Tag Archives: sfsu

Scientist Spotlight: Alennie Roldan

7 Jun
Alennie (they/them) graduated from SFSU in 2021 and will be working as a Bioinformatics Programmer in the lab of Dr. Marina Sirota.

Pleuni: Hi Alennie, congratulations on graduating this semester! 

Alennie: Thank you! I really enjoyed my time at SFSU and I’m excited to move onto the next chapter. 

Pleuni: You told me that you are starting a job at UCSF soon. Would you mind telling me what you’ll be doing there and how you found that job? 

Alennie: I’ll be working as a Bioinformatics Programmer in the lab of Dr. Marina Sirota. The work is very in line with the interdisciplinary concepts I learned through the PINC program–– coding meets life science and health data. Prior to getting the position, I heard about an event, “NIH Diversity Supplement Virtual Matchmaking,” from the PINC and SEO mailing list. At the event, I met with many different UCSF PIs and learned about their research. I kept in contact with some of the PIs I met whose research I thought was very interesting. From there I scheduled different meetings and interviews with each PI to see if we’d be a good match. I ended up moving forward with the Sirota lab because I wanted to be involved in their research and felt that I could learn a lot from the experience. 

Pleuni: When did you start to learn coding? 

Alennie: Honestly, I feel like my first stint with coding began with Tumblr. In middle and high school I picked up some HTML to personalize my Tumblr page. It was exciting to input strange strings of numbers and letters and churn out wacky graphics. When I stopped using Tumblr I didn’t seriously pick up coding until summer 2019 for the BDSP, where I learned that there were so many different ways programming could be used. 

Pleuni: Did you always want to learn coding? 

Alennie: When I was younger, I’d watch the crime show “Criminal Minds’” with my mother. One of my favorite characters was Penelope Garcia, the show’s FBI Technical Analyst. She fills the tech-savvy role of the group and I always enjoyed seeing how she’d help solve the case by unlocking “digital secrets” or finding classified information. Based on portrayals like that, I always considered coding as an exclusive skill limited to cyber security and creating complex software. So I was always interested in coding, but the idea of learning how seemed too daunting. 

Pleuni: You did the entire PINC program – which part did you like most? Which part was frustrating? 

Alennie: I enjoyed the creative freedom of the PINC program. Many of the classes I took had final projects that encouraged us to come up with our own ideas. It was satisfying and challenging to take all that I’ve learned so far and use that knowledge to come up with my own projects. One of my favorite projects was for CSC 307: Machine Learning for Life Science Data Scientists. The goal of my group’s project was to address the lack of diversity in dermatology datasets by applying a machine-learning model that could identify various skin disorders; our dataset consisted of skin image samples from People of Color. The assignment was especially rewarding because it allowed me to combine my passion for health equity, social justice, and programming into a single project. 

The most frustrating part of the program was primarily due to the pandemic. It was difficult to communicate with my professors and classmates through a remote format. The experience sometimes felt isolating because I had been so used to seeing my mentors in-person or meeting up with classmates to work on an assignment/project. Thankfully, I had met many of the same classmates in person before switching to virtual learning so I felt like I had some familiar faces to interact with. 

Pleuni: Sometimes it looks like coding is something for only some kinds of people. There are a lot of stereotypes associated with coding. How do you feel about that? 

Alennie: This is a very good question, as there are many layers to the coder/programmer stereotype. If you were to ask people to draw a picture of a coder, the most common image you’d likely see is a lonely man furiously typing in a darkened room, hunched over in his chair and focused on screens covered with indecipherable numbers and symbols. Simply put, we often imagine a typical coder as a cisgender white man who typically exhibits loner or awkward behaviors. It’s a very narrow and negative stereotype which ultimately promotes negative connotations regarding neurodivergent individuals and excludes Women and People of Color from the narrative. 

The stereotype does little to encourage or welcome most people. But in reality, the coding community at large desperately needs a diverse range of people who can contribute their unique perspectives. Stereotypes can be discouraging and unwelcoming, so it’s important for institutions to emphasize inclusivity to show how students can be fantastic coders and still be true to their unique identities. 

…it’s important for institutions to emphasize inclusivity to show how students can be fantastic coders and still be true to their unique identities.

Pleuni: I know you are applying to medical school. Do you think it is useful for a doctor to know about computer science? 

For example, by having some knowledge in computer science a doctor could aid in the design of an app that patients can use to let them know if they’re experiencing side effects to their medication, create a website that shows local doctors who are LGBTQ+ friendly, or even better navigate electronic health records. The possibilities are endless! 

Alennie: I believe that computer science can be very useful to a physician because it can improve how they can take care of people. Since they are face-to-face with patients everyday, healthcare professionals are in a position where they can recognize and understand what unique problems need to be addressed in their communities. 

Pleuni: Do you have any tips for students who are just starting out? 

Alennie: Embrace your creativity! We often think of coding as a sterile and strict subject, but as you create new programs, websites, apps, etc you realize how much creative freedom you actually have. Learning how to code can be very daunting so when you personalize programs to fit your style or reflect things that you like, it makes the journey seem less scary and more fun. When I started coding, I had the most bare-bones of tools at my disposal, but I could still find ways to inject things to make my code feel like it belonged to me. The very first game I programmed, a basic recreation of Pong, I signed with my favorite color, pastel pink.

Alennie recreated the classic game of Pong with a little extra flair for one of their coding projects.

Pleuni: Thank you, Alennie! Please stay in touch!

Scientist Spotlight: Berenice Chavez Rojas

28 May

Berenice Chavez Rojas graduated from SFSU in 2021 with a major in biology and a minor in computing applications. She is moving to Boston to work in a lab at Harvard’s Medical School.

Pleuni: Hi Berenice, congratulations on graduating this semester! 
I know that you are starting a job at Harvard soon. Would you mind telling me what you’ll be doing there and how you found that job? Did your coding skills help you land this job?

Berenice: I’ll be working as a research assistant in a wet lab. The model organism is C. elegans and the project will focus on apical-basal polarity in neurons and glia. I found this job on Twitter! Having a science Twitter is a great way to find research and job opportunities as well as learn new science from other scientists. While I won’t be using my computational skills as part of this job, the research experience I have been able to obtain with my coding skills did help me. 

“coding always seemed intimidating and unattainable”

Pleuni: When did you start to learn coding? 

Berenice: I started coding after I was accepted to the Big Data Summer Program two years ago [Note from Pleuni: this is now the PINC Summer Program]. This was also my first exposure to research and I’m grateful I was given this opportunity. This opportunity really changed my experience here at SFSU and it gave me many new opportunities that I don’t think I would have gotten had I not started coding. Following the Big Data Summer Program I started working in Dr. Rori Rohlfs’ computational biology lab. I also received a fellowship [https://seo.sfsu.edu/] which allowed me to stop working my retail job, this gave me more time to focus on school and research. 

Pleuni: Did you always want to learn coding?

Berenice: Not at all, coding always seemed intimidating and unattainable. After my first exposure to coding, I still thought it was intimidating and I was slightly hesitant in taking CS classes. Once I started taking classes and the more I practiced everything began to make more sense. I also realized that Google and StackOverflow were great resources that I could access at any time. To this day, I still struggle and sometimes feel like I can’t make any progress on my code, but I remind myself that I’ve struggled many times before and I was able to persevere all those times. It just takes time!

The forensic genetics team at the Big Data Science Program in the summer of 2019. Berenice Chavez Rojas is in the middle.
The forensic genetics team at the Big Data Science Program in the summer of 2019. Berenice Chavez Rojas is in the middle.

“At the end of this project, I was able to see how much I had learned and accomplished”

Pleuni: You did the entire PINC program – which part did you like most? Which part was frustrating?

Berenice: My favorite part of the PINC program was working on a capstone project of our choice. At the end of this project, I was able to see how much I had learned and accomplished as part of the PINC program and it was a great, rewarding feeling. As with any project, our team goals changed as we made progress and as we faced new obstacles in our code. Despite taking many redirections, we made great progress and learned so much about coding, working in teams, time management, and writing scientific proposals/reports.

Link to a short video Berenice made about her capstone project: https://www.powtoon.com/c/eKaZB3kkxE5/0/m

Pleuni: Sometimes it looks like coding is something for only some kinds of people. There are a lot of stereotypes associated with coding. How do you feel about that? 

Berenice: I think computer science is seen as a male-dominated field and this makes it a lot more intimidating and may even push people away. The PINC program does a great job of creating a welcoming and accepting environment for everyone. As a minority myself, this type of environment made me feel safe and I felt like I actually belonged to a community. Programs like PINC that strive to get more students into coding are a great way to encourage students that might be nervous about taking CS classes due to stereotypes associated with such classes. 

“talking to classmates […] was really helpful”

Pleuni: Do you have any tips for students who are just starting out?

Berenice: You can do it! It is challenging to learn how to code and at times you will want to give up but you can absolutely do it. The PINC instructors and your classmates are always willing to help you. I found that talking to classmates and making a Slack channel where we could all communicate was really helpful. We would post any questions we had and anyone could help out and often times more than a few people had the same question. Since this past year was online, we would meet over Zoom if we were having trouble with homework and go over code together. Online resources such as W3Schools, YouTube tutorials and GeeksforGeeks helped me so much. Lastly, don’t bring yourself down when you’re struggling. You’ve come so far; you can and will accomplish many great things!

Pleuni: What’s your dog’s name and will it come with you to Boston?

Berenice: His name is Bowie and he’ll be staying with my family here in California. 

Pleuni: Final question. Python or R?

Berenice: I like Python, mostly because it’s the one I use the most. 

Pleuni: Thank you, Berenice! Please stay in touch!

SFSU bio and chem Master’s students do machine learning and scicomm

20 May

This semester (spring 2021) I taught a new class together with my colleagues Dax Ovid and Rori Rohlfs: Exploratory Data Science for Scientists. This class is part of our new GOLD program through which Master’s students can earn a certificate in Data Science for Biology and Chemistry (link). We were happily surprised when 38 students signed up for the class! 

In the last few weeks of the class I taught some machine learning and as their final project, students had to find their own images to do image classification with a convolutional neural network. Then they had to communicate their science to a wide audience through blog, video or twitter. Here are the results! I am very proud 🙂

If you are interested in the materials we used, let me know.

Videos

Two teams made videos about their final project: 

Anjum Gujral, Jan Mikhale Cajulao, Carlos Guzman and Cillian Variot classified flowers and trees. 

Ryan Acbay, Xavier Plasencia, Ramon Rodriguez and Amanda Verzosa looked at Asian and African elephants. 

Twitter 

Three teams decided to use Twitter to share their results. 

Jacob Gorneau, Pooneh Kalhori, Ariana Nagainis, Natassja Punak and Rachel Quock looked at male and female moths. 

Joshua Vargas Luna, Tatiana Marrone, Roberto (Jose) Rodrigues and Ale (Patricia) Castruita and Dacia Flores classified sand dollars. 

Jessica Magana, Casey Mitchell and Zachary Pope found cats and dogs. 

Blogs

Finally, four teams wrote blogs about their projects

Adrian Barrera-Velasquez, Rudolph Cheong, Huy Do and Joel Martinez studied bagels and donuts. 

Jeremiah Ets-Hokin, Carmen Le, Saul Gamboa Peinada and Rebecca Salcedo were excited about dogs! 

Teagan Bullock, Joaquin Magana, Austin Sanchez and Michael Ward worked with memes. 

Musette Caldera, Lorenzo Mena and Ana Rodriguez Vega classified trees and flowers. 

https://arodri393.wixsite.com/labsite/post/demystifying-machine-learning

How we run an inclusive & online coding program for biology and chem undergrads in 2020 

7 May

By: Nicole Adelstein, Pleuni Pennings, Rori Rohlfs

Coding summer program (BDSP) in 2018, when students were in the same room for 8 hours a week.

In 2018 this team (led by Chinomnso Okorie) met in the “yellow room” for 8 hours a week to learn R.  

We have been running combined coding/research summer programs for several years, with a  focus on undergraduate students, women, and students from historically underrepresented racial and ethnic groups. This summer, we will run our 9-week program as an online program. We think that others may be interested in doing this too, so we’ll share here how we plan to  do it. 

Some of the information below will also be published as a “ten rules paper” in Plos Computational Biology*, but we wanted to share this sooner and focus on doing things online vs in person. 

TL; DR version

  1. Have students work in teams of 4 or 5, for 2 hours per day, 4 days a week. Learning to code should be done part-time, even if your program is full time. 
  2. Use near-peer mentors to facilitate the team meetings (not to teach, but to facilitate). 
  3. Use existing online courses – we’ll share a few that we like. Don’t try to make your own curriculum last minute. There are good online courses available. 
  4. Give the students a simple (repeat: simple!) research project to work on together. 

1. Have students work in teams for two hours a day – with pre-set times. 

Learning to code is stressful and tiring. Even though many students may not have jobs this summer – it doesn’t mean that they can code for 8 hours a day. First, because they have other stuff to do (like taking care of family members) and second because there’s a limit to how long you can be an effective learner. 

Our program is 10 hours per week (8 hours of coding, 2 hours of “all-hands” meeting). We make it clear that no work is expected outside of these hours. For example, a team may meet from 10am to 12pm four days a week for coding. 

Check-ins, quiet working, shared problem solving. 

During the coding hours, the near-peer mentor is always present (on Zoom, of course!) and facilitates the meeting. The very first day should be all about introductions and expectations. After that, we suggest that every day, there is time for check-ins (everybody shares how they are doing, what they’re excited about or struggling with, or what music they’re listening to), quiet working (mute all microphones, set a timer, everybody works on the online class by themselves) and shared problem solving (for example, let’s talk about the assignment X from the online class). One of the mentors last year was successful with starting every meeting with a guided meditation. 

Each team has a faculty mentor in our program (this could be a postdoc or faculty member). Once a week, the faculty mentor joins the meeting for about 1 hour. This hour could consist of introductions / check-ins, a short presentation or story by the faculty mentor, and the opportunity for the team to ask questions. It’s great if the near-peer mentor and the team prepare questions beforehand. 

1B. Add a non-coding meeting (if you can/want)

In addition to the 8 coding hours per week, our students also meet for 2 hours per week in an “all hands meeting”. Such an all-hands meeting is not absolutely necessary, but if you have the bandwidth, it may be nice to meet once a week to do something other than coding. Maybe to read a paper together or meet with someone online (an alum who is now somewhere else? A faculty member or grad student?). 

If your program is full time (like an REU program), we suggest to still only do about 8-15 hours of coding per week. Fill up the rest with more standard things such as lectures, reading etc (and don’t make anyone do Zoom 40 hours a week!). If students are enjoying themselves with coding and getting more confident, they may do more coding by themselves, but in our program it is not the expectation. 

2. Mentors and teams are key 

When working alone, we’ve often seen students get stuck on technical problems, leaving many feeling lost and inadequate and wanting to discontinue learning this new skill. Working in a mentored team, however, students have access to immediate support from their peers and mentor. This helps them learn technical skills more efficiently, develop relationships with each other, and cultivate a shared sense of belonging in computational research (Kephart et al. 2008). We recommend that each participant in a coding summer program be assigned to a team of 4 to 5 students with similar technical skill levels led by a near-peer mentor. 

Mentors in our program are typically a year or two ahead of participants but belong to similar demographic groups and come from similar academic backgrounds. The mentor facilitates the meetings and leads the team in learning skills and applying them to a research question (without doing the work themselves). 

Each team also has a faculty advisor, who comes up with a research project that is likely to be completed in the available time and that is of interest to the students (Harackiewicz et al. 2008). The faculty advisor meets with the whole team at least once per week to guide learning and research. Of note, acting as a mentor improves students’ retention and success in STEM (Trujillo et al. 2015) therefore, this setup benefits mentors as well as mentees. 

2B. Who can be mentors? 

Over the years, we have found that near-peer mentors are incredibly useful for a number of reasons including 1) student participants are more likely to ask for help from a near-peer mentor than from a faculty advisor, 2) near-peer mentors serve as role models, giving participants an idea of what they can aim for in the next year or two, and 3) the use of mentors allows the program to serve many more participants than it could if it relied on a few time-pressed faculty advisors. Our selection criteria for mentors include essential knowledge (for example, the mentor for a team doing an advanced chemistry research project should have taken physical chemistry), mentoring experience or potential, logistical availability, and having a similar demographic background as the participants. Mentors don’t need experience with the specific coding language or research topic they will work on with their team. Rather than being the expert in the room, they are expected to help team members work together to find solutions or formulate questions for the faculty advisor. 

Mentors are crucial for the success of the program and need to be paid well for their work. Each week of the program, we pay our mentors a competitive wage for 8 contact hours with their team, a 2-hour all hands lunch meeting, a 2-hour mentor meeting, and 3-4 additional hours to account for preparation. However, we realize that this summer, things may be different for many! You may find that PhD students or Master’s students who can not work in the lab (but are still paid / on a fellowship) could be excellent near-peer mentors. Just make sure that the mentors know that this is a real commitment that will eat up a significant chunk of time each week. 

3. Identify an appropriate online course for each team

We have found that when learning basic coding skills, interactive online classes to learn computer programming (for example, from Datacamp, Udacity or Coursera) motivate and engage students better than books or online texts. Yet, when working individually, most students – especially beginners and historically underrepresented students – don’t finish online classes (Ihsen et al. 2013; Jordan 2015). As a solution, we have found that in teams, where students can work together and support each other, they learn a great deal from an online class. 

Each team’s faculty advisor picks a free, clearly structured online class with videos and assignments to teach participants coding skills. We have had good experiences with Udacity’s Exploratory Data Analysis course because this class is suitable for beginners. It does a good job motivating students to think about data and learn R. In early team meetings, participants spend time quietly working on the online class with their headphones on, followed by a team discussion or collaborative problem-solving session. If students encounter difficulty with any of the material, mentors may develop mini-lectures or create their own exercises to facilitate learning. Note, the students’ goal is not necessarily to finish the online course, but to learn enough to perform their research project. 

3B. Suggested classes:

Udacity Exploratory Data Analysis with R https://www.udacity.com/course/data-analysis-with-r–ud651

CodeHS https://codehs.com/ (the faculty mentor or the near-peer-mentor needs to create a section on Code HS, we use the introduction to python (rainforest).  

Coursera https://www.coursera.org/learn/r-programming (this one is a tip from our UCSF colleague Dr Kala Mehta)

4. Assign each team a simple and engaging research project 

Learning to code without a specific application in mind can feel boring and irrelevant, sometimes leading students to abandon the effort. In our summer program, teams carry out a research project to motivate them to learn coding skills, improve their sense of belonging in science (Jones, Barlow, and Villarejo 2010) and cultivate their team work and time/project management skills. Faculty advisors assign each team a research project early in the program. These projects should answer real questions so that participants feel their work is valuable (Woodin, Carter, and Fletcher 2017). The projects should also be relatively simple. Small and self contained projects that can be completed within a three week time frame are ideal to ensure completion and make participants feel that their efforts have been successful. For example, past research projects in our program, which reflect the interests of faculty advisors and the students, include writing computer simulations to model the evolution of gene expression, analyzing bee observations from a large citizen science project, examining trends in google search term data with respect to teen birth outcomes, and building an app for finding parking spots on or near campus. 

For 2020, we’d like to encourage you to pick a project that appears extremely simple if you normally use R or Python to make your plots / do stats, but that would be quite challenging if you’re new to coding. We also suggest that – unless the students are already quite advanced – you don’t give them a project that you want to publish on quickly. Nobody needs more pressure this summer.  

Here are some suggestions for simple research projects

  1. Let students plot the number of COVID19 cases in their county over time using R. Let them plot the number of cases in 5 different counties on the same figure. Add an arrow for when a stay-at-home order was implemented or terminated. Easy to download data are here: https://github.com/nytimes/covid-19-data 
  2. Let students keep track of how many steps they take each day for 10 days using their phone or watch. Let them plot the number of steps per day using R. Let them add a line for the mean. Collect data from 6 people and create a pdf with 6 plots in different colors. 
  3. If you have any data from your lab, let the students plot those data. Try making 4 different plots with the same data (scatter, box, histogram, etc). 
  4. Let students recreate an existing plot from a publication when the data are available. 
  5. Let students analyze (anonymized) data from your class. How strong is the correlation between midterm grades and final exam grades? Do students who hand in homework regularly do better on the test? 

* reference: Pleuni Pennings, Mayra M. Banuelos, Francisca L. Catalan, Victoria R. Caudill, Bozhidar Chakalov, Selena Hernandez, Jeanice Jones, Chinomnso Okorie, Sepideh Modrek, Rori Rohlfs, Nicole Adelstein Ten simple rules for an inclusive summer coding program for non-CS undergraduates, accepted for publication in Plos Computational Biology.

Five Reasons why you should attend the Annual SACNAS National Conference

27 Apr

Guest post by: Bridget Hansen, SFSU undergraduate researcher

BridgetHansenPosterSACNAS2015

First, who am I? What is SACNAS?

My name is Bridget Hansen and I am an undergraduate in Microbiology at San Francisco State University, doing research at the Romberg Tiburon Center for Environmental Studies. Over the summer, I participated in the Howard Hughes Medical Institute Excellent Research Opportunity Program (HHMI-ExROP) summer research and the AMGEN program at the University of California, Berkeley. I worked on a project that I then presented at the SACNAS Conference this past October.

SACNAS stands for the Society for Advancing Chicanos/Hispanics and Native Americans in Science. This society, made up of many successful Chicanos and Native Americans in science related careers, puts on a national conference once a year. The conference has opportunities for scientists at all levels, from undergraduates to professors and researchers. Many graduate school recruiters and other professional organizations come to this conference to recruit, providing a great platform for networking.

I used this opportunity to network for graduate schools! I will be attending a PhD program in the fall, in part, thanks to my interactions that I had at SACNAS.

What happens at the SACNAS National Conference?

Students from all over the country submit abstracts for the opportunity to present their work, either in the form of a poster or an oral presentation. The students had a scheduled time and room to present. Other than presentations, the meat of the conference was geared towards guest speakers and networking. The whole goal of the conference was to introduce young students to the world of research and science related careers! The best part is the graduate student recruiter booths where you have the opportunity to chat with recruiters, professors, and students from that university.

Five reasons why I recommend SACNAS

  1. The networking

There were hundreds of booths set up, all stocked with professors, recruiters, graduate students and pamphlets listing the reasons why you should come to their school. Nearly every research institution was in attendance, looking for the next round of graduate students to apply to their programs. They want you to apply to their programs but most importantly, they want to make sure their school lines up with your research interests. You can ask them about the programs, the application process, what it is like to live in that part of the United States and any funding opportunities. Exchanging business cards or information is very common and the badge that you are given upon arriving even has a scanner square that the recruiters can use to keep in touch with you (they scan your badge and your e-mail is logged with them).

I spoke to over a dozen booths about their programs and had all my questions answered. I was even recruited during my poster session presentation! Which brings me to my next point.

  1. The presentations

The presentations are great for two reasons: 1. You have an opportunity to talk about your work and receive feedback on your presentation skills and 2. Other schools can come by your presentation and see you as a researcher. This is fantastic! I am not the best on paper in some ways, so having other schools approach me based on my science, reassures me that I am more than just my GPA or my GRE scores. Not only that, I received written evaluations based on my presentation skills and my poster, which were all constructive and positive!

  1. The seminars

The guest speakers focus on their journeys as minorities in the sciences and how their transforming experiences have brought them to where they are today. They inspire us to continue to pursue our passions and create a sense of community, which I will get to in a minute. The seminars are also great opportunities for junior scientists, like myself, because they offer an opportunity to check out new areas of research, hear about different paths in science outside of academia and get insights into how to be successful. There are workshops on how to give a compelling interview, what to expect in graduate school and how to master networking. All of these skills are important ones that give you a competitive edge.

  1. The experience

The experience itself was wonderful. Surrounded by 3,600 other students, mentors and researchers, the conference felt grand. I say grand because the conference center was massive, the sheer number of attendees was at times, a bit overwhelming, and the hotel that we were assigned to left me in awe. The Gaylord National Conference Center in Washington D.C. was an incredible place to hold this conference this year. As apart of the conference fees, we were fed in a large hall, which also created a sense of community.

  1. The sense of community

The SACNAS conference creates a sense of community for young scientists; a community that they can be a part of throughout their careers in the sciences. The idea of having a supportive community that I can be part of is a great feeling, especially coming from a background that does not have any college graduates. It can be lonely sometimes, walking into a completely new field that no one you grew up around, has any experience in. So, when I attended the conference with other San Francisco State students who were also presenting, they immediately considered me one of the group, even though we had just met. Similarly, other students from other places also welcomed conversation with open arms. The inclusion that occurs at SACNAS is excellent.

Overall, I highly recommend attending a SACNAS national conference. It looks great on your CV, it is great for your future scientific career and definitely gives you an edge when applying for graduate school. Bring your own business cards!

If you have any questions about SACNAS, please refer to the SACNAS website: http://sacnas.org .

Hope to see you there next year! I will be attending as a graduate student!

Feel free to contact me with questions at: blhansen “at” mail.sfsu.edu or missbridgette4 “at” aol.com. and indicate you read this blog so I know where the questions are coming from!

 

SACNAS

Reading in the lab

11 Jan

The winter break is a great opportunity to spend time in the lab with my students. One of the things we do, is read papers. Last week, we spent a morning reading the following paper:

Triple-Antiretroviral Prophylaxis to Prevent Mother-To-Child HIV Transmission through Breastfeeding—The Kisumu Breastfeeding Study, Kenya: A Clinical Trial. PLoS Medicine, 2011. Thomas , Masaba, Borkowf, et al. 

The paper shows that antiretroviral drugs taken by an HIV-infected mother help prevent transmission to the baby through breastfeeding. The reported rates of HIV infection of the infants during breastfeeding were less than half the previously reported rates from untreated women.

After everyone read the paper, and we all discussed it together, two students worked together to write an abstract and three students worked together to draw an abstract. Here are the results:

Abstract (by Kadie and Melissa)

The Kisumu Breastfeeding Study was a single-arm trial conducted with 522 HIV–infected pregnant women who took a triple antiretroviral regimen from 34 weeks of pregnancy to 6 months after delivery. The triple-ARV regimen consisted of zidovudine and lamivudine and either nevirapine or the protease inhibitor nelfinavir. The purpose of the study was to investigate how various ARV regimens given to mother and/or their infants affect mother to child transmission of HIV.

Data collected showed that between 0 and 24 months, the cumulative HIV transmission rate rose from 2.5% to 7.0%. The cumulative HIV transmission or death rate was 15.7%. Three percent of babies born to mothers with a low viral load were HIV-positive compared to 8.7% of babies born to mothers with a high viral load. Similarly, 8.4% of babies born to mothers with low baseline CD4 cell counts were HIV positive compared to 4.1% of babies born to mothers with high baseline CD4 cell counts. Although these findings are limited by the single-arm design, this study supports the idea that a simple triple-ARV regimen given to HIV-positive pregnant women regardless of their baseline CD4 cell count can reduce MTCT during pregnancy and breastfeeding in a resource-limited setting.

Graphical abstract (by Olivia, Patricia and Dasha)

2016-01-07 12.40.27

 

Jobs in physiology and CS at SFSU

16 Nov

There are two job searches that interest me this year on our campus. One in our department (Biology) for an animal physiologist (the committee already started looking at applications, so if you are interested, you need to be fast!). The link to the ad is here .

The second search is in the Computer Science Department, and the ad is here. They are looking for someone with a “background in the database area, but also in areas related to social networking and collaboration, mobile computing, cloud computing and/or human/computer interaction.”

Both jobs are open to candidates at the assistant or associate professor level.

SFSU is a great place to work. Here are all the reasons why I am happy to be at SFSU.

If you are interested in doing research, training an extremely diverse student body and living in San Francisco, you should apply! Shoot me an email if you have any questions (pennings at sfsu dot edu).

 

 

 

End of summer poster session

19 Aug

Today was the last day that the summer students were in the lab (although some of them will be back next week when the semester starts). I asked each of them to make a poster with a figure they made this summer. They are learning to program in R, and making figures is a big part of what they’ve worked on. I took snap shots of some of the students with their posters. They did a great job!

2015-08-19 14.53.47 Pedro Zorzanelli da Vitoria from Brasil

2015-08-19 14.54.31 Brendan Kusuma (SFSU, undergrad)

2015-08-19 14.55.19 Julia Pyko (SFSU post bac) and Patricia Kabeja (SFSU undergrad)

 

2015-08-19 14.56.02 Dasha Fedorova (SFSU undergrad) made her poster together with Sidra Tufon (not in the picture).

 

2015-08-19 14.56.51Dwayne Evans (SFSU Master’s student)

 

No programming background? No problem! Learn R

14 Jun

Guest post by Rosana Callejas

Rosana Callejas

Rosana Callejas

Can someone with no programming knowledge learn “R”? The answer is yes! My name is Rosana Callejas. I am a Physiology major, and recent graduate from San Francisco State University. I began to learn the programming language “R” at the beginning of February of this year. Despite not having any previous programming experience , I analyzed my first data set of more than 20,000 data points in only a couple of months. Would you like to learn how I did it? Stay tuned.

The power of “R”

So what exactly is “R”? It is a programming language used by many data analysts, scientists, and statisticians, to analyze data, and perform statistical analysis with graphs and figures. “R” is a great tool when analyzing large data sets. It has many additional packages that can be downloaded, which allow the user to expand or simplify commands when analyzing data.

How R coded its way into my heart

Dr. Pleuni Pennings, an evolutionary biologist, and Professor at SFSU, introduced me to this wonderful tool. “I do all my research on my computer,” Dr. Pennings said, as she showed me the open program. At first, the idea puzzled me. In all my years as a biology student, I had never met a biologist like Dr. Pennings, who has made many discoveries from analyzing HIV DNA sequences using R. She explained to me that there is an accumulation of data collected by scientists everyday waiting to be analyzed. Therefore, there is a need for scientists with the skills to interpret, and draw conclusions from such large data sets. This interested me as biologist. I imagined all the new findings that could be made if all the data collected was analyzed. It would definitely contribute to the advancement of science. With this in mind, I embarked myself in the adventure of learning R.

One command at a time

I began by taking the online course “Exploratory Data Analysis with R” on Udacity.com. The course is composed of 6 lessons, in which I first learned the basics of R, a few basic commands, followed by the analysis of one variable, and how to make simple plots. In my learning, I used R, and R studio, which can be downloaded free online. I also used data sets provided by Udacity to analyze. In addition, R comes with other data sets I practiced with. My first graphing assignment was a simple bar plot (Figure 1), that represented friend count for Facebook users of different ages. This task required the package “ggplot2”, which allows graphing.

BlogFigure1

Figure 1. Friend count as function of age.

As I learned more, I began to work with different packages, new commands, and to make better graphs. I discovered how to add color to the graphs. I learned how to order variables, make subsets, group variables, add a new columns to my data sets, work with multiple variables, run correlation tests, and much more. The following are some figures that followed that first one, and show the progress of my learning as I added more detail to that first plot throughout the course.

BlogFigure2

Figure 2. Median friend count as function of age by gender.

BlogFigure3

Figure 3. Friend count as function of age.  In the green graph each point represents 20 data points in the data set. The black line represents the mean friend count. The blue line represents with the 50th quantile. The dotted lines represent the 90th and 10th quantiles.

 

Figure 4. The top graph represents friend count as function of age in months, with the blue line representing the mean. The middle graph represents friend count as a function of age with blue line represents the mean. The bottom graph represents friend count vs. age in moths rounded, multiplied, and divided by 5.

Figure 4. The top graph represents friend count as function of age in months, with the blue line representing the mean. The middle graph represents friend count as a function of age with blue line represents the mean. The bottom graph represents friend count vs. age in moths rounded, multiplied, and divided by 5.

Patience is the mother of all virtues

Learning R was definitely a challenge. Commands that in theory should work, sometimes did not work. As a new user, it was difficult to know exactly what had gone wrong. Fortunately, I had the guidance of Dr. Pennings who helped me through the process. I also looked for resources outside of Udacity. One great package to use along with R is “swirl,” which is a teaching package. With swirl, I learned commands not taught in the Udacity course. It has multiple lessons that give the user immediate feedback. Patience and persistence are key to learning R. Now I have seen what R can do, I know it was worth learning.

The possibilities are endless

My favorite feature of R is that the code used in a previous analysis can be saved, and reused. R users can also share pieces of code with one another, which helps expand the knowledge among users. If changes need to be made in the middle of analysis, this is rather simple, and there is no need to reanalyze the data. R can be used to study many different types of data of any size or background. Scientists such a Dr. Pennings make major findings in Biology using R.

Although new to R, I was able to begin the analysis of my own data set [1] within only a few months of learning about it. Below is a figure which resulted from the question: Which HIV regimens are most common and in what years? In order to answer this question, many hours of work were invested in preparing the data set, excluding undesired data points, sub setting, color coding, etc., ending up with 6255 HIV data points, which included only the 26 most common unique regimens as a function of time. The graph represents the most common regimens of HIV treatments taken by patients in different years. It is also organized in order of increasing number of drugs per regimen. Each regimen was color coded to include a NNRTI drug, a PI drug, or consist of nRTIs.

Figure 5. The graph represents the most common regimens of HIV treatments taken by patients in different years belonging either to NNRTI, nRTI, or PI.

Figure 5. The graph represents the most common regimens of HIV treatments taken by patients in different years belonging either to NNRTI, nRTI, or PI.

As the graph shows in 1989, and early 1990s, the HIV treatment consisted of the single drug AZT, and later in 1997, NVP. As the years progressed, regimens composed of two drugs became more common. It isn’t until 1996 that we begin to see regimens composed of three drugs. Regimens composed of three drugs are the most abundant and continue to be taken by patients up to 2013, while the single drug treatments seemed to have ceased in 2008. In 2002, we first observe regimens composed of four drugs (although RTV is often not counted as a drug, so these regimens may be considered 3-drug regimens as well), which also continue to be used along with the three drugs regimens.

R is a great program for data analysis. I believe that anyone who would like to learn it, with persistence can definitely do it. I will continue learning R, and analyzing my data set. I hope to use it as a useful tool for future investigations in my career.

[1] Thanks to Dr Robert Shafer from Stanford University for sharing the data with us!

15 papers on contemporary evolution in human viruses

29 May

In the fall semester of 2014 I taught a reading seminar for master students at SF State on contemporary evolution in human viruses. This blog post contains a list of the papers we read in the seminar.

I posted about this seminar previously here (about the seminar format) and here (no powerpoint allowed), and here (about being nervous for a talk).

The students’ work can be read and seen here (about H1N5), here (polio outbreak), here (Dengue), here (Ebola), here (HIV in court), here (doing my own homework), here (the origin of HIV), here (on bad small things) and here (Hep B).

These are the papers we read:

1. Fast evolution of drug resistance in HIV patient the 1980s

ReissLangeLancet

Resumption of HIV antigen production during continuous zidovudine treatment. Lancet. 1988 Feb 20;1(8582):421.
Reiss P, Lange JM, Boucher CA, Danner SA, Goudsmit J.

2. HIV: Doctor infects his ex-girlfriend, phylogenetic evidence in court

Metzker_HIV_criminalcase

Metzker, Michael L., et al. “Molecular evidence of HIV-1 transmission in a criminal case.” Proceedings of the National Academy of Sciences 99.22 (2002): 14292-14297.

3. Very contemporary: the genomics of the West-African Ebola epidemic

Gire_Ebola

Gire, Stephen K., et al. “Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak.” Science 345.6202 (2014): 1369-1372.

4. Using phylogenetics to determine origin of Dengue-3 outbreak in Australia

RitchieDENV
An explosive epidemic of DENV-3 in Cairns, Australia. PLoS One. 2013 Jul 16;8(7):e68137. doi: 10.1371/journal.pone.0068137. Print 2013. Ritchie SA1, Pyke AT, Hall-Mendelin S, Day A, Mores CN, Christofferson RC, Gubler DJ, Bennett SN, van den Hurk AF.

5. Classic paper from Beatrice Hahn’s lab on origin of HIV-1

Gao_HIV

Gao, Feng, et al. “Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes.” Nature 397.6718 (1999): 436-441.

6. Timing the start of the HIV-1 pandemic

Korber_HIVTiming

Korber, Bette, et al. “Timing the ancestor of the HIV-1 pandemic strains.”Science 288.5472 (2000): 1789-1796.

7. Where did the polio outbreak in Dominican Republic and Haiti come from?

KewEtAlPolio

Kew, Olen, et al. “Outbreak of poliomyelitis in Hispaniola associated with circulating type 1 vaccine-derived poliovirus.” Science 296.5566 (2002): 356-359.

8. Within-patient evolution of vaccine-derived polio virus

Martin_Polio

Martín, Javier, et al. “Evolution of the Sabin strain of type 3 poliovirus in an immunodeficient patient during the entire 637-day period of virus excretion.”Journal of Virology 74.7 (2000): 3001-3010.

 9. Hepatitis B within-patient evolution

LimRodrigo

Lim, Seng Gee, et al. “Viral quasi-species evolution during hepatitis Be antigen seroconversion.” Gastroenterology 133.3 (2007): 951-958.

10. Permissive mutations and the evolution of drug resistance in Influenza

Bloom_Influenza

Bloom JD, Gong LI, Baltimore D. Permissive Secondary Mutations Enable the Evolution of Influenza Oseltamivir Resistance. Science (New York, NY). 2010;328(5983):1272-1275. doi:10.1126/science.1187816.

11. Controversial experiments on H5N1 Influenza

HerfstInfluenza

Airborne transmission of influenza A/H5N1 virus between ferrets. Science. 2012 Jun 22;336(6088):1534-41. doi: 10.1126/science.1213362.
Herfst S1, Schrauwen EJ, Linster M, Chutinimitkul S, de Wit E, Munster VJ, Sorrell EM, Bestebroer TM, Burke DF, Smith DJ, Rimmelzwaan GF, Osterhaus AD, Fouchier RA.

12. Influential study on treatment to prevent HIV

GrantEtAlHIV

Grant, Robert M., et al. “Preexposure chemoprophylaxis for HIV prevention in men who have sex with men.” New England Journal of Medicine 363.27 (2010): 2587-2599.

 13. HIV drug resistance in women in Africa who were treated to prevent mother-to-child transmission

Eshleman_NVPHIV

Eshleman, Susan H., et al. “Nevirapine (NVP) resistance in women with HIV-1 subtype C, compared with subtypes A and D, after the administration of single-dose NVP.” Journal of Infectious Diseases 192.1 (2005): 30-36.

 14. Evolution of Acyclovir resistance in Varicalla-Zoster Virus

Morfin_VZV

Morfin, Florence, et al. “Phenotypic and genetic characterization of thymidine kinase from clinical strains of varicella-zoster virus resistant to acyclovir.”Antimicrobial agents and chemotherapy 43.10 (1999): 2412-2416.

 

15. Soft and hard sweeps during evolution of drug resistance in HIV

Pennings2014
Loss and recovery of genetic diversity in adapting populations of HIV. PLoS Genet. 2014 Jan;10(1):e1004000. doi: 10.1371/journal.pgen.1004000. Epub 2014 Jan 23.
Pennings PS1, Kryazhimskiy S2, Wakeley J3.