The PSP projects are listed below, but are also located on the website: https://live-sfsu-biology.pantheonsite.io/pincsummerprogram/psp-2024

Public Health

Programming Language: R

Project Description: [The] research [will be] focused on environmental health, more specifically air pollution and related health outcomes. In this project, [we will] explore air quality data from the Bay Area while focusing on environmental justice communities. Potential analysis could include summarizing data, identifying peak time of pollution, peak locations of pollution, or examining air pollution exposures and a health outcome of interest. We will also create visualizations, such as time series plots and air pollution maps. We will be using publicly available air quality data for this project.

Faculty Advisor(s): Nancy Carmona & RJ Reyes

Ecology and Evolution

Programming Language: Python

Project Description: We will be gathering behavioral data from video recordings of insects using an automated algorithm written in Python.

Faculty Advisor(s): Ray Esquerra & Candace Low

Conservation Biology

Programming Language: R

Project Description: California biodiversity and climate change. We will look at California plant and pollinator biodiversity to see if we can explain how they are affected by climate and habitat.

Faculty Advisor(s): Gretchen Lebuhn

Mathematics

Programming Language: Python

Project Description: We will be working with a nonprofit organization Resilience of Mind, which offers online therapeutic programming to help first responders cope with traumatic stress. Our ultimate goal is to provide Resilience of Mind with data analysis that they can use to obtain grants and funding, so they can expand their program and offer it for free to more people who need it. Resilience of Mind will share with us all the data they have on who uses their program, how they use it, and how they score on different psychological assessments in the program. It is up to us to craft a story from that data while gaining the computational training to do it. While learning to code in Python and make data visualizations in Tableau, students will learn to think about practical uses of data, how to present data, and extract insights from data about a business, which are a very important part of data science in the real world.

Faculty Advisor(s): Luella Fu

Genomics

Programming Language: R

Project Description: The moss Syntrichia princeps is common across western North America. Unlike most mosses, this species does not have separate sexes; each individual produces both egg and sperm and reproduces by selfing, yielding thousands of spores that are genetically identical to itself. The goal of this project is to explore the newly sequenced and assembled genome of S. princeps, testing hypotheses about genomic synteny, gene family abundance, and sex chromosomes, and the relationship of these features to this plant’s life cycle

Faculty Advisor(s): Jenna Ekwealor

Cancer Biology

Programming Language: Python

Project Description: Tumor suppressors are genes that prevent the development of cancer. Mutations that disable tumor suppressors are major events in the progression of cancer. The project will investigate how tumor suppressors avoid mutation. We will search for evidence that tumor suppressors have evolved to reduce their rate of ongoing disabling mutations; in addition, we will search for evidence that this mutation avoidance is more pronounced in mammals with higher risk of cancer (namely, long-lived and/or large species).

Faculty Advisor(s): Scott Roy

Antiobiotic Resistance Evolution & Transmission in Bacteria

Programming Language: R

Project Description: Antibiotic resistance is a big public health problem. Antibiotic resistance means that bacteria cannot be killed by the drugs (antibiotics) we normally use to fight the bacteria that make us sick. This can mean, for example, that when you take antibiotics for a urinary tract infection, the infection (caused by bacteria) may not go away if the bacteria in your urinary tract are resistant to the antibiotics you take. We will be using publicly available sequencing data and evolutionary analyses to learn about how often antibiotic resistance evolves in bacteria like Streptococcus pneumoniae and how often resistant strains are transmitted from person to person.

Faculty Advisor(s): Pleuni Pennings