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.

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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.

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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.

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This project is an exciting opportunity to work with FEM, a non-profit that aims to address social rights issues among rural communities in Colombia. The team is currently working with the mayor’s office in Cartagena, Colombia to access multiple datasets and data sources to establish who and where the extreme poor in Cartagena live. Her team piloted a smartphone-based collection strategy during IOTA Hurricane and obtained data on 4845 households in a very vulnerable area. The first data science challenge was to wrangle and analyze these data to better characterize the population and their needs. This part of the project was conducted in the Spring and Summer of 2021.

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Rights CoLab is working with the Sustainability Accounting Standards Board (SASB) to define a strengthened set of disclosure standards that investors can use to persuade companies to improve labor rights for both their workforce and workers in supply chains. The project has two components: 1) a data science project, and 2) an Expert Group.

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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.

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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.

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