Microbiology Answers

You discovered that a species of bacteria can break down StyrofoamTM (polystyrene) products due to an

enzyme it produces, polystyrenase. You wish to study the gene that codes for this enzyme.

Agarose Gel Electrophoresis

Your amplicon from PCR was subjected to AGE for analysis. You are shown the image of the gel loaded

with the following lanes: (A) negative control, (B) size ladder (1, 2, 3, 4, 6, and 10 kb), (C) gDNA extract, (D)

PCR amplicon. However, due to mishaps while loading the gel with the samples, you are not sure which lane

is which. You are shown a diagram of the obtained gel below.

a. Label each lane of the gel. Write only the corresponding letters in the wells above.

b. Above each band in the size ladder, write its size (in kb).

c. Approximate the size (in kb) of the polystyrenase gene. Write your answer above the band

corresponding to the gene.

Bonus: If you wish to identify the bacterial species in this scenario, what gene is most commonly and

routinely sequenced?

Answer

A few weeks after returning from visiting his parents in India, Navin developed a bad cough that persisted for the next three weeks. As he is a heavy smoker, he Over this period, he began to feel weak and lethargic and had trouble sleeping at night due to excessive sweating. He also started to lose weight as he didn’t have much of an appetite. He then began to cough up blood and feel pain in his chest, which prompted him to go see his doctor. Once his doctor heard his symptoms, he took a blood sample for testing based on his and gave Navin a chest X-ray. The doctor also collected a sample of his sputum coughed up from his lungs to perform an acid-fast stain for identification.

2. What is your diagnosis, and what specific features within the case were critical to your diagnosis of this disease?

4. How is the disease treated?

5. What symptoms might the patient develop if the disease is not treated?

6. What is the PROGNOSIS with treatment?

Exposure to weather can lead to a reduction in the performance and efficiency of photovoltaic systems. Researchers have used machine learning to develop a test which categorises solar panels as defective or non-defective, based on textural features of the solar cells extracted from thermal imaging [2].

In a sample of 260 solar panels, 130 were classified as defective by registered electrical power engineers. Of these, the machine learning test was able to correctly identify 126 defective solar panels. The test correctly classified all non-defective solar panels.

(a) Construct a binary classification table for the outcomes of the machine learning test, and calculate the sensitivity, specificity and accuracy of the test, showing all working.