These days, it seems like every day we learn of a new variant of SARS-CoV-2 (the virus that causes COVID-19). However, it’s hard to understand what a variant is and how it changes the virus. In this post, I wanted to introduce PyMOL, a program that students have access to through the University. This program can be used to see what the spike protein and its mutations actually look like.
But first, here’s some background on SARS-CoV-2: COVID-19 is a disease caused by a strain of coronavirus called SARS-CoV-2. This virus gets inside the human cells by using something called a spike protein. This spike protein binds to a receptor on the human cell called the ACE2 receptor, and this allows the virus to infiltrate the cell. The variants of SARS-CoV-2 that we keep hearing about typically have different mutations on the spike protein. In the case of the B.1.1.7 variant, which is a variant that is thought to be 30-50 percent more infectious than other variants in circulation, the mutations are at a location that allow the spike protein to bind better to the ACE2 receptor. If you bind better to the receptor, you’re better at infiltrating the cell. The spike is also the target of the vaccine and our natural immune system.
Now, let’s try and look at where these mutations actually are.
Most people’s New Years Resolutions, I imagine, are not about improving their knowledge of statistics. But I would argue that a little bit of knowledge about statistics is both useful and interesting. As it turns out, our brains are constantly doing statistics – in reality, our conscious selves are the only ones out of the loop! Learning and using statistics can help with interpreting data, making formal conclusions about data, and understanding the limitations and qualifications of those conclusions.
In my last post, I explained a project in my PSY/NEU 338 course that lent itself well to statistical analysis. I walked through the process of collecting the data, using a Google Spreadsheet for computing statistics, and making sense of what a ‘p-value’ is. In this post, however, I walk through how I went about visualizing these results. Interpretation of data is often not complete before getting a chance to see it. Plus, images are much more conducive than a wall of text when it comes to sharing results with other people.
In PSY/NEU 338, From Animal Learning to Changing People’s Minds, my group recently presented our capstone project for the course: we researched irrationality, trying to understand when humans make irrational decisions, how that is implemented in the brain, and if certain things might actually be incorrectly labeled as ‘irrational’. Our emotions are a leading example: although some call them irrational, in practice, they play a key role in fine-tuning our decision-making and reasoning abilities. When you’re happy, for example, everything might be going more positively than expected. Your mood is thus encouraging you to continue the behaviors that led to those rewards, since that positive trend might continue (for a neuroscientific discussion of this topic, see this paper).
To demonstrate this phenomenon first-hand, we had students in the class play what is known as the Ultimatum Game:
You are the proposer. You have been given $100. You are tasked with splitting your money with a stranger, the responder. If the responder accepts the split that you propose, you both keep the money after the game ends. If the responder does not accept, no one keeps the money.
The question: how much money do you decide to offer the responder?
After reading this, students had five seconds to provide their answer. They were then asked to report their mood. The question we wanted to answer was simple:
Is the amount of money people offered statistically different between those who reported “positive” versus “negative” moods?
In this post, I’ll explain some of the basic statistics I used to formally answer this question, bolding some key terms in the field along the way. In my next post, I’ll walk through the programming aspect for visualizing those statistics.
Getting PSETs done over Zoom can be a combination of awkward and challenging. To assist with that task, fellow PCUR Correspondent Ryan Champeau recently wrote a post with suggestions for working on PSETs in the age of remote learning. A great tip in that article is to collaborate with friends when permitted under a course’s collaboration policy. However, given that students can’t meet in person to work on assignments anymore, I’ve found the process of checking over PSETs to be a bit more difficult than usual.
Specifically, I’m taking QCB 455, an introductory course to quantitative and computational biology in which there are four total problem sets. As a neuroscience major in a class filled with computer science majors and some graduate students, I didn’t really know many people in the course. Going over the first PSET with people I didn’t know over Zoom felt a bit strange, but I’ve since found that there are actually a few benefits to going over PSETs that are specific to the remote experience. In this post, I’ll go over the three strategies I’ve started to use when collaborating on PSETs for my classes:
Recently, we’ve all had to do our best to adapt our coursework, extracurriculars, and past times to a remote format. Some activities – like hands-on research in a lab – may be difficult, or even impossible, to do over Zoom. For those of you looking to fill the gap, hackathons may be the solution. Hackathons are short programming events designed for students to learn new skills, meet new people, develop solutions to everyday problems, and win prizes. And because of the COVID-19 situation, many hackathons are turning virtual.
To hear a little more about what exactly hackathons are and who they might be a good fit for, I interviewed Princeton sophomore and Director of the TechTogether New York hackathon, Soumya Gottipati. For those of you who have recently hard your internships canceled, Soumya also let me know about internship opportunities you might be interested in!
For this year’s Winter Seasonal Series, entitled Research Resources: Unsung Heroes, each correspondent has selected a faculty member, staff member, or peer working for a research resource on campus to interview. We hope that these interviews will provide insight into the variety of resources available on campus and supply the unique perspective of the people behind these resources. Here, Kamron shares his interview.
A few weeks ago, I interviewed Sara Howard, the Gender and Sexuality Studies and Student Engagement librarian. I’ve found that I often don’t use all the available research resources to my benefit. Given that we have all recently transitioned on an online learning community, consider meeting with your librarian over Zoom!
So you’ve just finished your JP, a dean’s date assignment, or some other research project. Considering how fast things seem to move here, you might have already forgotten about it – that’s how I felt when I turned in my R3 my first year.
However, I ended up taking another look at my R3 to prepare my presentation last spring for the Mary W. George Research Conference – the biannual writing conference – (tips on doing that here). During that process, I recognized some significant changes and expansions I could make on my R3, but I didn’t think much of it at the time.
After presenting my R3, I was encouraged by my writing
seminar professor and some of my peers to expand my work and submit the
manuscript for a conference or for publication. After submitting to a
conference and to multiple research journals, here are some of my takeaways from
the publication process:
We spend a lot of time finding and deciding what internships and jobs to pursue over the summer. There are quite a few posts on this blog alone that help with that process, including this one. After exploring my options, I think I know what I’ll be doing this summer: staying on campus to do research in a neuroscience lab (an experience I’ll talk more about in a future post).
knowing what I’ll be doing this summer isn’t all there is to finalizing my
summer plans. For one, I don’t know how my experience will actually be funded. Second,
I’m unsure where I’ll be staying for the duration of my research.
To better finalize my plans, I turned to SAFE, the Student Activities Funding Engine. SAFE is a website where students can apply for funding for internships and other activities. In addition to finding a relevant funding source for my summer plans, I came across many other interesting funding opportunities for students who have secured unpaid internships over the summer. I’ve gone ahead and summarized a few of them below.
In the fall of my first year I wanted to join a neuroscience research lab. I was hoping to contribute to meaningful research, network with helpful mentors, and develop new skills and qualifications. In retrospect I should have waited to adjust to Princeton and my new course-load before even beginning to think about labs. I didn’t, though, and as I sent a flurry of emails to lab directors, I soon ran into a barrier: I found it incredibly difficult to be accepted into a lab.
In their response to my email, one lab director told me that they preferred students with significant experience in the programming language Matlab. Although I’d used Matlab before, my trial subscription had long expired. Using the free software links available through the Office of Information Technology (OIT) website, however, I was able to download and use Matlab once more. I soon realized that a laboratory setting wasn’t necessary for me to conduct my own research. In fact, I actually felt empowered by the ability to choose my own research topic.
The infamous Senior Thesis is a source of stress and anxiety for many students. Although there are information sessions galore for juniors, I didn’t feel like I actually understood the process until I started it. This summer, I began my thesis research process by traveling to Norway to collect observational data on the country’s prison system.