We would like a student to assist with conducting linear and logistic regression analyses using SAS, along with preparing charts, tables, and graphs of the data from our prospective cohort study.
Small molecule modulators have been transformative in immune-oncology, revealing the functional role of numerous immune pathways. The current paradigm for immunotherapy, however, excludes cancers such as PDA where MHC-I is not effectively expressed at the membrane. In this application, we combine an innovative chemical proteomic screening platform with genome wide CRISPR screening in advanced PDA models. The lead compounds and protein targets discovered herein should provide launching points for drug development programs to remove PDA’s cloak of invisibility from the immune system.
The Landweber Lab is looking for a computational student to work with us to analyze long-read DNA sequence datasets from Oxford Nanopore and PacBio (so-called third generation sequencing platforms). These datasets were collected across a time-course while single cells of the genus Oxytricha are undergoing RNA-guided natural genome editing. This process leads to a completely different “output” product genome from the precursor “input” or germline genome, and has been compared to a cellular computer. The goal will be to capture and classify long reads in these DNA datasets that represent the intermediate steps in genome rearrangements, when chromosomes mix and match hundreds of thousands of precursor building blocks to assemble a mature genome of 18,000 new chromosomes during programmed nuclear development.
Evidence-based Medicine (EBM) is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. The key difference between evidence-based medicine and traditional medicine is not that EBM considers the evidence while the latter does not, but rather that EBM demands better evidence. Given the exponential growth of the medical literature and the free text format of this big body of literature that hampers efficient evidence computing, researchers, patients and clinicians face significant challenges in evidence retrieval, appraisal, and synthesis. Our long-term goal is to develop natural language processing and text summarization methods to overcome these challenges. Our short-term goal is to build a computable evidence base for COVID-19 and to enable evidence synthesis and reasoning over COVID-19 study findings. Currently we have a database of structured data elements for PubMed abstracts for randomized controlled trials published within the past 20 years. For this scholar project, we expect the participating students to develop methods to analyze the evidence in our evidence base, to build COVID-19 knowledge graphs, and to enable evidence synthesis and appraisal at scale. On this basis, we will also compare the evidence in the literature to the evidence derived from the real world data of COVID-19 patients.
Stroke is devastating when left untreated. Early treatment significantly improves outcomes, and can be reliably detected using the FAST exam, which specifically assesses facial asymmetry, arm weakness, and speech deficits for signs of stroke. Our project aims to use smartphone technology to build a stroke detection algorithm based on this exam. We have collected video data of hospitalized stroke patients performing aspects of the standard neurology exam, which includes the factors previously mentioned. The next step is to build an algorithm that can detect facial asymmetry using this data.
Stroke is devastating when left untreated. Early treatment significantly improves outcomes, and can be reliably detected using the FAST exam. The exam looks for facial asymmetry, arm weakness, and speech deficits as signs of stroke. Our research project aims to use smartphone technology to build a stroke detection algorithm based on the FAST exam. We have collected speech data of hospitalized stroke patients. The next step is to build an algorithm that can detect abnormal speech.
While an normal semen sample contains hundreds of millions of sperm, men with azospermia have no sperm seen and azospermia represents an important cause of infertility. Men with azospermia typically have to use donor sperm in order to have a child. However, extended manual search of semen samples in men with azospermia reveal single-digit numbers of sperm in ~80% of cases. These few sperm are sufficient to fertility eggs in the IVF lab and allow the men to be biological fathers. Unfortunately, the manual search for sperm is only performed in a couple of sites in the world because of the enormous cost associated with having a specially trained andrologist search a sperm sample for roughly 8 hours. In this project, we aim to apply the semen sample to the custom designed microfluidics chip at a fixed flow rate. Next, several thousands of images will be captured at frame rate ranging between 4,000-40,000 fps using a high definition microscope camera as the sample flows through the microfluidics device. We will then build a training dataset of images with and without the rare cells of interest. These training datasets will be used to build an artificial intelligence (AI) model with high accuracy to detect rare cells of interest. The model will be tested for its accuracy on the ‘true’ sample containing rare cells. The Data Science Institute Scholar is expected to have basic programming skills (preferably in Python). Thus, if successful, there will literally be countless babies born as a result of this project.
