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584 results on '"Lo, C. M."'

4. Distinguishing cancerous from non-cancerous cells through analysis of electrical noise

Author

Lovelady, D. C., Richmond, T. C., Maggi, A. N., Lo, C. -M., and Rabson, D. A.

Subjects
Quantitative Biology - Cell Behavior, Quantitative Biology - Quantitative Methods
Abstract

Since 1984, electric cell-substrate impedance sensing (ECIS) has been used to monitor cell behavior in tissue culture and has proven sensitive to cell morphological changes and cell motility. We have taken ECIS measurements on several cultures of non-cancerous (HOSE) and cancerous (SKOV) human ovarian surface epithelial cells. By analyzing the noise in real and imaginary electrical impedance, we demonstrate that it is possible to distinguish the two cell types purely from signatures of their electrical noise. Our measures include power-spectral exponents, Hurst and detrended fluctuation analysis, and estimates of correlation time; principal-component analysis combines all the measures. The noise from both cancerous and non-cancerous cultures shows correlations on many time scales, but these correlations are stronger for the non-cancerous cells., Comment: 8 pages, 4 figures; submitted to PRE

Published
2007
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6. Electrically Assessing the Effect of TGF-β1 on MDA-MB-231 Cells

Author

Lai, C. -I., Chen, W. -T., Lai, Y. -T., Lo, C. -M., MAGJAREVIC, Ratko, Editor-in-chief, Ładyzynsk, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Su, Fong-Chin, editor, Wang, Shyh-Hau, editor, and Yeh, Ming-Long, editor

Published
2015
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16. Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation

Author

Hickman, Riley J., H��se, Florian, Roch, Lo��c M., and Aspuru-Guzik, Al��n

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
Abstract

Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation. However, expensive measurements are required to accurately estimate materials properties, and can quickly become a hindrance to exhaustive materials discovery campaigns. Here, we introduce Gemini: a data-driven model capable of using inexpensive measurements as proxies for expensive measurements by correcting systematic biases between property evaluation methods. We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values. In a regression setting, we showcase the ability of our method to make accurate predictions of DFT calculated bandgaps of hybrid organic-inorganic perovskite materials. We further demonstrate the benefits that Gemini provides to autonomous workflows by augmenting the Bayesian optimizer Phoenics to yeild a scalable optimization framework leveraging multiple sources of measurement. Finally, we simulate an autonomous materials discovery platform for optimizing the activity of electrocatalysts for the oxygen evolution reaction. Realizing autonomous workflows with Gemini, we show that the number of measurements of a composition space comprising expensive and rare metals needed to achieve a target overpotential is significantly reduced when measurements from a proxy composition system with less expensive metals are available., 12 pages, 5 figures, 2 tables

Published
2021

24. Intravenous bolus somatostatin after diagnostic cholangiopancreatography reduces the incidence of pancreatitis associated with therapeutic endoscopic retrograde cholangiopancreatography procedures: a randomised controlled trial

Author

Poon, R.T.-P., Yeung, C., Liu, C.-L., Lam, C.-M., Yuen, W.-K., Lo, C.-M., Tang, A., and Fan, S.-T.

Subjects
Endoscopic retrograde cholangiopancreatography -- Usage -- Physiological aspects -- Research -- Health aspects, Somatostatin -- Physiological aspects -- Dosage and administration -- Testing -- Research -- Usage -- Health aspects, Colorectal diseases -- Research -- Causes of -- Health aspects -- Care and treatment -- Complications and side effects -- Prevention, Patients -- Health aspects -- Care and treatment -- Physiological aspects -- Usage -- Research, Pancreatitis -- Health aspects -- Causes of -- Prevention -- Research -- Care and treatment -- Complications and side effects, Gastrointestinal diseases -- Research -- Causes of -- Health aspects -- Care and treatment -- Complications and side effects -- Prevention, Health
Abstract

Gut 2003;52:1768-1773 Background: Previous studies suggested that somatostatin given before endoscopic retrograde cholangiopancreatography (ERCP) may reduce the incidence of post-ERCP pancreatitis. However, the routine use of somatostatin in all patients [...]

Published
2003

36. Laparoscopic repeat liver resection for hepatocellular carcinoma: a multicentre propensity score-based study

Author

Morise, Z, primary, Aldrighetti, L, additional, Belli, G, additional, Ratti, F, additional, Belli, A, additional, Cherqui, D, additional, Tanabe, M, additional, Wakabayashi, G, additional, Cheung, T T, additional, Lo, C M, additional, Tanaka, S, additional, Kubo, S, additional, Okamura, Y, additional, Uesaka, K, additional, Monden, K, additional, Sadamori, H, additional, Hashida, K, additional, Kawamoto, K, additional, Gotohda, N, additional, Chen, K H, additional, Kanazawa, A, additional, Takeda, Y, additional, Ohmura, Y, additional, Ueno, M, additional, Ogura, T, additional, Suh, K S, additional, Kato, Y, additional, Sugioka, A, additional, Nitta, H, additional, Yasunaga, M, additional, Halium, N A, additional, Laurent, A, additional, Kaneko, H, additional, Otsuka, Y, additional, Kim, K H, additional, Cho, H-D, additional, Lin, C C-W, additional, Ome, Y, additional, Seyama, Y, additional, Troisi, R I, additional, Berardi, G, additional, Roteller, F, additional, Wilson, G C, additional, Geller, D A, additional, Soubrane, O, additional, Yoh, T, additional, Kaizu, T, additional, Kumamoto, Y, additional, Han, H-S, additional, Ekmekcigil, E, additional, Dagher, I, additional, Fuks, D, additional, Gayet, B, additional, Buell, J F, additional, Ciria, R, additional, Briceno, J, additional, O'Rourke, N, additional, Lewin, J, additional, Edwin, B, additional, Shinoda, M, additional, Abe, Y, additional, Hilal, M Abu, additional, and Alzoubi, M, additional

Published
2020
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40. From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning

Author

Roch, Lo��c M., Saikin, Semion K., H��se, Florian, Friederich, Pascal, Goldsmith, Randall H., Le��n, Salvador, and Aspuru-Guzik, Al��n

Subjects
Chemical Physics (physics.chem-ph), Condensed Matter - Materials Science, Physics - Chemical Physics, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences
Abstract

Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider PEDOT:PSS, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions, and that the electronic couplings can be predicted from the simulated spectra with an 88 % accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multi-scale computational workflow, they were able to leverage leverage experimental data.

Published
2019

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