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Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2016年第33期113卷 E4885-E4894页

作者： Sherman, Maxwell A. Lee, Shane Law, Robert Haegens, Saskia Thorn, Catherine A. Hamalainen, Matti S. Moore, Christopher I. Jones, Stephanie R. Brown Univ Dept Neurosci Providence RI 02912 USA Brown Univ Dept Appl Math Providence RI 02912 USA Nathan S Kline Inst Psychiat Res Cognit Neurosci & Schizophrenia Program Orangeburg NY 10962 USA Columbia Univ Coll Phys & Surg Dept Neurol Surg New York NY 10032 USA Massachusetts Gen Hosp Athinola A Martinos Ctr Boston MA 02129 USA Aalto Univ Sch Sci Dept Neurosci & Biomed Engn Espoo 02150 Finland Harvard Med Sch Boston MA 02115 USA

Human neocortical 15-29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting < 150 ms with a stereotypical waveform. computational modeling uniquely designed to infer the electrical currents underlying these signals showed that beta events could emerge from the integration of nearly synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (similar to 50 ms). This beta mechanism rigorously accounted for the beta event profiles;several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function.

关键词： beta rhythm magnetoencephalography computational modeling sensorimotor processing Parkinson's disease

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Spatiotemporal dynamics of the Ebola epidemic in Guinea and implications for vaccination and disease elimination: a computational modeling analysis

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BMC MEDICINE 2016年第1期14卷 1-10页

作者： Ajelli, Marco Merler, Stefano Fumanelli, Laura Pastore y Piontti, Ana Dean, Natalie E. Longini, Ira M., Jr. Halloran, M. Elizabeth Vespignani, Alessandro Bruno Kessler Fdn Via Sommar 18 I-38123 Trento Italy Northeastern Univ Lab Modeling Biol & Sociotech Syst 360 Huntington Ave Boston MA 02115 USA Univ Florida Dept Biostat 2004 Mowry Rd Gainesville FL 32611 USA Fred Hutchinson Canc Res Ctr Vaccine & Infect Dis Div 1100 Fairview Ave N Seattle WA 98109 USA Univ Washington Sch Publ Hlth 1959 NE Pacific St Seattle WA 98195 USA Harvard Univ Inst Quantitat Social Sci 1737 Cambridge St Cambridge MA 02138 USA Inst Sci Interchange Fdn Via Alassio 11-C I-10126 Turin Italy

Background: Among the three countries most affected by the Ebola virus disease outbreak in 2014-2015, Guinea presents an unusual spatiotemporal epidemic pattern, with several waves and a long tail in the decay of the epidemic incidence. Methods: Here, we develop a stochastic agent-based model at the level of a single household that integrates detailed data on Guinean demography, hospitals, Ebola treatment units, contact tracing, and safe burial interventions. The microsimulation-based model is used to assess the effect of each control strategy and the probability of elimination of the epidemic according to different intervention scenarios, including ring vaccination with the recombinant vesicular stomatitis virus-vectored vaccine. Results: The numerical results indicate that the dynamics of the Ebola epidemic in Guinea can be quantitatively explained by the timeline of the implemented interventions. In particular, the early availability of Ebola treatment units and the associated isolation of cases and safe burials helped to limit the number of Ebola cases experienced by Guinea. We provide quantitative evidence of a strong negative correlation between the time series of cases and the number of traced contacts. This result is confirmed by the computational model that suggests that contact tracing effort is a key determinant in the control and elimination of the disease. In data-driven microsimulations, we find that tracing at least 5-10 contacts per case is crucial in preventing epidemic resurgence during the epidemic elimination phase. The computational model is used to provide an analysis of the ring vaccination trial highlighting its potential effect on disease elimination. Conclusions: We identify contact tracing as one of the key determinants of the epidemic's behavior in Guinea, and we show that the early availability of Ebola treatment unit beds helped to limit the number of Ebola cases in Guinea.

关键词： computational modeling Intervention strategies Ebola epidemiology

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Allostatic Load as a Complex Clinical Construct: A Case-Based computational modeling Approach

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COMPLEXITY 2016年第S1期21卷 291-306页

作者： Buckwalter, J. Galen Castellani, Brian McEwen, Bruce Karlamangla, Arun S. Rizzo, Albert A. John, Bruce O'Donnell, Kyle Seeman, Teresa Univ Southern Calif Inst Creat Technol Los Angeles CA 90094 USA Kent State Univ Dept Sociol Kent OH 44240 USA Rockefeller Univ Neuroendocrinol Lab New York NY 10065 USA Univ Calif Los Angeles David Geffen Sch Med Div Geriatr Los Angeles CA 90095 USA

Allostatic load (AL) is a complex clinical construct, providing a unique window into the cumulative impact of stress. However, due to its inherent complexity, AL presents two major measurement challenges to conventional statistical modeling (the field's dominant methodology): it is comprised of a complex causal network of bioallostatic systems, represented by an even larger set of dynamic biomarkers;and, it is situated within a web of antecedent socioecological systems, linking AL to differences in health outcomes and disparities. To address these challenges, we employed case-based computational modeling (CBM), which allowed us to make four advances: (1) we developed a multisystem, 7-factor (20 biomarker) model of AL's network of allostatic systems;(2) used it to create a catalog of nine different clinical AL profiles (causal pathways);(3) linked each clinical profile to a typology of 23 health outcomes;and (4) explored our results (post hoc) as a function of gender, a key socioecological factor. In terms of highlights, (a) the Healthy clinical profile had few health risks;(b) the pro-inflammatory profile linked to high blood pressure and diabetes;(c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems;and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%);while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles. (C) 2015 Wiley Periodicals, Inc.

关键词： allostatic load health risk outcomes complexity theory artificial neural nets computational modeling case-based modeling

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A computational modeling of rapid attitude formation during surveys about immigrants and immigration

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COMPUTERS IN HUMAN BEHAVIOR 2016年第0期63卷 179-188页

作者： Lamb, Richard Firestone, Jonah B. Ardasheva, Yuliya SUNY Buffalo Buffalo NY 14260 USA Washington State Univ Tricities Richland WA USA

Several theoretical models provide the basis for the interaction of the constructs of external social pressures during rapid attitude formation. Current modes of survey research have difficulty dealing with the complex non-linear dynamic effects such as social pressures on attitudinal formation. The interaction of the resultant constructs within the context of this study is due to the inherent confounding nature of the constructs themselves during the critical formative time for attitudes during questioning. During questioning and attitude formation, overlapping constructs link together psychologically. While the linkage between similar constructs is understandable, the lack of descriptive and computational causal models creates difficulty in identifying a mechanism of action. More importantly, the underlying confounding factors reduce the effective targeting of items during survey administration. The authors of this study seek to develop a causal and empirical computational model through a structural equation model and an underlying artificial neural network allowing for an argument outlining the psychological nature of the linkage between two interrelated constructs. Results suggest good model fit for both the structural equation model and the underlying artificial neural network. Results further illustrate that the artificial neural network acts as a sufficient means to model rapid attitudinal formation. (C) 2016 Published by Elsevier Ltd.

关键词： computational modeling Artificial neural network Machine learning Psychosocial factors Survey design

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Design Thinking and computational modeling to stop illegal poaching

Design Thinking and computational modeling to stop illegal p...

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IEEE Integrated STEM Education Conference (ISEC)

作者： Pradyuta Padmanabhan Alexander Baez Carmen Caiseda Kathleen McLane Nithin Ellanki Padmanabhan Seshaiyer Byong Kwon Erick Massawe Foxcroft School Inter American University Puerto Rico George Mason University Arizona State University Nelson Mandela African Institute of Science and Technology Tanzania

ISBN: (纸本)9781509053803

In this paper, we describe an interdisciplinary approach using STEM to solving a real-world problem involving poaching. This integrated STEM experience helped a team of researchers that included students from K-12, undergraduate students, graduate students and faculty to collaboratively understand, design and test potential solutions to the problem. The team employed an engineering based Design-Thinking framework combined with effective brainstorming strategies that have been well-established in business, marketing and commercial environments along with a computational mathematics problem solving approach. Not only this integrated STEM experience provided an opportunity for the team of researchers to develop professionally but also gave them an opportunity to become change agents to transform institutional practices and to engage the broader community to inspire the next generation STEM workforce. Specifically, the project framework guided the research team to propose potential integrated STEM based solutions including the application of drones to track illegal poaching as well as the development of an intelligent sensor based tracking system which are validated via sophisticated mathematical and probabilistic algorithms. This overall experience has clearly helped to establish STEM education partnerships and alliances outside traditional boundaries and has helped to create new synergies for all stakeholders who are invested in solving the problem of poaching. Specifically, it has helped to develop an international research experience for high school, undergraduate and graduate students who now have a unique perspective on solving real-world problems in developing countries. The program has helped to promote the much needed awareness of using STEM based solutions to solve global challenges through a multi-disciplinary collaborative approach involving students and faculty from mathematical sciences and engineering who continue to collaborate.

关键词： Drones Conferences Stakeholders Animals Collaboration STEM computational modeling

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Tri-m-cresyl phosphate and PPAR/LXR interactions in seabream hepatocytes: revealed by computational modeling (docking) and transcriptional regulation of signaling pathways

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TOXICOLOGY RESEARCH 2016年第2期5卷 471-481页

作者： Palermo, Francesco Alessandro Cocci, Paolo Mozzicafreddo, Matteo Arukwe, Augustine Angeletti, Mauro Aretusi, Graziano Mosconi, Gilberto Univ Camerino Sch Biosci & Vet Med Via Gentile III Da Varano I-62032 Camerino MC Italy Norwegian Univ Sci & Technol NTNU Dept Biol Hogskoleringen 5 N-7491 Trondheim Norway Controllo Stat Pescara Italy Marine Protected Area Torre Cerrano I-64025 Pineto TE Italy

The interactions between tri-m-cresyl phosphate (TMCP;an organophosphate flame retardant) and peroxisome proliferator activated receptors (PPARs) or liver X receptor alpha (LXR alpha) were investigated in seabream hepatocytes. The study was designed to characterize the binding of TMCP to PPAR alpha, PPAR. and LXR alpha by computational modeling (docking) and transcriptional regulation of signaling pathways. TMCP mainly established a non-polar interaction with each receptor. These findings reflect the hydrophobic nature of this binding site, with fish LXR alpha showing the highest binding efficiency. Further, we have investigated the ability of TMCP to activate PPAR and LXR controlled transcriptional processes involved in lipid/cholesterol metabolism. TMCP induced the expression of all the target genes measured. All target genes were up-regulated at all exposure doses, except for fatty acid binding protein 7 (FABP7) and carnitine palmitoyltransferase 1B. Collectively, our data indicate that TMCP can affect fatty acid synthesis/uptake and cholesterol metabolism through LXR alpha and PPARs, together with interactions between these transcription factors in seabream liver.

关键词： cholesterol metabolism Transcriptional Regulation Transcription Factors Fatty Acid-Binding Protein 7 computational modeling Hepatocytes Fire Retardants Berthing Signal Pathways Adrenergic alpha-Antagonists Sparidae Liver X Receptor Alpha Peroxisome Proliferator-Activated Receptors Sea Bream

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Method for Developing Optical Sensors Using a Synthetic Dye-Fluorescent Protein FRET Pair and computational modeling and Assessment

Method for Developing Optical Sensors Using a Synthetic Dye-...

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作者： Joshua A. Mitchell William H. Zhang Michel K. Herde Christian Henneberger Harald Janovjak Megan L. O’Mara Colin J. Jackson

Biosensors that exploit Förster resonance energy transfer (FRET) can be used to visualize biological and physiological processes and are capable of providing detailed information in both spatial and temporal dimensions. In a FRET-based biosensor, substrate binding is associated with a change in the relative positions of two fluorophores, leading to a change in FRET efficiency that may be observed in the fluorescence spectrum. As a result, their design requires a ligand-binding protein that exhibits a conformational change upon binding. However, not all ligand-binding proteins produce responsive sensors upon conjugation to fluorescent proteins or dyes, and identifying the optimum locations for the fluorophores often involves labor-intensive iterative design or high-throughput screening. Combining the genetic fusion of a fluorescent protein to the ligand-binding protein with site-specific covalent attachment of a fluorescent dye can allow fine control over the positions of the two fluorophores, allowing the construction of very sensitive sensors. This relies upon the accurate prediction of the locations of the two fluorophores in bound and unbound states. In this chapter, we describe a method for computational identification of dye-attachment sites that allows the use of cysteine modification to attach synthetic dyes that can be paired with a fluorescent protein for the purposes of creating FRET sensors. less

关键词： FRET Biosensor technique High-throughput Screening Mutagenesis Protein Purification Gibson Assembly PCR Agarose Gel Electrophoresis Nickel Affinity Chromatography Size-exclusion Chromatography PyMol Colony PCR Python computational modeling Optical sensor Synthetic dye

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computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice

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BMC BIOINFORMATICS 2016年第1期17卷 111-111页

作者： Kang, Mingon Tang, Liping Gao, Jean Univ Texas Arlington Dept Comp Sci & Engn 500 UTA Blvd Arlington TX 76019 USA Univ Texas Arlington Dept Bioengn 500 UTA Blvd Arlington TX 76019 USA

Background: computational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling discloses the nature of foreign body responses. Such understanding will shed insight on the cause of foreign body responses, which will lead to improved biomaterial design and will reduce foreign body reactions. One of the major obstacles in computational modeling is to build a mathematical model that represents the biological system and to quantitatively define the model parameters. Results: In this paper, we considered quantitative inter connections and logical relationships among diverse proteins and cells, which have been reported in biological experiments and literature. Based on the established biological discovery, we have built a mathematical model while unveiling the key components that contribute to biomaterial-mediated inflammatory responses. For the parameter estimation of the mathematical model, we proposed a global optimization algorithm, called Discrete Selection Levenberg-Marquardt (DSLM). This is an extension of Levenberg-Marquardt (LM) algorithm which is a gradient-based local optimization algorithm. The proposed DSLM suggests a new approach for the selection of optimal parameters in the discrete space with fast computational convergence. Conclusions: The computational modeling not only provides critical clues to recognize current knowledge of fibrosis development but also enables the prediction of yet-to-be observed biological phenomena.

关键词： computational modeling Foreign body responses Phagocyte transmigration Parameter estimation

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Perceiving Abstract Concepts Via Evolving computational Cognitive modeling

Perceiving Abstract Concepts Via Evolving Computational Cogn...

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International Joint Conference on Neural Networks

作者： Rahul Sharma Bernardete Ribeiro Alexandre Miguel Pinto F. Amilcar Cardoso Department of Informatics Engineering University of Coimbra Portugal

Any cognizable thing (object, or idea) can be recognized as a concept, and they are usually described by modality-specific, experience-dependent, or localist-distributed representations. There has been a significant effort in studying concrete concepts, along with their underlying cognitive and psychological processes through conceptual representations produced by computational approaches (like Artificial Neural Networks). However, the notion of abstract concepts, though debated, is seldom explored. In this article, we propose an approach to learn abstract representation of concepts with Regulated Activation Networks (RANs) evolving computational modeling. RANs methodology is illustrated using a toy-data problem, and evaluated for metrics Precision, Recall, F1-score, and Accuracy, along with ROC curve analysis. RANs comparison is presented using a benchmark data from human activity recognition domain, yielding Precision= 98.20% (ca.), Recall= 97.62% (ca.), F1-score=97.8 (ca.), and Accuracy = 97.62% (ca.). Sleep Detection data from SOCIALITE project is used to model active and inactive students, showing a convincing performance. Further, the model is used in studying 3 students in order to deduce biomarkers for understanding their psychological conditions.

关键词： computational modeling Artificial intelligence Mathematical model Psychology Biological system modeling Neural networks Manganese

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A computational modeling of semantic knowledge in reading comprehension: Integrating the landscape model with latent semantic analysis

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BEHAVIOR RESEARCH METHODS 2016年第3期48卷 880-896页

作者： Yeari, Menahem van den Broek, Paul Bar Ilan Univ Ramat Gan Israel Leiden Univ Leiden Netherlands

It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.

关键词： Reading comprehension computational modeling Landscape model Latent semantic analysis Semantic knowledge Inference generation Information centrality Text recall

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