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1.  Functional Molecular Ecological Networks 
mBio  2010;1(4):e00169-10.
Biodiversity and its responses to environmental changes are central issues in ecology and for society. Almost all microbial biodiversity research focuses on “species” richness and abundance but not on their interactions. Although a network approach is powerful in describing ecological interactions among species, defining the network structure in a microbial community is a great challenge. Also, although the stimulating effects of elevated CO2 (eCO2) on plant growth and primary productivity are well established, its influences on belowground microbial communities, especially microbial interactions, are poorly understood. Here, a random matrix theory (RMT)-based conceptual framework for identifying functional molecular ecological networks was developed with the high-throughput functional gene array hybridization data of soil microbial communities in a long-term grassland FACE (free air, CO2 enrichment) experiment. Our results indicate that RMT is powerful in identifying functional molecular ecological networks in microbial communities. Both functional molecular ecological networks under eCO2 and ambient CO2 (aCO2) possessed the general characteristics of complex systems such as scale free, small world, modular, and hierarchical. However, the topological structures of the functional molecular ecological networks are distinctly different between eCO2 and aCO2, at the levels of the entire communities, individual functional gene categories/groups, and functional genes/sequences, suggesting that eCO2 dramatically altered the network interactions among different microbial functional genes/populations. Such a shift in network structure is also significantly correlated with soil geochemical variables. In short, elucidating network interactions in microbial communities and their responses to environmental changes is fundamentally important for research in microbial ecology, systems microbiology, and global change.
IMPORTANCE
Microorganisms are the foundation of the Earth’s biosphere and play integral and unique roles in various ecosystem processes and functions. In an ecosystem, various microorganisms interact with each other to form complicated networks. Elucidating network interactions and their responses to environmental changes is difficult due to the lack of appropriate experimental data and an appropriate theoretical framework. This study provides a conceptual framework to construct interaction networks in microbial communities based on high-throughput functional gene array hybridization data. It also first documents that elevated carbon dioxide in the atmosphere dramatically alters the network interactions in soil microbial communities, which could have important implications in assessing the responses of ecosystems to climate change. The conceptual framework developed allows microbiologists to address research questions unapproachable previously by focusing on network interactions beyond the listing of, e.g., the number and abundance of species. Thus, this study could represent transformative research and a paradigm shift in microbial ecology.
doi:10.1128/mBio.00169-10
PMCID: PMC2953006  PMID: 20941329
2.  25th Annual Computational Neuroscience Meeting: CNS-2016 
Sharpee, Tatyana O. | Destexhe, Alain | Kawato, Mitsuo | Sekulić, Vladislav | Skinner, Frances K. | Wójcik, Daniel K. | Chintaluri, Chaitanya | Cserpán, Dorottya | Somogyvári, Zoltán | Kim, Jae Kyoung | Kilpatrick, Zachary P. | Bennett, Matthew R. | Josić, Kresimir | Elices, Irene | Arroyo, David | Levi, Rafael | Rodriguez, Francisco B. | Varona, Pablo | Hwang, Eunjin | Kim, Bowon | Han, Hio-Been | Kim, Tae | McKenna, James T. | Brown, Ritchie E. | McCarley, Robert W. | Choi, Jee Hyun | Rankin, James | Popp, Pamela Osborn | Rinzel, John | Tabas, Alejandro | Rupp, André | Balaguer-Ballester, Emili | Maturana, Matias I. | Grayden, David B. | Cloherty, Shaun L. | Kameneva, Tatiana | Ibbotson, Michael R. | Meffin, Hamish | Koren, Veronika | Lochmann, Timm | Dragoi, Valentin | Obermayer, Klaus | Psarrou, Maria | Schilstra, Maria | Davey, Neil | Torben-Nielsen, Benjamin | Steuber, Volker | Ju, Huiwen | Yu, Jiao | Hines, Michael L. | Chen, Liang | Yu, Yuguo | Kim, Jimin | Leahy, Will | Shlizerman, Eli | Birgiolas, Justas | Gerkin, Richard C. | Crook, Sharon M. | Viriyopase, Atthaphon | Memmesheimer, Raoul-Martin | Gielen, Stan | Dabaghian, Yuri | DeVito, Justin | Perotti, Luca | Kim, Anmo J. | Fenk, Lisa M. | Cheng, Cheng | Maimon, Gaby | Zhao, Chang | Widmer, Yves | Sprecher, Simon | Senn, Walter | Halnes, Geir | Mäki-Marttunen, Tuomo | Keller, Daniel | Pettersen, Klas H. | Andreassen, Ole A. | Einevoll, Gaute T. | Yamada, Yasunori | Steyn-Ross, Moira L. | Alistair Steyn-Ross, D. | Mejias, Jorge F. | Murray, John D. | Kennedy, Henry | Wang, Xiao-Jing | Kruscha, Alexandra | Grewe, Jan | Benda, Jan | Lindner, Benjamin | Badel, Laurent | Ohta, Kazumi | Tsuchimoto, Yoshiko | Kazama, Hokto | Kahng, B. | Tam, Nicoladie D. | Pollonini, Luca | Zouridakis, George | Soh, Jaehyun | Kim, DaeEun | Yoo, Minsu | Palmer, S. E. | Culmone, Viviana | Bojak, Ingo | Ferrario, Andrea | Merrison-Hort, Robert | Borisyuk, Roman | Kim, Chang Sub | Tezuka, Taro | Joo, Pangyu | Rho, Young-Ah | Burton, Shawn D. | Bard Ermentrout, G. | Jeong, Jaeseung | Urban, Nathaniel N. | Marsalek, Petr | Kim, Hoon-Hee | Moon, Seok-hyun | Lee, Do-won | Lee, Sung-beom | Lee, Ji-yong | Molkov, Yaroslav I. | Hamade, Khaldoun | Teka, Wondimu | Barnett, William H. | Kim, Taegyo | Markin, Sergey | Rybak, Ilya A. | Forro, Csaba | Dermutz, Harald | Demkó, László | Vörös, János | Babichev, Andrey | Huang, Haiping | Verduzco-Flores, Sergio | Dos Santos, Filipa | Andras, Peter | Metzner, Christoph | Schweikard, Achim | Zurowski, Bartosz | Roach, James P. | Sander, Leonard M. | Zochowski, Michal R. | Skilling, Quinton M. | Ognjanovski, Nicolette | Aton, Sara J. | Zochowski, Michal | Wang, Sheng-Jun | Ouyang, Guang | Guang, Jing | Zhang, Mingsha | Michael Wong, K. Y. | Zhou, Changsong | Robinson, Peter A. | Sanz-Leon, Paula | Drysdale, Peter M. | Fung, Felix | Abeysuriya, Romesh G. | Rennie, Chris J. | Zhao, Xuelong | Choe, Yoonsuck | Yang, Huei-Fang | Mi, Yuanyuan | Lin, Xiaohan | Wu, Si | Liedtke, Joscha | Schottdorf, Manuel | Wolf, Fred | Yamamura, Yoriko | Wickens, Jeffery R. | Rumbell, Timothy | Ramsey, Julia | Reyes, Amy | Draguljić, Danel | Hof, Patrick R. | Luebke, Jennifer | Weaver, Christina M. | He, Hu | Yang, Xu | Ma, Hailin | Xu, Zhiheng | Wang, Yuzhe | Baek, Kwangyeol | Morris, Laurel S. | Kundu, Prantik | Voon, Valerie | Agnes, Everton J. | Vogels, Tim P. | Podlaski, William F. | Giese, Martin | Kuravi, Pradeep | Vogels, Rufin | Seeholzer, Alexander | Podlaski, William | Ranjan, Rajnish | Vogels, Tim | Torres, Joaquin J. | Baroni, Fabiano | Latorre, Roberto | Gips, Bart | Lowet, Eric | Roberts, Mark J. | de Weerd, Peter | Jensen, Ole | van der Eerden, Jan | Goodarzinick, Abdorreza | Niry, Mohammad D. | Valizadeh, Alireza | Pariz, Aref | Parsi, Shervin S. | Warburton, Julia M. | Marucci, Lucia | Tamagnini, Francesco | Brown, Jon | Tsaneva-Atanasova, Krasimira | Kleberg, Florence I. | Triesch, Jochen | Moezzi, Bahar | Iannella, Nicolangelo | Schaworonkow, Natalie | Plogmacher, Lukas | Goldsworthy, Mitchell R. | Hordacre, Brenton | McDonnell, Mark D. | Ridding, Michael C. | Zapotocky, Martin | Smit, Daniel | Fouquet, Coralie | Trembleau, Alain | Dasgupta, Sakyasingha | Nishikawa, Isao | Aihara, Kazuyuki | Toyoizumi, Taro | Robb, Daniel T. | Mellen, Nick | Toporikova, Natalia | Tang, Rongxiang | Tang, Yi-Yuan | Liang, Guangsheng | Kiser, Seth A. | Howard, James H. | Goncharenko, Julia | Voronenko, Sergej O. | Ahamed, Tosif | Stephens, Greg | Yger, Pierre | Lefebvre, Baptiste | Spampinato, Giulia Lia Beatrice | Esposito, Elric | et Olivier Marre, Marcel Stimberg | Choi, Hansol | Song, Min-Ho | Chung, SueYeon | Lee, Dan D. | Sompolinsky, Haim | Phillips, Ryan S. | Smith, Jeffrey | Chatzikalymniou, Alexandra Pierri | Ferguson, Katie | Alex Cayco Gajic, N. | Clopath, Claudia | Angus Silver, R. | Gleeson, Padraig | Marin, Boris | Sadeh, Sadra | Quintana, Adrian | Cantarelli, Matteo | Dura-Bernal, Salvador | Lytton, William W. | Davison, Andrew | Li, Luozheng | Zhang, Wenhao | Wang, Dahui | Song, Youngjo | Park, Sol | Choi, Ilhwan | Shin, Hee-sup | Choi, Hannah | Pasupathy, Anitha | Shea-Brown, Eric | Huh, Dongsung | Sejnowski, Terrence J. | Vogt, Simon M. | Kumar, Arvind | Schmidt, Robert | Van Wert, Stephen | Schiff, Steven J. | Veale, Richard | Scheutz, Matthias | Lee, Sang Wan | Gallinaro, Júlia | Rotter, Stefan | Rubchinsky, Leonid L. | Cheung, Chung Ching | Ratnadurai-Giridharan, Shivakeshavan | Shomali, Safura Rashid | Ahmadabadi, Majid Nili | Shimazaki, Hideaki | Nader Rasuli, S. | Zhao, Xiaochen | Rasch, Malte J. | Wilting, Jens | Priesemann, Viola | Levina, Anna | Rudelt, Lucas | Lizier, Joseph T. | Spinney, Richard E. | Rubinov, Mikail | Wibral, Michael | Bak, Ji Hyun | Pillow, Jonathan | Zaho, Yuan | Park, Il Memming | Kang, Jiyoung | Park, Hae-Jeong | Jang, Jaeson | Paik, Se-Bum | Choi, Woochul | Lee, Changju | Song, Min | Lee, Hyeonsu | Park, Youngjin | Yilmaz, Ergin | Baysal, Veli | Ozer, Mahmut | Saska, Daniel | Nowotny, Thomas | Chan, Ho Ka | Diamond, Alan | Herrmann, Christoph S. | Murray, Micah M. | Ionta, Silvio | Hutt, Axel | Lefebvre, Jérémie | Weidel, Philipp | Duarte, Renato | Morrison, Abigail | Lee, Jung H. | Iyer, Ramakrishnan | Mihalas, Stefan | Koch, Christof | Petrovici, Mihai A. | Leng, Luziwei | Breitwieser, Oliver | Stöckel, David | Bytschok, Ilja | Martel, Roman | Bill, Johannes | Schemmel, Johannes | Meier, Karlheinz | Esler, Timothy B. | Burkitt, Anthony N. | Kerr, Robert R. | Tahayori, Bahman | Nolte, Max | Reimann, Michael W. | Muller, Eilif | Markram, Henry | Parziale, Antonio | Senatore, Rosa | Marcelli, Angelo | Skiker, K. | Maouene, M. | Neymotin, Samuel A. | Seidenstein, Alexandra | Lakatos, Peter | Sanger, Terence D. | Menzies, Rosemary J. | McLauchlan, Campbell | van Albada, Sacha J. | Kedziora, David J. | Neymotin, Samuel | Kerr, Cliff C. | Suter, Benjamin A. | Shepherd, Gordon M. G. | Ryu, Juhyoung | Lee, Sang-Hun | Lee, Joonwon | Lee, Hyang Jung | Lim, Daeseob | Wang, Jisung | Lee, Heonsoo | Jung, Nam | Anh Quang, Le | Maeng, Seung Eun | Lee, Tae Ho | Lee, Jae Woo | Park, Chang-hyun | Ahn, Sora | Moon, Jangsup | Choi, Yun Seo | Kim, Juhee | Jun, Sang Beom | Lee, Seungjun | Lee, Hyang Woon | Jo, Sumin | Jun, Eunji | Yu, Suin | Goetze, Felix | Lai, Pik-Yin | Kim, Seonghyun | Kwag, Jeehyun | Jang, Hyun Jae | Filipović, Marko | Reig, Ramon | Aertsen, Ad | Silberberg, Gilad | Bachmann, Claudia | Buttler, Simone | Jacobs, Heidi | Dillen, Kim | Fink, Gereon R. | Kukolja, Juraj | Kepple, Daniel | Giaffar, Hamza | Rinberg, Dima | Shea, Steven | Koulakov, Alex | Bahuguna, Jyotika | Tetzlaff, Tom | Kotaleski, Jeanette Hellgren | Kunze, Tim | Peterson, Andre | Knösche, Thomas | Kim, Minjung | Kim, Hojeong | Park, Ji Sung | Yeon, Ji Won | Kim, Sung-Phil | Kang, Jae-Hwan | Lee, Chungho | Spiegler, Andreas | Petkoski, Spase | Palva, Matias J. | Jirsa, Viktor K. | Saggio, Maria L. | Siep, Silvan F. | Stacey, William C. | Bernar, Christophe | Choung, Oh-hyeon | Jeong, Yong | Lee, Yong-il | Kim, Su Hyun | Jeong, Mir | Lee, Jeungmin | Kwon, Jaehyung | Kralik, Jerald D. | Jahng, Jaehwan | Hwang, Dong-Uk | Kwon, Jae-Hyung | Park, Sang-Min | Kim, Seongkyun | Kim, Hyoungkyu | Kim, Pyeong Soo | Yoon, Sangsup | Lim, Sewoong | Park, Choongseok | Miller, Thomas | Clements, Katie | Ahn, Sungwoo | Ji, Eoon Hye | Issa, Fadi A. | Baek, JeongHun | Oba, Shigeyuki | Yoshimoto, Junichiro | Doya, Kenji | Ishii, Shin | Mosqueiro, Thiago S. | Strube-Bloss, Martin F. | Smith, Brian | Huerta, Ramon | Hadrava, Michal | Hlinka, Jaroslav | Bos, Hannah | Helias, Moritz | Welzig, Charles M. | Harper, Zachary J. | Kim, Won Sup | Shin, In-Seob | Baek, Hyeon-Man | Han, Seung Kee | Richter, René | Vitay, Julien | Beuth, Frederick | Hamker, Fred H. | Toppin, Kelly | Guo, Yixin | Graham, Bruce P. | Kale, Penelope J. | Gollo, Leonardo L. | Stern, Merav | Abbott, L. F. | Fedorov, Leonid A. | Giese, Martin A. | Ardestani, Mohammad Hovaidi | Faraji, Mohammad Javad | Preuschoff, Kerstin | Gerstner, Wulfram | van Gendt, Margriet J. | Briaire, Jeroen J. | Kalkman, Randy K. | Frijns, Johan H. M. | Lee, Won Hee | Frangou, Sophia | Fulcher, Ben D. | Tran, Patricia H. P. | Fornito, Alex | Gliske, Stephen V. | Lim, Eugene | Holman, Katherine A. | Fink, Christian G. | Kim, Jinseop S. | Mu, Shang | Briggman, Kevin L. | Sebastian Seung, H. | Wegener, Detlef | Bohnenkamp, Lisa | Ernst, Udo A. | Devor, Anna | Dale, Anders M. | Lines, Glenn T. | Edwards, Andy | Tveito, Aslak | Hagen, Espen | Senk, Johanna | Diesmann, Markus | Schmidt, Maximilian | Bakker, Rembrandt | Shen, Kelly | Bezgin, Gleb | Hilgetag, Claus-Christian | van Albada, Sacha Jennifer | Sun, Haoqi | Sourina, Olga | Huang, Guang-Bin | Klanner, Felix | Denk, Cornelia | Glomb, Katharina | Ponce-Alvarez, Adrián | Gilson, Matthieu | Ritter, Petra | Deco, Gustavo | Witek, Maria A. G. | Clarke, Eric F. | Hansen, Mads | Wallentin, Mikkel | Kringelbach, Morten L. | Vuust, Peter | Klingbeil, Guido | De Schutter, Erik | Chen, Weiliang | Zang, Yunliang | Hong, Sungho | Takashima, Akira | Zamora, Criseida | Gallimore, Andrew R. | Goldschmidt, Dennis | Manoonpong, Poramate | Karoly, Philippa J. | Freestone, Dean R. | Soundry, Daniel | Kuhlmann, Levin | Paninski, Liam | Cook, Mark | Lee, Jaejin | Fishman, Yonatan I. | Cohen, Yale E. | Roberts, James A. | Cocchi, Luca | Sweeney, Yann | Lee, Soohyun | Jung, Woo-Sung | Kim, Youngsoo | Jung, Younginha | Song, Yoon-Kyu | Chavane, Frédéric | Soman, Karthik | Muralidharan, Vignesh | Srinivasa Chakravarthy, V. | Shivkumar, Sabyasachi | Mandali, Alekhya | Pragathi Priyadharsini, B. | Mehta, Hima | Davey, Catherine E. | Brinkman, Braden A. W. | Kekona, Tyler | Rieke, Fred | Buice, Michael | De Pittà, Maurizio | Berry, Hugues | Brunel, Nicolas | Breakspear, Michael | Marsat, Gary | Drew, Jordan | Chapman, Phillip D. | Daly, Kevin C. | Bradle, Samual P. | Seo, Sat Byul | Su, Jianzhong | Kavalali, Ege T. | Blackwell, Justin | Shiau, LieJune | Buhry, Laure | Basnayake, Kanishka | Lee, Sue-Hyun | Levy, Brandon A. | Baker, Chris I. | Leleu, Timothée | Philips, Ryan T. | Chhabria, Karishma
BMC Neuroscience  2016;17(Suppl 1):54.
Table of contents
A1 Functional advantages of cell-type heterogeneity in neural circuits
Tatyana O. Sharpee
A2 Mesoscopic modeling of propagating waves in visual cortex
Alain Destexhe
A3 Dynamics and biomarkers of mental disorders
Mitsuo Kawato
F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons
Vladislav Sekulić, Frances K. Skinner
F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains
Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári
F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.
Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić
O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators
Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona
O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain
Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi
O3 Modeling auditory stream segregation, build-up and bistability
James Rankin, Pamela Osborn Popp, John Rinzel
O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields
Alejandro Tabas, André Rupp, Emili Balaguer-Ballester
O5 A simple model of retinal response to multi-electrode stimulation
Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin
O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task
Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer
O7 Input-location dependent gain modulation in cerebellar nucleus neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber
O8 Analytic solution of cable energy function for cortical axons and dendrites
Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu
O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network
Jimin Kim, Will Leahy, Eli Shlizerman
O10 Is the model any good? Objective criteria for computational neuroscience model selection
Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook
O11 Cooperation and competition of gamma oscillation mechanisms
Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen
O12 A discrete structure of the brain waves
Yuri Dabaghian, Justin DeVito, Luca Perotti
O13 Direction-specific silencing of the Drosophila gaze stabilization system
Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon
O14 What does the fruit fly think about values? A model of olfactory associative learning
Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn
O15 Effects of ionic diffusion on power spectra of local field potentials (LFP)
Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll
O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits
Yasunori Yamada
O17 Spatial coarse-graining the brain: origin of minicolumns
Moira L. Steyn-Ross, D. Alistair Steyn-Ross
O18 Modeling large-scale cortical networks with laminar structure
Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang
O19 Information filtering by partial synchronous spikes in a neural population
Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner
O20 Decoding context-dependent olfactory valence in Drosophila
Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama
P1 Neural network as a scale-free network: the role of a hub
B. Kahng
P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging
Nicoladie D. Tam
P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique
Nicoladie D.Tam, Luca Pollonini, George Zouridakis
P4 Modeling jamming avoidance of weakly electric fish
Jaehyun Soh, DaeEun Kim
P5 Synergy and redundancy of retinal ganglion cells in prediction
Minsu Yoo, S. E. Palmer
P6 A neural field model with a third dimension representing cortical depth
Viviana Culmone, Ingo Bojak
P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord
Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk
P8 The recognition dynamics in the brain
Chang Sub Kim
P9 Multivariate spike train analysis using a positive definite kernel
Taro Tezuka
P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia
Pangyu Joo
P11 The ionic basis of heterogeneity affects stochastic synchrony
Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban
P12 Circular statistics of noise in spike trains with a periodic component
Petr Marsalek
P14 Representations of directions in EEG-BCI using Gaussian readouts
Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong
P15 Action selection and reinforcement learning in basal ganglia during reaching movements
Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak
P17 Axon guidance: modeling axonal growth in T-Junction assay
Csaba Forro, Harald Dermutz, László Demkó, János Vörös
P19 Transient cell assembly networks encode persistent spatial memories
Yuri Dabaghian, Andrey Babichev
P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons
Haiping Huang
P21 Design of biologically-realistic simulations for motor control
Sergio Verduzco-Flores
P22 Towards understanding the functional impact of the behavioural variability of neurons
Filipa Dos Santos, Peter Andras
P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia
Christoph Metzner, Achim Schweikard, Bartosz Zurowski
P24 Memory recall and spike frequency adaptation
James P. Roach, Leonard M. Sander, Michal R. Zochowski
P25 Stability of neural networks and memory consolidation preferentially occur near criticality
Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski
P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems
Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou
P27 Neurofield: a C++ library for fast simulation of 2D neural field models
Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao
P28 Action-based grounding: Beyond encoding/decoding in neural code
Yoonsuck Choe, Huei-Fang Yang
P29 Neural computation in a dynamical system with multiple time scales
Yuanyuan Mi, Xiaohan Lin, Si Wu
P30 Maximum entropy models for 3D layouts of orientation selectivity
Joscha Liedtke, Manuel Schottdorf, Fred Wolf
P31 A behavioral assay for probing computations underlying curiosity in rodents
Yoriko Yamamura, Jeffery R. Wickens
P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models
Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver
P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm
Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang
P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis
Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon
P35 Dynamics of cooperative excitatory and inhibitory plasticity
Everton J. Agnes, Tim P. Vogels
P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons
William F. Podlaski, Tim P. Vogels
P37 Phenomenological neural model for adaptation of neurons in area IT
Martin Giese, Pradeep Kuravi, Rufin Vogels
P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment
Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels
P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations
Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona
P40 Different roles for transient and sustained activity during active visual processing
Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden
P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications
Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh
P42 High frequency neuron can facilitate propagation of signal in neural networks
Aref Pariz, Shervin S. Parsi, Alireza Valizadeh
P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus
Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova
P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP
Florence I. Kleberg, Jochen Triesch
P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex
Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch
P46 Structure and dynamics of axon network formed in primary cell culture
Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau
P47 Efficient signal processing and sampling in random networks that generate variability
Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi
P48 Modeling the effect of riluzole on bursting in respiratory neural networks
Daniel T. Robb, Nick Mellen, Natalia Toporikova
P49 Mapping relaxation training using effective connectivity analysis
Rongxiang Tang, Yi-Yuan Tang
P50 Modeling neuron oscillation of implicit sequence learning
Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang
P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus
Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber
P52 Nonlinear response of noisy neurons
Sergej O. Voronenko, Benjamin Lindner
P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion
Tosif Ahamed, Greg Stephens
P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings
Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre
P55 Sufficient sampling rates for fast hand motion tracking
Hansol Choi, Min-Ho Song
P56 Linear readout of object manifolds
SueYeon Chung, Dan D. Lee, Haim Sompolinsky
P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves
Ryan S. Phillips, Jeffrey Smith
P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus
Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner
P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning
N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver
P60 A set of curated cortical models at multiple scales on Open Source Brain
Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver
P61 A synaptic story of dynamical information encoding in neural adaptation
Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu
P62 Physical modeling of rule-observant rodent behavior
Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin
P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes
Hannah Choi, Anitha Pasupathy, Eric Shea-Brown
P65 Stability of FORCE learning on spiking and rate-based networks
Dongsung Huh, Terrence J. Sejnowski
P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments
Simon M. Vogt, Arvind Kumar, Robert Schmidt
P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation
Stephen Van Wert, Steven J. Schiff
P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo
Richard Veale, Matthias Scheutz
P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions
Sang Wan Lee
P70 Maturation of sensory networks through homeostatic structural plasticity
Júlia Gallinaro, Stefan Rotter
P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings
Paula Sanz-Leon, Peter A. Robinson
P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study
Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan
P73 Exact spike-timing distribution reveals higher-order interactions of neurons
Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli
P74 Neural mechanism of visual perceptual learning using a multi-layered neural network
Xiaochen Zhao, Malte J. Rasch
P75 Inferring collective spiking dynamics from mostly unobserved systems
Jens Wilting, Viola Priesemann
P76 How to infer distributions in the brain from subsampled observations
Anna Levina, Viola Priesemann
P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons
Lucas Rudelt, Joseph T. Lizier, Viola Priesemann
P78 A nearest-neighbours based estimator for transfer entropy between spike trains
Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann
P79 Active learning of psychometric functions with multinomial logistic models
Ji Hyun Bak, Jonathan Pillow
P81 Inferring low-dimensional network dynamics with variational latent Gaussian process
Yuan Zaho, Il Memming Park
P82 Computational investigation of energy landscapes in the resting state subcortical brain network
Jiyoung Kang, Hae-Jeong Park
P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map
Jaeson Jang, Se-Bum Paik
P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition
Woochul Choi, Se-Bum Paik
P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map
Changju Lee, Jaeson Jang, Se-Bum Paik
P86 Computational method classifying neural network activity patterns for imaging data
Min Song, Hyeonsu Lee, Se-Bum Paik
P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory
Youngjin Park, Woochul Choi, Se-Bum Paik
P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons
Ergin Yilmaz, Veli Baysal, Mahmut Ozer
P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance
Veronika Koren, Klaus Obermayer
P90 Methods for building accurate models of individual neurons
Daniel Saska, Thomas Nowotny
P91 A full size mathematical model of the early olfactory system of honeybees
Ho Ka Chan, Alan Diamond, Thomas Nowotny
P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks
Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre
P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input
Philipp Weidel, Renato Duarte, Abigail Morrison
P94 Modulation of tuning induced by abrupt reduction of SST cell activity
Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas
P95 The functional role of VIP cell activation during locomotion
Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas
P96 Stochastic inference with spiking neural networks
Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier
P97 Modeling orientation-selective electrical stimulation with retinal prostheses
Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin
P98 Ion channel noise can explain firing correlation in auditory nerves
Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell
P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit
Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram
P100 On the representation of arm reaching movements: a computational model
Antonio Parziale, Rosa Senatore, Angelo Marcelli
P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior
Rosa Senatore, Antonio Parziale, Angelo Marcelli
P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge
K. Skiker, M. Maouene
P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia
Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton
P104 Effect of network size on computational capacity
Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr
P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks
Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton
P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas
Juhyoung Ryu, Sang-Hun Lee
P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception
Joonwon Lee, Sang-Hun Lee
P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making
Hyang Jung Lee, Sang-Hun Lee
P110 A Bayesian algorithm for phoneme Perception and its neural implementation
Daeseob Lim, Sang-Hun Lee
P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol
Jisung Wang, Heonsoo Lee
P112 Self-organized criticality of neural avalanche in a neural model on complex networks
Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee
P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model
Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee
P114 Computational model to replicate seizure suppression effect by electrical stimulation
Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee
P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy
Felix Goetze, Pik-Yin Lai
P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell
Seonghyun Kim, Jeehyun Kwag
P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model
Hyun Jae Jang, Jeehyun Kwag
P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum
Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar
P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease
Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison
P120 Learning sparse representations in the olfactory bulb
Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov
P121 Functional classification of homologous basal-ganglia networks
Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski
P122 Short term memory based on multistability
Tim Kunze, Andre Peterson, Thomas Knösche
P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units
Minjung Kim, Hojeong Kim
P125 Decoding laser-induced somatosensory information from EEG
Ji Sung Park, Ji Won Yeon, Sung-Phil Kim
P126 Phase synchronization of alpha activity for EEG-based personal authentication
Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim
P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model
Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa
P130 Epileptic seizures in the unfolding of a codimension-3 singularity
Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa
P131 Incremental dimensional exploratory reasoning under multi-dimensional environment
Oh-hyeon Choung, Yong Jeong
P132 A low-cost model of eye movements and memory in personal visual cognition
Yong-il Lee, Jaeseung Jeong
P133 Complex network analysis of structural connectome of autism spectrum disorder patients
Su Hyun Kim, Mir Jeong, Jaeseung Jeong
P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip
Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong
P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making
Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong
P136 Detecting purchase decision based on hyperfrontality of the EEG
Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong
P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome
Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong
P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans
Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong
P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?
Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong
P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish
Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa
P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data
JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii
P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems
Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta
P146 Swinging networks
Michal Hadrava, Jaroslav Hlinka
P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions
Hannah Bos, Moritz Helias
P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning
Charles M. Welzig, Zachary J. Harper
P149 Multiscale complexity analysis for the segmentation of MRI images
Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han
P150 A neuro-computational model of emotional attention
René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker
P151 Multi-site delayed feedback stimulation in parkinsonian networks
Kelly Toppin, Yixin Guo
P152 Bistability in Hodgkin–Huxley-type equations
Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden
P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency
Mark D. McDonnell, Bruce P. Graham
P154 Quantifying resilience patterns in brain networks: the importance of directionality
Penelope J. Kale, Leonardo L. Gollo
P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations
Merav Stern, L. F. Abbott
P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues
Leonid A. Fedorov, Martin A. Giese
P157 Spiking model for the interaction between action recognition and action execution
Mohammad Hovaidi Ardestani, Martin Giese
P158 Surprise-modulated belief update: how to learn within changing environments?
Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner
P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation
Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns
P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks
Won Hee Lee, Sophia Frangou
P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis
Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito
P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations
Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink
P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse
Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers
P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes
Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst
P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology
Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll
P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach
Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen
P167 Local field potentials in a 4 × 4 mm2 multi-layered network model
Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann
P168 A spiking network model explains multi-scale properties of cortical dynamics
Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada
P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups
Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk
P170 Tensor decomposition reveals RSNs in simulated resting state fMRI
Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco
P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening
Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust
P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors
Guido Klingbeil, Erik De Schutter
P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS
Weiliang Chen, Erik De Schutter
P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input
Yunliang Zang, Erik De Schutter
P175 Dendritic morphology determines how dendrites are organized into functional subunits
Sungho Hong, Akira Takashima, Erik De Schutter
P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells
Criseida Zamora, Andrew R. Gallimore, Erik De Schutter
P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents
Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta
P178 Data-driven neural models part II: connectivity patterns of human seizures
Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook
P179 Data-driven neural models part I: state and parameter estimation
Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook
P180 Spectral and spatial information processing in human auditory streaming
Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen
P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain
Leonardo L. Gollo, James A. Roberts, Luca Cocchi
P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles
Yann Sweeney, Claudia Clopath
P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography
Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi
P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep
Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi
P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response
Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi
P186 Neural field model of localized orientation selective activation in V1
James Rankin, Frédéric Chavane
P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia
Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P189 A computational architecture to model the microanatomy of the striatum and its functional properties
Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy
P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching
Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy
P191 Emergence of radial orientation selectivity from synaptic plasticity
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
P192 How do hidden units shape effective connections between neurons?
Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice
P193 Characterization of neural firing in the presence of astrocyte-synapse signaling
Maurizio De Pittà, Hugues Berry, Nicolas Brunel
P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics
James A. Roberts, Leonardo L. Gollo, Michael Breakspear
P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings
Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley
P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses
Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell
P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons
LieJune Shiau, Laure Buhry, Kanishka Basnayake
P200 Visual face representations during memory retrieval compared to perception
Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker
P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics
Timothée Leleu, Kazuyuki Aihara
Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics
Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy
doi:10.1186/s12868-016-0283-6
PMCID: PMC5001212  PMID: 27534393
3.  Molecular ecological network analyses 
BMC Bioinformatics  2012;13:113.
Background
Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.
Results
Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA).
Conclusions
The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.
doi:10.1186/1471-2105-13-113
PMCID: PMC3428680  PMID: 22646978
Ecological network; Random Matrix Theory; Microbial community; Microbiological ecology; Network interaction; Environmental changes
4.  How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience 
PLoS Biology  2016;14(8):e1002527.
Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities’ response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., “multiplex networks”), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full “entangled bank” of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
An ecological network synthesizing all known interactions between species exhibits a clear pattern of organization that reflects evolutionary and ecological constraints operating in this entangled bank of species.
Author Summary
Within an ecosystem, species interact with each other in many different ways, including predation, competition, and facilitation, and this can be modelled as a network of multiple interaction types. The variety of interaction types that link species to each other has long been recognized but has rarely been synthesized for entire multi-species ecosystems. Here, we leverage a unique marine ecological network that integrates thousands of trophic and non-trophic interactions. We show that, despite its multidimensional complexity, this ecological network collapses into a small set of “functional groups,” i.e., groups of species that resemble each other in the way they interact with others in their combined trophic and non-trophic interactions. These groups are taxonomically coherent and predictable by species attributes. Moreover, dynamic simulations suggest that the way the different interaction types relate to each other allows for higher species persistence and higher total biomass than is expected by chance alone, and that this tends to promote a higher robustness to extinctions. Our results will help to guide future empirical studies and to develop a more general theory of the dynamics of complex ecological systems.
doi:10.1371/journal.pbio.1002527
PMCID: PMC4972357  PMID: 27487303
5.  Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science 
Lewis, Cara | Darnell, Doyanne | Kerns, Suzanne | Monroe-DeVita, Maria | Landes, Sara J. | Lyon, Aaron R. | Stanick, Cameo | Dorsey, Shannon | Locke, Jill | Marriott, Brigid | Puspitasari, Ajeng | Dorsey, Caitlin | Hendricks, Karin | Pierson, Andria | Fizur, Phil | Comtois, Katherine A. | Palinkas, Lawrence A. | Chamberlain, Patricia | Aarons, Gregory A. | Green, Amy E. | Ehrhart, Mark. G. | Trott, Elise M. | Willging, Cathleen E. | Fernandez, Maria E. | Woolf, Nicholas H. | Liang, Shuting Lily | Heredia, Natalia I. | Kegler, Michelle | Risendal, Betsy | Dwyer, Andrea | Young, Vicki | Campbell, Dayna | Carvalho, Michelle | Kellar-Guenther, Yvonne | Damschroder, Laura J. | Lowery, Julie C. | Ono, Sarah S. | Carlson, Kathleen F. | Cottrell, Erika K. | O’Neil, Maya E. | Lovejoy, Travis L. | Arch, Joanna J. | Mitchell, Jill L. | Lewis, Cara C. | Marriott, Brigid R. | Scott, Kelli | Coldiron, Jennifer Schurer | Bruns, Eric J. | Hook, Alyssa N. | Graham, Benjamin C. | Jordan, Katelin | Hanson, Rochelle F. | Moreland, Angela | Saunders, Benjamin E. | Resnick, Heidi S. | Stirman, Shannon Wiltsey | Gutner, Cassidy A. | Gamarra, Jennifer | Vogt, Dawne | Suvak, Michael | Wachen, Jennifer Schuster | Dondanville, Katherine | Yarvis, Jeffrey S. | Mintz, Jim | Peterson, Alan L. | Borah, Elisa V. | Litz, Brett T. | Molino, Alma | McCaughan, Stacey Young | Resick, Patricia A. | Pandhi, Nancy | Jacobson, Nora | Serrano, Neftali | Hernandez, Armando | Schreiter, Elizabeth Zeidler- | Wietfeldt, Natalie | Karp, Zaher | Pullmann, Michael D. | Lucenko, Barbara | Pavelle, Bridget | Uomoto, Jacqueline A. | Negrete, Andrea | Cevasco, Molly | Kerns, Suzanne E. U. | Franks, Robert P. | Bory, Christopher | Miech, Edward J. | Damush, Teresa M. | Satterfield, Jason | Satre, Derek | Wamsley, Maria | Yuan, Patrick | O’Sullivan, Patricia | Best, Helen | Velasquez, Susan | Barnett, Miya | Brookman-Frazee, Lauren | Regan, Jennifer | Stadnick, Nicole | Hamilton, Alison | Lau, Anna | Regan, Jennifer | Hamilton, Alison | Stadnick, Nicole | Barnett, Miya | Lau, Anna | Brookman-Frazee, Lauren | Stadnick, Nicole | Lau, Anna | Barnett, Miya | Regan, Jennifer | Roesch, Scott | Brookman-Frazee, Lauren | Powell, Byron J. | Waltz, Thomas J. | Chinman, Matthew J. | Damschroder, Laura | Smith, Jeffrey L. | Matthieu, Monica M. | Proctor, Enola K. | Kirchner, JoAnn E. | Waltz, Thomas J. | Powell, Byron J. | Chinman, Matthew J. | Damschroder, Laura J. | Smith, Jeffrey L. | Matthieu, Monica J. | Proctor, Enola K. | Kirchner, JoAnn E. | Matthieu, Monica M. | Rosen, Craig S. | Waltz, Thomas J. | Powell, Byron J. | Chinman, Matthew J. | Damschroder, Laura J. | Smith, Jeffrey L. | Proctor, Enola K. | Kirchner, JoAnn E. | Walker, Sarah C. | Bishop, Asia S. | Lockhart, Mariko | Rodriguez, Allison L. | Manfredi, Luisa | Nevedal, Andrea | Rosenthal, Joel | Blonigen, Daniel M. | Mauricio, Anne M. | Dishion, Thomas D. | Rudo-Stern, Jenna | Smith, Justin D. | Locke, Jill | Wolk, Courtney Benjamin | Harker, Colleen | Olsen, Anne | Shingledecker, Travis | Barg, Frances | Mandell, David | Beidas, Rinad S. | Hansen, Marissa C. | Aranda, Maria P. | Torres-Vigil, Isabel | Hartzler, Bryan | Steinfeld, Bradley | Gildred, Tory | Harlin, Zandrea | Shephard, Fredric | Ditty, Matthew S. | Doyle, Andrea | Bickel, John A. | Cristaudo, Katharine | Fox, Dan | Combs, Sonia | Lischner, David H. | Van Dorn, Richard A. | Tueller, Stephen J. | Hinde, Jesse M. | Karuntzos, Georgia T. | Monroe-DeVita, Maria | Peterson, Roselyn | Darnell, Doyanne | Berliner, Lucy | Dorsey, Shannon | Murray, Laura K. | Botanov, Yevgeny | Kikuta, Beverly | Chen, Tianying | Navarro-Haro, Marivi | DuBose, Anthony | Korslund, Kathryn E. | Linehan, Marsha M. | Harker, Colleen M. | Karp, Elizabeth A. | Edmunds, Sarah R. | Ibañez, Lisa V. | Stone, Wendy L. | Andrews, Jack H. | Johnides, Benjamin D. | Hausman, Estee M. | Hawley, Kristin M. | Prusaczyk, Beth | Ramsey, Alex | Baumann, Ana | Colditz, Graham | Proctor, Enola K. | Botanov, Yevgeny | Kikuta, Beverly | Chen, Tianying | Navarro-Haro, Marivi | DuBose, Anthony | Korslund, Kathryn E. | Linehan, Marsha M. | Harker, Colleen M. | Karp, Elizabeth A. | Edmunds, Sarah R. | Ibañez, Lisa V. | Stone, Wendy L. | Choy-Brown, Mimi | Andrews, Jack H. | Johnides, Benjamin D. | Hausman, Estee M. | Hawley, Kristin M. | Prusaczyk, Beth | Ramsey, Alex | Baumann, Ana | Colditz, Graham | Proctor, Enola K. | Meza, Rosemary D. | Dorsey, Shannon | Wiltsey-Stirman, Shannon | Sedlar, Georganna | Lucid, Leah | Dorsey, Caitlin | Marriott, Brigid | Zounlome, Nelson | Lewis, Cara | Gutner, Cassidy A. | Monson, Candice M. | Shields, Norman | Mastlej, Marta | Landy, Meredith SH | Lane, Jeanine | Stirman, Shannon Wiltsey | Finn, Natalie K. | Torres, Elisa M. | Ehrhart, Mark. G. | Aarons, Gregory A. | Malte, Carol A. | Lott, Aline | Saxon, Andrew J. | Boyd, Meredith | Scott, Kelli | Lewis, Cara C. | Pierce, Jennifer D. | Lorthios-Guilledroit, Agathe | Richard, Lucie | Filiatrault, Johanne | Hallgren, Kevin | Crotwell, Shirley | Muñoz, Rosa | Gius, Becky | Ladd, Benjamin | McCrady, Barbara | Epstein, Elizabeth | Clapp, John D. | Ruderman, Danielle E. | Barwick, Melanie | Barac, Raluca | Zlotkin, Stanley | Salim, Laila | Davidson, Marnie | Bunger, Alicia C. | Powell, Byron J. | Robertson, Hillary A. | Botsko, Christopher | Landes, Sara J. | Smith, Brandy N. | Rodriguez, Allison L. | Trent, Lindsay R. | Matthieu, Monica M. | Powell, Byron J. | Proctor, Enola K. | Harned, Melanie S. | Navarro-Haro, Marivi | Korslund, Kathryn E. | Chen, Tianying | DuBose, Anthony | Ivanoff, André | Linehan, Marsha M. | Garcia, Antonio R. | Kim, Minseop | Palinkas, Lawrence A. | Snowden, Lonnie | Landsverk, John | Sweetland, Annika C. | Fernandes, Maria Jose | Santos, Edilson | Duarte, Cristiane | Kritski, Afrânio | Krawczyk, Noa | Nelligan, Caitlin | Wainberg, Milton L. | Aarons, Gregory A. | Sommerfeld, David H. | Chi, Benjamin | Ezeanolue, Echezona | Sturke, Rachel | Kline, Lydia | Guay, Laura | Siberry, George | Bennett, Ian M. | Beidas, Rinad | Gold, Rachel | Mao, Johnny | Powers, Diane | Vredevoogd, Mindy | Unutzer, Jurgen | Schroeder, Jennifer | Volpe, Lane | Steffen, Julie | Dorsey, Shannon | Pullmann, Michael D | Kerns, Suzanne E. U. | Jungbluth, Nathaniel | Berliner, Lucy | Thompson, Kelly | Segell, Eliza | McGee-Vincent, Pearl | Liu, Nancy | Walser, Robyn | Runnals, Jennifer | Shaw, R. Keith | Landes, Sara J. | Rosen, Craig | Schmidt, Janet | Calhoun, Patrick | Varkovitzky, Ruth L. | Landes, Sara J. | Drahota, Amy | Martinez, Jonathan I. | Brikho, Brigitte | Meza, Rosemary | Stahmer, Aubyn C. | Aarons, Gregory A. | Williamson, Anna | Rubin, Ronnie M. | Powell, Byron J. | Hurford, Matthew O. | Weaver, Shawna L. | Beidas, Rinad S. | Mandell, David S. | Evans, Arthur C. | Powell, Byron J. | Beidas, Rinad S. | Rubin, Ronnie M. | Stewart, Rebecca E. | Wolk, Courtney Benjamin | Matlin, Samantha L. | Weaver, Shawna | Hurford, Matthew O. | Evans, Arthur C. | Hadley, Trevor R. | Mandell, David S. | Gerke, Donald R. | Prusaczyk, Beth | Baumann, Ana | Lewis, Ericka M. | Proctor, Enola K. | McWilliam, Jenna | Brown, Jacquie | Tucker, Michelle | Conte, Kathleen P | Lyon, Aaron R. | Boyd, Meredith | Melvin, Abigail | Lewis, Cara C. | Liu, Freda | Jungbluth, Nathaniel | Kotte, Amelia | Hill, Kaitlin A. | Mah, Albert C. | Korathu-Larson, Priya A. | Au, Janelle R. | Izmirian, Sonia | Keir, Scott | Nakamura, Brad J. | Higa-McMillan, Charmaine K. | Cooper, Brittany Rhoades | Funaiole, Angie | Dizon, Eleanor | Hawkins, Eric J. | Malte, Carol A. | Hagedorn, Hildi J. | Berger, Douglas | Frank, Anissa | Lott, Aline | Achtmeyer, Carol E. | Mariano, Anthony J. | Saxon, Andrew J. | Wolitzky-Taylor, Kate | Rawson, Richard | Ries, Richard | Roy-Byrne, Peter | Craske, Michelle | Simmons, Dena | Torrente, Catalina | Nathanson, Lori | Carroll, Grace | Smith, Justin D. | Brown, Kimbree | Ramos, Karina | Thornton, Nicole | Dishion, Thomas J. | Stormshak, Elizabeth A. | Shaw, Daniel S. | Wilson, Melvin N. | Choy-Brown, Mimi | Tiderington, Emmy | Smith, Bikki Tran | Padgett, Deborah K. | Rubin, Ronnie M. | Ray, Marilyn L. | Wandersman, Abraham | Lamont, Andrea | Hannah, Gordon | Alia, Kassandra A. | Hurford, Matthew O. | Evans, Arthur C. | Saldana, Lisa | Schaper, Holle | Campbell, Mark | Chamberlain, Patricia | Shapiro, Valerie B. | Kim, B.K. Elizabeth | Fleming, Jennifer L. | LeBuffe, Paul A. | Landes, Sara J. | Lewis, Cara C. | Rodriguez, Allison L. | Marriott, Brigid R. | Comtois, Katherine Anne | Lewis, Cara C. | Stanick, Cameo | Weiner, Bryan J. | Halko, Heather | Dorsey, Caitlin
Implementation Science : IS  2016;11(Suppl 1):85.
Table of contents
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
Sarah S. Ono, Kathleen F. Carlson, Erika K. Cottrell, Maya E. O’Neil, Travis L. Lovejoy
A7: Building patient-practitioner partnerships in community oncology settings to implement behavioral interventions for anxious and depressed cancer survivors
Joanna J. Arch, Jill L. Mitchell
A8: Tailoring a Cognitive Behavioral Therapy implementation protocol using mixed methods, conjoint analysis, and implementation teams
Cara C. Lewis, Brigid R. Marriott, Kelli Scott
A9: Wraparound Structured Assessment and Review (WrapSTAR): An efficient, yet comprehensive approach to Wraparound implementation evaluation
Jennifer Schurer Coldiron, Eric J. Bruns, Alyssa N. Hook
A10: Improving the efficiency of standardized patient assessment of clinician fidelity: A comparison of automated actor-based and manual clinician-based ratings
Benjamin C. Graham, Katelin Jordan
A11: Measuring fidelity on the cheap
Rochelle F. Hanson, Angela Moreland, Benjamin E. Saunders, Heidi S. Resnick
A12: Leveraging routine clinical materials to assess fidelity to an evidence-based psychotherapy
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 McCaughanPatricia A. Resick
A13: The video vignette survey: An efficient process for gathering diverse community opinions to inform an intervention
Nancy Pandhi, Nora Jacobson, Neftali Serrano, Armando Hernandez, Elizabeth Zeidler- Schreiter, Natalie Wietfeldt, Zaher Karp
A14: Using integrated administrative data to evaluate implementation of a behavioral health and trauma screening for children and youth in foster care
Michael D. Pullmann, Barbara Lucenko, Bridget Pavelle, Jacqueline A. Uomoto, Andrea Negrete, Molly Cevasco, Suzanne E. U. Kerns
A15: Intermediary organizations as a vehicle to promote efficiency and speed of implementation
Robert P. Franks, Christopher Bory
A16: Applying the Consolidated Framework for Implementation Research constructs directly to qualitative data: The power of implementation science in action
Edward J. Miech, Teresa M. Damush
A17: Efficient and effective scaling-up, screening, brief interventions, and referrals to treatment (SBIRT) training: a snowball implementation model
Jason Satterfield, Derek Satre, Maria Wamsley, Patrick Yuan, Patricia O’Sullivan
A18: Matching models of implementation to system needs and capacities: addressing the human factor
Helen Best, Susan Velasquez
A19: Agency characteristics that facilitate efficient and successful implementation efforts
Miya Barnett, Lauren Brookman-Frazee, Jennifer Regan, Nicole Stadnick, Alison Hamilton, Anna Lau
A20: Rapid assessment process: Application to the Prevention and Early Intervention transformation in Los Angeles County
Jennifer Regan, Alison Hamilton, Nicole Stadnick, Miya Barnett, Anna Lau, Lauren Brookman-Frazee
A21: The development of the Evidence-Based Practice-Concordant Care Assessment: An assessment tool to examine treatment strategies across practices
Nicole Stadnick, Anna Lau, Miya Barnett, Jennifer Regan, Scott Roesch, Lauren Brookman-Frazee
A22: Refining a compilation of discrete implementation strategies and determining their importance and feasibility
Byron J. Powell, Thomas J. Waltz, Matthew J. Chinman, Laura Damschroder, Jeffrey L. Smith, Monica M. Matthieu, Enola K. Proctor, JoAnn E. Kirchner
A23: Structuring complex recommendations: Methods and general findings
Thomas J. Waltz, Byron J. Powell, Matthew J. Chinman, Laura J. Damschroder, Jeffrey L. Smith, Monica J. Matthieu, Enola K. Proctor, JoAnn E. Kirchner
A24: Implementing prolonged exposure for post-traumatic stress disorder in the Department of Veterans Affairs: Expert recommendations from the Expert Recommendations for Implementing Change (ERIC) project
Monica M. Matthieu, Craig S. Rosen, Thomas J. Waltz, Byron J. Powell, Matthew J. Chinman, Laura J. Damschroder, Jeffrey L. Smith, Enola K. Proctor, JoAnn E. Kirchner
A25: When readiness is a luxury: Co-designing a risk assessment and quality assurance process with violence prevention frontline workers in Seattle, WA
Sarah C. Walker, Asia S. Bishop, Mariko Lockhart
A26: Implementation potential of structured recidivism risk assessments with justice- involved veterans: Qualitative perspectives from providers
Allison L. Rodriguez, Luisa Manfredi, Andrea Nevedal, Joel Rosenthal, Daniel M. Blonigen
A27: Developing empirically informed readiness measures for providers and agencies for the Family Check-Up using a mixed methods approach
Anne M. Mauricio, Thomas D. Dishion, Jenna Rudo-Stern, Justin D. Smith
A28: Pebbles, rocks, and boulders: The implementation of a school-based social engagement intervention for children with autism
Jill Locke, Courtney Benjamin Wolk, Colleen Harker, Anne Olsen, Travis Shingledecker, Frances Barg, David Mandell, Rinad S. Beidas
A29: Problem Solving Teletherapy (PST.Net): A stakeholder analysis examining the feasibility and acceptability of teletherapy in community based aging services
Marissa C. Hansen, Maria P. Aranda, Isabel Torres-Vigil
A30: A case of collaborative intervention design eventuating in behavior therapy sustainment and diffusion
Bryan Hartzler
A31: Implementation of suicide risk prevention in an integrated delivery system: Mental health specialty services
Bradley Steinfeld, Tory Gildred, Zandrea Harlin, Fredric Shephard
A32: Implementation team, checklist, evaluation, and feedback (ICED): A step-by-step approach to Dialectical Behavior Therapy program implementation
Matthew S. Ditty, Andrea Doyle, John A. Bickel III, Katharine Cristaudo
A33: The challenges in implementing muliple evidence-based practices in a community mental health setting
Dan Fox, Sonia Combs
A34: Using electronic health record technology to promote and support evidence-based practice assessment and treatment intervention
David H. Lischner
A35: Are existing frameworks adequate for measuring implementation outcomes? Results from a new simulation methodology
Richard A. Van Dorn, Stephen J. Tueller, Jesse M. Hinde, Georgia T. Karuntzos
A36: Taking global local: Evaluating training of Washington State clinicians in a modularized cogntive behavioral therapy approach designed for low-resource settings
Maria Monroe-DeVita, Roselyn Peterson, Doyanne Darnell, Lucy Berliner, Shannon Dorsey, Laura K. Murray
A37: Attitudes toward evidence-based practices across therapeutic orientations
Yevgeny Botanov, Beverly Kikuta, Tianying Chen, Marivi Navarro-Haro, Anthony DuBose, Kathryn E. Korslund, Marsha M. Linehan
A38: Predicting the use of an evidence-based intervention for autism in birth-to-three programs
Colleen M. Harker, Elizabeth A. Karp, Sarah R. Edmunds, Lisa V. Ibañez, Wendy L. Stone
A39: Supervision practices and improved fidelity across evidence-based practices: A literature review
Mimi Choy-Brown
A40: Beyond symptom tracking: clinician perceptions of a hybrid measurement feedback system for monitoring treatment fidelity and client progress
Jack H. Andrews, Benjamin D. Johnides, Estee M. Hausman, Kristin M. Hawley
A41: A guideline decision support tool: From creation to implementation
Beth Prusaczyk, Alex Ramsey, Ana Baumann, Graham Colditz, Enola K. Proctor
A42: Dabblers, bedazzlers, or total makeovers: Clinician modification of a common elements cognitive behavioral therapy approach
Rosemary D. Meza, Shannon Dorsey, Shannon Wiltsey-Stirman, Georganna Sedlar, Leah Lucid
A43: Characterization of context and its role in implementation: The impact of structure, infrastructure, and metastructure
Caitlin Dorsey, Brigid Marriott, Nelson Zounlome, Cara Lewis
A44: Effects of consultation method on implementation of cognitive processing therapy for post-traumatic stress disorder
Cassidy A. Gutner, Candice M. Monson, Norman Shields, Marta Mastlej, Meredith SH Landy, Jeanine Lane, Shannon Wiltsey Stirman
A45: Cross-validation of the Implementation Leadership Scale factor structure in child welfare service organizations
Natalie K. Finn, Elisa M. Torres, Mark. G. Ehrhart, Gregory A. Aarons
A46: Sustainability of integrated smoking cessation care in Veterans Affairs posttraumatic stress disorder clinics: A qualitative analysis of focus group data from learning collaborative participants
Carol A. Malte, Aline Lott, Andrew J. Saxon
A47: Key characteristics of effective mental health trainers: The creation of the Measure of Effective Attributes of Trainers (MEAT)
Meredith Boyd, Kelli Scott, Cara C. Lewis
A48: Coaching to improve teacher implementation of evidence-based practices (EBPs)
Jennifer D. Pierce
A49: Factors influencing the implementation of peer-led health promotion programs targeting seniors: A literature review
Agathe Lorthios-Guilledroit, Lucie Richard, Johanne Filiatrault
A50: Developing treatment fidelity rating systems for psychotherapy research: Recommendations and lessons learned
Kevin Hallgren, Shirley Crotwell, Rosa Muñoz, Becky Gius, Benjamin Ladd, Barbara McCrady, Elizabeth Epstein
A51: Rapid translation of alcohol prevention science
John D. Clapp, Danielle E. Ruderman
A52: Factors implicated in successful implementation: evidence to inform improved implementation from high and low-income countries
Melanie Barwick, Raluca Barac, Stanley Zlotkin, Laila Salim, Marnie
Davidson
A53: Tracking implementation strategies prospectively: A practical approach
Alicia C. Bunger, Byron J. Powell, Hillary A. Robertson
A54: Trained but not implementing: the need for effective implementation planning tools
Christopher Botsko
A55: Evidence, context, and facilitation variables related to implementation of Dialectical Behavior Therapy: Qualitative results from a mixed methods inquiry in the Department of Veterans Affairs
Sara J. Landes, Brandy N. Smith, Allison L. Rodriguez, Lindsay R. Trent, Monica M. Matthieu
A56: Learning from implementation as usual in children’s mental health
Byron J. Powell, Enola K. Proctor
A57: Rates and predictors of implementation after Dialectical Behavior Therapy Intensive Training
Melanie S. Harned, Marivi Navarro-Haro, Kathryn E. Korslund, Tianying Chen, Anthony DuBose, André Ivanoff, Marsha M. Linehan
A58: Socio-contextual determinants of research evidence use in public-youth systems of care
Antonio R. Garcia, Minseop Kim, Lawrence A. Palinkas, Lonnie Snowden, John Landsverk
A59: Community resource mapping to integrate evidence-based depression treatment in primary care in Brazil: A pilot project
Annika C. Sweetland, Maria Jose Fernandes, Edilson Santos, Cristiane Duarte, Afrânio Kritski, Noa Krawczyk, Caitlin Nelligan, Milton L. Wainberg
A60: The use of concept mapping to efficiently identify determinants of implementation in the National Institute of Health--President’s Emergent Plan for AIDS Relief Prevention of Mother to Child HIV Transmission Implementation Science Alliance
Gregory A. Aarons, David H. Sommerfeld, Benjamin Chi, Echezona Ezeanolue, Rachel Sturke, Lydia Kline, Laura Guay, George Siberry
A61: Longitudinal remote consultation for implementing collaborative care for depression
Ian M. Bennett, Rinad Beidas, Rachel Gold, Johnny Mao, Diane Powers, Mindy Vredevoogd, Jurgen Unutzer
A62: Integrating a peer coach model to support program implementation and ensure long- term sustainability of the Incredible Years in community-based settings
Jennifer Schroeder, Lane Volpe, Julie Steffen
A63: Efficient sustainability: Existing community based supervisors as evidence-based treatment supports
Shannon Dorsey, Michael D Pullmann, Suzanne E. U. Kerns, Nathaniel Jungbluth, Lucy Berliner, Kelly Thompson, Eliza Segell
A64: Establishment of a national practice-based implementation network to accelerate adoption of evidence-based and best practices
Pearl McGee-Vincent, Nancy Liu, Robyn Walser, Jennifer Runnals, R. Keith Shaw, Sara J. Landes, Craig Rosen, Janet Schmidt, Patrick Calhoun
A65: Facilitation as a mechanism of implementation in a practice-based implementation network: Improving care in a Department of Veterans Affairs post-traumatic stress disorder outpatient clinic
Ruth L. Varkovitzky, Sara J. Landes
A66: The ACT SMART Toolkit: An implementation strategy for community-based organizations providing services to children with autism spectrum disorder
Amy Drahota, Jonathan I. Martinez, Brigitte Brikho, Rosemary Meza, Aubyn C. Stahmer, Gregory A. Aarons
A67: Supporting Policy In Health with Research: An intervention trial (SPIRIT) - protocol and early findings
Anna Williamson
A68: From evidence based practice initiatives to infrastructure: Lessons learned from a public behavioral health system’s efforts to promote evidence based practices
Ronnie M. Rubin, Byron J. Powell, Matthew O. Hurford, Shawna L. Weaver, Rinad S. Beidas, David S. Mandell, Arthur C. Evans
A69: Applying the policy ecology model to Philadelphia’s behavioral health transformation efforts
Byron J. Powell, Rinad S. Beidas, Ronnie M. Rubin, Rebecca E. Stewart, Courtney Benjamin Wolk, Samantha L. Matlin, Shawna Weaver, Matthew O. Hurford, Arthur C. Evans, Trevor R. Hadley, David S. Mandell
A70: A model for providing methodological expertise to advance dissemination and implementation of health discoveries in Clinical and Translational Science Award institutions
Donald R. Gerke, Beth Prusaczyk, Ana Baumann, Ericka M. Lewis, Enola K. Proctor
A71: Establishing a research agenda for the Triple P Implementation Framework
Jenna McWilliam, Jacquie Brown, Michelle Tucker
A72: Cheap and fast, but what is “best?”: Examining implementation outcomes across sites in a state-wide scaled-up evidence-based walking program, Walk With Ease
Kathleen P Conte
A73: Measurement feedback systems in mental health: Initial review of capabilities and characteristics
Aaron R. Lyon, Meredith Boyd, Abigail Melvin, Cara C. Lewis, Freda Liu, Nathaniel Jungbluth
A74: A qualitative investigation of case managers’ attitudes toward implementation of a measurement feedback system in a public mental health system for youth
Amelia Kotte, Kaitlin A. Hill, Albert C. Mah, Priya A. Korathu-Larson, Janelle R. Au, Sonia Izmirian, Scott Keir, Brad J. Nakamura, Charmaine K. Higa-McMillan
A75: Multiple pathways to sustainability: Using Qualitative Comparative Analysis to uncover the necessary and sufficient conditions for successful community-based implementation
Brittany Rhoades Cooper, Angie Funaiole, Eleanor Dizon
A76: Prescribers’ perspectives on opioids and benzodiazepines and medication alerts to reduce co-prescribing of these medications
Eric J. Hawkins, Carol A. Malte, Hildi J. Hagedorn, Douglas Berger, Anissa Frank, Aline Lott, Carol E. Achtmeyer, Anthony J. Mariano, Andrew J. Saxon
A77: Adaptation of Coordinated Anxiety Learning and Management for comorbid anxiety and substance use disorders: Delivery of evidence-based treatment for anxiety in addictions treatment centers
Kate Wolitzky-Taylor, Richard Rawson, Richard Ries, Peter Roy-Byrne, Michelle Craske
A78: Opportunities and challenges of measuring program implementation with online surveys
Dena Simmons, Catalina Torrente, Lori Nathanson, Grace Carroll
A79: Observational assessment of fidelity to a family-centered prevention program: Effectiveness and efficiency
Justin D. Smith, Kimbree Brown, Karina Ramos, Nicole Thornton, Thomas J. Dishion, Elizabeth A. Stormshak, Daniel S. Shaw, Melvin N. Wilson
A80: Strategies and challenges in housing first fidelity: A multistate qualitative analysis
Mimi Choy-Brown, Emmy Tiderington, Bikki Tran Smith, Deborah K. Padgett
A81: Procurement and contracting as an implementation strategy: Getting To Outcomes® contracting
Ronnie M. Rubin, Marilyn L. Ray, Abraham Wandersman, Andrea Lamont, Gordon Hannah, Kassandra A. Alia, Matthew O. Hurford, Arthur C. Evans
A82: Web-based feedback to aid successful implementation: The interactive Stages of Implementation Completion (SIC)TM tool
Lisa Saldana, Holle Schaper, Mark Campbell, Patricia Chamberlain
A83: Efficient methodologies for monitoring fidelity in routine implementation: Lessons from the Allentown Social Emotional Learning Initiative
Valerie B. Shapiro, B.K. Elizabeth Kim, Jennifer L. Fleming, Paul A. LeBuffe
A84: The Society for Implementation Research Collaboration (SIRC) implementation development workshop: Results from a new methodology for enhancing implementation science proposals
Sara J. Landes, Cara C. Lewis, Allison L. Rodriguez, Brigid R. Marriott, Katherine Anne Comtois
A85: An update on the Society for Implementation Research Collaboration (SIRC) Instrument Review Project
doi:10.1186/s13012-016-0428-0
PMCID: PMC4928139  PMID: 27357964
6.  Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 
mBio  2011;2(4):e00122-11.
ABSTRACT
Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.
IMPORTANCE
The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.
doi:10.1128/mBio.00122-11
PMCID: PMC3143843  PMID: 21791581
7.  What can ecosystems learn? Expanding evolutionary ecology with learning theory 
Biology Direct  2015;10:69.
Background
The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole?
Results
Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, ‘unsupervised learning’, well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community’s response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts.
Conclusions
This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.
Reviewers
This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.
doi:10.1186/s13062-015-0094-1
PMCID: PMC4672551  PMID: 26643685
Evolutionary ecology; Alternative stable states; Lotka-Volterra dynamics; Theoretical ecology; Community assembly; Network structures; Ecological memory; Associative learning; Regime shifts; Community matrix
8.  NEXCADE: Perturbation Analysis for Complex Networks 
PLoS ONE  2012;7(8):e41827.
Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the ‘robust, yet fragile’ nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.
doi:10.1371/journal.pone.0041827
PMCID: PMC3411682  PMID: 22870252
9.  Linkage Rules for Plant–Pollinator Networks: Trait Complementarity or Exploitation Barriers? 
PLoS Biology  2007;5(2):e31.
Recent attempts to examine the biological processes responsible for the general characteristics of mutualistic networks focus on two types of explanations: nonmatching biological attributes of species that prevent the occurrence of certain interactions (“forbidden links”), arising from trait complementarity in mutualist networks (as compared to barriers to exploitation in antagonistic ones), and random interactions among individuals that are proportional to their abundances in the observed community (“neutrality hypothesis”). We explored the consequences that simple linkage rules based on the first two hypotheses (complementarity of traits versus barriers to exploitation) had on the topology of plant–pollination networks. Independent of the linkage rules used, the inclusion of a small set of traits (two to four) sufficed to account for the complex topological patterns observed in real-world networks. Optimal performance was achieved by a “mixed model” that combined rules that link plants and pollinators whose trait ranges overlap (“complementarity models”) and rules that link pollinators to flowers whose traits are below a pollinator-specific barrier value (“barrier models”). Deterrence of floral parasites (barrier model) is therefore at least as important as increasing pollination efficiency (complementarity model) in the evolutionary shaping of plant–pollinator networks.
Author Summary
Whether they are antagonistic—as between predator and prey—or beneficial—as between pollinator and flower, interactions among all the key species in an ecosystem follow regular patterns. Connectivity (the proportion of possible interactions that are actually realised), for instance, decreases with network size. The “forbidden links” hypothesis proposes that connectivity decreases because interactions are prevented by a mismatch of biological attributes between certain species. Mismatches could arise from the evolution of complementary traits in mutualistic relationships (such as insects preferring to pollinate only flowers of a certain colour) or of traits that prevent exploitation in antagonistic ones (such as a plant growing a long corolla so that insects without a long proboscis cannot reach the nectar reward). We explored the consequences of simple linkage rules based on these two variants on the topology of plant–pollination networks. When compared to data for 37 real plant–pollinator networks, we show that a “mixed” model that combines simple rules from both “complementarity” and “barrier” models best explains the pattern of interactions. This implies, for example, that deterring floral parasites is at least as important as increasing pollination efficiency in the evolution of plant–pollinator networks. Our work emphasises the value of explaining the underlying ecological and evolutionary mechanisms generating such patterns.
The topology of plant-pollinator networks can be explained by relatively simple rules incorporating both "complementarity" and "barrier" traits, thus providing insights into the possible evolutionary and ecological processes driving the pattern.
doi:10.1371/journal.pbio.0050031
PMCID: PMC1779813  PMID: 17253905
10.  Integrating network ecology with applied conservation: a synthesis and guide to implementation 
AoB Plants  2015;7:plv076.
Ecological networks are a useful tool to study the complexity of biotic interactions at a community level. We introduce a framework for network analysis to be harnessed to advance biodiversity conservation by using plant–pollinator networks and islands as model systems. Conservation practitioners require indicators to assess management effectiveness and validate overall conservation goals. We propose the use of several network metrics that indicate human-induced changes to plant-pollinator communities, and illustrate an implementation pathway to successfully embed a network approach in biodiversity conservation. We list potential obstacles to the framework, highlight the shortfall in experimental network data, and discuss solutions.
Ecological networks are a useful tool to study the complexity of biotic interactions at a community level. Advances in the understanding of network patterns encourage the application of a network approach in other disciplines than theoretical ecology, such as biodiversity conservation. So far, however, practical applications have been meagre. Here we present a framework for network analysis to be harnessed to advance conservation management by using plant–pollinator networks and islands as model systems. Conservation practitioners require indicators to monitor and assess management effectiveness and validate overall conservation goals. By distinguishing between two network attributes, the ‘diversity’ and ‘distribution’ of interactions, on three hierarchical levels (species, guild/group and network) we identify seven quantitative metrics to describe changes in network patterns that have implications for conservation. Diversity metrics are partner diversity, vulnerability/generality, interaction diversity and interaction evenness, and distribution metrics are the specialization indices d′ and H2′, and modularity. Distribution metrics account for sampling bias and may therefore be suitable indicators to detect human-induced changes to plant–pollinator communities, thus indirectly assessing the structural and functional robustness and integrity of ecosystems. We propose an implementation pathway that outlines the stages that are required to successfully embed a network approach in biodiversity conservation. Most importantly, only if conservation action and study design are aligned by practitioners and ecologists through joint experiments, are the findings of a conservation network approach equally beneficial for advancing adaptive management and ecological network theory. We list potential obstacles to the framework, highlight the shortfall in empirical, mostly experimental, network data and discuss possible solutions.
doi:10.1093/aobpla/plv076
PMCID: PMC4564002  PMID: 26162897
Adaptive management; biodiversity conservation; ecological integrity; ecosystem functions; indicators; interaction networks; islands; pollination
11.  Using an agent-based model to analyze the dynamic communication network of the immune response 
Background
The immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions.
Results
Analyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win) versus persistent infection (loss), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the loss outcomes.
Conclusions
An agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies.
doi:10.1186/1742-4682-8-1
PMCID: PMC3032717  PMID: 21247471
12.  What Can Interaction Webs Tell Us About Species Roles? 
PLoS Computational Biology  2015;11(7):e1004330.
The group model is a useful tool to understand broad-scale patterns of interaction in a network, but it has previously been limited in use to food webs, which contain only predator-prey interactions. Natural populations interact with each other in a variety of ways and, although most published ecological networks only include information about a single interaction type (e.g., feeding, pollination), ecologists are beginning to consider networks which combine multiple interaction types. Here we extend the group model to signed directed networks such as ecological interaction webs. As a specific application of this method, we examine the effects of including or excluding specific interaction types on our understanding of species roles in ecological networks. We consider all three currently available interaction webs, two of which are extended plant-mutualist networks with herbivores and parasitoids added, and one of which is an extended intertidal food web with interactions of all possible sign structures (+/+, -/0, etc.). Species in the extended food web grouped similarly with all interactions, only trophic links, and only nontrophic links. However, removing mutualism or herbivory had a much larger effect in the extended plant-pollinator webs. Species removal even affected groups that were not directly connected to those that were removed, as we found by excluding a small number of parasitoids. These results suggest that including additional species in the network provides far more information than additional interactions for this aspect of network structure. Our methods provide a useful framework for simplifying networks to their essential structure, allowing us to identify generalities in network structure and better understand the roles species play in their communities.
Author Summary
Ecological interactions are highly diverse even when considering a single species: the species might feed on a first, disperse the seeds of a second, and pollinate a third. Here we extend the group model, a method for identifying broad patterns of interaction across a food web, to networks which contain multiple types of interactions. Using this new method, we ask whether the traditional approach of building a network for each type of interaction (food webs for consumption, pollination webs, seed-dispersal webs, host-parasite webs) can be improved by merging all interaction types in a single network. In particular, we test whether combining different interaction types leads to a better definition of the roles species play in ecological communities. We find that, although having more information necessarily leads to better results, the improvement is only incremental if the linked species remain unchanged. However, including a new interaction type that attaches new species to the network substantially improves performance. This method provides insight into possible implications of merging different types of interactions and allows for the study of coarse-grained structure in any signed network, including ecological interaction webs, gene regulation networks, and social networks.
doi:10.1371/journal.pcbi.1004330
PMCID: PMC4511233  PMID: 26197151
13.  Parallel ecological networks in ecosystems 
In ecosystems, species interact with other species directly and through abiotic factors in multiple ways, often forming complex networks of various types of ecological interaction. Out of this suite of interactions, predator–prey interactions have received most attention. The resulting food webs, however, will always operate simultaneously with networks based on other types of ecological interaction, such as through the activities of ecosystem engineers or mutualistic interactions. Little is known about how to classify, organize and quantify these other ecological networks and their mutual interplay. The aim of this paper is to provide new and testable ideas on how to understand and model ecosystems in which many different types of ecological interaction operate simultaneously. We approach this problem by first identifying six main types of interaction that operate within ecosystems, of which food web interactions are one. Then, we propose that food webs are structured among two main axes of organization: a vertical (classic) axis representing trophic position and a new horizontal ‘ecological stoichiometry’ axis representing decreasing palatability of plant parts and detritus for herbivores and detrivores and slower turnover times. The usefulness of these new ideas is then explored with three very different ecosystems as test cases: temperate intertidal mudflats; temperate short grass prairie; and tropical savannah.
doi:10.1098/rstb.2008.0222
PMCID: PMC2685422  PMID: 19451126
food webs; predator–prey interactions; ecological networks; non-trophic interactions; ecosystem engineers; ecological stoichiometry
14.  Proceedings of the 8th Annual Conference on the Science of Dissemination and Implementation 
Chambers, David | Simpson, Lisa | Hill-Briggs, Felicia | Neta, Gila | Vinson, Cynthia | Chambers, David | Beidas, Rinad | Marcus, Steven | Aarons, Gregory | Hoagwood, Kimberly | Schoenwald, Sonja | Evans, Arthur | Hurford, Matthew | Rubin, Ronnie | Hadley, Trevor | Barg, Frances | Walsh, Lucia | Adams, Danielle | Mandell, David | Martin, Lindsey | Mignogna, Joseph | Mott, Juliette | Hundt, Natalie | Kauth, Michael | Kunik, Mark | Naik, Aanand | Cully, Jeffrey | McGuire, Alan | White, Dominique | Bartholomew, Tom | McGrew, John | Luther, Lauren | Rollins, Angie | Salyers, Michelle | Cooper, Brittany | Funaiole, Angie | Richards, Julie | Lee, Amy | Lapham, Gwen | Caldeiro, Ryan | Lozano, Paula | Gildred, Tory | Achtmeyer, Carol | Ludman, Evette | Addis, Megan | Marx, Larry | Bradley, Katharine | VanDeinse, Tonya | Wilson, Amy Blank | Stacey, Burgin | Powell, Byron | Bunger, Alicia | Cuddeback, Gary | Barnett, Miya | Stadnick, Nicole | Brookman-Frazee, Lauren | Lau, Anna | Dorsey, Shannon | Pullmann, Michael | Mitchell, Shannon | Schwartz, Robert | Kirk, Arethusa | Dusek, Kristi | Oros, Marla | Hosler, Colleen | Gryczynski, Jan | Barbosa, Carolina | Dunlap, Laura | Lounsbury, David | O’Grady, Kevin | Brown, Barry | Damschroder, Laura | Waltz, Thomas | Powell, Byron | Ritchie, Mona | Waltz, Thomas | Atkins, David | Imel, Zac E. | Xiao, Bo | Can, Doğan | Georgiou, Panayiotis | Narayanan, Shrikanth | Berkel, Cady | Gallo, Carlos | Sandler, Irwin | Brown, C. Hendricks | Wolchik, Sharlene | Mauricio, Anne Marie | Gallo, Carlos | Brown, C. Hendricks | Mehrotra, Sanjay | Chandurkar, Dharmendra | Bora, Siddhartha | Das, Arup | Tripathi, Anand | Saggurti, Niranjan | Raj, Anita | Hughes, Eric | Jacobs, Brian | Kirkendall, Eric | Loeb, Danielle | Trinkley, Katy | Yang, Michael | Sprowell, Andrew | Nease, Donald | Lyon, Aaron | Lewis, Cara | Boyd, Meredith | Melvin, Abigail | Nicodimos, Semret | Liu, Freda | Jungbluth, Nathanial | Lyon, Aaron | Lewis, Cara | Boyd, Meredith | Melvin, Abigail | Nicodimos, Semret | Liu, Freda | Jungbluth, Nathanial | Flynn, Allen | Landis-Lewis, Zach | Sales, Anne | Baloh, Jure | Ward, Marcia | Zhu, Xi | Bennett, Ian | Unutzer, Jurgen | Mao, Johnny | Proctor, Enola | Vredevoogd, Mindy | Chan, Ya-Fen | Williams, Nathaniel | Green, Phillip | Bernstein, Steven | Rosner, June-Marie | DeWitt, Michelle | Tetrault, Jeanette | Dziura, James | Hsiao, Allen | Sussman, Scott | O’Connor, Patrick | Toll, Benjamin | Jones, Michael | Gassaway, Julie | Tobin, Jonathan | Zatzick, Douglas | Bradbury, Angela R. | Patrick-Miller, Linda | Egleston, Brian | Olopade, Olufunmilayo I. | Hall, Michael J. | Daly, Mary B. | Fleisher, Linda | Grana, Generosa | Ganschow, Pamela | Fetzer, Dominique | Brandt, Amanda | Farengo-Clark, Dana | Forman, Andrea | Gaber, Rikki S. | Gulden, Cassandra | Horte, Janice | Long, Jessica | Chambers, Rachelle Lorenz | Lucas, Terra | Madaan, Shreshtha | Mattie, Kristin | McKenna, Danielle | Montgomery, Susan | Nielsen, Sarah | Powers, Jacquelyn | Rainey, Kim | Rybak, Christina | Savage, Michelle | Seelaus, Christina | Stoll, Jessica | Stopfer, Jill | Yao, Shirley | Domchek, Susan | Hahn, Erin | Munoz-Plaza, Corrine | Wang, Jianjin | Delgadillo, Jazmine Garcia | Mittman, Brian | Gould, Michael | Liang, Shuting (Lily) | Kegler, Michelle C. | Cotter, Megan | Phillips, Emily | Hermstad, April | Morton, Rentonia | Beasley, Derrick | Martinez, Jeremy | Riehman, Kara | Gustafson, David | Marsch, Lisa | Mares, Louise | Quanbeck, Andrew | McTavish, Fiona | McDowell, Helene | Brown, Randall | Thomas, Chantelle | Glass, Joseph | Isham, Joseph | Shah, Dhavan | Liebschutz, Jane | Lasser, Karen | Watkins, Katherine | Ober, Allison | Hunter, Sarah | Lamp, Karen | Ewing, Brett | Iwelunmor, Juliet | Gyamfi, Joyce | Blackstone, Sarah | Quakyi, Nana Kofi | Plange-Rhule, Jacob | Ogedegbe, Gbenga | Kumar, Pritika | Van Devanter, Nancy | Nguyen, Nam | Nguyen, Linh | Nguyen, Trang | Phuong, Nguyet | Shelley, Donna | Rudge, Sian | Langlois, Etienne | Tricco, Andrea | Ball, Sherry | Lambert-Kerzner, Anne | Sulc, Christine | Simmons, Carol | Shell-Boyd, Jeneen | Oestreich, Taryn | O’Connor, Ashley | Neely, Emily | McCreight, Marina | Labebue, Amy | DiFiore, Doreen | Brostow, Diana | Ho, P. Michael | Aron, David | Harvey, Jillian | McHugh, Megan | Scanlon, Dennis | Lee, Rebecca | Soltero, Erica | Parker, Nathan | McNeill, Lorna | Ledoux, Tracey | McIsaac, Jessie-Lee | MacLeod, Kate | Ata, Nicole | Jarvis, Sherry | Kirk, Sara | Purtle, Jonathan | Dodson, Elizabeth | Brownson, Ross | Mittman, Brian | Curran, Geoffrey | Curran, Geoffrey | Pyne, Jeffrey | Aarons, Gregory | Ehrhart, Mark | Torres, Elisa | Miech, Edward | Miech, Edward | Stevens, Kathleen | Hamilton, Alison | Cohen, Deborah | Padgett, Deborah | Morshed, Alexandra | Patel, Rupa | Prusaczyk, Beth | Aron, David C. | Gupta, Divya | Ball, Sherry | Hand, Rosa | Abram, Jenica | Wolfram, Taylor | Hastings, Molly | Moreland-Russell, Sarah | Tabak, Rachel | Ramsey, Alex | Baumann, Ana | Kryzer, Emily | Montgomery, Katherine | Lewis, Ericka | Padek, Margaret | Powell, Byron | Brownson, Ross | Mamaril, Cezar Brian | Mays, Glen | Branham, Keith | Timsina, Lava | Mays, Glen | Hogg, Rachel | Fagan, Abigail | Shapiro, Valerie | Brown, Eric | Haggerty, Kevin | Hawkins, David | Oesterle, Sabrina | Hawkins, David | Catalano, Richard | McKay, Virginia | Dolcini, M. Margaret | Hoffer, Lee | Moin, Tannaz | Li, Jinnan | Duru, O. Kenrik | Ettner, Susan | Turk, Norman | Chan, Charles | Keckhafer, Abigail | Luchs, Robert | Ho, Sam | Mangione, Carol | Selby, Peter | Zawertailo, Laurie | Minian, Nadia | Balliunas, Dolly | Dragonetti, Rosa | Hussain, Sarwar | Lecce, Julia | Chinman, Matthew | Acosta, Joie | Ebener, Patricia | Malone, Patrick S. | Slaughter, Mary | Freedman, Darcy | Flocke, Susan | Lee, Eunlye | Matlack, Kristen | Trapl, Erika | Ohri-Vachaspati, Punam | Taggart, Morgan | Borawski, Elaine | Parrish, Amanda | Harris, Jeffrey | Kohn, Marlana | Hammerback, Kristen | McMillan, Becca | Hannon, Peggy | Swindle, Taren | Curran, Geoffrey | Whiteside-Mansell, Leanne | Ward, Wendy | Holt, Cheryl | Santos, Sheri Lou | Tagai, Erin | Scheirer, Mary Ann | Carter, Roxanne | Bowie, Janice | Haider, Muhiuddin | Slade, Jimmie | Wang, Min Qi | Masica, Andrew | Ogola, Gerald | Berryman, Candice | Richter, Kathleen | Shelton, Rachel | Jandorf, Lina | Erwin, Deborah | Truong, Khoa | Javier, Joyce R. | Coffey, Dean | Schrager, Sheree M. | Palinkas, Lawrence | Miranda, Jeanne | Johnson, Veda | Hutcherson, Valerie | Ellis, Ruth | Kharmats, Anna | Marshall-King, Sandra | LaPradd, Monica | Fonseca-Becker, Fannie | Kepka, Deanna | Bodson, Julia | Warner, Echo | Fowler, Brynn | Shenkman, Elizabeth | Hogan, William | Odedina, Folakami | De Leon, Jessica | Hooper, Monica | Carrasquillo, Olveen | Reams, Renee | Hurt, Myra | Smith, Steven | Szapocznik, Jose | Nelson, David | Mandal, Prabir | Teufel, James
Implementation Science : IS  2016;11(Suppl 2):100.
Table of contents
A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health
David Chambers, Lisa Simpson
D1 Discussion forum: Population health D&I research
Felicia Hill-Briggs
D2 Discussion forum: Global health D&I research
Gila Neta, Cynthia Vinson
D3 Discussion forum: Precision medicine and D&I research
David Chambers
S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system
Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell
S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field
Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully
S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes
Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers
S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach
Brittany Cooper, Angie Funaiole
S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment
Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley
S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study
Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback
S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform
Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau
S8 Leveraging an existing resource for technical assistance: Community-based supervisors in public mental health
Shannon Dorsey, Michael Pullmann
S9 SBIRT implementation for adolescents in urban federally qualified health centers: Implementation outcomes
Shannon Mitchell, Robert Schwartz, Arethusa Kirk, Kristi Dusek, Marla Oros, Colleen Hosler, Jan Gryczynski, Carolina Barbosa, Laura Dunlap, David Lounsbury, Kevin O'Grady, Barry Brown
S10 PANEL: Tailoring Implementation Strategies to Context - Expert recommendations for tailoring strategies to context
Laura Damschroder, Thomas Waltz, Byron Powell
S11 PANEL: Tailoring Implementation Strategies to Context - Extreme facilitation: Helping challenged healthcare settings implement complex programs
Mona Ritchie
S12 PANEL: Tailoring Implementation Strategies to Context - Using menu-based choice tasks to obtain expert recommendations for implementing three high-priority practices in the VA
Thomas Waltz
S13 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Siri, rate my therapist: Using technology to automate fidelity ratings of motivational interviewing
David Atkins, Zac E. Imel, Bo Xiao, Doğan Can, Panayiotis Georgiou, Shrikanth Narayanan
S14 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Identifying indicators of implementation quality for computer-based ratings
Cady Berkel, Carlos Gallo, Irwin Sandler, C. Hendricks Brown, Sharlene Wolchik, Anne Marie Mauricio
S15 PANEL: The Use of Technology to Improve Efficient Monitoring of Implementation of Evidence-based Programs - Improving implementation of behavioral interventions by monitoring emotion in spoken speech
Carlos Gallo, C. Hendricks Brown, Sanjay Mehrotra
S16 Scorecards and dashboards to assure data quality of health management information system (HMIS) using R
Dharmendra Chandurkar, Siddhartha Bora, Arup Das, Anand Tripathi, Niranjan Saggurti, Anita Raj
S17 A big data approach for discovering and implementing patient safety insights
Eric Hughes, Brian Jacobs, Eric Kirkendall
S18 Improving the efficacy of a depression registry for use in a collaborative care model
Danielle Loeb, Katy Trinkley, Michael Yang, Andrew Sprowell, Donald Nease
S19 Measurement feedback systems as a strategy to support implementation of measurement-based care in behavioral health
Aaron Lyon, Cara Lewis, Meredith Boyd, Abigail Melvin, Semret Nicodimos, Freda Liu, Nathanial Jungbluth
S20 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Common loop assay: Methods of supporting learning collaboratives
Allen Flynn
S21 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Innovating audit and feedback using message tailoring models for learning health systems
Zach Landis-Lewis
S22 PANEL: Implementation Science and Learning Health Systems: Intersections and Commonalities - Implementation science and learning health systems: Connecting the dots
Anne Sales
S23 Facilitation activities of Critical Access Hospitals during TeamSTEPPS implementation
Jure Baloh, Marcia Ward, Xi Zhu
S24 Organizational and social context of federally qualified health centers and variation in maternal depression outcomes
Ian Bennett, Jurgen Unutzer, Johnny Mao, Enola Proctor, Mindy Vredevoogd, Ya-Fen Chan, Nathaniel Williams, Phillip Green
S25 Decision support to enhance treatment of hospitalized smokers: A randomized trial
Steven Bernstein, June-Marie Rosner, Michelle DeWitt, Jeanette Tetrault, James Dziura, Allen Hsiao, Scott Sussman, Patrick O’Connor, Benjamin Toll
S26 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - A patient-centered approach to successful community transition after catastrophic injury
Michael Jones, Julie Gassaway
S27 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - Conducting PCOR to integrate mental health and cancer screening services in primary care
Jonathan Tobin
S28 PANEL: Developing Sustainable Strategies for the Implementation of Patient-Centered Care across Diverse US Healthcare Systems - A comparative effectiveness trial of optimal patient-centered care for US trauma care systems
Douglas Zatzick
S29 Preferences for in-person communication among patients in a multi-center randomized study of in-person versus telephone communication of genetic test results for cancer susceptibility
Angela R Bradbury, Linda Patrick-Miller, Brian Egleston, Olufunmilayo I Olopade, Michael J Hall, Mary B Daly, Linda Fleisher, Generosa Grana, Pamela Ganschow, Dominique Fetzer, Amanda Brandt, Dana Farengo-Clark, Andrea Forman, Rikki S Gaber, Cassandra Gulden, Janice Horte, Jessica Long, Rachelle Lorenz Chambers, Terra Lucas, Shreshtha Madaan, Kristin Mattie, Danielle McKenna, Susan Montgomery, Sarah Nielsen, Jacquelyn Powers, Kim Rainey, Christina Rybak, Michelle Savage, Christina Seelaus, Jessica Stoll, Jill Stopfer, Shirley Yao and Susan Domchek
S30 Working towards de-implementation: A mixed methods study in breast cancer surveillance care
Erin Hahn, Corrine Munoz-Plaza, Jianjin Wang, Jazmine Garcia Delgadillo, Brian Mittman Michael Gould
S31Integrating evidence-based practices for increasing cancer screenings in safety-net primary care systems: A multiple case study using the consolidated framework for implementation research
Shuting (Lily) Liang, Michelle C. Kegler, Megan Cotter, Emily Phillips, April Hermstad, Rentonia Morton, Derrick Beasley, Jeremy Martinez, Kara Riehman
S32 Observations from implementing an mHealth intervention in an FQHC
David Gustafson, Lisa Marsch, Louise Mares, Andrew Quanbeck, Fiona McTavish, Helene McDowell, Randall Brown, Chantelle Thomas, Joseph Glass, Joseph Isham, Dhavan Shah
S33 A multicomponent intervention to improve primary care provider adherence to chronic opioid therapy guidelines and reduce opioid misuse: A cluster randomized controlled trial protocol
Jane Liebschutz, Karen Lasser
S34 Implementing collaborative care for substance use disorders in primary care: Preliminary findings from the summit study
Katherine Watkins, Allison Ober, Sarah Hunter, Karen Lamp, Brett Ewing
S35 Sustaining a task-shifting strategy for blood pressure control in Ghana: A stakeholder analysis
Juliet Iwelunmor, Joyce Gyamfi, Sarah Blackstone, Nana Kofi Quakyi, Jacob Plange-Rhule, Gbenga Ogedegbe
S36 Contextual adaptation of the consolidated framework for implementation research (CFIR) in a tobacco cessation study in Vietnam
Pritika Kumar, Nancy Van Devanter, Nam Nguyen, Linh Nguyen, Trang Nguyen, Nguyet Phuong, Donna Shelley
S37 Evidence check: A knowledge brokering approach to systematic reviews for policy
Sian Rudge
S38 Using Evidence Synthesis to Strengthen Complex Health Systems in Low- and Middle-Income Countries
Etienne Langlois
S39 Does it matter: timeliness or accuracy of results? The choice of rapid reviews or systematic reviews to inform decision-making
Andrea Tricco
S40 Evaluation of the veterans choice program using lean six sigma at a VA medical center to identify benefits and overcome obstacles
Sherry Ball, Anne Lambert-Kerzner, Christine Sulc, Carol Simmons, Jeneen Shell-Boyd, Taryn Oestreich, Ashley O'Connor, Emily Neely, Marina McCreight, Amy Labebue, Doreen DiFiore, Diana Brostow, P. Michael Ho, David Aron
S41 The influence of local context on multi-stakeholder alliance quality improvement activities: A multiple case study
Jillian Harvey, Megan McHugh, Dennis Scanlon
S42 Increasing physical activity in early care and education: Sustainability via active garden education (SAGE)
Rebecca Lee, Erica Soltero, Nathan Parker, Lorna McNeill, Tracey Ledoux
S43 Marking a decade of policy implementation: The successes and continuing challenges of a provincial school food and nutrition policy in Canada
Jessie-Lee McIsaac, Kate MacLeod, Nicole Ata, Sherry Jarvis, Sara Kirk
S44 Use of research evidence among state legislators who prioritize mental health and substance abuse issues
Jonathan Purtle, Elizabeth Dodson, Ross Brownson
S45 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 1 designs
Brian Mittman, Geoffrey Curran
S46 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 2 designs
Geoffrey Curran
S47 PANEL: Effectiveness-Implementation Hybrid Designs: Clarifications, Refinements, and Additional Guidance Based on a Systematic Review and Reports from the Field - Hybrid type 3 designs
Jeffrey Pyne
S48 Linking team level implementation leadership and implementation climate to individual level attitudes, behaviors, and implementation outcomes
Gregory Aarons, Mark Ehrhart, Elisa Torres
S49 Pinpointing the specific elements of local context that matter most to implementation outcomes: Findings from qualitative comparative analysis in the RE-inspire study of VA acute stroke care
Edward Miech
S50 The GO score: A new context-sensitive instrument to measure group organization level for providing and improving care
Edward Miech
S51 A research network approach for boosting implementation and improvement
Kathleen Stevens, I.S.R.N. Steering Council
S52 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - The value of qualitative methods in implementation research
Alison Hamilton
S53 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - Learning evaluation: The role of qualitative methods in dissemination and implementation research
Deborah Cohen
S54 PANEL: Qualitative methods in D&I Research: Value, rigor and challenge - Qualitative methods in D&I research
Deborah Padgett
S55 PANEL: Maps & models: The promise of network science for clinical D&I - Hospital network of sharing patients with acute and chronic diseases in California
Alexandra Morshed
S56 PANEL: Maps & models: The promise of network science for clinical D&I - The use of social network analysis to identify dissemination targets and enhance D&I research study recruitment for pre-exposure prophylaxis for HIV (PrEP) among men who have sex with men
Rupa Patel
S57 PANEL: Maps & models: The promise of network science for clinical D&I - Network and organizational factors related to the adoption of patient navigation services among rural breast cancer care providers
Beth Prusaczyk
S58 A theory of de-implementation based on the theory of healthcare professionals’ behavior and intention (THPBI) and the becker model of unlearning
David C. Aron, Divya Gupta, Sherry Ball
S59 Observation of registered dietitian nutritionist-patient encounters by dietetic interns highlights low awareness and implementation of evidence-based nutrition practice guidelines
Rosa Hand, Jenica Abram, Taylor Wolfram
S60 Program sustainability action planning: Building capacity for program sustainability using the program sustainability assessment tool
Molly Hastings, Sarah Moreland-Russell
S61 A review of D&I study designs in published study protocols
Rachel Tabak, Alex Ramsey, Ana Baumann, Emily Kryzer, Katherine Montgomery, Ericka Lewis, Margaret Padek, Byron Powell, Ross Brownson
S62 PANEL: Geographic variation in the implementation of public health services: Economic, organizational, and network determinants - Model simulation techniques to estimate the cost of implementing foundational public health services
Cezar Brian Mamaril, Glen Mays, Keith Branham, Lava Timsina
S63 PANEL: Geographic variation in the implementation of public health services: Economic, organizational, and network determinants - Inter-organizational network effects on the implementation of public health services
Glen Mays, Rachel Hogg
S64 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Implementation fidelity, coalition functioning, and community prevention system transformation using communities that care
Abigail Fagan, Valerie Shapiro, Eric Brown
S65 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Expanding capacity for implementation of communities that care at scale using a web-based, video-assisted training system
Kevin Haggerty, David Hawkins
S66 PANEL: Building capacity for implementation and dissemination of the communities that care prevention system at scale to promote evidence-based practices in behavioral health - Effects of communities that care on reducing youth behavioral health problems
Sabrina Oesterle, David Hawkins, Richard Catalano
S68 When interventions end: the dynamics of intervention de-adoption and replacement
Virginia McKay, M. Margaret Dolcini, Lee Hoffer
S69 Results from next-d: can a disease specific health plan reduce incident diabetes development among a national sample of working-age adults with pre-diabetes?
Tannaz Moin, Jinnan Li, O. Kenrik Duru, Susan Ettner, Norman Turk, Charles Chan, Abigail Keckhafer, Robert Luchs, Sam Ho, Carol Mangione
S70 Implementing smoking cessation interventions in primary care settings (STOP): using the interactive systems framework
Peter Selby, Laurie Zawertailo, Nadia Minian, Dolly Balliunas, Rosa Dragonetti, Sarwar Hussain, Julia Lecce
S71 Testing the Getting To Outcomes implementation support intervention in prevention-oriented, community-based settings
Matthew Chinman, Joie Acosta, Patricia Ebener, Patrick S Malone, Mary Slaughter
S72 Examining the reach of a multi-component farmers’ market implementation approach among low-income consumers in an urban context
Darcy Freedman, Susan Flocke, Eunlye Lee, Kristen Matlack, Erika Trapl, Punam Ohri-Vachaspati, Morgan Taggart, Elaine Borawski
S73 Increasing implementation of evidence-based health promotion practices at large workplaces: The CEOs Challenge
Amanda Parrish, Jeffrey Harris, Marlana Kohn, Kristen Hammerback, Becca McMillan, Peggy Hannon
S74 A qualitative assessment of barriers to nutrition promotion and obesity prevention in childcare
Taren Swindle, Geoffrey Curran, Leanne Whiteside-Mansell, Wendy Ward
S75 Documenting institutionalization of a health communication intervention in African American churches
Cheryl Holt, Sheri Lou Santos, Erin Tagai, Mary Ann Scheirer, Roxanne Carter, Janice Bowie, Muhiuddin Haider, Jimmie Slade, Min Qi Wang
S76 Reduction in hospital utilization by underserved patients through use of a community-medical home
Andrew Masica, Gerald Ogola, Candice Berryman, Kathleen Richter
S77 Sustainability of evidence-based lay health advisor programs in African American communities: A mixed methods investigation of the National Witness Project
Rachel Shelton, Lina Jandorf, Deborah Erwin
S78 Predicting the long-term uninsured population and analyzing their gaps in physical access to healthcare in South Carolina
Khoa Truong
S79 Using an evidence-based parenting intervention in churches to prevent behavioral problems among Filipino youth: A randomized pilot study
Joyce R. Javier, Dean Coffey, Sheree M. Schrager, Lawrence Palinkas, Jeanne Miranda
S80 Sustainability of elementary school-based health centers in three health-disparate southern communities
Veda Johnson, Valerie Hutcherson, Ruth Ellis
S81 Childhood obesity prevention partnership in Louisville: creative opportunities to engage families in a multifaceted approach to obesity prevention
Anna Kharmats, Sandra Marshall-King, Monica LaPradd, Fannie Fonseca-Becker
S82 Improvements in cervical cancer prevention found after implementation of evidence-based Latina prevention care management program
Deanna Kepka, Julia Bodson, Echo Warner, Brynn Fowler
S83 The OneFlorida data trust: Achieving health equity through research & training capacity building
Elizabeth Shenkman, William Hogan, Folakami Odedina, Jessica De Leon, Monica Hooper, Olveen Carrasquillo, Renee Reams, Myra Hurt, Steven Smith, Jose Szapocznik, David Nelson, Prabir Mandal
S84 Disseminating and sustaining medical-legal partnerships: Shared value and social return on investment
James Teufel
doi:10.1186/s13012-016-0452-0
PMCID: PMC4977475  PMID: 27490260
15.  Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations 
BMC Ecology  2016;16:33.
Background
This work combines multivariate time series analysis and graph theory to detect synchronization and causality among certain ecological variables and to represent significant correlations via network projections. Four different statistical tools (cross-correlations, partial cross-correlations, Granger causality and partial Granger causality) utilized to quantify correlation strength and causality among biological entities. These indices correspond to different ways to estimate the relationships between different variables and to construct ecological networks using the variables as nodes and the indices as edges. Specifically, correlations and Granger causality indices introduce rules that define the associations (links) between the ecological variables (nodes). This approach is used for the first time to analyze time series of moth populations as well as temperature and relative humidity in order to detect spatiotemporal synchronization over an agricultural study area and to illustrate significant correlations and causality interactions via graphical models.
Results
The networks resulting from the different approaches are trimmed and show how the network configurations are affected by each construction technique. The Granger statistical rules provide a simple test to determine whether one series (population) is caused by another series (i.e. environmental variable or other population) even when they are not correlated. In most cases, the statistical analysis and the related graphical models, revealed intra-specific links, a fact that may be linked to similarities in pest population life cycles and synchronizations. Graph theoretic landscape projections reveal that significant associations in the populations are not subject to landscape characteristics. Populations may be linked over great distances through physical features such as rivers and not only at adjacent locations in which significant interactions are more likely to appear. In some cases, incidental connections, with no ecological explanation, were also observed; however, this was expected because some of the statistical methods used to define non trivial associations show connections that cannot be interpreted phenomenologically.
Conclusions
Incorporating multivariate causal interactions in a probabilistic sense comes closer to reality than doing per se binary theoretic constructs because the former conceptually incorporate the dynamics of all kinds of ecological variables within the network. The advantage of Granger rules over correlations is that Granger rules have dynamic features and provide an easy way to examine the dynamic causal relations of multiple time-series variables. The constructed networks may provide an intuitive, advantageous representation of multiple populations’ associations that can be realized within an agro-ecosystem. These relationships may be due to life cycle synchronizations, exposure to a shared climate or even more complicated ecological interactions such as moving behavior, dispersal patterns and host allocation. Moreover, they are useful for drawing inferences regarding pest population dynamics and their spatial management. Extending these models by including more variables should allow the exploration of intra and interspecies relationships in larger ecological systems, and the identification of specific population traits that might constrain their structures in larger areas.
Electronic supplementary material
The online version of this article (doi:10.1186/s12898-016-0087-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12898-016-0087-7
PMCID: PMC4974811  PMID: 27495149
Population modelling; Graph theory; MVAR models; Correlation and partial; Correlation; Final discovery rate; Synchronization; Causality
16.  A Genomewide Functional Network for the Laboratory Mouse 
PLoS Computational Biology  2008;4(9):e1000165.
Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.
Author Summary
Functionally related proteins interact in diverse ways to carry out biological processes, and each protein often participates in multiple pathways. Proteins are therefore organized into a complex network through which different functions of the cell are carried out. An accurate description of such a network is invaluable to our understanding of both the system-level features of a cell and those of an individual biological process. In this study, we used a probabilistic model to combine information from diverse genome-scale studies as well as individual investigations to generate a global functional network for mouse. Our analysis of the global topology of this network reveals biologically relevant systems-level characteristics of the mouse proteome, including conservation of functional neighborhoods and network features characteristic of known disease genes and key transcriptional regulators. We have made this network publicly available for search and dynamic exploration by researchers in the community. Our Web interface enables users to easily generate hypotheses regarding potential functional roles of uncharacterized proteins, investigate possible links between their proteins of interest and disease, and identify new players in specific biological processes.
doi:10.1371/journal.pcbi.1000165
PMCID: PMC2527685  PMID: 18818725
17.  Disrupting the Networks of Cancer 
Ecosystems are interactive systems involving communities of species and their abiotic environment. Tumors are ecosystems in which cancer cells act as invasive species interacting with native host cell species in an established microenvironment within the larger host biosphere. At its heart, to study ecology is to study interconnectedness. In ecologic science, an ecologic network is a representation of the biotic interactions in an ecosystem in which species (nodes) are connected by pairwise interactions (links). Ecologic networks and signaling network models have been used to describe and compare the structures of ecosystems. It has been shown that disruption of ecologic networks through the loss of species or disruption of interactions between them can lead to the destruction of the ecosystem. Often, the destruction of a single node or link is not enough to disrupt the entire ecosystem. The more complex the network and its interactions, the more difficult it is to cause the extinction of a species, especially without leveraging other aspects of the ecosystem. Similarly, successful treatment of cancer with a single agent is rarely enough to cure a patient without strategically modifying the support systems conducive to survival of cancer. Cancer cells and the ecologic systems they reside in can be viewed as a series of nested networks. The most effective new paradigms for treatment will be developed through application of scaled network disruption.
doi:10.1158/1078-0432.CCR-12-0366
PMCID: PMC4154593  PMID: 22442061
18.  Eco-evolutionary Model of Rapid Phenotypic Diversification in Species-Rich Communities 
PLoS Computational Biology  2016;12(10):e1005139.
Evolutionary and ecosystem dynamics are often treated as different processes –operating at separate timescales– even if evidence reveals that rapid evolutionary changes can feed back into ecological interactions. A recent long-term field experiment has explicitly shown that communities of competing plant species can experience very fast phenotypic diversification, and that this gives rise to enhanced complementarity in resource exploitation and to enlarged ecosystem-level productivity. Here, we build on progress made in recent years in the integration of eco-evolutionary dynamics, and present a computational approach aimed at describing these empirical findings in detail. In particular we model a community of organisms of different but similar species evolving in time through mechanisms of birth, competition, sexual reproduction, descent with modification, and death. Based on simple rules, this model provides a rationalization for the emergence of rapid phenotypic diversification in species-rich communities. Furthermore, it also leads to non-trivial predictions about long-term phenotypic change and ecological interactions. Our results illustrate that the presence of highly specialized, non-competing species leads to very stable communities and reveals that phenotypically equivalent species occupying the same niche may emerge and coexist for very long times. Thus, the framework presented here provides a simple approach –complementing existing theories, but specifically devised to account for the specificities of the recent empirical findings for plant communities– to explain the collective emergence of diversification at a community level, and paves the way to further scrutinize the intimate entanglement of ecological and evolutionary processes, especially in species-rich communities.
Author Summary
Population ecology and evolutionary biology have been traditionally studied as separate disciplines, even if feedbacks between community and evolutionary processes are known to exist, having been empirically characterized in recent years in different types of communities (from microbes to plants and vertebrates), and theoretically analyzed with novel and powerful mathematical tools. Recent long-term field experiments with plants have proven that rapid co-evolution and diversification of species traits results in an overall enhancement of the ecosystem productivity, with important consequences for agriculture and conservation. Here, we propose a relatively simple computational eco-evolutionary model specifically devised to describe rapid phenotypic diversification in this type of species-rich communities. Our model captures the main phenomenology observed experimentally, and it also makes non-trivial predictions for long term phenotypic change and ecological interactions, such as the stable coexistence of highly specialized species or the possible emergence of phenotypically equivalent species occupying the same niche. Finally, the model is easily generalizable to analyze different eco-evolutionary problems within a relatively simple and unified computational framework.
doi:10.1371/journal.pcbi.1005139
PMCID: PMC5063285  PMID: 27736874
19.  Biology, Methodology or Chance? The Degree Distributions of Bipartite Ecological Networks 
PLoS ONE  2011;6(3):e17645.
The distribution of the number of links per species, or degree distribution, is widely used as a summary of the topology of complex networks. Degree distributions have been studied in a range of ecological networks, including both mutualistic bipartite networks of plants and pollinators or seed dispersers and antagonistic bipartite networks of plants and their consumers. The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network. The skewed degree distributions of bipartite mutualistic and antagonistic networks are usually assumed to show that ecological or co-evolutionary processes constrain the relative numbers of specialists and generalists in the network. I show that a simple null model based on the principle of maximum entropy cannot be rejected as a model for the degree distributions in most of the 115 bipartite ecological networks tested here. The model requires knowledge of the number of nodes and links in the network, but needs no other ecological information. The model cannot be rejected for 159 (69%) of the 230 degree distributions of the 115 networks tested. It performed equally well on the plant and animal degree distributions, and cannot be rejected for 81 (70%) of the 115 plant distributions and 78 (68%) of the animal distributions. There are consistent differences between the degree distributions of mutualistic and antagonistic networks, suggesting that different processes are constraining these two classes of networks. Fit to the MaxEnt null model is consistently poor among the largest mutualistic networks. Potential ecological and methodological explanations for deviations from the model suggest that spatial and temporal heterogeneity are important drivers of the structure of these large networks.
doi:10.1371/journal.pone.0017645
PMCID: PMC3048397  PMID: 21390231
20.  RevEcoR: an R package for the reverse ecology analysis of microbiomes 
BMC Bioinformatics  2016;17:294.
Background
All species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecological methods can only be applied on a small scale and for relatively well-understood biological systems. Recently, a graph-theory-based algorithm called the reverse ecology approach has been developed that can analyze the metabolic networks of all the species in a microbial community, and predict the metabolic interface between species and their environment.
Results
Here, we present RevEcoR, an R package and a Shiny Web application that implements the reverse ecology algorithm for determining microbe–microbe interactions in microbial communities. This software allows users to obtain large-scale ecological insights into species’ ecology directly from high-throughput metagenomic data. The software has great potential for facilitating the study of microbiomes.
Conclusions
RevEcoR is open source software for the study of microbial community ecology. The RevEcoR R package is freely available under the GNU General Public License v. 2.0 at http://cran.r-project.org/web/packages/RevEcoR/ with the vignette and typical usage examples, and the interactive Shiny web application is available at http://yiluheihei.shinyapps.io/shiny-RevEcoR, or can be installed locally with the source code accessed from https://github.com/yiluheihei/shiny-RevEcoR.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-1088-4) contains supplementary material, which is available to authorized users.
doi:10.1186/s12859-016-1088-4
PMCID: PMC4965897  PMID: 27473172
Metabolic network; Microbiome; Reverse ecology
21.  Environments that Induce Synthetic Microbial Ecosystems 
PLoS Computational Biology  2010;6(11):e1001002.
Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.
Author Summary
Microbial metabolism affects biogeochemical cycles and human health. In most natural environments, multiple microbial species interact with each other, forming complex ecosystems whose properties are poorly understood. In an effort to understand inter-microbial interactions, and to explore new metabolic engineering avenues, researchers have started building artificial microbial ecosystems, e.g. pairs of genetically engineered strains that require each other for survival. Here we computationally explore the possibility of creating artificial microbial ecosystems without re-engineering the microbes themselves, but rather by manipulating the environment in which they grow. Specifically, using the framework of flux balance analysis, we predict environments in which either one or both microbes in a pair would not be able to grow without the other, inducing commensal (one-way) or mutualistic (two-way) interactions, respectively. Our algorithms can successfully recapitulate known inter-microbial interactions, and predict millions of new ones across any pair amongst different microbial species. Surprisingly, we find that it is always possible to identify conditions that induce mutualistic or commensal interactions between any two species. Hence, our method should help in mapping naturally occurring microbe-microbe interactions, and in engineering new ones through a novel, environment-driven branch of synthetic ecology.
doi:10.1371/journal.pcbi.1001002
PMCID: PMC2987903  PMID: 21124952
22.  Cooperation Is Not Enough—Exploring Social-Ecological Micro-Foundations for Sustainable Common-Pool Resource Use 
PLoS ONE  2016;11(8):e0157796.
Cooperation amongst resource users holds the key to overcoming the social dilemma that characterizes community-based common-pool resource management. But is cooperation alone enough to achieve sustainable resource use? The short answer is no. Developing management strategies in a complex social-ecological environment also requires ecological knowledge and approaches to deal with perceived environmental uncertainty. Recent behavioral experimental research indicates variation in the degree to which a group of users can identify a sustainable exploitation level. In this paper, we identify social-ecological micro-foundations that facilitate cooperative sustainable common-pool resource use. We do so by using an agent-based model (ABM) that is informed by behavioral common-pool resource experiments. In these experiments, groups that cooperate do not necessarily manage the resource sustainably, but also over- or underexploit. By reproducing the patterns of the behavioral experiments in a qualitative way, the ABM represents a social-ecological explanation for the experimental observations. We find that the ecological knowledge of each group member cannot sufficiently explain the relationship between cooperation and sustainable resource use. Instead, the development of a sustainable exploitation level depends on the distribution of ecological knowledge among the group members, their influence on each other’s knowledge, and the environmental uncertainty the individuals perceive. The study provides insights about critical social-ecological micro-foundations underpinning collective action and sustainable resource management. These insights may inform policy-making, but also point to future research needs regarding the mechanisms of social learning, the development of shared management strategies and the interplay of social and ecological uncertainty.
doi:10.1371/journal.pone.0157796
PMCID: PMC4996507  PMID: 27556175
23.  Top‐down network analysis characterizes hidden termite–termite interactions 
Ecology and Evolution  2016;6(17):6178-6188.
Abstract
The analysis of ecological networks is generally bottom‐up, where networks are established by observing interactions between individuals. Emergent network properties have been indicated to reflect the dominant mode of interactions in communities that might be mutualistic (e.g., pollination) or antagonistic (e.g., host–parasitoid communities). Many ecological communities, however, comprise species interactions that are difficult to observe directly. Here, we propose that a comparison of the emergent properties from detail‐rich reference communities with known modes of interaction can inform our understanding of detail‐sparse focal communities. With this top‐down approach, we consider patterns of coexistence between termite species that live as guests in mounds built by other host termite species as a case in point. Termite societies are extremely sensitive to perturbations, which precludes determining the nature of their interactions through direct observations. We perform a literature review to construct two networks representing termite mound cohabitation in a Brazilian savanna and in the tropical forest of Cameroon. We contrast the properties of these cohabitation networks with a total of 197 geographically diverse mutualistic plant–pollinator and antagonistic host–parasitoid networks. We analyze network properties for the networks, perform a principal components analysis (PCA), and compute the Mahalanobis distance of the termite networks to the cloud of mutualistic and antagonistic networks to assess the extent to which the termite networks overlap with the properties of the reference networks. Both termite networks overlap more closely with the mutualistic plant–pollinator communities than the antagonistic host–parasitoid communities, although the Brazilian community overlap with mutualistic communities is stronger. The analysis raises the hypothesis that termite–termite cohabitation networks may be overall mutualistic. More broadly, this work provides support for the argument that cryptic communities may be analyzed via comparison to well‐characterized communities.
doi:10.1002/ece3.2313
PMCID: PMC5016641  PMID: 27648235
Antagonism; community interactions; host–parasitoid; inquilines; mound; mutualism; network structure; plant; pollinator; termite
24.  Opportunities and challenges in deriving phytoplankton diversity measures from individual trait-based data obtained by scanning flow-cytometry 
In the context of understanding and predicting the effects of human-induced environmental change (EC) on biodiversity (BD), and the consequences of BD change for ecosystem functioning (EF), microbial ecologists face the challenge of linking individual level variability in functional traits to larger-scale ecosystem processes. Since lower level BD at genetic, individual, and population levels largely determines the functionality and resilience of natural populations and communities, individual level measures promise to link EC-induced physiological, ecological, and evolutionary responses to EF. Intraspecific trait differences, while representing among the least-understood aspects of natural microbial communities, have recently become easier to measure due to new technology. For example, recent advance in scanning flow-cytometry (SCF), automation of phytoplankton sampling and integration with environmental sensors allow to measure morphological and physiological traits of individual algae with high spatial and temporal resolution. Here we present emerging features of automated SFC data from natural phytoplankton communities and the opportunities that they provide for understanding the functioning of complex aquatic microbial communities. We highlight some current limitations and future needs, particularly focusing on the large amount of individual level data that, for the purpose of understanding the EC-BD-EF link, need to be translated into meaningful BD indices. We review the available functional diversity (FD) indices that, despite having been designed for mean trait values at the species level, can be adapted to individual-based trait data and provide links to ecological theory. We conclude that, considering some computational, mathematical and ecological issues, a set of multi-dimensional indices that address richness, evenness and divergence in overall community trait space represent the most promising BD metrics to study EC-BD-EF using individual level data.
doi:10.3389/fmicb.2014.00324
PMCID: PMC4076614  PMID: 25071737
biodiversity; environmental change; ecosystem functioning; scanning flow-cytometry; individual level data; traits; functional diversity; biodiversity indices
25.  Connectivity, Cycles, and Persistence Thresholds in Metapopulation Networks 
PLoS Computational Biology  2010;6(8):e1000876.
Synthesising the relationships between complexity, connectivity, and the stability of large biological systems has been a longstanding fundamental quest in theoretical biology and ecology. With the many exciting developments in modern network theory, interest in these issues has recently come to the forefront in a range of multidisciplinary areas. Here we outline a new theoretical analysis specifically relevant for the study of ecological metapopulations focusing primarily on marine systems, where subpopulations are generally connected via larval dispersal. Our work determines the qualitative and quantitative conditions by which dispersal and network structure control the persistence of a set of age-structured patch populations. Mathematical modelling combined with a graph theoretic analysis demonstrates that persistence depends crucially on the topology of cycles in the dispersal network which tend to enhance the effect of larvae “returning home.” Our method clarifies the impact directly due to network structure, but this almost by definition can only be achieved by examining the simplified case in which patches are identical; an assumption that we later relax. The methodology identifies critical migration routes, whose presence are vital to overall stability, and therefore should have high conservation priority. In contrast, “lonely links,” or links in the network that do not participate in a cyclical component, have no impact on persistence and thus have low conservation priority. A number of other intriguing criteria for persistence are derived. Our modelling framework reveals new insights regarding the determinants of persistence, stability, and thresholds in complex metapopulations. In particular, while theoretical arguments have, in the past, suggested that increasing connectivity is a destabilizing feature in complex systems, this is not evident in metapopulation networks where connectivity, cycles, coherency, and heterogeneity all tend to enhance persistence. The results should be of interest for many other scientific contexts that make use of network theory.
Author Summary
Taking advantage of modern network theory, we present a model formulation for determining those factors that control the stability and persistence of complex biological systems. As a case study, we focus on ecological metapopulations, which may be viewed as a set of distinct subpopulations (/sites) that are connected via a dispersal network of arbitrary complexity. Metapopulation persistence is found to depend critically on the topology of cycles, and cyclical components in the connectivity network, because they allow the offspring of the population to eventually “return home” to the sites from which they originated. The methodology identifies critical migration routes, whose presence are vital to overall stability, and are thus of high conservation priority – information that may be of value when designing networks of marine protected areas. In contrast, links that do not participate in a cyclical component have no impact on persistence and thus have low conservation priority. While network theory is highly fashionable in biology, only few studies go deeper than descriptive statistical applications as attempted here. Moreover, the key results are easily extended to other biological contexts (e.g., disease networks), particularly in situations whereby the network controls the dynamics of a complex system.
doi:10.1371/journal.pcbi.1000876
PMCID: PMC2916855  PMID: 20700494

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