| 1. |
- Hong, Danfeng, et al.
(författare)
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Interpretable Hyperspectral Artificial Intelligence : When nonconvex modeling meets hyperspectral remote sensing
- 2021
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Ingår i: IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2397. ; 9:2, s. 52-87
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Tidskriftsartikel (refereegranskat)abstract
- Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these HS products, mainly by seasoned experts. However, with an ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges for reducing the burden of manual labor and improving efficiency. For this reason, it is urgent that more intelligent and automatic approaches for various HS RS applications be developed. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications; however, their ability to handle complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher-dimensional HS signals. Compared to convex models, nonconvex modeling, which is capable of characterizing more complex real scenes and providing model interpretability technically and theoretically, has proven to be a feasible solution that reduces the gap between challenging HS vision tasks and currently advanced intelligent data processing models.
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| 2. |
- Ljungars, A., et al.
(författare)
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A platform for phenotypic discovery of therapeutic antibodies and targets applied on Chronic Lymphocytic Leukemia
- 2018
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Ingår i: JCO Precision Oncology. - : Springer Science and Business Media LLC. - 2473-4284 .- 2397-768X. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- Development of antibody drugs against novel targets and pathways offers great opportunities to improve current cancer treatment. We here describe a phenotypic discovery platform enabling efficient identification of therapeutic antibody-target combinations. The platform utilizes primary patient cells throughout the discovery process and includes methods for differential phage display cell panning, high-throughput cell-based specificity screening, phenotypic in vitro screening, target deconvolution, and confirmatory in vivo screening. In this study the platform was applied on cancer cells from patients with Chronic Lymphocytic Leukemia resulting in discovery of antibodies with improved cytotoxicity in vitro compared to the standard of care, the CD20-specific monoclonal antibody rituximab. Isolated antibodies were found to target six different receptors on Chronic Lymphocytic Leukemia cells; CD21, CD23, CD32, CD72, CD200, and HLA-DR of which CD32, CD200, and HLA-DR appeared as the most potent targets for antibody-based cytotoxicity treatment. Enhanced antibody efficacy was confirmed in vivo using a patient-derived xenograft model.
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