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4. The impact of completion and follow-up endoleaks on survival, reintervention, and rupture.

Author

Li, Chun, de Guerre, Livia, Dansey, Kirsten, Lu, Jinny, Patel, Priya, Yao, Mengdi, Jones, Douglas, Schermerhorn, Marc, and Malas, Mahmoud

Subjects
AAA, Aneurysm, Completion endoleak, EVAR, Follow-up, Humans, Aged, United States, Treatment Outcome, Follow-Up Studies, Blood Vessel Prosthesis Implantation, Risk Factors, Aortic Aneurysm, Abdominal, Medicare, Endoleak, Endovascular Procedures, Retrospective Studies
Abstract

OBJECTIVE: Endoleaks may be seen at case completion of endovascular abdominal aortic aneurysm repair (EVAR), and the presence of an endoleak may impact outcomes. However, the clinical implications of various endoleaks seen during follow-up is not well-described. Therefore, we studied the impact of endoleaks at completion and at follow-up on mid-term outcomes. METHODS: We reviewed patients who underwent EVAR from 2003 to 2016 within the Vascular Quality Initiative-Medicare database and identified patients with endoleak at procedure completion and during follow-up, excluding those presenting with rupture. We stratified cohorts by presence of completion and follow-up endoleak subtypes. The primary outcome was 5-year survival, and secondary outcomes included 5-year freedom from reintervention and freedom from rupture. We used Kaplan-Meier estimates and log-rank tests to analyze differences in time-to-event endpoints. RESULTS: Of 21,745 patients with completion endoleak data, 5085 (23%) had an endoleak. Compared with those without endoleak, those with type I endoleaks had lower 5-year survival (69% vs 75%; P < .001), type II endoleaks had higher survival (79%; P < .001), and types III, IV, and indeterminate were not statistically different (73%, 73%, and 75%, respectively). Freedom from reintervention for types I and III endoleaks were significantly lower than no endoleak cohort (I: 76%; P < .001; III: 72%; P < .001 vs 83%), but freedom from rupture was higher for those with type II and III endoleak (95% and 97% vs 94%; P < .001). Of 14,479 patients with detailed follow-up endoleak data, 2290 (16%) had an endoleak. Compared with those without endoleak, types I and III had significantly lower 5-year survival (I: 80%; P = .002; III: 66%; P < .001 vs 84%), but there were no differences for types II (82%) and indeterminate (77%). Those with any type of follow-up endoleak had lower 5-year freedom from reintervention (I: 70%; P < .001; II: 76%; P = .006; III: 36%; P < .001; indeterminate: 60%; P = .007 vs 84%), and lower freedom from rupture (I: 92%; P < .001; II: 91%; P = .16; III: 88%; P = .01; indeterminate: 90%; P = .11 vs 94%). CONCLUSIONS: Compared with patients with no endoleak, those with type I completion endoleaks have lower 5-year survival and freedom from reintervention. Patients with types I and III follow-up endoleaks also have lower survival, and any endoleak at follow-up is associated with lower freedom from reintervention and freedom from rupture. These data highlight the importance of careful patient selection and close postoperative follow-up after EVAR, as the presence of endoleaks, specifically type I and III, over time portends worse outcomes.

Published
2023

6. SALSA-Lite: A Fast and Effective Feature for Polyphonic Sound Event Localization and Detection with Microphone Arrays

Author

Nguyen, Thi Ngoc Tho, Jones, Douglas L., Watcharasupat, Karn N., Phan, Huy, and Gan, Woon-Seng

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

Polyphonic sound event localization and detection (SELD) has many practical applications in acoustic sensing and monitoring. However, the development of real-time SELD has been limited by the demanding computational requirement of most recent SELD systems. In this work, we introduce SALSA-Lite, a fast and effective feature for polyphonic SELD using microphone array inputs. SALSA-Lite is a lightweight variation of a previously proposed SALSA feature for polyphonic SELD. SALSA, which stands for Spatial Cue-Augmented Log-Spectrogram, consists of multichannel log-spectrograms stacked channelwise with the normalized principal eigenvectors of the spectrotemporally corresponding spatial covariance matrices. In contrast to SALSA, which uses eigenvector-based spatial features, SALSA-Lite uses normalized inter-channel phase differences as spatial features, allowing a 30-fold speedup compared to the original SALSA feature. Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset showed that the SALSA-Lite feature achieved competitive performance compared to the full SALSA feature, and significantly outperformed the traditional feature set of multichannel log-mel spectrograms with generalized cross-correlation spectra. Specifically, using SALSA-Lite features increased localization-dependent F1 score and class-dependent localization recall by 15% and 5%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra., Comment: arXiv admin note: text overlap with arXiv:2110.00275

Published
2021
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8. Preparing Patients for Oral Immunotherapy (PPOINT): International Delphi consensus for procedural preparation and consent

Author

Mack, Douglas P., Dribin, Timothy E., Turner, Paul J., Wasserman, Richard L., Hanna, Mariam A., Shaker, Marcus, Tang, Mimi L.K., Rodríguez del Río, Pablo, Sobolewski, Brad, Abrams, Elissa M., Anagnostou, Aikaterini, Arasi, Stefania, Bajowala, Sakina, Bégin, Philippe, Cameron, Scott B., Chan, Edmond S., Chinthrajah, Sharon, Clark, Andrew T., Detjen, Paul, du Toit, George, Ebisawa, Motohiro, Elizur, Arnon, Factor, Jeffrey M., Greiwe, Justin, O’B Hourihane, Jonathan, Hughes, Sarah W., Jones, Douglas H., Muraro, Antonella, Nowak-Wegrzyn, Anna, Patel, Nandinee B., Scurlock, Amy M., Shah, Atul N., Sindher, Sayantani B., Tilles, Stephen, Vickery, Brian P., Wang, Julie, Windom, Hugh H., and Greenhawt, Matthew

Published
2024
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9. SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection

Author

Nguyen, Thi Ngoc Tho, Watcharasupat, Karn N., Nguyen, Ngoc Khanh, Jones, Douglas L., and Gan, Woon-Seng

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses amplitude and/or phase differences between microphones to estimate source directions. As a result, it is often difficult to jointly optimize these two subtasks. We propose a novel feature called Spatial cue-Augmented Log-SpectrogrAm (SALSA) with exact time-frequency mapping between the signal power and the source directional cues, which is crucial for resolving overlapping sound sources. The SALSA feature consists of multichannel log-spectrograms stacked along with the normalized principal eigenvector of the spatial covariance matrix at each corresponding time-frequency bin. Depending on the microphone array format, the principal eigenvector can be normalized differently to extract amplitude and/or phase differences between the microphones. As a result, SALSA features are applicable for different microphone array formats such as first-order ambisonics (FOA) and multichannel microphone array (MIC). Experimental results on the TAU-NIGENS Spatial Sound Events 2021 dataset with directional interferences showed that SALSA features outperformed other state-of-the-art features. Specifically, the use of SALSA features in the FOA format increased the F1 score and localization recall by 6% each, compared to the multichannel log-mel spectrograms with intensity vectors. For the MIC format, using SALSA features increased F1 score and localization recall by 16% and 7%, respectively, compared to using multichannel log-mel spectrograms with generalized cross-correlation spectra., Comment: (c) 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2021
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10. Improving Polyphonic Sound Event Detection on Multichannel Recordings with the S{\o}rensen-Dice Coefficient Loss and Transfer Learning

Author

Watcharasupat, Karn N., Nguyen, Thi Ngoc Tho, Nguyen, Ngoc Khanh, Lee, Zhen Jian, Jones, Douglas L., and Gan, Woon Seng

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Signal Processing
Abstract

The S{\o}rensen--Dice Coefficient has recently seen rising popularity as a loss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples, such as semantic segmentation, natural language processing, and sound event detection. Conventional training of polyphonic sound event detection systems with binary cross-entropy loss often results in suboptimal detection performance as the training is often overwhelmed by updates from negative samples. In this paper, we investigated the effect of the Dice loss, intra- and inter-modal transfer learning, data augmentation, and recording formats, on the performance of polyphonic sound event detection systems with multichannel inputs. Our analysis showed that polyphonic sound event detection systems trained with Dice loss consistently outperformed those trained with cross-entropy loss across different training settings and recording formats in terms of F1 score and error rate. We achieved further performance gains via the use of transfer learning and an appropriate combination of different data augmentation techniques., Comment: Submitted to the 6th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2021

Published
2021

11. What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis

Author

Nguyen, Thi Ngoc Tho, Watcharasupat, Karn N., Lee, Zhen Jian, Nguyen, Ngoc Khanh, Jones, Douglas L., and Gan, Woon Seng

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Signal Processing
Abstract

Sound event localization and detection (SELD) is an emerging research topic that aims to unify the tasks of sound event detection and direction-of-arrival estimation. As a result, SELD inherits the challenges of both tasks, such as noise, reverberation, interference, polyphony, and non-stationarity of sound sources. Furthermore, SELD often faces an additional challenge of assigning correct correspondences between the detected sound classes and directions of arrival to multiple overlapping sound events. Previous studies have shown that unknown interferences in reverberant environments often cause major degradation in the performance of SELD systems. To further understand the challenges of the SELD task, we performed a detailed error analysis on two of our SELD systems, which both ranked second in the team category of DCASE SELD Challenge, one in 2020 and one in 2021. Experimental results indicate polyphony as the main challenge in SELD, due to the difficulty in detecting all sound events of interest. In addition, the SELD systems tend to make fewer errors for the polyphonic scenario that is dominant in the training set., Comment: Accepted for the 6th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2021

Published
2021

13. Yet another view of Trobriand kinship categories, from optimality to conceptual structure

Author

Jones, Douglas

Subjects
kinship, kin terms, optimality theory, Trobriand
Abstract

In “Another view of Trobriand kin categories,” Lounsbury analyzes Trobriand kin terms by providing a core genealogical definition for each term, and then showing how a set of reduction rules make it possible to supply terms for more distant relatives. This article revisits Lounsbury’s analysis in the light of recent advances in linguistics and cognitive science. We show that Trobriand kin terms express a conventionalized tradeoff between expressing relevant information and avoiding marked forms. Formally, we follow Optimality Theory in developing a constraint-based approach, an alternative to Lounsbury’s derivational approach, in which reduction rules are not just stipulated but derived. Kin terms are polysemous, with core and extended senses: a collection of markedness scales and a ranked set of distinctive features (1) marshal core referents of kin terms, and (2) select optimal, best-fit terms for kin types outside the core. Apart from its formal merits, this approach clarifies the connection between the Trobrianders’ Crow-type kin terminology and their matrilineal institutions. It may also have implications for the “the Crow-Omaha problem” – the relationship between skewed and unskewed cross-parallel distinctions. Finally, the organization of kin terms may provide a window onto an evolved domain of conceptual structure: our discussion concludes with some thoughts on the relationship between kinship, genealogy, and biological relatedness.

Published
2022

36. Outcomes of thoracic endovascular aortic repair in patients with concomitant blunt thoracic aortic injury and traumatic brain injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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37. Outcomes and practice patterns of medical management of blunt thoracic aortic injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Dipasupil, Edmundo, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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47. Investigator attitudes on equipoise and practice patterns in the BEST-CLI trial

Author

Farber, Alik, primary, Siracuse, Jeffrey J., additional, Giles, Kristina, additional, Jones, Douglas W., additional, Laskowski, Igor A., additional, Powell, Richard J., additional, Rosenfield, Kenneth, additional, Strong, Michael B., additional, White, Christopher J., additional, Doros, Gheorghe, additional, and Menard, Matthew T., additional

Published
2023
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