| dc.contributor.advisor | Eric Alm. | en_US |
| dc.contributor.author | Dai, Chengzhen L. | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2019-07-15T20:32:20Z | |
| dc.date.available | 2019-07-15T20:32:20Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1721.1/121666 | |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 43-46). | en_US |
| dc.description.abstract | The spread of antibiotic resistance across human and environmental habitats is a global public health challenge. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing of wastewater environments with risk prioritization of resistance genes. We find that many of the genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the antibiotic resistance genes in wastewater are not directly clinically relevant. Residential sewage was found to be of greater risk to human health than wastewater treatment plants and can be as risky as hospital effluent. Across countries, we show that differences in antibiotic resistance can, in some cases, resemble differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. | en_US |
| dc.description.statementofresponsibility | by Chengzhen L. Dai. | en_US |
| dc.format.extent | 46 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| dc.rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Multi-site sampling and risk prioritization of antibiotic resistance genes in sewage environments | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
| dc.identifier.oclc | 1102055765 | en_US |
| dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
| dspace.imported | 2019-07-15T20:32:18Z | en_US |
| mit.thesis.degree | Master | en_US |
| mit.thesis.department | EECS | en_US |