For this Spring Seasonal Series, entitled Doing Research in a Pandemic, each correspondent has selected a researcher to interview about the impact of the pandemic on their research. We hope that these interviews document the nuanced ways the pandemic has affected research experiences, and serve as a resource for students and other researchers. Here, Ryan shares her interview.
For this seasonal series, I decided to interview Yael Niv, a Professor in Neuroscience and Psychology at Princeton University. Professor Niv has conducted key research on reinforcement learning and decision-making and she continues to contribute to this field at her lab at the Princeton Neuroscience Institute. I decided to interview Professor Niv because I have taken two courses with her (FRS172: Our Subjective Reality and PSY338: From Animal Learning to Changing Minds) and have truly been inspired by the work she has done and the positive attitude that she brings to the classroom every day. I know that we can all learn from Professor Niv, especially at a time like right now. In our interview, we discuss the importance of hallway encounters, research opportunities during the pandemic, and the future of her lab.
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.
Due to the Covid-19 pandemic, a lot of in-person internships and research positions for students have been transitioned to remote opportunities. Last summer after my first year of college, I opted to take online classes over the summer because in-person opportunities were not a possibility. For this upcoming summer, I was hoping to gain laboratory experience in person, but my internship was also transitioned online. With only one summer left before I apply to graduate school, this left me with a looming question: will I have enough in-person research experience before I apply to graduate school?
Although this question bothered me for a while, I realized that I was approaching this issue from the wrong perspective. I feel as though many students are probably dealing with similar issues now that many summer opportunities have been canceled or moved online due to the pandemic. In this article, I am going to walk through the reasoning behind why you should not worry too much about lacking in-person research experience and also include some additional opportunities you should be on the lookout for.
As I just passed the deadline for my junior independent work (JIW), I wanted to explore strategies that could be helpful in composing a research proposal. In the chemistry department, JIW usually involves lab work and collecting raw data. However, this year, because of the pandemic, there is limited benchwork involved and most of the emphasis has shifted to designing a research proposal that would segue into one’s senior thesis. So far, I have only had one prior experience composing a research proposal, and it was from a virtual summer research program in my department. For this program, I was able to write a proposal on modifying a certain chemical inhibitor that could be used in reducing cancer cell proliferation. Using that experience as a guide, I will outline the steps I followed when I wrote my proposal. (Most of these steps are oriented towards research in the natural sciences, but there are many aspects common to research in other fields).
When writing a research manuscript or a lab report, I have been conditioned to complete all of my experiments first and then start by writing the results section. My mentors have always encouraged me to start with the section that ‘writes itself’, given that when you obtain your experimental results, you cannot alter them. I started the school year thinking I would use this approach for my thesis – focus strictly on experiments during the fall and the start of spring semester and transition into the written portion of the thesis during late spring semester. However, while I was at home and outside of the lab between Thanksgiving break and early February, I knew I could not spend more than 2 months without thesis progress. Although I did not have my results nearly ready by that point, I began to brainstorm different ways in which I could work on my lab-based thesis without access to the lab. In this post, I will highlight ideas and resources that can help you make progress on your thesis, even while you are outside of the lab.
For this Spring Seasonal Series, entitled Doing Research in a Pandemic, each correspondent has selected a researcher to interview about the impact of the pandemic on their research. We hope that these interviews document the nuanced ways the pandemic has affected research experiences, and serve as a resource for students and other researchers. Here, Nanako shares her interview.
For this seasonal series, I decided to interview Emily Mesev, a Ph.D. candidate in the Department of Molecular Biology. I was interested in how her experience as a graduate student differed from my experience as an undergrad. Because undergrads aren’t allowed to be in the laboratory (at least for Molecular Biology), I’ve had to change my thesis topic and redirect it to become computational. I was excited to find out whether the graduate student experience had changed in similar ways!
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.
Specializing in legal thought and critical theory, Daniela Gandorfer is a graduate of the doctoral program in Princeton’s Department of Comparative Literature, a postdoctoral scholar at UC Santa Cruz, and co-director and head of research at the Logische Phantasie Lab (LoPh). LoPh, a research collective recently founded by Princeton alumni and current students, describes itself as a “comprehensive research agency that actively challenges injustices resulting from political, legal, economic, social, physical, and environmental entanglements by means of specific investigations.”I want to thank former PCUR correspondent Rafi Lehman, now the Development Coordinator at LoPh, for putting me in touch with the research collective’s team.
Over email and Zoom, I was able to talk to Daniela about the critical methods employed by LoPh, its relationship with the established academy, and the benefits and limits of an interdisciplinary research approach.
This interview has been edited for length and clarity. Below is part two of a two-part interview. You can read Part I here.
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.
Like most students at Princeton, I am really looking forward to next semester. Having taken into account the pandemic and the Princeton community’s well-being, the university is offering all undergraduates the option to return to campus, even though most classes will still largely be held online. Consequently, Spring 2021 will be the second time since the pandemic began where we can experience a different side of Princeton – a hybrid semester, where there will be a mix of in-person and virtual classes. A hybrid semester presents a lot of opportunities to enhance the educational experience from a fully virtual semester like the one we had this fall. Next semester, I am looking forward to the small things — like seeing more students outside of classes and interacting with them as guidelines allow. However, it is likely that there will be new and old challenges for students on and off-campus. Although it is difficult to predict exactly how the semester will unfold, I outline three challenges that stand out to me, so that we can prepare for them beforehand.