large-scale-sars-cov-2-testing-utilizing-saliva-transposition-sample

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Identification and isolation of contagious individuals along with quarantine of close contacts, is critical for slowing the spread of COVID-19. ...

Michigan State University

In human breast cancer, mortality is associated with metastasis to distant sites. Therefore, it is critical to elucidate the biological mechanisms that underlie tumor progression and metastasis. Using signaling pathway signatures we previously predicted a role for E2F transcription factors in Myc induced tumors. To test this role we interbred MMTV-Myc transgenic mice with E2F knockouts. Surprisingly, we observed that the loss of E2F2 sharply increased the percentage of lung metastasis in MMTV-Myc transgenic mice. Examining the gene expression profile from these tumors, we identified genetic components that were potentially involved in mediating metastasis. These genes were filtered to uncover the genes involved in metastasis that also impacted distant metastasis free survival in human breast cancer. In order to elucidate the mechanism by which E2F2 loss enhanced metastasis we generated knockdowns of E2F2 in MDA-MB-231 cells and observed increased migration in vitro and increased lung colonization in vivo. We then examined genes that were differentially regulated between tumors from MMTV-Myc, MMTV-Myc E2F2-/-, and lung metastases samples and identified PTPRD. To test the role of PTPRD in E2F2-mediated breast cancer metastasis, we generated a knockdown of PTPRD in MDA-MB-231 cells. We noted that decreased levels of PTPRD resulted in decreased migration in vitro and decreased lung colonization in vivo. Taken together, these data indicate that E2F2 loss results in increased metastasis in breast cancer, potentially functioning through a PTPRD dependent mechanism.

Increased metastasis with loss of E2F2 in Myc-driven tumors.

Many and varied methods currently exist for featurization, which is the process of mapping persistence diagrams to Euclidean space, with the goal of maximally preserving structure. However, and to our knowledge, there are presently no methodical comparisons of existing approaches, nor a standardized collection of test data sets. This paper provides a comparative study of several such methods. In particular, we review, evaluate, and compare the stable multi-scale kernel, persistence landscapes, persistence images, the ring of algebraic functions, template functions, and adaptive template systems. Using these approaches for feature extraction, we apply and compare popular machine learning methods on five data sets: MNIST, Shape retrieval of non-rigid 3D Human Models (SHREC14), extracts from the Protein Classification Benchmark Collection (Protein), MPEG7 shape matching, and HAM10000 skin lesion data set. These data sets are commonly used in the above methods for featurization, and we use them to evaluate predictive utility in real-world applications.

Article	Year
Increased metastasis with loss of E2F2 in Myc-driven tumors. Oncotarget\| PubMed ID: 26474282	2015
A Comparative Study of Machine Learning Methods for Persistence Diagrams. Frontiers in artificial intelligence\| PubMed ID: 34396089	2021

Danielle Barnes

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