Browsing by Author "Li, Meng"

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    Association Between Actual and Perceived U.S. COVID-19 Policies and Preventive Behavior
    (Oxford UP, 2021-03-31) Li, Meng; Colby, Helen A.; Kelley School of Business
    BACKGROUND: COVID-19 related policies in the USA can be confusing: some states, but not others, implemented mask mandates mid-pandemic, and states reopened their economies to different levels with different timelines after initial shutdowns. PURPOSE: The current research asks: How well does the public's perception of such policies align with actual policies, and how well do actual versus perceived policies predict the public's mask-wearing and social distancing behaviors during the COVID-19 pandemic? METHODS: We conducted a preregistered cross-sectional study among 1,073 online participants who were representative of the U.S. population on age, gender, and education on Monday-Tuesday, July 20-21, 2020. We asked participants which locations they visited in the past weekend, and their mask-wearing and social distancing behaviors at each location. We also measured participants' beliefs about their state's policies on mask mandate and business opening and obtained objective measures of these policies from publicly available data. RESULTS: Perception about the existence of mask mandate was 91% accurate in states with a mask mandate but only 46% accurate in states without one. Perception of state reopening level did not correlate with policy. It was the perceived but not actual state mask mandate that positively predicted both mask-wearing and social distancing, controlling for state COVID-19 cases, demographic factors, and participants' numeracy and COVID-19 history. CONCLUSIONS: The public's perception of state-level mask mandates erred on the side of assuming there is one. Perception of reopening is almost completely inaccurate. Paradoxically, public perception that a mask mandate exists predicts preventive behaviors better than actual mandates.
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    Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer
    (BioMed Central, 2016-08-22) Jiang, Guanglong; Zhang, Shijun; Yazdanparast, Aida; Li, Meng; Pawar, Aniruddha Vikram; Liu, Yunlong; Inavolu, Sai Mounika; Cheng, Lijun; Department of Medical and Molecular Genetics, IU School of Medicine
    Background: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer’s research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Results: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. Conclusions: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.
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    Decitabine reactivated pathways in platinum resistant ovarian cancer
    (Impact Journals, 2014) Fang, Fang; Zuo, Qingyao; Pilrose, Jay; Wang, Yinu; Shen, Changyu; Li, Meng; Wulfridge, Phillip; Matei, Daniela; Nephew, Kenneth P.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Combination therapy with decitabine, a DNMTi and carboplatin resensitized chemoresistant ovarian cancer (OC) to platinum inducing promising clinical activity. We investigated gene-expression profiles in tumor biopsies to identify decitabine-reactivated pathways associated with clinical response. Gene-expression profiling was performed using RNA from paired tumor biopsies before and 8 days after decitabine from 17 patients with platinum resistant OC. Bioinformatic analysis included unsupervised hierarchical-clustering, pathway and GSEA distinguishing profiles of "responders" (progression-free survival, PFS>6 months) and "non-responders" (PFS< 6 months). Functional validation of selected results was performed in OC cells/tumors. Pre-treatment tumors from responders expressed genes associated with enhanced glycosphingolipid biosynthesis, translational misregulation, decreased ABC transporter expression, TGF-β signaling, and numerous metabolic pathways. Analysis of post-treatment biopsies from responders revealed overexpression of genes associated with reduced Hedgehog pathway signaling, reduced DNA repair/replication, and cancer-associated metabolism. GO and GSEA analyses revealed upregulation of genes associated with glycosaminoglycan binding, cell-matrix adhesion, and cell-substrate adhesion. Computational findings were substantiated by experimental validation of expression of key genes involved in two critical pathways affected by decitabine (TGF-β and Hh). Gene-expression profiling identified specific pathways altered by decitabine and associated with platinum-resensitization and clinical benefit in OC. Our data could influence patient stratification for future studies using epigenetic therapies.
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    Dodging dietary defaults: Choosing away from healthy nudges
    (Elsevier, 2020-11) Colby, Helen A.; Li, Meng; Chapman, Gretchen; Kelley School of Business - Indianapolis
    The default effect has been identified as a powerful tool to influence behavior; however, the current studies demonstrate that consumers dodge the effects of healthy defaults by selecting away from the healthy default environment, thereby reducing its effect. Two studies with real consequences and three hypothetical scenario studies in restaurant settings demonstrate that healthy defaults promote healthy food choice in the moment, but consumers choose to put themselves in environments with unhealthy defaults over those with healthy defaults. That is, healthy defaults negatively impact sales and willingness of consumers to return to the restaurant that offers them. Study 1 provides initial evidence that a healthy default reduces sales of the product compared to a less healthy default in a real gift shop. Study 2 uses an online survey with real consequences and demonstrates that participants prefer to receive meal kits from a company with unhealthy defaults over one with healthy defaults. Studies 3–5 use hypothetical scenarios to demonstrate the tendency for consumers to dodge healthy defaults. Study 3 shows that a healthy default can drive away future sales. Study 4 demonstrates that advertising a healthy default reduces interest in visiting the restaurant; that is, advertising healthy defaults drives away first-time sales. Finally, Study 5 shows that this dodge effect is robust in a between-subject manipulations using a well-known brand. The results demonstrate that consumers dodge healthy defaults by migrating to environments where unhealthy defaults are in place.
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    Efficiency for Lives, Equality for Everything Else: How Allocation Preference Shifts Across Domains
    (SAGE, 2019-07-01) Li, Meng; Colby, Helen A.; Fernbach, Philip
    The allocation of scarce public resources such as transplant organs and limited public funding involves a trade-off between equality—equal access and efficiency—maximizing total benefit. The current research explores how preferences shift when allocation decisions involve human lives versus when they do not. Fifteen experiments test this question using a variety of allocation scenarios including allocation of lifesaving medical aid, money, road construction, vaccines, and other resources. The results consistently show an increased preference for efficiency, when the allocation involves saving human lives, and equality, when the allocation involves outcomes with other consequences. We found no preference shift when stakes were manipulated in allocations where lives were not on the line, suggesting that the effect cannot be explained by lifesaving resources simply being higher stakes. These findings suggest a unique preference for efficiency for allocations involving life-and-death consequences that has implications for designing and conveying public resource allocation policies.
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    ExonImpact: Prioritizing Pathogenic Alternative Splicing Events
    (Wiley, 2017-01) Li, Meng; Feng, Weixing; Zhang, Xinjun; Yang, Yuedong; Wang, Kejun; Mort, Matthew; Cooper, David N.; Wang, Yue; Zhou, Yaoqi; Liu, Yunlong; Medicine, School of Medicine
    Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, in amongst the pathogenic AS events there are numerous “passenger” events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact
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    High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
    (Elsevier, 2023) Duan, Yuan-Yuan; Chen, Xiao-Feng; Zhu, Ren-Jie; Jia, Ying-Ying; Huang, Xiao-Ting; Zhang, Meng; Yang, Ning; Dong, Shan-Shan; Zeng, Mengqi; Feng, Zhihui; Zhu, Dong-Li; Wu, Hao; Jiang, Feng; Shi, Wei; Hu, Wei-Xin; Ke, Xin; Chen, Hao; Liu, Yunlong; Jing, Rui-Hua; Guo, Yan; Li, Meng; Yang, Tie-Lin; Medical and Molecular Genetics, School of Medicine
    Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.
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    Lipopolysaccharide treatment induces genome-wide pre-mRNA splicing pattern changes in mouse bone marrow stromal stem cells
    (BioMed Central, 2016-08-22) Zhou, Ao; Li, Meng; He, Bo; Feng, Weixing; Huang, Fei; Xu, Bing; Dunker, A. Keith; Balch, Curt; Li, Baiyan; Liu, Yunlong; Wang, Yue; Department of Medical and Molecular Genetics, IU School of Medicine
    Background Lipopolysaccharide (LPS) is a gram-negative bacterial antigen that triggers a series of cellular responses. LPS pre-conditioning was previously shown to improve the therapeutic efficacy of bone marrow stromal cells/bone-marrow derived mesenchymal stem cells (BMSCs) for repairing ischemic, injured tissue. Results In this study, we systematically evaluated the effects of LPS treatment on genome-wide splicing pattern changes in mouse BMSCs by comparing transcriptome sequencing data from control vs. LPS-treated samples, revealing 197 exons whose BMSC splicing patterns were altered by LPS. Functional analysis of these alternatively spliced genes demonstrated significant enrichment of phosphoproteins, zinc finger proteins, and proteins undergoing acetylation. Additional bioinformatics analysis strongly suggest that LPS-induced alternatively spliced exons could have major effects on protein functions by disrupting key protein functional domains, protein-protein interactions, and post-translational modifications. Conclusion Although it is still to be determined whether such proteome modifications improve BMSC therapeutic efficacy, our comprehensive splicing characterizations provide greater understanding of the intracellular mechanisms that underlie the therapeutic potential of BMSCs. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2898-5) contains supplementary material, which is available to authorized users.
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    Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
    (Springer Nature, 2017-09) Petrov, Dmitry; Gutman, Boris A.; Yu, Shih-Hua (Julie); van Erp, Theo G.M.; Turner, Jessica A.; Schmaal, Lianne; Veltman, Dick; Wang, Lei; Alpert, Kathryn; Isaev, Dmitry; Zavaliangos-Petropulu, Artemis; Ching, Christopher R.K.; Calhoun, Vince; Glahn, David; Satterthwaite, Theodore D.; Andreasen, Ole Andreas; Borgwardt, Stefan; Howells, Fleur; Groenewold, Nynke; Voineskos, Aristotle; Radua, Joaquim; Potkin, Steven G.; Crespo-Facorro, Benedicto; Tordesillas-Gutirrez, Diana; Shen, Li; Lebedeva, Irina; Spalletta, Gianfranco; Donohoe, Gary; Kochunov, Peter; Rosa, Pedro G.P.; James, Anthony; Dannlowski, Udo; Baune, Berhard T.; Aleman, Andre; Gotlib, Ian H.; Walter, Henrik; Walter, Martin; Soares, Jair C.; Ehrlich, Stefan; Gur, Ruben C.; Doan, N. Trung; Agartz, Ingrid; Westlye, Lars T.; Harrisberger, Fabienne; Richer-Rossler, Anita; Uhlmann, Anne; Stein, Dan J.; Dickie, Erin W.; Pomarol-Clotet, Edith; Fuentes-Claramonte, Paola; Canales-Rodriguez, Erick Jorge; Salvador, Raymond; Huang, Alexander J.; Roiz-Santianez, Roberto; Cong, Shan; Tomyshev, Alexander; Piras, Fabrizio; Vecchio, Daniela; Banaj, Nerisa; Ciullo, Valentina; Hong, Elliot; Busatto, Geraldo; Zanetti, Marcus V.; Serpa, Mauricio H.; Cervenka, Simon; Kelly, Sinead; Grotegerd, Dominik; Sacchet, Matthew D.; Veer, Illya M.; Li, Meng; Wu, Mon-Ju; Irungu, Benson; Walton, Esther; Thompson, Paul M.; Medicine, School of Medicine
    As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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    Physicians' flawed heuristics in the delivery room.
    (AAAS, 2021-10-15) Li, Meng; Colby, Helen; Kelley School of Business