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2,165 results on '"Tsai, T."'

10. Magnetopause location modeling using machine learning: inaccuracy due to solar wind parameter propagation.

Author

Nafchi, M. Aghabozorgi, Němec, F., Pi, G., Němeček, Z., Šafránková, J., Grygorov, K., Šimůnek, J., Tsai, T.-C., Desai, Ravindra, and Kajdic, Primoz

Subjects
SOLAR wind, MAGNETOPAUSE, ARTIFICIAL neural networks, INTERPLANETARY magnetic fields, DYNAMIC pressure, WIND pressure
Abstract

An intrinsic limitation of empirical models of the magnetopause location is a predefined magnetopause shape and assumed functional dependences on relevant parameters. We overcome this limitation using a machine learning approach (artificial neural networks), allowing us to incorporate general, purely data-driven dependences. For the training and testing of the developed neural network model, a data set of about 15,000 magnetopause crossings identified in the THEMIS A-E, Magion 4, Geotail, and Interball-1 satellite data in the subsolar region is used. A cylindrical symmetry around the direction of the impinging solar wind is assumed, and solar wind dynamic pressure, interplanetary magnetic field magnitude, cone angle, clock angle, tilt angle, and corrected Dst index are considered as parameters. The effect of these parameters on the magnetopause location is revealed. The performance of the developed model is compared with other empirical magnetopause models. Finally, we demonstrate and discuss the inaccuracy of magnetopause models due to the inaccurate information about the impinging solar wind parameters based on measurements near the L1 point. This inaccuracy imposes a theoretical limit on the precision of magnetopause predictions, a limit that our model closely approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Guselkumab : III

Author

Langley, R.G., Tsai, T.‐F., Flavin, S., Song, M., Randazzo, B., Wasfi, Y., Jiang, J., Li, S., and Puig, L.

Published
2018
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32. Metformin induces lactate accumulation and accelerates renal cyst progression in Pkd1-deficient mice

Author

Chang, M.-Y., Tsai, T.-I., Chou, L.-F., Hsu, S.H., Yang, H.-Y., Hung, C.-C., Tian, Y.-C., Ong, A.C.M., and Yang, C.-W.

Subjects
endocrine system diseases, urogenital system, nutritional and metabolic diseases
Abstract

Metabolic reprogramming is a potential treatment strategy for autosomal dominant polycystic kidney disease (ADPKD). Metformin has been shown to inhibit the early stages of cyst formation in animal models. However, metformin can lead to lactic acidosis in diabetic patients with advanced chronic kidney disease, and its efficacy in ADPKD is still not fully understood. Here, we investigated the effect of metformin in an established hypomorphic mouse model of PKD that presents stable and heritable knockdown of Pkd1. The Pkd1 miRNA transgenic mice of both genders were randomized to receive metformin or saline injections. Metformin was administrated through daily intraperitoneal injection from postnatal day 35 for 4 weeks. Unexpectedly, metformin treatment at a concentration of 150 mg/kg increased disease severity, including kidney-to-body weight ratio, cystic index and plasma BUN levels, and was associated with increased renal tubular cell proliferation and plasma lactate levels. Functional enrichment analysis for cDNA microarrays from kidney samples revealed significant enrichment of several pro-proliferative pathways including β-catenin, hypoxia-inducible factor-1α, protein kinase Cα and Notch signaling pathways in the metformin-treated mutant mice. The plasma metformin concentrations were still within the recommended therapeutic range for type 2 diabetic patients. Short-term metformin treatment in a second Pkd1 hypomorphic model (Pkd1RC/RC) was however neutral. These results demonstrate that metformin may exacerbate late-stage cyst growth associated with the activation of lactate-related signaling pathways in Pkd1 deficiency. Our findings indicate that using metformin in the later stage of ADPKD might accelerate disease progression and call for the cautious use of metformin in these patients.

Published
2022

37. FLEXDTW: DYNAMIC TIME WARPING WITH FLEXIBLE BOUNDARY CONDITIONS.

Author

Bükey, Irmak, Zhang, Jason, and Tsai, T. J.

Subjects
BOUNDARY value problems, MATRICES (Mathematics), DYNAMIC programming, ALGORITHMS, A priori
Abstract

Alignment algorithms like DTW and subsequence DTW assume specific boundary conditions on where an alignment path can begin and end in the cost matrix. In practice, the boundary conditions may not be known a priori or may not satisfy such strict assumptions. This paper introduces an alignment algorithm called FlexDTW that is designed to handle a wide range of boundary conditions. FlexDTW allows alignment paths to start anywhere on the bottom or left edge of the cost matrix (adjacent to the origin) and to end anywhere on the top or right edge. In order to properly compare paths of very different lengths, we use a normalized path cost measure that normalizes the cumulative path cost by the path length. The key insight of FlexDTW is that the Manhattan length of a path can be computed by simply knowing the starting point of the path, which can be computed recursively during dynamic programming. We artificially generate a suite of 16 benchmarks based on the Chopin Mazurka dataset in order to characterize audio alignment performance under a variety of boundary conditions. We show that FlexDTW has consistently strong performance that is comparable or better than commonly used alignment algorithms, and it is the only system with strong performance in some boundary conditions. [ABSTRACT FROM AUTHOR]

Published
2023

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