About

André J. Butler is an Industry Professor in the Civil, Urban, and Environmental Engineering Department at NYU Tandon School of Engineering, having joined the department in August 2022. Prior to this, he served as an associate professor and chair of the Environmental and Civil Engineering Department at Mercer University in Macon, Georgia. During his tenure at Mercer University, he received two School of Engineering Teacher of the Year Awards in 2003 and 2017, as well as the Minority Mentor Trailblazer of the Year Award for Excellence in Teaching in 2019. Dr. Butler’s teaching interests and research activities primarily focus on air quality and respiratory health. His research group has worked on understanding the spatio-temporal distributions of ozone and particulate matter in Georgia, improving access to clean drinking water in Malawi, Africa, and designing low-cost measurement techniques for indoor particulate matter in the Dominican Republic. His educational background includes a Ph.D. in Environmental Engineering from Georgia Institute of Technology, a Master of Engineering in Mechanical Engineering and Environmental Management from Carnegie Mellon University, and a Bachelor of Science in Mechanical Engineering from the University of Illinois-Urbana.

Research topics

• Artificial Intelligence
• Computer Science
• Biology
• Computational biology
• Immunology
• Genetics
• Bioinformatics

Selected publications

• Therapeutic Endoluminal Vacuum Therapy (EVT): A gold standard management for oesophageal and selected gastric and duodenal perforations

  The Surgeon · 2025-07-15

  article
  PublisherDOI

• EGS SO29 - Therapeutic Endoluminal Vacuum Therapy (EVT) as first line management for oesophageal and selected gastric and duodenal perforations

  British journal of surgery · 2024-11-01

  articleOpen access

  Abstract Background Upper gastro-intestinal (GI) perforations and leaks affect roughly 2,800 patients annually in the UK. They can occur spontaneously, as a complication of medical intervention, or be due to trauma. Mortality rates are around 20%, rising to 50% if diagnosis and treatment is delayed. Around a third of cases fall into the delayed category. Traditionally, management of this group of patients involved surgery with long hospital stays and poor outcomes. Our Unit has adopted EVT as first line management for these cases, using an ad-hoc Endoluminal Vacuum Device (EVD), which has significantly improved patient outcomes compared to traditional treatment strategies. Method The primary aim of this study was to assess the successful perforation/ leak healing rate, the overall mortality rate, and the complication rate of EVT. A retrospective analysis of a prospectively collated database for all patients who received EVT between May 2011 to November 2023 was performed. Patients who had oesophageal, gastric or duodenal perforations were included. The ad-hoc EVD was constructed using an open pore sponge (granufoam, KCI) sutured to the end of a nasogastric (NG) tube. This was inserted endoscopically into the site of perforation and with negative pressure (125mmHg) applied using a negative pressure vacuum pump. Results 104 patients received EVT, with a median age of 65years (range 23-92years), and median ASA of 3 (range 1-5) at presentation. Eighty-five cases were oesophageal, 15 gastric and 4 duodenal. Post-operative leaks accounted for 42 cases, 26 were iatrogenic, 30 spontaneous, and 6 traumatic. Leak resolution was achieved in 94 patients (90.4%). Twelve (11.5%) patients died; 7 (6.7%) deaths were due to treatment failure. Seven (6.7%) patients had a significant bleeding event during EVT of which 4 (3.8%) were directly related to the leak because of undrained sepsis. One patient had an oesophageal mucosal injury during EVD removal. Conclusion EVT is a safe and effective treatment for upper GI tract leaks regardless of their cause. It achieves a significant reduction in mortality compared to traditional treatments, especially in patients with delayed presentation. Adequate drainage of surrounding sepsis is essential to ensure therapy success and avoid bleeding complications. Given the improvement in patient outcomes that EVT delivers, it should be considered for first line management in patients with delayed diagnosis and presentation of oesophageal perforation, following anastomotic leak after resectional surgery, as well as in select patients with gastric and duodenal perforations.

  PublisherOA PDFDOI

• Influenza virus transcription and progeny production are poorly correlated in single cells

  eLife · 2023-09-07 · 2 citations

  articleOpen access

  The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here, we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.

  PublisherDOI

• Influenza virus transcription and progeny production are poorly correlated in single cells

  2023-07-12 · 5 citations

  preprintOpen access

  Abstract The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza-virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny, and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.

  PublisherOA PDFDOI

• Influenza virus transcription and progeny production are poorly correlated in single cells

  eLife · 2023-07-12 · 21 citations

  articleOpen access

  The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here, we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell's contribution to the progeny population.

  PublisherDOI

• Author Response: Influenza virus transcription and progeny production are poorly correlated in single cells

  2023-09-07

  peer-reviewOpen access
  PublisherDOI

• Author Response: Influenza virus transcription and progeny production are poorly correlated in single cells

  2023-07-12

  peer-reviewOpen access

  The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza-virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny, and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.

  PublisherDOI

• Influenza virus transcription and progeny production are poorly correlated in single cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2022-08-30 · 5 citations

  preprintOpen access

  Abstract The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza-virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny, and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell’s contribution to the progeny population.

  PublisherOA PDFDOI

• geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

  bioRxiv (Cold Spring Harbor Laboratory) · 2021-08-10

  preprintOpen access

  Abstract The problem of selecting targeted gene panels that capture maximum variability encoded in scRNA-sequencing data has become of great practical importance. scRNA-seq datasets are increasingly being used to identify gene panels that can be probed using alternative molecular technologies, such as spatial transcriptomics. In this context, the number of genes that can be probed is an important limiting factor, so choosing the best subset of genes is vital. Existing methods for this task are limited by either a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cell types. We resolve this by introducing an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. We demonstrate, using a variety of metrics and diverse datasets, that our approach outperforms existing strategies, and can not only resolve cell types but also more subtle cell state differences. Our approach is available as an open source, easy-to-use, documented R package ( https://github.com/MarioniLab/geneBasisR ).

  PublisherOA PDFDOI

• Additional file 6 of geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

  Figshare · 2021-01-01

  datasetOpen access

  Additional file 6: Table S3. List of cell type specific markers for the spleen dataset.

  PublisherDOI

Frequent coauthors

• Rahul Satija

  68 shared

• Allison J. Greaney

  Fred Hutch Cancer Center

  40 shared

• Jesse D. Bloom

  Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa

  40 shared

• David J. Bacsik

  Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa

  40 shared

• Bernadeta Dadonaite

  37 shared

• Nicholas S. Heaton

  37 shared

• Efthymia Papalexi

  New York University

  26 shared

• John C. Marioni

  European Bioinformatics Institute

  21 shared

Awards & honors

• Mercer University Minority Mentor Trailblazer of the Year Aw…
• Mercer University School of Engineering Teacher of the Year…
• Mercer University School of Engineering Teacher of the Year…