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Unveiling socio-psychological determinants behind residential treated greywater adoption: Integrating theory of planned behavior and norm activation model

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Journal of Environmental Management 2025年 387卷 125867页

作者： Sadegh Danesh-pajooh Massoud Tabesh Seyyed Ahmadreza Shahangian Masoud Yazdanpanah Tahereh Zobeidi Erfan Rezaei School of Civil Engineering College of Engineering University of Tehran Tehran Iran Advancing System Analysis Program International Institute for Applied Systems Analysis (IIASA) Laxenburg Austria

Persistent water scarcity and resource degradation threaten urban water security, driving water utilities to promote alternative sources such as residential treated greywater (RTG) as a sustainable solution. However, public resistance remains the primary barrier to the long-term and widespread implementation of any water reuse initiative. Hence, it is paramount to gain exhaustive insights into the motivations and mechanisms behind voluntary RTG adoption behavior, contributing to devising more impactful strategies for promoting such initiatives. Although environmental psychology has laid a robust groundwork in understanding pro-environmental behaviors and identifying entry points of behavioral intervention, a notable gap persists in RTG research, particularly in the Middle East, such as Iran. Hence, this research sought to explore the psychosocial drivers underlying households' RTG adoption through introducing an innovative theoretical framework that integrates the theory of planned behavior with the norm activation model, further extended by perceived risk and anticipated emotions (guilt and pride). A questionnaire was developed based on an extensive literature review and distributed in Isfahan City via an online self-administered survey, utilizing a blend of convenience sampling and cluster sampling techniques (N = 375). Data analysis was conducted using PLS-SEM with SmartPLS software, revealing that: (1) the framework explained 74.7 % and 73.3 % of the variance in intention and behavior; (2) attitude manifested as the most significant determinant of intention, followed by moral norms, subjective norms, perceived behavioral control (PBC), and perceived risk; and (3) behavior was predicted by intention, perceived risk, and PBC. Beyond its novel theoretical contributions, the findings laid a solid foundation for policies promoting RTG adoption: (1) launching awareness campaigns to raise public knowledge of water scarcity and its associated risks; (2) enhancing indivi

关键词： Anticipated emotions Greywater recycling Risk perception Structural equation modeling Wastewater reuse Water conservation

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Modeling supports the implementation of urban greening as a response to the challenges of sustainable spatial planning

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Journal of Environmental Management 2025年 386卷 125648页

作者： Kim, Eun Sub Hyun, Jung Hee Kim, Na Yeon Heo, Su Jeong Kim, Da Seul Lee, Dong Kun Low-Carbon and Climate Impact Research Centre School of Energy and Environment City University of Hong Kong Tat Chee Ave Hong Kong Kowloon Tong China Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Forest Disaster & Environment Department National Institute of Forest Science Seoul Korea Republic of Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of

Urban greening is critical for sustainable urban development, climate change mitigation, and biodiversity conservation. However, the effectiveness of urban greening varies depending on the specific goals (e.g., enhancing biodiversity, reducing urban heat, or both) and their spatial implementation. To address the spatial variability in the effectiveness of greening, we propose a spatial decision support model based on the non-dominated sorting genetic algorithm-II (NSGA-II). This model aims to optimize urban greening locations to maximize biomass density, mitigate urban heat stress, and improve landscape connectivity. Applied to Suwon City, South Korea, the model's effectiveness was evaluated against a business-as-usual (BAU) scenario across four scenarios: connectivity-based, biomass density-based, heat stress-based, and an integrated-based scenario. The integrated approach, balancing trade-offs between ecological benefits and implementation costs, outperformed the BAU scenario by 8.84 %. Despite highlighting a weaker correlation with heat stress mitigation, this outcome indicates significant improvements in biomass density and landscape connectivity. Our findings underscore the necessity of an integrated planning approach to urban greening, as it can contribute toward attaining urban development goals. Additionally, by proposing an app-based model for policymakers, our outputs should enable the reconciliation of multiple environmental objectives in urban landscapes. © 2025 Elsevier Ltd

关键词： Biotic

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Introducing a Novel Hybrid Agent-Based Framework for Simulating the Adoption of Residential Water Conservation Behaviors

Introducing a Novel Hybrid Agent-Based Framework for Simulat...

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World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Shahangian, Seyyed Ahmadreza Tabesh, Massoud Yazdanpanah, Masoud Akbarzadeh, Abbas Raoof, Mohammad Amin Zobeidi, Tahereh Hajibabaei, Mohsen Sitzenfrei, Robert School of Civil Engineering College of Engineering Univ. of Tehran Tehran Iran Water and Wastewater Research Center Water Research Institute Tehran Iran Dept. of Agricultural Extension and Education Agricultural Sciences and Natural Resources Univ. of Khuzestan Mollasani Iran Dept. of Family Youth and Community Sciences Univ. of Florida GainesvilleFL United States Dept. of Civil Engineering Sharif Univ. of Technology Tehran Iran Advancing System Analysis Program International Institute for Applied Systems Analysis Laxenburg Austria Unit of Environmental Engineering Dept. of Infrastructure Engineering Univ. of Innsbruck Innsbruck Austria

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

Promoting household adoption of water conservation behaviors (WCBs) has emerged as a vital strategic response to address water scarcity. Nevertheless, it is imperative to recognize that acceptance of residential WCBs relies on households' voluntary engagement, as are interventions designed to encourage households to become involved in such actions. Meanwhile, environmental psychology plays a key role in understanding individuals' WCBs and exploring the psychosocial determinants underlying these behaviors. Furthermore, the spread of technology and behavior among a population are known as a complex adaptive system (CAS), and agent-based modeling (ABM) offers a comprehensive and practical approach to studying CASs. Hence, the main aim of the present research is to introduce a novel, hybrid agent-based framework for simulating the adoption of residential WCBs, in which an extended model of the theory of planned behavior acts as household agents' behavioral rule for adopting water-efficiency behaviors (WEBs). The framework is also grounded in various theories rooted in psychological and social sciences. Moreover, the presented framework integrates ABM and structural equation modeling approaches. Although this study mainly focuses on WEBs, the framework includes a mechanism to update agents' water use based on their adoption level of water curtailment behaviors. Thus, the presented framework allows for simultaneously but separately exploring the preferred level of both types of WCBs among household agents. Finally, total water consumption is systematically calculated based on agents' aggregated behavior. © World Environmental and Water Resources Congress *** rights reserved

关键词： Water conservation

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Nature-Based Solutions Can Potentially Contribute to Biomass Density, Heat Stress Mitigation, and Biodiversity

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SSRN 2023年

作者： Kim, Eun Sub Hyun, Jung Hee Kim, Na Yeon Heo, Su Jeong Kim, Shin Woo Kim, Da Sle Lee, Dong Kun Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of

Nature-based solutions (NBS) are actions to protect, sustainably manage, or restore natural ecosystems. They are increasingly recognized worldwide as a key strategy for sustainable development, integrating climate change adaptation and mitigation and biodiversity conservation. NBS is fundamental to the quality of life of residents, and urban planning often includes strategies that are directly or indirectly related to greening. Although numerous studies have been conducted to quantitatively evaluate the benefits of NBS, analysis of the potential effect of NBS remains insufficient. Therefore, we developed a spatial decision-making support model that optimizes the location of NBS on multiple effects (biomass density, urban heat stress mitigation, and landscape connectivity) using a Non-Sorting dominated Genetic Algorithm-II (NSGA-II). Potential was assessed by comparing the NBS plan considering co-benefits with NBS strategy scenarios focused on individual objectives connectivity, biomass density, heat stress based scenario(CBS, BBS, and HBS). The model was applied to Suwon City, South Korea, demonstrating the potential of an integrated approach to maximize co-benefits simultaneously. The analysis revealed a trade-off between NBS benefits and implementation costs, with performance improvement in one objective often leading to decreased performance in others. Correlation analysis revealed significant positive correlations among the three objectives, with heat stress showing the lowest correlation. Simulations under four distinct scenarios highlighted the diverse potential arrangements of NBS and their effects. An integrated strategy demonstrated a consistently high overall value (a 221% improvement), which proved to be the most efficient approach when considering all aspects collectively. This study underscores the potential benefits of integrating multiple objectives into urban green space planning, thereby providing valuable insights for sustainable urban development.

关键词： Biodiversity

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Modeling Supports the Implementation of Urban Greening as a Response to the Challenges of Sustainable Spatial Planning

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SSRN 2024年

作者： Kim, Eun Sub Lee, Dong Kun Hyun, Jung Hee Kim, Na Yeon Heo, Su Jeong Kim, Da Sle Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of

Urban greening is a critical strategy for sustainable urban development, climate change mitigation, and biodiversity conservation. However, the effectiveness of urban greening varies depending on the specific goals (e.g., enhancing biodiversity, reducing urban heat, or both) and their spatial implementation. To address the spatial variability in the effectiveness of greening, we propose a spatial decision support model based on the non-dominated sorting genetic algorithm-II (NSGA-II). This model aims to optimize urban greening locations to maximize biomass density, mitigate urban heat stress, and simultaneously improve landscape connectivity. Applied to Suwon City, South Korea, the model's effectiveness was evaluated against a Business-As-Usual (BAU) scenario across four scenarios: Connectivity-based, Biomass density-based, Heat stress-based, and an Integrated-based scenario. The integrated approach, balancing trade-offs between ecological benefits and implementation costs, outperformed the BAU scenario by 8.84%. This outcome indicated of significant improvements in biomass density and landscape connectivity, despite highlighting a weaker correlation with heat stress mitigation. Our findings underscore the necessity of an integrated planning approach to urban greening, as it can contribute toward the attainment of urban development goals. Additionally, by proposing an app-based model for policymakers to utilize, our outputs should enable the reconciliation of multiple environmental objectives in urban landscapes. © 2024, The Authors. All rights reserved.

关键词： Climate change

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Landscape Design for Improved Thermal Environment: An Optimized Tree Arrangement Design for Climate-Responsive Outdoor Spaces in Residential Buildings

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SSRN 2023年

作者： Kim, Ji Yeon Park, Chae Yeon Hyun, Jung Hee Kim, Sang Hyuck Yun, Seok Hwan Lee, Dong Kun Interdisciplinary Program in Landscape Architecture Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul Korea Republic of Specialized Graduate School of Intelligent Eco-Science Seoul National University Seoul Korea Republic of Smart Green Solution team SK forest Seoul Korea Republic of National Institute for Environmental Studies 16-2 Onogawa Ibaraki Tsukuba305-8506 Japan Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul Korea Republic of

The heat reduction effect of trees has been investigated through numerical simulations;however, there are still challenges to applying the scientific results to the planning process due to the model's complexity and the computational resources required. This study investigates a rapid spatial evaluation method for heat stress potential, measured by mean radiant temperature (MRT), by decomposing radiation into sub-radiation using a multilayer MRT model. This method also enables the reproduction of optimized layouts considering the effect of tree arrangement in residential buildings. Multi-objectives were achieved through an evolutionary algorithm, resulting in more effective design layouts combining tree types and arrangements, all within a standard budget. By adopting this study's approach, landscape designers can create climate-responsive tree layouts with reduced heat exposure and generate customized planting designs tailored to their preferences. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Sequential Action-Based Dynamic Decision-Support Model for Urban Ecological Planning

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SSRN 2023年

作者： Kim, Eun Sub Yun, Seok Hwan Kim, Ji Yeon Thorne, James H. Hyun, Jung Hee Lee, Dong Kun Interdisciplinary Program in Landscape Architecture Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Program Seoul National University Seoul08826 Korea Republic of Specialized Graduate School of Intelligent Eco-Science 4 Dept. of Landscape Architecture Seoul National University Seoul08826 Korea Republic of Smart Green Solution Team SK forest Department of Environmental Science and Policy University of California One Shields Ave DavisCA95616 United States Systemic Risk and Resilience Research Group Advancing Systems Analysis International Institute for Applied Systems Analysis Austria Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul08826 Korea Republic of

Decision-makers often encounter the challenge of efficient long-term ecological planning to consider economic and environmental trade-offs. However, tools to support stepwise mitigation strategies that account for dynamic ecological responses to continuing urban development are limited. Therefore, we developed a sequential action-based dynamic decision-making model to simulate stepwise mitigation measures in space over time using a multi-objective optimization algorithm. We focused on exploring the ecological corridors and habitat creation as mitigation measures through four decades to identify trade-offs and optimal plans that minimize landscape connectivity and edge density as ecological responses to urban development and minimize implementation costs per action. The results showed that adopting an initial low-investment strategy with sequential planning significantly enhanced potential ecological benefits beyond the 2000s compared with one-action planning. In contrast, the initial high-cost investment exhibited a gradual decline in the ecological benefits over time from 1995. The initial low-cost investment presented an opportunity to enhance ecological benefits by implementing early mitigation measures in locations capable of sustaining long-term ecological functions. The results suggest that sequential planning offers valuable insights into effective urban ecological planning by analyzing time-series data. This study enables step-by-step scenario testing, thereby supporting the facilitation of urban and regional sustainability planning. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science : Seattle, WA, USA. 24-26 September 2015

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Implementation science : IS 2016年第1期11 Suppl 1卷 85页

作者： Cara Lewis Doyanne Darnell Suzanne Kerns Maria Monroe-DeVita Sara J. Landes Aaron R. Lyon Cameo Stanick Shannon Dorsey Jill Locke Brigid Marriott Ajeng Puspitasari Caitlin Dorsey Karin Hendricks Andria Pierson Phil Fizur Katherine A. Comtois Lawrence A. Palinkas Patricia Chamberlain Gregory A. Aarons Amy E. Green Mark. G. Ehrhart Elise M. Trott Cathleen E. Willging Maria E. Fernandez Nicholas H. Woolf Shuting Lily Liang Natalia I. Heredia Michelle Kegler Betsy Risendal Andrea Dwyer Vicki Young Dayna Campbell Michelle Carvalho Yvonne Kellar-Guenther Laura J. Damschroder Julie C. Lowery Sarah S. Ono Kathleen F. Carlson Erika K. Cottrell Maya E. O’Neil Travis L. Lovejoy Joanna J. Arch Jill L. Mitchell Cara C. Lewis Brigid R. Marriott Kelli Scott Jennifer Schurer Coldiron Eric J. Bruns Alyssa N. Hook Benjamin C. Graham Katelin Jordan Rochelle F. Hanson Angela Moreland Benjamin E. Saunders Heidi S. Resnick Shannon Wiltsey Stirman Cassidy A. Gutner Jennifer Gamarra Dawne Vogt Michael Suvak Jennifer Schuster Wachen Katherine Dondanville Jeffrey S. Yarvis Jim Mintz Alan L. Peterson Elisa V. Borah Brett T. Litz Alma Molino Stacey Young McCaughan Patricia A. Resick Nancy Pandhi Nora Jacobson Neftali Serrano Armando Hernandez Elizabeth Zeidler- Schreiter Natalie Wietfeldt Zaher Karp Michael D. Pullmann Barbara Lucenko Bridget Pavelle Jacqueline A. Uomoto Andrea Negrete Molly Cevasco Suzanne E. U. Kerns Robert P. Franks Christopher Bory Edward J. Miech Teresa M. Damush Jason Satterfield Derek Satre Maria Wamsley Patrick Yuan Patricia O’Sullivan Helen Best Susan Velasquez Miya Barnett Lauren Brookman-Frazee Jennifer Regan Nicole Stadnick Alison Hamilton Anna Lau Scott Roesch Byron J. Powell Thomas J. Waltz Matthew J. Chinman Laura Damschroder Jeffrey L. Smith Monica M. Matthieu Enola K. Proctor JoAnn E. Kirchner Monica J. Matthieu Craig S. Rosen Sarah C. Walker Asia S. Bishop Mariko Lockhart Allison L. Rodriguez Luisa Manfredi Andrea Nevedal Joel Rosenthal Daniel M. Blonigen Anne M. Mauricio Thomas D. Dishion Jenna Rudo-Stern Department of Psychological and Brain Sciences Indiana University Bloomington USA Department of Psychiatry & Behavioral Sciences University of Washington Seattle USA Psychology Department University of Montana Missoula USA Department of Psychology La Salle University Philadelphia USA Division of Public Behavioral Health and Justice Policy University of Washington Seattle USA National Center for PTSD VA Palo Alto Health Care System Menlo Park USA Department of Psychiatry University of Arkansas for Medical Sciences Little Rock USA Department of Psychology University of Washington Seattle USA Department of Speech and Hearing Sciences University of Washington Seattle USA Department of Psychological Sciences University of Missouri Columbia USA Department of Children Youth and Families School of Social Work University of Southern California Los Angeles USA Oregon Social Learning Center Eugene USA Department of Psychiatry University of California San Diego La Jolla USA Child and Adolescent Services Research Center (CASRC) San Diego USA Department of Psychology San Diego State University San Diego USA Pacific Institute for Research and Evaluation Behavioral Health Research Center of the Southwest Albuquerque USA School of Public Health University of Texas Health Science Center at Houston Houston USA Woolf Consulting Santa Barbara USA Rollins School of Public Health Emory University Atlanta USA University of Colorado Cancer Center at Denver Aurora USA South Carolina Primary Health Care Association Columbia USA Veterans Affairs (VA) Ann Arbor Center for Clinical Management Research Ann Arbor USA Personalizing Options through Veteran Engagement (PROVE) Quality Enhancement Research Initiative (QUERI) Program Ann Arbor USA Center to Improve Veteran Involvement in Care (CIVIC) Veterns Affairs (VA) Portland Health Care System (VAPORHCS) Portland USA Department of Family Medicine Oregon Health & Science University Portland USA Division of General Internal Medi

Introduction to the 3rd Biennial Conference of the Society for Implementation Research Collaboration: advancing efficient methodologies through team science and community partnerships Cara Lewis, Doyanne Darnell, Suzanne Kerns, Maria Monroe-DeVita, Sara J. Landes, Aaron R. Lyon, Cameo Stanick, Shannon Dorsey, Jill Locke, Brigid Marriott, Ajeng Puspitasari, Caitlin Dorsey, Karin Hendricks, Andria Pierson, Phil Fizur, Katherine A. Comtois A1: A behavioral economic perspective on adoption, implementation, and sustainment of evidence-based interventions Lawrence A. Palinkas A2: Towards making scale up of evidence-based practices in child welfare systems more efficient and affordable Patricia Chamberlain A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health Centers: Learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health Centers: Learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A5: Efficient synthesis: Using qualitative comparative analysis and the Consolidated Framework for Implementation Research across diverse studies Laura J. Damschroder, Julie C. Lowery A6: Establishing a veterans engagement group to empower patients and inform Veterans Affairs (VA) health services research Sara

关键词： Veteran Affair Motivational Interview Veteran Health Administration Dialectical Behavior Therapy Qualitative Comparative analysis

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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