Quick Biology Test

[Frederic Laureano]

Try this free biology practice test to see how prepared you are for a biology exam. Whether you are in high school or college, you are likely to have a biology requirement. Biology tests cover such subjects as the chemistry of life, evolution, genetics and ecology.

For a more comprehensive study of biology, try our 400 question Biology Practice Exam.

The A&P I Assessment Test is a 50-question multiple choice exam. The A&P I Assessment covers the pertinent state-wide objectives of high school biology and chemistry. The test is completed on the computer at a testing center. Students are allowed one piece of scratch paper.
A&P I Assessment Test Objectives

You take the Assessment Test in the testing period preceding the semester you wish to register. For example, if you want to take A&P I (BIOL 2401) during the Fall semester, you will need to take the Assessment Test during the Summer testing period before the Fall semester begins.

Upon passing, you will have permission to register for three (3) consecutive semesters. If you need to register beyond this you will have to retest for additional permission to register. Attempts are limited to three (3) per testing period.

Students taking the A&P I Assessment Test have the option to test multiple times in a day, but the Biology Department does not recommend this approach. Regardless, only their first three (3) attempts will apply per testing period, any additional attempts beyond the first three will be considered invalid.

This is the goal of researchers at the Human Longevity Lab at the Northwestern University Feinberg School of Medicine. They're recruiting study participants so they can test what kinds of interventions may slow the rate of aging. To that end, I decided to roll up my sleeve for science.

When I arrived, the first step was a quick blood draw. The Potocsnak Longevity Institute is housed on the light-filled 21st floor of Northwestern Memorial Hospital, overlooking Lake Michigan. It felt more like a spa than a doctor's office. I didn't anticipate the vast range of data and insights scientists could glean from a battery of tests.

But then, the tests got a lot more interesting. Inside a small exam room, a medical assistant opened the hinge of a BodPod, a capsule that looks like a submersible. The machine assessed my body composition, determining the ratio of fatty mass to lean mass, which includes muscle. Strength is a key marker of healthy aging, helping us fend off frailty and falls.

Then I went through a slew of cardiovascular health tests. They measured my endothelial function, which keeps blood flowing smoothly through the body. They looked at my heart rate variability and pulse-wave velocity, which is an indicator of stiffness of the arteries. I had electrodes placed onto my chest for an electrocardiogram.

Of all the tests they performed, the most intriguing is the GrimAge test. This test predicts biological age. It's gauging whether your DNA age is younger, or older, than your actual age, known as chronological age. Conjure images of the Grim Reaper? Yep, that's the idea: The test can estimate how quickly, or slowly, you're aging.

To figure this out, researchers use a technique based on DNA methylation, which is a measure of modifications in our DNA. Basically, as we age, compounds called methyl groups attach to some of our DNA molecules, which can turn genes on or off. Researchers have shown that the higher the proportion of methylated DNA in certain locations, the more accelerated a person's biological age. Published research suggests this is a reliable way to predict life span and health span.

For example, smoking has a very strong effect on methylation. "Tens of thousands of locations gain methylation when you smoke," explains researcher Steve Horvath, who developed the epigenetic clock used as part of the GrimAge test. People with obesity also exhibit higher methylation at certain locations. "Conversely, if you eat vegetables, if you are lean, if you exercise, that slows methylation age," he explains.

Vaughan's deep interest in aging took off when he identified a distinct genetic variant in an Amish community in Indiana. People who have the variant are protected from diabetes and have healthier cardiovascular systems compared to people who don't. In the laboratory, when Vaughan engineered mice to have only a 50% level of a protein associated with this mutation, their life spans increased by nearly fourfold. "This was a eureka moment," he says.

"I don't know exactly what that's going to be. It might be a drug. It might be a lifestyle intervention, for all I know it might be gene editing," Vaughan says. "But there are going to be ways that we are going to slow down this process and give people a longer health span."

People who live in the upscale Chicago neighborhood where the Human Longevity Lab is located can expect to live a much longer, healthier life compared to people who live just a few miles away. Vaughan wants to help close this gap.

"I'm worried about the poor soul in south Chicago who has a life expectancy of 55, compared to 92 in the neighborhood where we're standing right now," he says. A stunning difference of more than 30 years. (You can check out life expectancy in your ZIP code here.)

"There are lots of people who've been dealt a bad hand with regard to aging," Vaughan says. Their goal is to find affordable, evidence-based interventions that can benefit everyone, regardless of socioeconomic status.

For example, there's interest in studying stress, which Vaughan says could be "part of the reason for the discrepancy in the life expectancy in different neighborhoods of Chicago." To study this, he could measure people's biological age at baseline, have them try a stress-reduction program, and test again to see if their results changed.

Vaughan is also interested in studying people with chronic HIV, who tend to age at an accelerated rate. A charitable gift from a Chicago family with a shared interest helped launch the institute. Vaughan's team is considering a range of interventions to test whether they can slow down aging in this population.

"It might be weight training, it might be intermittent fasting, it might be dietary manipulations, it might be drugs that are available now that might have anti-aging effects," Vaughan explains, citing the diabetes drug metformin.

Longevity and health span research is attracting lots of funding and attention, from places like the Hevolution Foundation, which provides grants and early stage investments, and Altos Labs, a biotechnology company, founded by Dr. Rick Klausner, which is investigating ways to reprogram or rejuvenate cells.

"Teams have to come to the starting line and we're going to set up the frameworks by which they prove their therapeutic works," says XPRIZE's Jamie Justice, who is also a researcher at Wake Forest University School of Medicine.

With muscle mass, if you don't use it, you lose it. After the age of 30 to 35, muscle starts to slowly decline. And after age 65 or so, this loss accelerates. So it's never too soon to start building a reserve. My goal for this year is to build muscle through resistance training and an optimal diet. And also, to reduce stress.

As part of this project, we hope you'll share your healthy aging tips with us. What habits or lifestyle hacks have you've adopted to thrive as you age? Please use this form to share your thoughts or email us at Thr...@npr.org.

Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.

Recent advances in high-throughput sequencing technologies have made large biological datasets available to the scientific community. Together with the growth of these datasets, internet web services expanded, and enabled biologists to put large data online for scientific audiences.

As a result, scientists have begun to search for novel ways to interrogate, analyze, and process data, and therefore infer knowledge about molecular biology, physiology, electronic health records, and biomedicine in general. Because of its particular ability to handle large datasets, and to make predictions on them through accurate statistical models, machine learning was able to spread rapidly and to be used commonly in the computational biology community.

To avoid those situations, we present here ten quick tips to take advantage of machine learning in any computational biology project. Ten best practices, or ten pieces of advice, that we developed especially for machine learning beginners, and for biologists and healthcare scientists who have limited experience with data mining.

We organize our ten tips as follows. At the beginning, the first five tips regard practices to consider before commencing to program a machine learning software (the dataset check and arrangement in Tip 1, the dataset subset split in Tip 2, the problem category framing in Tip 3, the algorithm choice in Tip 4, and the handling of imbalanced dataset problem in Tip 5). After them, the next two tips regard relevant practices to adopt during the machine learning program development (the hyper-parameter optimization in Tip 6, and the handling of the overfitting problem in Tip 7). Moreover, the following tip refers to what to do at the end of a machine learning algorithm execution (the performance score evaluation in Tip 8). Finally, the last two tips regard broad general best practices on how to arrange a project, and are valid not only in machine learning and computational biology, but in any scientific field (choosing open source programming platforms in Tip 9, and asking feedback and help from experts in Tip 10).

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