Point mutations that confer resistance to a drug can't occur unless that drug is in the environment.
b. Antibiotic resistance genes aren't found on the main bacterial chromosome.
c. Plasmids can be passed among cells-allowing rapid spread, even between species of bacteria.
d. Antibiotics and antibiotic resistance genes are produced naturally, often by soil-dwelling bacteria.
6. Recall what you learned during the "Introduction to Statistics and Data Analysis" lab and from "Using the Chi-

Squared Test." What is the correct statistical test for the combinations of variables listed?
Variable