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Deep learning-enhanced paper-based vertical flow assay for high-sensitivity troponin detection using nanoparticle amplification
Authors:
Gyeo-Re Han,
Artem Goncharov,
Merve Eryilmaz,
Hyou-Arm Joung,
Rajesh Ghosh,
Geon Yim,
Nicole Chang,
Minsoo Kim,
Kevin Ngo,
Marcell Veszpremi,
Kun Liao,
Omai B. Garner,
Dino Di Carlo,
Aydogan Ozcan
Abstract:
Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-se…
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Successful integration of point-of-care testing (POCT) into clinical settings requires improved assay sensitivity and precision to match laboratory standards. Here, we show how innovations in amplified biosensing, imaging, and data processing, coupled with deep learning, can help improve POCT. To demonstrate the performance of our approach, we present a rapid and cost-effective paper-based high-sensitivity vertical flow assay (hs-VFA) for quantitative measurement of cardiac troponin I (cTnI), a biomarker widely used for measuring acute cardiac damage and assessing cardiovascular risk. The hs-VFA includes a colorimetric paper-based sensor, a portable reader with time-lapse imaging, and computational algorithms for digital assay validation and outlier detection. Operating at the level of a rapid at-home test, the hs-VFA enabled the accurate quantification of cTnI using 50 uL of serum within 15 min per test and achieved a detection limit of 0.2 pg/mL, enabled by gold ion amplification chemistry and time-lapse imaging. It also achieved high precision with a coefficient of variation of < 7% and a very large dynamic range, covering cTnI concentrations over six orders of magnitude, up to 100 ng/mL, satisfying clinical requirements. In blinded testing, this computational hs-VFA platform accurately quantified cTnI levels in patient samples and showed a strong correlation with the ground truth values obtained by a benchtop clinical analyzer. This nanoparticle amplification-based computational hs-VFA platform can democratize access to high-sensitivity point-of-care diagnostics and provide a cost-effective alternative to laboratory-based biomarker testing.
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Submitted 17 February, 2024;
originally announced February 2024.
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The discrete direct deconvolution model in the large eddy simulation of turbulence
Authors:
Ning Chang,
Zelong Yuan,
Yunpeng Wang,
Jianchun Wang
Abstract:
The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution model studied by Chang et al. ["The effect of sub-filter scale dynamics in large eddy simulation of turbulence," Phys. Fluids 34, 095104 (2022)]. For the first type model D3M-1, the original Gaussian filter is approximate…
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The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution model studied by Chang et al. ["The effect of sub-filter scale dynamics in large eddy simulation of turbulence," Phys. Fluids 34, 095104 (2022)]. For the first type model D3M-1, the original Gaussian filter is approximated by local discrete formulation of different orders, and direct inverse of the discrete filter is applied to reconstruct the unfiltered flow field. The inverse of original Gaussian filter can be also approximated by local discrete formulation, leading to a fully local model D3M-2. Compared to traditional models including the dynamic Smagorinsky model (DSM) and the dynamic mixed model (DMM), the D3M-1 and D3M-2 exhibit much larger correlation coefficients and smaller relative errors in the a priori studies. In the a posteriori validations, both D3M-1 and D3M-2 can accurately predict turbulence statistics, including velocity spectra, probability density functions (PDFs) of sub-filter scale (SFS) stresses and SFS energy flux, as well as time-evolving kinetic energy spectra, momentum thickness, and Reynolds stresses in turbulent mixing layer. Moreover, the proposed model can also well capture spatial structures of the Q-criterion iso surfaces. Thus, the D3M holds potential as an effective SFS modeling approach in turbulence simulations.
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Submitted 14 February, 2024; v1 submitted 13 February, 2024;
originally announced February 2024.
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Development of Pipetting Devices to Separate Protein Complexes
Authors:
Christopher M. Altenderfer,
Frank N. Chang,
Parsaoran Hutapea
Abstract:
The objective of this project is to develop an automated device used to spot protein samples on a hydrophobic membrane to be used for the patented electrophoresis method developed by Chang and Yonan in 2008 [1]. This novel method performs electrophoresis directly on hydrophobic blot membranes as opposed to the previous popular methods such as the 2-D polyacrylamide gel method [2, 3]. This new elec…
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The objective of this project is to develop an automated device used to spot protein samples on a hydrophobic membrane to be used for the patented electrophoresis method developed by Chang and Yonan in 2008 [1]. This novel method performs electrophoresis directly on hydrophobic blot membranes as opposed to the previous popular methods such as the 2-D polyacrylamide gel method [2, 3]. This new electrophoresis method significantly reduces the processing time to about 40 minutes, as opposed to the 2-D PAGE method which can take one or two days. Special methods to spot samples on hydrophobic blot membranes were established for successful separation of protein and protein complexes. These spotting methods are used in conjunction with the patented electrophoresis method [1] for effective separation. The automated device effectively replicates these special methods that are repeatable and user independent. The goal of the automated device is to effectively spot and separate protein complexes in addition to single proteins under non-denaturing conditions, thus eliminating the variability of the manual spotting method.
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Submitted 6 December, 2022;
originally announced March 2023.
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A Thermal Machine Learning Solver For Chip Simulation
Authors:
Rishikesh Ranade,
Haiyang He,
Jay Pathak,
Norman Chang,
Akhilesh Kumar,
Jimin Wen
Abstract:
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a…
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Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning Simulator) with modifications to the solution algorithm to handle constant and distributed HTC. The proposed method is validated against commercial solvers, such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different scenarios to demonstrate its enhanced accuracy, scalability, and generalizability.
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Submitted 10 September, 2022;
originally announced September 2022.
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A composable autoencoder-based iterative algorithm for accelerating numerical simulations
Authors:
Rishikesh Ranade,
Chris Hill,
Haiyang He,
Amir Maleki,
Norman Chang,
Jay Pathak
Abstract:
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but computationally costly. On the other hand, Machine Learning (ML) methods offer a significant computational speedup but face challenges with accuracy and generalization to different PDE conditions, such as geometry, boundary…
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Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but computationally costly. On the other hand, Machine Learning (ML) methods offer a significant computational speedup but face challenges with accuracy and generalization to different PDE conditions, such as geometry, boundary conditions, initial conditions and PDE source terms. In this work, we propose a novel ML-based approach, CoAE-MLSim (Composable AutoEncoder Machine Learning Simulation), which is an unsupervised, lower-dimensional, local method, that is motivated from key ideas used in commercial PDE solvers. This allows our approach to learn better with relatively fewer samples of PDE solutions. The proposed ML-approach is compared against commercial solvers for better benchmarks as well as latest ML-approaches for solving PDEs. It is tested for a variety of complex engineering cases to demonstrate its computational speed, accuracy, scalability, and generalization across different PDE conditions. The results show that our approach captures physics accurately across all metrics of comparison (including measures such as results on section cuts and lines).
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Submitted 7 October, 2021;
originally announced October 2021.
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Variation of the transition energies and oscillator strengths for the 3C and 3D lines of the Ne-like ions under plasma environment
Authors:
Chensheng Wu,
Shaomin Chen,
T. N. Chang,
Xiang Gao
Abstract:
We present the results of a detailed theoretical study which meets the spatial and temporal criteria of the Debye-Huckel (DH) approximation on the variation of the transition energies as well as the oscillator strengths for the ${2p^53d\ ^1P_1\rightarrow2p^6\ ^1S_0}$ (3C line) and the ${2p^53d\ ^3D_1\rightarrow2p^6\ ^1S_0}$ (3D line) transitions of the Ne-like ions subject to external plasma.\ Our…
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We present the results of a detailed theoretical study which meets the spatial and temporal criteria of the Debye-Huckel (DH) approximation on the variation of the transition energies as well as the oscillator strengths for the ${2p^53d\ ^1P_1\rightarrow2p^6\ ^1S_0}$ (3C line) and the ${2p^53d\ ^3D_1\rightarrow2p^6\ ^1S_0}$ (3D line) transitions of the Ne-like ions subject to external plasma.\ Our study shows that the redshifts of the transition energy follow the general scaling behaviors similar to the ones for the simple H-like and He-like ions.\ Whereas the oscillator strength for the 3C line decreases, the oscillator strength for the spin-flipped 3D line increases as the strength of the outside plasma increases.\ As a result, their ratio is amplified subject to outside plasma environment.\ We further demonstrate that the plasma-induced variation between the relative strength of the 3C and 3D transitions is mainly due to the spin-dependent interactions which dictate the mixing of the $^1P_1$ component in the $^3D_1$ upper state of the 3D transition.\ In addition, we are able to find that the ratio between the relative oscillator strengths of the 3C and 3D lines in the presence of the plasma to their respective plasma-free values varies as a nearly universal function of $[(Z-9.2)DZ]^{-1.8}$, with $Z$ the nuclear charge and $D$ the Debye length.\ The results of this study should be of great help in the modeling and diagnostic of astrophysical plasmas as well as laboratory plasmas.
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Submitted 17 April, 2019;
originally announced April 2019.
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Exact Solution of the Harmonic Oscillator in Arbitrary Dimensions with Minimal Length Uncertainty Relations
Authors:
Lay Nam Chang,
Djordje Minic,
Naotoshi Okamura,
Tatsu Takeuchi
Abstract:
We determine the energy eigenvalues and eigenfunctions of the harmonic oscillator where the coordinates and momenta are assumed to obey the modified commutation relations [x_i,p_j]=i hbar[(1+ beta p^2) delta_{ij} + beta' p_i p_j]. These commutation relations are motivated by the fact they lead to the minimal length uncertainty relations which appear in perturbative string theory. Our solutions i…
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We determine the energy eigenvalues and eigenfunctions of the harmonic oscillator where the coordinates and momenta are assumed to obey the modified commutation relations [x_i,p_j]=i hbar[(1+ beta p^2) delta_{ij} + beta' p_i p_j]. These commutation relations are motivated by the fact they lead to the minimal length uncertainty relations which appear in perturbative string theory. Our solutions illustrate how certain features of string theory may manifest themselves in simple quantum mechanical systems through the modification of the canonical commutation relations. We discuss whether such effects are observable in precision measurements on electrons trapped in strong magnetic fields.
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Submitted 15 March, 2002; v1 submitted 20 November, 2001;
originally announced November 2001.