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448 results on '"Hu, Tony"'

1. AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

Author

Tang, Zhiqiang, Fang, Haoyang, Zhou, Su, Yang, Taojiannan, Zhong, Zihan, Hu, Tony, Kirchhoff, Katrin, and Karypis, George

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundation models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities spanning classification, regression, object detection, semantic matching, and image segmentation. Experiments across diverse datasets and tasks showcases AutoMM's superior performance in basic classification and regression tasks compared to existing AutoML tools, while also demonstrating competitive results in advanced tasks, aligning with specialized toolboxes designed for such purposes., Comment: Accepted at AutoML 2024 Conference

Published
2024

2. A frequency-independent solver for systems of first order linear ordinary differential equations

Author

Hu, Tony and Bremer, James

Subjects
Mathematics - Numerical Analysis
Abstract

When a system of first order linear ordinary differential equations has eigenvalues of large magnitude, its solutions exhibit complicated behaviour, such as high-frequency oscillations, rapid growth or rapid decay. The cost of representing such solutions using standard techniques typically grows with the magnitudes of the eigenvalues. As a consequence, the running times of standard solvers for ordinary differential equations also grow with the size of these eigenvalues. The solutions of scalar equations with slowly-varying coefficients, however, can be efficiently represented via slowly-varying phase functions, regardless of the magnitudes of the eigenvalues of the corresponding coefficient matrix. Here, we couple an existing solver for scalar equations which exploits this observation with a well-known technique for transforming a system of linear ordinary differential equations into scalar form. The result is a method for solving a large class of systems of linear ordinary differential equations in time independent of the magnitudes of the eigenvalues of their coefficient matrices. We discuss the results of numerical experiments demonstrating the properties of our algorithm., Comment: arXiv admin note: text overlap with arXiv:2308.03288

Published
2023

3. AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

Author

Shchur, Oleksandr, Turkmen, Caner, Erickson, Nick, Shen, Huibin, Shirkov, Alexander, Hu, Tony, and Wang, Yuyang

Subjects
Computer Science - Machine Learning
Abstract

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods., Comment: Published at AutoML Conference 2023

Published
2023

5. Altered COVID-19 immunity in children with asthma by atopic status

Author

Tong, Sherry, Scott, Jordan C., Eyoh, Enwono, Werthmann, Derek W., Stone, Addison E., Murrell, Amelie E., Sabino-Santos, Gilberto, Trinh, Ivy V., Chandra, Sruti, Elliott, Debra H., Smira, Ashley R., Velazquez, Jalene V., Schieffelin, John, Ning, Bo, Hu, Tony, Kolls, Jay K., Landry, Samuel J., Zwezdaryk, Kevin J., Robinson, James E., Gunn, Bronwyn M., Rabito, Felicia A., and Norton, Elizabeth B.

Published
2024
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6. Blood-Based microRNA Biomarker Signature of Early-Stage Pancreatic Ductal Adenocarcinoma With Lead-Time Trajectory in Prediagnostic Samples

Author

Treekitkarnmongkol, Warapen, Dai, Jianliang, Liu, Suyu, Sankaran, Deivendran, Nguyen, Tristian, Balasenthil, Seetharaman, Hurd, Mark W., Chen, Meng, Katayama, Hiroshi, Roy-Chowdhuri, Sinchita, Calin, George A., Brand, Randall E., Lampe, Paul D., Hu, Tony Y., Maitra, Anirban, Koay, Eugene J., Killary, Ann M., and Sen, Subrata

Published
2024
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9. Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic β cells

Author

Xu, Weiwei, Qadir, M.M. Fahd, Nasteska, Daniela, Mota de Sa, Paula, Gorvin, Caroline M., Blandino-Rosano, Manuel, Evans, Charles R., Ho, Thuong, Potapenko, Evgeniy, Veluthakal, Rajakrishnan, Ashford, Fiona B., Bitsi, Stavroula, Fan, Jia, Bhondeley, Manika, Song, Kejing, Sure, Venkata N., Sakamuri, Siva S.V.P., Schiffer, Lina, Beatty, Wandy, Wyatt, Rachael, Frigo, Daniel E., Liu, Xiaowen, Katakam, Prasad V., Arlt, Wiebke, Buck, Jochen, Levin, Lonny R., Hu, Tony, Kolls, Jay, Burant, Charles F., Tomas, Alejandra, Merrins, Matthew J., Thurmond, Debbie C., Bernal-Mizrachi, Ernesto, Hodson, David J., and Mauvais-Jarvis, Franck

Published
2023
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11. Diagnosis of paediatric tuberculosis by optically detecting two virulence factors on extracellular vesicles in blood samples

Author

Zheng, Wenshu, LaCourse, Sylvia M., Song, Bofan, Singh, Dhiraj Kumar, Khanna, Mayank, Olivo, Juan, Stern, Joshua, Escudero, Jaclyn N., Vergara, Carlos, Zhang, Fangfang, Li, Shaobai, Wang, Shu, Cranmer, Lisa M., Huang, Zhen, Bojanowski, Christine M., Bao, Duran, Njuguna, Irene, Xiao, Yating, Wamalwa, Dalton C., Nguyen, Duc T., Yang, Li, Maleche-Obimbo, Elizabeth, Nguyen, Nhung, Zhang, Lili, Phan, Ha, Fan, Jia, Ning, Bo, Li, Chenzhong, Lyon, Christopher J., Graviss, Edward A., John-Stewart, Grace, Mitchell, Charles D., Ramsay, Alistair J., Kaushal, Deepak, Liang, Rongguang, Pérez-Then, Eddy, and Hu, Tony Y.

Published
2022
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16. Neuropathology and virus in brain of SARS-CoV-2 infected non-human primates

Author

Rutkai, Ibolya, Mayer, Meredith G., Hellmers, Linh M., Ning, Bo, Huang, Zhen, Monjure, Christopher J., Coyne, Carol, Silvestri, Rachel, Golden, Nadia, Hensley, Krystle, Chandler, Kristin, Lehmicke, Gabrielle, Bix, Gregory J., Maness, Nicholas J., Russell-Lodrigue, Kasi, Hu, Tony Y., Roy, Chad J., Blair, Robert V., Bohm, Rudolf, Doyle-Meyers, Lara A., Rappaport, Jay, and Fischer, Tracy

Published
2022
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18. CRISPR for companion diagnostics in low-resource settings.

Author

Qian, Xu, Xu, Qiang, Lyon, Christopher J., and Hu, Tony Y.

Subjects
RESOURCE-limited settings, EMERGING infectious diseases, MEDICAL screening, COMPANION diagnostics, NUCLEIC acids
Abstract

New point-of-care tests (POCTs), which are especially useful in low-resource settings, are needed to expand screening capacity for diseases that cause significant mortality: tuberculosis, multiple cancers, and emerging infectious diseases. Recently, clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostic (CRISPR-Dx) assays have emerged as powerful and versatile alternatives to traditional nucleic acid tests, revealing a strong potential to meet this need for new POCTs. In this review, we discuss CRISPR-Dx assay techniques that have been or could be applied to develop POCTs, including techniques for sample processing, target amplification, multiplex assay design, and signal readout. This review also describes current and potential applications for POCTs in disease diagnosis and includes future opportunities and challenges for such tests. These tests need to advance beyond initial assay development efforts to broadly meet criteria for use in low-resource settings. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Valence-Change MnO2-Coated Arsenene Nanosheets as a Pin1 Inhibitor for Hepatocellular Carcinoma Treatment

Author

Wang, Jingguo, Liang, Siping, Zhu, Dongdong, Ma, Xiaocao, Peng, Qin, Wang, Guanzhao, Wang, Yuting, Chen, Tiantian, Wu, Minhao, Hu, Tony Y., and Zhang, Yuanqing

Abstract

The heterogeneity of hepatocellular carcinoma (HCC) can prevent effective treatment, emphasizing the need for more effective therapies. Herein, we employed arsenene nanosheets coated with manganese dioxide and polyethylene glycol (AMPNs) for the degradation of Pin1, which is universally overexpressed in HCC. By employing an “AND gate”, AMPNs exhibited responsiveness toward excessive glutathione and hydrogen peroxide within the tumor microenvironment, thereby selectively releasing AsxOyto mitigate potential side effects of As2O3. Notably, AMPNs induced the suppressing Pin1 expression while simultaneously upregulation PD-L1, thereby eliciting a robust antitumor immune response and enhancing the efficacy of anti-PD-1/anti-PD-L1 therapy. The combination of AMPNs and anti-PD-1 synergistically enhanced tumor suppression and effectively induced long-lasting immune memory. This approach did not reveal As2O3-associated toxicity, indicating that arsenene-based nanotherapeutic could be employed to amplify the response rate of anti-PD-1/anti-PD-L1 therapy to improve the clinical outcomes of HCC patients and potentially other solid tumors (e.g., breast cancer) that are refractory to anti-PD-1/anti-PD-L1 therapy.

Published
2024
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34. Sensitive Blood-Based Detection of HIV-1 and Mycobacterium tuberculosis Peptides for Disease Diagnosis by Immuno-Affinity Liquid Chromatography–Tandem Mass Spectrometry: A Method Development and Proof-of-Concept Study

Author

Li, Lin, primary, Lyon, Christopher J, additional, LaCourse, Sylvia M, additional, Zheng, Wenshu, additional, Stern, Joshua, additional, Escudero, Jaclyn N, additional, Murithi, Wilfred Bundi, additional, Njagi, Lilian, additional, John-Stewart, Grace, additional, Hawn, Thomas R, additional, Nduba, Videlis, additional, Abdelgaliel, Wael, additional, Tombler, Thomas, additional, Horne, David, additional, Jiang, Li, additional, and Hu, Tony Y, additional

Published
2023
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44. Structure‐based design of peptidomimetic inhibitors of PSD‐95 with improved affinity for the PDZ3 domain.

Author

Naik, Mandar T., Naik, Nandita, Hu, Tony, Wang, Szu‐Huan, and Marshall, John

Subjects
BRAIN-derived neurotrophic factor, ANGELMAN syndrome, NEUROLOGICAL disorders, STRUCTURE-activity relationships, TROPOMYOSINS
Abstract

Aberrant brain‐derived neurotrophic factor (BDNF) signaling has been proposed to contribute to the pathophysiology of depression and other neurological disorders such as Angelman syndrome. We have previously shown that targeting the tropomyosin receptor kinase B/postsynaptic density protein‐95 (PSD‐95) nexus in the BDNF signaling pathway by peptidomimetic inhibitors is a promising approach for therapeutic intervention. Here, we used structure‐based knowledge to develop a new Syn3 peptidomimetic compound series that fuses peptides derived from the PSD‐95‐binding protein SynGAP to our prototype compound CN2097. The new compounds target the PSD‐95 PDZ3 domain and adjoining αC helix to achieve bivalent binding that results in up to 7‐fold stronger affinity compared to CN2097. These compounds were designed to improve CN2097 specificity for the PSD‐95 PDZ3 domain, and structure–activity relationship studies were performed to improve their resistance to proteolysis. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Sensitive Blood-Based Detection of HIV-1 and Mycobacterium tuberculosis Peptides for Disease Diagnosis by Immuno-Affinity Liquid Chromatography-Tandem Mass Spectrometry: A Method Development and Proof-of-Concept Study.

Author

Lin Li, Lyon, Christopher J., LaCourse, Sylvia M., Wenshu Zheng, Stern, Joshua, Escudero, Jaclyn N., Murithi, Wilfred Bundi, Njagi, Lilian, John-Stewart, Grace, Hawn, Thomas R., Nduba, Videlis, Abdelgaliel, Wael, Tombler, Thomas, Horne, David, Li Jiang, and Hu, Tony Y.

Published
2023
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50. Best Practices for Using AI When Writing Scientific Manuscripts: Caution, Care, and Consideration: Creative Science Depends on It

Author

Buriak, Jillian M., Akinwande, Deji, Artzi, Natalie, Brinker, C. Jeffrey, Burrows, Cynthia, Chan, Warren C.W., Chen, Chunying, Chen, Xiaodong, Chhowalla, Manish, Chi, Lifeng, Chueh, William, Crudden, Cathleen M., Di Carlo, Dino, Glotzer, Sharon C., Hersam, Mark C., Ho, Dean, Hu, Tony Y., Huang, Jiaxing, Javey, Ali, Kamat, Prashant V., Kim, Il Doo, Kotov, Nicholas A., Lee, T. Randall, Lee, Young Hee, Li, Yan, Liz-Marzán, Luis M., Mulvaney, Paul, Narang, Prineha, Nordlander, Peter, Oklu, Rahmi, Parak, Wolfgang J., Rogach, Andrey L., Salanne, Mathieu, Samorì, Paolo, Schaak, Raymond E., Schanze, Kirk S., Sekitani, Tsuyoshi, Skrabalak, Sara, Sood, Ajay K., Voets, Ilja K., Wang, Shu, Wang, Shutao, Wee, Andrew T.S., Ye, Jinhua, Buriak, Jillian M., Akinwande, Deji, Artzi, Natalie, Brinker, C. Jeffrey, Burrows, Cynthia, Chan, Warren C.W., Chen, Chunying, Chen, Xiaodong, Chhowalla, Manish, Chi, Lifeng, Chueh, William, Crudden, Cathleen M., Di Carlo, Dino, Glotzer, Sharon C., Hersam, Mark C., Ho, Dean, Hu, Tony Y., Huang, Jiaxing, Javey, Ali, Kamat, Prashant V., Kim, Il Doo, Kotov, Nicholas A., Lee, T. Randall, Lee, Young Hee, Li, Yan, Liz-Marzán, Luis M., Mulvaney, Paul, Narang, Prineha, Nordlander, Peter, Oklu, Rahmi, Parak, Wolfgang J., Rogach, Andrey L., Salanne, Mathieu, Samorì, Paolo, Schaak, Raymond E., Schanze, Kirk S., Sekitani, Tsuyoshi, Skrabalak, Sara, Sood, Ajay K., Voets, Ilja K., Wang, Shu, Wang, Shutao, Wee, Andrew T.S., and Ye, Jinhua

Published
2023

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