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1. RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance

Author

Sun, Zhicheng, Yang, Zhenhao, Jin, Yang, Chi, Haozhe, Xu, Kun, Chen, Liwei, Jiang, Hao, Song, Yang, Gai, Kun, and Mu, Yadong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Customizing diffusion models to generate identity-preserving images from user-provided reference images is an intriguing new problem. The prevalent approaches typically require training on extensive domain-specific images to achieve identity preservation, which lacks flexibility across different use cases. To address this issue, we exploit classifier guidance, a training-free technique that steers diffusion models using an existing classifier, for personalized image generation. Our study shows that based on a recent rectified flow framework, the major limitation of vanilla classifier guidance in requiring a special classifier can be resolved with a simple fixed-point solution, allowing flexible personalization with off-the-shelf image discriminators. Moreover, its solving procedure proves to be stable when anchored to a reference flow trajectory, with a convergence guarantee. The derived method is implemented on rectified flow with different off-the-shelf image discriminators, delivering advantageous personalization results for human faces, live subjects, and certain objects. Code is available at https://github.com/feifeiobama/RectifID., Comment: NeurIPS 2024

Published
2024

2. Harder Tasks Need More Experts: Dynamic Routing in MoE Models

Author

Huang, Quzhe, An, Zhenwei, Zhuang, Nan, Tao, Mingxu, Zhang, Chen, Jin, Yang, Xu, Kun, Chen, Liwei, Huang, Songfang, and Feng, Yansong

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty. Unlike traditional MoE approaches that rely on fixed Top-K routing, which activates a predetermined number of experts regardless of the input's complexity, our method dynamically selects experts based on the confidence level in expert selection for each input. This allows for a more efficient utilization of computational resources, activating more experts for complex tasks requiring advanced reasoning and fewer for simpler tasks. Through extensive evaluations, our dynamic routing method demonstrates substantial improvements over conventional Top-2 routing across various benchmarks, achieving an average improvement of 0.7% with less than 90% activated parameters. Further analysis shows our model dispatches more experts to tasks requiring complex reasoning skills, like BBH, confirming its ability to dynamically allocate computational resources in alignment with the input's complexity. Our findings also highlight a variation in the number of experts needed across different layers of the transformer model, offering insights into the potential for designing heterogeneous MoE frameworks. The code and models are available at https://github.com/ZhenweiAn/Dynamic_MoE.

Published
2024

6. Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study

Author

Gimbel, Ronald W, Rennert, Lior M, Crawford, Paul, Little, Jeanette R, Truong, Khoa, Williams, Joel E, Griffin, Sarah F, Shi, Lu, Chen, Liwei, Zhang, LingLing, Moss, Jennie B, Marshall, Robert C, Edwards, Karen W, Crawford, Kristy J, Hing, Marie, Schmeltz, Amanda, Lumsden, Brandon, Ashby, Morgan, Haas, Elizabeth, and Palazzo, Kelly

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundPast mobile health (mHealth) efforts to empower type 2 diabetes (T2D) self-management include portals, text messaging, collection of biometric data, electronic coaching, email, and collection of lifestyle information. ObjectiveThe primary objective was to enhance patient activation and self-management of T2D using the US Department of Defense’s Mobile Health Care Environment (MHCE) in a patient-centered medical home setting. MethodsA multisite study, including a user-centered design and a controlled trial, was conducted within the US Military Health System. Phase I assessed preferences regarding the enhancement of the enabling technology. Phase II was a single-blinded 12-month feasibility study that randomly assigned 240 patients to either the intervention (n=123, received mHealth technology and behavioral messages tailored to Patient Activation Measure [PAM] level at baseline) or the control group (n=117, received equipment but not messaging. The primary outcome measure was PAM scores. Secondary outcome measures included Summary of Diabetes Self-Care Activities (SDSCA) scores and cardiometabolic outcomes. We used generalized estimating equations to estimate changes in outcomes. ResultsThe final sample consisted of 229 patients. Participants were 61.6% (141/229) male, had a mean age of 62.9 years, mean glycated hemoglobin (HbA1c) of 7.5%, mean BMI of 32.7, and a mean duration of T2D diagnosis of 9.8 years. At month 12, the control group showed significantly greater improvements compared with the intervention group in PAM scores (control mean 7.49, intervention mean 1.77; P=.007), HbA1c (control mean −0.53, intervention mean −0.11; P=.006), and low-density lipoprotein cholesterol (control mean −7.14, intervention mean 4.38; P=.01). Both groups showed significant improvement in SDSCA, BMI, waist size, and diastolic blood pressure; between-group differences were not statistically significant. Except for patients with the highest level of activation (PAM level 4), intervention group patients exhibited significant improvements in PAM scores. For patients with the lowest level of activation (PAM level 1), the intervention group showed significantly greater improvement compared with the control group in HbA1c (control mean −0.09, intervention mean −0.52; P=.04), BMI (control mean 0.58, intervention mean −1.22; P=.01), and high-density lipoprotein cholesterol levels (control mean −4.86, intervention mean 3.56; P

Published
2020
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7. Probing Multimodal Large Language Models for Global and Local Semantic Representations

Author

Tao, Mingxu, Huang, Quzhe, Xu, Kun, Chen, Liwei, Feng, Yansong, and Zhao, Dongyan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works leverage image-caption datasets to train MLLMs, achieving state-of-the-art performance on image-to-text tasks. However, there are few studies exploring which layers of MLLMs make the most effort to the global image information, which plays vital roles in multimodal comprehension and generation. In this study, we find that the intermediate layers of models can encode more global semantic information, whose representation vectors perform better on visual-language entailment tasks, rather than the topmost layers. We further probe models regarding local semantic representations through object recognition tasks. We find that the topmost layers may excessively focus on local information, leading to a diminished ability to encode global information. Our code and data are released via https://github.com/kobayashikanna01/probing_MLLM_rep., Comment: Accepted by LREC-COLING 2024 as a short paper (Camera Ready)

Published
2024

8. Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization

Author

Jin, Yang, Sun, Zhicheng, Xu, Kun, Chen, Liwei, Jiang, Hao, Huang, Quzhe, Song, Chengru, Liu, Yuliang, Zhang, Di, Song, Yang, Gai, Kun, and Mu, Yadong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Compared to static images, video poses unique challenges for effective large-scale pre-training due to the modeling of its spatiotemporal dynamics. In this paper, we address such limitations in video-language pre-training with an efficient video decomposition that represents each video as keyframes and temporal motions. These are then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text. At inference, the generated tokens from the LLM are carefully recovered to the original continuous pixel space to create various video content. Our proposed framework is both capable of comprehending and generating image and video content, as demonstrated by its competitive performance across 13 multimodal benchmarks in image and video understanding and generation. Our code and models are available at https://video-lavit.github.io.

Published
2024

12. A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

Author

Cao, Hejing, An, Zhenwei, Feng, Jiazhan, Xu, Kun, Chen, Liwei, and Zhao, Dongyan

Subjects
Computer Science - Computation and Language
Abstract

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations. Challenges arise when these models grapple with understanding multi-hop relations in complex questions or lack the necessary knowledge for a comprehensive response. To address this issue, we introduce the "Decompose-and-Query" framework (D&Q). This framework guides the model to think and utilize external knowledge similar to ReAct, while also restricting its thinking to reliable information, effectively mitigating the risk of hallucinations. Experiments confirm the effectiveness of D&Q: On our ChitChatQA dataset, D&Q does not lose to ChatGPT in 67% of cases; on the HotPotQA question-only setting, D&Q achieved an F1 score of 59.6%. Our code is available at https://github.com/alkaidpku/DQ-ToolQA.

Published
2023

13. Hardy spaces and Szeg\H{o} projection on quotient domains

Author

Chen, Liwei and Yuan, Yuan

Subjects
Mathematics - Complex Variables, Mathematics - Classical Analysis and ODEs, Primary: 32A25, Secondary: 32A35, 32A07, 42B20, 42B30
Abstract

The Hardy spaces are defined on the quotient domain of a bounded complete Reinhardt domain by a finite subgroup of $U(n)$. The Szeg\H{o} projection on the quotient domain can be studied by lifting to the covering space. This setting builds on the solution of a boundary value problem for holomorphic functions. In particular, when the covering space is either the polydisc or the unit ball in $\mathbb{C}^n$, the boundary value problem can be solved. Applying this theory in $\mathbb{C}^2$, we further obtain sharp results on the $L^p$ regularity of the Szeg\H{o} projection on the symmetrized bidisc, generalized Thullen domains, and the minimal ball., Comment: 33 pages

Published
2023

14. Stein's theorem on infinite type domains

Author

Chen, Liwei and Yuan, Yuan

Subjects
Mathematics - Complex Variables, Mathematics - Classical Analysis and ODEs, Primary: 32A10, 32A50, Secondary: 32T20
Abstract

The disc property is formulated for domains in $\mathbb{C}^n$. Holomorphic Lipschitz functions enjoy a gain in the order of Lipschitz regularity along the complex tangential direction on domains with disc property. Disc property is studied on various domains of infinite type. As applications, the local version of Stein's theorem is obtained on these domains, including the worm domains., Comment: To appear in Comm. Anal. Geom., 17 pages

Published
2023

15. Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Author

Jin, Yang, Xu, Kun, Chen, Liwei, Liao, Chao, Tan, Jianchao, Huang, Quzhe, Chen, Bin, Lei, Chenyi, Liu, An, Song, Chengru, Lei, Xiaoqiang, Zhang, Di, Ou, Wenwu, Gai, Kun, and Mu, Yadong

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models are available at https://github.com/jy0205/LaVIT., Comment: ICLR 2024

Published
2023

16. Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women.

Author

Chen, Liwei, Dai, Jin, Yu, Guoqi, Pang, Wei, Rahman, Mohammad, Liu, Xinyue, Fiehn, Oliver, Guivarch, Claire, Chen, Zhen, and Zhang, Cuilin

Subjects
DASH diet, biomarkers, metabolomics, pregnant women, Humans, Female, Pregnancy, Dietary Approaches To Stop Hypertension, Pregnant Women, Dietary Patterns, Case-Control Studies, Hypertension, Lipids, Biomarkers
Abstract

Objective: the aim of this study was to identify plasma metabolomic markers of Dietary Approaches to Stop Hypertension (DASH) dietary patterns in pregnant women. Methods: This study included 186 women who had both dietary intake and metabolome measured from a nested case-control study within the NICHD Fetal Growth Studies-Singletons cohort (FGS). Dietary intakes were ascertained at 8-13 gestational weeks (GW) using the Food Frequency Questionnaire (FFQ) and DASH scores were calculated based on eight food and nutrient components. Fasting plasma samples were collected at 15-26 GW and untargeted metabolomic profiling was performed. Multivariable linear regression models were used to examine the association of individual metabolites with the DASH score. Least absolute shrinkage and selection operator (LASSO) regression was used to select a panel of metabolites jointly associated with the DASH score. Results: Of the total 460 known metabolites, 92 were individually associated with DASH score in linear regressions, 25 were selected as a panel by LASSO regressions, and 18 were identified by both methods. Among the top 18 metabolites, there were 11 lipids and lipid-like molecules (i.e., TG (49:1), TG (52:2), PC (31:0), PC (35:3), PC (36:4) C, PC (36:5) B, PC (38:4) B, PC (42:6), SM (d32:0), gamma-tocopherol, and dodecanoic acid), 5 organic acids and derivatives (i.e., asparagine, beta-alanine, glycine, taurine, and hydroxycarbamate), 1 organic oxygen compound (i.e., xylitol), and 1 organoheterocyclic compound (i.e., maleimide). Conclusions: our study identified plasma metabolomic markers for DASH dietary patterns in pregnant women, with most of being lipids and lipid-like molecules.

Published
2024

17. Longitudinal lipidomic profiles during pregnancy and associations with neonatal anthropometry: findings from a multiracial cohort.

Author

Song, Yiqing, Lu, Ruijin, Yu, Guoqi, Rahman, Mohammad, Chen, Liwei, Zhu, Yeiyi, Tsai, Michael, Chen, Zhen, Zhang, Cuilin, and Fiehn, Oliver

Subjects
Birthweight, Body length, Lipidomics, Neonatal anthropometry, Pregnancy, Sum of body circumferences, Sum of skinfolds, Infant, Newborn, Child, Pregnancy, Humans, Female, Lipidomics, Anthropometry, Fetal Development, Birth Weight, Lipids
Abstract

BACKGROUND: Maternal lipidomic profiling offers promise for characterizing lipid metabolites during pregnancy, but longitudinal data are limited. This study aimed to examine associations of longitudinal lipidomic profiles during pregnancy with multiple neonatal anthropometry using data from a multiracial cohort. METHODS: We measured untargeted plasma lipidome profiles among 321 pregnant women from the NICHD Fetal Growth Study-Singletons using plasma samples collected longitudinally during four study visits at gestational weeks (GW) 10-14, 15-26, 23-31, and 33-39, respectively. We evaluated individual lipidomic metabolites at each study visit in association with neonatal anthropometry. We also evaluated the associations longitudinally by constructing lipid networks using weighted correlation network analysis and common networks using consensus network analysis across four visits using linear mixed-effects models with the adjustment of false discover rate. FINDINGS: Multiple triglycerides (TG) were positively associated with birth weight (BW), BW Z-score, length and head circumference, while some cholesteryl ester (CE), phosphatidylcholine (PC), sphingomyelines (SM), phosphatidylethanolamines (PE), and lysophosphatidylcholines (LPC 20:3) families were inversely associated with BW, length, abdominal and head circumference at different GWs. Longitudinal trajectories of TG, PC, and glucosylcermides (GlcCer) were associated with BW, and CE (18:2) with BW z-score, length, and sum of skinfolds (SS), while some PC and PE were significantly associated with abdominal and head circumference. Modules of TG at GW 10-14 and 15-26 mainly were associated with BW. At GW 33-39, two networks of LPC (20:3) and of PC, TG, and CE, showed inverse associations with abdominal circumference. Distinct trajectories within two consensus modules with changes in TG, CE, PC, and LPC showed significant differences in BW and length. INTERPRETATION: The results demonstrated that longitudinal changes of TGs during early- and mid-pregnancy and changes of PC, LPC, and CE during late-pregnancy were significantly associated with neonatal anthropometry. FUNDING: National Institute of Child Health and Human Development intramural funding.

Published
2023

23. In-situ monitoring additive manufacturing process with AI edge computing

Author

Zhu, Wenkang, Li, Hui, Zhang, Yikai, Hou, Yuqing, and Chen, Liwei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In-situ monitoring system can be used to monitor the quality of additive manufacturing (AM) processes. In the case of digital image correlation (DIC) based in-situ monitoring systems, high-speed cameras were used to capture images of high resolutions. This paper proposed a novel in-situ monitoring system to accelerate the process of digital images using artificial intelligence (AI) edge computing board. It built a visual transformer based video super resolution (ViTSR) network to reconstruct high resolution (HR) videos frames. Fully convolutional network (FCN) was used to simultaneously extract the geometric characteristics of molten pool and plasma arc during the AM processes. Compared with 6 state-of-the-art super resolution methods, ViTSR ranks first in terms of peak signal to noise ratio (PSNR). The PSNR of ViTSR for 4x super resolution reached 38.16 dB on test data with input size of 75 pixels x 75 pixels. Inference time of ViTSR and FCN was optimized to 50.97 ms and 67.86 ms on AI edge board after operator fusion and model pruning. The total inference time of the proposed system was 118.83 ms, which meets the requirement of real-time quality monitoring with low cost in-situ monitoring equipment during AM processes. The proposed system achieved an accuracy of 96.34% on the multi-objects extraction task and can be applied to different AM processes.

Published
2023

24. Scheduled Execution-Based Binary Indirect Call Targets Refinement

Author

Shi, Yangyang, Tian, Linan, Chen, Liwei, Yang, Yanqi, Shi, Gang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Garcia-Alfaro, Joaquin, editor, Kozik, Rafał, editor, Choraś, Michał, editor, and Katsikas, Sokratis, editor

Published
2024
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25. MPR Node Selection Improvement in OLSR Based on Binary Cuckoo Search

Author

Wang, Tong, Yu, Kai, Ouyang, Min, Gao, Shan, Chen, Liwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Lei, editor, Qiu, Tie, editor, Lin, Chi, editor, and Wang, Xinbing, editor

Published
2024
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26. Arterial Traffic Optimization Algorithm Based on Deep Reinforcement Learning and Green Wave Coordination Control in Complex Lane Queuing Conditions

Author

Wang, Tong, Liu, Songming, Chen, Liwei, Ouyang, Min, Gao, Shan, Zhang, Yingxue, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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27. Research on Traffic Flow Prediction and Traffic Light Timing Recommendation Technology Based on Vehicle Data Analysis

Author

Wang, Tong, Xue, Shuyu, Yang, Guangxin, Gao, Shan, Ouyang, Min, Chen, Liwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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28. Vessel Traffic Flow Prediction and Analysis Based on Ship Big Data

Author

Wang, Tong, Gai, Xiaoyang, Liu, Songming, Gao, Shan, Ouyang, Min, Chen, Liwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Wang, Tong, editor, Fan, Hongfei, editor, Liu, Dongning, editor, and Du, Bowen, editor

Published
2024
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29. Identify Vulnerability Types: A Cross-Project Multiclass Vulnerability Classification System Based on Deep Domain Adaptation

Author

Du, Gewangzi, Chen, Liwei, Wu, Tongshuai, Zhu, Chenguang, Shi, Gang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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30. In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study.

Author

Chehab, Rana, Ferrara, Assiamira, Zheng, Siwen, Barupal, Dinesh, Ngo, Amanda, Chen, Liwei, Zhu, Yeyi, and Fiehn, Oliver

Subjects
biomarker, gestational diabetes, prediction, refined grains, untargeted metabolomics, Pregnancy, Female, Humans, Diabetes, Gestational, Metabolome, Risk Factors, Sphingolipids, Biomarkers, Edible Grain, Glycerophospholipids
Abstract

BACKGROUND: Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear. OBJECTIVES: We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk. METHODS: In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM). RESULTS: Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values

Published
2023

31. Metabolomic biomarkers of the mediterranean diet in pregnant individuals: A prospective study.

Author

Chen, Liwei, Dai, Jin, Fei, Zhe, Liu, Xinyue, Zhu, Yeyi, Rahman, Mohammad L, Lu, Ruijin, Mitro, Susanna D, Yang, Jiaxi, Hinkle, Stefanie N, Chen, Zhen, Song, Yiqing, and Zhang, Cuilin

Subjects
Humans, Fasting, Prospective Studies, Diet, Mediterranean, Metabolomics, Biomarkers, Dietary patterns, Mediterranean diet, Plasma biomarkers, Pregnant individuals, Nutrition, Prevention, Basic Behavioral and Social Science, Behavioral and Social Science, Nutrition and Dietetics, Nutrition & Dietetics
Abstract

Background and aimsMetabolomic profiling is a systematic approach to identifying biomarkers for dietary patterns. Yet, metabolomic markers for dietary patterns in pregnant individuals have not been investigated. The aim of this study was to identify plasma metabolomic markers and metabolite panels that are associated with the Mediterranean diet in pregnant individuals.MethodsThis is a prospective study of 186 pregnant individuals who had both dietary intake and metabolomic profiles measured from the Fetal Growth Studies-Singletons cohort. Dietary intakes during the peri-conception/1st trimester and the second trimester were accessed at 8-13 and 16-22 weeks of gestation, respectively. Adherence to the Mediterranean diet was measured by the alternate Mediterranean Diet (aMED) score. Fasting plasma samples were collected at 16-22 weeks and untargeted metabolomics profiling was performed using the mass spectrometry-based platforms. Metabolites individually or jointly associated with aMED scores were identified using linear regression and least absolute shrinkage and selection operator (LASSO) regression models with adjustment for potential confounders, respectively.ResultsAmong 459 annotated metabolites, 64 and 41 were individually associated with the aMED scores of the diet during the peri-conception/1st trimester and during the second trimester, respectively. Fourteen metabolites were associated with the Mediterranean diet in both time windows. Most Mediterranean diet-related metabolites were lipids (e.g., acylcarnitine, cholesteryl esters (CEs), linoleic acid, long-chain triglycerides (TGs), and phosphatidylcholines (PCs), amino acids, and sugar alcohols. LASSO regressions also identified a 10 metabolite-panel that were jointly associated with aMED score of the diet during the peri-conception/1st trimester (AUC: 0.74; 95% CI: 0.57, 0.91) and a 3 metabolites-panel in the 2nd trimester (AUC: 0.68; 95% CI: 0.50, 0.86).ConclusionWe identified plasma metabolomic markers for the Mediterranean diet among pregnant individuals. Some of them have also been reported in previous studies among non-pregnant populations, whereas others are novel. The results from our study warrant replication in pregnant individuals by future studies.Clinical trial registration numberThis study was registered at ClinicalTrials.gov.

Published
2023

32. Associations of racial and ethnic discrimination with adverse changes in exercise and screen time during the COVID-19 pandemic in the United States

Author

Xia, Tong, Gee, Gilbert C, Li, Jian, Liu, Xinyue, Dai, Jin, Shi, Lu, Zhang, Donglan, Chen, Zhuo, Han, Xuesong, Li, Yan, Li, Hongmei, Wen, Ming, Su, Dejun, and Chen, Liwei

Subjects
Public Health, Health Sciences, Prevention, Mind and Body, Basic Behavioral and Social Science, Behavioral and Social Science, Adult, Humans, Black or African American, COVID-19, Cross-Sectional Studies, Pandemics, Screen Time, United States, White, Asian, Hispanic or Latino, Racism, Exercise, Life style, Racial and ethnic discrimination, Screen time
Abstract

ObjectivesDuring the coronavirus disease 2019 (COVID-19) pandemic, a growing prevalence of racial and ethnic discrimination occurred when many Americans struggled to maintain healthy lifestyles. This study investigated the associations of racial and ethnic discrimination with changes in exercise and screen time during the pandemic in the United States.MethodsWe included 2,613 adults who self-identified as non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, or Hispanic from the Health, Ethnicity, and Pandemic study, a cross-sectional survey conducted among a nationally representative sample of United States adults between October and November 2020. We assessed self-reported racial and ethnic discrimination by measuring COVID-19-related racial and ethnic bias and examined its associations with changes in exercise and screen time using multivariable logistic regression models. We analyzed data between September 2021 and March 2022.ResultsCOVID-19-related racial and ethnic bias was associated with decreased exercise time among non-Hispanic Asian (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.13 to 1.89) and Hispanic people (OR, 1.91; 95% CI, 1.32 to 2.77), and with increased screen time among non-Hispanic Black people (OR, 1.94; 95% CI, 1.33 to 2.85), adjusting for age, sex, education, marital status, annual household income, insurance, and employment status.ConclusionsRacial and ethnic discrimination may have adversely influenced exercise and screen time changes among racial and ethnic minorities during the COVID-19 pandemic in the United States. Further studies are needed to investigate the mechanisms through which racial and ethnic discrimination can impact lifestyles and to develop potential strategies to address racial and ethnic discrimination as a barrier to healthy lifestyles.

Published
2023

33. Physical activity and individual plasma phospholipid SFAs in pregnancy: a longitudinal study in a multiracial/multiethnic cohort in the United States.

Author

Liu, Xinyue, Chen, Liwei, Fei, Zhe, Zhao, Sifang, Zhu, Yeyi, Xia, Tong, Dai, Jin, Rahman, Mohammad, Wu, Jing, Weir, Natalie, Tsai, Michael, and Zhang, Cuilin

Subjects
PA, SFAs, maternal exercise, physical activity, pregnancy health, pregnant females, prospective cohort, saturated fatty acids, Female, Humans, Pregnancy, Exercise, Longitudinal Studies, Phospholipids, Prospective Studies, United States
Abstract

BACKGROUND: Circulating individual SFAs in pregnant females are critical for maternal and fetal health. However, research on identifying their modifiable factors is limited. OBJECTIVES: We aimed to examine the associations of total physical activity (PA) and types of PA with circulating individual SFAs during pregnancy in a multiracial/multiethnic cohort of pregnant females in the United States. METHODS: The study included participants in a nested case-control study (n = 321) from the Eunice Kennedy Shriver NICHD Fetal Growth Studies-Singleton Cohort. Sampling weights were applied, so the results represented the entire Fetal Growth Cohort. Plasma phospholipid SFAs were measured at 4 visits [10-14 (visit 1), 15-26 (visit 2), 23-31 (visit 3), and 33-39 (visit 4) weeks of gestation] throughout pregnancy. PA of the previous year at visit 1 and since the previous visit at the subsequent visits was assessed using the validated Pregnancy PA Questionnaire. Time-specific and longitudinal associations were examined using multivariable linear and generalized estimating equation models. RESULTS: Total PA (metabolic equivalent of task-h/wk) was positively associated with circulating heptadecanoic acid (17:0) at visit 1 (β × 103: 0.07; 95% CI: 0.02, 0.11) and pentadecanoic acid (15:0) at visit 3 (β × 103: 0.09; 95% CI: 0.03, 0.14) independent of sociodemographic, reproductive, pregnancy, and dietary factors. Across the 4 visits, the positive associations with total PA were consistent for pentadecanoic acid (β × 103: 0.06; 95% CI: 0.02, 0.10) and heptadecanoic acid (β × 103: 0.10; 95% CI: 0.06, 0.14). Out of the 4 PA types (i.e., sports/exercise, household/caregiving, transportation, and occupational PA) considered, the magnitude of positive associations was the largest for sports/exercise PA. CONCLUSIONS: Our findings suggest that maternal PA is positively associated with circulating pentadecanoic and heptadecanoic acids. The findings warrant confirmation by future studies.This trial was registered at clinicaltrials.gov as NCT00912132.

Published
2022

43. Understanding Determinants of Consumer Mobile Health Usage Intentions, Assimilation, and Channel Preferences

Author

Rai, Arun, Chen, Liwei, Pye, Jessica, and Baird, Aaron

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundConsumer use of mobile devices as health service delivery aids (mHealth) is growing, especially as smartphones become ubiquitous. However, questions remain as to how consumer traits, health perceptions, situational characteristics, and demographics may affect consumer mHealth usage intentions, assimilation, and channel preferences. ObjectiveWe examine how consumers’ personal innovativeness toward mobile services (PIMS), perceived health conditions, health care availability, health care utilization, demographics, and socioeconomic status affect their (1) mHealth usage intentions and extent of mHealth assimilation, and (2) preference for mHealth as a complement or substitute for in-person doctor visits. MethodsLeveraging constructs from research in technology acceptance, technology assimilation, consumer behavior, and health informatics, we developed a cross-sectional online survey to study determinants of consumers’ mHealth usage intentions, assimilation, and channel preferences. Data were collected from 1132 nationally representative US consumers and analyzed by using moderated multivariate regressions and ANOVA. ResultsThe results indicate that (1) 430 of 1132 consumers in our sample (37.99%) have started using mHealth, (2) a larger quantity of consumers are favorable to using mHealth as a complement to in-person doctor visits (758/1132, 66.96%) than as a substitute (532/1132, 47.00%), and (3) consumers’ PIMS and perceived health conditions have significant positive direct influences on mHealth usage intentions, assimilation, and channel preferences, and significant positive interactive influences on assimilation and channel preferences. The independent variables within the moderated regressions collectively explained 59.70% variance in mHealth usage intentions, 60.41% in mHealth assimilation, 34.29% in preference for complementary use of mHealth, and 45.30% in preference for substitutive use of mHealth. In a follow-up ANOVA examination, we found that those who were more favorable toward using mHealth as a substitute for in-person doctor visits than as a complement indicated stronger intentions to use mHealth (F1,702=20.14, P

Published
2013
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45. Identification of the conductivity type of single-walled carbon nanotubes via dual-modulation dielectric force microscopy.

Author

Lai, Junqi, Wang, Wenyuan, Liu, Shuai, Chen, Bowen, Kang, Lixing, Chen, Qi, and Chen, Liwei

Subjects
SINGLE walled carbon nanotubes, CARBON nanotubes, CARBON nanotube testing, DIELECTRICS, CHARGE carriers, OHMIC contacts
Abstract

The conductivity type is one of the most fundamental transport properties of semiconductors, which is usually identified by fabricating the field-effect transistor, the Hall-effect device, etc. However, it is challenging to obtain an Ohmic contact if the sample is down to nanometer-scale because of the small size and intrinsic heterogeneity. Noncontact dielectric force microscopy (DFM) can identify the conductivity type of the sample by applying a DC gate voltage to the tip, which is effective in tuning the accumulation or depletion of charge carriers. Here, we further developed a dual-modulation DFM, which simplified the conductivity type identification from multiple scan times under different DC gate voltages to a single scan under an AC gate voltage. Taking single-walled carbon nanotubes as testing samples, the semiconducting-type sample exhibits a more significant charge carrier accumulation/depletion under each half-period of the AC gate voltage than the metallic-type sample due to the stronger rectification effect. The charge carrier accumulation or depletion of the p-type sample is opposite to that of the n-type sample at the same half-period of the AC gate voltage because of the reversed charge carrier type. [ABSTRACT FROM AUTHOR]

Published
2024
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