Chuan Bi

University of Maryland, Baltimore

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Shuo Chen

University of Maryland, Baltimore

Peter Kochunov

University of Maryland, Baltimore

Tianzhou Ma

University of Maryland, College Park

Yezhi Pan

University of Maryland, Baltimore

L. Elliot Hong

The University of Texas Health Science Center at Houston

Zhenyao Ye

University of Maryland, Baltimore

Rozalina G. McCoy

University of Maryland, Baltimore

Yizhou Ma

The University of Texas Health Science Center at Houston

Mustapha Bouhrara

National Institute on Aging

Si Gao

Peking University

Cooperative Institutions

University of Maryland, Baltimore

University of Maryland, College Park

University of Mary

The University of Texas Health Science Center at Houston

Neuroscience Research Australia

Shanghai Jiao Tong University

National Institute on Aging

National Institutes of Health

Johns Hopkins University

UNSW Sydney

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An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping

Molecular and Cellular Neuroscience (2023)

Chen Mo Zhenyao Ye Yezhi Pan Yuan Zhang Qiong Wu

Genome-wide Association Study

Linkage Disequilibrium

Genetic Association

SNP

10.1016/j.mcn.2023.103895

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Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst

International Journal on Cybernetics & Informatics (2023)

Keer Yang Guanqun Zhang Chuan Bi Qiang Guan Hailu Xu

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.

Normalization

Trading strategy

Stock (firearms)

10.5121/ijci.2023.120201

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Author response for "Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation"

Michael Rozowski Jonathan Palumbo Jay Bisen Chuan Bi Mustapha Bouhrara

Regularization

10.1002/mrc.5289/v2/response1

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Integral Representation of Hydraulic Permeability

arXiv (Cornell University) (2021)

Chuan Bi Miao‐Jung Yvonne Ou Shangyou Zhang

In this paper, we show that the permeability of a porous {material} and that of a bubbly fluid are limiting cases of the complexified version of the two-fluid models posed in {Lipton_Avellaneda_1990}. We assume the viscosity of the inclusion fluid is $z\mu_1$ and the viscosity of the hosting fluid is $\mu_1\in \mathbb{R}^+$, $z\in\mathbb{C}$. The proof is carried out by the construction of solutions for large $|z|$ and small $|z|$ with an iteration process and the analytic continuation. Moreover, we also show that for a fixed microstructure, the permeabilities of these three cases share the same integral representation formula (IRF) in Equation (99) with different values of contrast parameter $s:=1/(z-1)$, as long as $s$ is outside the interval $[-\frac{2E_2^2}{1+2E_2^2},-\frac{1}{1+2E_1^2}]$, where the positive constants $E_1$ and $E_2$ are the extension constants that depend only on the geometry of the periodic pore space of the material.

Limiting

10.48550/arxiv.2101.11862

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QTL(量的形質遺伝子座)マッピングに基づくヒマ(Ricinus communisL.)の草高に関連する形質の遺伝的解析【Powered by NICT】

Zuowu Xuebao (2014)

Liu Chen LU Jian-nong Xuegui Yin Chuan Bi Wen Danyou

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Solution of Non-negative Least Squares Inverse Problems Using a Span of Regularized Solutions, with Application to Magnetic Resonance Relaxometry

arXiv (Cornell University) (2021)

Chuan Bi Miao‐Jung Yvonne Ou Mustapha Bouhrara Richard G. Spencer

We present a fundamentally new regularization method for the solution of the Fredholm integral equation of the first kind, in which we incorporate solutions corresponding to a range of Tikhonov regularizers into the end result. This method identifies solutions within a much larger function space, spanned by this set of regularized solutions, than is available to conventional regularizaton methods. Each of these solutions is regularized to a different extent. In effect, we combine the stability of solutions with greater degrees of regularization with the resolution of those that are less regularized. In contrast, current methods involve selection of a single, or in some cases several, regularization parameters that define an optimal degree of regularization. Because the identified solution is within the span of a set of differently-regularized solutions, we call this method \textit{span of regularizations}, or SpanReg. We demonstrate the performance of SpanReg through a non-negative least squares analysis employing a Gaussian basis, and demonstrate the improved recovery of bimodal Gaussian distribution functions as compared to conventional methods. We also demonstrate that this method exhibits decreased dependence of the end result on the optimality of regularization parameter selection. We further illustrate the method with an application to myelin water fraction mapping in the human brain from experimental magnetic resonance imaging relaxometry data. We expect SpanReg to be widely applicable as an effective new method for regularization of inverse problems.

Regularization

Backus–Gilbert method

10.48550/arxiv.2102.10039

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Neural Network Analysis of Biexponential Decay Curves Using Regularized Input Data

Bulletin of the American Physical Society (2021)

Michael Rozowski Jay Bisen Chuan Bi Wojciech Czaja R.R. Spencer

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APOE4poses opposite effects of plasma LDL on white matter integrity in older adults

bioRxiv (Cold Spring Harbor Laboratory) (2023)

Zhenyao Ye Yezhi Pan Rozalina G. McCoy Chuan Bi Chen Mo

APOE4 is a strong genetic risk factor of Alzheimer's disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown.In the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging.The increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]).The results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

Moderation

10.1101/2023.10.24.563796

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A novel cortical biomarker signature accurately and reliably predicts individual pain sensitivity: The PREDICT longitudinal analytical validation study

medRxiv (Cold Spring Harbor Laboratory) (2024)

Nahian S. Chowdhury Chuan Bi Andrew J. Furman Alan Chiang Patrick Skippen

Abstract Background Biomarkers would greatly assist chronic pain management. The present study aimed to undertake analytical validation of a sensorimotor cortical biomarker signature for pain consisting of two measures: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME), using a human model of prolonged temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). Methods 150 participants received an injection of NGF to the right masseter muscle on Days 0 and 2, inducing prolonged pain lasting up to 4 weeks. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Days 0, 2 and 5. We determined the predictive accuracy of the PAF/CME biomarker signature using a nested control-test scheme: machine learning models were run on a training set (n = 100), where PAF and CME were predictors and pain sensitivity was the outcome. The winning classifier was assessed on a test set (n = 50) comparing the predicted pain labels against the true labels. Results The winning classifier was logistic regression, with an outstanding area under the curve (AUC=1.00). The locked model assessed on the test set had excellent performance (AUC=0.88). Results were reproduced across a range of methodological parameters. Moreover, inclusion of sex and pain catastrophizing as covariates did not improve model performance, suggesting the model including biomarkers only was more robust. PAF and CME biomarkers showed good-excellent test-retest reliability. Conclusions This study provides evidence for a sensorimotor cortical biomarker signature for pain sensitivity. The combination of accuracy, reproducibility, and reliability, suggests the PAF/CME biomarker signature has substantial potential for clinical translation. Key Points Question Can individuals be accurately classified as high or low pain sensitive based on two features of cortical activity: sensorimotor peak alpha frequency (PAF) and corticomotor excitability (CME)? Findings In a cohort study of 150 healthy participants, the performance of a logistic regression model was outstanding in a training set (n=100) and excellent in a test set (n=50), with the combination of slower PAF and CME depression predicting higher pain. Results were reproduced across a range of methodological parameters, and inclusion of covariates did not improve model performance Meaning A novel cortical biomarker comprised of PAF and CME can accurately distinguish high and low pain sensitive individuals

Signature (topology)

10.1101/2024.06.16.24309005

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Direct and inverse problems for composite materials

Chuan Bi

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