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Vasenina A, Fu Y, O'Toole GA, Mucha PJ. Local control: a hub-based model for the c-di-GMP network. mSphere 2024:e0017824. [PMID: 38591888 DOI: 10.1128/msphere.00178-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
The genome of Pseudomonas fluorescens encodes >50 proteins predicted to play a role in bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP)-mediated biofilm formation. We built a network representation of protein-protein interactions and extracted key information via multidimensional scaling (i.e., principal component analysis) of node centrality measures, which measure features of proteins in a network. Proteins of different domain types (diguanylate cyclase, dual domain, phosphodiesterase, PilZ) exhibit unique network behavior and can be accurately classified by their network centrality values (i.e., roles in the network). The predictive power of protein-protein interactions in biofilm formation indicates the possibility of localized pools of c-di-GMP. A regression model showed a statistically significant impact of protein-protein interactions on the extent of biofilm formation in various environments. These results highlight the importance of a localized c-di-GMP signaling, extend our understanding of signaling by this second messenger beyond the current "Bow-tie Model," support a newly proposed "Hub Model," and suggest future avenues of investigation.
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Affiliation(s)
- Anna Vasenina
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, USA
| | - Yu Fu
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - George A O'Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, USA
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2
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He X, Ghasemian A, Lee E, Clauset A, Mucha PJ. Sequential stacking link prediction algorithms for temporal networks. Nat Commun 2024; 15:1364. [PMID: 38355612 PMCID: PMC10866871 DOI: 10.1038/s41467-024-45598-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
Link prediction algorithms are indispensable tools in many scientific applications by speeding up network data collection and imputing missing connections. However, in many systems, links change over time and it remains unclear how to optimally exploit such temporal information for link predictions in such networks. Here, we show that many temporal topological features, in addition to having high computational cost, are less accurate in temporal link prediction than sequentially stacked static network features. This sequential stacking link prediction method uses 41 static network features that avoid detailed feature engineering choices and is capable of learning a highly accurate predictive distribution of future connections from historical data. We demonstrate that this algorithm works well for both partially observed and completely unobserved target layers, and on two temporal stochastic block models achieves near-oracle-level performance when combined with other single predictor methods as an ensemble learning method. Finally, we empirically illustrate that stacking multiple predictive methods together further improves performance on 19 real-world temporal networks from different domains.
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Affiliation(s)
- Xie He
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Amir Ghasemian
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
| | - Eun Lee
- Department of Scientific Computing, Pukyong National University, Busan, South Korea
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado, Boulder, Boulder, CO, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA.
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3
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Kang Y, Ahn J, Cosme D, Mwilambwe-Tshilobo L, McGowan A, Zhou D, Boyd ZM, Jovanova M, Stanoi O, Mucha PJ, Ochsner KN, Bassett DS, Lydon-Staley D, Falk EB. Frontoparietal functional connectivity moderates the link between time spent on social media and subsequent negative affect in daily life. Sci Rep 2023; 13:20501. [PMID: 37993522 PMCID: PMC10665348 DOI: 10.1038/s41598-023-46040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 10/26/2023] [Indexed: 11/24/2023] Open
Abstract
Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.
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Affiliation(s)
- Yoona Kang
- Department of Psychology, Rutgers, The State University of New Jersey, Camden, NJ, 08102, USA.
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Jeesung Ahn
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Amanda McGowan
- Department of Psychology, Concordia University, Montreal, QC, H4B 1R6, Canada
| | - Dale Zhou
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Zachary M Boyd
- Department of Mathematics, Brigham Young University, Provo, UT, 84604, USA
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, NY, 10027, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, 03755, USA
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, NY, 10027, USA
| | - Dani S Bassett
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Wharton Marketing Department, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Wharton Operations, Information and Decisions Department, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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4
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Jovanova M, Cosme D, Doré B, Kang Y, Stanoi O, Cooper N, Helion C, Lomax S, McGowan AL, Boyd ZM, Bassett DS, Mucha PJ, Ochsner KN, Lydon-Staley DM, Falk EB. Psychological distance intervention reminders reduce alcohol consumption frequency in daily life. Sci Rep 2023; 13:12045. [PMID: 37491371 PMCID: PMC10368637 DOI: 10.1038/s41598-023-38478-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 07/09/2023] [Indexed: 07/27/2023] Open
Abstract
Modifying behaviors, such as alcohol consumption, is difficult. Creating psychological distance between unhealthy triggers and one's present experience can encourage change. Using two multisite, randomized experiments, we examine whether theory-driven strategies to create psychological distance-mindfulness and perspective-taking-can change drinking behaviors among young adults without alcohol dependence via a 28-day smartphone intervention (Study 1, N = 108 participants, 5492 observations; Study 2, N = 218 participants, 9994 observations). Study 2 presents a close replication with a fully remote delivery during the COVID-19 pandemic. During weeks when they received twice-a-day intervention reminders, individuals in the distancing interventions reported drinking less frequently than on control weeks-directionally in Study 1, and significantly in Study 2. Intervention reminders reduced drinking frequency but did not impact amount. We find that smartphone-based mindfulness and perspective-taking interventions, aimed to create psychological distance, can change behavior. This approach requires repeated reminders, which can be delivered via smartphones.
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Affiliation(s)
- Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA.
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
| | - Bruce Doré
- Desautels Faculty of Management, McGill University, Montreal, Canada
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, USA
| | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
| | - Chelsea Helion
- Department of Psychology, Temple University, Philadelphia, USA
| | - Silicia Lomax
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
| | - Amanda L McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
| | - Zachary M Boyd
- Mathematics Department, Brigham Young University, Provo, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, USA
- The Santa Fe Institute, Santa Fe, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, USA
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, USA
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, USA
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, USA.
- Department of Psychology, University of Pennsylvania, Philadelphia, USA.
- Wharton Marketing Department, University of Pennsylvania, Philadelphia, USA.
- Wharton Operations, Information and Decisions Department, University of Pennsylvania, Philadelphia, USA.
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5
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McGowan AL, Sayed F, Boyd ZM, Jovanova M, Kang Y, Speer ME, Cosme D, Mucha PJ, Ochsner KN, Bassett DS, Falk EB, Lydon-Staley DM. Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones. Biol Psychiatry 2023; 93:681-689. [PMID: 36797176 PMCID: PMC10038886 DOI: 10.1016/j.biopsych.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.
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Affiliation(s)
- Amanda L McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Concordia University, Montréal, Québec, Canada
| | - Farah Sayed
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zachary M Boyd
- Department of Mathematics, Brigham Young University, Provo, Utah
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Megan E Speer
- Department of Psychology, Columbia University, New York, New York
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, New York
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; Operations, Information and Decisions, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.
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6
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Stanford W, Mucha PJ, Dayan E. Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks. bioRxiv 2023:2023.02.17.528999. [PMID: 36824776 PMCID: PMC9949152 DOI: 10.1101/2023.02.17.528999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we used diffusion MRI data from cognitively intact participants (n=480, ages 40-90) to study age-associated changes in the controllability of structural brain networks, features that could mitigate these changes, and the overall effect on cognitive function. We found age-associated declines in controllability in control hubs and large-scale networks, particularly within the and frontoparietal control and default mode networks. Redundancy, quantified via the assessment of multi-step paths within networks, mitigated the effects of changes in topology on network controllability. Lastly, network controllability, redundancy, and grey matter volume each played important complementary roles in cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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7
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McGowan AL, Boyd ZM, Kang Y, Bennett L, Mucha PJ, Ochsner KN, Bassett DS, Falk EB, Lydon-Staley DM. Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students. Psychosom Med 2023; 85:141-153. [PMID: 36728904 PMCID: PMC9918680 DOI: 10.1097/psy.0000000000001159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE A holistic understanding of the naturalistic dynamics among physical activity, sleep, emotions, and purpose in life as part of a system reflecting wellness is key to promoting well-being. The main aim of this study was to examine the day-to-day dynamics within this wellness system. METHODS Using self-reported emotions (happiness, sadness, anger, anxiousness) and physical activity periods collected twice per day, and daily reports of sleep and purpose in life via smartphone experience sampling, more than 28 days as college students ( n = 226 young adults; mean [standard deviation] = 20.2 [1.7] years) went about their daily lives, we examined day-to-day temporal and contemporaneous dynamics using multilevel vector autoregressive models that consider the network of wellness together. RESULTS Network analyses revealed that higher physical activity on a given day predicted an increase of happiness the next day. Higher sleep quality on a given night predicted a decrease in negative emotions the next day, and higher purpose in life predicted decreased negative emotions up to 2 days later. Nodes with the highest centrality were sadness, anxiety, and happiness in the temporal network and purpose in life, anxiety, and anger in the contemporaneous network. CONCLUSIONS Although the effects of sleep and physical activity on emotions and purpose in life may be shorter term, a sense of purpose in life is a critical component of wellness that can have slightly longer effects, bleeding into the next few days. High-arousal emotions and purpose in life are central to motivating people into action, which can lead to behavior change.
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Affiliation(s)
- Amanda L. McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, Concordia University, Montréal, Québec, Canada
| | - Zachary M. Boyd
- Department of Mathematics, Brigham Young University, Provo, UT, USA
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | - Logan Bennett
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Kevin N. Ochsner
- Department of Psychology, Columbia University, New York City, NY, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Emily B. Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
- Marketing Department, and Operations, Information and Decision Department, Wharton School, University of Pennsylvania, PA, USA
| | - David M. Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
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8
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Zou Z, Potter RF, McCoy WH, Wildenthal JA, Katumba GL, Mucha PJ, Dantas G, Henderson JP. E. coli catheter-associated urinary tract infections are associated with distinctive virulence and biofilm gene determinants. JCI Insight 2023; 8:e161461. [PMID: 36512427 PMCID: PMC9977300 DOI: 10.1172/jci.insight.161461] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
Urinary catheterization facilitates urinary tract colonization by E. coli and increases infection risk. Here, we aimed to identify strain-specific characteristics associated with the transition from colonization to infection in catheterized patients. In a single-site study population, we compared E. coli isolates from patients with catheter-associated asymptomatic bacteriuria (CAASB) to those with catheter-associated urinary tract infection (CAUTI). CAUTI isolates were dominated by a phylotype B2 subclade containing the multidrug-resistant ST131 lineage relative to CAASB isolates, which were phylogenetically more diverse. A distinctive combination of virulence-associated genes was present in the CAUTI-associated B2 subclade. Catheter-associated biofilm formation was widespread among isolates and did not distinguish CAUTI from CAASB strains. Preincubation with CAASB strains could inhibit catheter colonization by multiple ST131 CAUTI isolates. Comparative genomic analysis identified a group of variable genes associated with high catheter biofilm formation present in both CAUTI and CAASB strains. Among these, ferric citrate transport (Fec) system genes were experimentally associated with enhanced catheter biofilm formation using reporter and fecA deletion strains. These results are consistent with a variable role for catheter biofilm formation in promoting CAUTI by ST131-like strains or resisting CAUTI by lower-risk strains that engage in niche exclusion.
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Affiliation(s)
- Zongsen Zou
- Center for Women’s Infectious Diseases Research
- Department of Internal Medicine, Division of Infectious Diseases
| | - Robert F. Potter
- The Edison Family Center for Genome Sciences and Systems Biology
- Department of Pathology and Immunology, and
| | - William H. McCoy
- Center for Women’s Infectious Diseases Research
- Department of Internal Medicine, Division of Dermatology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - John A. Wildenthal
- Center for Women’s Infectious Diseases Research
- Department of Internal Medicine, Division of Infectious Diseases
| | - George L. Katumba
- Center for Women’s Infectious Diseases Research
- Department of Internal Medicine, Division of Infectious Diseases
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, USA
| | - Gautam Dantas
- The Edison Family Center for Genome Sciences and Systems Biology
- Department of Pathology and Immunology, and
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Department of Biomedical Engineering, Washington University in St. Louis, Missouri, USA
| | - Jeffrey P. Henderson
- Center for Women’s Infectious Diseases Research
- Department of Internal Medicine, Division of Infectious Diseases
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Kang Y, Cosme D, Lydon-Staley D, Ahn J, Jovanova M, Corbani F, Lomax S, Stanoi O, Strecher V, Mucha PJ, Ochsner K, Bassett DS, Falk EB. Purpose in life, neural alcohol cue reactivity and daily alcohol use in social drinkers. Addiction 2022; 117:3049-3057. [PMID: 35915548 DOI: 10.1111/add.16012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 07/07/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIM Alcohol craving is an urge to consume alcohol that commonly precedes drinking; however, craving does not lead to drinking for all people under all circumstances. The current study measured the correlation between neural reactivity and alcohol cues as a risk, and purpose in daily life as a protective factor that may influence the link between alcohol craving and the subsequent amount of consumption. DESIGN Observational study that correlated functional magnetic resonance imaging (fMRI) data on neural cue reactivity and ecological momentary assessments (EMA) on purpose in life and alcohol use. SETTING Two college campuses in the United States. PARTICIPANTS A total of 54 college students (37 women, 16 men, and 1 other) recruited via campus-based groups from January 2019 to October 2020. MEASUREMENTS Participants underwent fMRI while viewing images of alcohol; we examined activity within the ventral striatum, a key region of interest implicated in reward and craving. Participants then completed 28 days of EMA and answered questions about daily levels of purpose in life and alcohol use, including how much they craved and consumed alcohol. FINDINGS A significant three-way interaction indicated that greater alcohol cue reactivity within the ventral striatum was associated with heavier alcohol use following craving in daily life only when people were previously feeling a lower than usual sense of purpose. By contrast, individuals with heightened neural alcohol cue reactivity drank less in response to craving if they were feeling a stronger than their usual sense of purpose in the preceding moments (binteraction = -0.086, P < 0.001, 95% CI = -0.137, -0.035). CONCLUSIONS Neural sensitivity to alcohol cues within the ventral striatum appears to be a potential risk for increased alcohol use in social drinkers, when people feel less purposeful. Enhancing daily levels of purpose in life may promote alcohol moderation among social drinkers who show relatively higher reactivity to alcohol cues.
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Affiliation(s)
- Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jeesung Ahn
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Faustine Corbani
- Department of Psychology, Columbia University, New York, New York, United States
| | - Silicia Lomax
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, New York, United States
| | - Victor Strecher
- School of Public Health, University of Michigan, Ann Arbor, Michigan, United States
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, United States
| | - Kevin Ochsner
- Department of Psychology, Columbia University, New York, New York, United States
| | - Dani S Bassett
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Wharton Marketing Department, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States.,Wharton Operations, Information and Decisions Department, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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10
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Yin W, Li T, Mucha PJ, Cohen JR, Zhu H, Zhu Z, Lin W. Altered neural flexibility in children with attention-deficit/hyperactivity disorder. Mol Psychiatry 2022; 27:4673-4679. [PMID: 35869272 PMCID: PMC9734048 DOI: 10.1038/s41380-022-01706-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 12/14/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.
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Affiliation(s)
- Weiyan Yin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica R Cohen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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11
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Weir WH, Mucha PJ, Kim WY. A bipartite graph-based expected networks approach identifies DDR genes not associated with TMB yet predictive of immune checkpoint blockade response. Cell Rep Med 2022; 3:100602. [PMID: 35584624 PMCID: PMC9133403 DOI: 10.1016/j.xcrm.2022.100602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/07/2022] [Accepted: 03/20/2022] [Indexed: 11/26/2022]
Abstract
Immune checkpoint blockade (ICB) has had remarkable success for treatment of solid tumors. However, as only a subset of patients exhibit responses, there is a continued need for biomarker development. Numerous reports have shown a link between tumor mutational burden (TMB) and ICB response, while others have identified a link between ICB response and mutation in DNA damage repair (DDR) genes. However, it remains unclear to what extent mutations in DDR genes hold predictive value above and beyond their association with TMB. Herein, we present a networks-based test and bipartite graph-based expected TMB score (BiG-BETS) with higher specificity for discriminating DDR genes and pathways that are associated with elevated TMB. Moreover, we find that mutations in certain DDR genes that are not associated with elevated TMB (low BiG-BETS) are nevertheless predictive of ICB benefit in high TMB patients, demonstrating that their inactivation contributes to ICB response in a TMB-independent manner.
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Affiliation(s)
- William H Weir
- Curriculum in Bioinformatics & Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Curriculum in Bioinformatics & Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Mathematics, Dartmouth College, Hanover, NH, USA.
| | - William Y Kim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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12
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Hinton AL, Mucha PJ. A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data. Front Microbiol 2022; 13:837396. [PMID: 35387076 PMCID: PMC8978828 DOI: 10.3389/fmicb.2022.837396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/15/2022] [Indexed: 12/14/2022] Open
Abstract
Numerous metagenomic studies aim to discover associations between the microbial composition of an environment (e.g., gut, skin, oral) and a phenotype of interest. Multivariate analysis is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. This approach typically reduces statistical power in settings where the true associations among only a few taxa are obscured by high dimensionality (i.e., sparse association signals). At the same time, low sample size and compositional sample space constraints may reduce beyond-study generalizability if not properly accounted for. To address these difficulties, we developed the Selection-Energy-Permutation (SelEnergyPerm) method, a nonparametric group association test with embedded feature selection that directly accounts for compositional constraints using parsimonious logratio signatures between taxonomic features, for characterizing and understanding alterations in microbial community structure. Simulation results show SelEnergyPerm selects small independent sets of logratios that capture strong associations in a range of scenarios. Additionally, our simulation results demonstrate SelEnergyPerm consistently detects/rejects associations in synthetic data with sparse, dense, or no association signals. We demonstrate the novel benefits of our method in four case studies utilizing publicly available 16S amplicon and whole-genome sequencing datasets. Our R implementation of Selection-Energy-Permutation, including an example demonstration and the code to generate all of the scenarios used here, is available at https://www.github.com/andrew84830813/selEnergyPermR.
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Affiliation(s)
- Andrew L Hinton
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, United States.,School of Medicine, University of North Carolina at Chapel Hill Food Allergy Initiative, Chapel Hill, NC, United States
| | - Peter J Mucha
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, United States.,Departments of Mathematics and Applied Physical Sciences, University of North Carolina, Chapel Hill, NC, United States.,Department of Mathematics, Dartmouth College, Hanover, NH, United States
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13
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Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, Shapiro JT, Solis A, Soarimalala V, Tortosa P, Kramer R, Moody J, Mucha PJ, Nunn C. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface 2022; 19:20210690. [PMID: 35016555 PMCID: PMC8753172 DOI: 10.1098/rsif.2021.0690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.
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Affiliation(s)
- Kayla Kauffman
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Courtney S. Werner
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Georgia Titcomb
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | | | - Jean Yves Rabezara
- Science de la Nature et Valorisation des Ressources Naturelles, Centre Universitaire Régional de la SAVA, Antalaha, Madagascar
| | | | - Julie Teresa Shapiro
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
| | | | - Pablo Tortosa
- UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Université de La Réunion, Ile de La Réunion, France
| | - Randall Kramer
- Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, NC 27708, USA
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Charles Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
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14
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Langella S, Mucha PJ, Giovanello KS, Dayan E. The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy. Neurobiol Aging 2021; 108:179-188. [PMID: 34614422 DOI: 10.1016/j.neurobiolaging.2021.09.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 08/16/2021] [Accepted: 09/01/2021] [Indexed: 10/20/2022]
Abstract
Hippocampal neurodegeneration, a primary component of Alzheimer's disease pathology, relates to poor cognition; however, the mechanisms underlying this relationship are not well understood. Using a sample of cognitively normal older adults and individuals with mild cognitive impairment, this study aims to determine the topological properties of functional networks accompanying hippocampal atrophy in aging, along with their association to cognition and clinical progression. We considered two conceptually differing topological properties: redundancy (the existence of alternative channels of functional commutation) and local efficiency (the efficiency of local information exchange). Hippocampal redundancy, but not local efficiency, mediated the association between low hippocampal volume and low memory in both the whole sample and in ß-amyloid positive participants. Additionally, participants with high hippocampal volume, redundancy, and memory clustered separately from those with low values on all three measures, with the latter group showing higher conversion rates to dementia within three years. Together, these results demonstrate that reduced hippocampal redundancy is one mechanism through which hippocampal atrophy associates with memory impairment in healthy and pathological aging.
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Affiliation(s)
- Stephanie Langella
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, NH 03755, USA
| | - Kelly S Giovanello
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27514, USA; Department of Radiology, University of North Carolina at Chapel Hill, NC 27599, USA.
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15
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Kroma-Wiley KA, Mucha PJ, Bassett DS. Synchronization of coupled Kuramoto oscillators under resource constraints. Phys Rev E 2021; 104:014211. [PMID: 34412254 DOI: 10.1103/physreve.104.014211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 03/25/2021] [Indexed: 11/07/2022]
Abstract
A fundamental understanding of synchronized behavior in multiagent systems can be acquired by studying analytically tractable Kuramoto models. However, such models typically diverge from many real systems whose dynamics evolve under nonnegligible resource constraints. Here we construct a system of coupled Kuramoto oscillators that consume or produce resources as a function of their oscillation frequency. At high coupling, we observe strongly synchronized dynamics, whereas at low coupling, we observe independent oscillator dynamics as expected from standard Kuramoto models. For intermediate coupling, which typically induces a partially synchronized state, we empirically observe that (and theoretically explain why) the system can exist in either: (i) a state in which the order parameter oscillates in time, or (ii) a state in which multiple synchronization states are simultaneously stable. Whether (i) or (ii) occurs depends upon whether the oscillators consume or produce resources, respectively. Relevant for systems as varied as coupled neurons and social groups, our paper lays important groundwork for future efforts to develop quantitative predictions of synchronized dynamics for systems embedded in environments marked by sparse resources.
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Affiliation(s)
- Keith A Kroma-Wiley
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Peter J Mucha
- Department of Mathematics and Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Danielle S Bassett
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.,Department of Bioengineering, Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.,Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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16
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Paydarfar DA, Paydarfar D, Mucha PJ, Chang J. Optimizing Emergency Stroke Transport Strategies Using Physiological Models. Stroke 2021; 52:4010-4020. [PMID: 34407639 PMCID: PMC8607917 DOI: 10.1161/strokeaha.120.031633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Supplemental Digital Content is available in the text. The criteria for choosing between drip and ship and mothership transport strategies in emergency stroke care is widely debated. Although existing data-driven probability models can inform transport decision-making at an epidemiological level, we propose a novel mathematical, physiologically derived framework that provides insight into how patient characteristics underlying infarct core growth influence these decisions.
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Affiliation(s)
- Daniel A Paydarfar
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics (D.A.P., P.J.M.), University of North Carolina, Chapel Hill
| | - David Paydarfar
- Departments of Neurology (D.P., J.C.), Dell Medical School, Mulva Clinic for the Neurosciences and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics (D.A.P., P.J.M.), University of North Carolina, Chapel Hill
| | - Joshua Chang
- Departments of Neurology (D.P., J.C.), Dell Medical School, Mulva Clinic for the Neurosciences and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin.,Population Health (J.C.), Dell Medical School, Mulva Clinic for the Neurosciences and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin
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17
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Sadiq MU, Langella S, Giovanello KS, Mucha PJ, Dayan E. Accrual of functional redundancy along the lifespan and its effects on cognition. Neuroimage 2021; 229:117737. [PMID: 33486125 PMCID: PMC8022200 DOI: 10.1016/j.neuroimage.2021.117737] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/04/2021] [Accepted: 01/05/2021] [Indexed: 12/01/2022] Open
Abstract
Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.
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Affiliation(s)
- Muhammad Usman Sadiq
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Stephanie Langella
- Department of Psychology and Neuroscience, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Kelly S Giovanello
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Psychology and Neuroscience, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Peter J Mucha
- Department of Mathematics, UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Applied Physical Sciences, UNC-Chapel Hill, Chapel Hill, NC 27599, United States
| | - Eran Dayan
- Biomedical Research Imaging Center (BRIC), UNC-Chapel Hill, Chapel Hill, NC 27599, United States; Department of Radiology, UNC-Chapel Hill, Chapel Hill, NC 27599, United States.
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18
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Smeekens JM, Johnson-Weaver BT, Hinton AL, Azcarate-Peril MA, Moran TP, Immormino RM, Kesselring JR, Steinbach EC, Orgel KA, Staats HF, Burks AW, Mucha PJ, Ferris MT, Kulis MD. Fecal IgA, Antigen Absorption, and Gut Microbiome Composition Are Associated With Food Antigen Sensitization in Genetically Susceptible Mice. Front Immunol 2021; 11:599637. [PMID: 33542716 PMCID: PMC7850988 DOI: 10.3389/fimmu.2020.599637] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/25/2020] [Indexed: 01/04/2023] Open
Abstract
Food allergy is a potentially fatal disease affecting 8% of children and has become increasingly common in the past two decades. Despite the prevalence and severe nature of the disease, the mechanisms underlying sensitization remain to be further elucidated. The Collaborative Cross is a genetically diverse panel of inbred mice that were specifically developed to study the influence of genetics on complex diseases. Using this panel of mouse strains, we previously demonstrated CC027/GeniUnc mice, but not C3H/HeJ mice, develop peanut allergy after oral exposure to peanut in the absence of a Th2-skewing adjuvant. Here, we investigated factors associated with sensitization in CC027/GeniUnc mice following oral exposure to peanut, walnut, milk, or egg. CC027/GeniUnc mice mounted antigen-specific IgE responses to peanut, walnut and egg, but not milk, while C3H/HeJ mice were not sensitized to any antigen. Naïve CC027/GeniUnc mice had markedly lower total fecal IgA compared to C3H/HeJ, which was accompanied by stark differences in gut microbiome composition. Sensitized CC027/GeniUnc mice had significantly fewer CD3+ T cells but higher numbers of CXCR5+ B cells and T follicular helper cells in the mesenteric lymph nodes compared to C3H/HeJ mice, which is consistent with their relative immunoglobulin production. After oral challenge to the corresponding food, peanut- and walnut-sensitized CC027/GeniUnc mice experienced anaphylaxis, whereas mice exposed to milk and egg did not. Ara h 2 was detected in serum collected post-challenge from peanut-sensitized mice, indicating increased absorption of this allergen, while Bos d 5 and Gal d 2 were not detected in mice exposed to milk and egg, respectively. Machine learning on the change in gut microbiome composition as a result of food protein exposure identified a unique signature in CC027/GeniUnc mice that experienced anaphylaxis, including the depletion of Akkermansia. Overall, these results demonstrate several factors associated with enteral sensitization in CC027/GeniUnc mice, including diminished total fecal IgA, increased allergen absorption and altered gut microbiome composition. Furthermore, peanuts and tree nuts may have inherent properties distinct from milk and eggs that contribute to allergy.
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Affiliation(s)
- Johanna M. Smeekens
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | | | - Andrew L. Hinton
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, United States
| | - M. Andrea Azcarate-Peril
- Department of Medicine, Division of Gastroenterology and Hepatology, University of North Carolina, Chapel Hill, NC, United States
- UNC Microbiome Core, Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, United States
| | - Timothy P. Moran
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Robert M. Immormino
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Janelle R. Kesselring
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Erin C. Steinbach
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Kelly A. Orgel
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Herman F. Staats
- Department of Pathology, Duke University School of Medicine, Durham, NC, United States
- Duke Human Vaccine Institute, Duke University School of Medicine, Durham, NC, United States
- Department of Immunology, Duke University School of Medicine, Durham, NC, United States
| | - A. Wesley Burks
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Peter J. Mucha
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, United States
- Department of Mathematics and Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, NC, United States
| | - Martin T. Ferris
- Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Michael D. Kulis
- Department of Pediatrics, Division of Rheumatology, Allergy and Immunology, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
- UNC Food Allergy Initiative, School of Medicine, University of North Carolina, Chapel Hill, NC, United States
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19
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Langella S, Sadiq MU, Mucha PJ, Giovanello KS, Dayan E. Lower functional hippocampal redundancy in mild cognitive impairment. Transl Psychiatry 2021; 11:61. [PMID: 33462184 PMCID: PMC7813821 DOI: 10.1038/s41398-020-01166-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 12/07/2020] [Accepted: 12/10/2020] [Indexed: 12/24/2022] Open
Abstract
With an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.
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Affiliation(s)
- Stephanie Langella
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Muhammad Usman Sadiq
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eran Dayan
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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20
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Tan KR, Santacroce SJ, Wood WA, Mayer DK, Santos H, Mucha PJ, Schwartz TA, Fredrickson BL. Positive psychological states and stress responses in caregivers of adults receiving an allogeneic bone marrow transplant: A study protocol. J Adv Nurs 2021; 77:2073-2084. [PMID: 33460207 DOI: 10.1111/jan.14742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 12/10/2020] [Indexed: 12/24/2022]
Abstract
AIMS This protocol directs a study that aims to: (a) describe the caregiver's experience over 8-12 weeks after an index adult patient's allogeneic bone marrow transplant (BMT) for advanced cancer using a case-oriented approach and mixed methods, with qualitative methods in the foreground; and (b) explore networks of relationships among psycho-neurological symptoms, positive psychological states and caregiver health. DESIGN Case-oriented longitudinal design using multiple data types and analytic approaches. METHODS Data will be collected from 10-12 caregivers. The sample will be recruited from a large public hospital in the southeastern United States using maximum variation sampling (e.g., caregiver race/ethnicity, relationship to patient, age, education, and number of caregiving roles). Participants will be asked to complete weekly surveys, have their blood drawn bi-weekly and participate in an interview each month during the study period (~100 days). Aim 1 analysis will include directed content analysis and case-oriented visual analysis. Aim 2 analysis will include symptom network estimation of psycho-neurological symptoms, positive psychological states, and caregiver health. Institutional review board approval was obtained August 2018. DISCUSSION Results will provide an in-depth description of caregivers' experiences in the 100 days after BMT. Findings will inform generation of hypotheses and identification of targets for interventions to improve caregiver's experiences after BMT. IMPACT This in-depth multi-method longitudinal study to describe caregivers of adult patients receiving an allogeneic BMT is an essential step in understanding caregivers' complex responses to chronic stress and the role of positive psychological states. The results from this study will inform future research on chronic stress processes, intense caregiving, and intervention development.
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Affiliation(s)
- Kelly R Tan
- School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Sheila J Santacroce
- School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - William A Wood
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA.,Division of Hematology/Oncology, Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Deborah K Mayer
- School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Hudson Santos
- School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Peter J Mucha
- Department of Mathematics and Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Todd A Schwartz
- School of Nursing, University of North Carolina, Chapel Hill, North Carolina, USA.,Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Barbara L Fredrickson
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA.,Department Of Psychology and Neuroscience, University of North Carolina, Chapel Hill, North Carolina, USA
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21
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Abstract
Networked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception. We find that individuals' ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of "churn" to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.
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Affiliation(s)
- Caleb Pomeroy
- Department of Political Science, The Ohio State University, Columbus, OH, 43210, USA
| | - Robert M Bond
- School of Communication, The Ohio State University, Columbus, OH, 43210, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Skyler J Cranmer
- Department of Political Science, The Ohio State University, Columbus, OH, 43210, USA.
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22
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Paydarfar DA, Paydarfar D, Mucha PJ, Chang J. Abstract WP291: Stochastic Methods Can Resolve the Dilemma of Emergency Stroke Transport. Stroke 2020. [DOI: 10.1161/str.51.suppl_1.wp291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
Drip and Ship (DNS) and Mothership (MS) are well-known emergency transport strategies in acute stroke care, but the criteria for choosing between the two is widely debated. Existing models define time-dependent outcomes but cannot resolve this debate with statistical significance because the independent variables are deterministic. We propose a novel stochastic framework that quantifies statistical significance between DNS and MS in a network of primary and comprehensive stroke centers.
Methods:
We represented the physiology of ischemic core growth as a stochastic first-order differential equation, enabling infarct volume at time of reperfusion to be calculated and mapped to 90-day mRS. Using Texas as a case study, we configured the state’s stroke network within 15,811 geographic blocks as defined by census data. For each block, we ran Monte Carlo simulations to generate Beta distributions of large- and small-vessel infarct volumes, which were then translated into cumulative distribution functions of mRS. A two-sample Kolmogorov-Smirnov test for significance, and Cohen’s d effect size statistic for practical significance were computed between each DNS and MS pair. Stable effect sizes were assured by sampling
>
5,000 total infarct volumes for each block. All model parameters were established from large cohort studies or trials.
Results:
Of the 13,113 blocks where the primary stroke center is the closest hospital from origin, DNS produces significantly better stroke outcomes than MS in 79.0% (0.3% SEM;
P
< 0.05; 0.2 < d < 0.5). For the subset of patients with large-vessel strokes, MS produces significantly better outcomes in 44.6% of blocks (1.3% SEM;
P
< 0.05; 0.4 < d < 0.85).
Conclusion:
Stochastic methods enable the use of clinically relevant metrics for comparative significance of DNS and MS in a geographic region. This formalism, which has not been incorporated in previous models, can be further generalized beyond stochastic infarct volumes if sufficiently large datasets become available. For example, the kinetic growth model can integrate the statistical distributions of times (pre-hospital and hospital) leading up to intervention, and patient attributes that affect outcomes, such as the degree of collateral flow and comorbidities.
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Affiliation(s)
| | | | - Peter J Mucha
- Mathematics, The Univ of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Joshua Chang
- Neurology, The Univ of Texas at Austin, Austin, TX
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23
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Jackson JC, Watts J, Henry TR, List JM, Forkel R, Mucha PJ, Greenhill SJ, Gray RD, Lindquist KA. Emotion semantics show both cultural variation and universal structure. Science 2019; 366:1517-1522. [DOI: 10.1126/science.aaw8160] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Accepted: 11/20/2019] [Indexed: 01/21/2023]
Abstract
Many human languages have words for emotions such as “anger” and “fear,” yet it is not clear whether these emotions have similar meanings across languages, or why their meanings might vary. We estimate emotion semantics across a sample of 2474 spoken languages using “colexification”—a phenomenon in which languages name semantically related concepts with the same word. Analyses show significant variation in networks of emotion concept colexification, which is predicted by the geographic proximity of language families. We also find evidence of universal structure in emotion colexification networks, with all families differentiating emotions primarily on the basis of hedonic valence and physiological activation. Our findings contribute to debates about universality and diversity in how humans understand and experience emotion.
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Affiliation(s)
- Joshua Conrad Jackson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph Watts
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
- Religion Programme, University of Otago, Dunedin, New Zealand
- Center for Research on Evolution, Belief, and Behaviour, University of Otago, Dunedin, New Zealand
- Social and Evolutionary Neuroscience Research Group, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Teague R. Henry
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Johann-Mattis List
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
| | - Robert Forkel
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
| | - Peter J. Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Simon J. Greenhill
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
- ARC Centre of Excellence for the Dynamics of Language, ANU College of Asia and the Pacific, Australian National University, Canberra, Australia
| | - Russell D. Gray
- Department of Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Jena, Germany
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Kristen A. Lindquist
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Abstract
Network properties govern the rate and extent of various spreading processes, from simple contagions to complex cascades. Recently, the analysis of spreading processes has been extended from static networks to temporal networks, where nodes and links appear and disappear. We focus on the effects of accessibility, whether there is a temporally consistent path from one node to another, and reachability, the density of the corresponding accessibility graph representation of the temporal network. The level of reachability thus inherently limits the possible extent of any spreading process on the temporal network. We study reachability in terms of the overall levels of temporal concurrency between edges and the structural cohesion of the network agglomerating over all edges. We use simulation results and develop heterogeneous mean-field model predictions for random networks to better quantify how the properties of the underlying temporal network regulate reachability.
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Affiliation(s)
- Eun Lee
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Scott Emmons
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Ryan Gibson
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - James Moody
- Duke Network Analysis Center and Department of Sociology, Duke University, Durham, North Carolina 27708, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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25
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Abstract
Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata are well aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.
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Affiliation(s)
- Scott Emmons
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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26
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Trogdon JG, Weir WH, Shai S, Mucha PJ, Kuo TM, Meyer AM, Stitzenberg KB. Comparing Shared Patient Networks Across Payers. J Gen Intern Med 2019; 34:2014-2020. [PMID: 30945065 PMCID: PMC6816773 DOI: 10.1007/s11606-019-04978-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 11/21/2018] [Accepted: 02/19/2019] [Indexed: 11/24/2022]
Abstract
BACKGROUND Measuring care coordination in administrative data facilitates important research to improve care quality. OBJECTIVE To compare shared patient networks constructed from administrative claims data across multiple payers. DESIGN Social network analysis of pooled cross sections of physicians treating prevalent colorectal cancer patients between 2003 and 2013. PARTICIPANTS Surgeons, medical oncologists, and radiation oncologists identified from North Carolina Central Cancer Registry data linked to Medicare claims (N = 1735) and private insurance claims (N = 1321). MAIN MEASURES Provider-level measures included the number of patients treated, the number of providers with whom they share patients (by specialty), the extent of patient sharing with each specialty, and network centrality. Network-level measures included the number of providers and shared patients, the density of shared-patient relationships among providers, and the size and composition of clusters of providers with a high level of patient sharing. RESULTS For 24.5% of providers, total patient volume rank differed by at least one quintile group between payers. Medicare claims missed 14.6% of all shared patient relationships between providers, but captured a greater number of patient-sharing relationships per provider compared with the private insurance database, even after controlling for the total number of patients (27.242 vs 26.044, p < 0.001). Providers in the private network shared a higher fraction of patients with other providers (0.226 vs 0.127, p < 0.001) compared to the Medicare network. Clustering coefficients for providers, weighted betweenness, and eigenvector centrality varied greatly across payers. Network differences led to some clusters of providers that existed in the combined network not being detected in Medicare alone. CONCLUSION Many features of shared patient networks constructed from a single-payer database differed from similar networks constructed from other payers' data. Depending on a study's goals, shortcomings of single-payer networks should be considered when using claims data to draw conclusions about provider behavior.
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Affiliation(s)
- Justin G Trogdon
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - W H Weir
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - S Shai
- Department of Mathematics and Computer Science, Wesleyan University, Marion, IN, USA
| | - P J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - T M Kuo
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - K B Stitzenberg
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Division of Surgical Oncology and Endocrinology Surgery, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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27
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Abstract
Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods.
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Affiliation(s)
- Ian Barnett
- University of Pennsylvania, Department of Biostatistics, Philadelphia, 19104, USA
| | - Nishant Malik
- Rochester Institute of Technology, Rochester, 14623, USA
| | | | - Peter J Mucha
- University of North Carolina, Department of Mathematics, Chapel Hill, 27599, USA
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28
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Robinson JI, Weir WH, Crowley JR, Hink T, Reske KA, Kwon JH, Burnham CAD, Dubberke ER, Mucha PJ, Henderson JP. Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections. J Clin Invest 2019; 129:3792-3806. [PMID: 31403473 DOI: 10.1172/jci126905] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 06/11/2019] [Indexed: 12/15/2022] Open
Abstract
Clostridioides difficile infection (CDI) accounts for a substantial proportion of deaths attributable to antibiotic-resistant bacteria in the United States. Although C. difficile can be an asymptomatic colonizer, its pathogenic potential is most commonly manifested in patients with antibiotic-modified intestinal microbiomes. In a cohort of 186 hospitalized patients, we showed that host and microbe-associated shifts in fecal metabolomes had the potential to distinguish patients with CDI from those with non-C. difficile diarrhea and C. difficile colonization. Patients with CDI exhibited a chemical signature of Stickland amino acid fermentation that was distinct from those of uncolonized controls. This signature suggested that C. difficile preferentially catabolizes branched chain amino acids during CDI. Unexpectedly, we also identified a series of noncanonical, unsaturated bile acids that were depleted in patients with CDI. These bile acids may derive from an extended host-microbiome dehydroxylation network in uninfected patients. Bile acid composition and leucine fermentation defined a prototype metabolomic model with potential to distinguish clinical CDI from asymptomatic C. difficile colonization.
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Affiliation(s)
- John I Robinson
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - William H Weir
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, and Curriculum in Bioinformatics & Computational Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jan R Crowley
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Tiffany Hink
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Kimberly A Reske
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jennie H Kwon
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Carey-Ann D Burnham
- Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Erik R Dubberke
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, and Curriculum in Bioinformatics & Computational Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Jeffrey P Henderson
- Center for Women's Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
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29
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Abstract
Even though transitivity is a central structural feature of social networks, its influence on epidemic spread on coevolving networks has remained relatively unexplored. Here we introduce and study an adaptive susceptible-infected-susceptible (SIS) epidemic model wherein the infection and network coevolve with nontrivial probability to close triangles during edge rewiring, leading to substantial reinforcement of network transitivity. This model provides an opportunity to study the role of transitivity in altering the SIS dynamics on a coevolving network. Using numerical simulations and approximate master equations (AMEs), we identify and examine a rich set of dynamical features in the model. In many cases, AMEs including transitivity reinforcement provide accurate predictions of stationary-state disease prevalence and network degree distributions. Furthermore, for some parameter settings, the AMEs accurately trace the temporal evolution of the system. We show that higher transitivity reinforcement in the model leads to lower levels of infective individuals in the population, when closing a triangle is the dominant rewiring mechanism. These methods and results may be useful in developing ideas and modeling strategies for controlling SIS-type epidemics.
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Affiliation(s)
- Hsuan-Wei Lee
- Institute of Sociology, Academia Sinica, Taipei 115, Taiwan
| | - Nishant Malik
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, New York 14623, USA
| | - Feng Shi
- Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Peter J Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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30
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Gates KM, Fisher ZF, Arizmendi C, Henry TR, Duffy KA, Mucha PJ. Assessing the robustness of cluster solutions obtained from sparse count matrices. Psychol Methods 2019; 24:675-689. [PMID: 30742473 DOI: 10.1037/met0000204] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging). Increasingly, community detection methods are being used to subset the data in a data-driven manner. While many of these approaches perform well in simulation studies and thus offer some improvement upon traditional clustering approaches, there is no readily available approach for evaluating the robustness of these solutions in empirical data. Researchers have no way of knowing if their results are due to noise. We describe here 2 approaches novel to the field of psychology that enable evaluation of cluster solution robustness. This tutorial also explains the use of an associated R package, perturbR, which provides researchers with the ability to use the methods described herein. In the first approach, the cluster assignment from the original matrix is compared against cluster assignments obtained by randomly perturbing the edges in the matrix. Stable cluster solutions should not demonstrate large changes in the presence of small perturbations. For the second approach, Monte Carlo simulations of random matrices that have the same properties as the original matrix are generated. The distribution of quality scores ("modularity") obtained from the cluster solutions from these matrices are then compared with the score obtained from the original matrix results. From this, one can assess if the results are better than what would be expected by chance. perturbR automates these 2 methods, providing an easy-to-use resource for psychological researchers. We demonstrate the utility of this package using benchmark simulated data generated from a previous study and then apply the methods to publicly available empirical data obtained from social networks and structural neuroimaging. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Affiliation(s)
- Kathleen M Gates
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Zachary F Fisher
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Cara Arizmendi
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Teague R Henry
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Kelly A Duffy
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill
| | - Peter J Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill
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31
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Abstract
The spreading of epidemics is very much determined by the structure of the contact network, which may be impacted by the mobility dynamics of the individuals themselves. In confined scenarios where a small, closed population spends most of its time in localized environments and has easily identifiable mobility patterns—such as workplaces, university campuses, or schools—it is of critical importance to identify the factors controlling the rate of disease spread. Here, we present a discrete-time, metapopulation-based model to describe the transmission of susceptible-infected-susceptible-like diseases that take place in confined scenarios where the mobilities of the individuals are not random but, rather, follow clear recurrent travel patterns. This model allows analytical determination of the onset of epidemics, as well as the ability to discern which contact structures are most suited to prevent the infection to spread. It thereby determines whether common prevention mechanisms, as isolation, are worth implementing in such a scenario and their expected impact.
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Affiliation(s)
- Clara Granell
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain.,Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, E-08007 Barcelona, Spain.,Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA
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32
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Kirk JM, Kim SO, Inoue K, Smola MJ, Lee DM, Schertzer MD, Wooten JS, Baker AR, Sprague D, Collins DW, Horning CR, Wang S, Chen Q, Weeks KM, Mucha PJ, Calabrese JM. Functional classification of long non-coding RNAs by k-mer content. Nat Genet 2018; 50:1474-1482. [PMID: 30224646 PMCID: PMC6262761 DOI: 10.1038/s41588-018-0207-8] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 07/24/2018] [Indexed: 12/30/2022]
Abstract
The functions of most long non-coding RNAs (lncRNAs) are unknown. In contrast to proteins, lncRNAs with similar functions often lack linear sequence homology; thus, the identification of function in one lncRNA rarely informs the identification of function in others. We developed a sequence comparison method to deconstruct linear sequence relationships in lncRNAs and evaluate similarity based on the abundance of short motifs called kmers. We found that lncRNAs of related function often had similar kmer profiles despite lacking linear homology, and that kmer profiles correlated with protein binding to lncRNAs and with their subcellular localization. Using a novel assay to quantify Xist-like regulatory potential, we directly demonstrated that evolutionarily unrelated lncRNAs can encode similar function through different spatial arrangements of related sequence motifs. Kmer-based classification is a powerful approach to detect recurrent relationships between sequence and function in lncRNAs.
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Affiliation(s)
- Jessime M Kirk
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Susan O Kim
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Kaoru Inoue
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Matthew J Smola
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Ribometrix, Durham, NC, USA
| | - David M Lee
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Megan D Schertzer
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joshua S Wooten
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Allison R Baker
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Harvard Medical School, Ph.D. Program in Biological and Biomedical Sciences, Boston, MA, USA
| | - Daniel Sprague
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David W Collins
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher R Horning
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shuo Wang
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qidi Chen
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kevin M Weeks
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - J Mauro Calabrese
- Department of Pharmacology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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33
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Heroy S, Taylor D, Shi FB, Forest MG, Mucha PJ. RIGID GRAPH COMPRESSION: MOTIF-BASED RIGIDITY ANALYSIS FOR DISORDERED FIBER NETWORKS. Multiscale Model Simul 2018; 16:1283-1304. [PMID: 30450018 PMCID: PMC6234004 DOI: 10.1137/17m1157271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Using particle-scale models to accurately describe property enhancements and phase transitions in macroscopic behavior is a major engineering challenge in composite materials science. To address some of these challenges, we use the graph theoretic property of rigidity to model mechanical reinforcement in composites with stiff rod-like particles. We develop an efficient algorithmic approach called rigid graph compression (RGC) to describe the transition from floppy to rigid in disordered fiber networks ("rod-hinge systems"), which form the reinforcing phase in many composite systems. To establish RGC on a firm theoretical foundation, we adapt rigidity matroid theory to identify primitive topological network motifs that serve as rules for composing interacting rigid particles into larger rigid components. This approach is computationally efficient and stable, because RGC requires only topological information about rod interactions (encoded by a sparse unweighted network) rather than geometrical details such as rod locations or pairwise distances (as required in rigidity matroid theory). We conduct numerical experiments on simulated two-dimensional rod-hinge systems to demonstrate that RGC closely approximates the rigidity percolation threshold for such systems, through comparison with the pebble game algorithm (which is exact in two dimensions). Importantly, whereas the pebble game is derived from Laman's condition and is only valid in two dimensions, the RGC approach naturally extends to higher dimensions.
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Affiliation(s)
- Samuel Heroy
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599
| | - Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599
| | - F Bill Shi
- The Odum Institute for Research in Social Science, University of North Carolina, Chapel Hill, NC 27599
| | - M Gregory Forest
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599
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34
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Abstract
Assessing whether a given network is typical or atypical for a random-network ensemble (i.e., network-ensemble comparison) has widespread applications ranging from null-model selection and hypothesis testing to clustering and classifying networks. We develop a framework for network-ensemble comparison by subjecting the network to stochastic rewiring. We study two rewiring processes-uniform and degree-preserved rewiring-which yield random-network ensembles that converge to the Erdős-Rényi and configuration-model ensembles, respectively. We study convergence through von Neumann entropy (VNE)-a network summary statistic measuring information content based on the spectra of a Laplacian matrix-and develop a perturbation analysis for the expected effect of rewiring on VNE. Our analysis yields an estimate for how many rewires are required for a given network to resemble a typical network from an ensemble, offering a computationally efficient quantity for network-ensemble comparison that does not require simulation of the corresponding rewiring process.
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Affiliation(s)
- Zichao Li
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Mathematics, University at Buffalo, State University of New York (SUNY), Buffalo, NY 14260, USA
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35
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Abstract
We study a model for switching strategies in the Prisoner's Dilemma game on adaptive networks of player pairings that coevolve as players attempt to maximize their return. We use a node-based strategy model wherein each player follows one strategy at a time (cooperate or defect) across all of its neighbors, changing that strategy and possibly changing partners in response to local changes in the network of player pairing and in the strategies used by connected partners. We compare and contrast numerical simulations with existing pair approximation differential equations for describing this system, as well as more accurate equations developed here using the framework of approximate master equations. We explore the parameter space of the model, demonstrating the relatively high accuracy of the approximate master equations for describing the system observations made from simulations. We study two variations of this partner-switching model to investigate the system evolution, predict stationary states, and compare the total utilities and other qualitative differences between these two model variants.
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Affiliation(s)
- Hsuan-Wei Lee
- Department of Sociology, University of Nebraska-Lincoln
| | | | - Peter J Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill
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36
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Strano E, Giometto A, Shai S, Bertuzzo E, Mucha PJ, Rinaldo A. The scaling structure of the global road network. R Soc Open Sci 2017; 4:170590. [PMID: 29134071 PMCID: PMC5666254 DOI: 10.1098/rsos.170590] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 09/18/2017] [Indexed: 05/30/2023]
Abstract
Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.
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Affiliation(s)
- Emanuele Strano
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, D-82234 Wessling, Germany
- Laboratory of Geographic Information Systems (LaSig), Polytechnical School of Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Andrea Giometto
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- Laboratory of Ecohydrology, École Polytechnique Fédérale Lausanne (EPFL), Lausanne CH-1015, Switzerland
| | - Saray Shai
- Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Enrico Bertuzzo
- Laboratory of Ecohydrology, École Polytechnique Fédérale Lausanne (EPFL), Lausanne CH-1015, Switzerland
- Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, Venezia Mestre 30170, Italy
| | - Peter J. Mucha
- Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, École Polytechnique Fédérale Lausanne (EPFL), Lausanne CH-1015, Switzerland
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova 35131, Italy
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Abstract
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.
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Affiliation(s)
- William H. Weir
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
- Correspondence:
| | - Scott Emmons
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ryan Gibson
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Peter J. Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
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38
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Abstract
Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K* . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than 𝒪(L-1/2). Moreover, we find that thresholding the summation can, in some cases, cause K* to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.
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Affiliation(s)
- Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, New York 14260, USA
| | - Rajmonda S. Caceres
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02420, USA
| | - Peter J. Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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39
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Abstract
Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.
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Affiliation(s)
- Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA; and Statistical and Applied Mathematical Sciences Institute (SAMSI), Research Triangle Park, NC, 27709, USA
| | - Sean A Myers
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA (Current address: Department of Economics, Stanford University, Stanford, CA 94305-6072, USA)
| | - Aaron Clauset
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA; Santa Fe Institute, Santa Fe, NM 87501, USA; and BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA
| | - Mason A Porter
- Mathematical Institute, University of Oxford, OX2 6GG, UK; CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK; and Department of Mathematics, University of California, Los Angeles, CA 90095, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599-3250, USA
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Aikat J, Carsey TM, Fecho K, Jeffay K, Krishnamurthy A, Mucha PJ, Rajasekar A, Ahalt SC. Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education. Big Data 2017; 5:12-18. [PMID: 28287837 DOI: 10.1089/big.2016.0014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.
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Affiliation(s)
- Jay Aikat
- 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
- 2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Thomas M Carsey
- 3 Odum Institute for Research in Social Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
- 4 Department of Political Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Karamarie Fecho
- 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Kevin Jeffay
- 2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Ashok Krishnamurthy
- 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Peter J Mucha
- 5 Department of Mathematics, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Arcot Rajasekar
- 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
- 6 School of Information and Library Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
| | - Stanley C Ahalt
- 1 Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
- 2 Department of Computer Science, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina
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41
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Malik N, Shi F, Lee HW, Mucha PJ. Transitivity reinforcement in the coevolving voter model. Chaos 2016; 26:123112. [PMID: 28039984 PMCID: PMC5848690 DOI: 10.1063/1.4972116] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 11/29/2016] [Indexed: 06/06/2023]
Abstract
One of the fundamental structural properties of many networks is triangle closure. Whereas the influence of this transitivity on a variety of contagion dynamics has been previously explored, existing models of coevolving or adaptive network systems typically use rewiring rules that randomize away this important property, raising questions about their applicability. In contrast, we study here a modified coevolving voter model dynamics that explicitly reinforces and maintains such clustering. Carrying out numerical simulations for a variety of parameter settings, we establish that the transitions and dynamical states observed in coevolving voter model networks without clustering are altered by reinforcing transitivity in the model. We then use a semi-analytical framework in terms of approximate master equations to predict the dynamical behaviors of the model for a variety of parameter settings.
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Affiliation(s)
- Nishant Malik
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire 03755, USA
| | - Feng Shi
- Computation Institute, University of Chicago, Chicago, Illinois 60637, USA
| | - Hsuan-Wei Lee
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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42
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Entwisle B, Williams NE, Verdery AM, Rindfuss RR, Walsh SJ, Malanson GP, Mucha PJ, Frizzelle BG, McDaniel PM, Yao X, Heumann BW, Prasartkul P, Sawangdee Y, Jampaklay A. Climate Shocks and Migration: An Agent-Based Modeling Approach. Popul Environ 2016; 38:47-71. [PMID: 27594725 PMCID: PMC5004973 DOI: 10.1007/s11111-016-0254-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
This is a study of migration responses to climate shocks. We construct an agent-based model that incorporates dynamic linkages between demographic behaviors, such as migration, marriage, and births, and agriculture and land use, which depend on rainfall patterns. The rules and parameterization of our model are empirically derived from qualitative and quantitative analyses of a well-studied demographic field site, Nang Rong district, Northeast Thailand. With this model, we simulate patterns of migration under four weather regimes in a rice economy: 1) a reference, 'normal' scenario; 2) seven years of unusually wet weather; 3) seven years of unusually dry weather; and 4) seven years of extremely variable weather. Results show relatively small impacts on migration. Experiments with the model show that existing high migration rates and strong selection factors, which are unaffected by climate change, are likely responsible for the weak migration response.
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Affiliation(s)
- Barbara Entwisle
- Office of the Vice Chancellor for Research, University of North Carolina at Chapel Hill, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, USA
- Department of Sociology, University of North Carolina at Chapel Hill, USA
| | - Nathalie E. Williams
- Jackson School of International Studies, University of Washington, USA
- Department of Sociology, University of Washington, USA
| | - Ashton M. Verdery
- Carolina Population Center, University of North Carolina at Chapel Hill, USA
- Department of Sociology, University of North Carolina at Chapel Hill, USA
| | - Ronald R. Rindfuss
- Carolina Population Center, University of North Carolina at Chapel Hill, USA
- Department of Sociology, University of North Carolina at Chapel Hill, USA
- East-West Center, Honolulu, HI, USA
| | - Stephen J. Walsh
- Carolina Population Center, University of North Carolina at Chapel Hill, USA
- Department of Geography, University of North Carolina at Chapel Hill, USA
| | | | - Peter J. Mucha
- Department of Mathematics, University of North Carolina at Chapel Hill, USA
| | - Brian G. Frizzelle
- Carolina Population Center, University of North Carolina at Chapel Hill, USA
| | | | | | - Benjamin W. Heumann
- Dept. of Geography; Center for Geographic Information Science, Central Michigan University, USA
| | - Pramote Prasartkul
- Institute for Population and Social Research, Mahidol University, Thailand
| | - Yothin Sawangdee
- Institute for Population and Social Research, Mahidol University, Thailand
| | - Aree Jampaklay
- Institute for Population and Social Research, Mahidol University, Thailand
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43
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Taylor D, Shai S, Stanley N, Mucha PJ. Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation. Phys Rev Lett 2016; 116:228301. [PMID: 27314740 PMCID: PMC5125641 DOI: 10.1103/physrevlett.116.228301] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Indexed: 05/24/2023]
Abstract
Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common-but not well understood-practice of thresholding pairwise-interaction data to obtain sparse network representations.
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Affiliation(s)
- Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Saray Shai
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Natalie Stanley
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Peter J. Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA
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44
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Abstract
Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.
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Affiliation(s)
- Natalie Stanley
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill
| | - Saray Shai
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill
| | - Dane Taylor
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill
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45
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Abstract
In this study, we provide a comprehensive analysis of trends in the extremes during the Indian summer monsoon (ISM) months (June to September) at different temporal and spatial scales. Our goal is to identify and quantify spatiotemporal patterns and trends that have emerged during the recent decades and may be associated with changing climatic conditions. Our analysis primarily relies on quantile regression that avoids making any subjective choices on spatial, temporal, or intensity pattern of extreme rainfall events. Our analysis divides the Indian monsoon region into climatic compartments that show different and partly opposing trends. These include strong trends towards intensified droughts in Northwest India, parts of Peninsular India, and Myanmar; in contrast, parts of Pakistan, Northwest Himalaya, and Central India show increased extreme daily rain intensity leading to higher flood vulnerability. Our analysis helps explain previously contradicting results of trends in average ISM rainfall.
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Affiliation(s)
- Nishant Malik
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, CB #3250, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Bodo Bookhagen
- Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14467 Potsdam-Golm, Germany
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, CB #3250, University of North Carolina, Chapel Hill, NC 27599, USA
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46
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47
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Abstract
This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.
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Affiliation(s)
- Ashton M. Verdery
- Department of Sociology and Criminology, Population Research Institute, and Institute for CyberScience, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Ted Mouw
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Shawn Bauldry
- Department of Sociology, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Peter J. Mucha
- Department of Applied Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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48
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Parker KS, Wilson JD, Marschall J, Mucha PJ, Henderson JP. Network Analysis Reveals Sex- and Antibiotic Resistance-Associated Antivirulence Targets in Clinical Uropathogens. ACS Infect Dis 2015; 1:523-532. [PMID: 26985454 PMCID: PMC4788272 DOI: 10.1021/acsinfecdis.5b00022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Indexed: 01/29/2023]
Abstract
Increasing antibiotic resistance among uropathogenic Escherichia coli (UPEC) is driving interest in therapeutic targeting of nonconserved virulence factor (VF) genes. The ability to formulate efficacious combinations of antivirulence agents requires an improved understanding of how UPEC deploy these genes. To identify clinically relevant VF combinations, we applied contemporary network analysis and biclustering algorithms to VF profiles from a large, previously characterized inpatient clinical cohort. These mathematical approaches identified four stereotypical VF combinations with distinctive relationships to antibiotic resistance and patient sex that are independent of traditional phylogenetic grouping. Targeting resistance- or sex-associated VFs based upon these contemporary mathematical approaches may facilitate individualized anti-infective therapies and identify synergistic VF combinations in bacterial pathogens.
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Affiliation(s)
| | | | - Jonas Marschall
- Department
of Infectious Diseases, Bern University Hospital and University of Bern, 3010 Bern, Switzerland
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49
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Jeub LGS, Balachandran P, Porter MA, Mucha PJ, Mahoney MW. Think locally, act locally: detection of small, medium-sized, and large communities in large networks. Phys Rev E Stat Nonlin Soft Matter Phys 2015; 91:012821. [PMID: 25679670 PMCID: PMC5125638 DOI: 10.1103/physreve.91.012821] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Indexed: 06/04/2023]
Abstract
It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic networks, the output of locally biased methods that focus on communities that are centered around a given seed node (or set of seed nodes) might have better conceptual grounding and greater practical utility than the output of global community-detection methods. They also illustrate structural properties that are important to consider in the development of better benchmark networks to test methods for community detection.
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Affiliation(s)
- Lucas G S Jeub
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Prakash Balachandran
- Morgan Stanley, Montreal, Quebec, H3C 3S4, Canada and Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA
| | - Mason A Porter
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom and CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, United Kingdom
| | - Peter J Mucha
- Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA
| | - Michael W Mahoney
- International Computer Science Institute, Berkeley, California 94704, USA and Department of Statistics, University of California at Berkeley, Berkeley, California 94720, USA
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Wilson JD, Wang S, Mucha PJ, Bhamidi S, Nobel AB. A testing based extraction algorithm for identifying significant communities in networks. Ann Appl Stat 2014. [DOI: 10.1214/14-aoas760] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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