Harvest patterns and effort dynamics of indigenous and non-indigenous commercial sectors of the eastern Torres Strait reef line fishery

NASA Astrophysics Data System (ADS)

Williams, Ashley J.; Ballagh, Aaron C.; Begg, Gavin A.; Murchie, Cameron D.; Currey, Leanne M.

2008-09-01

The reef line fishery (RLF) in eastern Torres Strait (ETS) is unique in that it has both a commercial indigenous sector and a commercial non-indigenous sector. Recently, concerns have been expressed by all stakeholders about the long-term sustainability of the fishery. These concerns have been exacerbated by the lack of detailed catch and effort information from both sectors, which has precluded any formal assessment of the fishery. In this paper, we characterise the harvest patterns and effort dynamics of the indigenous and non-indigenous commercial sectors of the ETS RLF using a range of data sources including commercial logbooks, community freezer records, voluntary logbooks and observer surveys. We demonstrate that bycatch is a significant component of the catch for both sectors and identify substantial differences in harvest patterns and effort dynamics between the sectors. Differences between sectors were observed in species composition and spatial and temporal patterns in catch, effort and catch per unit effort. These results highlight the inherent variation in catch and effort dynamics between the two commercial sectors of the ETS RLF and provide valuable information for the development of future assessments and appropriate management strategies for the fishery. The more reliable estimates of harvest patterns and effort dynamics for both sectors obtained from observer surveys will also assist in resolving issues relating to allocation of reef fish resources in Torres Strait.

Assured Information Sharing for Ad-Hoc Collaboration

ERIC Educational Resources Information Center

Jin, Jing

2009-01-01

Collaborative information sharing tends to be highly dynamic and often ad hoc among organizations. The dynamic natures and sharing patterns in ad-hoc collaboration impose a need for a comprehensive and flexible approach to reflecting and coping with the unique access control requirements associated with the environment. This dissertation…

Dual-Responsive SPMA-Modified Polymer Photonic Crystals and Their Dynamic Display Patterns.

PubMed

Gao, Zewen; Gao, Dongsheng; Huang, Chao; Zhang, Hanbing; Guo, Jinbao; Wei, Jie

2018-05-28

Light and electrothermal responsive polymer photonic crystals (PCs) modified with 1'-acryloyl chloride-3',3'-dimethyl-6-nitro-spiro(2H-1-benzopyran-2,2'-indoline) (SPMA) are proposed, and their dynamic display patterns are achieved through the combination of the SPMA-modified PCs and a patterned graphite layer. These PCs exhibit fluorescence under UV light irradiation because of the isomerization of the SPMA, which is restricted in the shell of the polymer colloidal spheres. After a voltage is applied to the patterned graphite layer, the fluorescence of PCs in the specific area disappears, and dynamic display patterns are obtained. Under UV light irradiation, the PCs change from the "partial-fluorescence" state to the initial "fluorescence" state, and the patterns disappear. Using this technique, the PC pattern "M L N" on the glass substrate and PC patterns from "0" to "9" on the paper substrate are fabricated. Thus, these dual-responsive PCs have potential applications in information recording, anticounterfeiting, dynamic display, and photoelectric devices. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dynamic search and working memory in social recall.

PubMed

Hills, Thomas T; Pachur, Thorsten

2012-01-01

What are the mechanisms underlying search in social memory (e.g., remembering the people one knows)? Do the search mechanisms involve dynamic local-to-global transitions similar to semantic search, and are these transitions governed by the general control of attention, associated with working memory span? To find out, we asked participants to recall individuals from their personal social networks and measured each participant's working memory capacity. Additionally, participants provided social-category and contact-frequency information about the recalled individuals as well as information about the social proximity among the recalled individuals. On the basis of these data, we tested various computational models of memory search regarding their ability to account for the patterns in which participants recalled from social memory. Although recall patterns showed clustering based on social categories, models assuming dynamic transitions between representations cued by social proximity and frequency information predicted participants' recall patterns best-no additional explanatory power was gained from social-category information. Moreover, individual differences in the time between transitions were positively correlated with differences in working memory capacity. These results highlight the role of social proximity in structuring social memory and elucidate the role of working memory for maintaining search criteria during search within that structure.

Fluid-dynamics modelling of the human left ventricle with dynamic mesh for normal and myocardial infarction: preliminary study.

PubMed

Khalafvand, S S; Ng, E Y K; Zhong, L; Hung, T K

2012-08-01

Pulsating blood flow patterns in the left ventricular (LV) were computed for three normal subjects and three patients after myocardial infarction (MI). Cardiac magnetic resonance (MR) images were obtained, segmented and transformed into 25 frames of LV for a computational fluid dynamics (CFD) study. Multi-block structure meshes were generated for 25 frames and 75 intermediate grids. The complete LV cycle was modelled by using ANSYS-CFX 12. The flow patterns and pressure drops in the LV chamber of this study provided some useful information on intra-LV flow patterns with heart diseases. Copyright © 2012 Elsevier Ltd. All rights reserved.

Stochastic feeding dynamics arise from the need for information and energy.

PubMed

Scholz, Monika; Dinner, Aaron R; Levine, Erel; Biron, David

2017-08-29

Animals regulate their food intake in response to the available level of food. Recent observations of feeding dynamics in small animals showed feeding patterns of bursts and pauses, but their function is unknown. Here, we present a data-driven decision-theoretical model of feeding in Caenorhabditis elegans Our central assumption is that food intake serves a dual purpose: to gather information about the external food level and to ingest food when the conditions are good. The model recapitulates experimentally observed feeding patterns. It naturally implements trade-offs between speed versus accuracy and exploration versus exploitation in responding to a dynamic environment. We find that the model predicts three distinct regimes in responding to a dynamical environment, with a transition region where animals respond stochastically to periodic signals. This stochastic response accounts for previously unexplained experimental data.

Elements of the cellular metabolic structure

PubMed Central

De la Fuente, Ildefonso M.

2015-01-01

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell. PMID:25988183

Phase-selective entrainment of nonlinear oscillator ensembles

DOE PAGES

Zlotnik, Anatoly V.; Nagao, Raphael; Kiss, Istvan Z.; ...

2016-03-18

The ability to organize and finely manipulate the hierarchy and timing of dynamic processes is important for understanding and influencing brain functions, sleep and metabolic cycles, and many other natural phenomena. However, establishing spatiotemporal structures in biological oscillator ensembles is a challenging task that requires controlling large collections of complex nonlinear dynamical units. In this report, we present a method to design entrainment signals that create stable phase patterns in ensembles of heterogeneous nonlinear oscillators without using state feedback information. We demonstrate the approach using experiments with electrochemical reactions on multielectrode arrays, in which we selectively assign ensemble subgroups intomore » spatiotemporal patterns with multiple phase clusters. As a result, the experimentally confirmed mechanism elucidates the connection between the phases and natural frequencies of a collection of dynamical elements, the spatial and temporal information that is encoded within this ensemble, and how external signals can be used to retrieve this information.« less

Phase-selective entrainment of nonlinear oscillator ensembles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zlotnik, Anatoly V.; Nagao, Raphael; Kiss, Istvan Z.

The ability to organize and finely manipulate the hierarchy and timing of dynamic processes is important for understanding and influencing brain functions, sleep and metabolic cycles, and many other natural phenomena. However, establishing spatiotemporal structures in biological oscillator ensembles is a challenging task that requires controlling large collections of complex nonlinear dynamical units. In this report, we present a method to design entrainment signals that create stable phase patterns in ensembles of heterogeneous nonlinear oscillators without using state feedback information. We demonstrate the approach using experiments with electrochemical reactions on multielectrode arrays, in which we selectively assign ensemble subgroups intomore » spatiotemporal patterns with multiple phase clusters. As a result, the experimentally confirmed mechanism elucidates the connection between the phases and natural frequencies of a collection of dynamical elements, the spatial and temporal information that is encoded within this ensemble, and how external signals can be used to retrieve this information.« less

Phase-selective entrainment of nonlinear oscillator ensembles

NASA Astrophysics Data System (ADS)

Zlotnik, Anatoly; Nagao, Raphael; Kiss, István Z.; Li-Shin, Jr.

2016-03-01

The ability to organize and finely manipulate the hierarchy and timing of dynamic processes is important for understanding and influencing brain functions, sleep and metabolic cycles, and many other natural phenomena. However, establishing spatiotemporal structures in biological oscillator ensembles is a challenging task that requires controlling large collections of complex nonlinear dynamical units. In this report, we present a method to design entrainment signals that create stable phase patterns in ensembles of heterogeneous nonlinear oscillators without using state feedback information. We demonstrate the approach using experiments with electrochemical reactions on multielectrode arrays, in which we selectively assign ensemble subgroups into spatiotemporal patterns with multiple phase clusters. The experimentally confirmed mechanism elucidates the connection between the phases and natural frequencies of a collection of dynamical elements, the spatial and temporal information that is encoded within this ensemble, and how external signals can be used to retrieve this information.

Dynamics of ecosystem service values in response to landscape pattern changes from 1995 to 2005 in Guangzhou, Southern China

Treesearch

Yanqiong Ye; Jia' en Zhang; Lili Chen; Ying Ouyang; Prem Parajuli

2015-01-01

This study analyzed the landscape pattern changes, the dynamics of the ecosystem service values (ESVs) and the spatial distribution of ESVs from 1995 to 2005 in Guangzhou, which is the capital of Guangdong Province and a regional central city in South China. Remote sensing data and geographic information system techniques, in conjunction with spatial metrics, were used...

Dynamic analysis of patterns of renal sympathetic nerve activity: implications for renal function.

PubMed

DiBona, Gerald F

2005-03-01

Methods of dynamic analysis are used to provide additional understanding of the renal sympathetic neural control of renal function. The concept of functionally specific subgroups of renal sympathetic nerve fibres conveying information encoded in the frequency domain is presented. Analog pulse modulation and pseudorandom binary sequence stimulation patterns are used for the determination of renal vascular frequency response. Transfer function analysis is used to determine the effects of non-renal vasoconstrictor and vasoconstrictor intensities of renal sympathetic nerve activity on dynamic autoregulation of renal blood flow.

Information flow in layered networks of non-monotonic units

NASA Astrophysics Data System (ADS)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

Search Regimes and the Industrial Dynamics of Science

ERIC Educational Resources Information Center

Bonaccorsi, Andrea

2008-01-01

The article addresses the issue of dynamics of science, in particular of new sciences born in twentieth century and developed after the Second World War (information science, materials science, life science). The article develops the notion of search regime as an abstract characterization of dynamic patterns, based on three dimensions: the rate of…

The perception-action dynamics of action competency are altered by both physical and observational training.

PubMed

Buchanan, John J; Ramos, Jorge; Robson, Nina

2015-04-01

Action competency is defined as the ability of an individual to self-evaluate their own performance capabilities. The current experiment demonstrated that physical and observational training with a motor skill alters action competency ratings in a similar manner. Using a pre-test and post-test protocol, the results revealed that action competency is constrained prior to training by the intrinsic dynamics of relative phase (ϕ), with in-phase (ϕ = 0°) and anti-phase (ϕ = 180°) patterns receiving higher competency ratings than other relative phase patterns. After 2 days of training, action competency ratings for two trained relative phase patterns, +60° and +120°, increased following physical practice or observational practice. A transfer test revealed that both physical performance ability and action competency ability transferred to the symmetry partners (-60° and -120°) of the two trained relative phase patterns following physical or observational training. The findings also revealed that relative motion direction acts as categorical information that helps to organize action production and facilitate action competency. The results are interpreted based on the coordination dynamics theory of perception-action coupling, and extend this theory by showing that visual perception, action production, and action competency are all constrained in a consistent manner by the dynamics of the order parameter relative phase. As a whole, the findings revealed that relative motion, relative phase, and possibly relative amplitude information are all distinct sources of information that contribute to the emergence of a kinematic understanding of action in the nervous system.

Morphology-Induced Collective Behaviors: Dynamic Pattern Formation in Water-Floating Elements

PubMed Central

Nakajima, Kohei; Ngouabeu, Aubery Marchel Tientcheu; Miyashita, Shuhei; Göldi, Maurice; Füchslin, Rudolf Marcel; Pfeifer, Rolf

2012-01-01

Complex systems involving many interacting elements often organize into patterns. Two types of pattern formation can be distinguished, static and dynamic. Static pattern formation means that the resulting structure constitutes a thermodynamic equilibrium whose pattern formation can be understood in terms of the minimization of free energy, while dynamic pattern formation indicates that the system is permanently dissipating energy and not in equilibrium. In this paper, we report experimental results showing that the morphology of elements plays a significant role in dynamic pattern formation. We prepared three different shapes of elements (circles, squares, and triangles) floating in a water-filled container, in which each of the shapes has two types: active elements that were capable of self-agitation with vibration motors, and passive elements that were mere floating tiles. The system was purely decentralized: that is, elements interacted locally, and subsequently elicited global patterns in a process called self-organized segregation. We showed that, according to the morphology of the selected elements, a different type of segregation occurs. Also, we quantitatively characterized both the local interaction regime and the resulting global behavior for each type of segregation by means of information theoretic quantities, and showed the difference for each case in detail, while offering speculation on the mechanism causing this phenomenon. PMID:22715370

Development of a dynamic framework to explain population patterns of leisure-time physical activity through agent-based modeling.

PubMed

Garcia, Leandro M T; Diez Roux, Ana V; Martins, André C R; Yang, Yong; Florindo, Alex A

2017-08-22

Despite the increasing body of evidences on the factors influencing leisure-time physical activity, our understanding of the mechanisms and interactions that lead to the formation and evolution of population patterns is still limited. Moreover, most frameworks in this field fail to capture dynamic processes. Our aim was to create a dynamic conceptual model depicting the interaction between key psychological attributes of individuals and main aspects of the built and social environments in which they live. This conceptual model will inform and support the development of an agent-based model aimed to explore how population patterns of LTPA in adults may emerge from the dynamic interplay between psychological traits and built and social environments. We integrated existing theories and models as well as available empirical data (both from literature reviews), and expert opinions (based on a systematic expert assessment of an intermediary version of the model). The model explicitly presents intention as the proximal determinant of leisure-time physical activity, a relationship dynamically moderated by the built environment (access, quality, and available activities) - with the strength of the moderation varying as a function of the person's intention- and influenced both by the social environment (proximal network's and community's behavior) and the person's behavior. Our conceptual model is well supported by evidence and experts' opinions and will inform the design of our agent-based model, as well as data collection and analysis of future investigations on population patterns of leisure-time physical activity among adults.

Modality-specific spectral dynamics in response to visual and tactile sequential shape information processing tasks: An MEG study using multivariate pattern classification analysis.

PubMed

Gohel, Bakul; Lee, Peter; Jeong, Yong

2016-08-01

Brain regions that respond to more than one sensory modality are characterized as multisensory regions. Studies on the processing of shape or object information have revealed recruitment of the lateral occipital cortex, posterior parietal cortex, and other regions regardless of input sensory modalities. However, it remains unknown whether such regions show similar (modality-invariant) or different (modality-specific) neural oscillatory dynamics, as recorded using magnetoencephalography (MEG), in response to identical shape information processing tasks delivered to different sensory modalities. Modality-invariant or modality-specific neural oscillatory dynamics indirectly suggest modality-independent or modality-dependent participation of particular brain regions, respectively. Therefore, this study investigated the modality-specificity of neural oscillatory dynamics in the form of spectral power modulation patterns in response to visual and tactile sequential shape-processing tasks that are well-matched in terms of speed and content between the sensory modalities. Task-related changes in spectral power modulation and differences in spectral power modulation between sensory modalities were investigated at source-space (voxel) level, using a multivariate pattern classification (MVPC) approach. Additionally, whole analyses were extended from the voxel level to the independent-component level to take account of signal leakage effects caused by inverse solution. The modality-specific spectral dynamics in multisensory and higher-order brain regions, such as the lateral occipital cortex, posterior parietal cortex, inferior temporal cortex, and other brain regions, showed task-related modulation in response to both sensory modalities. This suggests modality-dependency of such brain regions on the input sensory modality for sequential shape-information processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study

PubMed Central

Borge-Holthoefer, Javier; Rivero, Alejandro; García, Iñigo; Cauhé, Elisa; Ferrer, Alfredo; Ferrer, Darío; Francos, David; Iñiguez, David; Pérez, María Pilar; Ruiz, Gonzalo; Sanz, Francisco; Serrano, Fermín; Viñas, Cristina; Tarancón, Alfonso; Moreno, Yamir

2011-01-01

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics. PMID:21886834

Opinion dynamics on interacting networks: media competition and social influence.

PubMed

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-27

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

Opinion dynamics on interacting networks: media competition and social influence

NASA Astrophysics Data System (ADS)

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-01

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

Dynamic modeling of patient and physician eye gaze to understand the effects of electronic health records on doctor-patient communication and attention.

PubMed

Montague, Enid; Asan, Onur

2014-03-01

The aim of this study was to examine eye gaze patterns between patients and physicians while electronic health records were used to support patient care. Eye gaze provides an indication of physician attention to patient, patient/physician interaction, and physician behaviors such as searching for information and documenting information. A field study was conducted where 100 patient visits were observed and video recorded in a primary care clinic. Videos were then coded for gaze behaviors where patients' and physicians' gaze at each other and artifacts such as electronic health records were coded using a pre-established objective coding scheme. Gaze data were then analyzed using lag sequential methods. Results showed that there are several eye gaze patterns significantly dependent to each other. All doctor-initiated gaze patterns were followed by patient gaze patterns. Some patient-initiated gaze patterns were also followed by doctor gaze patterns significantly unlike the findings in previous studies. Health information technology appears to contribute to some of the new significant patterns that have emerged. Differences were also found in gaze patterns related to technology that differ from patterns identified in studies with paper charts. Several sequences related to patient-doctor-technology were also significant. Electronic health records affect the patient-physician eye contact dynamic differently than paper charts. This study identified several patterns of patient-physician interaction with electronic health record systems. Consistent with previous studies, physician initiated gaze is an important driver of the interactions between patient and physician and patient and technology. Published by Elsevier Ireland Ltd.

Dynamic modeling of patient and physician eye gaze to understand the effects of electronic health records on doctor-patient communication and attention

PubMed Central

Montague, Enid; Asan, Onur

2014-01-01

Objective The aim of this study was to examine eye gaze patterns between patients and physicians while electronic health records were used to support patient care. Background Eye gaze provides an indication of physician attention to patient, patient/physician interaction, and physician behaviors such as searching for information and documenting information. Methods A field study was conducted where 100 patient visits were observed and video recorded in a primary care clinic. Videos were then coded for gaze behaviors where patients’ and physicians’ gaze at each other and artifacts such as electronic health records were coded using a pre-established objective coding scheme. Gaze data were then analyzed using lag sequential methods. Results Results showed that there are several eye gaze patterns significantly dependent to each other. All doctor-initiated gaze patterns were followed by patient gaze patterns. Some patient-initiated gaze patterns were also followed by doctor gaze patterns significantly unlike the findings in previous studies. Health information technology appears to contribute to some of the new significant patterns that have emerged. Differences were also found in gaze patterns related to technology that differ from patterns identified in studies with paper charts. Several sequences related to patient-doctor- technology were also significant. Electronic health records affect the patient-physician eye contact dynamic differently than paper charts. Conclusion This study identified several patterns of patient-physician interaction with electronic health record systems. Consistent with previous studies, physician initiated gaze is an important driver of the interactions between patient and physician and patient and technology. PMID:24380671

Cognitive Invariants of Geographic Event Conceptualization: What Matters and What Refines?

NASA Astrophysics Data System (ADS)

Klippel, Alexander; Li, Rui; Hardisty, Frank; Weaver, Chris

Behavioral experiments addressing the conceptualization of geographic events are few and far between. Our research seeks to address this deficiency by developing an experimental framework on the conceptualization of movement patterns. In this paper, we report on a critical experiment that is designed to shed light on the question of cognitively salient invariants in such conceptualization. Invariants have been identified as being critical to human information processing, particularly for the processing of dynamic information. In our experiment, we systematically address cognitive invariants of one class of geographic events: single entity movement patterns. To this end, we designed 72 animated icons that depict the movement patterns of hurricanes around two invariants: size difference and topological equivalence class movement patterns endpoints. While the endpoint hypothesis, put forth by Regier (2007), claims a particular focus of human cognition to ending relations of events, other research suggests that simplicity principles guide categorization and, additionally, that static information is easier to process than dynamic information. Our experiments show a clear picture: Size matters. Nonetheless, we also find categorization behaviors consistent with experiments in both the spatial and temporal domain, namely that topology refines these behaviors and that topological equivalence classes are categorized consistently. These results are critical steppingstones in validating spatial formalism from a cognitive perspective and cognitively grounding work on ontologies.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Unraveling dynamics of human physical activity patterns in chronic pain conditions

NASA Astrophysics Data System (ADS)

Paraschiv-Ionescu, Anisoara; Buchser, Eric; Aminian, Kamiar

2013-06-01

Chronic pain is a complex disabling experience that negatively affects the cognitive, affective and physical functions as well as behavior. Although the interaction between chronic pain and physical functioning is a well-accepted paradigm in clinical research, the understanding of how pain affects individuals' daily life behavior remains a challenging task. Here we develop a methodological framework allowing to objectively document disruptive pain related interferences on real-life physical activity. The results reveal that meaningful information is contained in the temporal dynamics of activity patterns and an analytical model based on the theory of bivariate point processes can be used to describe physical activity behavior. The model parameters capture the dynamic interdependence between periods and events and determine a `signature' of activity pattern. The study is likely to contribute to the clinical understanding of complex pain/disease-related behaviors and establish a unified mathematical framework to quantify the complex dynamics of various human activities.

Wnt-regulated dynamics of positional information in zebrafish somitogenesis

PubMed Central

Bajard, Lola; Morelli, Luis G.; Ares, Saúl; Pécréaux, Jacques; Jülicher, Frank; Oates, Andrew C.

2014-01-01

How signaling gradients supply positional information in a field of moving cells is an unsolved question in patterning and morphogenesis. Here, we ask how a Wnt signaling gradient regulates the dynamics of a wavefront of cellular change in a flow of cells during somitogenesis. Using time-controlled perturbations of Wnt signaling in the zebrafish embryo, we changed segment length without altering the rate of somite formation or embryonic elongation. This result implies specific Wnt regulation of the wavefront velocity. The observed Wnt signaling gradient dynamics and timing of downstream events support a model for wavefront regulation in which cell flow plays a dominant role in transporting positional information. PMID:24595291

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks.

PubMed

de Arruda, Henrique Ferraz; Comin, Cesar Henrique; Miazaki, Mauro; Viana, Matheus Palhares; Costa, Luciano da Fontoura

2015-04-30

A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. To our best understanding, there are no similar analyses to compare with. A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity. Copyright © 2015. Published by Elsevier B.V.

Investigating the Temporal Patterns within and between Intrinsic Connectivity Networks under Eyes-Open and Eyes-Closed Resting States: A Dynamical Functional Connectivity Study Based on Phase Synchronization

PubMed Central

Wang, Xun-Heng; Li, Lihua; Xu, Tao; Ding, Zhongxiang

2015-01-01

The brain active patterns were organized differently under resting states of eyes open (EO) and eyes closed (EC). The altered voxel-wise and regional-wise resting state active patterns under EO/EC were found by static analysis. More importantly, dynamical spontaneous functional connectivity has been observed in the resting brain. To the best of our knowledge, the dynamical mechanisms of intrinsic connectivity networks (ICNs) under EO/EC remain largely unexplored. The goals of this paper were twofold: 1) investigating the dynamical intra-ICN and inter-ICN temporal patterns during resting state; 2) analyzing the altered dynamical temporal patterns of ICNs under EO/EC. To this end, a cohort of healthy subjects with scan conditions of EO/EC were recruited from 1000 Functional Connectomes Project. Through Hilbert transform, time-varying phase synchronization (PS) was applied to evaluate the inter-ICN synchrony. Meanwhile, time-varying amplitude was analyzed as dynamical intra-ICN temporal patterns. The results found six micro-states of inter-ICN synchrony. The medial visual network (MVN) showed decreased intra-ICN amplitude during EC relative to EO. The sensory-motor network (SMN) and auditory network (AN) exhibited enhanced intra-ICN amplitude during EC relative to EO. Altered inter-ICN PS was found between certain ICNs. Particularly, the SMN and AN exhibited enhanced PS to other ICNs during EC relative to EO. In addition, the intra-ICN amplitude might influence the inter-ICN synchrony. Moreover, default mode network (DMN) might play an important role in information processing during EO/EC. Together, the dynamical temporal patterns within and between ICNs were altered during different scan conditions of EO/EC. Overall, the dynamical intra-ICN and inter-ICN temporal patterns could benefit resting state fMRI-related research, and could be potential biomarkers for human functional connectome. PMID:26469182

Pseudo-polar drive patterns for brain electrical impedance tomography.

PubMed

Shi, Xuetao; Dong, Xiuzhen; Shuai, Wanjun; You, Fusheng; Fu, Feng; Liu, Ruigang

2006-11-01

Brain electrical impedance tomography (EIT) is a difficult task as brain tissues are enclosed by the skull of high resistance and cerebrospinal fluid (CSF) of low resistance, which makes internal resistivity information more difficult to extract. In order to seek a single source drive pattern that is more suitable for brain EIT, we built a more realistic experimental setting that simulates a head with the resistivity of the scalp, skull, CSF and brain, and compared the performance of adjacent, cross, polar and pseudo-polar drive patterns in terms of the boundary voltage dynamic range, independent measurement number, total boundary voltage changes and anti-noise performance based on it. The results demonstrate that the pseudo-polar drive pattern is optimal in all the aspects except for the dynamic range. The polar and cross drive patterns come next, and the adjacent drive pattern is the worst. Therefore, the pseudo-polar drive pattern should be chosen for brain EIT.

Probing Atomic Dynamics and Structures Using Optical Patterns

NASA Astrophysics Data System (ADS)

Schmittberger, Bonnie L.; Gauthier, Daniel J.

2015-05-01

Pattern formation is a widely studied phenomenon that can provide fundamental insights into nonlinear systems. Emergent patterns in cold atoms are of particular interest in condensed matter physics and quantum information science because one can relate optical patterns to spatial structures in the atoms. In our experimental system, we study multimode optical patterns generated from a sample of cold, thermal atoms. We observe this nonlinear optical phenomenon at record low input powers due to the highly nonlinear nature of the spatial bunching of atoms in an optical lattice. We present a detailed study of the dynamics of these bunched atoms during optical pattern formation. We show how small changes in the atomic density distribution affect the symmetry of the generated patterns as well as the nature of the nonlinearity that describes the light-atom interaction. We gratefully acknowledge the financial support of the National Science Foundation through Grant #PHY-1206040.

Informational Aspects of Isotopic Diversity in Biology and Medicine

NASA Astrophysics Data System (ADS)

Berezin, Alexander A.

2004-10-01

Use of stable and radioactive isotopes in biology and medicine is intensive, yet informational aspects of isotopes as such are largely neglected (A.A.Berezin, J.Theor.Biol.,1992). Classical distinguishability (``labelability'') of isotopes allows for pattern generation dynamics. Quantum mechanically advantages of isotopicity (diversity of stable isotopes) arise from (almost perfect) degeneracy of various isotopic configurations; this in turn allows for isotopic sweeps (hoppings) by resonance neutron tunneling (Eccles mechanism). Isotopic variations of de Broglie wavelength affect quantum tunneling, diffusivity, magnetic interactions (e.g. by Lorentz force), etc. Ergodicity principle (all isoenergetic states are eventually accessed) implies possibility of fast scanning of library of morphogenetic patterns (cf metaphors of universal ``Platonic'' Library of Patterns: e.g. J.L.Borges, R.Sheldrake) with subsequent Darwinian reinforcement (e.g. by targeted mutations) of evolutionary advantageous patterns and structures. Isotopic shifts in organisms, from viruses and protozoa to mammalians, (e.g. DNA with enriched or depleted C-13) are tools to elucidate possible informational (e.g. Shannon entropy) role of isotopicity in genetic (e.g. evolutionary and morphological), dynamical (e.g. physiological and neurological) as well as medical (e.g. carcinogenesis, aging) aspects of biology and medicine.

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

Branching dynamics of viral information spreading.

PubMed

Iribarren, José Luis; Moro, Esteban

2011-10-01

Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking, or marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants' decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models. Here we present a detailed analysis of our study of real viral marketing campaigns where tracking the propagation of a controlled message allowed us to analyze the structure and dynamics of a diffusion graph involving over 31,000 individuals. We found that information spreading displays a non-Markovian branching dynamics that can be modeled by a two-step Bellman-Harris branching process that generalizes the static models known in the literature and incorporates the high variability of human behavior. It explains accurately all the features of information propagation under the "tipping point" and can be used for prediction and management of viral information spreading processes.

Branching dynamics of viral information spreading

NASA Astrophysics Data System (ADS)

Iribarren, José Luis; Moro, Esteban

2011-10-01

Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking, or marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants’ decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models. Here we present a detailed analysis of our study of real viral marketing campaigns where tracking the propagation of a controlled message allowed us to analyze the structure and dynamics of a diffusion graph involving over 31 000 individuals. We found that information spreading displays a non-Markovian branching dynamics that can be modeled by a two-step Bellman-Harris branching process that generalizes the static models known in the literature and incorporates the high variability of human behavior. It explains accurately all the features of information propagation under the “tipping point” and can be used for prediction and management of viral information spreading processes.

Dynamic Encoding of Face Information in the Human Fusiform Gyrus

PubMed Central

Ghuman, Avniel Singh; Brunet, Nicolas M.; Li, Yuanning; Konecky, Roma O.; Pyles, John A.; Walls, Shawn A.; Destefino, Vincent; Wang, Wei; Richardson, R. Mark

2014-01-01

Humans’ ability to rapidly and accurately detect, identify, and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing, however temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly upon FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200-500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses. PMID:25482825

Dynamic encoding of face information in the human fusiform gyrus.

PubMed

Ghuman, Avniel Singh; Brunet, Nicolas M; Li, Yuanning; Konecky, Roma O; Pyles, John A; Walls, Shawn A; Destefino, Vincent; Wang, Wei; Richardson, R Mark

2014-12-08

Humans' ability to rapidly and accurately detect, identify and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing; however, temporal dynamics of face information processing in FFA remains unclear. Here we use multivariate pattern classification to decode the temporal dynamics of expression-invariant face information processing using electrodes placed directly on FFA in humans. Early FFA activity (50-75 ms) contained information regarding whether participants were viewing a face. Activity between 200 and 500 ms contained expression-invariant information about which of 70 faces participants were viewing along with the individual differences in facial features and their configurations. Long-lasting (500+ms) broadband gamma frequency activity predicted task performance. These results elucidate the dynamic computational role FFA plays in multiple face processing stages and indicate what information is used in performing these visual analyses.

Using measures of information content and complexity of time series as hydrologic metrics

USDA-ARS?s Scientific Manuscript database

The information theory has been previously used to develop metrics that allowed to characterize temporal patterns in soil moisture dynamics, and to evaluate and to compare performance of soil water flow models. The objective of this study was to apply information and complexity measures to characte...

Modeling the influence of information on the coevolution of contact networks and the dynamics of infectious diseases

NASA Astrophysics Data System (ADS)

Zhang, Haifeng; Small, Michael; Fu, Xinchu; Sun, Guiquan; Wang, Binghong

2012-09-01

Outbreaks of infectious diseases may awaken the awareness of individuals, consequently, they may adjust their contact patterns according to the perceived risk from disease. In this paper, we assume that individuals make decisions on breaking or recovering links according to the information of diseases spreading which they have acquired. They will reduce some links when diseases are prevalent and have high risks; otherwise, they will recover some original links when the diseases are controlled or present minimal risk. Under such an assumption, we study the effects of information of diseases on the contact patterns within the population and on the dynamics of epidemics. By extensive simulations and theoretical analysis, we find that, due to the time-delayed information of diseases, both the density of the disease and the topology of the network vary with time in a periodic form. Our results indicate that the quality of information available to individuals can have an important effect on the spreading of infectious diseases and implications for related problems.

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

PubMed

Dutt-Mazumder, Aviroop; Button, Chris; Robins, Anthony; Bartlett, Roger

2011-12-01

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

Generating Spatiotemporal Joint Torque Patterns from Dynamical Synchronization of Distributed Pattern Generators

PubMed Central

Pitti, Alexandre; Lungarella, Max; Kuniyoshi, Yasuo

2009-01-01

Pattern generators found in the spinal cord are no more seen as simple rhythmic oscillators for motion control. Indeed, they achieve flexible and dynamical coordination in interaction with the body and the environment dynamics giving to rise motor synergies. Discovering the mechanisms underlying the control of motor synergies constitutes an important research question not only for neuroscience but also for robotics: the motors coordination of high dimensional robotic systems is still a drawback and new control methods based on biological solutions may reduce their overall complexity. We propose to model the flexible combination of motor synergies in embodied systems via partial phase synchronization of distributed chaotic systems; for specific coupling strength, chaotic systems are able to phase synchronize their dynamics to the resonant frequencies of one external force. We take advantage of this property to explore and exploit the intrinsic dynamics of one specified embodied system. In two experiments with bipedal walkers, we show how motor synergies emerge when the controllers phase synchronize to the body's dynamics, entraining it to its intrinsic behavioral patterns. This stage is characterized by directed information flow from the sensors to the motors exhibiting the optimal situation when the body dynamics drive the controllers (mutual entrainment). Based on our results, we discuss the relevance of our findings for modeling the modular control of distributed pattern generators exhibited in the spinal cord, and for exploring the motor synergies in robots. PMID:20011216

Linking dynamics of the inhibitory network to the input structure

PubMed Central

Komarov, Maxim

2017-01-01

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives. PMID:27650865

Visual search for facial expressions of emotions: a comparison of dynamic and static faces.

PubMed

Horstmann, Gernot; Ansorge, Ulrich

2009-02-01

A number of past studies have used the visual search paradigm to examine whether certain aspects of emotional faces are processed preattentively and can thus be used to guide attention. All these studies presented static depictions of facial prototypes. Emotional expressions conveyed by the movement patterns of the face have never been examined for their preattentive effect. The present study presented for the first time dynamic facial expressions in a visual search paradigm. Experiment 1 revealed efficient search for a dynamic angry face among dynamic friendly faces, but inefficient search in a control condition with static faces. Experiments 2 to 4 suggested that this pattern of results is due to a stronger movement signal in the angry than in the friendly face: No (strong) advantage of dynamic over static faces is revealed when the degree of movement is controlled. These results show that dynamic information can be efficiently utilized in visual search for facial expressions. However, these results do not generally support the hypothesis that emotion-specific movement patterns are always preattentively discriminated. (c) 2009 APA, all rights reserved

Introduction to focus issue: intrinsic and designed computation: information processing in dynamical systems--beyond the digital hegemony.

PubMed

Crutchfield, James P; Ditto, William L; Sinha, Sudeshna

2010-09-01

How dynamical systems store and process information is a fundamental question that touches a remarkably wide set of contemporary issues: from the breakdown of Moore's scaling laws--that predicted the inexorable improvement in digital circuitry--to basic philosophical problems of pattern in the natural world. It is a question that also returns one to the earliest days of the foundations of dynamical systems theory, probability theory, mathematical logic, communication theory, and theoretical computer science. We introduce the broad and rather eclectic set of articles in this Focus Issue that highlights a range of current challenges in computing and dynamical systems.

Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

NASA Astrophysics Data System (ADS)

Kirst, Christoph

It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

PubMed

Yoo, Peter E; Hagan, Maureen A; John, Sam E; Opie, Nicholas L; Ordidge, Roger J; O'Brien, Terence J; Oxley, Thomas J; Moffat, Bradford A; Wong, Yan T

2018-06-01

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization. © 2018 Wiley Periodicals, Inc.

Distributed and Dynamic Storage of Working Memory Stimulus Information in Extrastriate Cortex

PubMed Central

Sreenivasan, Kartik K.; Vytlacil, Jason; D'Esposito, Mark

2015-01-01

The predominant neurobiological model of working memory (WM) posits that stimulus information is stored via stable elevated activity within highly selective neurons. Based on this model, which we refer to as the canonical model, the storage of stimulus information is largely associated with lateral prefrontal cortex (lPFC). A growing number of studies describe results that cannot be fully explained by the canonical model, suggesting that it is in need of revision. In the present study, we directly test key elements of the canonical model. We analyzed functional MRI data collected as participants performed a task requiring WM for faces and scenes. Multivariate decoding procedures identified patterns of activity containing information about the items maintained in WM (faces, scenes, or both). While information about WM items was identified in extrastriate visual cortex (EC) and lPFC, only EC exhibited a pattern of results consistent with a sensory representation. Information in both regions persisted even in the absence of elevated activity, suggesting that elevated population activity may not represent the storage of information in WM. Additionally, we observed that WM information was distributed across EC neural populations that exhibited a broad range of selectivity for the WM items rather than restricted to highly selective EC populations. Finally, we determined that activity patterns coding for WM information were not stable, but instead varied over the course of a trial, indicating that the neural code for WM information is dynamic rather than static. Together, these findings challenge the canonical model of WM. PMID:24392897

In Situ Carbon Stable Isotope Tracer Experiments Elucidate Carbon Translocation Rates and Allocation Patterns in Zostera marina L. (eelgrass)

EPA Science Inventory

The intertidal seagrass Zostera marina is an important species that provides critical habitat for a number of estuarine species. Despite its widespread distribution, there is limited information on seasonal patterns of carbon dynamics of plants growing in situ, particularly esti...

Linking dynamic patterns of neural activity in orbitofrontal cortex with decision making.

PubMed

Rich, Erin L; Stoll, Frederic M; Rudebeck, Peter H

2018-04-01

Humans and animals demonstrate extraordinary flexibility in choice behavior, particularly when deciding based on subjective preferences. We evaluate options on different scales, deliberate, and often change our minds. Little is known about the neural mechanisms that underlie these dynamic aspects of decision-making, although neural activity in orbitofrontal cortex (OFC) likely plays a central role. Recent evidence from studies in macaques shows that attention modulates value responses in OFC, and that ensembles of OFC neurons dynamically signal different options during choices. When contexts change, these ensembles flexibly remap to encode the new task. Determining how these dynamic patterns emerge and relate to choices will inform models of decision-making and OFC function. Copyright © 2017 Elsevier Ltd. All rights reserved.

Opinion dynamics on interacting networks: media competition and social influence

PubMed Central

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-01-01

The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

Sonic Hedgehog functions through dynamic changes in temporal competence in the developing ventral telencephalon

PubMed Central

Sousa, Vitor H.; Fishell, Gord

2010-01-01

Morphogens act during development to provide graded spatial information that controls patterning and cell lineage specification in the nervous system. The role of morphogen signaling in instructing the expression of downstream effector transcription factors has been well established. However, a key requirement for morphogen signaling is the existence of functional intracellular machinery able to mediate the appropriate response in target cells. Here we suggest that dynamic changes in the temporal responses to Shh in the developing ventral telencephalon occur through alterations in progenitor competence. We suggest these developmental changes in competence are mediated by a transcriptional mechanism that intrinsically integrates information from the distinct signaling pathways that act to pattern the telencephalic neuroepithelium. PMID:20466536

In Vivo Measurement of Glenohumeral Joint Contact Patterns

NASA Astrophysics Data System (ADS)

Bey, Michael J.; Kline, Stephanie K.; Zauel, Roger; Kolowich, Patricia A.; Lock, Terrence R.

2009-12-01

The objectives of this study were to describe a technique for measuring in-vivo glenohumeral joint contact patterns during dynamic activities and to demonstrate application of this technique. The experimental technique calculated joint contact patterns by combining CT-based 3D bone models with joint motion data that were accurately measured from biplane x-ray images. Joint contact patterns were calculated for the repaired and contralateral shoulders of 20 patients who had undergone rotator cuff repair. Significant differences in joint contact patterns were detected due to abduction angle and shoulder condition (i.e., repaired versus contralateral). Abduction angle had a significant effect on the superior/inferior contact center position, with the average joint contact center of the repaired shoulder 12.1% higher on the glenoid than the contralateral shoulder. This technique provides clinically relevant information by calculating in-vivo joint contact patterns during dynamic conditions and overcomes many limitations associated with conventional techniques for quantifying joint mechanics.

The development of a dynamic software for the user interaction from the geographic information system environment with the database of the calibration site of the satellite remote electro-optic sensors

NASA Astrophysics Data System (ADS)

Zyelyk, Ya. I.; Semeniv, O. V.

2015-12-01

The state of the problem of the post-launch calibration of the satellite electro-optic remote sensors and its solutions in Ukraine is analyzed. The database is improved and dynamic services for user interaction with database from the environment of open geographical information system Quantum GIS for information support of calibration activities are created. A dynamic application under QGIS is developed, implementing these services in the direction of the possibility of data entering, editing and extraction from the database, using the technology of object-oriented programming and of modern complex program design patterns. The functional and algorithmic support of this dynamic software and its interface are developed.

Markov and non-Markov processes in complex systems by the dynamical information entropy

NASA Astrophysics Data System (ADS)

Yulmetyev, R. M.; Gafarov, F. M.

1999-12-01

We consider the Markov and non-Markov processes in complex systems by the dynamical information Shannon entropy (DISE) method. The influence and important role of the two mutually dependent channels of entropy alternation (creation or generation of correlation) and anti-correlation (destroying or annihilation of correlation) have been discussed. The developed method has been used for the analysis of the complex systems of various natures: slow neutron scattering in liquid cesium, psychology (short-time numeral and pattern human memory and effect of stress on the dynamical taping-test), random dynamics of RR-intervals in human ECG (problem of diagnosis of various disease of the human cardio-vascular systems), chaotic dynamics of the parameters of financial markets and ecological systems.

Time and Category Information in Pattern-Based Codes

PubMed Central

Eyherabide, Hugo Gabriel; Samengo, Inés

2010-01-01

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response. PMID:21151371

Model of polar auxin transport coupled to mechanical forces retrieves robust morphogenesis along the Arabidopsis root

NASA Astrophysics Data System (ADS)

Romero-Arias, J. Roberto; Hernández-Hernández, Valeria; Benítez, Mariana; Alvarez-Buylla, Elena R.; Barrio, Rafael A.

2017-03-01

Stem cells are identical in many scales, they share the same molecular composition, DNA, genes, and genetic networks, yet they should acquire different properties to form a functional tissue. Therefore, they must interact and get some external information from their environment, either spatial (dynamical fields) or temporal (lineage). In this paper we test to what extent coupled chemical and physical fields can underlie the cell's positional information during development. We choose the root apical meristem of Arabidopsis thaliana to model the emergence of cellular patterns. We built a model to study the dynamics and interactions between the cell divisions, the local auxin concentration, and physical elastic fields. Our model recovers important aspects of the self-organized and resilient behavior of the observed cellular patterns in the Arabidopsis root, in particular, the reverse fountain pattern observed in the auxin transport, the PIN-FORMED (protein family of auxin transporters) polarization pattern and the accumulation of auxin near the region of maximum curvature in a bent root. Our model may be extended to predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions.

Multistability of the Brain Network for Self-other Processing

PubMed Central

Chen, Yi-An; Huang, Tsung-Ren

2017-01-01

Early fMRI studies suggested that brain areas processing self-related and other-related information were highly overlapping. Hypothesising functional localisation of the cortex, researchers have tried to locate “self-specific” and “other-specific” regions within these overlapping areas by subtracting suspected confounding signals in task-based fMRI experiments. Inspired by recent advances in whole-brain dynamic modelling, we instead explored an alternative hypothesis that similar spatial activation patterns could be associated with different processing modes in the form of different synchronisation patterns. Combining an automated synthesis of fMRI data with a presumption-free diffusion spectrum image (DSI) fibre-tracking algorithm, we isolated a network putatively composed of brain areas and white matter tracts involved in self-other processing. We sampled synchronisation patterns from the dynamical systems of this network using various combinations of physiological parameters. Our results showed that the self-other processing network, with simulated gamma-band activity, tended to stabilise at a number of distinct synchronisation patterns. This phenomenon, termed “multistability,” could serve as an alternative model in theorising the mechanism of processing self-other information. PMID:28256520

Measuring patterns in team interaction sequences using a discrete recurrence approach.

PubMed

Gorman, Jamie C; Cooke, Nancy J; Amazeen, Polemnia G; Fouse, Shannon

2012-08-01

Recurrence-based measures of communication determinism and pattern information are described and validated using previously collected team interaction data. Team coordination dynamics has revealed that"mixing" team membership can lead to flexible interaction processes, but keeping a team "intact" can lead to rigid interaction processes. We hypothesized that communication of intact teams would have greater determinism and higher pattern information compared to that of mixed teams. Determinism and pattern information were measured from three-person Uninhabited Air Vehicle team communication sequences over a series of 40-minute missions. Because team members communicated using push-to-talk buttons, communication sequences were automatically generated during each mission. The Composition x Mission determinism effect was significant. Intact teams' determinism increased over missions, whereas mixed teams' determinism did not change. Intact teams had significantly higher maximum pattern information than mixed teams. Results from these new communication analysis methods converge with content-based methods and support our hypotheses. Because they are not content based, and because they are automatic and fast, these new methods may be amenable to real-time communication pattern analysis.

Spatial patterns of close relationships across the lifespan

NASA Astrophysics Data System (ADS)

Jo, Hang-Hyun; Saramäki, Jari; Dunbar, Robin I. M.; Kaski, Kimmo

2014-11-01

The dynamics of close relationships is important for understanding the migration patterns of individual life-courses. The bottom-up approach to this subject by social scientists has been limited by sample size, while the more recent top-down approach using large-scale datasets suffers from a lack of detail about the human individuals. We incorporate the geographic and demographic information of millions of mobile phone users with their communication patterns to study the dynamics of close relationships and its effect in their life-course migration. We demonstrate how the close age- and sex-biased dyadic relationships are correlated with the geographic proximity of the pair of individuals, e.g., young couples tend to live further from each other than old couples. In addition, we find that emotionally closer pairs are living geographically closer to each other. These findings imply that the life-course framework is crucial for understanding the complex dynamics of close relationships and their effect on the migration patterns of human individuals.

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics.

PubMed

Teka, Wondimu W; Upadhyay, Ranjit Kumar; Mondal, Argha

2017-09-01

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cavity solitons and localized patterns in a finite-size optical cavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kozyreff, G.; Gelens, L.

2011-08-15

In appropriate ranges of parameters, laser-driven nonlinear optical cavities can support a wide variety of optical patterns, which could be used to carry information. The intensity peaks appearing in these patterns are called cavity solitons and are individually addressable. Using the Lugiato-Lefever equation to model a perfectly homogeneous cavity, we show that cavity solitons can only be located at discrete points and at a minimal distance from the edges. Other localized states which are attached to the edges are identified. By interpreting these patterns in an information coding frame, the information capacity of this dynamical system is evaluated. The resultsmore » are explained analytically in terms of the the tail characteristics of the cavity solitons. Finally, the influence of boundaries and of cavity imperfections on cavity solitons are compared.« less

Real-time determination of fringe pattern frequencies: An application to pressure measurement

NASA Astrophysics Data System (ADS)

Sciammarella, Cesar A.; Piroozan, Parham

2007-05-01

Retrieving information in real time from fringe patterns is a topic of a great deal of interest in scientific and engineering applications of optical methods. This paper presents a method for fringe frequency determination based on the capability of neural networks to recognize signals that are similar but not identical to signals used to train the neural network. Sampled patterns are generated by calibration and stored in memory. Incoming patterns are analyzed by a back-propagation neural network at the speed of the recording device, a CCD camera. This method of information retrieval is utilized to measure pressures on a boundary layer flow. The sensor combines optics and electronics to analyze dynamic pressure distributions and to feed information to a control system that is capable to preserve the stability of the flow.

Emergence of multicellular organisms with dynamic differentiation and spatial pattern.

PubMed

Furusawa, C; Kaneko, K

1998-01-01

The origin of multicellular organisms and the mechanism of development in cell societies are studied by choosing a model with intracellular biochemical dynamics allowing for oscillations, cell-cell interaction through diffusive chemicals on a two-dimensional grid, and state-dependent cell adhesion. Cells differentiate due to a dynamical instability, as described by our "isologous diversification" theory. A fixed spatial pattern of differentiated cells emerges, where spatial information is sustained by cell-cell interactions. This pattern is robust against perturbations. With an adequate cell adhesion force, active cells are release that form the seed of a new generation of multicellular organisms, accompanied by death of the original multicellular unit as a halting state. It is shown that the emergence of multicellular organisms with differentiation, regulation, and life cycle is not an accidental event, but a natural consequence in a system of replicating cells with growth.

Topics in Complexity: Dynamical Patterns in the Cyberworld

NASA Astrophysics Data System (ADS)

Qi, Hong

Quantitative understanding of mechanism in complex systems is a common "difficult" problem across many fields such as physical, biological, social and economic sciences. Investigation on underlying dynamics of complex systems and building individual-based models have recently been fueled by big data resulted from advancing information technology. This thesis investigates complex systems in social science, focusing on civil unrests on streets and relevant activities online. Investigation consists of collecting data of unrests from open digital source, featuring dynamical patterns underlying, making predictions and constructing models. A simple law governing the progress of two-sided confrontations is proposed with data of activities at micro-level. Unraveling the connections between activity of organizing online and outburst of unrests on streets gives rise to a further meso-level pattern of human behavior, through which adversarial groups evolve online and hyper-escalate ahead of real-world uprisings. Based on the patterns found, noticeable improvement of prediction of civil unrests is achieved. Meanwhile, novel model created from combination of mobility dynamics in the cyberworld and a traditional contagion model can better capture the characteristics of modern civil unrests and other contagion-like phenomena than the original one.

Goldspotted oak borer effects on tree health and colonization patterns at six newly-established sites

Treesearch

Laurel J. Haavik; Mary L. Flint; Tom W. Coleman; Robert C. Venette; Steven J. Seybold

2015-01-01

Newly-established populations of invasive wood-inhabiting insects provide an opportunity for the study of invasion dynamics and for collecting information to improve management options for these cryptic species. From 2011 to 2013, we studied the dynamics of the goldspotted oak borer Agrilus auroguttatus Schaeffer (Coleoptera: Buprestidae), a new pest...

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons

PubMed Central

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C.; Bunney, Benjamin S.; Peterson, Bradley S.

2012-01-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. PMID:22831464

Multi-Dimensional Pattern Discovery of Trajectories Using Contextual Information

NASA Astrophysics Data System (ADS)

Sharif, M.; Alesheikh, A. A.

2017-10-01

Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Single-shot thermal ghost imaging using wavelength-division multiplexing

NASA Astrophysics Data System (ADS)

Deng, Chao; Suo, Jinli; Wang, Yuwang; Zhang, Zhili; Dai, Qionghai

2018-01-01

Ghost imaging (GI) is an emerging technique that reconstructs the target scene from its correlated measurements with a sequence of patterns. Restricted by the multi-shot principle, GI usually requires long acquisition time and is limited in observation of dynamic scenes. To handle this problem, this paper proposes a single-shot thermal ghost imaging scheme via a wavelength-division multiplexing technique. Specifically, we generate thousands of correlated patterns simultaneously by modulating a broadband light source with a wavelength dependent diffuser. These patterns carry the scene's spatial information and then the correlated photons are coupled into a spectrometer for the final reconstruction. This technique increases the speed of ghost imaging and promotes the applications in dynamic ghost imaging with high scalability and compatibility.

An Adaptive Sensor Mining Framework for Pervasive Computing Applications

NASA Astrophysics Data System (ADS)

Rashidi, Parisa; Cook, Diane J.

Analyzing sensor data in pervasive computing applications brings unique challenges to the KDD community. The challenge is heightened when the underlying data source is dynamic and the patterns change. We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model. In our framework, the frequent and periodic patterns of data are first discovered by the Frequent and Periodic Pattern Miner (FPPM) algorithm; and then any changes in the discovered patterns over the lifetime of the system are discovered by the Pattern Adaptation Miner (PAM) algorithm, in order to adapt to the changing environment. This framework also captures vital context information present in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.

Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics

NASA Astrophysics Data System (ADS)

Miritello, Giovanna; Lara, Rubén; Moro, Esteban

Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

Cortical and thalamic contributions to response dynamics across layers of the primary somatosensory cortex during tactile discrimination

PubMed Central

Pais-Vieira, Miguel; Kunicki, Carolina; Tseng, Po-He; Martin, Joel; Lebedev, Mikhail

2015-01-01

Tactile information processing in the rodent primary somatosensory cortex (S1) is layer specific and involves modulations from both thalamocortical and cortico-cortical loops. However, the extent to which these loops influence the dynamics of the primary somatosensory cortex while animals execute tactile discrimination remains largely unknown. Here, we describe neural dynamics of S1 layers across the multiple epochs defining a tactile discrimination task. We observed that neuronal ensembles within different layers of the S1 cortex exhibited significantly distinct neurophysiological properties, which constantly changed across the behavioral states that defined a tactile discrimination. Neural dynamics present in supragranular and granular layers generally matched the patterns observed in the ventral posterior medial nucleus of the thalamus (VPM), whereas the neural dynamics recorded from infragranular layers generally matched the patterns from the posterior nucleus of the thalamus (POM). Selective inactivation of contralateral S1 specifically switched infragranular neural dynamics from POM-like to those resembling VPM neurons. Meanwhile, ipsilateral M1 inactivation profoundly modulated the firing suppression observed in infragranular layers. This latter effect was counterbalanced by contralateral S1 block. Tactile stimulus encoding was layer specific and selectively affected by M1 or contralateral S1 inactivation. Lastly, causal information transfer occurred between all neurons in all S1 layers but was maximal from infragranular to the granular layer. These results suggest that tactile information processing in the S1 of awake behaving rodents is layer specific and state dependent and that its dynamics depend on the asynchronous convergence of modulations originating from ipsilateral M1 and contralateral S1. PMID:26180115

Modelling information dissemination under privacy concerns in social media

NASA Astrophysics Data System (ADS)

Zhu, Hui; Huang, Cheng; Lu, Rongxing; Li, Hui

2016-05-01

Social media has recently become an important platform for users to share news, express views, and post messages. However, due to user privacy preservation in social media, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this study, a general stochastic model using dynamic evolution equations was introduced to illustrate how privacy concerns impact the process of information dissemination. Extensive simulations and analyzes involving the privacy settings of general users, privileged users, and pure observers were conducted on real-world networks, and the results demonstrated that user privacy settings affect information differently. Finally, we also studied the process of information diffusion analytically and numerically with different privacy settings using two classic networks.

Reactive immunization on complex networks

NASA Astrophysics Data System (ADS)

Alfinito, Eleonora; Beccaria, Matteo; Fachechi, Alberto; Macorini, Guido

2017-01-01

Epidemic spreading on complex networks depends on the topological structure as well as on the dynamical properties of the infection itself. Generally speaking, highly connected individuals play the role of hubs and are crucial to channel information across the network. On the other hand, static topological quantities measuring the connectivity structure are independent of the dynamical mechanisms of the infection. A natural question is therefore how to improve the topological analysis by some kind of dynamical information that may be extracted from the ongoing infection itself. In this spirit, we propose a novel vaccination scheme that exploits information from the details of the infection pattern at the moment when the vaccination strategy is applied. Numerical simulations of the infection process show that the proposed immunization strategy is effective and robust on a wide class of complex networks.

Patterns In Contingencies: The Interlocking of Formal and Informal Political Institutions in Contemporary Argentina

ERIC Educational Resources Information Center

Llamazares, Ivan

2005-01-01

This article explores how the interlocking of formal and informal political institutions has affected the dynamics and performance of the Argentine democracy. Key institutional features of the Argentine political system have been a competitive form of federalism, loosely structured and political parties that are not ideologically unified,…

The Lure of Hegemonic Masculinity: Investigating the Dynamics of Gender Relations in Two Male Elementary School Teachers' Lives

ERIC Educational Resources Information Center

Martino, Wayne

2008-01-01

This paper is based on an investigation into the dynamics of masculinity in two male elementary school teachers' lives. It draws on a poststructuralist approach to empirical analysis that is informed by Sondergaard who argues for the need to attend to the "constitution of social practices and cultural patterns" through which subjects…

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

PubMed

Gao, Zhongke; Jin, Ningde

2009-06-01

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

Regulative feedback in pattern formation: towards a general relativistic theory of positional information.

PubMed

Jaeger, Johannes; Irons, David; Monk, Nick

2008-10-01

Positional specification by morphogen gradients is traditionally viewed as a two-step process. A gradient is formed and then interpreted, providing a spatial metric independent of the target tissue, similar to the concept of space in classical mechanics. However, the formation and interpretation of gradients are coupled, dynamic processes. We introduce a conceptual framework for positional specification in which cellular activity feeds back on positional information encoded by gradients, analogous to the feedback between mass-energy distribution and the geometry of space-time in Einstein's general theory of relativity. We discuss how such general relativistic positional information (GRPI) can guide systems-level approaches to pattern formation.

Experimental resource pulses influence social-network dynamics and the potential for information flow in tool-using crows

PubMed Central

St Clair, James J. H.; Burns, Zackory T.; Bettaney, Elaine M.; Morrissey, Michael B.; Otis, Brian; Ryder, Thomas B.; Fleischer, Robert C.; James, Richard; Rutz, Christian

2015-01-01

Social-network dynamics have profound consequences for biological processes such as information flow, but are notoriously difficult to measure in the wild. We used novel transceiver technology to chart association patterns across 19 days in a wild population of the New Caledonian crow—a tool-using species that may socially learn, and culturally accumulate, tool-related information. To examine the causes and consequences of changing network topology, we manipulated the environmental availability of the crows' preferred tool-extracted prey, and simulated, in silico, the diffusion of information across field-recorded time-ordered networks. Here we show that network structure responds quickly to environmental change and that novel information can potentially spread rapidly within multi-family communities, especially when tool-use opportunities are plentiful. At the same time, we report surprisingly limited social contact between neighbouring crow communities. Such scale dependence in information-flow dynamics is likely to influence the evolution and maintenance of material cultures. PMID:26529116

Temporal and spatial evolution characteristics of gas-liquid two-phase flow pattern based on image texture spectrum descriptor

NASA Astrophysics Data System (ADS)

Zhou, Xi-Guo; Jin, Ning-De; Wang, Zhen-Ya; Zhang, Wen-Yin

2009-11-01

The dynamic image information of typical gas-liquid two-phase flow patterns in vertical upward pipe is captured by a highspeed dynamic camera. The texture spectrum descriptor is used to describe the texture characteristics of the processed images whose content is represented in the form of texture spectrum histogram, and four time-varying characteristic parameter indexes which represent image texture structure of different flow patterns are extracted. The study results show that the amplitude fluctuation of texture characteristic parameter indexes of bubble flow is lowest and shows very random complex dynamic behavior; the amplitude fluctuation of slug flow is higher and shows intermittent motion behavior between gas slug and liquid slug, and the amplitude fluctuation of churn flow is the highest and shows better periodicity; the amplitude fluctuation of bubble-slug flow is from low to high and oscillating frequence is higher than that of slug flow, and includes the features of both slug flow and bubble flow; the slug-churn flow loses the periodicity of slug flow and churn flow, and the amplitude fluctuation is high. The results indicate that the image texture characteristic parameter indexes of different flow pattern can reflect the flow characteristics of gas-liquid two-phase flow, which provides a new approach to understand the temporal and spatial evolution of flow pattern dynamics.

Dynamical patterns of cattle trade movements.

PubMed

Bajardi, Paolo; Barrat, Alain; Natale, Fabrizio; Savini, Lara; Colizza, Vittoria

2011-01-01

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions.

Dynamical Patterns of Cattle Trade Movements

PubMed Central

Bajardi, Paolo; Barrat, Alain; Natale, Fabrizio; Savini, Lara; Colizza, Vittoria

2011-01-01

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions. PMID:21625633

Weak connections form an infinite number of patterns in the brain

NASA Astrophysics Data System (ADS)

Ren, Hai-Peng; Bai, Chao; Baptista, Murilo S.; Grebogi, Celso

2017-04-01

Recently, much attention has been paid to interpreting the mechanisms for memory formation in terms of brain connectivity and dynamics. Within the plethora of collective states a complex network can exhibit, we show that the phenomenon of Collective Almost Synchronisation (CAS), which describes a state with an infinite number of patterns emerging in complex networks for weak coupling strengths, deserves special attention. We show that a simulated neuron network with neurons weakly connected does produce CAS patterns, and additionally produces an output that optimally model experimental electroencephalograph (EEG) signals. This work provides strong evidence that the brain operates locally in a CAS regime, allowing it to have an unlimited number of dynamical patterns, a state that could explain the enormous memory capacity of the brain, and that would give support to the idea that local clusters of neurons are sufficiently decorrelated to independently process information locally.

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

Use of soil moisture dynamics and patterns for the investigation of runoff generation processes with emphasis on preferential flow

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2007-08-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

Use of soil moisture dynamics and patterns at different spatio-temporal scales for the investigation of subsurface flow processes

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2009-07-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and binary indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

The heparanome--the enigma of encoding and decoding heparan sulfate sulfation.

PubMed

Lamanna, William C; Kalus, Ina; Padva, Michael; Baldwin, Rebecca J; Merry, Catherine L R; Dierks, Thomas

2007-04-30

Heparan sulfate (HS) is a cell surface carbohydrate polymer modified with sulfate moieties whose highly ordered composition is central to directing specific cell signaling events. The ability of the cell to generate these information rich glycans with such specificity has opened up a new field of "heparanomics" which seeks to understand the systems involved in generating these cell type and developmental stage specific HS sulfation patterns. Unlike other instances where biological information is encrypted as linear sequences in molecules such as DNA, HS sulfation patterns are generated through a non-template driven process. Thus, deciphering the sulfation code and the dynamic nature of its generation has posed a new challenge to system biologists. The recent discovery of two sulfatases, Sulf1 and Sulf2, with the unique ability to edit sulfation patterns at the cell surface, has opened up a new dimension as to how we understand the regulation of HS sulfation patterning and pattern-dependent cell signaling events. This review will focus on the functional relationship between HS sulfation patterning and biological processes. Special attention will be given to Sulf1 and Sulf2 and how these key editing enzymes might act in concert with the HS biosynthetic enzymes to generate and regulate specific HS sulfation patterns in vivo. We will further explore the use of knock out mice as biological models for understanding the dynamic systems involved in generating HS sulfation patterns and their biological relevance. A brief overview of new technologies and innovations summarizes advances in the systems biology field for understanding non-template molecular networks and their influence on the "heparanome".

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

PubMed

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

2012-11-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Exploring spatial evolution of economic clusters: A case study of Beijing

NASA Astrophysics Data System (ADS)

Yang, Zhenshan; Sliuzas, Richard; Cai, Jianming; Ottens, Henk F. L.

2012-10-01

An identification of economic clusters and analysing their changing spatial patterns is important for understanding urban economic space dynamics. Previous studies, however, suffer from limitations as a consequence of using fixed geographically areas and not combining functional and spatial dynamics. The paper presents an approach, based on local spatial statistics and the case of Beijing to understand the spatial clustering of industries that are functionally interconnected by common or complementary patterns of demand or supply relations. Using register data of business establishments, it identifies economic clusters and analyses their pattern based on postcodes at different time slices during the period 1983-2002. The study shows how the advanced services occupy the urban centre and key sub centres. The Information and Communication Technology (ICT) cluster is mainly concentrated in the north part of the city and circles the urban centre, and the main manufacturing clusters are evolved in the key sub centers. This type of outcomes improves understanding of urban-economic dynamics, which can support spatial and economic planning.

The bioelectric code: An ancient computational medium for dynamic control of growth and form.

PubMed

Levin, Michael; Martyniuk, Christopher J

2018-02-01

What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. Cracking the bioelectric code will enable much-improved control over biological patterning, advancing basic evolutionary developmental biology as well as enabling numerous applications in regenerative medicine and synthetic bioengineering. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamic analysis and pattern visualization of forest fires.

PubMed

Lopes, António M; Tenreiro Machado, J A

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Dynamic Analysis and Pattern Visualization of Forest Fires

PubMed Central

Lopes, António M.; Tenreiro Machado, J. A.

2014-01-01

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns. PMID:25137393

Neural nets with terminal chaos for simulation of non-deterministic patterns

NASA Technical Reports Server (NTRS)

Zak, Michail

1993-01-01

Models for simulating some aspects of neural intelligence are presented and discussed. Special attention is given to terminal neurodynamics as a particular architecture of terminal dynamics suitable for modeling information flows. Applications of terminal chaos to information fusion as well as to planning and modeling coordination among neurons in biological systems are disussed.

Corn rootworms (Coleoptera: Chrysomelidae) in space and time

NASA Astrophysics Data System (ADS)

Park, Yong-Lak

Spatial dispersion is a main characteristic of insect populations. Dispersion pattern provides useful information for developing effective sampling and scouting programs because it affects sampling accuracy, efficiency, and precision. Insect dispersion, however, is dynamic in space and time and largely dependent upon interactions among insect, plant and environmental factors. This study investigated the spatial and temporal dynamics of corn rootworm dispersion at different spatial scales by using the global positioning system, the geographic information system, and geostatistics. Egg dispersion pattern was random or uniform in 8-ha cornfields, but could be aggregated at a smaller scale. Larval dispersion pattern was aggregated regardless of spatial scales used in this study. Soil moisture positively affected corn rootworm egg and larval dispersions. Adult dispersion tended to be aggregated during peak population period and random or uniform early and late in the season and corn plant phenology was a major factor to determine dispersion patterns. The dispersion pattern of root injury by corn rootworm larval feeding was aggregated and the degree of aggregation increased as the root injury increased within the range of root injury observed in microscale study. Between-year relationships in dispersion among eggs, larvae, adult, and environment provided a strategy that could predict potential root damage the subsequent year. The best prediction map for the subsequent year's potential root damage was the dispersion maps of adults during population peaked in the cornfield. The prediction map was used to develop site-specific pest management that can reduce chemical input and increase control efficiency by controlling pests only where management is needed. This study demonstrated the spatio-temporal dynamics of insect population and spatial interactions among insects, plants, and environment.

The epidemic spreading model and the direction of information flow in brain networks.

PubMed

Meier, J; Zhou, X; Hillebrand, A; Tewarie, P; Stam, C J; Van Mieghem, P

2017-05-15

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural dynamics based on the recognition of neural fingerprints

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2015-01-01

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

Change Semantic Constrained Online Data Cleaning Method for Real-Time Observational Data Stream

NASA Astrophysics Data System (ADS)

Ding, Yulin; Lin, Hui; Li, Rongrong

2016-06-01

Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment's status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The "big but dirty" real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not stationary or static but changing and dynamic. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

Evol and ProDy for bridging protein sequence evolution and structural dynamics

PubMed Central

Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R.; Bahar, Ivet

2014-01-01

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. Availability and implementation: ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. Contact: bahar@pitt.edu PMID:24849577

Sexual affordances, perceptual-motor invariance extraction and intentional nonlinear dynamics: sexually deviant and non-deviant patterns in male subjects.

PubMed

Renaud, Patrice; Goyette, Mathieu; Chartier, Sylvain; Zhornitski, Simon; Trottier, Dominique; Rouleau, Joanne-L; Proulx, Jean; Fedoroff, Paul; Bradford, John-P; Dassylva, Benoit; Bouchard, Stephane

2010-10-01

Sexual arousal and gaze behavior dynamics are used to characterize deviant sexual interests in male subjects. Pedophile patients and non-deviant subjects are immersed with virtual characters depicting relevant sexual features. Gaze behavior dynamics as indexed from correlation dimensions (D2) appears to be fractal in nature and significantly different from colored noise (surrogate data tests and recurrence plot analyses were performed). This perceptual-motor fractal dynamics parallels sexual arousal and differs from pedophiles to non-deviant subjects when critical sexual information is processed. Results are interpreted in terms of sexual affordance, perceptual invariance extraction and intentional nonlinear dynamics.

Context-aware pattern discovery for moving object trajectories

NASA Astrophysics Data System (ADS)

Sharif, Mohammad; Asghar Alesheikh, Ali; Kaffash Charandabi, Neda

2018-05-01

Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Enabling the First Ever Measurement of Coherent Neutrino Scattering Through Background Neutron Measurements.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reyna, David; Betty, Rita

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis,thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities. The purpose of this project was to computationally model the impact of neural population dynamics within the neurobiological memory system in order to examine how subareas in the brain enable pattern separation and completion of information in memory across time as associated experiences.

Speed and direction changes induce the perception of animacy in 7-month-old infants

PubMed Central

Träuble, Birgit; Pauen, Sabina; Poulin-Dubois, Diane

2014-01-01

A large body of research has documented infants’ ability to classify animate and inanimate objects based on static or dynamic information. It has been shown that infants less than 1 year of age transfer animacy-specific expectations from dynamic point-light displays to static images. The present study examined whether basic motion cues that typically trigger judgments of perceptual animacy in older children and adults lead 7-month-olds to infer an ambiguous object’s identity from dynamic information. Infants were tested with a novel paradigm that required inferring the animacy status of an ambiguous moving shape. An ambiguous shape emerged from behind a screen and its identity could only be inferred from its motion. Its motion pattern varied distinctively between scenes: it either changed speed and direction in an animate way, or it moved along a straight path at a constant speed (i.e., in an inanimate way). At test, the identity of the shape was revealed and it was either consistent or inconsistent with its motion pattern. Infants looked longer on trials with the inconsistent outcome. We conclude that 7-month-olds’ representations of animates and inanimates include category-specific associations between static and dynamic attributes. Moreover, these associations seem to hold for simple dynamic cues that are considered minimal conditions for animacy perception. PMID:25346712

Time series analyses of breathing patterns of lung cancer patients using nonlinear dynamical system theory.

PubMed

Tewatia, D K; Tolakanahalli, R P; Paliwal, B R; Tomé, W A

2011-04-07

The underlying requirements for successful implementation of any efficient tumour motion management strategy are regularity and reproducibility of a patient's breathing pattern. The physiological act of breathing is controlled by multiple nonlinear feedback and feed-forward couplings. It would therefore be appropriate to analyse the breathing pattern of lung cancer patients in the light of nonlinear dynamical system theory. The purpose of this paper is to analyse the one-dimensional respiratory time series of lung cancer patients based on nonlinear dynamics and delay coordinate state space embedding. It is very important to select a suitable pair of embedding dimension 'm' and time delay 'τ' when performing a state space reconstruction. Appropriate time delay and embedding dimension were obtained using well-established methods, namely mutual information and the false nearest neighbour method, respectively. Establishing stationarity and determinism in a given scalar time series is a prerequisite to demonstrating that the nonlinear dynamical system that gave rise to the scalar time series exhibits a sensitive dependence on initial conditions, i.e. is chaotic. Hence, once an appropriate state space embedding of the dynamical system has been reconstructed, we show that the time series of the nonlinear dynamical systems under study are both stationary and deterministic in nature. Once both criteria are established, we proceed to calculate the largest Lyapunov exponent (LLE), which is an invariant quantity under time delay embedding. The LLE for all 16 patients is positive, which along with stationarity and determinism establishes the fact that the time series of a lung cancer patient's breathing pattern is not random or irregular, but rather it is deterministic in nature albeit chaotic. These results indicate that chaotic characteristics exist in the respiratory waveform and techniques based on state space dynamics should be employed for tumour motion management.

Emergent mechanics, quantum and un-quantum

NASA Astrophysics Data System (ADS)

Ralston, John P.

2013-10-01

There is great interest in quantum mechanics as an "emergent" phenomenon. The program holds that nonobvious patterns and laws can emerge from complicated physical systems operating by more fundamental rules. We find a new approach where quantum mechanics itself should be viewed as an information management tool not derived from physics nor depending on physics. The main accomplishment of quantum-style theory comes in expanding the notion of probability. We construct a map from macroscopic information as data" to quantum probability. The map allows a hidden variable description for quantum states, and efficient use of the helpful tools of quantum mechanics in unlimited circumstances. Quantum dynamics via the time-dependent Shroedinger equation or operator methods actually represents a restricted class of classical Hamiltonian or Lagrangian dynamics, albeit with different numbers of degrees of freedom. We show that under wide circumstances such dynamics emerges from structureless dynamical systems. The uses of the quantum information management tools are illustrated by numerical experiments and practical applications

Patterns of Information-Seeking for Cancer on the Internet: An Analysis of Real World Data

PubMed Central

Ofran, Yishai; Paltiel, Ora; Pelleg, Dan; Rowe, Jacob M.; Yom-Tov, Elad

2012-01-01

Although traditionally the primary information sources for cancer patients have been the treating medical team, patients and their relatives increasingly turn to the Internet, though this source may be misleading and confusing. We assess Internet searching patterns to understand the information needs of cancer patients and their acquaintances, as well as to discern their underlying psychological states. We screened 232,681 anonymous users who initiated cancer-specific queries on the Yahoo Web search engine over three months, and selected for study users with high levels of interest in this topic. Searches were partitioned by expected survival for the disease being searched. We compared the search patterns of anonymous users and their contacts. Users seeking information on aggressive malignancies exhibited shorter search periods, focusing on disease- and treatment-related information. Users seeking knowledge regarding more indolent tumors searched for longer periods, alternated between different subjects, and demonstrated a high interest in topics such as support groups. Acquaintances searched for longer periods than the proband user when seeking information on aggressive (compared to indolent) cancers. Information needs can be modeled as transitioning between five discrete states, each with a unique signature representing the type of information of interest to the user. Thus, early phases of information-seeking for cancer follow a specific dynamic pattern. Areas of interest are disease dependent and vary between probands and their contacts. These patterns can be used by physicians and medical Web site authors to tailor information to the needs of patients and family members. PMID:23029317

Sea Level Change due to Time-Dependent Long-Wavelength Dynamic Topography Inferred from Plate Tectonic Reconstructions

NASA Astrophysics Data System (ADS)

Conrad, Clinton P.; Steinberger, Bernhard; Torsvik, Trond H.

2017-04-01

Earth's surface is deflected vertically by stresses associated with convective mantle flow. Although dynamic topography is important for both sea level change and continental uplift and subsidence, the time history of dynamic topography is difficult to constrain because the time-dependence of mantle flow is not known. However, the motions of the tectonic plates contain information about the mantle flow patterns that drive them. In particular, we show that the longest wavelengths of mantle flow are tightly linked to the dipole and quadrupole moments (harmonic degrees 1 and 2) of plate motions. This coupling allows us to infer patterns of long-wavelength mantle flow, and the associated dynamic topography, from tectonic plate motions. After calibrating this linkage using models of present-day mantle flow, we can use reconstructions of global plate motions to infer the basic patterns of long-wavelength dynamic topography back to 250 Ma. We find relatively stable dynamic uplift persists above large-scale mantle upwelling beneath Africa and the Central Pacific. Regions of major downwelling encircled the periphery of these stable upwellings, alternating between primarily east-west and north-south orientations. The amplitude of long-wavelength dynamic topography was likely largest in the Cretaceous, when global plate motions were fastest. Continental motions over this time-evolving dynamic topography predict patterns of continental uplift and subsidence that are confirmed by geological observations of continental surfaces relative to sea level. Net uplift or subsidence of the global seafloor can also induce eustatic sea level changes. We infer that dispersal of the Pangean supercontinent away from stable upwelling beneath Africa may have exposed the seafloor to an increasingly larger area of growing positive dynamic topography during the Mesozoic. This net uplift of the seafloor caused 60 m of sea level rise during the Triassic and Jurassic, ceasing in the Cenozoic once continents fully override degree-2 downwellings. These sea level changes represent a significant component of the estimated 200 m of sea level variations during the Phanerozoic, which exhibit a similar temporal pattern.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

PubMed

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity

PubMed Central

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition. PMID:29615882

Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

NASA Astrophysics Data System (ADS)

Dubovyk, Olena; Landmann, Tobias; Erasmus, Barend F. N.; Tewes, Andreas; Schellberg, Jürgen

2015-06-01

Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000-2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000-2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.

Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

NASA Astrophysics Data System (ADS)

Cui, Ling-xiao; Long, Wen

2016-11-01

Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial-temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market.

Dynamic stratification of the landscape of Mexico: analysis of vegetation patterns observed with multitemporal remotely sensed images

Treesearch

Franz Mora; Louis R. Iverson; Louis R. Iverson

1997-01-01

Rapid deforestation in Mexico, when coupled with poor access to current and consistent ecological information across the country underscores the need for an ecological classification system that can be readily updated as new data become available. In this study, regional vegetation resources in Mexico were evaluated using remotely sensed information. Multitemporal...

The Coordination Dynamics of Observational Learning: Relative Motion Direction and Relative Phase as Informational Content Linking Action-Perception to Action-Production.

PubMed

Buchanan, John J

2016-01-01

The primary goal of this chapter is to merge together the visual perception perspective of observational learning and the coordination dynamics theory of pattern formation in perception and action. Emphasis is placed on identifying movement features that constrain and inform action-perception and action-production processes. Two sources of visual information are examined, relative motion direction and relative phase. The visual perception perspective states that the topological features of relative motion between limbs and joints remains invariant across an actor's motion and therefore are available for pickup by an observer. Relative phase has been put forth as an informational variable that links perception to action within the coordination dynamics theory. A primary assumption of the coordination dynamics approach is that environmental information is meaningful only in terms of the behavior it modifies. Across a series of single limb tasks and bimanual tasks it is shown that the relative motion and relative phase between limbs and joints is picked up through visual processes and supports observational learning of motor skills. Moreover, internal estimations of motor skill proficiency and competency are linked to the informational content found in relative motion and relative phase. Thus, the chapter links action to perception and vice versa and also links cognitive evaluations to the coordination dynamics that support action-perception and action-production processes.

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior

PubMed Central

Carlson, Bruce A.

2010-01-01

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge (EOD). In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals and band-pass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally-relevant stimulus information encoded into temporal patterns of activity by sensory neurons. PMID:19641105

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior.

PubMed

Carlson, Bruce A

2009-07-29

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

UniEnt: uniform entropy model for the dynamics of a neuronal population

NASA Astrophysics Data System (ADS)

Hernandez Lahme, Damian; Nemenman, Ilya

Sensory information and motor responses are encoded in the brain in a collective spiking activity of a large number of neurons. Understanding the neural code requires inferring statistical properties of such collective dynamics from multicellular neurophysiological recordings. Questions of whether synchronous activity or silence of multiple neurons carries information about the stimuli or the motor responses are especially interesting. Unfortunately, detection of such high order statistical interactions from data is especially challenging due to the exponentially large dimensionality of the state space of neural collectives. Here we present UniEnt, a method for the inference of strengths of multivariate neural interaction patterns. The method is based on the Bayesian prior that makes no assumptions (uniform a priori expectations) about the value of the entropy of the observed multivariate neural activity, in contrast to popular approaches that maximize this entropy. We then study previously published multi-electrode recordings data from salamander retina, exposing the relevance of higher order neural interaction patterns for information encoding in this system. This work was supported in part by Grants JSMF/220020321 and NSF/IOS/1208126.

Pattern-based information portal for business plan co-creation

NASA Astrophysics Data System (ADS)

Bontchev, Boyan; Ruskov, Petko; Tanev, Stoyan

2011-03-01

Creation of business plans helps entrepreneurs in managing identification of business opportunities and committing necessary resources for process evolution. Applying patterns in business plan creation facilitates the identification of effective solutions that were adopted in the past and may provide a basis for adopting similar solutions in the future within given business context. The article presents the system design of an information portal for business plan co-creation based on patterns. The portal is going to provide start-up and entrepreneurs with ready-to-modify business plan patterns in order to help them in development of effective and efficient business plans. It will facilitate entrepreneurs in co-experimenting and co-learning more frequently and faster. Moreover, the paper focuses on the software architecture of the pattern based portal and explains the functionality of its modules, namely the pattern designer, pattern repository services and agent-based pattern implementers. It explains their role for business process co-creation, storing and managing patterns described formally, and selecting patterns best suited for specific business case. Thus, innovative entrepreneurs will be guided by the portal in co-writing winning business plans and staying competitive in the present day dynamic globalized environment.

Pattern-based information portal for business plan co-creation

NASA Astrophysics Data System (ADS)

Bontchev, Boyan; Ruskov, Petko; Tanev, Stoyan

2010-10-01

Creation of business plans helps entrepreneurs in managing identification of business opportunities and committing necessary resources for process evolution. Applying patterns in business plan creation facilitates the identification of effective solutions that were adopted in the past and may provide a basis for adopting similar solutions in the future within given business context. The article presents the system design of an information portal for business plan co-creation based on patterns. The portal is going to provide start-up and entrepreneurs with ready-to-modify business plan patterns in order to help them in development of effective and efficient business plans. It will facilitate entrepreneurs in co-experimenting and co-learning more frequently and faster. Moreover, the paper focuses on the software architecture of the pattern based portal and explains the functionality of its modules, namely the pattern designer, pattern repository services and agent-based pattern implementers. It explains their role for business process co-creation, storing and managing patterns described formally, and selecting patterns best suited for specific business case. Thus, innovative entrepreneurs will be guided by the portal in co-writing winning business plans and staying competitive in the present day dynamic globalized environment.

Workplace Friendship in the Electronically Connected Organization

ERIC Educational Resources Information Center

Sias, Patricia M.; Pedersen, Hannah; Gallagher, Erin B.; Kopaneva, Irina

2012-01-01

This study examined information communication technologies and workplace friendship dynamics. Employees reported factors that influenced their initiation of friendship with a coworker and reported patterns and perceptions of communication with their workplace friend via different communication methods. Results indicated that personality, shared…

Analysis of crevasse patterns on Helheim and Kangerdlugssuaq Glaciers in Greenland

NASA Astrophysics Data System (ADS)

Udell, K.; Walker, C. C.; Schmidt, B. E.

2017-12-01

As a tidewater glacier flows through a valley, it accumulates fractures that provide qualitative information on how glacier thickness, climate forcing, and areas of compression and extension conspire within the ice. These fracture patterns remain and evolve on the glacier, and rapid changes in the pattern can be indicative of a transition in the movement of the glacier. Not only can the fractures provide qualitative information about a glacier, they can also provide quantitative information that allows for the calculation of the stress field and dynamics that the ice experiences. Helheim and Kangerdlugssuaq both terminate in the ocean, potentially providing information on the transition from solid glacier to mélange, which is an important but not well understood process. Using satellite imagery, we traced surface crevasses present along each glacier for available images between 2001-2016 using geospatial software QGIS. We also qualitatively tracked any surface melt ponds present, and monitored for large fractures or regions of the terminus that appeared to be susceptible to or currently calving. With the trace maps, we will use spatial analysis techniques to allow us to quantify the visible patterns and compare the information from year to year and glacier to glacier. Once we can quantitatively describe fracture density in different areas of the glacier, we will also be able to better describe the transition within the glacier from solid mass to highly-fractured and collapsing. Having this data for each glacier allows for comparisons to be made within regions of individual glaciers as well as between glaciers. Using this information, we can answer questions about the relationship between density and pattern of fractures to the stability of the terminus, the stresses that drive glacial fractures, and what effects climate has on glacier dynamics and calving. Preliminary observations include the increasing prevalence of melt ponds beginning in 2004 as well as the retreat of the terminus during the same period. More recently the location of the terminus has remained relatively constant. Overall, understanding the processes of glacial fracturing has implications for both better understanding climate change and analyzing ice fracturing on other planetary bodies such as Europa.

A Typical Synergy

NASA Astrophysics Data System (ADS)

van Noort, Thomas; Achten, Peter; Plasmeijer, Rinus

We present a typical synergy between dynamic types (dynamics) and generalised algebraic datatypes (GADTs). The former provides a clean approach to integrating dynamic typing in a statically typed language. It allows values to be wrapped together with their type in a uniform package, deferring type unification until run time using a pattern match annotated with the desired type. The latter allows for the explicit specification of constructor types, as to enforce their structural validity. In contrast to ADTs, GADTs are heterogeneous structures since each constructor type is implicitly universally quantified. Unfortunately, pattern matching only enforces structural validity and does not provide instantiation information on polymorphic types. Consequently, functions that manipulate such values, such as a type-safe update function, are cumbersome due to boilerplate type representation administration. In this paper we focus on improving such functions by providing a new GADT annotation via a natural synergy with dynamics. We formally define the semantics of the annotation and touch on novel other applications of this technique such as type dispatching and enforcing type equality invariants on GADT values.

The expert advantage in dynamic pattern recall persists across both attended and unattended display elements.

PubMed

Gorman, Adam D; Abernethy, Bruce; Farrow, Damian

2013-07-01

We examined how differences in attention influence how expert and novice basketball players encode into memory the specific structural information contained within patterns of play from their sport. Our participants were primed during a typical recall task to focus attention on either attacking or defending player formations before being asked to recall the attended or unattended portion of the pattern. Adherence to the instructional set was confirmed through an analysis of gaze distributions. Recall performance was superior for the experts relative to the novices across both the attended and unattended attacking and defensive pattern structures. Expert recall of attacker positions was unchanged with and without attention, whereas recall accuracy for the positions of defenders diminished without attention, as did the novices' recall of both attack and defense formations. The findings suggest that experienced performers are better than novices at encoding the elements from a complex and dynamic pattern in the absence of focused attention, with this advantage being especially evident in relation to the recall of attacking structure. Some revision of long-term memory theories of expertise will be necessary to accommodate these findings.

Controls of Isotopic Patterns in Saprotrophic and Ectomycorrhizal Fungi

EPA Science Inventory

Isotopes of nitrogen (δ15N) and carbon (δ13C) in ectomycorrhizal and saprotrophic fungi contain important information about ecological functioning, but the complexity of physiological and ecosystem processes contributing to fungal carbon and nitrogen dynamics has limited our abil...

Photon statistics and speckle visibility spectroscopy with partially coherent X-rays.

PubMed

Li, Luxi; Kwaśniewski, Paweł; Orsi, Davide; Wiegart, Lutz; Cristofolini, Luigi; Caronna, Chiara; Fluerasu, Andrei

2014-11-01

A new approach is proposed for measuring structural dynamics in materials from multi-speckle scattering patterns obtained with partially coherent X-rays. Coherent X-ray scattering is already widely used at high-brightness synchrotron lightsources to measure dynamics using X-ray photon correlation spectroscopy, but in many situations this experimental approach based on recording long series of images (i.e. movies) is either not adequate or not practical. Following the development of visible-light speckle visibility spectroscopy, the dynamic information is obtained instead by analyzing the photon statistics and calculating the speckle contrast in single scattering patterns. This quantity, also referred to as the speckle visibility, is determined by the properties of the partially coherent beam and other experimental parameters, as well as the internal motions in the sample (dynamics). As a case study, Brownian dynamics in a low-density colloidal suspension is measured and an excellent agreement is found between correlation functions measured by X-ray photon correlation spectroscopy and the decay in speckle visibility with integration time obtained from the analysis presented here.

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

NeuroRhythmics: software for analyzing time-series measurements of saltatory movements in neuronal processes.

PubMed

Kerlin, Aaron M; Lindsley, Tara A

2008-08-15

Time-lapse imaging of living neurons both in vivo and in vitro has revealed that the growth of axons and dendrites is highly dynamic and characterized by alternating periods of extension and retraction. These growth dynamics are associated with important features of neuronal development and are differentially affected by experimental treatments, but the underlying cellular mechanisms are poorly understood. NeuroRhythmics was developed to semi-automate specific quantitative tasks involved in analysis of two-dimensional time-series images of processes that exhibit saltatory elongation. This software provides detailed information on periods of growth and nongrowth that it identifies by transitions in elongation (i.e. initiation time, average rate, duration) and information regarding the overall pattern of saltatory growth (i.e. time of pattern onset, frequency of transitions, relative time spent in a state of growth vs. nongrowth). Plots and numeric output are readily imported into other applications. The user has the option to specify criteria for identifying transitions in growth behavior, which extends the potential application of the software to neurons of different types or developmental stage and to other time-series phenomena that exhibit saltatory dynamics. NeuroRhythmics will facilitate mechanistic studies of periodic axonal and dendritic growth in neurons.

Corticonic models of brain mechanisms underlying cognition and intelligence

NASA Astrophysics Data System (ADS)

Farhat, Nabil H.

The concern of this review is brain theory or more specifically, in its first part, a model of the cerebral cortex and the way it: (a) interacts with subcortical regions like the thalamus and the hippocampus to provide higher-level-brain functions that underlie cognition and intelligence, (b) handles and represents dynamical sensory patterns imposed by a constantly changing environment, (c) copes with the enormous number of such patterns encountered in a lifetime by means of dynamic memory that offers an immense number of stimulus-specific attractors for input patterns (stimuli) to select from, (d) selects an attractor through a process of “conjugation” of the input pattern with the dynamics of the thalamo-cortical loop, (e) distinguishes between redundant (structured) and non-redundant (random) inputs that are void of information, (f) can do categorical perception when there is access to vast associative memory laid out in the association cortex with the help of the hippocampus, and (g) makes use of “computation” at the edge of chaos and information driven annealing to achieve all this. Other features and implications of the concepts presented for the design of computational algorithms and machines with brain-like intelligence are also discussed. The material and results presented suggest, that a Parametrically Coupled Logistic Map network (PCLMN) is a minimal model of the thalamo-cortical complex and that marrying such a network to a suitable associative memory with re-entry or feedback forms a useful, albeit, abstract model of a cortical module of the brain that could facilitate building a simple artificial brain. In the second part of the review, the results of numerical simulations and drawn conclusions in the first part are linked to the most directly relevant works and views of other workers. What emerges is a picture of brain dynamics on the mesoscopic and macroscopic scales that gives a glimpse of the nature of the long sought after brain code underlying intelligence and other higher level brain functions.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

PubMed

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

PubMed Central

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert. PMID:27764203

Ecosystem functional assessment based on the "optical type" concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

NASA Astrophysics Data System (ADS)

Huesca, Margarita; Merino-de-Miguel, Silvia; Eklundh, Lars; Litago, Javier; Cicuéndez, Victor; Rodríguez-Rastrero, Manuel; Ustin, Susan L.; Palacios-Orueta, Alicia

2015-12-01

Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of "optical types" (OT), meaning "optically distinguishable functional types", as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation. This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found. Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

PubMed

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Social mixing in Fiji: Who-eats-with-whom contact patterns and the implications of age and ethnic heterogeneity for disease dynamics in the Pacific Islands.

PubMed

Watson, Conall H; Coriakula, Jeremaia; Ngoc, Dung Tran Thi; Flasche, Stefan; Kucharski, Adam J; Lau, Colleen L; Thieu, Nga Tran Vu; le Polain de Waroux, Olivier; Rawalai, Kitione; Van, Tan Trinh; Taufa, Mere; Baker, Stephen; Nilles, Eric J; Kama, Mike; Edmunds, W John

2017-01-01

Empirical data on contact patterns can inform dynamic models of infectious disease transmission. Such information has not been widely reported from Pacific islands, nor strongly multi-ethnic settings, and few attempts have been made to quantify contact patterns relevant for the spread of gastrointestinal infections. As part of enteric fever investigations, we conducted a cross-sectional survey of the general public in Fiji, finding that within the 9,650 mealtime contacts reported by 1,814 participants, there was strong like-with-like mixing by age and ethnicity, with higher contact rates amongst iTaukei than non-iTaukei Fijians. Extra-domiciliary lunchtime contacts follow these mixing patterns, indicating the overall data do not simply reflect household structures. Inter-ethnic mixing was most common amongst school-age children. Serological responses indicative of recent Salmonella Typhi infection were found to be associated, after adjusting for age, with increased contact rates between meal-sharing iTaukei, with no association observed for other contact groups. Animal ownership and travel within the geographical division were common. These are novel data that identify ethnicity as an important social mixing variable, and use retrospective mealtime contacts as a socially acceptable metric of relevance to enteric, contact and respiratory diseases that can be collected in a single visit to participants. Application of these data to other island settings will enable communicable disease models to incorporate locally relevant mixing patterns in parameterisation.

Social mixing in Fiji: Who-eats-with-whom contact patterns and the implications of age and ethnic heterogeneity for disease dynamics in the Pacific Islands

PubMed Central

Coriakula, Jeremaia; Ngoc, Dung Tran Thi; Flasche, Stefan; Kucharski, Adam J.; Lau, Colleen L.; Thieu, Nga Tran Vu; le Polain de Waroux, Olivier; Rawalai, Kitione; Van, Tan Trinh; Taufa, Mere; Baker, Stephen; Nilles, Eric J.; Kama, Mike; Edmunds, W. John

2017-01-01

Empirical data on contact patterns can inform dynamic models of infectious disease transmission. Such information has not been widely reported from Pacific islands, nor strongly multi-ethnic settings, and few attempts have been made to quantify contact patterns relevant for the spread of gastrointestinal infections. As part of enteric fever investigations, we conducted a cross-sectional survey of the general public in Fiji, finding that within the 9,650 mealtime contacts reported by 1,814 participants, there was strong like-with-like mixing by age and ethnicity, with higher contact rates amongst iTaukei than non-iTaukei Fijians. Extra-domiciliary lunchtime contacts follow these mixing patterns, indicating the overall data do not simply reflect household structures. Inter-ethnic mixing was most common amongst school-age children. Serological responses indicative of recent Salmonella Typhi infection were found to be associated, after adjusting for age, with increased contact rates between meal-sharing iTaukei, with no association observed for other contact groups. Animal ownership and travel within the geographical division were common. These are novel data that identify ethnicity as an important social mixing variable, and use retrospective mealtime contacts as a socially acceptable metric of relevance to enteric, contact and respiratory diseases that can be collected in a single visit to participants. Application of these data to other island settings will enable communicable disease models to incorporate locally relevant mixing patterns in parameterisation. PMID:29211731

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Uncovering the transmission dynamics of Plasmodium vivax using population genetics

PubMed Central

Barry, Alyssa E.; Waltmann, Andreea; Koepfli, Cristian; Barnadas, Celine; Mueller, Ivo

2015-01-01

Population genetic analysis of malaria parasites has the power to reveal key insights into malaria epidemiology and transmission dynamics with the potential to deliver tools to support control and elimination efforts. Analyses of parasite genetic diversity have suggested that Plasmodium vivax populations are more genetically diverse and less structured than those of Plasmodium falciparum indicating that P. vivax may be a more ancient parasite of humans and/or less susceptible to population bottlenecks, as well as more efficient at disseminating its genes. These population genetic insights into P. vivax transmission dynamics provide an explanation for its relative resilience to control efforts. Here, we describe current knowledge on P. vivax population genetic structure, its relevance to understanding transmission patterns and relapse and how this information can inform malaria control and elimination programmes. PMID:25891915

Movement Patterns, Social Dynamics, and the Evolution of Cooperation

PubMed Central

Smaldino, Paul E.; Schank, Jeffrey C.

2012-01-01

The structure of social interactions influences many aspects of social life, including the spread of information and behavior, and the evolution of social phenotypes. After dispersal, organisms move around throughout their lives, and the patterns of their movement influence their social encounters over the course of their lifespan. Though both space and mobility are known to influence social evolution, there is little analysis of the influence of specific movement patterns on evolutionary dynamics. We explored the effects of random movement strategies on the evolution of cooperation using an agent-based prisoner’s dilemma model with mobile agents. This is the first systematic analysis of a model in which cooperators and defectors can use different random movement strategies, which we chose to fall on a spectrum between highly exploratory and highly restricted in their search tendencies. Because limited dispersal and restrictions to local neighborhood size are known to influence the ability of cooperators to effectively assort, we also assessed the robustness of our findings with respect to dispersal and local capacity constraints. We show that differences in patterns of movement can dramatically influence the likelihood of cooperator success, and that the effects of different movement patterns are sensitive to environmental assumptions about offspring dispersal and local space constraints. Since local interactions implicitly generate dynamic social interaction networks, we also measured the average number of unique and total interactions over a lifetime and considered how these emergent network dynamics helped explain the results. This work extends what is known about mobility and the evolution of cooperation, and also has general implications for social models with randomly moving agents. PMID:22838026

Cell Patterns Emerge from Coupled Chemical and Physical Fields with Cell Proliferation Dynamics: The Arabidopsis thaliana Root as a Study System

PubMed Central

Barrio, Rafael A.; Romero-Arias, José Roberto; Noguez, Marco A.; Azpeitia, Eugenio; Ortiz-Gutiérrez, Elizabeth; Hernández-Hernández, Valeria; Cortes-Poza, Yuriria; Álvarez-Buylla, Elena R.

2013-01-01

A central issue in developmental biology is to uncover the mechanisms by which stem cells maintain their capacity to regenerate, yet at the same time produce daughter cells that differentiate and attain their ultimate fate as a functional part of a tissue or an organ. In this paper we propose that, during development, cells within growing organs obtain positional information from a macroscopic physical field that is produced in space while cells are proliferating. This dynamical interaction triggers and responds to chemical and genetic processes that are specific to each biological system. We chose the root apical meristem of Arabidopsis thaliana to develop our dynamical model because this system is well studied at the molecular, genetic and cellular levels and has the key traits of multicellular stem-cell niches. We built a dynamical model that couples fundamental molecular mechanisms of the cell cycle to a tension physical field and to auxin dynamics, both of which are known to play a role in root development. We perform extensive numerical calculations that allow for quantitative comparison with experimental measurements that consider the cellular patterns at the root tip. Our model recovers, as an emergent pattern, the transition from proliferative to transition and elongation domains, characteristic of stem-cell niches in multicellular organisms. In addition, we successfully predict altered cellular patterns that are expected under various applied auxin treatments or modified physical growth conditions. Our modeling platform may be extended to explicitly consider gene regulatory networks or to treat other developmental systems. PMID:23658505

The Neuronal Transition Probability (NTP) Model for the Dynamic Progression of Non-REM Sleep EEG: The Role of the Suprachiasmatic Nucleus

PubMed Central

Merica, Helli; Fortune, Ronald D.

2011-01-01

Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) – in fitting the data well – successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN) activity we show that the SCN has the information required to provide a threshold-triggered flip-flop for timing the towards-and-away alternations, information provided by sleep-relevant feedback to the SCN. NTP then determines the pattern of spectral power within each dynamic-state. NTP was fitted to individual NREM episodes 1–4, using data from 30 healthy subjects aged 20–30 years, and the quality of fit for each NREM measured. We show that the model fits well all NREM episodes and the best-fit probability-set is found to be effectively the same in fitting all subject data. The significant model-data agreement, the constant probability parameter and the proposed role of the SCN add considerable strength to the model. With it we link for the first time findings at cellular level and detailed time-course data at EEG level, to give a coherent picture of NREM dynamics over the entire night and over hierarchic brain levels all the way from the SCN to the EEG. PMID:21886801

Study on Dynamic Development of Three-dimensional Weld Pool Surface in Stationary GTAW

NASA Astrophysics Data System (ADS)

Huang, Jiankang; He, Jing; He, Xiaoying; Shi, Yu; Fan, Ding

2018-04-01

The weld pool contains abundant information about the welding process. In particular, the type of the weld pool surface shape, i. e., convex or concave, is determined by the weld penetration. To detect it, an innovative laser-vision-based sensing method is employed to observe the weld pool surface of the gas tungsten arc welding (GTAW). A low-power laser dots pattern is projected onto the entire weld pool surface. Its reflection is intercepted by a screen and captured by a camera. Then the dynamic development process of the weld pool surface can be detected. By observing and analyzing, the change of the reflected laser dots reflection pattern, for shape of the weld pool surface shape, was found to closely correlate to the penetration of weld pool in the welding process. A mathematical model was proposed to correlate the incident ray, reflected ray, screen and surface of weld pool based on structured laser specular reflection. The dynamic variation of the weld pool surface and its corresponding dots laser pattern were simulated and analyzed. By combining the experimental data and the mathematical analysis, the results show that the pattern of the reflected laser dots pattern is closely correlated to the development of weld pool, such as the weld penetration. The concavity of the pool surface was found to increase rapidly after the surface shape was changed from convex to concave during the stationary GTAW process.

Predicting atmospheric states from local dynamical properties of the underlying attractor

NASA Astrophysics Data System (ADS)

Faranda, Davide; Rodrigues, David; Alvarez-Castro, M. Carmen; Messori, Gabriele; Yiou, Pascal

2017-04-01

Mid-latitude flows are characterized by a chaotic dynamics and recurring patterns hinting to the existence of an atmospheric attractor. In 1963 Lorenz described this object as: "the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid" and analyzed a low dimensional system describing a convective dynamics whose attractor has the shape of a butterfly. Since then, many studies try to find equivalent of the Lorenz butterfly in the complex atmospheric dynamics. Most of the studies where focused to determine the average dimension D of the attractor i.e. the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of D has proved challenging. Moreover, D does not provide information on transient atmospheric motions, such as those leading to weather extremes. Using recent developments in dynamical systems theory, we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at lead times of over two weeks. Instantaneous properties of the attractor therefore provide an efficient way of evaluating and informing operational weather forecasts.

Human systems dynamics: Toward a computational model

NASA Astrophysics Data System (ADS)

Eoyang, Glenda H.

2012-09-01

A robust and reliable computational model of complex human systems dynamics could support advancements in theory and practice for social systems at all levels, from intrapersonal experience to global politics and economics. Models of human interactions have evolved from traditional, Newtonian systems assumptions, which served a variety of practical and theoretical needs of the past. Another class of models has been inspired and informed by models and methods from nonlinear dynamics, chaos, and complexity science. None of the existing models, however, is able to represent the open, high dimension, and nonlinear self-organizing dynamics of social systems. An effective model will represent interactions at multiple levels to generate emergent patterns of social and political life of individuals and groups. Existing models and modeling methods are considered and assessed against characteristic pattern-forming processes in observed and experienced phenomena of human systems. A conceptual model, CDE Model, based on the conditions for self-organizing in human systems, is explored as an alternative to existing models and methods. While the new model overcomes the limitations of previous models, it also provides an explanatory base and foundation for prospective analysis to inform real-time meaning making and action taking in response to complex conditions in the real world. An invitation is extended to readers to engage in developing a computational model that incorporates the assumptions, meta-variables, and relationships of this open, high dimension, and nonlinear conceptual model of the complex dynamics of human systems.

New Perspectives: Wave Mechanical Interpretations of Dark Matter, Baryon and Dark Energy

NASA Astrophysics Data System (ADS)

Russell, Esra

We model the cosmic components: dark matter, dark energy and baryon distributions in the Cosmic Web by means of highly nonlinear Schrodinger type and reaction diffusion type wave mechanical descriptions. The construction of these wave mechanical models of the structure formation is achieved by introducing the Fisher information measure and its comparison with highly nonlinear term which has dynamical analogy to infamous quantum potential in the wave equations. Strikingly, the comparison of this nonlinear term and the Fisher information measure provides a dynamical distinction between lack of self-organization and self-organization in the dynamical evolution of the cosmic components. Mathematically equivalent to the standard cosmic fluid equations, these approaches make it possible to follow the evolution of the matter distribution even into the highly nonlinear regime by circumventing singularities. Also, numerical realizations of the emerging web-like patterns are presented from the nonlinear dynamics of the baryon component while dark energy component shows Gaussian type dynamics corresponding to soliton-like solutions.

Examining inter-family differences in intra-family (parent-adolescent) dynamics using grid-sequence analysis.

PubMed

Brinberg, Miriam; Fosco, Gregory M; Ram, Nilam

2017-12-01

Family systems theorists have forwarded a set of theoretical principles meant to guide family scientists and practitioners in their conceptualization of patterns of family interaction-intra-family dynamics-that, over time, give rise to family and individual dysfunction and/or adaptation. In this article, we present an analytic approach that merges state space grid methods adapted from the dynamic systems literature with sequence analysis methods adapted from molecular biology into a "grid-sequence" method for studying inter-family differences in intra-family dynamics. Using dyadic data from 86 parent-adolescent dyads who provided up to 21 daily reports about connectedness, we illustrate how grid-sequence analysis can be used to identify a typology of intrafamily dynamics and to inform theory about how specific types of intrafamily dynamics contribute to adolescent behavior problems and family members' mental health. Methodologically, grid-sequence analysis extends the toolbox of techniques for analysis of family experience sampling and daily diary data. Substantively, we identify patterns of family level microdynamics that may serve as new markers of risk/protective factors and potential points for intervention in families. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

PubMed Central

Kim, Yongsoo; Kim, Taek-Kyun; Kim, Yungu; Yoo, Jiho; You, Sungyong; Lee, Inyoul; Carlson, George; Hood, Leroy; Choi, Seungjin; Hwang, Daehee

2011-01-01

Motivation: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. Results: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. Availability: The web-based software is available at: http://sbm.postech.ac.kr/pna. Contact: dhhwang@postech.ac.kr; seungjin@postech.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21193522

Rotation-invariant image and video description with local binary pattern features.

PubMed

Zhao, Guoying; Ahonen, Timo; Matas, Jiří; Pietikäinen, Matti

2012-04-01

In this paper, we propose a novel approach to compute rotation-invariant features from histograms of local noninvariant patterns. We apply this approach to both static and dynamic local binary pattern (LBP) descriptors. For static-texture description, we present LBP histogram Fourier (LBP-HF) features, and for dynamic-texture recognition, we present two rotation-invariant descriptors computed from the LBPs from three orthogonal planes (LBP-TOP) features in the spatiotemporal domain. LBP-HF is a novel rotation-invariant image descriptor computed from discrete Fourier transforms of LBP histograms. The approach can be also generalized to embed any uniform features into this framework, and combining the supplementary information, e.g., sign and magnitude components of the LBP, together can improve the description ability. Moreover, two variants of rotation-invariant descriptors are proposed to the LBP-TOP, which is an effective descriptor for dynamic-texture recognition, as shown by its recent success in different application problems, but it is not rotation invariant. In the experiments, it is shown that the LBP-HF and its extensions outperform noninvariant and earlier versions of the rotation-invariant LBP in the rotation-invariant texture classification. In experiments on two dynamic-texture databases with rotations or view variations, the proposed video features can effectively deal with rotation variations of dynamic textures (DTs). They also are robust with respect to changes in viewpoint, outperforming recent methods proposed for view-invariant recognition of DTs.

Heritable stress response dynamics revealed by single-cell genealogy

PubMed Central

2018-01-01

Cells often respond to environmental stimuli by activating specific transcription factors. Upon exposure to glucose limitation stress, it is known that yeast Saccharomyces cerevisiae cells dephosphorylate the general stress response factor Msn2, leading to its nuclear localization, which in turn activates the expression of many genes. However, the precise dynamics of Msn2 nucleocytoplasmic translocations and whether they are inherited over multiple generations in a stress-dependent manner are not well understood. Tracking Msn2 localization events in yeast lineages grown on a microfluidic chip, here we report how cells modulate the amplitude, duration, frequency, and dynamic pattern of the localization events in response to glucose limitation stress. Single yeast cells were found to modulate the amplitude and frequency of Msn2 nuclear localization, but not its duration. Moreover, the Msn2 localization frequency was epigenetically inherited in descendants of mother cells, leading to a decrease in cell-to-cell variation in localization frequency. An analysis of the time dynamic patterns of nuclear localizations between genealogically related cell pairs using an information theory approach found that the magnitude of pattern similarity increased with stress intensity and was strongly inherited by the descendant cells at the highest stress level. By dissecting how general stress response dynamics is contributed by different modulation schemes over long time scales, our work provides insight into which scheme evolution might have acted on to optimize fitness in stressful environments. PMID:29675464

HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition.

PubMed

Lagorce, Xavier; Orchard, Garrick; Galluppi, Francesco; Shi, Bertram E; Benosman, Ryad B

2017-07-01

This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.

New digital anti-copy/scan and verification technologies

NASA Astrophysics Data System (ADS)

Phillips, George K.

2004-06-01

This white paper reviews the method for making bearer printed information indistinguishable on a non-copyable substrate when a copied attempt is made on either an analog or digital electrostatic photocopier device. In 1995 we received patent number 5,704,651 for a non-copyable technology trademarked MetallicSafe. In this patent the abstract describes the usage of a reflective layer, formed on a complex pattern region and having graphic or font size shapes and type coordinating to particular patterns in the complex pattern region. The technology used in this patent has now been improved and evolved to new methods of creating a non-copyable substrate trademarked CopySafe+. CopySafe+ is formed of a metallic specular light reflector, a white camouflaged diffused light reflector, and the content information 'light absorption' layer. The synthesizing of these layers on a substrate creates dynamic camouflaged interference patterns and the phenomena of image chaos on a copy. In short, the orientation of a plurality of spectral and diffused light reflection camouflaged layers, mixed and coordinated with light absorption printed information, inhibits the copying device from reproducing the printed content.

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics.

PubMed

Ramanathan, Arvind; Pullum, Laura L; Hobson, Tanner C; Steed, Chad A; Quinn, Shannon P; Chennubhotla, Chakra S; Valkova, Silvia

2015-01-01

The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

PubMed Central

2011-01-01

Background The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. Methods We considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects. Results We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic. Conclusions These results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics. Please see related article BMC Medicine, 2011, 9:88 PMID:21771290

Dynamic semantic cognition: Characterising coherent and controlled conceptual retrieval through time using magnetoencephalography and chronometric transcranial magnetic stimulation.

PubMed

Teige, Catarina; Mollo, Giovanna; Millman, Rebecca; Savill, Nicola; Smallwood, Jonathan; Cornelissen, Piers L; Jefferies, Elizabeth

2018-06-01

Distinct neural processes are thought to support the retrieval of semantic information that is (i) coherent with strongly-encoded aspects of knowledge, and (ii) non-dominant yet relevant for the current task or context. While the brain regions that support readily coherent and more controlled patterns of semantic retrieval are relatively well-characterised, the temporal dynamics of these processes are not well-understood. This study used magnetoencephalography (MEG) and dual-pulse chronometric transcranial magnetic stimulation (cTMS) in two separate experiments to examine temporal dynamics during the retrieval of strong and weak associations. MEG results revealed a dissociation within left temporal cortex: anterior temporal lobe (ATL) showed greater oscillatory response for strong than weak associations, while posterior middle temporal gyrus (pMTG) showed the reverse pattern. Left inferior frontal gyrus (IFG), a site associated with semantic control and retrieval, showed both patterns at different time points. In the cTMS experiment, stimulation of ATL at ∼150 msec disrupted the efficient retrieval of strong associations, indicating a necessary role for ATL in coherent conceptual activations. Stimulation of pMTG at the onset of the second word disrupted the retrieval of weak associations, suggesting this site may maintain information about semantic context from the first word, allowing efficient engagement of semantic control. Together these studies provide converging evidence for a functional dissociation within the temporal lobe, across both tasks and time. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Flower Development as an Interplay between Dynamical Physical Fields and Genetic Networks

PubMed Central

Barrio, Rafael Ángel; Hernández-Machado, Aurora; Varea, C.; Romero-Arias, José Roberto; Álvarez-Buylla, Elena

2010-01-01

In this paper we propose a model to describe the mechanisms by which undifferentiated cells attain gene configurations underlying cell fate determination during morphogenesis. Despite the complicated mechanisms that surely intervene in this process, it is clear that the fundamental fact is that cells obtain spatial and temporal information that bias their destiny. Our main hypothesis assumes that there is at least one macroscopic field that breaks the symmetry of space at a given time. This field provides the information required for the process of cell differentiation to occur by being dynamically coupled to a signal transduction mechanism that, in turn, acts directly upon the gene regulatory network (GRN) underlying cell-fate decisions within cells. We illustrate and test our proposal with a GRN model grounded on experimental data for cell fate specification during organ formation in early Arabidopsis thaliana flower development. We show that our model is able to recover the multigene configurations characteristic of sepal, petal, stamen and carpel primordial cells arranged in concentric rings, in a similar pattern to that observed during actual floral organ determination. Such pattern is robust to alterations of the model parameters and simulated failures predict altered spatio-temporal patterns that mimic those described for several mutants. Furthermore, simulated alterations in the physical fields predict a pattern equivalent to that found in Lacandonia schismatica, the only flowering species with central stamens surrounded by carpels. PMID:21048956

Flower development as an interplay between dynamical physical fields and genetic networks.

PubMed

Barrio, Rafael Ángel; Hernández-Machado, Aurora; Varea, C; Romero-Arias, José Roberto; Alvarez-Buylla, Elena

2010-10-27

In this paper we propose a model to describe the mechanisms by which undifferentiated cells attain gene configurations underlying cell fate determination during morphogenesis. Despite the complicated mechanisms that surely intervene in this process, it is clear that the fundamental fact is that cells obtain spatial and temporal information that bias their destiny. Our main hypothesis assumes that there is at least one macroscopic field that breaks the symmetry of space at a given time. This field provides the information required for the process of cell differentiation to occur by being dynamically coupled to a signal transduction mechanism that, in turn, acts directly upon the gene regulatory network (GRN) underlying cell-fate decisions within cells. We illustrate and test our proposal with a GRN model grounded on experimental data for cell fate specification during organ formation in early Arabidopsis thaliana flower development. We show that our model is able to recover the multigene configurations characteristic of sepal, petal, stamen and carpel primordial cells arranged in concentric rings, in a similar pattern to that observed during actual floral organ determination. Such pattern is robust to alterations of the model parameters and simulated failures predict altered spatio-temporal patterns that mimic those described for several mutants. Furthermore, simulated alterations in the physical fields predict a pattern equivalent to that found in Lacandonia schismatica, the only flowering species with central stamens surrounded by carpels.

Using big data to map the network organization of the brain.

PubMed

Swain, James E; Sripada, Chandra; Swain, John D

2014-02-01

The past few years have shown a major rise in network analysis of "big data" sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension - individuality versus sociality - might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

Using big data to map the network organization of the brain

PubMed Central

Swain, James E.; Sripada, Chandra; Swain, John D.

2015-01-01

The past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization. PMID:24572243

RS- and GIS-based study on landscape pattern change in the Poyang Lake wetland area, China

NASA Astrophysics Data System (ADS)

Chen, Xiaoling; Li, Hui; Bao, Shuming; Wu, Zhongyi; Fu, Weijuan; Cai, Xiaobin; Zhao, Hongmei; Guo, Peng

2006-10-01

As wetland has been recognized as an important component of ecosystem, it is received ever-increasing attention worldwide. Poyang Lake wetlands, the international wetlands and the largest bird habitat in Asia, play an important role in biodiversity and ecologic protection. However, with the rapid economic growth and urbanization, landscape patterns in the wetlands have dramatically changed in the past three decades. To better understand the wetland landscape dynamics, remote sensing, geographic information system technologies, and the FRAGSTATS landscape analysis program were used to measure landscape patterns. Statistical approach was employed to illustrate the driving forces. In this study, Landsat images (TM and ETM+) from 1989 and 2000 were acquired for the wetland area. The landscapes in the wetland area were classified as agricultural land, urban, wetland, forest, grassland, unused land, and water body using a combination of supervised and unsupervised classification techniques integrated with Digital Elevation Model (DEM). Landscape indices, which are popular for the quantitative analysis of landscape pattern, were then employed to analyze the landscape pattern changes between the two dates in a GIS. From this analysis an understanding of the spatial-temporal patterns of landscape evolution was generated. The results show that wetland area was reduced while fragmentation was increased over the study period. Further investigation was made to examine the relationship between landscape metrics and some other parameters such as urbanization to address the driving forces for those changes. The urban was chosen as center to conduct buffer analysis in a GIS to study the impact of human-induced activities on landscape pattern dynamics. It was found that the selected parameters were significantly correlated with the landscape metrics, which may well indicate the impact of human-induced activities on the wetland landscape pattern dynamics and account for the driving forces.

Quantification of brain macrostates using dynamical nonstationarity of physiological time series.

PubMed

Latchoumane, Charles-Francois Vincent; Jeong, Jaeseung

2011-04-01

The brain shows complex, nonstationarity temporal dynamics, with abrupt micro- and macrostate transitions during its information processing. Detecting and characterizing these transitions in dynamical states of the brain is a critical issue in the field of neuroscience and psychiatry. In the current study, a novel method is proposed to quantify brain macrostates (e.g., sleep stages or cognitive states) from shifts of dynamical microstates or dynamical nonstationarity. A ``dynamical microstate'' is a temporal unit of the information processing in the brain with fixed dynamical parameters and specific spatial distribution. In this proposed approach, a phase-space-based dynamical dissimilarity map (DDM) is used to detect transitions between dynamically stationary microstates in the time series, and Tsallis time-dependent entropy is applied to quantify dynamical patterns of transitions in the DDM. We demonstrate that the DDM successfully detects transitions between microstates of different temporal dynamics in the simulated physiological time series against high levels of noise. Based on the assumption of nonlinear, deterministic brain dynamics, we also demonstrate that dynamical nonstationarity analysis is useful to quantify brain macrostates (sleep stages I, II, III, IV, and rapid eye movement (REM) sleep) from sleep EEGs with an overall accuracy of 77%. We suggest that dynamical nonstationarity is a useful tool to quantify macroscopic mental states (statistical integration) of the brain using dynamical transitions at the microscopic scale in physiological data.

Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

NASA Astrophysics Data System (ADS)

Aoyagi, Toshio; Nomura, Masaki

1999-08-01

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

Emotional First Aid: Crisis Development and Systems of Intervention.

ERIC Educational Resources Information Center

Rosenbluh, Edward S.; And Others

This instructional manual takes a developmental approach toward understanding the psychological, social and behavioral dynamics of human crisis. The manual describes the behavior patterns characterizing various psychological and physical crises, and provides background information and methods of crisis intervention with which to manage each. In…

Patterns in stable isotope ratios of particulate material from the eastern US continental shelf

EPA Science Inventory

Stable isotope measurements of nitrogen and carbon (δ15N, δ13C) in estuarine, nearshore, and open ocean ecosystems are often utilized in order to characterize human influences, elucidate food web dynamics, or better understand nitrogen cycling. Reliable information a...

Automating Network Node Behavior Characterization by Mining Communication Patterns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carroll, Thomas E.; Chikkagoudar, Satish; Arthur-Durett, Kristine M.

Enterprise networks of scale are complex, dynamic computing environments that respond to evolv- ing business objectives and requirements. Characteriz- ing system behaviors in these environments is essential for network management and cyber security operations. Characterization of system’s communication is typical and is supported using network flow information (NetFlow). Related work has characterized behavior using theoretical graph metrics; results are often difficult to interpret by enterprise staff. We propose a different approach, where flow information is mapped to sets of tags that contextualize the data in terms of network principals and enterprise concepts. Frequent patterns are then extracted and are expressedmore » as behaviors. Behaviors can be com- pared, identifying systems expressing similar behaviors. We evaluate the approach using flow information collected by a third party.« less

Evol and ProDy for bridging protein sequence evolution and structural dynamics.

PubMed

Bakan, Ahmet; Dutta, Anindita; Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R; Bahar, Ivet

2014-09-15

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Detecting Deception in Movement: The Case of the Side-Step in Rugby

PubMed Central

Brault, Sébastien; Bideau, Benoit; Kulpa, Richard; Craig, Cathy M.

2012-01-01

Although coordinated patterns of body movement can be used to communicate action intention, they can also be used to deceive. Often known as deceptive movements, these unpredictable patterns of body movement can give a competitive advantage to an attacker when trying to outwit a defender. In this particular study, we immersed novice and expert rugby players in an interactive virtual rugby environment to understand how the dynamics of deceptive body movement influence a defending player’s decisions about how and when to act. When asked to judge final running direction, expert players who were found to tune into prospective tau-based information specified in the dynamics of ‘honest’ movement signals (Centre of Mass), performed significantly better than novices who tuned into the dynamics of ‘deceptive’ movement signals (upper trunk yaw and out-foot placement) (p<.001). These findings were further corroborated in a second experiment where players were able to move as if to intercept or ‘tackle’ the virtual attacker. An analysis of action responses showed that experts waited significantly longer before initiating movement (p<.001). By waiting longer and picking up more information that would inform about future running direction these experts made significantly fewer errors (p<.05). In this paper we not only present a mathematical model that describes how deception in body-based movement is detected, but we also show how perceptual expertise is manifested in action expertise. We conclude that being able to tune into the ‘honest’ information specifying true running action intention gives a strong competitive advantage. PMID:22701569

Dynamic model updating based on strain mode shape and natural frequency using hybrid pattern search technique

NASA Astrophysics Data System (ADS)

Guo, Ning; Yang, Zhichun; Wang, Le; Ouyang, Yan; Zhang, Xinping

2018-05-01

Aiming at providing a precise dynamic structural finite element (FE) model for dynamic strength evaluation in addition to dynamic analysis. A dynamic FE model updating method is presented to correct the uncertain parameters of the FE model of a structure using strain mode shapes and natural frequencies. The strain mode shape, which is sensitive to local changes in structure, is used instead of the displacement mode for enhancing model updating. The coordinate strain modal assurance criterion is developed to evaluate the correlation level at each coordinate over the experimental and the analytical strain mode shapes. Moreover, the natural frequencies which provide the global information of the structure are used to guarantee the accuracy of modal properties of the global model. Then, the weighted summation of the natural frequency residual and the coordinate strain modal assurance criterion residual is used as the objective function in the proposed dynamic FE model updating procedure. The hybrid genetic/pattern-search optimization algorithm is adopted to perform the dynamic FE model updating procedure. Numerical simulation and model updating experiment for a clamped-clamped beam are performed to validate the feasibility and effectiveness of the present method. The results show that the proposed method can be used to update the uncertain parameters with good robustness. And the updated dynamic FE model of the beam structure, which can correctly predict both the natural frequencies and the local dynamic strains, is reliable for the following dynamic analysis and dynamic strength evaluation.

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning

PubMed Central

Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-01-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. Here we show that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) are dynamically reformatted in the network at the timescale of a single breath, giving rise to separated patterns of activity in ensemble of output neurons (mitral/tufted cells; M/T). Strikingly, the extent of pattern separation in M/T assemblies predicts behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimuli distinction, a process that is sculpted by synaptic inhibition. PMID:26301325

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning.

PubMed

Gschwend, Olivier; Abraham, Nixon M; Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-10-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features, thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. We found that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) were dynamically reformatted in the network on the timescale of a single breath, giving rise to separated patterns of activity in an ensemble of output neurons, mitral/tufted (M/T) cells. Notably, the extent of pattern separation in M/T assemblies predicted behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimulus distinction, a process that is sculpted by synaptic inhibition.

Coupling of replisome movement with nucleosome dynamics can contribute to the parent-daughter information transfer.

PubMed

Bameta, Tripti; Das, Dibyendu; Padinhateeri, Ranjith

2018-06-01

Positioning of nucleosomes along the genomic DNA is crucial for many cellular processes that include gene regulation and higher order packaging of chromatin. The question of how nucleosome-positioning information from a parent chromatin gets transferred to the daughter chromatin is highly intriguing. Accounting for experimentally known coupling between replisome movement and nucleosome dynamics, we propose a model that can obtain de novo nucleosome assembly similar to what is observed in recent experiments. Simulating nucleosome dynamics during replication, we argue that short pausing of the replication fork, associated with nucleosome disassembly, can be a event crucial for communicating nucleosome positioning information from parent to daughter. We show that the interplay of timescales between nucleosome disassembly (τp) at the replication fork and nucleosome sliding behind the fork (τs) can give rise to a rich 'phase diagram' having different inherited patterns of nucleosome organization. Our model predicts that only when τp ≥ τs the daughter chromatin can inherit nucleosome positioning of the parent.

Secure remote synchronization and secure key distribution in electro-optic networks revealed by symmetries

NASA Astrophysics Data System (ADS)

Xu, Mingfeng; Pan, Wei; Zhang, Liyue

2018-07-01

Despite the intuition that synchronization of different nodes in coupled oscillator networks results from information exchange between them, it has recently been shown that remote nodes could be partially synchronous even when they are separated by intermediately unsynchronized nodes. Here based on electro-optic system, we report on a more stronger form of such synchronization pattern that is termed as secure remote synchronization, in which two remotely separated nodes could have identically synchronized dynamical behaviors while the rest of the network are both statistically and information-theoretically incoherent relative to the two synchronized nodes. The generalized form of mirror symmetry in the network structure is identified to be a key mechanism allowing for secure remote synchronization. Moreover, this synchronization mode is robust against a wild range of system parameters and noise perturbing the intermediary dynamics. The lack of information about the synchronized dynamics in the rest of the network suggests that our results could potentially lead to network-based solutions for secure key distribution and secure communication.

Reorganization in Semantic Memory: An Interpretation of the Facilitation Effect

ERIC Educational Resources Information Center

Hopf-Weichel, Rosemarie

1977-01-01

A model is proposed in which information processing is accompanied by dynamic processes, including the reorganization of items into active patterns and their subsequent displacement. Research using category names and instances showed that reaction times decreased with each successive repetition under one condition, but longer latencies were…

Principal Turnover: Upheaval and Uncertainty in Charter Schools?

ERIC Educational Resources Information Center

Ni, Yongmei; Sun, Min; Rorrer, Andrea

2015-01-01

Purpose: Informed by literature on labor market and school choice, this study aims to examine the dynamics of principal career movements in charter schools by comparing principal turnover rates and patterns between charter schools and traditional public schools. Research Methods/Approach: This study uses longitudinal data on Utah principals and…

Reinforced communication and social navigation: Remember your friends and remember yourself

NASA Astrophysics Data System (ADS)

Mirshahvalad, A.; Rosvall, M.

2011-09-01

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples’ ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Hepatopulmonary shunting on Tc99m-MAA liver mapping: correlation with dynamic cross-sectional imaging and description of different shunting patterns.

PubMed

Bermo, Mohammed; Matesan, Manuela C; Itani, Malak; Behnia, Fatemeh; Vesselle, Hubert J

2018-04-09

The purpose of the study was to correlate lung shunt fraction (LSF) calculated by intra-arterial injection of Technetium-99m (Tc-99m)-labeled macroaggregated albumin (MAA) in a hepatic artery branch with the presence of certain patterns of vascular shunts on dynamic CT or MRI of the liver. This retrospective study was approved by the institutional review board and informed consent was waived. We reviewed 523 MAA scans in 453 patients (301 men, 152 women) performed from July 2007 to June 2015 and their correlative cross-sectional imaging. Patterns of vascular shunts on dynamic CT or MRI performed within 3 months of the MAA study and that potentially divert hepatic arterial inflow to the systemic venous return were defined as "target shunts." Dynamic CT or MRI was classified into three groups with target shunt present, absent, or indeterminate. The mean LSF was compared across the first and second groups using paired t test. 342 CT and MRI studies met inclusion criteria: target shunts were present in 63 studies, absent in 271 studies, and 8 studies were indeterminate. When target shunts were visualized, the mean LSF on corresponding MAA scans was 12.9 ± 10.36% (95% CI 10.29-15.15%) compared to 4.3 ± 3.17% (95% CI 3.93-4.68%) when no target shunt was visualized. The difference was statistically significant (p value < 0.001). Identified target shunts were either direct (arteriohepatic venous shunt) or indirect (arterioportal shunt combined with a portosystemic shunt). Visualizing certain patterns of vascular shunting on a dynamic CT or MRI scan is associated with high LSF.

Effects of paddy rice agriculture on the seasonal dynamics of atmospheric methane concentration

NASA Astrophysics Data System (ADS)

Zhang, G.; Xiao, X.; Dong, J.; Zhang, Y.; Xin, F.; Zhou, Y.; Wang, J.; Wu, X.; Moore, B., III

2017-12-01

Methane (CH4) is an important greenhouse gas (GHG) and may account for 20 % of anticipated global warming. The atmospheric CH4 concentration was nearly constant from 1999 to 2006, following with a strong growth resumed since 2007. Previous study attributed the increase in CH4 to agriculture. Specifically, paddy rice agriculture is a significant source of CH4, but large uncertainty still exists on methane emission estimates from rice paddies, largely due to lack of detailed geospatial datasets of rice paddies. In this study, based on a pixel- and phenology-based image analysis system with multi-temporal MODIS imagery (MODIS-RICE), we generated the paddy rice map in 2005 to document the spatiotemporal pattern of paddy rice dynamics in Monsoon Asia, which accounts for more than 90% of the global rice production. Furthermore, we examined the effects of paddy rice agriculture on atmospheric CH4 concentration over Monsoon Asia, by comparing atmospheric CH4 concentration data from SCIAMACHY sensor and the paddy rice maps in 2005. We found a significant spatial consistency between spatial patterns of paddy rice and atmospheric CH4 concentration. Based on the high resolution paddy rice map, different seasonal dynamics of CH4 concentration, including single, double to triple peaks, were found based on the rice paddy distribution information. That suggests paddy rice agriculture contributes substantially to the spatial and seasonal pattern of atmospheric CH4 concentration in Monsoon Asia. This study provides satellite evidence for seasonal cycle of CH4 dynamics at regional scale, and suggests that shifting regime of paddy rice agriculture and cropping intensity could affect the seasonal dynamics and spatial pattern of atmospheric methane concentration.

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

PubMed Central

Vanleer, Ann C; Blanco, Justin A; Wagenaar, Joost B; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-01-01

Objective Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from local field potential spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two dimensional spike patterns during seizures were different from those between seizures. Main results We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state. PMID:26859260

Millimeter-scale epileptiform spike propagation patterns and their relationship to seizures

NASA Astrophysics Data System (ADS)

Vanleer, Ann C.; Blanco, Justin A.; Wagenaar, Joost B.; Viventi, Jonathan; Contreras, Diego; Litt, Brian

2016-04-01

Objective. Current mapping of epileptic networks in patients prior to epilepsy surgery utilizes electrode arrays with sparse spatial sampling (∼1.0 cm inter-electrode spacing). Recent research demonstrates that sub-millimeter, cortical-column-scale domains have a role in seizure generation that may be clinically significant. We use high-resolution, active, flexible surface electrode arrays with 500 μm inter-electrode spacing to explore epileptiform local field potential (LFP) spike propagation patterns in two dimensions recorded from subdural micro-electrocorticographic signals in vivo in cat. In this study, we aimed to develop methods to quantitatively characterize the spatiotemporal dynamics of epileptiform activity at high-resolution. Approach. We topically administered a GABA-antagonist, picrotoxin, to induce acute neocortical epileptiform activity leading up to discrete electrographic seizures. We extracted features from LFP spikes to characterize spatiotemporal patterns in these events. We then tested the hypothesis that two-dimensional spike patterns during seizures were different from those between seizures. Main results. We showed that spatially correlated events can be used to distinguish ictal versus interictal spikes. Significance. We conclude that sub-millimeter-scale spatiotemporal spike patterns reveal network dynamics that are invisible to standard clinical recordings and contain information related to seizure-state.

Smart Meter Driven Segmentation: What Your Consumption Says About You

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, A; Rajagopal, R

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploitedmore » for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.« less

Root Water Uptake and Soil Moisture Pattern Dynamics - Capturing Connections, Controls and Causalities

NASA Astrophysics Data System (ADS)

Blume, T.; Heidbuechel, I.; Hassler, S. K.; Simard, S.; Guntner, A.; Stewart, R. D.; Weiler, M.

2015-12-01

We hypothesize that there is a shift in controls on landscape scale soil moisture patterns when plants become active during the growing season. Especially during the summer soil moisture patterns are not only controlled by soils, topography and related abiotic site characteristics but also by root water uptake. Root water uptake influences soil moisture patterns both in the lateral and vertical direction. Plant water uptake from different soil depths is estimated based on diurnal fluctuations in soil moisture content and was investigated with a unique setup of 46 field sites in Luxemburg and 15 field sites in Germany. These sites cover a range of geologies, soils, topographic positions and types of vegetation. Vegetation types include pasture, pine forest (young and old) and different deciduous forest stands. Available data at all sites includes information at high temporal resolution from 3-5 soil moisture and soil temperature profiles, matrix potential, piezometers and sapflow sensors as well as standard climate data. At sites with access to a stream, discharge or water level is also recorded. The analysis of soil moisture patterns over time indicates a shift in regime depending on season. Depth profiles of root water uptake show strong differences between different forest stands, with maximum depths ranging between 50 and 200 cm. Temporal dynamics of signal strength within the profile furthermore suggest a locally shifting spatial distribution of root water uptake depending on water availability. We will investigate temporal thresholds (under which conditions spatial patterns of root water uptake become most distinct) as well as landscape controls on soil moisture and root water uptake dynamics.

Quantitative characterization of the regressive ecological succession by fractal analysis of plant spatial patterns

USGS Publications Warehouse

Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.

2003-01-01

We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.

Audio aided electro-tactile perception training for finger posture biofeedback.

PubMed

Vargas, Jose Gonzalez; Yu, Wenwei

2008-01-01

Visual information is one of the prerequisites for most biofeedback studies. The aim of this study is to explore how the usage of an audio aided training helps in the learning process of dynamical electro-tactile perception without any visual feedback. In this research, the electrical simulation patterns associated with the experimenter's finger postures and motions were presented to the subjects. Along with the electrical stimulation patterns 2 different types of information, verbal and audio information on finger postures and motions, were presented to the verbal training subject group (group 1) and audio training subject group (group 2), respectively. The results showed an improvement in the ability to distinguish and memorize electrical stimulation patterns correspondent to finger postures and motions without visual feedback, and with audio tones aid, the learning was faster and the perception became more precise after training. Thus, this study clarified that, as a substitution to visual presentation, auditory information could help effectively in the formation of electro-tactile perception. Further research effort needed to make clear the difference between the visual guided and audio aided training in terms of information compilation, post-training effect and robustness of the perception.

Reconstruction of network topology using status-time-series data

NASA Astrophysics Data System (ADS)

Pandey, Pradumn Kumar; Badarla, Venkataramana

2018-01-01

Uncovering the heterogeneous connection pattern of a networked system from the available status-time-series (STS) data of a dynamical process on the network is of great interest in network science and known as a reverse engineering problem. Dynamical processes on a network are affected by the structure of the network. The dependency between the diffusion dynamics and structure of the network can be utilized to retrieve the connection pattern from the diffusion data. Information of the network structure can help to devise the control of dynamics on the network. In this paper, we consider the problem of network reconstruction from the available status-time-series (STS) data using matrix analysis. The proposed method of network reconstruction from the STS data is tested successfully under susceptible-infected-susceptible (SIS) diffusion dynamics on real-world and computer-generated benchmark networks. High accuracy and efficiency of the proposed reconstruction procedure from the status-time-series data define the novelty of the method. Our proposed method outperforms compressed sensing theory (CST) based method of network reconstruction using STS data. Further, the same procedure of network reconstruction is applied to the weighted networks. The ordering of the edges in the weighted networks is identified with high accuracy.

Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study.

PubMed

Bicho, Estela; Erlhagen, Wolfram; Louro, Luis; e Silva, Eliana Costa

2011-10-01

In this paper we present a model for action preparation and decision making in cooperative tasks that is inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. It implements the coordination of actions and goals among the partners as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of others' motor behavior. The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode task-relevant information about action means, task goals and context in the form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic model of joint action is evaluated in a task in which a robot and a human jointly construct a toy object. We show that the highly context sensitive mapping from action observation onto appropriate complementary actions allows coping with dynamically changing joint action situations. Copyright © 2010 Elsevier B.V. All rights reserved.

Complex amplitude reconstruction for dynamic beam quality M² factor measurement with self-referencing interferometer wavefront sensor.

PubMed

Du, Yongzhao; Fu, Yuqing; Zheng, Lixin

2016-12-20

A real-time complex amplitude reconstruction method for determining the dynamic beam quality M² factor based on a Mach-Zehnder self-referencing interferometer wavefront sensor is developed. By using the proposed complex amplitude reconstruction method, full characterization of the laser beam, including amplitude (intensity profile) and phase information, can be reconstructed from a single interference pattern with the Fourier fringe pattern analysis method in a one-shot measurement. With the reconstructed complex amplitude, the beam fields at any position z along its propagation direction can be obtained by first utilizing the diffraction integral theory. Then the beam quality M² factor of the dynamic beam is calculated according to the specified method of the Standard ISO11146. The feasibility of the proposed method is demonstrated with the theoretical analysis and experiment, including the static and dynamic beam process. The experimental method is simple, fast, and operates without movable parts and is allowed in order to investigate the laser beam in inaccessible conditions using existing methods.

On The Ubiquity of Nonstationary Fluvial Suspended Sediment Dynamics: A Call for Long Term Monitoring and Dynamical Sediment Management Strategies

NASA Astrophysics Data System (ADS)

Gray, A. B.

2017-12-01

Watersheds with sufficient monitoring data have been predominantly found to display nonstationary suspended sediment dynamics, whereby the relationship between suspended sediment concentration and discharge changes over time. Despite the importance of suspended sediment as a keystone of geophysical and biochemical processes, and as a primary mediator of water quality, stationary behavior remains largely assumed in the context of these applications. This study presents an investigation into the time dependent behavior of small mountainous rivers draining the coastal ranges of the western continental US over interannual to interdecadal time scales. Of the 250+ small coastal (drainage area < 2x104 km2) watersheds in this region, only 23 have discharge associated suspended sediment concentration time series with base periods of 10 years or more. Event to interdecadal scale nonstationary suspended sediment dynamics were identified throughout these systems. Temporal patterns of non-stationary behavior provided some evidence for spatial coherence, which may be related to synoptic hydro-metrological patterns and regional scale changes in land use patterns. However, the results also highlight the complex, integrative nature of watershed scale fluvial suspended sediment dynamics. This underscores the need for in-depth, forensic approaches for initial processes identification, which require long term, high resolution monitoring efforts in order to adequately inform management. The societal implications of nonstationary sediment dynamics and their controls were further explored through the case of California, USA, where over 150 impairment listings have resulted in more than 50 sediment TMDLs, only 3 of which are flux based - none of which account for non-stationary behavior.

A Neural Correlate of Predicted and Actual Reward-Value Information in Monkey Pedunculopontine Tegmental and Dorsal Raphe Nucleus during Saccade Tasks

PubMed Central

Okada, Ken-ichi; Nakamura, Kae; Kobayashi, Yasushi

2011-01-01

Dopamine, acetylcholine, and serotonin, the main modulators of the central nervous system, have been proposed to play important roles in the execution of movement, control of several forms of attentional behavior, and reinforcement learning. While the response pattern of midbrain dopaminergic neurons and its specific role in reinforcement learning have been revealed, the role of the other neuromodulators remains rather elusive. Here, we review our recent studies using extracellular recording from neurons in the pedunculopontine tegmental nucleus, where many cholinergic neurons exist, and the dorsal raphe nucleus, where many serotonergic neurons exist, while monkeys performed eye movement tasks to obtain different reward values. The firing patterns of these neurons are often tonic throughout the task period, while dopaminergic neurons exhibited a phasic activity pattern to the task event. The different modulation patterns, together with the activity of dopaminergic neurons, reveal dynamic information processing between these different neuromodulator systems. PMID:22013541

Dynamic texture recognition using local binary patterns with an application to facial expressions.

PubMed

Zhao, Guoying; Pietikäinen, Matti

2007-06-01

Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation.

Pattern-process interactions at alpine treeline in southwest Yukon, Canada

NASA Astrophysics Data System (ADS)

Danby, R.

2011-12-01

Results from an ensemble of studies conduced in southwest Yukon have uncovered a distinct "top-down/bottom-up" interaction at alpine treeline whereby terrain-induced gradients of solar radiation result in fundamental differences in plant-scale biological processes which, in turn, structure vegetation pattern at the landscape scale. Varied insolation creates differences in snow depth and timing of melt, soil temperature, and permafrost on opposing slopes that result in distinct physiological differences in white spruce (Picea glauca), the dominant treeline conifer. Measurement of young individuals indicated that secondary growth and lateral growth was significantly greater on south-facing slopes. Photosynthetic efficiency was reduced in individuals on south-facing slopes, while over-winter damage and mortality was significantly greater. Population-level processes also differed. Dendroecology and repeat photography indicated that treeline advanced on south-facing slopes during the 20th century, but that range expansion was limited on north-facing slopes. These process-related differences appear to be the mechanism for differences in treeline pattern at the landscape scale, including a higher treeline elevation and greater clustering of individuals on south-facing slopes. These results can be used to inform theory on the functional causation of treeline, rationalize differential treeline dynamics observed worldwide, and better inform predictions of future treeline dynamics.

Population-based learning of load balancing policies for a distributed computer system

NASA Technical Reports Server (NTRS)

Mehra, Pankaj; Wah, Benjamin W.

1993-01-01

Effective load-balancing policies use dynamic resource information to schedule tasks in a distributed computer system. We present a novel method for automatically learning such policies. At each site in our system, we use a comparator neural network to predict the relative speedup of an incoming task using only the resource-utilization patterns obtained prior to the task's arrival. Outputs of these comparator networks are broadcast periodically over the distributed system, and the resource schedulers at each site use these values to determine the best site for executing an incoming task. The delays incurred in propagating workload information and tasks from one site to another, as well as the dynamic and unpredictable nature of workloads in multiprogrammed multiprocessors, may cause the workload pattern at the time of execution to differ from patterns prevailing at the times of load-index computation and decision making. Our load-balancing policy accommodates this uncertainty by using certain tunable parameters. We present a population-based machine-learning algorithm that adjusts these parameters in order to achieve high average speedups with respect to local execution. Our results show that our load-balancing policy, when combined with the comparator neural network for workload characterization, is effective in exploiting idle resources in a distributed computer system.

A framework for quantification of groundwater dynamics - concepts and hydro(geo-)logical metrics

NASA Astrophysics Data System (ADS)

Haaf, Ezra; Heudorfer, Benedikt; Stahl, Kerstin; Barthel, Roland

2017-04-01

Fluctuation patterns in groundwater hydrographs are generally assumed to contain information on aquifer characteristics, climate and environmental controls. However, attempts to disentangle this information and map the dominant controls have been few. This is due to the substantial heterogeneity and complexity of groundwater systems, which is reflected in the abundance of morphologies of groundwater time series. To describe the structure and shape of hydrographs, descriptive terms like "slow"/ "fast" or "flashy"/ "inert" are frequently used, which are subjective, irreproducible and limited. This lack of objective and refined concepts limit approaches for regionalization of hydrogeological characteristics as well as our understanding of dominant processes controlling groundwater dynamics. Therefore, we propose a novel framework for groundwater hydrograph characterization in an attempt to categorize morphologies explicitly and quantitatively based on perceptual concepts of aspects of the dynamics. This quantitative framework is inspired by the existing and operational eco-hydrological classification frameworks for streamflow. The need for a new framework for groundwater systems is justified by the fundamental differences between the state variable groundwater head and the flow variable streamflow. Conceptually, we extracted exemplars of specific dynamic patterns, attributing descriptive terms for means of systematisation. Metrics, primarily taken from streamflow literature, were subsequently adapted to groundwater and assigned to the described patterns for means of quantification. In this study, we focused on the particularities of groundwater as a state variable. Furthermore, we investigated the descriptive skill of individual metrics as well as their usefulness for groundwater hydrographs. The ensemble of categorized metrics result in a framework, which can be used to describe and quantify groundwater dynamics. It is a promising tool for the setup of a successful similarity classification framework for groundwater hydrographs. However, the overabundance of metrics available calls for a systematic redundancy analysis of the metrics, which we describe in a second study (Heudorfer et al., 2017). Heudorfer, B., Haaf, E., Barthel, R., Stahl, K., 2017. A framework for quantification of groundwater dynamics - redundancy and transferability of hydro(geo-)logical metrics. EGU General Assembly 2017, Vienna, Austria.

A dynamical system perspective to understanding badminton singles game play.

PubMed

Chow, Jia Yi; Seifert, Ludovic; Hérault, Romain; Chia, Shannon Jing Yi; Lee, Miriam Chang Yi

2014-02-01

By altering the task constraints of cooperative and competitive game contexts in badminton, insights can be obtained from a dynamical systems perspective to investigate the underlying processes that results in either a gradual shift or transition of playing patterns. Positional data of three pairs of skilled female badminton players (average age 20.5±1.38years) were captured and analyzed. Local correlation coefficient, which provides information on the relationship of players' displacement data, between each pair of players was computed for angle and distance from base position. Speed scalar product was in turn established from speed vectors of the players. The results revealed two patterns of playing behaviors (i.e., in-phase and anti-phase patterns) for movement displacement. Anti-phase relation was the dominant coupling pattern for speed scalar relationships among the pairs of players. Speed scalar product, as a collective variable, was different between cooperative and competitive plays with a greater variability in amplitude seen in competitive plays leading to a winning point. The findings from this study provide evidence for increasing stroke variability to perturb existing stable patterns of play and highlights the potential for speed scalar product to be a collective variable to distinguish different patterns of play (e.g., cooperative and competitive). Copyright © 2013 Elsevier B.V. All rights reserved.

Differential dynamics of spatial attention, position, and color coding within the parietofrontal network.

PubMed

Astrand, Elaine; Ibos, Guilhem; Duhamel, Jean-René; Ben Hamed, Suliann

2015-02-18

Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network. Copyright © 2015 the authors 0270-6474/15/353174-16$15.00/0.

Micromagnetic computer simulations of spin waves in nanometre-scale patterned magnetic elements

NASA Astrophysics Data System (ADS)

Kim, Sang-Koog

2010-07-01

Current needs for further advances in the nanotechnologies of information-storage and -processing devices have attracted a great deal of interest in spin (magnetization) dynamics in nanometre-scale patterned magnetic elements. For instance, the unique dynamic characteristics of non-uniform magnetic microstructures such as various types of domain walls, magnetic vortices and antivortices, as well as spin wave dynamics in laterally restricted thin-film geometries, have been at the centre of extensive and intensive researches. Understanding the fundamentals of their unique spin structure as well as their robust and novel dynamic properties allows us to implement new functionalities into existing or future devices. Although experimental tools and theoretical approaches are effective means of understanding the fundamentals of spin dynamics and of gaining new insights into them, the limitations of those same tools and approaches have left gaps of unresolved questions in the pertinent physics. As an alternative, however, micromagnetic modelling and numerical simulation has recently emerged as a powerful tool for the study of a variety of phenomena related to spin dynamics of nanometre-scale magnetic elements. In this review paper, I summarize the recent results of simulations of the excitation and propagation and other novel wave characteristics of spin waves, highlighting how the micromagnetic computer simulation approach contributes to an understanding of spin dynamics of nanomagnetism and considering some of the merits of numerical simulation studies. Many examples of micromagnetic modelling for numerical calculations, employing various dimensions and shapes of patterned magnetic elements, are given. The current limitations of continuum micromagnetic modelling and of simulations based on the Landau-Lifshitz-Gilbert equation of motion of magnetization are also discussed, along with further research directions for spin-wave studies.

A neural coding scheme reproducing foraging trajectories

NASA Astrophysics Data System (ADS)

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-12-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series.

Modelling the control of interceptive actions.

PubMed Central

Beek, P J; Dessing, J C; Peper, C E; Bullock, D

2003-01-01

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics. PMID:14561342

Combining rules, background knowledge and change patterns to maintain semantic annotations.

PubMed

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Combining rules, background knowledge and change patterns to maintain semantic annotations

PubMed Central

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system. PMID:29854115

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

PubMed Central

2018-01-01

Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. PMID:29537963

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

PubMed

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

ISS method for coordination control of nonlinear dynamical agents under directed topology.

PubMed

Wang, Xiangke; Qin, Jiahu; Yu, Changbin

2014-10-01

The problems of coordination of multiagent systems with second-order locally Lipschitz continuous nonlinear dynamics under directed interaction topology are investigated in this paper. A completely nonlinear input-to-state stability (ISS)-based framework, drawing on ISS methods, with the aid of results from graph theory, matrix theory, and the ISS cyclic-small-gain theorem, is proposed for the coordination problem under directed topology, which can effectively tackle the technical challenges caused by locally Lipschitz continuous dynamics. Two coordination problems, i.e., flocking with a virtual leader and containment control, are considered. For both problems, it is assumed that only a portion of the agents can obtain the information from the leader(s). For the first problem, the proposed strategy is shown effective in driving a group of nonlinear dynamical agents reach the prespecified geometric pattern under the condition that at least one agent in each strongly connected component of the information-interconnection digraph with zero in-degree has access to the state information of the virtual leader; and the strategy proposed for the second problem can guarantee the nonlinear dynamical agents moving to the convex hull spanned by the positions of multiple leaders under the condition that for each agent there exists at least one leader that has a directed path to this agent.

Toward conservation of midcontinental shorebird migrations

USGS Publications Warehouse

Skagen, Susan K.; Knopf, Fritz L.

1993-01-01

Shorebirds represent a highly diverse group of species, many of which experience tremendous energy demands associated with long-distance migratory flights. Transcontinental migrants are dependant upon dynamic freshwater wetlands for stopover resources essential for replenishment of lipid reserves and completion of migration. Patterns of shorebird migration across midcontinental wetlands were detected from migration reports to American Birds and information provided by U.S. Fish and Wildlife Service national wildlife refuges. Patterns in species composition and abundance varied geographically, emphasizing the uniqueness of different regions to migrating shorebirds. Smaller species and neotropical migrants moved primarily across the Great Plains, whereas larger species and North American migrants predominated in assemblages in the intermountain west. Shorebirds were broadly dispersed in wetland habitats with dynamic water regimes. Whereas populations of shorebirds in coastal system appear to concentrate at sites of seasonally predictable and abundant food resources, we propose that transcontinental shorebirds disperse and use wetlands opportunistically. This migration system exemplifies the need for large-scale, coordinated regional management efforts that recognize the dynamic nature of ecosystem processes.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

PubMed Central

Gonzalez-Castillo, Javier; Hoy, Colin W.; Handwerker, Daniel A.; Robinson, Meghan E.; Buchanan, Laura C.; Saad, Ziad S.; Bandettini, Peter A.

2015-01-01

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition. PMID:26124112

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grosso, Marcos; Kalstein, Adrian; Parisi, Gustavo

The native state of a protein consists of an equilibrium of conformational states on an energy landscape rather than existing as a single static state. The co-existence of conformers with different ligand-affinities in a dynamical equilibrium is the basis for the conformational selection model for ligand binding. In this context, the development of theoretical methods that allow us to analyze not only the structural changes but also changes in the fluctuation patterns between conformers will contribute to elucidate the differential properties acquired upon ligand binding. Molecular dynamics simulations can provide the required information to explore these features. Its use inmore » combination with subsequent essential dynamics analysis allows separating large concerted conformational rearrangements from irrelevant fluctuations. We present a novel procedure to define the size and composition of essential dynamics subspaces associated with ligand-bound and ligand-free conformations. These definitions allow us to compare essential dynamics subspaces between different conformers. Our procedure attempts to emphasize the main similarities and differences between the different essential dynamics in an unbiased way. Essential dynamics subspaces associated to conformational transitions can also be analyzed. As a test case, we study the glutaminase interacting protein (GIP), composed of a single PDZ domain. Both GIP ligand-free state and glutaminase L peptide-bound states are analyzed. Our findings concerning the relative changes in the flexibility pattern upon binding are in good agreement with experimental Nuclear Magnetic Resonance data.« less

SPATIAL AND TEMPORAL VARIABILITY IN ZOOPLANKTON COMMUNITY DYNAMICS IN THREE URBANIZED BAYOUS OF THE PENSACOLA BAY SYSTEM, FLORIDA, USA

EPA Science Inventory

Spatial and temporal patterns in zooplankton community composition and abundance in near-coastal areas of the Gulf of Mexico are not well understood. This survey provides information on spatial and temporal differences in zoolplankton community composition and abundance for a coa...

Recognition of Amodal Language Identity Emerges in Infancy

ERIC Educational Resources Information Center

Lewkowicz, David J.; Pons, Ferran

2013-01-01

Audiovisual speech consists of overlapping and invariant patterns of dynamic acoustic and optic articulatory information. Research has shown that infants can perceive a variety of basic auditory-visual (A-V) relations but no studies have investigated whether and when infants begin to perceive higher order A-V relations inherent in speech. Here, we…

Clonal evolution in relapsed and refractory diffuse large B-cell lymphoma is characterized by high dynamics of subclones.

PubMed

Melchardt, Thomas; Hufnagl, Clemens; Weinstock, David M; Kopp, Nadja; Neureiter, Daniel; Tränkenschuh, Wolfgang; Hackl, Hubert; Weiss, Lukas; Rinnerthaler, Gabriel; Hartmann, Tanja N; Greil, Richard; Weigert, Oliver; Egle, Alexander

2016-08-09

Little information is available about the role of certain mutations for clonal evolution and the clinical outcome during relapse in diffuse large B-cell lymphoma (DLBCL). Therefore, we analyzed formalin-fixed-paraffin-embedded tumor samples from first diagnosis, relapsed or refractory disease from 28 patients using next-generation sequencing of the exons of 104 coding genes. Non-synonymous mutations were present in 74 of the 104 genes tested. Primary tumor samples showed a median of 8 non-synonymous mutations (range: 0-24) with the used gene set. Lower numbers of non-synonymous mutations in the primary tumor were associated with a better median OS compared with higher numbers (28 versus 15 months, p=0.031). We observed three patterns of clonal evolution during relapse of disease: large global change, subclonal selection and no or minimal change possibly suggesting preprogrammed resistance. We conclude that targeted re-sequencing is a feasible and informative approach to characterize the molecular pattern of relapse and it creates novel insights into the role of dynamics of individual genes.

Differential dynamic microscopy of bidisperse colloidal suspensions.

PubMed

Safari, Mohammad S; Poling-Skutvik, Ryan; Vekilov, Peter G; Conrad, Jacinta C

2017-01-01

Research tasks in microgravity include monitoring the dynamics of constituents of varying size and mobility in processes such as aggregation, phase separation, or self-assembly. We use differential dynamic microscopy, a method readily implemented with equipment available on the International Space Station, to simultaneously resolve the dynamics of particles of radius 50 nm and 1 μm in bidisperse aqueous suspensions. Whereas traditional dynamic light scattering fails to detect a signal from the larger particles at low concentrations, differential dynamic microscopy exhibits enhanced sensitivity in these conditions by accessing smaller wavevectors where scattering from the large particles is stronger. Interference patterns due to scattering from the large particles induce non-monotonic decay of the amplitude of the dynamic correlation function with the wavevector. We show that the position of the resulting minimum contains information on the vertical position of the particles. Together with the simple instrumental requirements, the enhanced sensitivity of differential dynamic microscopy makes it an appealing alternative to dynamic light scattering to characterize samples with complex dynamics.

A design philosophy for multi-layer neural networks with applications to robot control

NASA Technical Reports Server (NTRS)

Vadiee, Nader; Jamshidi, MO

1989-01-01

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation.

PubMed

Werkle-Bergner, Markus; Grandy, Thomas H; Chicherio, Christian; Schmiedek, Florian; Lövdén, Martin; Lindenberger, Ulman

2014-06-18

Higher cognitive functions, such as human perceptual decision making, require information processing and transmission across wide-spread cortical networks. Temporally synchronized neural firing patterns are advantageous for efficiently representing and transmitting information within and between assemblies. Computational, empirical, and conceptual considerations all lead to the expectation that the informational redundancy of neural firing rates is positively related to their synchronization. Recent theorizing and initial evidence also suggest that the coding of stimulus characteristics and their integration with behavioral goal states require neural interactions across a hierarchy of timescales. However, most studies thus have focused on neural activity in a single frequency range or on a restricted set of brain regions. Here we provide evidence for cooperative spatiotemporal dynamics of slow and fast EEG signals during perceptual decision making at the single-trial level. Participants performed three masked two-choice decision tasks, one each with numerical, verbal, or figural content. Decrements in posterior α power (8-14 Hz) were paralleled by increments in high-frequency (>30 Hz) signal entropy in trials demanding active sensory processing. Simultaneously, frontocentral θ power (4-7 Hz) increased, indicating evidence integration. The coordinated α/θ dynamics were tightly linked to decision speed and remarkably similar across tasks, suggesting a domain-general mechanism. In sum, we demonstrate an inverse association between decision-related changes in widespread low-frequency power and local high-frequency entropy. The cooperation among mechanisms captured by these changes enhances the informational density of neural response patterns and qualifies as a neural coding system in the service of perceptual decision making. Copyright © 2014 the authors 0270-6474/14/348519-10$15.00/0.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

PubMed Central

Seaton, Daniel D; Krishnan, J

2016-01-01

Cell cycle progression is carefully coordinated with a cell’s intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments. PMID:26741131

Multiple scales modelling approaches to social interaction in crowd dynamics and crisis management. Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al.

NASA Astrophysics Data System (ADS)

Trucu, Dumitru

2016-09-01

In this comprehensive review concerning the modelling of human behaviours in crowd dynamics [3], the authors explore a wide range of mathematical approaches spanning over multiple scales that are suitable to describe emerging crowd behaviours in extreme situations. Focused on deciphering the key aspects leading to emerging crowd patterns evolutions in challenging times such as those requiring an evacuation on a complex venue, the authors address this complex dynamics at both microscale (individual level), mesoscale (probability distributions of interacting individuals), and macroscale (population level), ultimately aiming to gain valuable understanding and knowledge that would inform decision making in managing crisis situations.

The contribution of visual and proprioceptive information to the perception of leaning in a dynamic motorcycle simulator.

PubMed

Lobjois, Régis; Dagonneau, Virginie; Isableu, Brice

2016-11-01

Compared with driving or flight simulation, little is known about self-motion perception in riding simulation. The goal of this study was to examine whether or not continuous roll motion supports the sensation of leaning into bends in dynamic motorcycle simulation. To this end, riders were able to freely tune the visual scene and/or motorcycle simulator roll angle to find a pattern that matched their prior knowledge. Our results revealed idiosyncrasy in the combination of visual and proprioceptive information. Some subjects relied more on the visual dimension, but reported increased sickness symptoms with the visual roll angle. Others relied more on proprioceptive information, tuning the direction of the visual scenery to match three possible patterns. Our findings also showed that these two subgroups tuned the motorcycle simulator roll angle in a similar way. This suggests that sustained inertially specified roll motion have contributed to the sensation of leaning in spite of the occurrence of unexpected gravito-inertial stimulation during the tilt. Several hypotheses are discussed. Practitioner Summary: Self-motion perception in motorcycle simulation is a relatively new research area. We examined how participants combined visual and proprioceptive information. Findings revealed individual differences in the visual dimension. However, participants tuned the simulator roll angle similarly, supporting the hypothesis that sustained inertially specified roll motion contributes to a leaning sensation.

Quantifying Information Flow During Emergencies

NASA Astrophysics Data System (ADS)

Gao, Liang; Song, Chaoming; Gao, Ziyou; Barabási, Albert-László; Bagrow, James P.; Wang, Dashun

2014-02-01

Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.

Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

NASA Astrophysics Data System (ADS)

Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

Stable and Dynamic Coding for Working Memory in Primate Prefrontal Cortex

PubMed Central

Watanabe, Kei; Funahashi, Shintaro; Stokes, Mark G.

2017-01-01

Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM. SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled “working memory” (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding. PMID:28559375

Understanding neuromotor strategy during functional upper extremity tasks using symbolic dynamics.

PubMed

Nathan, Dominic E; Guastello, Stephen J; Prost, Robert W; Jeutter, Dean C

2012-01-01

The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.

ORBiT: Oak Ridge Bio-surveillance Toolkit for Public Health Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Pullum, Laura L; Hobson, Tanner C

With novel emerging infectious diseases being reported across different parts of the world, there is a need to build effective bio-surveillance systems that can track, monitor and report such events in a timely manner. Apart from monitoring for emerging disease outbreaks, it is also important to identify susceptible geographic regions and populations where these diseases may have a significant impact. The digitization of health related information through electronic health records (EHR) and electronic healthcare claim reimbursements (eHCR) and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing novel public health surveillancemore » tools. In this paper, we present an overview of Oak Ridge Bio-surveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various bio-surveillance related tasks. We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using eHCR data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the eHCR data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as bridge regions contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. These quantitative insights show how the eHCR data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.« less

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics

PubMed Central

2015-01-01

Background The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. Methods In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. Results We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. Conclusions These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance. PMID:26679008

On the analysis and comparison of conformer-specific essential dynamics upon ligand binding to a protein.

PubMed

Grosso, Marcos; Kalstein, Adrian; Parisi, Gustavo; Roitberg, Adrian E; Fernandez-Alberti, Sebastian

2015-06-28

The native state of a protein consists of an equilibrium of conformational states on an energy landscape rather than existing as a single static state. The co-existence of conformers with different ligand-affinities in a dynamical equilibrium is the basis for the conformational selection model for ligand binding. In this context, the development of theoretical methods that allow us to analyze not only the structural changes but also changes in the fluctuation patterns between conformers will contribute to elucidate the differential properties acquired upon ligand binding. Molecular dynamics simulations can provide the required information to explore these features. Its use in combination with subsequent essential dynamics analysis allows separating large concerted conformational rearrangements from irrelevant fluctuations. We present a novel procedure to define the size and composition of essential dynamics subspaces associated with ligand-bound and ligand-free conformations. These definitions allow us to compare essential dynamics subspaces between different conformers. Our procedure attempts to emphasize the main similarities and differences between the different essential dynamics in an unbiased way. Essential dynamics subspaces associated to conformational transitions can also be analyzed. As a test case, we study the glutaminase interacting protein (GIP), composed of a single PDZ domain. Both GIP ligand-free state and glutaminase L peptide-bound states are analyzed. Our findings concerning the relative changes in the flexibility pattern upon binding are in good agreement with experimental Nuclear Magnetic Resonance data.

On the analysis and comparison of conformer-specific essential dynamics upon ligand binding to a protein

NASA Astrophysics Data System (ADS)

Grosso, Marcos; Kalstein, Adrian; Parisi, Gustavo; Roitberg, Adrian E.; Fernandez-Alberti, Sebastian

2015-06-01

The native state of a protein consists of an equilibrium of conformational states on an energy landscape rather than existing as a single static state. The co-existence of conformers with different ligand-affinities in a dynamical equilibrium is the basis for the conformational selection model for ligand binding. In this context, the development of theoretical methods that allow us to analyze not only the structural changes but also changes in the fluctuation patterns between conformers will contribute to elucidate the differential properties acquired upon ligand binding. Molecular dynamics simulations can provide the required information to explore these features. Its use in combination with subsequent essential dynamics analysis allows separating large concerted conformational rearrangements from irrelevant fluctuations. We present a novel procedure to define the size and composition of essential dynamics subspaces associated with ligand-bound and ligand-free conformations. These definitions allow us to compare essential dynamics subspaces between different conformers. Our procedure attempts to emphasize the main similarities and differences between the different essential dynamics in an unbiased way. Essential dynamics subspaces associated to conformational transitions can also be analyzed. As a test case, we study the glutaminase interacting protein (GIP), composed of a single PDZ domain. Both GIP ligand-free state and glutaminase L peptide-bound states are analyzed. Our findings concerning the relative changes in the flexibility pattern upon binding are in good agreement with experimental Nuclear Magnetic Resonance data.

Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis

PubMed Central

Gan, Muye; Deng, Jinsong; Zheng, Xinyu; Hong, Yang; Wang, Ke

2014-01-01

Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents’ quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development. PMID:25375176

Survey of decentralized control methods. [for large scale dynamic systems

NASA Technical Reports Server (NTRS)

Athans, M.

1975-01-01

An overview is presented of the types of problems that are being considered by control theorists in the area of dynamic large scale systems with emphasis on decentralized control strategies. Approaches that deal directly with decentralized decision making for large scale systems are discussed. It is shown that future advances in decentralized system theory are intimately connected with advances in the stochastic control problem with nonclassical information pattern. The basic assumptions and mathematical tools associated with the latter are summarized, and recommendations concerning future research are presented.

3D-shape of objects with straight line-motion by simultaneous projection of color coded patterns

NASA Astrophysics Data System (ADS)

Flores, Jorge L.; Ayubi, Gaston A.; Di Martino, J. Matías; Castillo, Oscar E.; Ferrari, Jose A.

2018-05-01

In this work, we propose a novel technique to retrieve the 3D shape of dynamic objects by the simultaneous projection of a fringe pattern and a homogeneous light pattern which are both coded in two of the color channels of a RGB image. The fringe pattern, red channel, is used to retrieve the phase by phase-shift algorithms with arbitrary phase-step, while the homogeneous pattern, blue channel, is used to match pixels from the test object in consecutive images, which are acquired at different positions, and thus, to determine the speed of the object. The proposed method successfully overcomes the standard requirement of projecting fringes of two different frequencies; one frequency to extract object information and the other one to retrieve the phase. Validation experiments are presented.

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

PubMed Central

Yeh, Hsiang J.; Guindani, Michele; Vannucci, Marina; Haneef, Zulfi; Stern, John M.

2018-01-01

Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function. PMID:29320526

A neural coding scheme reproducing foraging trajectories

PubMed Central

Gutiérrez, Esther D.; Cabrera, Juan Luis

2015-01-01

The movement of many animals may follow Lévy patterns. The underlying generating neuronal dynamics of such a behavior is unknown. In this paper we show that a novel discovery of multifractality in winnerless competition (WLC) systems reveals a potential encoding mechanism that is translatable into two dimensional superdiffusive Lévy movements. The validity of our approach is tested on a conductance based neuronal model showing WLC and through the extraction of Lévy flights inducing fractals from recordings of rat hippocampus during open field foraging. Further insights are gained analyzing mice motor cortex neurons and non motor cell signals. The proposed mechanism provides a plausible explanation for the neuro-dynamical fundamentals of spatial searching patterns observed in animals (including humans) and illustrates an until now unknown way to encode information in neuronal temporal series. PMID:26648311

Patterns of variations in large pelagic fish: A comparative approach between the Indian and the Atlantic Oceans

NASA Astrophysics Data System (ADS)

Corbineau, A.; Rouyer, T.; Fromentin, J.-M.; Cazelles, B.; Fonteneau, A.; Ménard, F.

2010-07-01

Catch data of large pelagic fish such as tuna, swordfish and billfish are highly variable ranging from short to long term. Based on fisheries data, these time series are noisy and reflect mixed information on exploitation (targeting, strategy, fishing power), population dynamics (recruitment, growth, mortality, migration, etc.), and environmental forcing (local conditions or dominant climate patterns). In this work, we investigated patterns of variation of large pelagic fish (i.e. yellowfin tuna, bigeye tuna, swordfish and blue marlin) in Japanese longliners catch data from 1960 to 2004. We performed wavelet analyses on the yearly time series of each fish species in each biogeographic province of the tropical Indian and Atlantic Oceans. In addition, we carried out cross-wavelet analyses between these biological time series and a large-scale climatic index, i.e. the Southern Oscillation Index (SOI). Results showed that the biogeographic province was the most important factor structuring the patterns of variability of Japanese catch time series. Relationships between the SOI and the fish catches in the Indian and Atlantic Oceans also pointed out the role of climatic variability for structuring patterns of variation of catch time series. This work finally confirmed that Japanese longline CPUE data poorly reflect the underlying population dynamics of tunas.

Light-driven dynamic Archimedes spirals and periodic oscillatory patterns of topological solitons in anisotropic soft matter

DOE PAGES

Martinez, Angel; Smalyukh, Ivan I.

2015-02-12

Oscillatory and excitable systems very commonly exhibit formation of dynamic non-equilibrium patterns. For example, rotating spiral patterns are observed in biological, chemical, and physical systems ranging from organization of slime mold cells to Belousov-Zhabotinsky reactions, and to crystal growth from nuclei with screw dislocations. Here we describe spontaneous formation of spiral waves and a large variety of other dynamic patterns in anisotropic soft matter driven by low-intensity light. The unstructured ambient or microscope light illumination of thin liquid crystal films in contact with a self-assembled azobenzene monolayer causes spontaneous formation, rich spatial organization, and dynamics of twisted domains and topologicalmore » solitons accompanied by the dynamic patterning of azobenzene group orientations within the monolayer. Linearly polarized incident light interacts with the twisted liquid crystalline domains, mimicking their dynamics and yielding patterns in the polarization state of transmitted light, which can be transformed to similar dynamic patterns in its intensity and interference color. This shows that the delicate light-soft-matter interaction can yield complex self-patterning of both. Finally, we uncover underpinning physical mechanisms and discuss potential uses.« less

How physician electronic health record screen sharing affects patient and doctor non-verbal communication in primary care.

PubMed

Asan, Onur; Young, Henry N; Chewning, Betty; Montague, Enid

2015-03-01

Use of electronic health records (EHRs) in primary-care exam rooms changes the dynamics of patient-physician interaction. This study examines and compares doctor-patient non-verbal communication (eye-gaze patterns) during primary care encounters for three different screen/information sharing groups: (1) active information sharing, (2) passive information sharing, and (3) technology withdrawal. Researchers video recorded 100 primary-care visits and coded the direction and duration of doctor and patient gaze. Descriptive statistics compared the length of gaze patterns as a percentage of visit length. Lag sequential analysis determined whether physician eye-gaze influenced patient eye gaze, and vice versa, and examined variations across groups. Significant differences were found in duration of gaze across groups. Lag sequential analysis found significant associations between several gaze patterns. Some, such as DGP-PGD ("doctor gaze patient" followed by "patient gaze doctor") were significant for all groups. Others, such DGT-PGU ("doctor gaze technology" followed by "patient gaze unknown") were unique to one group. Some technology use styles (active information sharing) seem to create more patient engagement, while others (passive information sharing) lead to patient disengagement. Doctors can engage patients in communication by using EHRs in the visits. EHR training and design should facilitate this. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The dynamic-stimulus advantage of visual symmetry perception.

PubMed

Niimi, Ryosuke; Watanabe, Katsumi; Yokosawa, Kazuhiko

2008-09-01

It has been speculated that visual symmetry perception from dynamic stimuli involves mechanisms different from those for static stimuli. However, previous studies found no evidence that dynamic stimuli lead to active temporal processing and improve symmetry detection. In this study, four psychophysical experiments investigated temporal processing in symmetry perception using both dynamic and static stimulus presentations of dot patterns. In Experiment 1, rapid successive presentations of symmetric patterns (e.g., 16 patterns per 853 ms) produced more accurate discrimination of orientations of symmetry axes than static stimuli (single pattern presented through 853 ms). In Experiments 2-4, we confirmed that the dynamic-stimulus advantage depended upon presentation of a large number of unique patterns within a brief period (853 ms) in the dynamic conditions. Evidently, human vision takes advantage of temporal processing for symmetry perception from dynamic stimuli.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina).

PubMed

Espinosa, Manuel; Weinberg, Diego; Rotela, Camilo H; Polop, Francisco; Abril, Marcelo; Scavuzzo, Carlos Marcelo

2016-05-01

Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina)

PubMed Central

Espinosa, Manuel; Weinberg, Diego; Rotela, Camilo H.; Polop, Francisco; Abril, Marcelo; Scavuzzo, Carlos Marcelo

2016-01-01

Background Since 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. Methodology/Principal Findings Seasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). Conclusions/Significance This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control. PMID:27223693

Multiscale structure in eco-evolutionary dynamics

NASA Astrophysics Data System (ADS)

Stacey, Blake C.

In a complex system, the individual components are neither so tightly coupled or correlated that they can all be treated as a single unit, nor so uncorrelated that they can be approximated as independent entities. Instead, patterns of interdependency lead to structure at multiple scales of organization. Evolution excels at producing such complex structures. In turn, the existence of these complex interrelationships within a biological system affects the evolutionary dynamics of that system. I present a mathematical formalism for multiscale structure, grounded in information theory, which makes these intuitions quantitative, and I show how dynamics defined in terms of population genetics or evolutionary game theory can lead to multiscale organization. For complex systems, "more is different," and I address this from several perspectives. Spatial host--consumer models demonstrate the importance of the structures which can arise due to dynamical pattern formation. Evolutionary game theory reveals the novel effects which can result from multiplayer games, nonlinear payoffs and ecological stochasticity. Replicator dynamics in an environment with mesoscale structure relates to generalized conditionalization rules in probability theory. The idea of natural selection "acting at multiple levels" has been mathematized in a variety of ways, not all of which are equivalent. We will face down the confusion, using the experience developed over the course of this thesis to clarify the situation.

Salinity fluctuation influencing biological adaptation: growth dynamics and Na+ /K+ -ATPase activity in a euryhaline bacterium.

PubMed

Yang, Hao; Meng, Yang; Song, Youxin; Tan, Yalin; Warren, Alan; Li, Jiqiu; Lin, Xiaofeng

2017-07-01

Although salinity fluctuation is a prominent characteristic of many coastal ecosystems, its effects on biological adaptation have not yet been fully recognized. To test the salinity fluctuations on biological adaptation, population growth dynamics and Na + /K + -ATPase activity were investigated in the euryhaline bacterium Idiomarina sp. DYB, which was acclimated at different salinity exposure levels, exposure times, and shifts in direction of salinity. Results showed: (1) bacterial population growth dynamics and Na + /K + -ATPase activity changed significantly in response to salinity fluctuation; (2) patterns of variation in bacterial growth dynamics were related to exposure times, levels of salinity, and shifts in direction of salinity change; (3) significant tradeoffs were detected between growth rate (r) and carrying capacity (K) on the one hand, and Na + /K + -ATPase activity on the other; and (4) beneficial acclimation was confirmed in Idiomarina sp. DYB. In brief, this study demonstrated that salinity fluctuation can change the population growth dynamics, Na + /K + -ATPase activity, and tradeoffs between r, K, and Na + /K + -ATPase activity, thus facilitating bacterial adaption in a changing environment. These findings provide constructive information for determining biological response patterns to environmental change. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A nonlinear dynamics approach for incorporating wind-speed patterns into wind-power project evaluation.

PubMed

Huffaker, Ray; Bittelli, Marco

2015-01-01

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind-the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Pattern Recognition Control Design

NASA Technical Reports Server (NTRS)

Gambone, Elisabeth A.

2018-01-01

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

A gradient analysis on urban sprawl and urban landscape pattern between 1985 and 2000 in the Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Dai, Erfu; Wu, Zhuo; Du, Xiaodian

2017-04-01

Urbanization is an irreversible trend worldwide, especially in rapidly developing China. Accelerated urbanization has resulted in rapid urban sprawl and urban landscape pattern changes. Quantifying the spatiotemporal dynamics of urban land use and landscape pattern not only can reveal the characteristics of social transfer and economic development, but also can provide insights into the driving mechanisms of land use changes. In this study, we integrated remote sensing (RS), geographic information system (GIS), landscape metrics, and gradient analysis to quantitatively compare the spatiotemporal dynamics of land use, urban sprawl, and landscape pattern for nine cities in the Pearl River Delta from 1985‒2000. For the whole study region, urbanization was obvious. The results show an increase in urban buildup land and shrinkage of cropland in the Pearl River Delta. However, the nine cities differed greatly in terms of the process and magnitude of urban sprawl for both the spatial and temporal dimensions. This was most evident for the cities of Guangzhou and Shenzhen. Gradient analysis on urban landscape changes could deepen understanding of the stages of urban development and provide a scientific foundation for future urban planning and land management strategies in China.

Particle tracking and extended object imaging by interferometric super resolution microscopy

NASA Astrophysics Data System (ADS)

Gdor, Itay; Yoo, Seunghwan; Wang, Xiaolei; Daddysman, Matthew; Wilton, Rosemarie; Ferrier, Nicola; Hereld, Mark; Cossairt, Oliver (Ollie); Katsaggelos, Aggelos; Scherer, Norbert F.

2018-02-01

An interferometric fluorescent microscope and a novel theoretic image reconstruction approach were developed and used to obtain super-resolution images of live biological samples and to enable dynamic real time tracking. The tracking utilizes the information stored in the interference pattern of both the illuminating incoherent light and the emitted light. By periodically shifting the interferometer phase and a phase retrieval algorithm we obtain information that allow localization with sub-2 nm axial resolution at 5 Hz.

Memory Circuit Fault Simulator

NASA Technical Reports Server (NTRS)

Sheldon, Douglas J.; McClure, Tucker

2013-01-01

Spacecraft are known to experience significant memory part-related failures and problems, both pre- and postlaunch. These memory parts include both static and dynamic memories (SRAM and DRAM). These failures manifest themselves in a variety of ways, such as pattern-sensitive failures, timingsensitive failures, etc. Because of the mission critical nature memory devices play in spacecraft architecture and operation, understanding their failure modes is vital to successful mission operation. To support this need, a generic simulation tool that can model different data patterns in conjunction with variable write and read conditions was developed. This tool is a mathematical and graphical way to embed pattern, electrical, and physical information to perform what-if analysis as part of a root cause failure analysis effort.

Time delay induced different synchronization patterns in repulsively coupled chaotic oscillators

NASA Astrophysics Data System (ADS)

Yao, Chenggui; Yi, Ming; Shuai, Jianwei

2013-09-01

Time delayed coupling plays a crucial role in determining the system's dynamics. We here report that the time delay induces transition from the asynchronous state to the complete synchronization (CS) state in the repulsively coupled chaotic oscillators. In particular, by changing the coupling strength or time delay, various types of synchronous patterns, including CS, antiphase CS, antiphase synchronization (ANS), and phase synchronization, can be generated. In the transition regions between different synchronous patterns, bistable synchronous oscillators can be observed. Furthermore, we show that the time-delay-induced phase flip bifurcation is of key importance for the emergence of CS. All these findings may light on our understanding of neuronal synchronization and information processing in the brain.

Digital Geography and the Race for the White House

ERIC Educational Resources Information Center

Kenreich, Todd W.

2016-01-01

With the 2016 presidential election right around the corner, geography provides a dynamic view of the spatial patterns and processes that shape the electorate. The major presidential campaigns know that a winning strategy must use geography to make informed decisions about where to allocate limited resources such as money and staff. In the end,…

Stand dynamics of unthinned and thinned shortleaf pine plantations

Treesearch

Glendon W. Smalley

1986-01-01

Growth and yield information about unthinned and thinned shortleaf pine (Pinus echinata Mill.) plantations established mostly on old-fields in the Coastal Plain, Piedmont, Ridge and Valley, Cumberland Plateau, and Highland Rim physiographic provinces is covered in this paper. The growth and yield pattern of shortleaf pine is more suited to the production of sawlogs at...

Structure and development of old-growth, unmanaged second-growth, and extended rotation Pinus resinosa forests in Minnesota, USA

Treesearch

Emily J. Silver; Anthony W. D' Amato; Shawn Fraver; Brian J. Palik; John B. Bradford

2013-01-01

The structure and developmental dynamics of old-growth forests often serve as important baselines for restoration prescriptions aimed at promoting more complex structural conditions in managed forest landscapes. Nonetheless, long-term information on natural patterns of development is rare for many commercially important and ecologically widespread forest types....

Patterns of Oak Dominance in the Eastern Ouachita Mountains Suggested by Early Records

Treesearch

Don C. Bragg

2004-01-01

Many years of human influence across the Interior Highlands have caused profound changes in forest composition, disturbance regimes, and understory dynamics. However, information on the historical condition of these forests is limited. General Land Office (GLO) records, old documents, and contemporary studies provided data on the township encompassing the Lake Winona...

Biological Motion Preference in Humans at Birth: Role of Dynamic and Configural Properties

ERIC Educational Resources Information Center

Bardi, Lara; Regolin, Lucia; Simion, Francesca

2011-01-01

The present study addresses the hypothesis that detection of biological motion is an intrinsic capacity of the visual system guided by a non-species-specific predisposition for the pattern of vertebrate movement and investigates the role of global vs. local information in biological motion detection. Two-day-old babies exposed to a biological…

Designing and validating the joint battlespace infosphere

NASA Astrophysics Data System (ADS)

Peterson, Gregory D.; Alexander, W. Perry; Birdwell, J. Douglas

2001-08-01

Fielding and managing the dynamic, complex information systems infrastructure necessary for defense operations presents significant opportunities for revolutionary improvements in capabilities. An example of this technology trend is the creation and validation of the Joint Battlespace Infosphere (JBI) being developed by the Air Force Research Lab. The JBI is a system of systems that integrates, aggregates, and distributes information to users at all echelons, from the command center to the battlefield. The JBI is a key enabler of meeting the Air Force's Joint Vision 2010 core competencies such as Information Superiority, by providing increased situational awareness, planning capabilities, and dynamic execution. At the same time, creating this new operational environment introduces significant risk due to an increased dependency on computational and communications infrastructure combined with more sophisticated and frequent threats. Hence, the challenge facing the nation is the most effective means to exploit new computational and communications technologies while mitigating the impact of attacks, faults, and unanticipated usage patterns.

Localized analysis of paint-coat drying using dynamic speckle interferometry

NASA Astrophysics Data System (ADS)

Sierra-Sosa, Daniel; Tebaldi, Myrian; Grumel, Eduardo; Rabal, Hector; Elmaghraby, Adel

2018-07-01

The paint-coating is part of several industrial processes, including the automotive industry, architectural coatings, machinery and appliances. These paint-coatings must comply with high quality standards, for this reason evaluation techniques from paint-coatings are in constant development. One important factor from the paint-coating process is the drying, as it has influence on the quality of final results. In this work we present an assessment technique based on the optical dynamic speckle interferometry, this technique allows for the temporal activity evaluation of the paint-coating drying process, providing localized information from drying. This localized information is relevant in order to address the drying homogeneity, optimal drying, and quality control. The technique relies in the definition of a new temporal history of the speckle patterns to obtain the local activity; this information is then clustered to provide a convenient indicative of different drying process stages. The experimental results presented were validated using the gravimetric drying curves

Mitotic Cortical Waves Predict Future Division Sites by Encoding Positional and Size Information.

PubMed

Xiao, Shengping; Tong, Cheesan; Yang, Yang; Wu, Min

2017-11-20

Dynamic spatial patterns such as traveling waves could theoretically encode spatial information, but little is known about whether or how they are employed by biological systems, especially higher eukaryotes. Here, we show that concentric target or spiral waves of active Cdc42 and the F-BAR protein FBP17 are invoked in adherent cells at the onset of mitosis. These waves predict the future sites of cell divisions and represent the earliest known spatial cues for furrow assembly. Unlike interphase waves, the frequencies and wavelengths of the mitotic waves display size-dependent scaling properties. While the positioning role of the metaphase waves requires microtubule dynamics, spindle and microtubule-independent inhibitory signals are propagated by the mitotic waves to ensure the singularity of furrow formation. Taken together, we propose that metaphase cortical waves integrate positional and cell size information for division-plane specification in adhesion-dependent cytokinesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Reprogrammable Assembly of Molecular Motor on Solid Surfaces via Dynamic Bonds.

PubMed

Yu, Li; Sun, Jian; Wang, Qian; Guan, Yan; Zhou, Le; Zhang, Jingxuan; Zhang, Lanying; Yang, Huai

2017-06-01

Controllable assembly of molecular motors on solid surfaces is a fundamental issue for providing them to perform physical tasks. However, it can hardly be achieved by most previous methods due to their inherent limitations. Here, a general strategy is designed for the reprogrammable assembly of molecular motors on solid surfaces based on dynamic bonds. In this method, molecular motors with disulfide bonds can be remotely, reversibly, and precisely attached to solid surfaces with disulfide bonds, regardless of their chemical composition and microstructure. More importantly, it not only allows encoding geometric information referring to a pattern of molecular motors, but also enables erasing and re-encoding of geometric information via hemolytic photocleavage and recombination of disulfide bonds. Thus, solid surfaces can be regarded as "computer hardware", where molecular motors can be reformatted and reprogramed as geometric information. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand.

PubMed

Jeefoo, Phaisarn; Tripathi, Nitin Kumar; Souris, Marc

2011-01-01

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.

Millennial-scale faunal record reveals differential resilience of European large mammals to human impacts across the Holocene.

PubMed

Crees, Jennifer J; Carbone, Chris; Sommer, Robert S; Benecke, Norbert; Turvey, Samuel T

2016-03-30

The use of short-term indicators for understanding patterns and processes of biodiversity loss can mask longer-term faunal responses to human pressures. We use an extensive database of approximately 18,700 mammalian zooarchaeological records for the last 11,700 years across Europe to reconstruct spatio-temporal dynamics of Holocene range change for 15 large-bodied mammal species. European mammals experienced protracted, non-congruent range losses, with significant declines starting in some species approximately 3000 years ago and continuing to the present, and with the timing, duration and magnitude of declines varying individually between species. Some European mammals became globally extinct during the Holocene, whereas others experienced limited or no significant range change. These findings demonstrate the relatively early onset of prehistoric human impacts on postglacial biodiversity, and mirror species-specific patterns of mammalian extinction during the Late Pleistocene. Herbivores experienced significantly greater declines than carnivores, revealing an important historical extinction filter that informs our understanding of relative resilience and vulnerability to human pressures for different taxa. We highlight the importance of large-scale, long-term datasets for understanding complex protracted extinction processes, although the dynamic pattern of progressive faunal depletion of European mammal assemblages across the Holocene challenges easy identification of 'static' past baselines to inform current-day environmental management and restoration. © 2016 The Author(s).

The neural dynamics of task context in free recall.

PubMed

Polyn, Sean M; Kragel, James E; Morton, Neal W; McCluey, Joshua D; Cohen, Zachary D

2012-03-01

Multivariate pattern analysis (MVPA) is a powerful tool for relating theories of cognitive function to the neural dynamics observed while people engage in cognitive tasks. Here, we use the Context Maintenance and Retrieval model of free recall (CMR; Polyn et al., 2009a) to interpret variability in the strength of task-specific patterns of distributed neural activity as participants study and recall lists of words. The CMR model describes how temporal and source-related (here, encoding task) information combine in a contextual representation that is responsible for guiding memory search. Each studied word in the free-recall paradigm is associated with one of two encoding tasks (size and animacy) that have distinct neural representations during encoding. We find evidence for the context retrieval hypothesis central to the CMR model: Task-specific patterns of neural activity are reactivated during memory search, as the participant recalls an item previously associated with a particular task. Furthermore, we find that the fidelity of these task representations during study is related to task-shifting, the serial position of the studied item, and variability in the magnitude of the recency effect across participants. The CMR model suggests that these effects may be related to a central parameter of the model that controls the rate that an internal contextual representation integrates information from the surrounding environment. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

A lattice model for influenza spreading.

PubMed

Liccardo, Antonella; Fierro, Annalisa

2013-01-01

We construct a stochastic SIR model for influenza spreading on a D-dimensional lattice, which represents the dynamic contact network of individuals. An age distributed population is placed on the lattice and moves on it. The displacement from a site to a nearest neighbor empty site, allows individuals to change the number and identities of their contacts. The dynamics on the lattice is governed by an attractive interaction between individuals belonging to the same age-class. The parameters, which regulate the pattern dynamics, are fixed fitting the data on the age-dependent daily contact numbers, furnished by the Polymod survey. A simple SIR transmission model with a nearest neighbors interaction and some very basic adaptive mobility restrictions complete the model. The model is validated against the age-distributed Italian epidemiological data for the influenza A(H1N1) during the [Formula: see text] season, with sensible predictions for the epidemiological parameters. For an appropriate topology of the lattice, we find that, whenever the accordance between the contact patterns of the model and the Polymod data is satisfactory, there is a good agreement between the numerical and the experimental epidemiological data. This result shows how rich is the information encoded in the average contact patterns of individuals, with respect to the analysis of the epidemic spreading of an infectious disease.

Emergence of postural patterns as a function of vision and translation frequency

NASA Technical Reports Server (NTRS)

Buchanan, J. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1999-01-01

Emergence of postural patterns as a function of vision and translation frequency. We examined the frequency characteristics of human postural coordination and the role of visual information in this coordination. Eight healthy adults maintained balance in stance during sinusoidal support surface translations (12 cm peak to peak) in the anterior-posterior direction at six different frequencies. Changes in kinematic and dynamic measures revealed that both sensory and biomechanical constraints limit postural coordination patterns as a function of translation frequency. At slow frequencies (0.1 and 0.25 Hz), subjects ride the platform (with the eyes open or closed). For fast frequencies (1.0 and 1.25 Hz) with the eyes open, subjects fix their head and upper trunk in space. With the eyes closed, large-amplitude, slow-sway motion of the head and trunk occurred for fast frequencies above 0.5 Hz. Visual information stabilized posture by reducing the variability of the head's position in space and the position of the center of mass (CoM) within the support surface defined by the feet for all but the slowest translation frequencies. When subjects rode the platform, there was little oscillatory joint motion, with muscle activity limited mostly to the ankles. To support the head fixed in space and slow-sway postural patterns, subjects produced stable interjoint hip and ankle joint coordination patterns. This increase in joint motion of the lower body dissipated the energy input by fast translation frequencies and facilitated the control of upper body motion. CoM amplitude decreased with increasing translation frequency, whereas the center of pressure amplitude increased with increasing translation frequency. Our results suggest that visual information was important to maintaining a fixed position of the head and trunk in space, whereas proprioceptive information was sufficient to produce stable coordinative patterns between the support surface and legs. The CNS organizes postural patterns in this balance task as a function of available sensory information, biomechanical constraints, and translation frequency.

Outcome dependency alters the neural substrates of impression formation

PubMed Central

Ames, Daniel L.; Fiske, Susan T.

2015-01-01

How do people maintain consistent impressions of other people when other people are often inconsistent? The present research addresses this question by combining recent neuroscientific insights with ecologically meaningful behavioral methods. Participants formed impressions of real people whom they met in a personally involving situation. fMRI and supporting behavioral data revealed that outcome dependency (i.e., depending on another person for a desired outcome) alters previously identified neural dynamics of impression formation. Consistent with past research, a functional localizer identified a region of dorsomedial PFC previously linked to social impression formation. In the main task, this ROI revealed the predicted patterns of activity across outcome dependency conditions: greater BOLD response when information confirmed (vs. violated) social expectations if participants were outcome-independent and the reverse pattern if participants were outcome-dependent. We suggest that, although social perceivers often discount expectancy-disconfirming information as noise, being dependent on another person for a desired outcome focuses impression-formation processing on the most diagnostic information, rather than on the most tractable information. PMID:23850465

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

NASA Astrophysics Data System (ADS)

Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

2015-12-01

The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

Full-field dual-color 100-nm super-resolution imaging reveals organization and dynamics of mitochondrial and ER networks.

PubMed

Brunstein, Maia; Wicker, Kai; Hérault, Karine; Heintzmann, Rainer; Oheim, Martin

2013-11-04

Most structured illumination microscopes use a physical or synthetic grating that is projected into the sample plane to generate a periodic illumination pattern. Albeit simple and cost-effective, this arrangement hampers fast or multi-color acquisition, which is a critical requirement for time-lapse imaging of cellular and sub-cellular dynamics. In this study, we designed and implemented an interferometric approach allowing large-field, fast, dual-color imaging at an isotropic 100-nm resolution based on a sub-diffraction fringe pattern generated by the interference of two colliding evanescent waves. Our all-mirror-based system generates illumination pat-terns of arbitrary orientation and period, limited only by the illumination aperture (NA = 1.45), the response time of a fast, piezo-driven tip-tilt mirror (10 ms) and the available fluorescence signal. At low µW laser powers suitable for long-period observation of life cells and with a camera exposure time of 20 ms, our system permits the acquisition of super-resolved 50 µm by 50 µm images at 3.3 Hz. The possibility it offers for rapidly adjusting the pattern between images is particularly advantageous for experiments that require multi-scale and multi-color information. We demonstrate the performance of our instrument by imaging mitochondrial dynamics in cultured cortical astrocytes. As an illustration of dual-color excitation dual-color detection, we also resolve interaction sites between near-membrane mitochondria and the endoplasmic reticulum. Our TIRF-SIM microscope provides a versatile, compact and cost-effective arrangement for super-resolution imaging, allowing the investigation of co-localization and dynamic interactions between organelles--important questions in both cell biology and neurophysiology.

Testability of evolutionary game dynamics based on experimental economics data

NASA Astrophysics Data System (ADS)

Wang, Yijia; Chen, Xiaojie; Wang, Zhijian

In order to better understand the dynamic processes of a real game system, we need an appropriate dynamics model, so to evaluate the validity of a model is not a trivial task. Here, we demonstrate an approach, considering the dynamical macroscope patterns of angular momentum and speed as the measurement variables, to evaluate the validity of various dynamics models. Using the data in real time Rock-Paper-Scissors (RPS) games experiments, we obtain the experimental dynamic patterns, and then derive the related theoretical dynamic patterns from a series of typical dynamics models respectively. By testing the goodness-of-fit between the experimental and theoretical patterns, the validity of the models can be evaluated. One of the results in our study case is that, among all the nonparametric models tested, the best-known Replicator dynamics model performs almost worst, while the Projection dynamics model performs best. Besides providing new empirical macroscope patterns of social dynamics, we demonstrate that the approach can be an effective and rigorous tool to test game dynamics models. Fundamental Research Funds for the Central Universities (SSEYI2014Z) and the National Natural Science Foundation of China (Grants No. 61503062).

Geographic migration of black and white families over four generations.

PubMed

Sharkey, Patrick

2015-02-01

This article analyzes patterns of geographic migration of black and white American families over four consecutive generations. The analysis is based on a unique set of questions in the Panel Study of Income Dynamics (PSID) asking respondents about the counties and states in which their parents and grandparents were raised. Using this information along with the extensive geographic information available in the PSID survey, the article tracks the geographic locations of four generations of family members and considers the ways in which families and places are linked together over the course of a family's history. The patterns documented in the article are consistent with much of the demographic literature on the Great Migration of black Americans out of the South, but they reveal new insights into patterns of black migration after the Great Migration. In the most recent generation, black Americans have remained in place to a degree that is unique relative to the previous generation and relative to whites of the same generation. This new geographic immobility is the most pronounced change in black Americans' migration patterns after the Great Migration, and it is a pattern that has implications for the demography of black migration as well as the literature on racial inequality.

Smoking behavior of Mexicans: patterns by birth-cohort, gender, and education.

PubMed

Christopoulou, Rebekka; Lillard, Dean R; Balmori de la Miyar, Josè R

2013-06-01

Little is known about historical smoking patterns in Mexico. Policy makers must rely on imprecise predictions of human or fiscal burdens from smoking-related diseases. In this paper we document intergenerational patterns of smoking, project them for future cohorts, and discuss those patterns in the context of Mexico's impressive economic growth. We use retrospectively collected information to generate life-course smoking prevalence rates of five birth-cohorts, by gender and education. With dynamic panel data methods, we regress smoking rates on indicators of economic development. Smoking is most prevalent among men and the highly educated. Smoking rates peaked in the 1980s and have since decreased, slowly on average, and fastest among the highly educated. Development significantly contributed to this decline; a 1 % increase in development is associated with an average decline in smoking prevalence of 0.02 and 0.07 percentage points for women and men, respectively. Mexico's development may have triggered forces that decrease smoking, such as the spread of health information. Although smoking rates are falling, projections suggest that they will be persistently high for several future generations.

How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

NASA Astrophysics Data System (ADS)

Bettinardi, R. G.; Deco, G.; Karlaftis, V. M.; Van Hartevelt, T. J.; Fernandes, H. M.; Kourtzi, Z.; Kringelbach, M. L.; Zamora-López, G.

2017-04-01

Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

Testability of evolutionary game dynamics based on experimental economics data

NASA Astrophysics Data System (ADS)

Wang, Yijia; Chen, Xiaojie; Wang, Zhijian

2017-11-01

Understanding the dynamic processes of a real game system requires an appropriate dynamics model, and rigorously testing a dynamics model is nontrivial. In our methodological research, we develop an approach to testing the validity of game dynamics models that considers the dynamic patterns of angular momentum and speed as measurement variables. Using Rock-Paper-Scissors (RPS) games as an example, we illustrate the geometric patterns in the experiment data. We then derive the related theoretical patterns from a series of typical dynamics models. By testing the goodness-of-fit between the experimental and theoretical patterns, we show that the validity of these models can be evaluated quantitatively. Our approach establishes a link between dynamics models and experimental systems, which is, to the best of our knowledge, the most effective and rigorous strategy for ascertaining the testability of evolutionary game dynamics models.

Cortical responses to dynamic emotional facial expressions generalize across stimuli, and are sensitive to task-relevance, in adults with and without Autism.

PubMed

Kliemann, Dorit; Richardson, Hilary; Anzellotti, Stefano; Ayyash, Dima; Haskins, Amanda J; Gabrieli, John D E; Saxe, Rebecca R

2018-06-01

Individuals with Autism Spectrum Disorders (ASD) report difficulties extracting meaningful information from dynamic and complex social cues, like facial expressions. The nature and mechanisms of these difficulties remain unclear. Here we tested whether that difficulty can be traced to the pattern of activity in "social brain" regions, when viewing dynamic facial expressions. In two studies, adult participants (male and female) watched brief videos of a range of positive and negative facial expressions, while undergoing functional magnetic resonance imaging (Study 1: ASD n = 16, control n = 21; Study 2: ASD n = 22, control n = 30). Patterns of hemodynamic activity differentiated among facial emotional expressions in left and right superior temporal sulcus, fusiform gyrus, and parts of medial prefrontal cortex. In both control participants and high-functioning individuals with ASD, we observed (i) similar responses to emotional valence that generalized across facial expressions and animated social events; (ii) similar flexibility of responses to emotional valence, when manipulating the task-relevance of perceived emotions; and (iii) similar responses to a range of emotions within valence. Altogether, the data indicate that there was little or no group difference in cortical responses to isolated dynamic emotional facial expressions, as measured with fMRI. Difficulties with real-world social communication and social interaction in ASD may instead reflect differences in initiating and maintaining contingent interactions, or in integrating social information over time or context. Copyright © 2018 Elsevier Ltd. All rights reserved.

Scalable persistent identifier systems for dynamic datasets

NASA Astrophysics Data System (ADS)

Golodoniuc, P.; Cox, S. J. D.; Klump, J. F.

2016-12-01

Reliable and persistent identification of objects, whether tangible or not, is essential in information management. Many Internet-based systems have been developed to identify digital data objects, e.g., PURL, LSID, Handle, ARK. These were largely designed for identification of static digital objects. The amount of data made available online has grown exponentially over the last two decades and fine-grained identification of dynamically generated data objects within large datasets using conventional systems (e.g., PURL) has become impractical. We have compared capabilities of various technological solutions to enable resolvability of data objects in dynamic datasets, and developed a dataset-centric approach to resolution of identifiers. This is particularly important in Semantic Linked Data environments where dynamic frequently changing data is delivered live via web services, so registration of individual data objects to obtain identifiers is impractical. We use identifier patterns and pattern hierarchies for identification of data objects, which allows relationships between identifiers to be expressed, and also provides means for resolving a single identifier into multiple forms (i.e. views or representations of an object). The latter can be implemented through (a) HTTP content negotiation, or (b) use of URI querystring parameters. The pattern and hierarchy approach has been implemented in the Linked Data API supporting the United Nations Spatial Data Infrastructure (UNSDI) initiative and later in the implementation of geoscientific data delivery for the Capricorn Distal Footprints project using International Geo Sample Numbers (IGSN). This enables flexible resolution of multi-view persistent identifiers and provides a scalable solution for large heterogeneous datasets.

Metastable dynamical patterns and their stabilization in arrays of bidirectionally coupled sigmoidal neurons

NASA Astrophysics Data System (ADS)

Horikawa, Yo

2013-12-01

Transient patterns in a bistable ring of bidirectionally coupled sigmoidal neurons were studied. When the system had a pair of spatially uniform steady solutions, the instability of unstable spatially nonuniform steady solutions decreased exponentially with the number of neurons because of the symmetry of the system. As a result, transient spatially nonuniform patterns showed dynamical metastability: Their duration increased exponentially with the number of neurons and the duration of randomly generated patterns obeyed a power-law distribution. However, these metastable dynamical patterns were easily stabilized in the presence of small variations in coupling strength. Metastable rotating waves and their pinning in the presence of asymmetry in the direction of coupling and the disappearance of metastable dynamical patterns due to asymmetry in the output function of a neuron were also examined. Further, in a two-dimensional array of neurons with nearest-neighbor coupling, intrinsically one-dimensional patterns were dominant in transients, and self-excitation in these neurons affected the metastable dynamical patterns.

Percolator: Scalable Pattern Discovery in Dynamic Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choudhury, Sutanay; Purohit, Sumit; Lin, Peng

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walkingmore » through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.« less

Modelling hen harrier dynamics to inform human-wildlife conflict resolution: a spatially-realistic, individual-based approach.

PubMed

Heinonen, Johannes P M; Palmer, Stephen C F; Redpath, Steve M; Travis, Justin M J

2014-01-01

Individual-based models have gained popularity in ecology, and enable simultaneous incorporation of spatial explicitness and population dynamic processes to understand spatio-temporal patterns of populations. We introduce an individual-based model for understanding and predicting spatial hen harrier (Circus cyaneus) population dynamics in Great Britain. The model uses a landscape with habitat, prey and game management indices. The hen harrier population was initialised according to empirical census estimates for 1988/89 and simulated until 2030, and predictions for 1998, 2004 and 2010 were compared to empirical census estimates for respective years. The model produced a good qualitative match to overall trends between 1989 and 2010. Parameter explorations revealed relatively high elasticity in particular to demographic parameters such as juvenile male mortality. This highlights the need for robust parameter estimates from empirical research. There are clearly challenges for replication of real-world population trends, but this model provides a useful tool for increasing understanding of drivers of hen harrier dynamics and focusing research efforts in order to inform conflict management decisions.

Modelling Hen Harrier Dynamics to Inform Human-Wildlife Conflict Resolution: A Spatially-Realistic, Individual-Based Approach

PubMed Central

Heinonen, Johannes P. M.; Palmer, Stephen C. F.; Redpath, Steve M.; Travis, Justin M. J.

2014-01-01

Individual-based models have gained popularity in ecology, and enable simultaneous incorporation of spatial explicitness and population dynamic processes to understand spatio-temporal patterns of populations. We introduce an individual-based model for understanding and predicting spatial hen harrier (Circus cyaneus) population dynamics in Great Britain. The model uses a landscape with habitat, prey and game management indices. The hen harrier population was initialised according to empirical census estimates for 1988/89 and simulated until 2030, and predictions for 1998, 2004 and 2010 were compared to empirical census estimates for respective years. The model produced a good qualitative match to overall trends between 1989 and 2010. Parameter explorations revealed relatively high elasticity in particular to demographic parameters such as juvenile male mortality. This highlights the need for robust parameter estimates from empirical research. There are clearly challenges for replication of real-world population trends, but this model provides a useful tool for increasing understanding of drivers of hen harrier dynamics and focusing research efforts in order to inform conflict management decisions. PMID:25405860

Reduced attentional focus and the influence on expert anticipatory perception.

PubMed

Gorman, Adam D; Abernethy, Bruce; Farrow, Damian

2018-01-01

The anticipatory memory encodings of expert and novice basketball players were examined under conditions of both full (attended condition) and reduced (unattended condition) attention (see also Gorman, Abernethy, & Farrow in Attention, Perception, & Psychophysics, 75, 835-844, 2013a). Participants completed a typical pattern recall task using dynamic playing sequences from basketball, and their responses were compared to both the original target pattern as well as to the series of patterns that occurred immediately after and immediately before the target image. The latter had not previously been employed in a pattern recall task when examining the anticipatory encoding of pattern information. Results revealed that the overall extent of the forward displacement for both the attended and unattended patterns was generally significantly greater for the experts, with the expert advantage tending to be most prominent for the attacking patterns. The novel addition of both forward and backward scenes may provide a more precise measure of the anticipatory effect, suggesting that future research in this domain should use a similar methodological design.

A Nonlinear Dynamics Approach for Incorporating Wind-Speed Patterns into Wind-Power Project Evaluation

PubMed Central

Huffaker, Ray; Bittelli, Marco

2015-01-01

Wind-energy production may be expanded beyond regions with high-average wind speeds (such as the Midwest U.S.A.) to sites with lower-average speeds (such as the Southeast U.S.A.) by locating favorable regional matches between natural wind-speed and energy-demand patterns. A critical component of wind-power evaluation is to incorporate wind-speed dynamics reflecting documented diurnal and seasonal behavioral patterns. Conventional probabilistic approaches remove patterns from wind-speed data. These patterns must be restored synthetically before they can be matched with energy-demand patterns. How to accurately restore wind-speed patterns is a vexing problem spurring an expanding line of papers. We propose a paradigm shift in wind power evaluation that employs signal-detection and nonlinear-dynamics techniques to empirically diagnose whether synthetic pattern restoration can be avoided altogether. If the complex behavior of observed wind-speed records is due to nonlinear, low-dimensional, and deterministic system dynamics, then nonlinear dynamics techniques can reconstruct wind-speed dynamics from observed wind-speed data without recourse to conventional probabilistic approaches. In the first study of its kind, we test a nonlinear dynamics approach in an application to Sugarland Wind—the first utility-scale wind project proposed in Florida, USA. We find empirical evidence of a low-dimensional and nonlinear wind-speed attractor characterized by strong temporal patterns that match up well with regular daily and seasonal electricity demand patterns. PMID:25617767

Dynamic speech representations in the human temporal lobe.

PubMed

Leonard, Matthew K; Chang, Edward F

2014-09-01

Speech perception requires rapid integration of acoustic input with context-dependent knowledge. Recent methodological advances have allowed researchers to identify underlying information representations in primary and secondary auditory cortex and to examine how context modulates these representations. We review recent studies that focus on contextual modulations of neural activity in the superior temporal gyrus (STG), a major hub for spectrotemporal encoding. Recent findings suggest a highly interactive flow of information processing through the auditory ventral stream, including influences of higher-level linguistic and metalinguistic knowledge, even within individual areas. Such mechanisms may give rise to more abstract representations, such as those for words. We discuss the importance of characterizing representations of context-dependent and dynamic patterns of neural activity in the approach to speech perception research. Copyright © 2014 Elsevier Ltd. All rights reserved.

New segmentation-based tone mapping algorithm for high dynamic range image

NASA Astrophysics Data System (ADS)

Duan, Weiwei; Guo, Huinan; Zhou, Zuofeng; Huang, Huimin; Cao, Jianzhong

2017-07-01

The traditional tone mapping algorithm for the display of high dynamic range (HDR) image has the drawback of losing the impression of brightness, contrast and color information. To overcome this phenomenon, we propose a new tone mapping algorithm based on dividing the image into different exposure regions in this paper. Firstly, the over-exposure region is determined using the Local Binary Pattern information of HDR image. Then, based on the peak and average gray of the histogram, the under-exposure and normal-exposure region of HDR image are selected separately. Finally, the different exposure regions are mapped by differentiated tone mapping methods to get the final result. The experiment results show that the proposed algorithm achieve the better performance both in visual quality and objective contrast criterion than other algorithms.

Dissimilarity measure based on ordinal pattern for physiological signals

NASA Astrophysics Data System (ADS)

Wang, Jing; Shang, Pengjian; Shi, Wenbin; Cui, Xingran

2016-08-01

Complex physiologic signals may carry information of their underlying mechanisms. In this paper, we introduce a dissimilarity measure to capture the features of underlying dynamics from various types of physiologic signals based on rank order statistics of ordinal patterns. Simulated 1/f noise and white noise are used to evaluate the effect of data length, embedding dimension and time delay on this measure. We then apply this measure to different physiologic signals. The method can successfully characterize the unique underlying patterns of subjects at similar physiologic states. It can also serve as a good discriminative tool for the healthy young, healthy elderly, congestive heart failure, atrial fibrilation and white noise groups. Furthermore, when investigated into the details of underlying ordinal patterns for each group, it is found that the distributions of ordinal patterns varies significantly for healthy and pathologic states, as well as aging.

Dynamic Skin Patterns in Cephalopods

PubMed Central

How, Martin J.; Norman, Mark D.; Finn, Julian; Chung, Wen-Sung; Marshall, N. Justin

2017-01-01

Cephalopods are unrivaled in the natural world in their ability to alter their visual appearance. These mollusks have evolved a complex system of dermal units under neural, hormonal, and muscular control to produce an astonishing variety of body patterns. With parallels to the pixels on a television screen, cephalopod chromatophores can be coordinated to produce dramatic, dynamic, and rhythmic displays, defined collectively here as “dynamic patterns.” This study examines the nature, context, and potential functions of dynamic patterns across diverse cephalopod taxa. Examples are presented for 21 species, including 11 previously unreported in the scientific literature. These range from simple flashing or flickering patterns, to highly complex passing wave patterns involving multiple skin fields. PMID:28674500

Resting state networks in empirical and simulated dynamic functional connectivity.

PubMed

Glomb, Katharina; Ponce-Alvarez, Adrián; Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

2017-10-01

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions ("communities") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of helicopter simulator sickness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Horn, R.D.; Birdwell, J.D.; Allgood, G.O.

1990-01-01

Machine learning methods from artificial intelligence are used to identify information in sampled accelerometer signals and associative behavioral patterns which correlates pilot simulator sickness with helicopter simulator dynamics. These simulators are used to train pilots in fundamental procedures, tactics, and response to emergency conditions. Simulator sickness induced by these systems represents a risk factor to both the pilot and manufacturer. Simulator sickness symptoms are closely aligned with those of motion sickness. Previous studies have been performed by behavioral psychologists using information gathered with surveys and motor skills performance measures; however, the results are constrained by the limited information which ismore » accessible in this manner. In this work, accelerometers were installed in the simulator cab, enabling a complete record of flight dynamics and the pilot's control response as a function of time. Given the results of performance measures administered to detect simulator sickness symptoms, the problem was then to find functions of the recorded data which could be used to help predict the simulator sickness level and susceptibility. Methods based upon inductive inference were used, which yield decision trees whose leaves indicate the degree of simulator-induced sickness. The long-term goal is to develop a gauge'' which can provide an on-line prediction of simulator sickness level, given a pilot's associative behavioral patterns (learned expectations). This will allow informed decisions to be made on when to terminate a hop and provide an effective basis for determining training and flight restrictions placed upon the pilot after simulator use. 6 refs., 6 figs.« less

Hierarchical nonlinear dynamics of human attention.

PubMed

Rabinovich, Mikhail I; Tristan, Irma; Varona, Pablo

2015-08-01

Attention is the process of focusing mental resources on a specific cognitive/behavioral task. Such brain dynamics involves different partially overlapping brain functional networks whose interconnections change in time according to the performance stage, and can be stimulus-driven or induced by an intrinsically generated goal. The corresponding activity can be described by different families of spatiotemporal discrete patterns or sequential dynamic modes. Since mental resources are finite, attention modalities compete with each other at all levels of the hierarchy, from perception to decision making and behavior. Cognitive activity is a dynamical process and attention possesses some universal dynamical characteristics. Thus, it is time to apply nonlinear dynamical theory for the description and prediction of hierarchical attentional tasks. Such theory has to include the analyses of attentional control stability, the time cost of attention switching, the finite capacity of informational resources in the brain, and the normal and pathological bifurcations of attention sequential dynamics. In this paper we have integrated today's knowledge, models and results in these directions. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics

NASA Astrophysics Data System (ADS)

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-01

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter—the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics.

PubMed

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-03

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter-the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

Chaotic Dynamics of Linguistic-Like Processes at the Syntactical and Semantic Levels: in the Pursuit of a Multifractal Attractor

NASA Astrophysics Data System (ADS)

Nicolis, John S.; Katsikas, Anastassis A.

Collective parameters such as the Zipf's law-like statistics, the Transinformation, the Block Entropy and the Markovian character are compared for natural, genetic, musical and artificially generated long texts from generating partitions (alphabets) on homogeneous as well as on multifractal chaotic maps. It appears that minimal requirements for a language at the syntactical level such as memory, selectivity of few keywords and broken symmetry in one dimension (polarity) are more or less met by dynamically iterating simple maps or flows e.g. very simple chaotic hardware. The same selectivity is observed at the semantic level where the aim refers to partitioning a set of enviromental impinging stimuli onto coexisting attractors-categories. Under the regime of pattern recognition and classification, few key features of a pattern or few categories claim the lion's share of the information stored in this pattern and practically, only these key features are persistently scanned by the cognitive processor. A multifractal attractor model can in principle explain this high selectivity, both at the syntactical and the semantic levels.

Co-activation patterns in resting-state fMRI signals.

PubMed

Liu, Xiao; Zhang, Nanyin; Chang, Catie; Duyn, Jeff H

2018-02-08

The brain is a complex system that integrates and processes information across multiple time scales by dynamically coordinating activities over brain regions and circuits. Correlations in resting-state functional magnetic resonance imaging (rsfMRI) signals have been widely used to infer functional connectivity of the brain, providing a metric of functional associations that reflects a temporal average over an entire scan (typically several minutes or longer). Not until recently was the study of dynamic brain interactions at much shorter time scales (seconds to minutes) considered for inference of functional connectivity. One method proposed for this objective seeks to identify and extract recurring co-activation patterns (CAPs) that represent instantaneous brain configurations at single time points. Here, we review the development and recent advancement of CAP methodology and other closely related approaches, as well as their applications and associated findings. We also discuss the potential neural origins and behavioral relevance of CAPs, along with methodological issues and future research directions in the analysis of fMRI co-activation patterns. Copyright © 2018 Elsevier Inc. All rights reserved.

A pseudo-equilibrium thermodynamic model of information processing in nonlinear brain dynamics.

PubMed

Freeman, Walter J

2008-01-01

Computational models of brain dynamics fall short of performance in speed and robustness of pattern recognition in detecting minute but highly significant pattern fragments. A novel model employs the properties of thermodynamic systems operating far from equilibrium, which is analyzed by linearization near adaptive operating points using root locus techniques. Such systems construct order by dissipating energy. Reinforcement learning of conditioned stimuli creates a landscape of attractors and their basins in each sensory cortex by forming nerve cell assemblies in cortical connectivity. Retrieval of a selected category of stored knowledge is by a phase transition that is induced by a conditioned stimulus, and that leads to pattern self-organization. Near self-regulated criticality the cortical background activity displays aperiodic null spikes at which analytic amplitude nears zero, and which constitute a form of Rayleigh noise. Phase transitions in recognition and recall are initiated at null spikes in the presence of an input signal, owing to the high signal-to-noise ratio that facilitates capture of cortex by an attractor, even by very weak activity that is typically evoked by a conditioned stimulus.

Effect of body weight support variation on muscle activities during robot assisted gait: a dynamic simulation study.

PubMed

Hussain, Shahid; Jamwal, Prashant K; Ghayesh, Mergen H

2017-05-01

While body weight support (BWS) intonation is vital during conventional gait training of neurologically challenged subjects, it is important to evaluate its effect during robot assisted gait training. In the present research we have studied the effect of BWS intonation on muscle activities during robotic gait training using dynamic simulations. Two dimensional (2-D) musculoskeletal model of human gait was developed conjointly with another 2-D model of a robotic orthosis capable of actuating hip, knee and ankle joints simultaneously. The musculoskeletal model consists of eight major muscle groups namely; soleus (SOL), gastrocnemius (GAS), tibialis anterior (TA), hamstrings (HAM), vasti (VAS), gluteus maximus (GLU), uniarticular hip flexors (iliopsoas, IP), and Rectus Femoris (RF). BWS was provided at levels of 0, 20, 40 and 60% during the simulations. In order to obtain a feasible set of muscle activities during subsequent gait cycles, an inverse dynamics algorithm along with a quadratic minimization algorithm was implemented. The dynamic parameters of the robot assisted human gait such as joint angle trajectories, ground contact force (GCF), human limb joint torques and robot induced torques at different levels of BWS were derived. The patterns of muscle activities at variable BWS were derived and analysed. For most part of the gait cycle (GC) the muscle activation patterns are quite similar for all levels of BWS as is apparent from the mean of muscle activities for the complete GC. Effect of BWS variation during robot assisted gait on muscle activities was studied by developing dynamic simulation. It is expected that the proposed dynamic simulation approach will provide important inferences and information about the muscle function variations consequent upon a change in BWS during robot assisted gait. This information shall be quite important while investigating the influence of BWS intonation on neuromuscular parameters of interest during robotic gait training.

Revealing Hidden Structural Order Controlling Both Fast and Slow Glassy Dynamics in Supercooled Liquids

NASA Astrophysics Data System (ADS)

Tong, Hua; Tanaka, Hajime

2018-01-01

The dynamics of a supercooled liquid near the glass transition is characterized by two-step relaxation, fast β and slow α relaxations. Because of the apparently disordered nature of glassy structures, there have been long debates over whether the origin of drastic slowing-down of the α relaxation accompanied by heterogeneous dynamics is thermodynamic or dynamic. Furthermore, it has been elusive whether there is any deep connection between fast β and slow α modes. To settle these issues, here we introduce a set of new structural order parameters characterizing sterically favored structures with high local packing capability, and then access structure-dynamics correlation by a novel nonlocal approach. We find that the particle mobility is under control of the static order parameter field. The fast β process is controlled by the instantaneous order parameter field locally, resulting in short-time particle-scale dynamics. Then the mobility field progressively develops with time t , following the initial order parameter field from disorder to more ordered regions. As is well known, the heterogeneity in the mobility field (dynamic heterogeneity) is maximized with a characteristic length ξ4, when t reaches the relaxation time τα. We discover that this mobility pattern can be predicted solely by a spatial coarse graining of the initial order parameter field at t =0 over a length ξ without any dynamical information. Furthermore, we find a relation ξ ˜ξ4, indicating that the static length ξ grows coherently with the dynamic one ξ4 upon cooling. This further suggests an intrinsic link between τα and ξ : the growth of the static length ξ is the origin of dynamical slowing-down. These we confirm for the first time in binary glass formers both in two and three spatial dimensions. Thus, a static structure has two intrinsic characteristic lengths, particle size and ξ , which control dynamics in local and nonlocal manners, resulting in the emergence of the two key relaxation modes, fast β and slow α processes, respectively. Because the two processes share a common structural origin, we can even predict a dynamic propensity pattern at long timescale from the fast β pattern. The presence of such intrinsic structure-dynamics correlation strongly indicates a thermodynamic nature of glass transition.

Shape measurement and vibration analysis of moving speaker cone

NASA Astrophysics Data System (ADS)

Zhang, Qican; Liu, Yuankun; Lehtonen, Petri

2014-06-01

Surface three-dimensional (3-D) shape information is needed for many fast processes such as structural testing of material, standing waves on loudspeaker cone, etc. Usually measurement is done from limited number of points using electrical sensors or laser distance meters. Fourier Transform Profilometry (FTP) enables fast shape measurement of the whole surface. Method is based on angled sinusoidal fringe pattern projection and image capturing. FTP requires only one image of the deformed fringe pattern to restore the 3-D shape of the measured object, which makes real-time or dynamic data processing possible. In our experiment the method was used for loudspeaker cone distortion measurement in dynamic conditions. For sound quality issues it is important that the whole cone moves in same phase and there are no partial waves. Our imaging resolution was 1280x1024 pixels and frame rate was 200 fps. Using our setup we found unwanted spatial waves in our sample cone.

Human dynamics revealed through Web analytics

NASA Astrophysics Data System (ADS)

Gonçalves, Bruno; Ramasco, José J.

2008-08-01

The increasing ubiquity of Internet access and the frequency with which people interact with it raise the possibility of using the Web to better observe, understand, and monitor several aspects of human social behavior. Web sites with large numbers of frequently returning users are ideal for this task. If these sites belong to companies or universities, their usage patterns can furnish information about the working habits of entire populations. In this work, we analyze the properly anonymized logs detailing the access history to Emory University’s Web site. Emory is a medium-sized university located in Atlanta, Georgia. We find interesting structure in the activity patterns of the domain and study in a systematic way the main forces behind the dynamics of the traffic. In particular, we find that linear preferential linking, priority-based queuing, and the decay of interest for the contents of the pages are the essential ingredients to understand the way users navigate the Web.

Hidden scaling patterns and universality in written communication

NASA Astrophysics Data System (ADS)

Formentin, M.; Lovison, A.; Maritan, A.; Zanzotto, G.

2014-07-01

The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's "proper time" measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near -3/2. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bimodal character, which is empirically borne out by our newly collected years-long data on email. These perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, and emergency handling.

Complex patterns of abnormal heartbeats

NASA Technical Reports Server (NTRS)

Schulte-Frohlinde, Verena; Ashkenazy, Yosef; Goldberger, Ary L.; Ivanov, Plamen Ch; Costa, Madalena; Morley-Davies, Adrian; Stanley, H. Eugene; Glass, Leon

2002-01-01

Individuals having frequent abnormal heartbeats interspersed with normal heartbeats may be at an increased risk of sudden cardiac death. However, mechanistic understanding of such cardiac arrhythmias is limited. We present a visual and qualitative method to display statistical properties of abnormal heartbeats. We introduce dynamical "heartprints" which reveal characteristic patterns in long clinical records encompassing approximately 10(5) heartbeats and may provide information about underlying mechanisms. We test if these dynamics can be reproduced by model simulations in which abnormal heartbeats are generated (i) randomly, (ii) at a fixed time interval following a preceding normal heartbeat, or (iii) by an independent oscillator that may or may not interact with the normal heartbeat. We compare the results of these three models and test their limitations to comprehensively simulate the statistical features of selected clinical records. This work introduces methods that can be used to test mathematical models of arrhythmogenesis and to develop a new understanding of underlying electrophysiologic mechanisms of cardiac arrhythmia.

Complex Dynamics in Information Sharing Networks

NASA Astrophysics Data System (ADS)

Cronin, Bruce

This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

Exploring space-time structure of human mobility in urban space

NASA Astrophysics Data System (ADS)

Sun, J. B.; Yuan, J.; Wang, Y.; Si, H. B.; Shan, X. M.

2011-03-01

Understanding of human mobility in urban space benefits the planning and provision of municipal facilities and services. Due to the high penetration of cell phones, mobile cellular networks provide information for urban dynamics with a large spatial extent and continuous temporal coverage in comparison with traditional approaches. The original data investigated in this paper were collected by cellular networks in a southern city of China, recording the population distribution by dividing the city into thousands of pixels. The space-time structure of urban dynamics is explored by applying Principal Component Analysis (PCA) to the original data, from temporal and spatial perspectives between which there is a dual relation. Based on the results of the analysis, we have discovered four underlying rules of urban dynamics: low intrinsic dimensionality, three categories of common patterns, dominance of periodic trends, and temporal stability. It implies that the space-time structure can be captured well by remarkably few temporal or spatial predictable periodic patterns, and the structure unearthed by PCA evolves stably over time. All these features play a critical role in the applications of forecasting and anomaly detection.

Glassy dynamics in three-dimensional embryonic tissues

PubMed Central

Schötz, Eva-Maria; Lanio, Marcos; Talbot, Jared A.; Manning, M. Lisa

2013-01-01

Many biological tissues are viscoelastic, behaving as elastic solids on short timescales and fluids on long timescales. This collective mechanical behaviour enables and helps to guide pattern formation and tissue layering. Here, we investigate the mechanical properties of three-dimensional tissue explants from zebrafish embryos by analysing individual cell tracks and macroscopic mechanical response. We find that the cell dynamics inside the tissue exhibit features of supercooled fluids, including subdiffusive trajectories and signatures of caging behaviour. We develop a minimal, three-parameter mechanical model for these dynamics, which we calibrate using only information about cell tracks. This model generates predictions about the macroscopic bulk response of the tissue (with no fit parameters) that are verified experimentally, providing a strong validation of the model. The best-fit model parameters indicate that although the tissue is fluid-like, it is close to a glass transition, suggesting that small changes to single-cell parameters could generate a significant change in the viscoelastic properties of the tissue. These results provide a robust framework for quantifying and modelling mechanically driven pattern formation in tissues. PMID:24068179

How events determine spreading patterns: information transmission via internal and external influences on social networks

NASA Astrophysics Data System (ADS)

Liu, Chuang; Zhan, Xiu-Xiu; Zhang, Zi-Ke; Sun, Gui-Quan; Hui, Pak Ming

2015-11-01

Recently, information transmission models motivated by the classical epidemic propagation, have been applied to a wide-range of social systems, generally assume that information mainly transmits among individuals via peer-to-peer interactions on social networks. In this paper, we consider one more approach for users to get information: the out-of-social-network influence. Empirical analyzes of eight typical events’ diffusion on a very large micro-blogging system, Sina Weibo, show that the external influence has significant impact on information spreading along with social activities. In addition, we propose a theoretical model to interpret the spreading process via both internal and external channels, considering three essential properties: (i) memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts. Experimental and mathematical results indicate that the information indeed spreads much quicker and broader with mutual effects of the internal and external influences. More importantly, the present model reveals that the event characteristic would highly determine the essential spreading patterns once the network structure is established. The results may shed some light on the in-depth understanding of the underlying dynamics of information transmission on real social networks.

Parameter estimation by decoherence in the double-slit experiment

NASA Astrophysics Data System (ADS)

Matsumura, Akira; Ikeda, Taishi; Kukita, Shingo

2018-06-01

We discuss a parameter estimation problem using quantum decoherence in the double-slit interferometer. We consider a particle coupled to a massive scalar field after the particle passing through the double slit and solve the dynamics non-perturbatively for the coupling by the WKB approximation. This allows us to analyze the estimation problem which cannot be treated by master equation used in the research of quantum probe. In this model, the scalar field reduces the interference fringes of the particle and the fringe pattern depends on the field mass and coupling. To evaluate the contrast and the estimation precision obtained from the pattern, we introduce the interferometric visibility and the Fisher information matrix of the field mass and coupling. For the fringe pattern observed on the distant screen, we derive a simple relation between the visibility and the Fisher matrix. Also, focusing on the estimation precision of the mass, we find that the Fisher information characterizes the wave-particle duality in the double-slit interferometer.

Random patterns in fish schooling enhance alertness: A hydrodynamic perspective

NASA Astrophysics Data System (ADS)

Kadri, U.; Brümmer, F.; Kadri, A.

2016-11-01

One of the most highly debated questions in the field of animal swarming and social behaviour is the collective random patterns and chaotic behaviour formed by some animal species, in particular if there is a danger. Is such a behaviour beneficial or unfavourable for survival? Here we report on one of the most remarkable forms of animal swarming and social behaviour —fish schooling— from a hydrodynamic point of view. We found that some fish species do not have preferred orientation and they swarm in a random pattern mode, despite the excess of energy consumed. Our analyses, which include calculations of the hydrodynamic forces between slender bodies, show that such a behaviour may enhance the transfer of hydrodynamic information, and thus the survivability of the school could improve. These findings support the general hypothesis that a disordered and nontrivial collective behaviour of individuals within a nonlinear dynamical system is essential for optimising transfer of information —an optimisation that might be crucial for survival.

Structure preserving clustering-object tracking via subgroup motion pattern segmentation

NASA Astrophysics Data System (ADS)

Fan, Zheyi; Zhu, Yixuan; Jiang, Jiao; Weng, Shuqin; Liu, Zhiwen

2018-01-01

Tracking clustering objects with similar appearances simultaneously in collective scenes is a challenging task in the field of collective motion analysis. Recent work on clustering-object tracking often suffers from poor tracking accuracy and terrible real-time performance due to the neglect or the misjudgment of the motion differences among objects. To address this problem, we propose a subgroup motion pattern segmentation framework based on a multilayer clustering structure and establish spatial constraints only among objects in the same subgroup, which entails having consistent motion direction and close spatial position. In addition, the subgroup segmentation results are updated dynamically because crowd motion patterns are changeable and affected by objects' destinations and scene structures. The spatial structure information combined with the appearance similarity information is used in the structure preserving object tracking framework to track objects. Extensive experiments conducted on several datasets containing multiple real-world crowd scenes validate the accuracy and the robustness of the presented algorithm for tracking objects in collective scenes.

Development and assessment of 30-meter pine density maps for landscape-level modeling of mountain pine beetle dynamics

Treesearch

Benjamin A. Crabb; James A. Powell; Barbara J. Bentz

2012-01-01

Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population...

On the road to national mapping and attribution of the processes underlying U.S

Treesearch

Karen Schleeweis; Gretchen G. Moisen; Todd A. Schroeder; Chris Toney; Elizabeth A. Freeman

2015-01-01

Questions regarding the impact of natural and anthropogenic forest change events (temporary and persisting) on energy, water and nutrient cycling, forest sustainability and resilience, and ecosystem services call for a full suite of information on the spatial and temporal trends of forest dynamics. Temporal and spatial patterns of change along with their magnitude and...

Investigation of Dynamic Algorithms for Pattern Recognition Identified in Cerebral Cortex

DTIC Science & Technology

1991-12-02

oscillatory and possibly chaotic activity forin the actual cortical substrate of the diverse sensory, motor, and cognitive operations now studied in...September Neural Information Processing Systems - Natural and Synthetic, Denver, Colo., November 1989 U.C. San Diego, Cognitive Science Dept...Baird. Biologically applied neural networks may foster the co-evolution of neurobiology and cognitive psychology. Brain and Behavioral Sciences, 37

Distinct spatiotemporal activity in principal neurons of the mouse olfactory bulb in anesthetized and awake states

PubMed Central

Blauvelt, David G.; Sato, Tomokazu F.; Wienisch, Martin; Murthy, Venkatesh N.

2013-01-01

The acquisition of olfactory information and its early processing in mammals are modulated by brain states through sniffing behavior and neural feedback. We imaged the spatiotemporal pattern of odor-evoked activity in a population of output neurons (mitral/tufted cells, MTCs) in the olfactory bulb (OB) of head-restrained mice expressing a genetically-encoded calcium indicator. The temporal dynamics of MTC population activity were relatively simple in anesthetized animals, but were highly variable in awake animals. However, the apparently irregular activity in awake animals could be predicted well using sniff timing measured externally, or inferred through fluctuations in the global responses of MTC population even without explicit knowledge of sniff times. The overall spatial pattern of activity was conserved across states, but odor responses had a diffuse spatial component in anesthetized mice that was less prominent during wakefulness. Multi-photon microscopy indicated that MTC lateral dendrites were the likely source of spatially disperse responses in the anesthetized animal. Our data demonstrate that the temporal and spatial dynamics of MTCs can be significantly modulated by behavioral state, and that the ensemble activity of MTCs can provide information about sniff timing to downstream circuits to help decode odor responses. PMID:23543674

Contact, Travel, and Transmission: The Impact of Winter Holidays on Influenza Dynamics in the United States

PubMed Central

Ewing, Anne; Lee, Elizabeth C.; Viboud, Cécile

2017-01-01

Abstract Background. The seasonality of influenza is thought to vary according to environmental factors and human behavior. During winter holidays, potential disease-causing contact and travel deviate from typical patterns. We aim to understand these changes on age-specific and spatial influenza transmission. Methods. We characterized the changes to transmission and epidemic trajectories among children and adults in a spatial context before, during, and after the winter holidays among aggregated physician medical claims in the United States from 2001 to 2009 and among synthetic data simulated from a deterministic, age-specific spatial metapopulation model. Results. Winter holidays reduced influenza transmission and delayed the trajectory of influenza season epidemics. The holiday period was marked by a shift in the relative risk of disease from children toward adults. Model results indicated that holidays delayed epidemic peaks and synchronized incidence across locations, and that contact reductions from school closures, rather than age-specific mixing and travel, produced these observed holiday influenza dynamics. Conclusions. Winter holidays delay seasonal influenza epidemic peaks and shift disease risk toward adults because of changes in contact patterns. These findings may inform targeted influenza information and vaccination campaigns during holiday periods. PMID:28031259

A new similarity index for nonlinear signal analysis based on local extrema patterns

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Motie Nasrabadi, Ali; Shamsollahi, Mohammad Bagher

2018-02-01

Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By adding time information of local extrema as well as fuzzifying quantized values, this work proposes a new similarity index for nonlinear and long-term signal analysis, which extends the SBLE method. These new features provide more information about signals and reduce noise sensitivity by fuzzifying them. A number of practical tests were performed to demonstrate the ability of the method in nonlinear signal clustering and classification on synthetic data. In addition, epileptic seizure detection based on electroencephalography (EEG) signal processing was done by the proposed similarity to feature the potentials of the method as a real-world application tool.

A self-organized learning strategy for object recognition by an embedded line of attraction

NASA Astrophysics Data System (ADS)

Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

2012-04-01

For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based on this observation we developed a self- organizing line attractor, which is capable of generating new lines in the feature space to learn unrecognized patterns. Experiments performed on UMIST pose database and CMU face expression variant database for face recognition have shown that the proposed nonlinear line attractor is able to successfully identify the individuals and it provided better recognition rate when compared to the state of the art face recognition techniques. Experiments on FRGC version 2 database has also provided excellent recognition rate in images captured in complex lighting environments. Experiments performed on the Japanese female face expression database and Essex Grimace database using the self organizing line attractor have also shown successful expression invariant face recognition. These results show that the proposed model is able to create nonlinear manifolds in a multidimensional feature space to distinguish complex patterns.

Dynamic Imaging of Mouse Embryos and Cardiodynamics in Static Culture.

PubMed

Lopez, Andrew L; Larina, Irina V

2018-01-01

The heart is a dynamic organ that quickly undergoes morphological and mechanical changes through early embryonic development. Characterizing these early moments is important for our understanding of proper embryonic development and the treatment of heart disease. Traditionally, tomographic imaging modalities and fluorescence-based microscopy are excellent approaches to visualize structural features and gene expression patterns, respectively, and connect aberrant gene programs to pathological phenotypes. However, these approaches usually require static samples or fluorescent markers, which can limit how much information we can derive from the dynamic and mechanical changes that regulate heart development. Optical coherence tomography (OCT) is unique in this circumstance because it allows for the acquisition of three-dimensional structural and four-dimensional (3D + time) functional images of living mouse embryos without fixation or contrast reagents. In this chapter, we focus on how OCT can visualize heart morphology at different stages of development and provide cardiodynamic information to reveal mechanical properties of the developing heart.

Comparative primate genomics: emerging patterns of genome content and dynamics

PubMed Central

Rogers, Jeffrey; Gibbs, Richard A.

2014-01-01

Preface Advances in genome sequencing technologies have created new opportunities for comparative primate genomics. Genome assemblies have been published for several primates, with analyses of several others underway. Whole genome assemblies for the great apes provide remarkable new information about the evolutionary origins of the human genome and the processes involved. Genomic data for macaques and other nonhuman primates provide valuable insight into genetic similarities and differences among species used as models for disease-related research. This review summarizes current knowledge regarding primate genome content and dynamics and offers a series of goals for the near future. PMID:24709753

Mapping 180° polar domains using electron backscatter diffraction and dynamical scattering simulations

DOE PAGES

Burch, Matthew J.; Fancher, Chris M.; Patala, Srikanth; ...

2016-11-18

A novel technique, which directly and nondestructively maps polar domains using electron backscatter diffraction (EBSD) is described and demonstrated. Through dynamical diffraction simulations and quantitative comparison to experimental EBSD patterns, the absolute orientation of a non-centrosymmetric crystal can be determined. With this information, the polar domains of a material can be mapped. The technique is demonstrated by mapping the non-ferroelastic, or 180°, ferroelectric domains in periodically poled LiNbO 3 single crystals. Furthermore, the authors demonstrate the possibility of mapping polarity using this technique in other polar materials system.

Comparative primate genomics: emerging patterns of genome content and dynamics.

PubMed

Rogers, Jeffrey; Gibbs, Richard A

2014-05-01

Advances in genome sequencing technologies have created new opportunities for comparative primate genomics. Genome assemblies have been published for various primate species, and analyses of several others are underway. Whole-genome assemblies for the great apes provide remarkable new information about the evolutionary origins of the human genome and the processes involved. Genomic data for macaques and other non-human primates offer valuable insights into genetic similarities and differences among species that are used as models for disease-related research. This Review summarizes current knowledge regarding primate genome content and dynamics, and proposes a series of goals for the near future.

Reflow dynamics of thin patterned viscous films

NASA Astrophysics Data System (ADS)

Leveder, T.; Landis, S.; Davoust, L.

2008-01-01

This letter presents a study of viscous smoothening dynamics of a nanopatterned thin film. Ultrathin film manufacturing processes appearing to be a key point of nanotechnology engineering and numerous studies have been recently led in order to exhibit driving parameters of this transient surface motion, focusing on time scale accuracy method. Based on nanomechanical analysis, this letter shows that controlled shape measurements provided much more detailed information about reflow mechanism. Control of reflow process of any complex surface shape, or measurement of material parameter as thin film viscosity, free surface energy, or even Hamaker constant are therefore possible.

Transmission dynamics of schistosomiasis in Zimbabwe: A mathematical and GIS Approach

NASA Astrophysics Data System (ADS)

Ngarakana-Gwasira, E. T.; Bhunu, C. P.; Masocha, M.; Mashonjowa, E.

2016-06-01

Temperature and presence of water bodies are known to influence the transmission dynamics of schistosomiasis. In this work, effects of water bodies (taken in context of rainfall patterns) and temperature from 1950 to 2000 are considered in the model. With the aid of Geographic Information System (GIS), the reproduction number is mapped on the Zimbabwean country. Results of the mapping show high reproduction numbers along the Lowveld and the Zambezi valley catchment area. High reproduction numbers suggest high levels of schistosomiasis. This result suggests more control efforts should be targeted in these areas with high reproduction numbers.

Social behavior of bacteria: from physics to complex organization

NASA Astrophysics Data System (ADS)

Ben-Jacob, E.

2008-10-01

I describe how bacteria develop complex colonial patterns by utilizing intricate communication capabilities, such as quorum sensing, chemotactic signaling and exchange of genetic information (plasmids) Bacteria do not store genetically all the information required for generating the patterns for all possible environments. Instead, additional information is cooperatively generated as required for the colonial organization to proceed. Each bacterium is, by itself, a biotic autonomous system with its own internal cellular informatics capabilities (storage, processing and assessments of information). These afford the cell certain plasticity to select its response to biochemical messages it receives, including self-alteration and broadcasting messages to initiate alterations in other bacteria. Hence, new features can collectively emerge during self-organization from the intra-cellular level to the whole colony. Collectively bacteria store information, perform decision make decisions (e.g. to sporulate) and even learn from past experience (e.g. exposure to antibiotics)-features we begin to associate with bacterial social behavior and even rudimentary intelligence. I also take Schrdinger’s’ “feeding on negative entropy” criteria further and propose that, in addition organisms have to extract latent information embedded in the environment. By latent information we refer to the non-arbitrary spatio-temporal patterns of regularities and variations that characterize the environmental dynamics. In other words, bacteria must be able to sense the environment and perform internal information processing for thriving on latent information embedded in the complexity of their environment. I then propose that by acting together, bacteria can perform this most elementary cognitive function more efficiently as can be illustrated by their cooperative behavior.

Lindemann histograms as a new method to analyse nano-patterns and phases

NASA Astrophysics Data System (ADS)

Makey, Ghaith; Ilday, Serim; Tokel, Onur; Ibrahim, Muhamet; Yavuz, Ozgun; Pavlov, Ihor; Gulseren, Oguz; Ilday, Omer

The detection, observation, and analysis of material phases and atomistic patterns are of great importance for understanding systems exhibiting both equilibrium and far-from-equilibrium dynamics. As such, there is intense research on phase transitions and pattern dynamics in soft matter, statistical and nonlinear physics, and polymer physics. In order to identify phases and nano-patterns, the pair correlation function is commonly used. However, this approach is limited in terms of recognizing competing patterns in dynamic systems, and lacks visualisation capabilities. In order to solve these limitations, we introduce Lindemann histogram quantification as an alternative method to analyse solid, liquid, and gas phases, along with hexagonal, square, and amorphous nano-pattern symmetries. We show that the proposed approach based on Lindemann parameter calculated per particle maps local number densities to material phase or particles pattern. We apply the Lindemann histogram method on dynamical colloidal self-assembly experimental data and identify competing patterns.

Voltage Imaging of Waking Mouse Cortex Reveals Emergence of Critical Neuronal Dynamics

PubMed Central

Scott, Gregory; Fagerholm, Erik D.; Mutoh, Hiroki; Leech, Robert; Sharp, David J.; Shew, Woodrow L.

2014-01-01

Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain. PMID:25505314

Diel growth dynamics in tree stems: linking anatomy and ecophysiology.

PubMed

Steppe, Kathy; Sterck, Frank; Deslauriers, Annie

2015-06-01

Impacts of climate on stem growth in trees are studied in anatomical, ecophysiological, and ecological disciplines, but an integrative framework to assess those impacts remains lacking. In this opinion article, we argue that three research efforts are required to provide that integration. First, we need to identify the missing links in diel patterns in stem diameter and stem growth and relate those patterns to the underlying mechanisms that control water and carbon balance. Second, we should focus on the understudied mechanisms responsible for seasonal impacts on such diel patterns. Third, information on stem anatomy and ecophysiology should be integrated in the same experiments and mechanistic plant growth models to capture both diel and seasonal scales. Copyright © 2015 Elsevier Ltd. All rights reserved.

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination

PubMed Central

Stamoulis, Catherine; Schomer, Donald L.; Chang, Bernard S.

2013-01-01

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution [2, 31]. However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies <100 Hz, suggesting a potential interference role of high-frequency activity, to disrupt abnormal ictal synchrony at lower frequencies. These changes in network synchrony started at least 20–30 sec prior to seizure offset, depending on the seizure duration. Opposite patterns were estimated at frequencies ≤100 Hz in several seizures. These results raise the possibility that high-frequency interference may occur in the form of progressive network coordination during the ictal interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤100 Hz, at least in some seizures. PMID:23608198

Regional Eco-hydrologic Sensitivity to Projected Amazonian Land Use Scenarios

NASA Astrophysics Data System (ADS)

Knox, R. G.; Longo, M.; Zhang, K.; Levine, N. M.; Moorcroft, P. R.; Bras, R. L.

2011-12-01

Given business as usual land-use practices, it is estimated that by 2050 roughly half of the Amazon's pre-anthropogenic closed-canopy forest stands would remain. Of this, eight of the Amazon's twelve major hydrologic basins would lose more than half of their forest cover to deforestation. With the availability of these land-use projections, we may start to question the associated response of the region's hydrologic climate to significant land-cover change. Here the Ecosystem-Demography Model 2 (EDM2, a dynamic and spatially distributed terrestrial model of plant structure and composition, succession, disturbance and thermodynamic transfer) is coupled with the Brazilian Regional Atmospheric Model (BRAMS, a three-dimensional limited area model of the atmospheric fluid momentum equations and physics parameterizations for closing the system of equations at the lower boundary, convection, radiative transfer, microphysics, etc). This experiment conducts decadal simulations, framed with high-reliability lateral boundary conditions of reanalysis atmospheric data (ERA-40 interim) and variable impact of land-use scenarios (SimAmazonia). This is done by initializing the regional ecosystem structure with both aggressive and conservationist deforestation scenarios, and also by differentially allowing and not-allowing dynamic vegetation processes. While the lateral boundaries of the simulation will not reflect the future climate in the region, reanalysis data has provided improved realism as compared to results derived from GCM boundary data. Therefore, the ecosystem response (forest composition and structure) and the time-space patterns of hydrologic information (soil moisture, rainfall, evapotranspiration) are objectively compared in the context of a sensitivity experiment, as opposed to a forecast. The following questions are addressed. How do aggressive and conservative scenarios of Amazonian deforestation effect the regional patterning of hydrologic information in the Amazon and South American Convergence Zone, and does forest response in these regions influence that patterning of hydrologic information?

Computer Vision for Artificially Intelligent Robotic Systems

NASA Astrophysics Data System (ADS)

Ma, Chialo; Ma, Yung-Lung

1987-04-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts -- position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed bye the main control unit. In Pulse-Echo Signal Process Unit, we ultilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by u law coding method, and this data together with delay time T, angle information OH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Model, we use a narrow beam transducer and it's input voltage is 50V p-p. A RobOt equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Improved Performance Characteristics For Indium Antimonide Photovoltaic Detector Arrays Using A FET-Switched Multiplexing Technique

NASA Astrophysics Data System (ADS)

Ma, Yung-Lung; Ma, Chialo

1987-03-01

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable. The Acoustic Imaging Recognition System (AIRS) is consist of four parts _ position control unit, pulse-echo signal processing unit, pattern recognition unit and main control unit. The position control of AIRS can rotate an angle of ±5 degree Horizental and Vertical seperately, the purpose of rotation is to find the maximum reflection intensity area, from the distance, angles and intensity of the target we can decide the characteristic of this target, of course all the decision is target, of course all the decision is processed by the main control unit. In Pulse-Echo Signal Process Unit, we utilize the correlation method, to overcome the limitation of short burst of ultrasonic, because the Correlation system can transmit large time bandwidth signals and obtain their resolution and increased intensity through pulse compression in the correlation receiver. The output of correlator is sampled and transfer into digital data by p law coding method, and this data together with delay time T, angle information eH, eV will be sent into main control unit for further analysis. The recognition process in this paper, we use dynamic look up table method, in this method at first we shall set up serval recognition pattern table and then the new pattern scanned by Transducer array will be devided into serval stages and compare with the sampling table. The comparison is implemented by dynamic programing and Markovian process. All the hardware control signals, such as optimum delay time for correlator receiver, horizental and vertical rotation angle for transducer plate, are controlled by the Main Control Unit, the Main Control Unit also handles the pattern recognition process. The distance from the target to the transducer plate is limitted by the power and beam angle of transducer elements, in this AIRS Models, we use a narrow beam transducer and it's input voltage is 50V p-p. A Robot equipped with AIRS can not only measure the distance from the target but also recognize a three dimensional image of target from the image lab of Robot memory. Indexitems, Accoustic System, Supersonic transducer, Dynamic programming, Look-up-table, Image process, pattern Recognition, Quad Tree, Quadappoach.

Use of simulated satellite radiances from a mesoscale numerical model to understand kinematic and dynamic processes

NASA Technical Reports Server (NTRS)

Kalb, Michael; Robertson, Franklin; Jedlovec, Gary; Perkey, Donald

1987-01-01

Techniques by which mesoscale numerical weather prediction model output and radiative transfer codes are combined to simulate the radiance fields that a given passive temperature/moisture satellite sensor would see if viewing the evolving model atmosphere are introduced. The goals are to diagnose the dynamical atmospheric processes responsible for recurring patterns in observed satellite radiance fields, and to develop techniques to anticipate the ability of satellite sensor systems to depict atmospheric structures and provide information useful for numerical weather prediction (NWP). The concept of linking radiative transfer and dynamical NWP codes is demonstrated with time sequences of simulated radiance imagery in the 24 TIROS vertical sounder channels derived from model integrations for March 6, 1982.

[Dynamic hierarchy of regulatory peptides. Structure of the induction relations of regulators as the target for therapeutic agents].

PubMed

Koroleva, S V; Miasoedov, N F

2012-01-01

Based on the database information (literature period 1970-2010 gg.) on the effects of regulatory peptides (RP) and non-peptide neurotransmitters (dopamine, serotonin, norepi-nephrine, acetylcholine) it was analyzed of possible cascade processes of endogenous regulators. It was found that the entire continuum of RP and mediators is a chaotic soup of the ordered three-level compartments. Such a dynamic functional hierarchy of endogenous regulators allows to create start-up and corrective tasks for a variety of physiological functions. Some examples of static and dynamic patterns of induction processes of RP and mediators (that regulate the states of anxiety, depression, learning and memory, feeding behavior, reproductive processes, etc.) are considered.

Kinematic variability, fractal dynamics and local dynamic stability of treadmill walking

PubMed Central

2011-01-01

Background Motorized treadmills are widely used in research or in clinical therapy. Small kinematics, kinetics and energetics changes induced by Treadmill Walking (TW) as compared to Overground Walking (OW) have been reported in literature. The purpose of the present study was to characterize the differences between OW and TW in terms of stride-to-stride variability. Classical (Standard Deviation, SD) and non-linear (fractal dynamics, local dynamic stability) methods were used. In addition, the correlations between the different variability indexes were analyzed. Methods Twenty healthy subjects performed 10 min TW and OW in a random sequence. A triaxial accelerometer recorded trunk accelerations. Kinematic variability was computed as the average SD (MeanSD) of acceleration patterns among standardized strides. Fractal dynamics (scaling exponent α) was assessed by Detrended Fluctuation Analysis (DFA) of stride intervals. Short-term and long-term dynamic stability were estimated by computing the maximal Lyapunov exponents of acceleration signals. Results TW did not modify kinematic gait variability as compared to OW (multivariate T2, p = 0.87). Conversely, TW significantly modified fractal dynamics (t-test, p = 0.01), and both short and long term local dynamic stability (T2 p = 0.0002). No relationship was observed between variability indexes with the exception of significant negative correlation between MeanSD and dynamic stability in TW (3 × 6 canonical correlation, r = 0.94). Conclusions Treadmill induced a less correlated pattern in the stride intervals and increased gait stability, but did not modify kinematic variability in healthy subjects. This could be due to changes in perceptual information induced by treadmill walking that would affect locomotor control of the gait and hence specifically alter non-linear dependencies among consecutive strides. Consequently, the type of walking (i.e. treadmill or overground) is important to consider in each protocol design. PMID:21345241

Dynamic functional connectivity: Promise, issues, and interpretations

PubMed Central

Hutchison, R. Matthew; Womelsdorf, Thilo; Allen, Elena A.; Bandettini, Peter A.; Calhoun, Vince D.; Corbetta, Maurizio; Penna, Stefania Della; Duyn, Jeff H.; Glover, Gary H.; Gonzalez-Castillo, Javier; Handwerker, Daniel A.; Keilholz, Shella; Kiviniemi, Vesa; Leopold, David A.; de Pasquale, Francesco; Sporns, Olaf; Walter, Martin; Chang, Catie

2013-01-01

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations. PMID:23707587

Dynamic perfusion assessment during perforator flap surgery: an up-to-date

PubMed Central

MUNTEAN, MAXIMILIAN VLAD; MUNTEAN, VALENTIN; ARDELEAN, FILIP; GEORGESCU, ALEXANDRU

2015-01-01

Flap monitoring technology has progressed alongside flap design. The highly variable vascular anatomy and the complexity associated with modern perforator flaps demands dynamic, real-time, intraoperative information about the vessel location, perfusion patterns and flap physiology. Although most surgeons still assess flap perfusion and viability based solely on clinical experience, studies have shown that results may be highly variable and often misleading. Poor judgment of intraoperative perfusion leads to major complications. Employing dynamic perfusion imaging during flap reconstruction has led to a reduced complication rate, lower morbidity, shorter hospital stay, and an overall better result. With the emergence of multiple systems capable of intraoperative flap evaluation, the purpose of this article is to review the two systems that have been widely accepted and are currently used by plastic surgeons: Indocyanine green angiography (ICGA) and dynamic infrared thermography (DIRT). PMID:26609259

3D imaging of translucent media with a plenoptic sensor based on phase space optics

NASA Astrophysics Data System (ADS)

Zhang, Xuanzhe; Shu, Bohong; Du, Shaojun

2015-05-01

Traditional stereo imaging technology is not working for dynamical translucent media, because there are no obvious characteristic patterns on it and it's not allowed using multi-cameras in most cases, while phase space optics can solve the problem, extracting depth information directly from "space-spatial frequency" distribution of the target obtained by plenoptic sensor with single lens. This paper discussed the presentation of depth information in phase space data, and calculating algorithms with different transparency. A 3D imaging example of waterfall was given at last.

Contour junctions defined by dynamic image deformations enhance perceptual transparency.

PubMed

Kawabe, Takahiro; Nishida, Shin'ya

2017-11-01

The majority of work on the perception of transparency has focused on static images with luminance-defined contour junctions, but recent work has shown that dynamic image sequences with dynamic image deformations also provide information about transparency. The present study demonstrates that when part of a static image is dynamically deformed, contour junctions at which deforming and nondeforming contours are connected facilitate the deformation-based perception of a transparent layer. We found that the impression of a transparent layer was stronger when a dynamically deforming area was adjacent to static nondeforming areas than when presented alone. When contour junctions were not formed at the dynamic-static boundaries, however, the impression of a transparent layer was not facilitated by the presence of static surrounding areas. The effect of the deformation-defined junctions was attenuated when the spatial pattern of luminance contrast at the junctions was inconsistent with the perceived transparency related to luminance contrast, while the effect did not change when the spatial luminance pattern was consistent with it. In addition, the results showed that contour completions across the junctions were required for the perception of a transparent layer. These results indicate that deformation-defined junctions that involve contour completion between deforming and nondeforming regions enhance the perception of a transparent layer, and that the deformation-based perceptual transparency can be promoted by the simultaneous presence of appropriately configured luminance and contrast-other features that can also by themselves produce the sensation of perceiving transparency.

Conceptual challenges in the study of caregiver-care recipient relationships.

PubMed

Lingler, Jennifer Hagerty; Sherwood, Paula R; Crighton, Margaret H; Song, Mi-Kyung; Happ, Mary Beth

2008-01-01

In the literature on family caregiving, care receiving and caregiving are generally treated as distinct constructs, suggesting that informal care and support flow in a unidirectional manner from caregiver to care recipient. Yet, informal care dynamics are fundamentally relational and often reciprocal, and caregiving roles can be complex and overlapping. To illustrate ways care dynamics may depart from traditional notions of dyadic unidirectional family caregiving and to stimulate a discussion of the implications of complex relational care dynamics for caregiving science. Exemplar cases of informal care dynamics were drawn from three ongoing and completed investigations involving persons with serious illness and their family caregivers. The selected cases provide examples of three unique, but not uncommon, care exchange patterns: (a) care dyads who are aging, are chronically ill, and who compensate for one another's deficits in reciprocal relationships; (b) patients who present with a constellation of family members and other informal caregivers, as opposed to one primary caregiver; and (c) family care chains whereby a given individual functions as a caregiver to one relative or friend and care recipient to another. These cases illustrate such phenomena as multiple caregivers, shifting and shared caregiving roles, and care recipients as caregivers. As caregiving science enters a new era of complexity and maturity, there is a need for conceptual and methodological approaches that acknowledge, account for, and support the complex, web-like nature of family caregiving configurations. Research that contributes to, and is informed by, a broader understanding of the reality of family caregiving will yield findings that carry greater clinical relevance than has been possible previously.

Conceptual Challenges in the Study of Caregiver-Care Recipient Relationships

PubMed Central

Lingler, Jennifer Hagerty; Sherwood, Paula R.; Crighton, Margaret H.; Song, Mi-Kyung; Happ, Mary Beth

2010-01-01

Background In the literature on family caregiving, care receiving and caregiving are treated generally as distinct constructs, suggesting that informal care and support flow in a unidirectional manner from caregiver to care recipient. Yet, informal care dynamics are fundamentally relational and often reciprocal, and caregiving roles can be complex and overlapping. Objectives To illustrate ways care dynamics may depart from traditional notions of dyadic, unidirectional family caregiving; and to stimulate a discussion of the implications of complex, relational care dynamics for caregiving science. Approach Exemplar cases of informal care dynamics were drawn from three ongoing and completed investigations involving persons with serious illness and their family caregivers. The selected cases provide examples of three unique, but not uncommon, care exchange patterns: (a) aging and chronically ill care dyads who compensate for one another's deficits in reciprocal relationships; (b) patients who present with a constellation of family members and other informal caregivers, as opposed to one primary caregiver; and (c) family care chains whereby a given individual functions as a caregiver to one relative or friend and care recipient to another. Conclusions These cases illustrate such phenomena as multiple caregivers, shifting and shared caregiving roles, and care recipients as caregivers. As caregiving science enters a new era of complexity and maturity, there is a need for conceptual and methodological approaches that acknowledge, account for, and support the complex, web-like nature of family caregiving configurations. Research that contributes to, and is informed by, a broader understanding of the reality of family caregiving will yield findings that carry greater clinical relevance than has been possible previously. PMID:18794721

Exploring Entrainment Patterns of Human Emotion in Social Media

PubMed Central

Luo, Chuan; Zhang, Zhu

2016-01-01

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace. PMID:26953692

Pattern Recognition Control Design

NASA Technical Reports Server (NTRS)

Gambone, Elisabeth

2016-01-01

Spacecraft control algorithms must know the expected spacecraft response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach can be used to investigate the relationship between the control effector commands and the spacecraft responses. Instead of supplying the approximated vehicle properties and the effector performance characteristics, a database of information relating the effector commands and the desired vehicle response can be used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands can be analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center (Ref. 1) to analyze flight dynamics Monte Carlo data sets through pattern recognition methods can be used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands is established, it can be used in place of traditional control laws and gains set. This pattern recognition approach can be compared with traditional control algorithms to determine the potential benefits and uses.

Quantifying urban growth patterns in Hanoi using landscape expansion modes and time series spatial metrics.

PubMed

Nong, Duong H; Lepczyk, Christopher A; Miura, Tomoaki; Fox, Jefferson M

2018-01-01

Urbanization has been driven by various social, economic, and political factors around the world for centuries. Because urbanization continues unabated in many places, it is crucial to understand patterns of urbanization and their potential ecological and environmental impacts. Given this need, the objectives of our study were to quantify urban growth rates, growth modes, and resultant changes in the landscape pattern of urbanization in Hanoi, Vietnam from 1993 to 2010 and to evaluate the extent to which the process of urban growth in Hanoi conformed to the diffusion-coalescence theory. We analyzed the spatiotemporal patterns and dynamics of the built-up land in Hanoi using landscape expansion modes, spatial metrics, and a gradient approach. Urbanization was most pronounced in the periods of 2001-2006 and 2006-2010 at a distance of 10 to 35 km around the urban center. Over the 17 year period urban expansion in Hanoi was dominated by infilling and edge expansion growth modes. Our findings support the diffusion-coalescence theory of urbanization. The shift of the urban growth areas over time and the dynamic nature of the spatial metrics revealed important information about our understanding of the urban growth process and cycle. Furthermore, our findings can be used to evaluate urban planning policies and aid in urbanization issues in rapidly urbanizing countries.

Exploring Entrainment Patterns of Human Emotion in Social Media.

PubMed

He, Saike; Zheng, Xiaolong; Zeng, Daniel; Luo, Chuan; Zhang, Zhu

2016-01-01

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks

PubMed Central

Knöpfel, Thomas; Leech, Robert

2018-01-01

Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascades, known as neuronal avalanches, usually report the statistics of large numbers of avalanches, without probing the characteristic patterns produced by the avalanches themselves. This is partly due to limitations in the extent or spatiotemporal resolution of commonly used neuroimaging techniques. In this study, we overcome these limitations by using optical voltage (genetically encoded voltage indicators) imaging. This allows us to record cortical activity in vivo across an entire cortical hemisphere, at both high spatial (~30um) and temporal (~20ms) resolution in mice that are either in an anesthetized or awake state. We then use artificial neural networks to identify the characteristic patterns created by neuronal avalanches in our data. The avalanches in the anesthetized cortex are most accurately classified by an artificial neural network architecture that simultaneously connects spatial and temporal information. This is in contrast with the awake cortex, in which avalanches are most accurately classified by an architecture that treats spatial and temporal information separately, due to the increased levels of spatiotemporal complexity. This is in keeping with reports of higher levels of spatiotemporal complexity in the awake brain coinciding with features of a dynamical system operating close to criticality. PMID:29795654

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

PubMed Central

LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

2014-01-01

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

Submerged discontent and patterns of accommodation: a case study of doctors' pay in two public hospitals in China.

PubMed

Cao, Xuebing

2014-01-01

The article evaluates submerged discontent among Chinese public hospital doctors (Note1) regarding their pay and patterns of accommodation, including doctors' responses through formal and informal actions in the context of health service marketization. On the basis of a case study of two public hospitals, the article illustrates the dynamical impact of marketization on Chinese doctors' pay-related dissatisfaction and health service employment relationship. Because of the authoritarian management and compliant trade unions, the conflict between doctors and hospitals is unable to be accommodated through collective methods. Instead, doctors' discontent is often channelled through informal, individual and subtle activities. Meanwhile, doctors' professional society is gradually influential, showing its potential of developing doctors' group identity and protecting members' interests in future. Copyright © 2013 John Wiley & Sons, Ltd.

Dynamical interpretation of conditional patterns

NASA Technical Reports Server (NTRS)

Adrian, R. J.; Moser, R. D.; Moin, P.

1988-01-01

While great progress is being made in characterizing the 3-D structure of organized turbulent motions using conditional averaging analysis, there is a lack of theoretical guidance regarding the interpretation and utilization of such information. Questions concerning the significance of the structures, their contributions to various transport properties, and their dynamics cannot be answered without recourse to appropriate dynamical governing equations. One approach which addresses some of these questions uses the conditional fields as initial conditions and calculates their evolution from the Navier-Stokes equations, yielding valuable information about stability, growth, and longevity of the mean structure. To interpret statistical aspects of the structures, a different type of theory which deals with the structures in the context of their contributions to the statistics of the flow is needed. As a first step toward this end, an effort was made to integrate the structural information from the study of organized structures with a suitable statistical theory. This is done by stochastically estimating the two-point conditional averages that appear in the equation for the one-point probability density function, and relating the structures to the conditional stresses. Salient features of the estimates are identified, and the structure of the one-point estimates in channel flow is defined.

Use of sonification in the detection of anomalous events

NASA Astrophysics Data System (ADS)

Ballora, Mark; Cole, Robert J.; Kruesi, Heidi; Greene, Herbert; Monahan, Ganesh; Hall, David L.

2012-06-01

In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners. The soundtrack generation is flexible, allowing an individual listener to create a unique audio mix from the available information sources. Properly constructed, the display exploits the auditory system's sensitivities to periodicities, to dynamic changes, and to patterns. This type of display could be valuable in environments that demand high levels of situational awareness based on multiple sources of incoming information.

Revisiting the body-schema concept in the context of whole-body postural-focal dynamics.

PubMed

Morasso, Pietro; Casadio, Maura; Mohan, Vishwanathan; Rea, Francesco; Zenzeri, Jacopo

2015-01-01

The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory-motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.

Revisiting the Body-Schema Concept in the Context of Whole-Body Postural-Focal Dynamics

PubMed Central

Morasso, Pietro; Casadio, Maura; Mohan, Vishwanathan; Rea, Francesco; Zenzeri, Jacopo

2015-01-01

The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory–motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability. PMID:25741274

Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa

PubMed Central

Wesolowski, Amy; O’Meara, Wendy Prudhomme; Eagle, Nathan; Tatem, Andrew J.; Buckee, Caroline O.

2015-01-01

Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations. PMID:26158274

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

PubMed

Zhuang, Xiaowei; Walsh, Ryan R; Sreenivasan, Karthik; Yang, Zhengshi; Mishra, Virendra; Cordes, Dietmar

2018-05-15

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD. Copyright © 2018 Elsevier Inc. All rights reserved.

Interactive Sonification Exploring Emergent Behavior Applying Models for Biological Information and Listening

PubMed Central

Choi, Insook

2018-01-01

Sonification is an open-ended design task to construct sound informing a listener of data. Understanding application context is critical for shaping design requirements for data translation into sound. Sonification requires methodology to maintain reproducibility when data sources exhibit non-linear properties of self-organization and emergent behavior. This research formalizes interactive sonification in an extensible model to support reproducibility when data exhibits emergent behavior. In the absence of sonification theory, extensibility demonstrates relevant methods across case studies. The interactive sonification framework foregrounds three factors: reproducible system implementation for generating sonification; interactive mechanisms enhancing a listener's multisensory observations; and reproducible data from models that characterize emergent behavior. Supramodal attention research suggests interactive exploration with auditory feedback can generate context for recognizing irregular patterns and transient dynamics. The sonification framework provides circular causality as a signal pathway for modeling a listener interacting with emergent behavior. The extensible sonification model adopts a data acquisition pathway to formalize functional symmetry across three subsystems: Experimental Data Source, Sound Generation, and Guided Exploration. To differentiate time criticality and dimensionality of emerging dynamics, tuning functions are applied between subsystems to maintain scale and symmetry of concurrent processes and temporal dynamics. Tuning functions accommodate sonification design strategies that yield order parameter values to render emerging patterns discoverable as well as rehearsable, to reproduce desired instances for clinical listeners. Case studies are implemented with two computational models, Chua's circuit and Swarm Chemistry social agent simulation, generating data in real-time that exhibits emergent behavior. Heuristic Listening is introduced as an informal model of a listener's clinical attention to data sonification through multisensory interaction in a context of structured inquiry. Three methods are introduced to assess the proposed sonification framework: Listening Scenario classification, data flow Attunement, and Sonification Design Patterns to classify sound control. Case study implementations are assessed against these methods comparing levels of abstraction between experimental data and sound generation. Outcomes demonstrate the framework performance as a reference model for representing experimental implementations, also for identifying common sonification structures having different experimental implementations, identifying common functions implemented in different subsystems, and comparing impact of affordances across multiple implementations of listening scenarios. PMID:29755311

Study on Dissemination Patterns in Location-Aware Gossiping Networks

NASA Astrophysics Data System (ADS)

Kami, Nobuharu; Baba, Teruyuki; Yoshikawa, Takashi; Morikawa, Hiroyuki

We study the properties of information dissemination over location-aware gossiping networks leveraging location-based real-time communication applications. Gossiping is a promising method for quickly disseminating messages in a large-scale system, but in its application to information dissemination for location-aware applications, it is important to consider the network topology and patterns of spatial dissemination over the network in order to achieve effective delivery of messages to potentially interested users. To this end, we propose a continuous-space network model extended from Kleinberg's small-world model applicable to actual location-based applications. Analytical and simulation-based study shows that the proposed network achieves high dissemination efficiency resulting from geographically neutral dissemination patterns as well as selective dissemination to proximate users. We have designed a highly scalable location management method capable of promptly updating the network topology in response to node movement and have implemented a distributed simulator to perform dynamic target pursuit experiments as one example of applications that are the most sensitive to message forwarding delay. The experimental results show that the proposed network surpasses other types of networks in pursuit efficiency and achieves the desirable dissemination patterns.

Individual Movement Strategies Revealed through Novel Clustering of Emergent Movement Patterns

NASA Astrophysics Data System (ADS)

Valle, Denis; Cvetojevic, Sreten; Robertson, Ellen P.; Reichert, Brian E.; Hochmair, Hartwig H.; Fletcher, Robert J.

2017-03-01

Understanding movement is critical in several disciplines but analysis methods often neglect key information by adopting each location as sampling unit, rather than each individual. We introduce a novel statistical method that, by focusing on individuals, enables better identification of temporal dynamics of connectivity, traits of individuals that explain emergent movement patterns, and sites that play a critical role in connecting subpopulations. We apply this method to two examples that span movement networks that vary considerably in size and questions: movements of an endangered raptor, the snail kite (Rostrhamus sociabilis plumbeus), and human movement in Florida inferred from Twitter. For snail kites, our method reveals substantial differences in movement strategies for different bird cohorts and temporal changes in connectivity driven by the invasion of an exotic food resource, illustrating the challenge of identifying critical connectivity sites for conservation in the presence of global change. For human movement, our method is able to reliably determine the origin of Florida visitors and identify distinct movement patterns within Florida for visitors from different places, providing near real-time information on the spatial and temporal patterns of tourists. These results emphasize the need to integrate individual variation to generate new insights when modeling movement data.

Dynamical origin of complex motor patterns

NASA Astrophysics Data System (ADS)

Alonso, L. M.; Alliende, J. A.; Mindlin, G. B.

2010-11-01

Behavior emerges as the nervous system generates motor patterns in charge of driving a peripheral biomechanical device. For several cases in the animal kingdom, it has been identified that the motor patterns used in order to accomplish a diversity of tasks are the different solutions of a simple, low dimensional nonlinear dynamical system. Yet, motor patterns emerge from the interaction of an enormous number of individual dynamical units. In this work, we study the dynamics of the average activity of a large set of coupled excitable units which are periodically forced. We show that low dimensional, yet non trivial dynamics emerges. As a case study, we analyze the air sac pressure patterns used by domestic canaries during song, which consists of a succession of repetitions of different syllable types. We show that the pressure patterns used to generate different syllables can be approximated by the solutions of the investigated model. In this way, we are capable of integrating different description scales of our problem.

Computing by physical interaction in neurons.

PubMed

Aur, Dorian; Jog, Mandar; Poznanski, Roman R

2011-12-01

The electrodynamics of action potentials represents the fundamental level where information is integrated and processed in neurons. The Hodgkin-Huxley model cannot explain the non-stereotyped spatial charge density dynamics that occur during action potential propagation. Revealed in experiments as spike directivity, the non-uniform charge density dynamics within neurons carry meaningful information and suggest that fragments of information regarding our memories are endogenously stored in structural patterns at a molecular level and are revealed only during spiking activity. The main conceptual idea is that under the influence of electric fields, efficient computation by interaction occurs between charge densities embedded within molecular structures and the transient developed flow of electrical charges. This process of computation underlying electrical interactions and molecular mechanisms at the subcellular level is dissimilar from spiking neuron models that are completely devoid of physical interactions. Computation by interaction describes a more powerful continuous model of computation than the one that consists of discrete steps as represented in Turing machines.

Biometric verification in dynamic writing

NASA Astrophysics Data System (ADS)

George, Susan E.

2002-03-01

Pen-tablet devices capable of capturing the dynamics of writing record temporal and pressure information as well as the spatial pattern. This paper explores biometric verification based upon the dynamics of writing where writers are distinguished not on the basis of what they write (ie the signature), but how they write. We have collected samples of dynamic writing from 38 Chinese writers. Each writer was asked to provide 10 copies of a paragraph of text and the same number of signature samples. From the data we have extracted stroke-based primitives from the sentence data utilizing pen-up/down information and heuristic rules about the shape of the character. The x, y and pressure values of each primitive were interpolated into an even temporal range based upon a 20 msec sampling rate. We applied the Daubechies 1 wavelet transform to the x signal, y signal and pressure signal using the coefficients as inputs to a multi-layer perceptron trained with back-propagation on the sentence data. We found a sensitivity of 0.977 and specificity of 0.990 recognizing writers based on test primitives extracted from sentence data and measures of 0.916 and 0.961 respectively, from test primitives extracted from signature data.

Dynamic Connectivity Patterns in Conscious and Unconscious Brain

PubMed Central

Ma, Yuncong; Hamilton, Christina

2017-01-01

Abstract Brain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. In this study, we acquired resting-state functional magnetic resonance imaging data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding window method and k-means clustering. Our results demonstrate that whole-brain networks contained several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level. PMID:27846731

Wide-field depth-sectioning fluorescence microscopy using projector-generated patterned illumination

NASA Astrophysics Data System (ADS)

Delica, Serafin; Mar Blanca, Carlo

2007-10-01

We present a simple and cost-effective wide-field, depth-sectioning, fluorescence microscope utilizing a commercial multimedia projector to generate excitation patterns on the sample. Highly resolved optical sections of fluorescent pollen grains at 1.9 μm axial resolution are constructed using the structured illumination technique. This requires grid excitation patterns to be scanned across the sample, which is straightforwardly implemented by creating slideshows of gratings at different phases, projecting them onto the sample, and synchronizing camera acquisition with slide transition. In addition to rapid dynamic pattern generation, the projector provides high illumination power and spectral excitation selectivity. We exploit these properties by imaging mouse neural cells in cultures multistained with Alexa 488 and Cy3. The spectral and structural neural information is effectively resolved in three dimensions. The flexibility and commercial availability of this light source is envisioned to open multidimensional imaging to a broader user base.

From network heterogeneities to familiarity detection and hippocampal memory management

PubMed Central

Wang, Jane X.; Poe, Gina; Zochowski, Michal

2009-01-01

Hippocampal-neocortical interactions are key to the rapid formation of novel associative memories in the hippocampus and consolidation to long term storage sites in the neocortex. We investigated the role of network correlates during information processing in hippocampal-cortical networks. We found that changes in the intrinsic network dynamics due to the formation of structural network heterogeneities alone act as a dynamical and regulatory mechanism for stimulus novelty and familiarity detection, thereby controlling memory management in the context of memory consolidation. This network dynamic, coupled with an anatomically established feedback between the hippocampus and the neocortex, recovered heretofore unexplained properties of neural activity patterns during memory management tasks which we observed during sleep in multiunit recordings from behaving animals. Our simple dynamical mechanism shows an experimentally matched progressive shift of memory activation from the hippocampus to the neocortex and thus provides the means to achieve an autonomous off-line progression of memory consolidation. PMID:18999453

Reconstruction dynamics of recorded holograms in photochromic glass.

PubMed

Mihailescu, Mona; Pavel, Eugen; Nicolae, Vasile B

2011-06-20

We have investigated the dynamics of the record-erase process of holograms in photochromic glass using continuum Nd:YVO₄ laser radiation (λ=532 nm). A bidimensional microgrid pattern was formed and visualized in photochromic glass, and its diffraction efficiency decay versus time (during reconstruction step) gave us information (D, Δn) about the diffusion process inside the material. The recording and reconstruction processes were carried out in an off-axis setup, and the images of the reconstructed object were recorded by a CCD camera. Measurements realized on reconstructed object images using holograms recorded at a different incident power laser have shown a two-stage process involved in silver atom kinetics.

Reconstructing multi-mode networks from multivariate time series

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Yang, Yu-Xuan; Dang, Wei-Dong; Cai, Qing; Wang, Zhen; Marwan, Norbert; Boccaletti, Stefano; Kurths, Jürgen

2017-09-01

Unveiling the dynamics hidden in multivariate time series is a task of the utmost importance in a broad variety of areas in physics. We here propose a method that leads to the construction of a novel functional network, a multi-mode weighted graph combined with an empirical mode decomposition, and to the realization of multi-information fusion of multivariate time series. The method is illustrated in a couple of successful applications (a multi-phase flow and an epileptic electro-encephalogram), which demonstrate its powerfulness in revealing the dynamical behaviors underlying the transitions of different flow patterns, and enabling to differentiate brain states of seizure and non-seizure.

Long-range correlations and asymmetry in the Bitcoin market

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.; Ibarra-Valdez, C.

2018-02-01

This work studies long-range correlations and informational efficiency of the Bitcoin market for the period from June 30, 2013 to June 3rd, 2017. To this end, the detrended fluctuation analysis (DFA) was implemented over sliding windows to estimate long-range correlations for price returns. It was found that the Bitcoin market exhibits periods of efficiency alternating with periods where the price dynamics are driven by anti-persistence. The pattern is replicated by prices samples at day, hour and second frequencies. The Bitcoin market also presents asymmetric correlations with respect to increasing and decreasing price trending, with the former trend linked to anti-persistence of returns dynamics.

Dynamic combination of sensory and reward information under time pressure

PubMed Central

Farashahi, Shiva; Kao, Chang-Hao

2018-01-01

When making choices, collecting more information is beneficial but comes at the cost of sacrificing time that could be allocated to making other potentially rewarding decisions. To investigate how the brain balances these costs and benefits, we conducted a series of novel experiments in humans and simulated various computational models. Under six levels of time pressure, subjects made decisions either by integrating sensory information over time or by dynamically combining sensory and reward information over time. We found that during sensory integration, time pressure reduced performance as the deadline approached, and choice was more strongly influenced by the most recent sensory evidence. By fitting performance and reaction time with various models we found that our experimental results are more compatible with leaky integration of sensory information with an urgency signal or a decision process based on stochastic transitions between discrete states modulated by an urgency signal. When combining sensory and reward information, subjects spent less time on integration than optimally prescribed when reward decreased slowly over time, and the most recent evidence did not have the maximal influence on choice. The suboptimal pattern of reaction time was partially mitigated in an equivalent control experiment in which sensory integration over time was not required, indicating that the suboptimal response time was influenced by the perception of imperfect sensory integration. Meanwhile, during combination of sensory and reward information, performance did not drop as the deadline approached, and response time was not different between correct and incorrect trials. These results indicate a decision process different from what is involved in the integration of sensory information over time. Together, our results not only reveal limitations in sensory integration over time but also illustrate how these limitations influence dynamic combination of sensory and reward information. PMID:29584717

Dynamics of scroll waves with time-delay propagation in excitable media

NASA Astrophysics Data System (ADS)

Chen, Jiang-Xing; Xiao, Jie; Qiao, Li-Yan; Xu, Jiang-Rong

2018-06-01

Information transmission delay can be widely observed in various systems. Here, we study the dynamics of scroll waves with time-delay propagation among slices in excitable media. Weak time delay induces scroll waves to meander. Through increasing the time delay, we find a series of dynamical transitions. Firstly, the straight filament of a scroll wave becomes twisted. Then, the scroll wave breaks and forms interesting patterns. With long time delay, loosed scroll waves are maintained while their period are greatly decreased. Also, cylinder waves appears. The influences of diffusively coupling strength on the time-delay-induced scroll waves are studied. It is found that the critical time delay characterizing those transitions decreases as the coupling strength is increased. A phase diagram in the diffusive coupling-time delay plane is presented.

A stochastic multicellular model identifies biological watermarks from disorders in self-organized patterns of phyllotaxis

PubMed Central

Refahi, Yassin; Brunoud, Géraldine; Farcot, Etienne; Jean-Marie, Alain; Pulkkinen, Minna; Vernoux, Teva; Godin, Christophe

2016-01-01

Exploration of developmental mechanisms classically relies on analysis of pattern regularities. Whether disorders induced by biological noise may carry information on building principles of developmental systems is an important debated question. Here, we addressed theoretically this question using phyllotaxis, the geometric arrangement of plant aerial organs, as a model system. Phyllotaxis arises from reiterative organogenesis driven by lateral inhibitions at the shoot apex. Motivated by recurrent observations of disorders in phyllotaxis patterns, we revisited in depth the classical deterministic view of phyllotaxis. We developed a stochastic model of primordia initiation at the shoot apex, integrating locality and stochasticity in the patterning system. This stochastic model recapitulates phyllotactic patterns, both regular and irregular, and makes quantitative predictions on the nature of disorders arising from noise. We further show that disorders in phyllotaxis instruct us on the parameters governing phyllotaxis dynamics, thus that disorders can reveal biological watermarks of developmental systems. DOI: http://dx.doi.org/10.7554/eLife.14093.001 PMID:27380805

Pattern dynamics of the reaction-diffusion immune system.

PubMed

Zheng, Qianqian; Shen, Jianwei; Wang, Zhijie

2018-01-01

In this paper, we will investigate the effect of diffusion, which is ubiquitous in nature, on the immune system using a reaction-diffusion model in order to understand the dynamical behavior of complex patterns and control the dynamics of different patterns. Through control theory and linear stability analysis of local equilibrium, we obtain the optimal condition under which the system loses stability and a Turing pattern occurs. By combining mathematical analysis and numerical simulation, we show the possible patterns and how these patterns evolve. In addition, we establish a bridge between the complex patterns and the biological mechanism using the results from a previous study in Nature Cell Biology. The results in this paper can help us better understand the biological significance of the immune system.

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone.

PubMed

Zhang, Luyan; Liang, Yi; Li, Fali; Sun, Hongbin; Peng, Wenjing; Du, Peishan; Si, Yajing; Song, Limeng; Yu, Liang; Xu, Peng

2017-01-01

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

Dynamics of colour polymorphism in a changing environment: fire melanism and then what?

PubMed

Karlsson, Magnus; Caesar, Sofia; Ahnesjö, Jonas; Forsman, Anders

2008-01-01

Studies of whether disturbance events are associated with the changing genetic compositions of natural populations may provide insights into the importance of local selection events in maintaining diversity, and might inform plans for the conservation and protection of that diversity. We examined the dynamics of a colour pattern polymorphism in a natural population of pygmy grasshoppers Tetrix subulata (Orthoptera: Tetrigidae) inhabiting a previously burnt clear-cut area. Data on morph frequencies for wild-caught and captive-reared individuals indicated that the initial dominance of black phenotypes following the fire event was followed by an increased diversity of the polymorphism. This was manifested as the appearance of a novel morph, a decreased incidence of the black morph, and a more even distribution of individuals across alternative morphs following the recurrence of vegetation. We also found that the colour patterns of captive-reared individuals resembled those of their parents and that the degree of within-clutch diversity increased between generations. Our comparisons of morph frequencies across generations and between environments within generations point to a genetic determination of colour pattern, and indicate that the polymorphism is influenced more strongly by selection than by plasticity or migration.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Self-Supervised Dynamical Systems

NASA Technical Reports Server (NTRS)

Zak, Michail

2003-01-01

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and metal aspects of a monad is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. This feedback is what makes the evolution of probability densities nonlinear. The deviation from linear evolution can be characterized, in a sense, as an expression of free will. It has been demonstrated that probability densities can approach prescribed attractors while exhibiting such patterns as shock waves, solitons, and chaos in probability space. The concept of self-supervised dynamical systems has been considered for application to diverse phenomena, including information-based neural networks, cooperation, competition, deception, games, and control of chaos. In addition, a formal similarity between the mathematical structures of self-supervised dynamical systems and of quantum-mechanical systems has been investigated.

Invisible Electronic States and Their Dynamics Revealed by Perturbations

NASA Astrophysics Data System (ADS)

Merer, Anthony J.

2011-06-01

Sooner or later everyone working in the field of spectroscopy encounters perturbations. These can range in size from a small shift of a single rotational level to total destruction of the vibrational and rotational patterns of an electronic state. To some workers perturbations are a source of terror, but to others they are the most fascinating features of molecular spectra, because they give information about molecular dynamics, and about states that would otherwise be invisible as a result of unfavorable selection rules. An example of the latter is the essentially complete characterization of the tilde{b}^3A_2 state of SO_2 from the vibronic perturbations it causes in the tilde{a}^3B_1 state. The S_1-trans state of acetylene is a beautiful example of dynamics in action. The level patterns of the three bending vibrations change dramatically with increasing vibrational excitation as a result of the vibrational angular momentum and the approach to the isomerization barrier. Several vibrational levels of the S_1-cis isomer, previously thought to be unobservable, can now be assigned. They obtain their intensity through interactions with nearby levels of the trans isomer.

Dynamic deformation inspection of a human arm by using a line-scan imaging system

NASA Astrophysics Data System (ADS)

Hu, Eryi

2009-11-01

A line-scan imaging system is used in the dynamic deformation measurement of a human arm when the muscle is contracting and relaxing. The measurement principle is based on the projection grating profilometry, and the measuring system is consisted of a line-scan CCD camera, a projector, optical lens and a personal computer. The detected human arm is put upon a reference plane, and a sinusoidal grating is projected onto the object surface and reference plane at an incidence angle, respectively. The deformed fringe pattern in the same line of the dynamic detected arm is captured by the line-scan CCD camera with free trigger model, and the deformed fringe pattern is recorded in the personal computer for processing. A fast Fourier transform combining with a filtering and spectrum shifting method is used to extract the phase information caused by the profile of the detected object. Thus, the object surface profile can be obtained following the geometric relationship between the fringe deformation and the object surface height. Furthermore, the deformation procedure can be obtained line by line. Some experimental results are presented to prove the feasibility of the inspection system.

Regulating Cortical Neurodynamics for Past, Present and Future

NASA Astrophysics Data System (ADS)

Liljenström, Hans

2002-09-01

Behaving systems, biological as well as artificial, need to respond quickly and accurately to changes in the environment. The response is dependent on stored memories, and novel situations should be learnt for the guidance of future behavior. A highly nonlinear system dynamics is required in order to cope with a complex and changing environment, and this dynamics should be regulated to match the demands of the current situation, and to predict future behavior. In many cases the dynamics should be regulated to minimize processing time. We use computer simulations of cortical structures in order to investigate how the neurodynamics of these systems can be regulated for optimal performance in an unknown and changing environment. In particular, we study how cortical oscillations can serve to amplify weak signals and sustain an input pattern for more accurate information processing, and how chaotic-like behavior could increase the sensitivity in initial, exploratory states. We mimic regulating mechanisms based on neuromodulators, intrinsic noise levels, and various synchronizing effects. We find optimal noise levels where system performance is maximized, and neuromodulatory strategies for an efficient pattern recognition, where the anticipatory state of the system plays an important role.

Comparing the Cognitive Process of Circular Causality in Two Patients with Strokes through Qualitative Analysis.

PubMed

Derakhshanrad, Seyed Alireza; Piven, Emily; Ghoochani, Bahareh Zeynalzadeh

2017-10-01

Walter J. Freeman pioneered the neurodynamic model of brain activity when he described the brain dynamics for cognitive information transfer as the process of circular causality at intention, meaning, and perception (IMP) levels. This view contributed substantially to establishment of the Intention, Meaning, and Perception Model of Neuro-occupation in occupational therapy. As described by the model, IMP levels are three components of the brain dynamics system, with nonlinear connections that enable cognitive function to be processed in a circular causality fashion, known as Cognitive Process of Circular Causality (CPCC). Although considerable research has been devoted to study the brain dynamics by sophisticated computerized imaging techniques, less attention has been paid to study it through investigating the adaptation process of thoughts and behaviors. To explore how CPCC manifested thinking and behavioral patterns, a qualitative case study was conducted on two matched female participants with strokes, who were of comparable ages, affected sides, and other characteristics, except for their resilience and motivational behaviors. CPCC was compared by matrix analysis between two participants, using content analysis with pre-determined categories. Different patterns of thinking and behavior may have happened, due to disparate regulation of CPCC between two participants.

Ultrasound Imaging of Muscle Contraction of the Tibialis Anterior in Patients with Facioscapulohumeral Dystrophy.

PubMed

Gijsbertse, Kaj; Goselink, Rianne; Lassche, Saskia; Nillesen, Maartje; Sprengers, André; Verdonschot, Nico; van Alfen, Nens; de Korte, Chris

2017-11-01

A need exists for biomarkers to diagnose, quantify and longitudinally follow facioscapulohumeral muscular dystrophy (FSHD) and many other neuromuscular disorders. Furthermore, the pathophysiological mechanisms leading to muscle weakness in most neuromuscular disorders are not completely understood. Dynamic ultrasound imaging (B-mode image sequences) in combination with speckle tracking is an easy, applicable and patient-friendly imaging tool to visualize and quantify muscle deformation. This dynamic information provides insight in the pathophysiological mechanisms and may help to distinguish the various stages of diseased muscle in FSHD. In this proof-of-principle study, we applied a speckle tracking technique to 2-D ultrasound image sequences to quantify the deformation of the tibialis anterior muscle in patients with FSHD and in healthy controls. The resulting deformation patterns were compared with muscle ultrasound echo intensity analysis (a measure of fat infiltration and dystrophy) and clinical outcome measures. Of the four FSHD patients, two patients had severe peroneal weakness and two patients had mild peroneal weakness on clinical examination. We found a markedly varied muscle deformation pattern between these groups: patients with severe peroneal weakness showed a different motion pattern of the tibialis anterior, with overall less displacement of the central tendon region, while healthy patients showed a non-uniform displacement pattern, with the central aponeurosis showing the largest displacement. Hence, dynamic muscle ultrasound of the tibialis anterior muscle in patients with FSHD revealed a distinctively different tissue deformation pattern among persons with and without tibialis anterior weakness. These findings could clarify the understanding of the pathophysiology of muscle weakness in FSHD patients. In addition, the change in muscle deformation shows good correlation with clinical measures and quantitative muscle ultrasound measurements. In conclusion, dynamic ultrasound in combination with speckle tracking allows the study of the effects of muscle pathology in relation to strength, force transmission and movement generation. Although further research is required, this technique can develop into a biomarker to quantify muscle disease severity. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Fuzzy recurrence plots

NASA Astrophysics Data System (ADS)

Pham, T. D.

2016-12-01

Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.

Introduced earthworm species exhibited unique patterns of seasonal activity and vertical distribution, and Lumbricus terrestris burrows remained usable for at least 7 years in hardwood and pine stands

Treesearch

Lynette R. Potvin; Erik A. Lilleskov

2017-01-01

It is difficult to obtain non-destructive information on the seasonal dynamics of earthworms in northern forest soils. To overcome this, we used a Rhizotron facility to compile 7 years of data on the activity of anecic (Lumbricus terrestris) and endogeic (Aporrectodea caliginosa complex) earthworms in two contrasting soil/plant...

Contact, Travel, and Transmission: The Impact of Winter Holidays on Influenza Dynamics in the United States.

PubMed

Ewing, Anne; Lee, Elizabeth C; Viboud, Cécile; Bansal, Shweta

2017-03-01

The seasonality of influenza is thought to vary according to environmental factors and human behavior. During winter holidays, potential disease-causing contact and travel deviate from typical patterns. We aim to understand these changes on age-specific and spatial influenza transmission. We characterized the changes to transmission and epidemic trajectories among children and adults in a spatial context before, during, and after the winter holidays among aggregated physician medical claims in the United States from 2001 to 2009 and among synthetic data simulated from a deterministic, age-specific spatial metapopulation model. Winter holidays reduced influenza transmission and delayed the trajectory of influenza season epidemics. The holiday period was marked by a shift in the relative risk of disease from children toward adults. Model results indicated that holidays delayed epidemic peaks and synchronized incidence across locations, and that contact reductions from school closures, rather than age-specific mixing and travel, produced these observed holiday influenza dynamics. Winter holidays delay seasonal influenza epidemic peaks and shift disease risk toward adults because of changes in contact patterns. These findings may inform targeted influenza information and vaccination campaigns during holiday periods. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Quantitative imaging with Fucci and mathematics to uncover temporal dynamics of cell cycle progression.

PubMed

Saitou, Takashi; Imamura, Takeshi

2016-01-01

Cell cycle progression is strictly coordinated to ensure proper tissue growth, development, and regeneration of multicellular organisms. Spatiotemporal visualization of cell cycle phases directly helps us to obtain a deeper understanding of controlled, multicellular, cell cycle progression. The fluorescent ubiquitination-based cell cycle indicator (Fucci) system allows us to monitor, in living cells, the G1 and the S/G2/M phases of the cell cycle in red and green fluorescent colors, respectively. Since the discovery of Fucci technology, it has found numerous applications in the characterization of the timing of cell cycle phase transitions under diverse conditions and various biological processes. However, due to the complexity of cell cycle dynamics, understanding of specific patterns of cell cycle progression is still far from complete. In order to tackle this issue, quantitative approaches combined with mathematical modeling seem to be essential. Here, we review several studies that attempted to integrate Fucci technology and mathematical models to obtain quantitative information regarding cell cycle regulatory patterns. Focusing on the technological development of utilizing mathematics to retrieve meaningful information from the Fucci producing data, we discuss how the combined methods advance a quantitative understanding of cell cycle regulation. © 2015 Japanese Society of Developmental Biologists.

Model of rhythmic ball bouncing using a visually controlled neural oscillator.

PubMed

Avrin, Guillaume; Siegler, Isabelle A; Makarov, Maria; Rodriguez-Ayerbe, Pedro

2017-10-01

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture's ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment. NEW & NOTEWORTHY The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment. Copyright © 2017 the American Physiological Society.

Quantitative assessment of dynamic PET imaging data in cancer imaging.

PubMed

Muzi, Mark; O'Sullivan, Finbarr; Mankoff, David A; Doot, Robert K; Pierce, Larry A; Kurland, Brenda F; Linden, Hannah M; Kinahan, Paul E

2012-11-01

Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. This review focuses on the application of dynamic PET imaging to measuring regional cancer biologic features and especially in using dynamic PET imaging for quantitative therapeutic response monitoring for cancer clinical trials. Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach. Copyright © 2012 Elsevier Inc. All rights reserved.

Neutrality and Robustness in Evo-Devo: Emergence of Lateral Inhibition

PubMed Central

Munteanu, Andreea; Solé, Ricard V.

2008-01-01

Embryonic development is defined by the hierarchical dynamical process that translates genetic information (genotype) into a spatial gene expression pattern (phenotype) providing the positional information for the correct unfolding of the organism. The nature and evolutionary implications of genotype–phenotype mapping still remain key topics in evolutionary developmental biology (evo-devo). We have explored here issues of neutrality, robustness, and diversity in evo-devo by means of a simple model of gene regulatory networks. The small size of the system allowed an exhaustive analysis of the entire fitness landscape and the extent of its neutrality. This analysis shows that evolution leads to a class of robust genetic networks with an expression pattern characteristic of lateral inhibition. This class is a repertoire of distinct implementations of this key developmental process, the diversity of which provides valuable clues about its underlying causal principles. PMID:19023404

Climate change alters diffusion of forest pest: A model study

NASA Astrophysics Data System (ADS)

Jo, Woo Seong; Kim, Hwang-Yong; Kim, Beom Jun

2017-01-01

Population dynamics with spatial information is applied to understand the spread of pests. We introduce a model describing how pests spread in discrete space. The number of pest descendants at each site is controlled by local information such as temperature, precipitation, and the density of pine trees. Our simulation leads to a pest spreading pattern comparable to the real data for pine needle gall midge in the past. We also simulate the model in two different climate conditions based on two different representative concentration pathways scenarios for the future. We observe that after an initial stage of a slow spread of pests, a sudden change in the spreading speed occurs, which is soon followed by a large-scale outbreak. We found that a future climate change causes the outbreak point to occur earlier and that the detailed spatio-temporal pattern of the spread depends on the source position from which the initial pest infection starts.

Changing word usage predicts changing word durations in New Zealand English.

PubMed

Sóskuthy, Márton; Hay, Jennifer

2017-09-01

This paper investigates the emergence of lexicalized effects of word usage on word duration by looking at parallel changes in usage and duration over 130years in New Zealand English. Previous research has found that frequent words are shorter, informative words are longer, and words in utterance-final position are also longer. It has also been argued that some of these patterns are not simply online adjustments, but are incorporated into lexical representations. While these studies tend to focus on the synchronic aspects of such patterns, our corpus shows that word-usage patterns and word durations are not static over time. Many words change in duration and also change with respect to frequency, informativity and likelihood of occurring utterance-finally. Analysis of changing word durations over this time period shows substantial patterns of co-adaptation between word usage and word durations. Words that are increasing in frequency are becoming shorter. Words that are increasing/decreasing in informativity show a change in the same direction in duration (e.g. increasing informativity is associated with increasing duration). And words that are increasingly appearing utterance-finally are lengthening. These effects exist independently of the local effects of the predictors. For example, words that are increasing utterance-finally lengthen in all positions, including utterance-medially. We show that these results are compatible with a number of different views about lexical representations, but they cannot be explained without reference to a production-perception loop that allows speakers to update their representations dynamically on the basis of their experience. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Integrating vegetation index time series and meteorological data to understand the effect of the land use/land cover (LULC) in the climatic seasonality of the Brazilian Cerrado

NASA Astrophysics Data System (ADS)

Lins, D. B.; Zullo, J.; Friedel, M. J.

2013-12-01

The Cerrado (savanna ecosystem) of São Paulo state (Brazil) represent a complex mosaic of different typologies of uses, actors and biophysical and social restrictions. Originally, 14% of the state of São Paulo area was covered by the diversity of Cerrado phytophysiognomies. Currently, only 1% of this original composition remains fragmented into numerous relicts of biodiversity, mainly concentrated in the central-eastern of the state. A relevant part of the fragments are found in areas of intense coverage change by human activities, whereas the greatest pressure comes from sugar cane cultivation, either by direct replacement of Cerrado vegetation or occupying pasture areas in the fragments edges. As a result, new local level dynamics has been introduced, directly or indirectly, affecting the established of processes in climate systems. In this study, the main goal is analyzing the relationship between the Cerrado landscape changing and the climate dynamics in regional and local areas. The multi-temporal MODIS 250 m Vegetation Index (VI) datasets (period of 2000 to 2012) are integrated with precipitation data of the correspondent period (http://www.agritempo.gov.br/),one of the most important variable of the spatial phytophysiognomies distribution. The integration of meteorological data enable the development of an integrated approach to understand the relationship between climatic seasonality and the changes in the spatial patterns. A procedure to congregated diverse dynamics information is the Self Organizing Map (SOM, Kohonen, 2001), a technique that relies on unsupervised competitive learning (Kohonen and Somervuo 2002) to recognize patterns. In this approach, high-dimensional data are represented on two dimensions, making possible to obtain patterns that takes into account information from different natures. Observed advances will contribute to bring machine-learning techniques as a valid tool to provide improve in land use/land cover (LULC) analyzes at different hierarchical scales to support numerous science and policy applications.

Incorrect support and missing center tolerances of phasing algorithms

DOE PAGES

Huang, Xiaojing; Nelson, Johanna; Steinbrener, Jan; ...

2010-01-01

In x-ray diffraction microscopy, iterative algorithms retrieve reciprocal space phase information, and a real space image, from an object's coherent diffraction intensities through the use of a priori information such as a finite support constraint. In many experiments, the object's shape or support is not well known, and the diffraction pattern is incompletely measured. We describe here computer simulations to look at the effects of both of these possible errors when using several common reconstruction algorithms. Overly tight object supports prevent successful convergence; however, we show that this can often be recognized through pathological behavior of the phase retrieval transfermore » function. Dynamic range limitations often make it difficult to record the central speckles of the diffraction pattern. We show that this leads to increasing artifacts in the image when the number of missing central speckles exceeds about 10, and that the removal of unconstrained modes from the reconstructed image is helpful only when the number of missing central speckles is less than about 50. In conclusion, this simulation study helps in judging the reconstructability of experimentally recorded coherent diffraction patterns.« less

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2016-10-02

Here, we present ECoG ClusterFlow, a novel interactive visual analysis tool for the exploration of high-resolution Electrocorticography (ECoG) data. Our system detects and visualizes dynamic high-level structures, such as communities, using the time-varying spatial connectivity network derived from the high-resolution ECoG data. ECoG ClusterFlow provides a multi-scale visualization of the spatio-temporal patterns underlying the time-varying communities using two views: 1) an overview summarizing the evolution of clusters over time and 2) a hierarchical glyph-based technique that uses data aggregation and small multiples techniques to visualize the propagation of clusters in their spatial domain. ECoG ClusterFlow makes it possible 1) tomore » compare the spatio-temporal evolution patterns across various time intervals, 2) to compare the temporal information at varying levels of granularity, and 3) to investigate the evolution of spatial patterns without occluding the spatial context information. Lastly, we present case studies done in collaboration with neuroscientists on our team for both simulated and real epileptic seizure data aimed at evaluating the effectiveness of our approach.« less

Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

2015-03-01

Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

Vortex-Core Reversal Dynamics: Towards Vortex Random Access Memory

NASA Astrophysics Data System (ADS)

Kim, Sang-Koog

2011-03-01

An energy-efficient, ultrahigh-density, ultrafast, and nonvolatile solid-state universal memory is a long-held dream in the field of information-storage technology. The magnetic random access memory (MRAM) along with a spin-transfer-torque switching mechanism is a strong candidate-means of realizing that dream, given its nonvolatility, infinite endurance, and fast random access. Magnetic vortices in patterned soft magnetic dots promise ground-breaking applications in information-storage devices, owing to the very stable twofold ground states of either their upward or downward core magnetization orientation and plausible core switching by in-plane alternating magnetic fields or spin-polarized currents. However, two technologically most important but very challenging issues --- low-power recording and reliable selection of each memory cell with already existing cross-point architectures --- have not yet been resolved for the basic operations in information storage, that is, writing (recording) and readout. Here, we experimentally demonstrate a magnetic vortex random access memory (VRAM) in the basic cross-point architecture. This unique VRAM offers reliable cell selection and low-power-consumption control of switching of out-of-plane core magnetizations using specially designed rotating magnetic fields generated by two orthogonal and unipolar Gaussian-pulse currents along with optimized pulse width and time delay. Our achievement of a new device based on a new material, that is, a medium composed of patterned vortex-state disks, together with the new physics on ultrafast vortex-core switching dynamics, can stimulate further fruitful research on MRAMs that are based on vortex-state dot arrays.

Geographic patterns of networks derived from extreme precipitation over the Indian subcontinent

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Bookhagen, Bodo; Marwan, Norbert; Kurths, Juergen

2014-05-01

Complex networks (CN) and event synchronization (ES) methods have been applied to study a number of climate phenomena such as Indian Summer Monsoon (ISM), South-American Monsoon, and African Monsoon. These methods proved to be powerful tools to infer interdependencies in climate dynamics between geographical sites, spatial structures, and key regions of the considered climate phenomenon. Here, we use these methods to study the spatial temporal variability of the extreme rainfall over the Indian subcontinent, in order to filter the data by coarse-graining the network, and to identify geographic patterns that are signature features (spatial signatures) of the ISM. We find four main geographic patterns of networks derived from extreme precipitation over the Indian subcontinent using up-to-date satellite-derived, and high temporal and spatial resolution rain-gauge interpolated daily rainfall datasets. In order to prove that our results are also relevant for other climatic variables like pressure and temperature, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). We find that two of the patterns revealed from the CN extreme rainfall analysis coincide with those obtained for the pressure and temperature fields, and all four above mentioned patterns can be explained by topography, winds, and monsoon circulation. CN and ES enable to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to infer geographic pattern that are spatial signatures of the ISM. These patterns deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Dynamically Intuitive and Potentially Predicatable Three-Dimensional Structures in the Low Frequency Flow Variability of the Extratropical Northern Hemisphere

NASA Astrophysics Data System (ADS)

Wettstein, J. J.; Li, C.; Bradshaw, S.

2016-12-01

Canonical tropospheric climate variability patterns and their corresponding indices are ubiquitous, yet a firm dynamical interpretation has remained elusive for many of even the leading extratropical patterns. Part of the lingering difficulty in understanding and predicting atmospheric low frequency variability is the fact that the identification itself of the different patterns is indistinct. This study characterizes three-dimensional structures in the low frequency variability of the extratropical zonal wind field within the entire period of record of the ERA-Interim reanalysis and suggests the foundations for a new paradigm in identifying and predicting extratropical atmospheric low-frequency variability. In concert with previous results, there is a surprisingly rich three-dimensional structure to the variance of the zonal wind field that is not (cannot be) captured by traditional identification protocols that explore covariance of pressure in the lower troposphere, flow variability in the zonal mean or, for that matter, in any variable on any planar surface. Correspondingly, many of the pressure-based canonical indices of low frequency atmospheric variability exhibit inconsistent relationships to physically intuitive reorganizations of the subtropical and polar front jets and with other forcing mechanisms. Different patterns exhibit these inconsistencies to a greater or lesser extent. The three-dimensional variance of the zonal wind field is, by contrast, naturally organized around dynamically intuitive atmospheric redistributions that have a surprisingly large amount of physically intuitive information in the vertical. These conclusions are robust in a variety of seasons and also in intra-seasonal and inter-annual explorations. Similar results and conclusions are also derived using detrended data, other reanalyses, and state-of-the-art coupled climate model output. In addition to providing a clearer perspective on the distinct three-dimensional patterns of atmospheric low frequency variability, the time evolution and potential predictability of the resultant patterns can be explored with much greater clarity because of an intrinsic link between the patterns and the requisite conservation of momentum (i.e. to the primitive equations and candidate forcing mechanisms).

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study

PubMed Central

Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions (n = 6) and a group of healthy adolescent athletes (n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort. PMID:29357675

Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study.

PubMed

Muller, Angela M; Virji-Babul, Naznin

2018-01-01

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions ( n = 6) and a group of healthy adolescent athletes ( n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort.

Auditory cortical activity after intracortical microstimulation and its role for sensory processing and learning.

PubMed

Deliano, Matthias; Scheich, Henning; Ohl, Frank W

2009-12-16

Several studies have shown that animals can learn to make specific use of intracortical microstimulation (ICMS) of sensory cortex within behavioral tasks. Here, we investigate how the focal, artificial activation by ICMS leads to a meaningful, behaviorally interpretable signal. In natural learning, this involves large-scale activity patterns in widespread brain-networks. We therefore trained gerbils to discriminate closely neighboring ICMS sites within primary auditory cortex producing evoked responses largely overlapping in space. In parallel, during training, we recorded electrocorticograms (ECoGs) at high spatial resolution. Applying a multivariate classification procedure, we identified late spatial patterns that emerged with discrimination learning from the ongoing poststimulus ECoG. These patterns contained information about the preceding conditioned stimulus, and were associated with a subsequent correct behavioral response by the animal. Thereby, relevant pattern information was mainly carried by neuron populations outside the range of the lateral spatial spread of ICMS-evoked cortical activation (approximately 1.2 mm). This demonstrates that the stimulated cortical area not only encoded information about the stimulation sites by its focal, stimulus-driven activation, but also provided meaningful signals in its ongoing activity related to the interpretation of ICMS learned by the animal. This involved the stimulated area as a whole, and apparently required large-scale integration in the brain. However, ICMS locally interfered with the ongoing cortical dynamics by suppressing pattern formation near the stimulation sites. The interaction between ICMS and ongoing cortical activity has several implications for the design of ICMS protocols and cortical neuroprostheses, since the meaningful interpretation of ICMS depends on this interaction.

Dynamic visual noise reduces confidence in short-term memory for visual information.

PubMed

Kemps, Eva; Andrade, Jackie

2012-05-01

Previous research has shown effects of the visual interference technique, dynamic visual noise (DVN), on visual imagery, but not on visual short-term memory, unless retention of precise visual detail is required. This study tested the prediction that DVN does also affect retention of gross visual information, specifically by reducing confidence. Participants performed a matrix pattern memory task with three retention interval interference conditions (DVN, static visual noise and no interference control) that varied from trial to trial. At recall, participants indicated whether or not they were sure of their responses. As in previous research, DVN did not impair recall accuracy or latency on the task, but it did reduce recall confidence relative to static visual noise and no interference. We conclude that DVN does distort visual representations in short-term memory, but standard coarse-grained recall measures are insensitive to these distortions.

Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving Mammals

PubMed Central

Jercog, Pablo; Rogerson, Thomas; Schnitzer, Mark J.

2016-01-01

During long-term memory formation, cellular and molecular processes reshape how individual neurons respond to specific patterns of synaptic input. It remains poorly understood how such changes impact information processing across networks of mammalian neurons. To observe how networks encode, store, and retrieve information, neuroscientists must track the dynamics of large ensembles of individual cells in behaving animals, over timescales commensurate with long-term memory. Fluorescence Ca2+-imaging techniques can monitor hundreds of neurons in behaving mice, opening exciting avenues for studies of learning and memory at the network level. Genetically encoded Ca2+ indicators allow neurons to be targeted by genetic type or connectivity. Chronic animal preparations permit repeated imaging of neural Ca2+ dynamics over multiple weeks. Together, these capabilities should enable unprecedented analyses of how ensemble neural codes evolve throughout memory processing and provide new insights into how memories are organized in the brain. PMID:27048190

Bursting patterns and mixed-mode oscillations in reduced Purkinje model

NASA Astrophysics Data System (ADS)

Zhan, Feibiao; Liu, Shenquan; Wang, Jing; Lu, Bo

2018-02-01

Bursting discharge is a ubiquitous behavior in neurons, and abundant bursting patterns imply many physiological information. There exists a closely potential link between bifurcation phenomenon and the number of spikes per burst as well as mixed-mode oscillations (MMOs). In this paper, we have mainly explored the dynamical behavior of the reduced Purkinje cell and the existence of MMOs. First, we adopted the codimension-one bifurcation to illustrate the generation mechanism of bursting in the reduced Purkinje cell model via slow-fast dynamics analysis and demonstrate the process of spike-adding. Furthermore, we have computed the first Lyapunov coefficient of Hopf bifurcation to determine whether it is subcritical or supercritical and depicted the diagrams of inter-spike intervals (ISIs) to examine the chaos. Moreover, the bifurcation diagram near the cusp point is obtained by making the codimension-two bifurcation analysis for the fast subsystem. Finally, we have a discussion on mixed-mode oscillations and it is further investigated using the characteristic index that is Devil’s staircase.

Multiple-object permanence tracking: limitation in maintenance and transformation of perceptual objects.

PubMed

Saiki, Jun

2002-01-01

Research on change blindness and transsaccadic memory revealed that a limited amount of information is retained across visual disruptions in visual working memory. It has been proposed that visual working memory can hold four to five coherent object representations. To investigate their maintenance and transformation in dynamic situations, I devised an experimental paradigm called multiple-object permanence tracking (MOPT) that measures memory for multiple feature-location bindings in dynamic situations. Observers were asked to detect any color switch in the middle of a regular rotation of a pattern with multiple colored disks behind an occluder. The color-switch detection performance dramatically declined as the pattern rotation velocity increased, and this effect of object motion was independent of the number of targets. The MOPT task with various shapes and colors showed that color-shape conjunctions are not available in the MOPT task. These results suggest that even completely predictable motion severely reduces our capacity of object representations, from four to only one or two.

Discovering Coherent Structures Using Local Causal States

NASA Astrophysics Data System (ADS)

Rupe, Adam; Crutchfield, James P.; Kashinath, Karthik; Prabhat, Mr.

2017-11-01

Coherent structures were introduced in the study of fluid dynamics and were initially defined as regions characterized by high levels of coherent vorticity, i.e. regions where instantaneously space and phase correlated vorticity are high. In a more general spatiotemporal setting, coherent structures can be seen as localized broken symmetries which persist in time. Building off the computational mechanics framework, which integrates tools from computation and information theory to capture pattern and structure in nonlinear dynamical systems, we introduce a theory of coherent structures, in the more general sense. Central to computational mechanics is the causal equivalence relation, and a local spatiotemporal generalization of it is used to construct the local causal states, which are utilized to uncover a system's spatiotemporal symmetries. Coherent structures are then identified as persistent, localized deviations from these symmetries. We illustrate how novel patterns and structures can be discovered in cellular automata and outline the path from them to laminar, transitional and turbulent flows. Funded by Intel through the Big Data Center at LBNL and the IPCC at UC Davis.

Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles

NASA Technical Reports Server (NTRS)

Eichler, Gabriel S.; Huang, Sui; Ingber, Donald E.

2003-01-01

Genome-wide expression profiles contain global patterns that evade visual detection in current gene clustering analysis. Here, a Gene Expression Dynamics Inspector (GEDI) is described that uses self-organizing maps to translate high-dimensional expression profiles of time courses or sample classes into animated, coherent and robust mosaics images. GEDI facilitates identification of interesting patterns of molecular activity simultaneously across gene, time and sample space without prior assumption of any structure in the data, and then permits the user to retrieve genes of interest. Important changes in genome-wide activities may be quickly identified based on 'Gestalt' recognition and hence, GEDI may be especially useful for non-specialist end users, such as physicians. AVAILABILITY: GEDI v1.0 is written in Matlab, and binary Matlab.dll files which require Matlab to run can be downloaded for free by academic institutions at http://www.chip.org/ge/gedihome.html Supplementary information: http://www.chip.org/ge/gedihome.html.

Energy prediction using spatiotemporal pattern networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated bymore » the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.« less

Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions.

PubMed

Contini, Erika W; Wardle, Susan G; Carlson, Thomas A

2017-10-01

Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of Morphology Constraint on Electrophysiological Properties of Cortical Neurons

NASA Astrophysics Data System (ADS)

Zhu, Geng; Du, Liping; Jin, Lei; Offenhäusser, Andreas

2016-04-01

There is growing interest in engineering nerve cells in vitro to control architecture and connectivity of cultured neuronal networks or to build neuronal networks with predictable computational function. Pattern technologies, such as micro-contact printing, have been developed to design ordered neuronal networks. However, electrophysiological characteristics of the single patterned neuron haven’t been reported. Here, micro-contact printing, using polyolefine polymer (POP) stamps with high resolution, was employed to grow cortical neurons in a designed structure. The results demonstrated that the morphology of patterned neurons was well constrained, and the number of dendrites was decreased to be about 2. Our electrophysiological results showed that alterations of dendritic morphology affected firing patterns of neurons and neural excitability. When stimulated by current, though both patterned and un-patterned neurons presented regular spiking, the dynamics and strength of the response were different. The un-patterned neurons exhibited a monotonically increasing firing frequency in response to injected current, while the patterned neurons first exhibited frequency increase and then a slow decrease. Our findings indicate that the decrease in dendritic complexity of cortical neurons will influence their electrophysiological characteristics and alter their information processing activity, which could be considered when designing neuronal circuitries.

Beach-dune dynamics: Spatio-temporal patterns of aeolian sediment transport under complex offshore airflow

NASA Astrophysics Data System (ADS)

Lynch, K.; Jackson, D.; Delgado-Fernandez, I.; Cooper, J. A.; Baas, A. C.; Beyers, M.

2010-12-01

This study examines sand transport and wind speed across a beach at Magilligan Strand, Northern Ireland, under offshore wind conditions. Traditionally the offshore component of local wind regimes has been ignored when quantifying beach-dune sediment budgets, with the sheltering effect of the foredune assumed to prohibit grain entrainment on the adjoining beach. Recent investigations of secondary airflow patterns over coastal dunes have suggested this may not be the case, that the turbulent nature of the airflow in these zones enhances sediment transport potential. Beach sediment may be delivered to the dune toe by re-circulating eddies under offshore winds in coastal areas, which may explain much of the dynamics of aeolian dunes on coasts where the dominant wind direction is offshore. The present study investigated aeolian sediment transport patterns under an offshore wind event. Empirical data were collected using load cell traps, for aeolian sediment transport, co-located with 3-D ultrasonic anemometers. The instrument positioning on the sub-aerial beach was informed by prior analysis of the airflow patterns using computational fluid dynamics. The array covered a total beach area of 90 m alongshore by 65 m cross-shore from the dune crest. Results confirm that sediment transport occurred in the ‘sheltered’ area under offshore winds. Over short time and space scales the nature of the transport is highly complex; however, preferential zones for sand entrainment may be identified. Alongshore spatial heterogeneity of sediment transport seems to show a relationship to undulations in the dune crest, while temporal and spatial variations may also be related to the position of the airflow reattachment zone. These results highlight the important feedbacks between flow characteristics and transport in a complex three dimensional surface.

Heat exposure in cities: combining the dynamics of temperature and population

NASA Astrophysics Data System (ADS)

Hu, L.; Wilhelmi, O.; Uejio, C. K.

2017-12-01

Assessment of human exposure to extreme heat requires the distributions of temperature and population. However, both variables are dynamic, thus presenting many challenges in capturing temperature and population patterns spatially and over time in an urban context. This study aims to improve the understanding of spatiotemporal patterns of urban population exposure to heat, taking Chicago, USA as an example. We estimate the hourly, geographically variable, population distribution considering commute of workers and students in a regular weekday and analyze the diurnal air temperature patterns during different meteorological conditions from satellite observations. The results show a relatively larger temperature increase in less urbanized areas during extreme heat events (EHEs), resulting in a spatially homogeneous temperature distribution over Chicago Metropolitan area. A lake cooling effect is weaker during EHEs. Population dynamics due to daily commute determine higher population density in more urbanized areas during daytime. The city-wide analysis reveals that the exposure is more sensitive to the nighttime temperature increases, and EHEs enhance this sensitivity. The high exposure hotspots are identified at the northwest Chicago, Cicero and Oak Park areas, where the influence from Lake Michigan is weakened, while the spatial extent of high outdoor exposure areas varies diurnally. This study's findings have potential to better inform general heat mitigation strategies during hot summer months and facilitate emergency response during EHEs. Availability of remotely-sensed temperature observations as well as the workers and students commute-adjusted population data allows for the adoption of this study's methodology in other major metropolitan areas. A better understanding of space-time patterns of urban population's exposure to heat will further enable local decision makers to mitigate extreme heat health risks and develop more targeted heat preparedness and response strategies.

Information processing in the CNS: a supramolecular chemistry?

PubMed

Tozzi, Arturo

2015-10-01

How does central nervous system process information? Current theories are based on two tenets: (a) information is transmitted by action potentials, the language by which neurons communicate with each other-and (b) homogeneous neuronal assemblies of cortical circuits operate on these neuronal messages where the operations are characterized by the intrinsic connectivity among neuronal populations. In this view, the size and time course of any spike is stereotypic and the information is restricted to the temporal sequence of the spikes; namely, the "neural code". However, an increasing amount of novel data point towards an alternative hypothesis: (a) the role of neural code in information processing is overemphasized. Instead of simply passing messages, action potentials play a role in dynamic coordination at multiple spatial and temporal scales, establishing network interactions across several levels of a hierarchical modular architecture, modulating and regulating the propagation of neuronal messages. (b) Information is processed at all levels of neuronal infrastructure from macromolecules to population dynamics. For example, intra-neuronal (changes in protein conformation, concentration and synthesis) and extra-neuronal factors (extracellular proteolysis, substrate patterning, myelin plasticity, microbes, metabolic status) can have a profound effect on neuronal computations. This means molecular message passing may have cognitive connotations. This essay introduces the concept of "supramolecular chemistry", involving the storage of information at the molecular level and its retrieval, transfer and processing at the supramolecular level, through transitory non-covalent molecular processes that are self-organized, self-assembled and dynamic. Finally, we note that the cortex comprises extremely heterogeneous cells, with distinct regional variations, macromolecular assembly, receptor repertoire and intrinsic microcircuitry. This suggests that every neuron (or group of neurons) embodies different molecular information that hands an operational effect on neuronal computation.

Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.

PubMed

Wimmer, Klaus; Ramon, Marc; Pasternak, Tatiana; Compte, Albert

2016-01-13

Neuronal activity in the lateral prefrontal cortex (LPFC) reflects the structure and cognitive demands of memory-guided sensory discrimination tasks. However, we still do not know how neuronal activity articulates in network states involved in perceiving, remembering, and comparing sensory information during such tasks. Oscillations in local field potentials (LFPs) provide fingerprints of such network dynamics. Here, we examined LFPs recorded from LPFC of macaques while they compared the directions or the speeds of two moving random-dot patterns, S1 and S2, separated by a delay. LFP activity in the theta, beta, and gamma bands tracked consecutive components of the task. In response to motion stimuli, LFP theta and gamma power increased, and beta power decreased, but showed only weak motion selectivity. In the delay, LFP beta power modulation anticipated the onset of S2 and encoded the task-relevant S1 feature, suggesting network dynamics associated with memory maintenance. After S2 onset the difference between the current stimulus S2 and the remembered S1 was strongly reflected in broadband LFP activity, with an early sensory-related component proportional to stimulus difference and a later choice-related component reflecting the behavioral decision buildup. Our results demonstrate that individual LFP bands reflect both sensory and cognitive processes engaged independently during different stages of the task. This activation pattern suggests that during elementary cognitive tasks, the prefrontal network transitions dynamically between states and that these transitions are characterized by the conjunction of LFP rhythms rather than by single LFP bands. Neurons in the brain communicate through electrical impulses and coordinate this activity in ensembles that pulsate rhythmically, very much like musical instruments in an orchestra. These rhythms change with "brain state," from sleep to waking, but also signal with different oscillation frequencies rapid changes between sensory and cognitive processing. Here, we studied rhythmic electrical activity in the monkey prefrontal cortex, an area implicated in working memory, decision making, and executive control. Monkeys had to identify and remember a visual motion pattern and compare it to a second pattern. We found orderly transitions between rhythmic activity where the same frequency channels were active in all ongoing prefrontal computations. This supports prefrontal circuit dynamics that transitions rapidly between complex rhythmic patterns during structured cognitive tasks. Copyright © 2016 the authors 0270-6474/16/360489-17$15.00/0.

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

PubMed Central

Gerhard, Felipe; Kispersky, Tilman; Gutierrez, Gabrielle J.; Marder, Eve; Kramer, Mark; Eden, Uri

2013-01-01

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities. PMID:23874181

Grouping of optic flow stimuli during binocular rivalry is driven by monocular information.

PubMed

Holten, Vivian; Stuit, Sjoerd M; Verstraten, Frans A J; van der Smagt, Maarten J

2016-10-01

During binocular rivalry, perception alternates between two dissimilar images, presented dichoptically. Although binocular rivalry is thought to result from competition at a local level, neighboring image parts with similar features tend to be perceived together for longer durations than image parts with dissimilar features. This simultaneous dominance of two image parts is called grouping during rivalry. Previous studies have shown that this grouping depends on a shared eye-of-origin to a much larger extent than on image content, irrespective of the complexity of a static image. In the current study, we examine whether grouping of dynamic optic flow patterns is also primarily driven by monocular (eye-of-origin) information. In addition, we examine whether image parameters, such as optic flow direction, and partial versus full visibility of the optic flow pattern, affect grouping durations during rivalry. The results show that grouping of optic flow is, as is known for static images, primarily affected by its eye-of-origin. Furthermore, global motion can affect grouping durations, but only under specific conditions. Namely, only when the two full optic flow patterns were presented locally. These results suggest that grouping during rivalry is primarily driven by monocular information even for motion stimuli thought to rely on higher-level motion areas. Copyright © 2016 Elsevier Ltd. All rights reserved.

Entropic measures of individual mobility patterns

NASA Astrophysics Data System (ADS)

Gallotti, Riccardo; Bazzani, Armando; Degli Esposti, Mirko; Rambaldi, Sandro

2013-10-01

Understanding human mobility from a microscopic point of view may represent a fundamental breakthrough for the development of a statistical physics for cognitive systems and it can shed light on the applicability of macroscopic statistical laws for social systems. Even if the complexity of individual behaviors prevents a true microscopic approach, the introduction of mesoscopic models allows the study of the dynamical properties for the non-stationary states of the considered system. We propose to compute various entropy measures of the individual mobility patterns obtained from GPS data that record the movements of private vehicles in the Florence district, in order to point out new features of human mobility related to the use of time and space and to define the dynamical properties of a stochastic model that could generate similar patterns. Moreover, we can relate the predictability properties of human mobility to the distribution of time passed between two successive trips. Our analysis suggests the existence of a hierarchical structure in the mobility patterns which divides the performed activities into three different categories, according to the time cost, with different information contents. We show that a Markov process defined by using the individual mobility network is not able to reproduce this hierarchy, which seems the consequence of different strategies in the activity choice. Our results could contribute to the development of governance policies for a sustainable mobility in modern cities.

Quantifying urban growth patterns in Hanoi using landscape expansion modes and time series spatial metrics

PubMed Central

Lepczyk, Christopher A.; Miura, Tomoaki; Fox, Jefferson M.

2018-01-01

Urbanization has been driven by various social, economic, and political factors around the world for centuries. Because urbanization continues unabated in many places, it is crucial to understand patterns of urbanization and their potential ecological and environmental impacts. Given this need, the objectives of our study were to quantify urban growth rates, growth modes, and resultant changes in the landscape pattern of urbanization in Hanoi, Vietnam from 1993 to 2010 and to evaluate the extent to which the process of urban growth in Hanoi conformed to the diffusion-coalescence theory. We analyzed the spatiotemporal patterns and dynamics of the built-up land in Hanoi using landscape expansion modes, spatial metrics, and a gradient approach. Urbanization was most pronounced in the periods of 2001–2006 and 2006–2010 at a distance of 10 to 35 km around the urban center. Over the 17 year period urban expansion in Hanoi was dominated by infilling and edge expansion growth modes. Our findings support the diffusion-coalescence theory of urbanization. The shift of the urban growth areas over time and the dynamic nature of the spatial metrics revealed important information about our understanding of the urban growth process and cycle. Furthermore, our findings can be used to evaluate urban planning policies and aid in urbanization issues in rapidly urbanizing countries. PMID:29734346

The spatiotemporal order of signaling events unveils the logic of development signaling

PubMed Central

Zhu, Hao; Owen, Markus R.; Mao, Yanlan

2016-01-01

Motivation: Animals from worms and insects to birds and mammals show distinct body plans; however, the embryonic development of diverse body plans with tissues and organs within is controlled by a surprisingly few signaling pathways. It is well recognized that combinatorial use of and dynamic interactions among signaling pathways follow specific logic to control complex and accurate developmental signaling and patterning, but it remains elusive what such logic is, or even, what it looks like. Results: We have developed a computational model for Drosophila eye development with innovated methods to reveal how interactions among multiple pathways control the dynamically generated hexagonal array of R8 cells. We obtained two novel findings. First, the coupling between the long-range inductive signals produced by the proneural Hh signaling and the short-range restrictive signals produced by the antineural Notch and EGFR signaling is essential for generating accurately spaced R8s. Second, the spatiotemporal orders of key signaling events reveal a robust pattern of lateral inhibition conducted by Ato-coordinated Notch and EGFR signaling to collectively determine R8 patterning. This pattern, stipulating the orders of signaling and comparable to the protocols of communication, may help decipher the well-appreciated but poorly defined logic of developmental signaling. Availability and implementation: The model is available upon request. Contact: hao.zhu@ymail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153573

Coherent-fields, their responsive colloids, and life's origins.

NASA Astrophysics Data System (ADS)

Mitra-Delmotte, G.; Mitra, A. N.

2015-10-01

In living systems, evolvable sequence-encoded constraints control the incoming energy-matter flows, and are also sustained by their embedded flows/ processes. What's more, in such dynamic-organized liquid-state media, the flows can also produce novel materials/mechanisms. Thus, embedded processes of such media enable its spatiotemporal resilience via turnovers, as well as functional 'takeovers'. Further, the responsiveness of such constrained media to their environment enables adaptations, as they can mediate feedback between the changing environment & their embedded flows/processes. Now, the complexity of the constituent functional materials, make it very likely that they themselves emerged/got selected thanks to the creative properties of such dynamically constrained media. We have asked if such Maxwelldemon- like scenario could not be mimicked using other plausible ingredients to achieve similar ways of dissipative sustenance and coherent functioning. In particular, the potential of organizing coherent fields and their responsive anisotropic colloids to enhance the probability of life's emergence—akin to an adaptive transition—to a new way of evolving, seems promising. Note that pattern-sustenance in liquid state requires presence of the specific source that enabled it (c.f. spontaneously formed patterns). For example, external coherent heterogeneous fields (e.g. magnetic rocks) can act as sources both of 1) aperiodic information, and 2) useful energy, for inducing and sustaining (specific) structures of superparamagnetic mineral colloids (via their Brownianrotation) away-from-equilibrium, to access 3-way coupling between energy-information-matter in liquid-medium. Such dynamic functioning structures seem ideal for stable containment of bottom-up chemical systems; and similar scenario in the nanoscience engineering area can help in design/tests.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Local dynamics measured by hydrogen/deuterium exchange and mass spectrometry of creatine kinase digested by two proteases.

PubMed

Mazon, Hortense; Marcillat, Olivier; Forest, Eric; Vial, Christian

2005-12-01

Hydrogen/deuterium exchange coupled to mass spectrometry has been used to investigate the structure and dynamics of native dimeric cytosolic muscle creatine kinase. The protein was incubated in D2O for various time. After H/D exchange and rapid quenching of the reaction, the partially deuterated protein was cleaved in parallel by two different proteases (pepsin or type XIII protease from Aspergillus saitoi) to increase the sequence coverage and spatial resolution of deuterium incorporation. The resulting peptides were analyzed by liquid chromatography coupled to mass spectrometry. In comparison with the 3D structure of MM-CK, the analysis of the two independent proteolysis deuteration patterns allowed us to get new insights into CK local dynamics as compared to a previous study using pepsin [Mazon et al. Protein Science 13 (2004) 476-486]. In particular, we obtained more information on the kinetics and extent of deuterium exchange in the N- and C-terminal extremities represented by the 1-22 and 362-380 pepsin peptides. Indeed, we observed a very different behaviour of the 1-12 and 13-22 type XIII protease peptides, and similarly for the 362-373 and 374-380 peptides. Moreover, comparison of the deuteration patterns of type XIII protease segments of the large 90-126 pepsin peptide led us to identify a small relatively dynamic region (108-114).

How does "not left" become "right"? Electrophysiological evidence for a dynamic conflict-bound negation processing account.

PubMed

Dudschig, Carolin; Kaup, Barbara

2018-05-01

Human thought and language is traditionally considered as abstract, amodal, and symbolic. However, recent theories propose that high-level human cognition is directly linked to basic, modal biological systems such as sensorimotor areas. Despite this influential representational debate very little is known regarding whether the mechanisms involved in sensorimotor control are also shared with higher-level cognitive processes, such as language comprehension. We investigated negation as a universal of human language, addressing two key questions: (a) Does negation result in a conflict-like representation? (b) Does negation trigger executive control adjustments in a similar manner as standard information processing conflicts do (e.g., Simon, Flanker)? Electrophysiological data indicated that phrases such as "not left/not right" result in initial activation of the to-be-negated information and subsequently the outcome of the negation process. More importantly, our findings also suggest that negation triggers conflict-related adjustments in information processing in line with traditional conflict tasks. Trial-by-trial conflict adaptation patterns in both behavioral and electrophysiological data indicated that negation processing dynamically changes depending on the current cognitive state. In summary, negation processing results in cognitive conflict, and dynamic influences of the cognitive state determine conflict resolution, that is, negation implementation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Sensory flow shaped by active sensing: sensorimotor strategies in electric fish.

PubMed

Hofmann, Volker; Sanguinetti-Scheck, Juan I; Künzel, Silke; Geurten, Bart; Gómez-Sena, Leonel; Engelmann, Jacob

2013-07-01

Goal-directed behavior in most cases is composed of a sequential order of elementary motor patterns shaped by sensorimotor contingencies. The sensory information acquired thus is structured in both space and time. Here we review the role of motion during the generation of sensory flow focusing on how animals actively shape information by behavioral strategies. We use the well-studied examples of vision in insects and echolocation in bats to describe commonalities of sensory-related behavioral strategies across sensory systems, and evaluate what is currently known about comparable active sensing strategies in electroreception of electric fish. In this sensory system the sensors are dispersed across the animal's body and the carrier source emitting energy used for sensing, the electric organ, is moved while the animal moves. Thus ego-motions strongly influence sensory dynamics. We present, for the first time, data of electric flow during natural probing behavior in Gnathonemus petersii (Mormyridae), which provide evidence for this influence. These data reveal a complex interdependency between the physical input to the receptors and the animal's movements, posture and objects in its environment. Although research on spatiotemporal dynamics in electrolocation is still in its infancy, the emerging field of dynamical sensory systems analysis in electric fish is a promising approach to the study of the link between movement and acquisition of sensory information.

Dynamic divisive normalization predicts time-varying value coding in decision-related circuits.

PubMed

Louie, Kenway; LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

Intrinsic information carriers in combinatorial dynamical systems

NASA Astrophysics Data System (ADS)

Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter

2010-09-01

Many proteins are composed of structural and chemical features—"sites" for short—characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations—unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are self-consistent descriptors of system dynamics in that their time-evolution is governed by a closed system of kinetic equations. Taken together, fragments are endogenous distinctions that matter for the dynamics of a system, which warrants viewing them as the carriers of information. Although fragments can be thought of as multisets of molecular species (an extensional view), their self-consistency suggests treating them as autonomous aspects cut off from their microscopic realization (an intensional view). Fragmentation is a seeded process that depends on the choice of observables whose dynamics one insists to describe. Different observables can cause distinct fragmentations, in effect altering the set of information carriers that govern the behavior of a system, even though nothing has changed in its microscopic constitution. In this contribution, we present a mathematical specification of fragments, but not an algorithmic implementation. We have described the latter elsewhere in rather technical terms that, although effective, were lacking an embedding into a more general conceptual framework, which we here provide.

Intrinsic information carriers in combinatorial dynamical systems.

PubMed

Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter

2010-09-01

Many proteins are composed of structural and chemical features--"sites" for short--characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations-unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are self-consistent descriptors of system dynamics in that their time-evolution is governed by a closed system of kinetic equations. Taken together, fragments are endogenous distinctions that matter for the dynamics of a system, which warrants viewing them as the carriers of information. Although fragments can be thought of as multisets of molecular species (an extensional view), their self-consistency suggests treating them as autonomous aspects cut off from their microscopic realization (an intensional view). Fragmentation is a seeded process that depends on the choice of observables whose dynamics one insists to describe. Different observables can cause distinct fragmentations, in effect altering the set of information carriers that govern the behavior of a system, even though nothing has changed in its microscopic constitution. In this contribution, we present a mathematical specification of fragments, but not an algorithmic implementation. We have described the latter elsewhere in rather technical terms that, although effective, were lacking an embedding into a more general conceptual framework, which we here provide.

Using Contact Patterns to Inform HIV Interventions in Persons Who Inject Drugs in Northern Vietnam.

PubMed

Smith, M Kumi; Graham, Matthew; Latkin, Carl A; Go, Vivian L

2018-05-01

Population mixing patterns can greatly inform allocation of HIV prevention interventions such as treatment as prevention or preexposure prophylaxis. Characterizing contact patterns among subgroups can help identify the specific combinations of contact expected to result in the greatest number of new infections. Baseline data from an intervention to reduce HIV-related risk behaviors in male persons who inject drugs (PWID) in the Northern Vietnamese province of Thai Nguyen were used for the analysis. Egocentric network data were provided by PWID who reported any drug-injection equipment sharing in the previous 3 months. Age-dependent mixing was assessed to explore its epidemiological implications on risk of HIV transmission risk (among those HIV-infected) and HIV acquisition risk (among those not infected) in PWID. A total of 1139 PWID collectively reported 2070 equipment-sharing partnerships in the previous 3 months. Mixing by age identified the 30-34 and 35-39 years age groups as the groups from whom the largest number of new infections was transmitted, making them primary targets for treatment as prevention. Among the uninfected, 25-29, 30-35, and 35-39 years age groups had the highest HIV acquisition rate, making them the primary targets for preexposure prophylaxis. Collection and analysis of contact patterns in PWID is feasible and can greatly inform infectious disease dynamics and targeting of appropriate interventions. Results presented also provide much needed empirical data on mixing to improve mathematical models of disease transmission in this population.

Food and consumers: Where are we heading?

PubMed

Worsley, A

2000-09-01

The translation of recent advances in nutrition sciences into enhanced population health and well-being depends on the development of a deeper understanding of human food consumption patterns and the factors which influence them. Food consumption patterns are dynamic and are influenced by complex, interrelated biological, social, cultural and psychological processes. These are evident in recent attempts to discriminate nutrition and health-related dietary patterns in terms of consumer lifestyles and belief systems. Consumers' pursuit of health and well-being through food consumption will be illustrated through reference to recent Australian studies. Some of the effects of societal changes associated with globalization: gender, work and family roles; materialism; information technology; and increasing longevity, on food consumption trends will be explored. Finally, the implications of these developments for the activities of health professionals, food companies and other agencies will be raised.

The Ancient Maya Landscape: Facing the Challenges and Embracing the Promise of Integrating Archaeology, Remote Sensing, Soil Science and Hydrologic Modeling for Coupled Natural and Human Systems.

NASA Astrophysics Data System (ADS)

Murtha, T., Jr.; Duffy, C.; Cook, B. D.; Schroder, W.; Webster, D.; French, K. D.; Alcover, O.; Golden, C.; Balzotti, C.; Shaffer, D.

2016-12-01

Relying on a niche inheritance perspective, this paper discusses the long-term spatial and temporal dynamics of land-use management, agricultural decision making and patterns of resource availability in the tropical lowlands of Central America. We introduce and describe ongoing research that addresses a series of long standing questions about coupled natural and human history dynamics in the Central Maya lowlands, emphasizing the role of landscape and region to address these questions. First, we summarize the results of a CNH pilot study focused on the evolution of the regional landscape of Tikal, Guatemala. Particular attention is centered on how we integrated landscape survey, traditional archaeology and soil studies to understand the spatial and temporal dynamics of agricultural land use and intensification over a two thousand period. Additionally, we discuss how these results were integrated into remote sensing, hydrological and erosion models to better understand how past changes in available water and productive land compare to what we know about settlement patterns in the Tikal Region over that same time period. We not only describe how the Maya transformed this landscape, but also how the region influenced changing patterns of settlement and land use. We finish this section with a discussion of some of the unique challenges integrating archaeological information to study CNH dynamics during this pilot study. Second, we introduce a new project designed to `scale up' the pilot study for a macro-regional analysis of the lowland Maya landscape. The new project leverages a uniquely sampled LIDAR data set designed to refine measurements of above ground carbon storage. Our new project quantitatively examines these data for evidence for past human activity. Preliminary results offer a promising path for tightly integrating archaeology, natural science, remote sensing and modeling for studying CNH dynamics in the deep and recent past.

Nanopatterning dynamics on Si(100) during oblique 40-keV Ar+ erosion with metal codeposition: Morphological and compositional correlation

NASA Astrophysics Data System (ADS)

Redondo-Cubero, A.; Gago, R.; Palomares, F. J.; Mücklich, A.; Vinnichenko, M.; Vázquez, L.

2012-08-01

The formation and dynamics of nanopatterns produced on Si(100) surfaces by 40-keV Ar+ oblique (α = 60°) bombardment with concurrent Fe codeposition have been studied. Morphological and chemical analysis has been performed by ex situ atomic force microscopy, Rutherford backscattering spectrometry, x-ray photoelectron spectroscopy, and scanning and transmission electron microscopies. During irradiation, Fe atoms incorporated into the target surface react with Si to form silicides, a process enhanced at this medium-ion energy range. The silicides segregate at the nanoscale from the early irradiation stages. As the irradiation proceeds, a ripple pattern is formed without any correlation with silicide segregation. From the comparison with the pattern dynamics reported previously for metal-free conditions, it is demonstrated that the metal incorporation alters both the pattern dynamics and the morphology. Although the pattern formation and dynamics are delayed for decreasing metal content, once ripples emerge, the same qualitative pattern of morphological evolution is observed for different metal content, resulting in an asymptotic saw-tooth-like facetted surface pattern. Despite the medium ion energy employed, the nanopatterning process with concurrent Fe deposition can be explained by those mechanisms proposed for low-ion energy irradiations such as shadowing, height fluctuations, silicide formation and segregation, ensuing composition dependent sputter rate, and ion sculpting effects. In particular, the interplay between the ion irradiation and metal flux geometries, differences in sputtering rates, and the surface pattern morphology produces a dynamic compositional patterning correlated with the evolving morphological one.

Innovative Ge Quantum Dot Functional Sensing and Metrology Devices

DTIC Science & Technology

2017-08-21

information latency and power consumption . In contrast, optical interconnects have shown tremendous promise for replacing electrical wires thanks to...single oxidation step of Si0.85Ge0.15 nano-pillars patterned over a buffer layer of Si3N4 on top of the n-Si substrate. During the high- temperature ...exquisitely-controlled dynamic balance between the fluxes of oxygen and silicon interstitials. Results and Discussion: 1. Self-organized, gate

Neutral Theory and Scale-Free Neural Dynamics

NASA Astrophysics Data System (ADS)

Martinello, Matteo; Hidalgo, Jorge; Maritan, Amos; di Santo, Serena; Plenz, Dietmar; Muñoz, Miguel A.

2017-10-01

Neural tissues have been consistently observed to be spontaneously active and to generate highly variable (scale-free distributed) outbursts of activity in vivo and in vitro. Understanding whether these heterogeneous patterns of activity stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as criticality has been argued to entail many possible important functional advantages in biological computing systems. Here, we employ a well-accepted model for neural dynamics to elucidate an alternative scenario in which diverse neuronal avalanches, obeying scaling, can coexist simultaneously, even if the network operates in a regime far from the edge of any phase transition. We show that perturbations to the system state unfold dynamically according to a "neutral drift" (i.e., guided only by stochasticity) with respect to the background of endogenous spontaneous activity, and that such a neutral dynamics—akin to neutral theories of population genetics and of biogeography—implies marginal propagation of perturbations and scale-free distributed causal avalanches. We argue that causal information, not easily accessible to experiments, is essential to elucidate the nature and statistics of neural avalanches, and that neutral dynamics is likely to play an important role in the cortex functioning. We discuss the implications of these findings to design new empirical approaches to shed further light on how the brain processes and stores information.

Walking in simulated Martian gravity: influence of the portable life support system's design on dynamic stability.

PubMed

Scott-Pandorf, Melissa M; O'Connor, Daniel P; Layne, Charles S; Josić, Kresimir; Kurz, Max J

2009-09-01

With human exploration of the moon and Mars on the horizon, research considerations for space suit redesign have surfaced. The portable life support system (PLSS) used in conjunction with the space suit during the Apollo missions may have influenced the dynamic balance of the gait pattern. This investigation explored potential issues with the PLSS design that may arise during the Mars exploration. A better understanding of how the location of the PLSS load influences the dynamic stability of the gait pattern may provide insight, such that space missions may have more productive missions with a smaller risk of injury and damaging equipment while falling. We explored the influence the PLSS load position had on the dynamic stability of the walking pattern. While walking, participants wore a device built to simulate possible PLSS load configurations. Floquet and Lyapunov analysis techniques were used to quantify the dynamic stability of the gait pattern. The dynamic stability of the gait pattern was influenced by the position of load. PLSS loads that are placed high and forward on the torso resulted in less dynamically stable walking patterns than loads placed evenly and low on the torso. Furthermore, the kinematic results demonstrated that all joints of the lower extremity may be important for adjusting to different load placements and maintaining dynamic stability. Space scientists and engineers may want to consider PLSS designs that distribute loads evenly and low, and space suit designs that will not limit the sagittal plane range of motion at the lower extremity joints.

Multiple dynamics in a single predator-prey system: experimental effects of food quality.

PubMed Central

Nelson, W A; McCauley, E; Wrona, F J

2001-01-01

Recent work with the freshwater zooplankton Daphnia has suggested that the quality of its algal prey can have a significant effect on its demographic rates and life-history patterns. Predator-prey theory linking food quantity and food quality predicts that a single system should be able to display two distinct patterns of population dynamics. One pattern is predicted to have high herbivore and low algal biomass dynamics (high HBD), whereas the other is predicted to have low herbivore and high algal biomass dynamics (low HBD). Despite these predictions and the stoichiometric evidence that many phytoplankton communities may have poor access to food of quality, there have been few tests of whether a dynamic predator-prey system can display both of these distinct patterns. Here we report, to the authors' knowledge, the first evidence for two dynamical patterns, as predicted by theory, in a single predator-prey system. We show that the high HBD is a result of food quantity effects and that the low HBD is a result of food quality effects, which are maintained by phosphorus limitation in the predator. These results provide an important link between the known effects of nutrient limitation in herbivores and the significance of prey quality in predator-prey population dynamics in natural zooplankton communities. PMID:11410147

Decapod larvae distribution and species composition off the southern Portuguese coast

NASA Astrophysics Data System (ADS)

Pochelon, Patricia N.; Pires, Rita F. T.; Dubert, Jesús; Nolasco, Rita; Santos, A. Miguel P.; Queiroga, Henrique; dos Santos, Antonina

2017-12-01

For decapod crustaceans, the larval phase is the main responsible for dispersal, given the direct emission from adult habitats into the water column. Circulation patterns and behavioural mechanisms control the dispersal distance and connectivity between different areas. Information on larval distribution and abundance is required to predict the size and location of breeding populations, and correctly manage marine resources. Spatial distribution and abundance data of decapod larvae, and environmental parameters were assessed in winter surveys off the southern Portuguese coast. To better understand the oceanic structures driving larval distribution patterns, in situ physical parameters were measured and a hydrodynamical model used. Inter-annual, cross-shore and alongshore differences on decapod larvae distribution were found. Brachyuran crabs dominated the samples and similar taxa composition was observed in the most dynamic areas. Coastal taxa dominated the nearshore survey and were almost absent in the more offshore one, that registered much lower abundances. An upwelling front allowed a clear cross-shore species separation, also evident in the abundance values and number of taxa. Hydrodynamical conditions and adult habitats were the main factors explaining the observed patterns. Important missing information to understand the distribution patterns of decapod larval communities and the mechanisms behind them is given for the region.

A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal.

PubMed

Silva, Carmen; Cabral, João Alexandre; Hughes, Samantha Jane; Santos, Mário

2017-03-01

Worldwide ecological impact assessments of wind farms have gathered relevant information on bat activity patterns. Since conventional bat study methods require intensive field work, the prediction of bat activity might prove useful by anticipating activity patterns and estimating attractiveness concomitant with the wind farm location. A novel framework was developed, based on the stochastic dynamic methodology (StDM) principles, to predict bat activity on mountain ridges with wind farms. We illustrate the framework application using regional data from North Portugal by merging information from several environmental monitoring programmes associated with diverse wind energy facilities that enable integrating the multifactorial influences of meteorological conditions, land cover and geographical variables on bat activity patterns. Output from this innovative methodology can anticipate episodes of exceptional bat activity, which, if correlated with collision probability, can be used to guide wind farm management strategy such as halting wind turbines during hazardous periods. If properly calibrated with regional gradients of environmental variables from mountain ridges with windfarms, the proposed methodology can be used as a complementary tool in environmental impact assessments and ecological monitoring, using predicted bat activity to assist decision making concerning the future location of wind farms and the implementation of effective mitigation measures. Copyright © 2016 Elsevier B.V. All rights reserved.

Extracting quasi-steady Lagrangian transport patterns from the ocean circulation: An application to the Gulf of Mexico.

PubMed

Duran, R; Beron-Vera, F J; Olascoaga, M J

2018-03-26

We construct a climatology of Lagrangian coherent structures (LCSs)-the concealed skeleton that shapes transport-with a twelve-year-long data-assimilative simulation of the sea-surface circulation in the Gulf of Mexico (GoM). Computed as time-mean Cauchy-Green strain tensorlines of the climatological velocity, the climatological LCSs (cLCSs) unveil recurrent Lagrangian circulation patterns. The cLCSs strongly constrain the ensemble-mean Lagrangian circulation of the instantaneous model velocity, showing that a climatological velocity can preserve meaningful transport information. The quasi-steady transport patterns revealed by the cLCSs agree well with aspects of the GoM circulation described in several previous observational and numerical studies. For example, the cLCSs identify regions of persistent isolation, and suggest that coastal regions previously identified as high-risk for pollution impact are regions of maximal attraction. We also show that cLCSs are remarkably accurate at identifying transport patterns observed during the Deepwater Horizon and Ixtoc oil spills, and during the Grand LAgrangian Deployment (GLAD) experiment. Thus it is shown that computing cLCSs is an efficient and meaningful way of synthesizing vast amounts of Lagrangian information. The cLCS method confirms previous GoM studies, and contributes to our understanding by revealing the persistent nature of the dynamics and kinematics treated therein.

Human area MT+ shows load-dependent activation during working memory maintenance with continuously morphing stimulation.

PubMed

Galashan, Daniela; Fehr, Thorsten; Kreiter, Andreas K; Herrmann, Manfred

2014-07-11

Initially, human area MT+ was considered a visual area solely processing motion information but further research has shown that it is also involved in various different cognitive operations, such as working memory tasks requiring motion-related information to be maintained or cognitive tasks with implied or expected motion.In the present fMRI study in humans, we focused on MT+ modulation during working memory maintenance using a dynamic shape-tracking working memory task with no motion-related working memory content. Working memory load was systematically varied using complex and simple stimulus material and parametrically increasing retention periods. Activation patterns for the difference between retention of complex and simple memorized stimuli were examined in order to preclude that the reported effects are caused by differences in retrieval. Conjunction analysis over all delay durations for the maintenance of complex versus simple stimuli demonstrated a wide-spread activation pattern. Percent signal change (PSC) in area MT+ revealed a pattern with higher values for the maintenance of complex shapes compared to the retention of a simple circle and with higher values for increasing delay durations. The present data extend previous knowledge by demonstrating that visual area MT+ presents a brain activity pattern usually found in brain regions that are actively involved in working memory maintenance.

Stochastic nonlinear dynamics pattern formation and growth models

PubMed Central

Yaroslavsky, Leonid P

2007-01-01

Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature. PMID:17908341

Rayleigh Scattering Diagnostic for Simultaneous Measurements of Dynamic Density and Velocity

NASA Technical Reports Server (NTRS)

Seasholtz, Richard G.; Panda, J.

2000-01-01

A flow diagnostic technique based on the molecular Rayleigh scattering of laser light is used to obtain dynamic density and velocity data in turbulent flows. The technique is based on analyzing the Rayleigh scattered light with a Fabry-Perot interferometer and recording information about the interference pattern with a multiple anode photomultiplier tube (PMT). An artificial neural network is used to process the signals from the PMT to recover the velocity time history, which is then used to calculate the velocity power spectrum. The technique is illustrated using simulated data. The results of an experiment to measure the velocity power spectrum in a low speed (100 rn/sec) flow are also presented.

Dynamic Pattern Formation in Electron-Beam-Induced Etching [Emergent formation of dynamic topographic patterns in electron beam induced etching

DOE PAGES

Martin, Aiden A.; Bahm, Alan; Bishop, James; ...

2015-12-15

Here, we report highly ordered topographic patterns that form on the surface of diamond, span multiple length scales, and have a symmetry controlled by the precursor gas species used in electron-beam-induced etching (EBIE). The pattern formation dynamics reveals an etch rate anisotropy and an electron energy transfer pathway that is overlooked by existing EBIE models. Therefore, we, modify established theory such that it explains our results and remains universally applicable to EBIE. Furthermore, the patterns can be exploited in controlled wetting, optical structuring, and other emerging applications that require nano- and microscale surface texturing of a wide band-gap material.

The Informational Patterns of Laughter

NASA Astrophysics Data System (ADS)

Bea, José A.; Marijuán, Pedro C.

2003-06-01

Laughter is one of the most characteristic -and enigmatic- communicational traits of human individuals. Its analysis has to take into account a variety of emotional, social, cognitive, and communicational factors densely interconnected. In this article we study laughter just as an auditive signal (as a 'neutral' information carrier), and we compare its structure with the regular traits of linguistic signals. In the experimental records of human laughter that we have performed, the most noticeable trait is the disorder content of frequencies. In comparison with the sonograms of vowels, the information content of which appears as a characteristic, regular function of the first vibration modes of the dynamic system formed, for each vowel, by the vocal cords and the accompanying resonance of the vocalization apparatus, the sonograms of laughter are highly irregular. In the episodes of laughter, a highly random content in frequencies appears, reason why it cannot be considered as a genuine codification of patterned information like in linguistic signals. In order to numerically gauge the disorder content of laughter frequencies, we have performed several "entropy" measures of the spectra -trying to unambiguously identify spontaneous laughter from "faked", articulated laughter. Interestingly, Shannon's entropy (the most natural candidate) performs rather poorly.

Movement pattern recognition in basketball free-throw shooting.

PubMed

Schmidt, Andrea

2012-04-01

The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented. Copyright © 2012. Published by Elsevier B.V.

DMT-TAFM: a data mining tool for technical analysis of futures market

NASA Astrophysics Data System (ADS)

Stepanov, Vladimir; Sathaye, Archana

2002-03-01

Technical analysis of financial markets describes many patterns of market behavior. For practical use, all these descriptions need to be adjusted for each particular trading session. In this paper, we develop a data mining tool for technical analysis of the futures markets (DMT-TAFM), which dynamically generates rules based on the notion of the price pattern similarity. The tool consists of three main components. The first component provides visualization of data series on a chart with different ranges, scales, and chart sizes and types. The second component constructs pattern descriptions using sets of polynomials. The third component specifies the training set for mining, defines the similarity notion, and searches for a set of similar patterns. DMT-TAFM is useful to prepare the data, and then reveal and systemize statistical information about similar patterns found in any type of historical price series. We performed experiments with our tool on three decades of trading data fro hundred types of futures. Our results for this data set shows that, we can prove or disprove many well-known patterns based on real data, as well as reveal new ones, and use the set of relatively consistent patterns found during data mining for developing better futures trading strategies.

The effects of global awareness on the spreading of epidemics in multiplex networks

NASA Astrophysics Data System (ADS)

Zang, Haijuan

2018-02-01

It is increasingly recognized that understanding the complex interplay patterns between epidemic spreading and human behavioral is a key component of successful infection control efforts. In particular, individuals can obtain the information about epidemics and respond by altering their behaviors, which can affect the spreading dynamics as well. Besides, because the existence of herd-like behaviors, individuals are very easy to be influenced by the global awareness information. Here, in this paper, we propose a global awareness controlled spreading model (GACS) to explore the interplay between the coupled dynamical processes. Using the global microscopic Markov chain approach, we obtain the analytical results for the epidemic thresholds, which shows a high accuracy by comparison with lots of Monte Carlo simulations. Furthermore, considering other classical models used to describe the coupled dynamical processes, including the local awareness controlled contagion spreading (LACS) model, Susceptible-Infected-Susceptible-Unaware-Aware-Unaware (SIS-UAU) model and the single layer occasion, we make a detailed comparisons between the GACS with them. Although the comparisons and results depend on the parameters each model has, the GACS model always shows a strong restrain effects on epidemic spreading process. Our results give us a better understanding of the coupled dynamical processes and highlights the importance of considering the spreading of global awareness in the control of epidemics.

Modeling repetitive motions using structured light.

PubMed

Xu, Yi; Aliaga, Daniel G

2010-01-01

Obtaining models of dynamic 3D objects is an important part of content generation for computer graphics. Numerous methods have been extended from static scenarios to model dynamic scenes. If the states or poses of the dynamic object repeat often during a sequence (but not necessarily periodically), we call such a repetitive motion. There are many objects, such as toys, machines, and humans, undergoing repetitive motions. Our key observation is that when a motion-state repeats, we can sample the scene under the same motion state again but using a different set of parameters; thus, providing more information of each motion state. This enables robustly acquiring dense 3D information difficult for objects with repetitive motions using only simple hardware. After the motion sequence, we group temporally disjoint observations of the same motion state together and produce a smooth space-time reconstruction of the scene. Effectively, the dynamic scene modeling problem is converted to a series of static scene reconstructions, which are easier to tackle. The varying sampling parameters can be, for example, structured-light patterns, illumination directions, and viewpoints resulting in different modeling techniques. Based on this observation, we present an image-based motion-state framework and demonstrate our paradigm using either a synchronized or an unsynchronized structured-light acquisition method.

Phytoestrogens and Mycoestrogens Induce Signature Structure Dynamics Changes on Estrogen Receptor α

PubMed Central

Chen, Xueyan; Uzuner, Ugur; Li, Man; Shi, Weibing; Yuan, Joshua S.; Dai, Susie Y.

2016-01-01

Endocrine disrupters include a broad spectrum of chemicals such as industrial chemicals, natural estrogens and androgens, synthetic estrogens and androgens. Phytoestrogens are widely present in diet and food supplements; mycoestrogens are frequently found in grains. As human beings and animals are commonly exposed to phytoestrogens and mycoestrogens in diet and environment, it is important to understand the potential beneficial or hazardous effects of estrogenic compounds. Many bioassays have been established to study the binding of estrogenic compounds with estrogen receptor (ER) and provided rich data in the literature. However, limited assays can offer structure information with regard to the ligand/ER complex. Our current study surveys the global structure dynamics changes for ERα ligand binding domain (LBD) when phytoestrogens and mycoestrogens bind. The assay is based on the structure dynamics information probed by hydrogen deuterium exchange mass spectrometry and offers a unique viewpoint to elucidate the mechanism how phytoestrogens and mycoestrogens interact with estrogen receptor. The cluster analysis based on the hydrogen deuterium exchange (HDX) assay data reveals a unique pattern when phytoestrogens and mycoestrogens bind with ERα LBD compared to that of estradiol and synthetic estrogen modulators. Our study highlights that structure dynamics could play an important role in the structure function relationship when endocrine disrupters interact with estrogen receptors. PMID:27589781

Origin of Peer Influence in Social Networks

NASA Astrophysics Data System (ADS)

Pinheiro, Flávio L.; Santos, Marta D.; Santos, Francisco C.; Pacheco, Jorge M.

2014-03-01

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends' friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination.

PubMed

Stamoulis, Catherine; Schomer, Donald L; Chang, Bernard S

2013-08-01

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution (Andrade-Valenca et al., 2011; Stamoulis et al., 2012). However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤ 100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies <100 Hz, suggesting a potential interference role of high-frequency activity, to disrupt abnormal ictal synchrony at lower frequencies. These changes in network synchrony started at least 20-30s prior to seizure offset, depending on the seizure duration. Opposite patterns were estimated at frequencies ≤ 100 Hz in several seizures. These results raise the possibility that high-frequency interference may occur in the form of progressive network coordination during the ictal interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤ 100 Hz, at least in some seizures. Copyright © 2013 Elsevier B.V. All rights reserved.

Dynamic assessment of exposure to air pollution using mobile phone data.

PubMed

Dewulf, Bart; Neutens, Tijs; Lefebvre, Wouter; Seynaeve, Gerdy; Vanpoucke, Charlotte; Beckx, Carolien; Van de Weghe, Nico

2016-04-21

Exposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments. In this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO2 using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach). The mean exposure to NO2 increases with 1.27 μg/m(3) (4.3%) during the week and with 0.12 μg/m(3) (0.4%) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO2 exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO2 concentrations. It is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones).

Analysis of musical expression in audio signals

NASA Astrophysics Data System (ADS)

Dixon, Simon

2003-01-01

In western art music, composers communicate their work to performers via a standard notation which specificies the musical pitches and relative timings of notes. This notation may also include some higher level information such as variations in the dynamics, tempo and timing. Famous performers are characterised by their expressive interpretation, the ability to convey structural and emotive information within the given framework. The majority of work on audio content analysis focusses on retrieving score-level information; this paper reports on the extraction of parameters describing the performance, a task which requires a much higher degree of accuracy. Two systems are presented: BeatRoot, an off-line beat tracking system which finds the times of musical beats and tracks changes in tempo throughout a performance, and the Performance Worm, a system which provides a real-time visualisation of the two most important expressive dimensions, tempo and dynamics. Both of these systems are being used to process data for a large-scale study of musical expression in classical and romantic piano performance, which uses artificial intelligence (machine learning) techniques to discover fundamental patterns or principles governing expressive performance.

Macromolecular diffractive imaging using imperfect crystals

PubMed Central

Ayyer, Kartik; Yefanov, Oleksandr; Oberthür, Dominik; Roy-Chowdhury, Shatabdi; Galli, Lorenzo; Mariani, Valerio; Basu, Shibom; Coe, Jesse; Conrad, Chelsie E.; Fromme, Raimund; Schaffer, Alexander; Dörner, Katerina; James, Daniel; Kupitz, Christopher; Metz, Markus; Nelson, Garrett; Lourdu Xavier, Paulraj; Beyerlein, Kenneth R.; Schmidt, Marius; Sarrou, Iosifina; Spence, John C. H.; Weierstall, Uwe; White, Thomas A.; Yang, Jay-How; Zhao, Yun; Liang, Mengning; Aquila, Andrew; Hunter, Mark S.; Robinson, Joseph S.; Koglin, Jason E.; Boutet, Sébastien; Fromme, Petra; Barty, Anton; Chapman, Henry N.

2016-01-01

The three-dimensional structures of macromolecules and their complexes are predominantly elucidated by X-ray protein crystallography. A major limitation is access to high-quality crystals, to ensure X-ray diffraction extends to sufficiently large scattering angles and hence yields sufficiently high-resolution information that the crystal structure can be solved. The observation that crystals with shrunken unit-cell volumes and tighter macromolecular packing often produce higher-resolution Bragg peaks1,2 hints that crystallographic resolution for some macromolecules may be limited not by their heterogeneity but rather by a deviation of strict positional ordering of the crystalline lattice. Such displacements of molecules from the ideal lattice give rise to a continuous diffraction pattern, equal to the incoherent sum of diffraction from rigid single molecular complexes aligned along several discrete crystallographic orientations and hence with an increased information content3. Although such continuous diffraction patterns have long been observed—and are of interest as a source of information about the dynamics of proteins4 —they have not been used for structure determination. Here we show for crystals of the integral membrane protein complex photosystem II that lattice disorder increases the information content and the resolution of the diffraction pattern well beyond the 4.5 Å limit of measurable Bragg peaks, which allows us to directly phase5 the pattern. With the molecular envelope conventionally determined at 4.5 Å as a constraint, we then obtain a static image of the photosystem II dimer at 3.5 Å resolution. This result shows that continuous diffraction can be used to overcome long-supposed resolution limits of macromolecular crystallography, with a method that puts great value in commonly encountered imperfect crystals and opens up the possibility for model-free phasing6,7. PMID:26863980

How Temporal and Spatial Aspects of Presenting Visualizations Affect Learning about Locomotion Patterns

ERIC Educational Resources Information Center

Imhof, Birgit; Scheiter, Katharina; Edelmann, Jorg; Gerjets, Peter

2012-01-01

Two studies investigated the effectiveness of dynamic and static visualizations for a perceptual learning task (locomotion pattern classification). In Study 1, seventy-five students viewed either dynamic, static-sequential, or static-simultaneous visualizations. For tasks of intermediate difficulty, dynamic visualizations led to better…

Impacts of large dams on the complexity of suspended sediment dynamics in the Yangtze River

NASA Astrophysics Data System (ADS)

Wang, Yuankun; Rhoads, Bruce L.; Wang, Dong; Wu, Jichun; Zhang, Xiao

2018-03-01

The Yangtze River is one of the largest and most important rivers in the world. Over the past several decades, the natural sediment regime of the Yangtze River has been altered by the construction of dams. This paper uses multi-scale entropy analysis to ascertain the impacts of large dams on the complexity of high-frequency suspended sediment dynamics in the Yangtze River system, especially after impoundment of the Three Gorges Dam (TGD). In this study, the complexity of sediment dynamics is quantified by framing it within the context of entropy analysis of time series. Data on daily sediment loads for four stations located in the mainstem are analyzed for the past 60 years. The results indicate that dam construction has reduced the complexity of short-term (1-30 days) variation in sediment dynamics near the structures, but that complexity has actually increased farther downstream. This spatial pattern seems to reflect a filtering effect of the dams on the on the temporal pattern of sediment loads as well as decreased longitudinal connectivity of sediment transfer through the river system, resulting in downstream enhancement of the influence of local sediment inputs by tributaries on sediment dynamics. The TGD has had a substantial impact on the complexity of sediment series in the mainstem of the Yangtze River, especially after it became fully operational. This enhanced impact is attributed to the high trapping efficiency of this dam and its associated large reservoir. The sediment dynamics "signal" becomes more spatially variable after dam construction. This study demonstrates the spatial influence of dams on the high-frequency temporal complexity of sediment regimes and provides valuable information that can be used to guide environmental conservation of the Yangtze River.

Seasonal Biophysical Dynamics of the Amazon from Space Using MODIS Vegetation Indices

NASA Astrophysics Data System (ADS)

Huete, A. R.; Didan, K.; Ratana, P.; Ferreira, L.

2002-12-01

We utilized the Terra- Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) products to analyze the seasonal and spatial patterns of photosynthetic vegetation activity over the Amazon Basin and surrounding regions of Brazil. The seasonal patterns of vegetation activity were studied along two, eco-climatic transects extending from (1) the cerrado region (Brasilia National Park) to the seasonal tropical forest (Tapajos National Forest) and (2) the caatinga biome to the seasonal and per-humid tropical forests. In addition to the climatic transects, we also investigated the seasonal dynamics of altered, land conversion areas associated with pastures and clearcutting land use activities. Both the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) at 250-m, 500-m, and 1-km were used to extract seasonal profile curves. The quality assurance (QA) information of the output products was used in noise removal and data filtering prior to the generation of the seasonal profiles. Histogram analyses were also performed at coarse (biome) scale and fine, site intensive (flux towers) scale. The seasonal patterns of the cerrado and caatinga were very pronounced with distinct dry and wet seasonal trends. We observed decreasing dry-wet seasonal patterns in the transitional areas near Araguaia National Park. In contrast, the seasonal behavior of the tropical forests were much harder to assess, but indicated slight seasonal trends that ran counter to rainfall activity. This may be attributed to new leaf growth in the dry season. We further found MODIS VI seasonal patterns to vary significantly in land converted and land degraded areas.

Mechanical Influences on Morphogenesis of the Knee Joint Revealed through Morphological, Molecular and Computational Analysis of Immobilised Embryos

PubMed Central

Roddy, Karen A.; Prendergast, Patrick J.; Murphy, Paula

2011-01-01

Very little is known about the regulation of morphogenesis in synovial joints. Mechanical forces generated from muscle contractions are required for normal development of several aspects of normal skeletogenesis. Here we show that biophysical stimuli generated by muscle contractions impact multiple events during chick knee joint morphogenesis influencing differential growth of the skeletal rudiment epiphyses and patterning of the emerging tissues in the joint interzone. Immobilisation of chick embryos was achieved through treatment with the neuromuscular blocking agent Decamethonium Bromide. The effects on development of the knee joint were examined using a combination of computational modelling to predict alterations in biophysical stimuli, detailed morphometric analysis of 3D digital representations, cell proliferation assays and in situ hybridisation to examine the expression of a selected panel of genes known to regulate joint development. This work revealed the precise changes to shape, particularly in the distal femur, that occur in an altered mechanical environment, corresponding to predicted changes in the spatial and dynamic patterns of mechanical stimuli and region specific changes in cell proliferation rates. In addition, we show altered patterning of the emerging tissues of the joint interzone with the loss of clearly defined and organised cell territories revealed by loss of characteristic interzone gene expression and abnormal expression of cartilage markers. This work shows that local dynamic patterns of biophysical stimuli generated from muscle contractions in the embryo act as a source of positional information guiding patterning and morphogenesis of the developing knee joint. PMID:21386908

Time-Resolved Transposon Insertion Sequencing Reveals Genome-Wide Fitness Dynamics during Infection.

PubMed

Yang, Guanhua; Billings, Gabriel; Hubbard, Troy P; Park, Joseph S; Yin Leung, Ka; Liu, Qin; Davis, Brigid M; Zhang, Yuanxing; Wang, Qiyao; Waldor, Matthew K

2017-10-03

Transposon insertion sequencing (TIS) is a powerful high-throughput genetic technique that is transforming functional genomics in prokaryotes, because it enables genome-wide mapping of the determinants of fitness. However, current approaches for analyzing TIS data assume that selective pressures are constant over time and thus do not yield information regarding changes in the genetic requirements for growth in dynamic environments (e.g., during infection). Here, we describe structured analysis of TIS data collected as a time series, termed pattern analysis of conditional essentiality (PACE). From a temporal series of TIS data, PACE derives a quantitative assessment of each mutant's fitness over the course of an experiment and identifies mutants with related fitness profiles. In so doing, PACE circumvents major limitations of existing methodologies, specifically the need for artificial effect size thresholds and enumeration of bacterial population expansion. We used PACE to analyze TIS samples of Edwardsiella piscicida (a fish pathogen) collected over a 2-week infection period from a natural host (the flatfish turbot). PACE uncovered more genes that affect E. piscicida 's fitness in vivo than were detected using a cutoff at a terminal sampling point, and it identified subpopulations of mutants with distinct fitness profiles, one of which informed the design of new live vaccine candidates. Overall, PACE enables efficient mining of time series TIS data and enhances the power and sensitivity of TIS-based analyses. IMPORTANCE Transposon insertion sequencing (TIS) enables genome-wide mapping of the genetic determinants of fitness, typically based on observations at a single sampling point. Here, we move beyond analysis of endpoint TIS data to create a framework for analysis of time series TIS data, termed pattern analysis of conditional essentiality (PACE). We applied PACE to identify genes that contribute to colonization of a natural host by the fish pathogen Edwardsiella piscicida. PACE uncovered more genes that affect E. piscicida 's fitness in vivo than were detected using a terminal sampling point, and its clustering of mutants with related fitness profiles informed design of new live vaccine candidates. PACE yields insights into patterns of fitness dynamics and circumvents major limitations of existing methodologies. Finally, the PACE method should be applicable to additional "omic" time series data, including screens based on clustered regularly interspaced short palindromic repeats with Cas9 (CRISPR/Cas9). Copyright © 2017 Yang et al.

Information flow in the auditory cortical network

PubMed Central

Hackett, Troy A.

2011-01-01

Auditory processing in the cerebral cortex is comprised of an interconnected network of auditory and auditory-related areas distributed throughout the forebrain. The nexus of auditory activity is located in temporal cortex among several specialized areas, or fields, that receive dense inputs from the medial geniculate complex. These areas are collectively referred to as auditory cortex. Auditory activity is extended beyond auditory cortex via connections with auditory-related areas elsewhere in the cortex. Within this network, information flows between areas to and from countless targets, but in a manner that is characterized by orderly regional, areal and laminar patterns. These patterns reflect some of the structural constraints that passively govern the flow of information at all levels of the network. In addition, the exchange of information within these circuits is dynamically regulated by intrinsic neurochemical properties of projecting neurons and their targets. This article begins with an overview of the principal circuits and how each is related to information flow along major axes of the network. The discussion then turns to a description of neurochemical gradients along these axes, highlighting recent work on glutamate transporters in the thalamocortical projections to auditory cortex. The article concludes with a brief discussion of relevant neurophysiological findings as they relate to structural gradients in the network. PMID:20116421

Associating sex-biased and seasonal behaviour with contact patterns and transmission risk in Gopherus agassizii

USGS Publications Warehouse

Aiello, Christina M.; Esque, Todd; Nussear, K. E.; Emblidge, P. G.; Hudson, P. J.

2018-01-01

Interactions between wildlife hosts act as transmission routes for directly transmitted pathogens and vary in ways that affect transmission efficiency. Identifying drivers of contact variation can allow both contact inference and estimation of transmission dynamics despite limited data. In desert tortoises, mating strategy, burrow use and seasonal change influence numerous behaviours and likely shape contact patterns. In this study, we ask to what extent tortoise contact behaviour varies between sexes and seasons, and whether space or burrow-use data can be used to infer contact characteristics consistent with those recorded by proximity loggers. We identified sex and season-biased contact behaviour in both wild and captive populations indicative of female-female avoidance and seasonal male mate-seeking behaviour. Space and burrow-use patterns were informative, but did not always predict the extent of sex or seasonal biases on contact. We discuss the implications these findings have for transmission patterns and disease mitigation in tortoise populations.

Fast Fourier single-pixel imaging via binary illumination.

PubMed

Zhang, Zibang; Wang, Xueying; Zheng, Guoan; Zhong, Jingang

2017-09-20

Fourier single-pixel imaging (FSI) employs Fourier basis patterns for encoding spatial information and is capable of reconstructing high-quality two-dimensional and three-dimensional images. Fourier-domain sparsity in natural scenes allows FSI to recover sharp images from undersampled data. The original FSI demonstration, however, requires grayscale Fourier basis patterns for illumination. This requirement imposes a limitation on the imaging speed as digital micro-mirror devices (DMDs) generate grayscale patterns at a low refreshing rate. In this paper, we report a new strategy to increase the speed of FSI by two orders of magnitude. In this strategy, we binarize the Fourier basis patterns based on upsampling and error diffusion dithering. We demonstrate a 20,000 Hz projection rate using a DMD and capture 256-by-256-pixel dynamic scenes at a speed of 10 frames per second. The reported technique substantially accelerates image acquisition speed of FSI. It may find broad imaging applications at wavebands that are not accessible using conventional two-dimensional image sensors.

Neural computing for numeric-to-symbolic conversion in control systems

NASA Technical Reports Server (NTRS)

Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

1989-01-01

A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

Bio-inspired energy-harvesting mechanisms and patterns of dynamic soaring.

PubMed

Liu, Duo-Neng; Hou, Zhong-Xi; Guo, Zheng; Yang, Xi-Xiang; Gao, Xian-Zhong

2017-01-30

Albatrosses can make use of the dynamic soaring technique extracting energy from the wind field to achieve large-scale movement without a flap, which stimulates interest in effortless flight with small unmanned aerial vehicles (UAVs). However, mechanisms of energy harvesting in terms of the energy transfer from the wind to the flyer (albatross or UAV) are still indeterminate and controversial when using different reference frames in previous studies. In this paper, the classical four-phase Rayleigh cycle, includes sequentially upwind climb, downwind turn, downwind dive and upwind turn, is introduced in analyses of energy gain with the albatross's equation of motions and the simulated trajectory in dynamic soaring. Analytical and numerical results indicate that the energy gain in the air-relative frame mostly originates from large wind gradients at lower part of the climb and dive, while the energy gain in the inertial frame comes from the lift vector inclined to the wind speed direction during the climb, dive and downwind turn at higher altitude. These two energy-gain mechanisms are not equivalent in terms of energy sources and reference frames but have to be simultaneously satisfied in terms of the energy-neutral dynamic soaring cycle. For each reference frame, energy-loss phases are necessary to connect energy-gain ones. Based on these four essential phases in dynamic soaring and the albatrosses' flight trajectory, different dynamic soaring patterns are schematically depicted and corresponding optimal trajectories are computed. The optimal dynamic soaring trajectories are classified into two closed patterns including 'O' shape and '8' shape, and four travelling patterns including 'Ω' shape, 'α' shape, 'C' shape and 'S' shape. The correlation among these patterns are analysed and discussed. The completeness of the classification for different patterns is confirmed by listing and summarising dynamic soaring trajectories shown in studies over the past decades.

Geometric Morphometrics on Gene Expression Patterns Within Phenotypes: A Case Example on Limb Development

PubMed Central

Martínez-Abadías, Neus; Mateu, Roger; Niksic, Martina; Russo, Lucia; Sharpe, James

2016-01-01

How the genotype translates into the phenotype through development is critical to fully understand the evolution of phenotypes. We propose a novel approach to directly assess how changes in gene expression patterns are associated with changes in morphology using the limb as a case example. Our method combines molecular biology techniques, such as whole-mount in situ hybridization, with image and shape analysis, extending the use of Geometric Morphometrics to the analysis of nonanatomical shapes, such as gene expression domains. Elliptical Fourier and Procrustes-based semilandmark analyses were used to analyze the variation and covariation patterns of the limb bud shape with the expression patterns of two relevant genes for limb morphogenesis, Hoxa11 and Hoxa13. We devised a multiple thresholding method to semiautomatically segment gene domains at several expression levels in large samples of limb buds from C57Bl6 mouse embryos between 10 and 12 postfertilization days. Besides providing an accurate phenotyping tool to quantify the spatiotemporal dynamics of gene expression patterns within developing structures, our morphometric analyses revealed high, non-random, and gene-specific variation undergoing canalization during limb development. Our results demonstrate that Hoxa11 and Hoxa13, despite being paralogs with analogous functions in limb patterning, show clearly distinct dynamic patterns, both in shape and size, and are associated differently with the limb bud shape. The correspondence between our results and already well-established molecular processes underlying limb development confirms that this morphometric approach is a powerful tool to extract features of development regulating morphogenesis. Such multilevel analyses are promising in systems where not so much molecular information is available and will advance our understanding of the genotype–phenotype map. In systematics, this knowledge will increase our ability to infer how evolution modified a common developmental pattern to generate a wide diversity of morphologies, as in the vertebrate limb. PMID:26377442

Assessment of Multi-frequency Electromagnetic Induction for Determining Soil Moisture Patterns at the Hillslope Scale

NASA Astrophysics Data System (ADS)

Tromp-van Meerveld, I.; McDonnell, J.

2009-05-01

We present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map soil moisture patterns at the Panola (GA, USA) hillslope. We address the following questions regarding the applicability of EM measurements for hillslope hydrological investigations: (1) Can EM be used for soil moisture measurements in areas with shallow soils?; (2) Can EM represent the temporal and spatial patterns of soil moisture throughout the year?; and (3) can multiple frequencies be used to extract additional information content from the EM approach and explain the depth profile of soil moisture? We found that the apparent conductivity measured with the multi-frequency GEM-300 was linearly related to soil moisture measured with an Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in soil moisture well. During spring rainfall events that wetted only the surface soil layers the apparent conductivity measurements explained the soil moisture dynamics at depth better than the surface soil moisture dynamics. All four EM frequencies (7290, 9090, 11250, and 14010 Hz) were highly correlated and linearly related to each other and could be used to predict soil moisture. This limited our ability to use the four different EM frequencies to obtain a soil moisture profile with depth. The apparent conductivity patterns represented the observed spatial soil moisture patterns well when the individually fitted relationships between measured soil moisture and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the soil moisture patterns were smoothed and did not resemble the observed soil moisture patterns very well. In addition, the range in calculated soil moisture values was reduced compared to observed soil moisture. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the Aqua-pro soil moisture measurements.

Fluctuations in Wikipedia access-rate and edit-event data

NASA Astrophysics Data System (ADS)

Kämpf, Mirko; Tismer, Sebastian; Kantelhardt, Jan W.; Muchnik, Lev

2012-12-01

Internet-based social networks often reflect extreme events in nature and society by drastic increases in user activity. We study and compare the dynamics of the two major complex processes necessary for information spread via the online encyclopedia ‘Wikipedia’, i.e., article editing (information upload) and article access (information viewing) based on article edit-event time series and (hourly) user access-rate time series for all articles. Daily and weekly activity patterns occur in addition to fluctuations and bursting activity. The bursts (i.e., significant increases in activity for an extended period of time) are characterized by a power-law distribution of durations of increases and decreases. For describing the recurrence and clustering of bursts we investigate the statistics of the return intervals between them. We find stretched exponential distributions of return intervals in access-rate time series, while edit-event time series yield simple exponential distributions. To characterize the fluctuation behavior we apply detrended fluctuation analysis (DFA), finding that most article access-rate time series are characterized by strong long-term correlations with fluctuation exponents α≈0.9. The results indicate significant differences in the dynamics of information upload and access and help in understanding the complex process of collecting, processing, validating, and distributing information in self-organized social networks.

Information flow and causality as rigorous notions ab initio

NASA Astrophysics Data System (ADS)

Liang, X. San

2016-11-01

Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.

Quantitative dynamic ¹⁸FDG-PET and tracer kinetic analysis of soft tissue sarcomas.

PubMed

Rusten, Espen; Rødal, Jan; Revheim, Mona E; Skretting, Arne; Bruland, Oyvind S; Malinen, Eirik

2013-08-01

To study soft tissue sarcomas using dynamic positron emission tomography (PET) with the glucose analog tracer [(18)F]fluoro-2-deoxy-D-glucose ((18)FDG), to investigate correlations between derived PET image parameters and clinical characteristics, and to discuss implications of dynamic PET acquisition (D-PET). D-PET images of 11 patients with soft tissue sarcomas were analyzed voxel-by-voxel using a compartment tracer kinetic model providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Furthermore, standard uptake values (SUVs) in the early (2 min p.i.; SUVE) and late (45 min p.i.; SUVL) phases of the PET acquisition were obtained. The derived transfer rates K1, k2 and k3, along with the metabolic rate of (18)FDG (MRFDG) and the vascular fraction νp, was fused with the computed tomography (CT) images for visual interpretation. Correlations between D-PET imaging parameters and clinical parameters, i.e. tumor size, grade and clinical status, were calculated with a significance level of 0.05. The temporal uptake pattern of (18)FDG in the tumor varied considerably from patient to patient. SUVE peak was higher than SUVL peak for four patients. The images of the rate constants showed a systematic pattern, often with elevated intensity in the tumors compared to surrounding tissue. Significant correlations were found between SUVE/L and some of the rate parameters. Dynamic (18)FDG-PET may provide additional valuable information on soft tissue sarcomas not obtainable from conventional (18)FDG-PET. The prognostic role of dynamic imaging should be investigated.

A guide to calculating habitat-quality metrics to inform conservation of highly mobile species

USGS Publications Warehouse

Bieri, Joanna A.; Sample, Christine; Thogmartin, Wayne E.; Diffendorfer, James E.; Earl, Julia E.; Erickson, Richard A.; Federico, Paula; Flockhart, D. T. Tyler; Nicol, Sam; Semmens, Darius J.; Skraber, T.; Wiederholt, Ruscena; Mattsson, Brady J.

2018-01-01

Many metrics exist for quantifying the relative value of habitats and pathways used by highly mobile species. Properly selecting and applying such metrics requires substantial background in mathematics and understanding the relevant management arena. To address this multidimensional challenge, we demonstrate and compare three measurements of habitat quality: graph-, occupancy-, and demographic-based metrics. Each metric provides insights into system dynamics, at the expense of increasing amounts and complexity of data and models. Our descriptions and comparisons of diverse habitat-quality metrics provide means for practitioners to overcome the modeling challenges associated with management or conservation of such highly mobile species. Whereas previous guidance for applying habitat-quality metrics has been scattered in diversified tracks of literature, we have brought this information together into an approachable format including accessible descriptions and a modeling case study for a typical example that conservation professionals can adapt for their own decision contexts and focal populations.Considerations for Resource ManagersManagement objectives, proposed actions, data availability and quality, and model assumptions are all relevant considerations when applying and interpreting habitat-quality metrics.Graph-based metrics answer questions related to habitat centrality and connectivity, are suitable for populations with any movement pattern, quantify basic spatial and temporal patterns of occupancy and movement, and require the least data.Occupancy-based metrics answer questions about likelihood of persistence or colonization, are suitable for populations that undergo localized extinctions, quantify spatial and temporal patterns of occupancy and movement, and require a moderate amount of data.Demographic-based metrics answer questions about relative or absolute population size, are suitable for populations with any movement pattern, quantify demographic processes and population dynamics, and require the most data.More real-world examples applying occupancy-based, agent-based, and continuous-based metrics to seasonally migratory species are needed to better understand challenges and opportunities for applying these metrics more broadly.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

PubMed

Kaplan, Bernhard A; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

PubMed Central

Kaplan, Bernhard A.; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID:24570657

Emerging Concepts of Data Integration in Pathogen Phylodynamics.

PubMed

Baele, Guy; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

PubMed Central

Baele, Guy; Suchard, Marc A.; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics. PMID:28173504

Aortic isthmus and cardiac monitoring of the growth-restricted fetus.

PubMed

Acharya, Ganesh; Tronnes, Ashlie; Rasanen, Juha

2011-03-01

Aortic isthmus acts as an arterial watershed between the cerebral and placental circulations, connecting 2 parallel fetal ventricular pumps. It plays a crucial role in the fetal circulatory dynamics. Information about aortic isthmus blood flow may improve the management of sick fetuses. However, perceived technical difficulties limit the clinical use of aortic isthmus Doppler for fetal hemodynamic monitoring. Changes in aortic isthmus blood flow pattern seem to reflect fetal cardiovascular status accurately and predict perinatal and long-term neurodevelopmental outcome in intrauterine growth restriction. This review evaluates the available scientific information and discusses the role of aortic isthmus in fetal circulation. Copyright © 2011 Elsevier Inc. All rights reserved.

THE DYNAMICAL RELATIONSHIP BETWEEN THE BAR AND SPIRAL PATTERNS OF NGC 1365

DOE Office of Scientific and Technical Information (OSTI.GOV)

Speights, Jason C.; Rooke, Paul C., E-mail: jcspeights@frostburg.edu

2016-07-20

Theories that attempt to explain the dynamical relationship between bar and spiral patterns in galactic disks make different predictions about the radial profile of the pattern speed. These are tested for the H-alpha bar and spiral patterns of NGC 1365. The radial profile of the pattern speed is measured by fitting mathematical models that are based on the Tremaine–Weinberg method. The results show convincing evidence for the bar rotating at a faster rate than the spiral pattern, inconsistent with a global wave mode or a manifold. There is evidence for mode coupling of the bar and spiral patterns at themore » overlap of corotation and inner Lindblad resonances (ILRs), but the evidence is unreliable and inconsistent. The results are the most consistent with the bar and spiral patterns being dynamically distinct features. The pattern speed of the bar begins near an ILR and ends near the corotation resonance (CR). The radial profile of the pattern speed beyond the bar most closely resembles what is expected for coupled spiral modes and tidal interactions.« less

Perfusion information extracted from resting state functional magnetic resonance imaging.

PubMed

Tong, Yunjie; Lindsey, Kimberly P; Hocke, Lia M; Vitaliano, Gordana; Mintzopoulos, Dionyssios; Frederick, Blaise deB

2017-02-01

It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.

Participation Dynamics in Population-Based Longitudinal HIV Surveillance in Rural South Africa

PubMed Central

Larmarange, Joseph; Mossong, Joël; Bärnighausen, Till; Newell, Marie Louise

2015-01-01

Population-based HIV surveillance is crucial to inform understanding of the HIV pandemic and evaluate HIV interventions, but little is known about longitudinal participation patterns in such settings. We investigated the dynamics of longitudinal participation patterns in a high HIV prevalence surveillance setting in rural South Africa between 2003 and 2012, taking into account demographic dynamics. At any given survey round, 22,708 to 30,495 persons were eligible. Although the yearly participation rates were relatively modest (26% to 46%), cumulative rates increased substantially with multiple recruitment opportunities: 68% of eligible persons participated at least once, 48% at least twice and 31% at least three times after five survey rounds. We identified two types of study fatigue: at the individual level, contact and consent rates decreased with multiple recruitment opportunities and, at the population level, these rates also decreased over calendar time, independently of multiple recruitment opportunities. Using sequence analysis and hierarchical clustering, we identified three broad individual participation profiles: consenters (20%), switchers (43%) and refusers (37%). Men were over represented among refusers, women among consenters, and temporary non-residents among switchers. The specific subgroup of persons who were systemically not contacted or refusers constitutes a challenge for population-based surveillance and interventions. PMID:25875851

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

NASA Astrophysics Data System (ADS)

Florido, E.; Asencio-Cortés, G.; Aznarte, J. L.; Rubio-Escudero, C.; Martínez-Álvarez, F.

2018-06-01

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.

Time-resolved double-slit interference pattern measurement with entangled photons

PubMed Central

Kolenderski, Piotr; Scarcella, Carmelo; Johnsen, Kelsey D.; Hamel, Deny R.; Holloway, Catherine; Shalm, Lynden K.; Tisa, Simone; Tosi, Alberto; Resch, Kevin J.; Jennewein, Thomas

2014-01-01

The double-slit experiment strikingly demonstrates the wave-particle duality of quantum objects. In this famous experiment, particles pass one-by-one through a pair of slits and are detected on a distant screen. A distinct wave-like pattern emerges after many discrete particle impacts as if each particle is passing through both slits and interfering with itself. Here we present a temporally- and spatially-resolved measurement of the double-slit interference pattern using single photons. We send single photons through a birefringent double-slit apparatus and use a linear array of single-photon detectors to observe the developing interference pattern. The analysis of the buildup allows us to compare quantum mechanics and the corpuscular model, which aims to explain the mystery of single-particle interference. Finally, we send one photon from an entangled pair through our double-slit setup and show the dependence of the resulting interference pattern on the twin photon's measured state. Our results provide new insight into the dynamics of the buildup process in the double-slit experiment, and can be used as a valuable resource in quantum information applications. PMID:24770360

On the Interplay between Order Parameter Dynamics and System Parameter Dynamics in Human Perceptual-Cognitive-Behavioral Systems.

PubMed

Frank, T D

2015-04-01

Previous research has demonstrated that perceiving, thinking, and acting are human activities that correspond to self-organized patterns. The emergence of such patterns can be completely described in terms of the dynamics of the pattern amplitudes, which are referred to as order parameters. The patterns emerge at bifurcations points when certain system parameters internal and external to a human agent exceed critical values. At issue is how one might study the order parameter dynamics for sequences of consecutive, emergent perceptual, cognitive, or behavioral activities. In particular, these activities may in turn impact the system parameters that have led to the emergence of the activities in the first place. This interplay between order parameter dynamics and system parameter dynamics is discussed in general and formulated in mathematical terms. Previous work that has made use of this two-tiered framework of order parameter and system parameter dynamics are briefly addressed. As an application, a model for perception under functional fixedness is presented. Finally, it is argued that the phenomena that emerge in this framework and can be observed when human agents perceive, think, and act are just as likely to occur in pattern formation systems of the inanimate world. Consequently, these phenomena do not necessarily have a neurophysiological basis but should instead be understood from the perspective of the theory of self-organization.

Pathogen profiling for disease management and surveillance.

PubMed

Sintchenko, Vitali; Iredell, Jonathan R; Gilbert, Gwendolyn L

2007-06-01

The usefulness of rapid pathogen genotyping is widely recognized, but its effective interpretation and application requires integration into clinical and public health decision-making. How can pathogen genotyping data best be translated to inform disease management and surveillance? Pathogen profiling integrates microbial genomics data into communicable disease control by consolidating phenotypic identity-based methods with DNA microarrays, proteomics, metabolomics and sequence-based typing. Sharing data on pathogen profiles should facilitate our understanding of transmission patterns and the dynamics of epidemics.

Safe motion planning for mobile agents: A model of reactive planning for multiple mobile agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fujimura, Kikuo.

1990-01-01

The problem of motion planning for multiple mobile agents is studied. Each planning agent independently plans its own action based on its map which contains a limited information about the environment. In an environment where more than one mobile agent interacts, the motions of the robots are uncertain and dynamic. A model for reactive agents is described and simulation results are presented to show their behavior patterns. 18 refs., 2 figs.

Analyzing neuronal networks using discrete-time dynamics

NASA Astrophysics Data System (ADS)

Ahn, Sungwoo; Smith, Brian H.; Borisyuk, Alla; Terman, David

2010-05-01

We develop mathematical techniques for analyzing detailed Hodgkin-Huxley like models for excitatory-inhibitory neuronal networks. Our strategy for studying a given network is to first reduce it to a discrete-time dynamical system. The discrete model is considerably easier to analyze, both mathematically and computationally, and parameters in the discrete model correspond directly to parameters in the original system of differential equations. While these networks arise in many important applications, a primary focus of this paper is to better understand mechanisms that underlie temporally dynamic responses in early processing of olfactory sensory information. The models presented here exhibit several properties that have been described for olfactory codes in an insect’s Antennal Lobe. These include transient patterns of synchronization and decorrelation of sensory inputs. By reducing the model to a discrete system, we are able to systematically study how properties of the dynamics, including the complex structure of the transients and attractors, depend on factors related to connectivity and the intrinsic and synaptic properties of cells within the network.

Memory and betweenness preference in temporal networks induced from time series

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

2017-02-01

We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

Fractional-order information in the visual control of lateral locomotor interception.

PubMed

Bootsma, Reinoud J; Ledouit, Simon; Casanova, Remy; Zaal, Frank T J M

2016-04-01

Previous work on locomotor interception of a target moving in the transverse plane has suggested that interception is achieved by maintaining the target's bearing angle (often inadvertently confused and/or confounded with the target heading angle) at a constant value. However, dynamics-based model simulations testing the veracity of the underlying control strategy of nulling the rate of change in the bearing angle have been restricted to limited conditions of target motion, and only a few alternatives have been considered. Exploring a wide range of target motion characteristics with straight and curving ball trajectories in a virtual reality setting, we examined how soccer goalkeepers moved along the goal line to intercept long-range shots on goal, a situation in which interception is naturally constrained to movement along a single dimension. Analyses of the movement patterns suggested reliance on combinations of optical position and velocity for straight trajectories and optical velocity and acceleration for curving trajectories. As an alternative to combining such standard integer-order derivatives, we demonstrate with a simple dynamical model that nulling a single informational variable of a self-tuned fractional (rather than integer) order efficiently captures the timing and patterning of the observed interception behaviors. This new perspective could fundamentally change the conception of what perceptual systems may actually provide, both in humans and in other animals. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Cross-entropy clustering framework for catchment classification

NASA Astrophysics Data System (ADS)

Tongal, Hakan; Sivakumar, Bellie

2017-09-01

There is an increasing interest in catchment classification and regionalization in hydrology, as they are useful for identification of appropriate model complexity and transfer of information from gauged catchments to ungauged ones, among others. This study introduces a nonlinear cross-entropy clustering (CEC) method for classification of catchments. The method specifically considers embedding dimension (m), sample entropy (SampEn), and coefficient of variation (CV) to represent dimensionality, complexity, and variability of the time series, respectively. The method is applied to daily streamflow time series from 217 gauging stations across Australia. The results suggest that a combination of linear and nonlinear parameters (i.e. m, SampEn, and CV), representing different aspects of the underlying dynamics of streamflows, could be useful for determining distinct patterns of flow generation mechanisms within a nonlinear clustering framework. For the 217 streamflow time series, nine hydrologically homogeneous clusters that have distinct patterns of flow regime characteristics and specific dominant hydrological attributes with different climatic features are obtained. Comparison of the results with those obtained using the widely employed k-means clustering method (which results in five clusters, with the loss of some information about the features of the clusters) suggests the superiority of the cross-entropy clustering method. The outcomes from this study provide a useful guideline for employing the nonlinear dynamic approaches based on hydrologic signatures and for gaining an improved understanding of streamflow variability at a large scale.

Decoding Task and Stimulus Representations in Face-responsive Cortex

PubMed Central

Kliemann, Dorit; Jacoby, Nir; Anzellotti, Stefano; Saxe, Rebecca R.

2017-01-01

Faces provide rich social information about others’ stable traits (e.g., age) and fleeting states of mind (e.g., emotional expression). While some of these facial aspects may be processed automatically, observers can also deliberately attend to some features while ignoring others. It remains unclear how internal goals (e.g., task context) influence the representational geometry of variable and stable facial aspects in face-responsive cortex. We investigated neural response patterns related to decoding i) the intention to attend to a facial aspect before its perception, ii) the attended aspect of a face and iii) stimulus properties. We measured neural responses while subjects watched videos of dynamic positive and negative expressions, and judged the age or the expression’s valence. Split-half multivoxel pattern analyses (MVPA) showed that (i) the intention to attend to a specific aspect of a face can be decoded from left fronto-lateral, but not face-responsive regions; (ii) during face perception, the attend aspect (age vs emotion) could be robustly decoded from almost all face-responsive regions; and (iii) a stimulus property (valence), was represented in right posterior superior temporal sulcus and medial prefrontal cortices. The effect of deliberately shifting the focus of attention on representations suggest a powerful influence of top-down signals on cortical representation of social information, varying across cortical regions, likely reflecting neural flexibility to optimally integrate internal goals and dynamic perceptual input. PMID:27978778

Multi-temporal dynamics of suspended particulate matter in a macro-tidal river Plume (the Gironde) as observed by satellite data

NASA Astrophysics Data System (ADS)

Constantin, Sorin; Doxaran, David; Derkacheva, Anna; Novoa, Stéfani; Lavigne, Héloïse

2018-03-01

The Gironde River plume area is unique in terms of Suspended Particulate Matter (SPM) dynamics. Multiple factors contribute to the variations of SPM at multiple time scales, from river outputs to wind stress, currents and tidal cycles. The formation and evolution of the Maximum Turbidity Zone (MTZ) inside the estuary also plays a significant role. Thus, detailed analyses and monitoring of the region is important for better understanding the mechanisms governing the turbid plume dynamics, for proper future management and monitoring of SPM export from the estuary to the coastal ocean. In this study we use an unprecedented volume of satellite data to capture and better understand the dynamics of the river plume. We combine two types of satellite information in order to achieve these goals: data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors. The integrated information allows accounting for multiple time scales, i.e. from seasonal to diurnal cycles. We show and parameterize the overall effects of river discharge rates over the plume extension. Seasonal variations are also analyzed and an overall relationship between river discharge rates and plume magnitude is computed. For the first time, we clearly observe and explain the diurnal cycle of SPM dynamics in the river plume. Despite the limited capabilities of the SEVIRI sensor, geostationary data was successfully used to derive such information and results similar to in-situ datasets were obtained. The same patterns are observed, with significant increase in SPM plume during spring/ebb tide periods. Results from our study can be further used to refine sediment transport models and to gain a better perspective on the ecological implications of the sediment output in the continental shelf area.

Dynamics of Biofilm Regrowth in Drinking Water Distribution Systems.

PubMed

Douterelo, I; Husband, S; Loza, V; Boxall, J

2016-07-15

The majority of biomass within water distribution systems is in the form of attached biofilm. This is known to be central to drinking water quality degradation following treatment, yet little understanding of the dynamics of these highly heterogeneous communities exists. This paper presents original information on such dynamics, with findings demonstrating patterns of material accumulation, seasonality, and influential factors. Rigorous flushing operations repeated over a 1-year period on an operational chlorinated system in the United Kingdom are presented here. Intensive monitoring and sampling were undertaken, including time-series turbidity and detailed microbial analysis using 16S rRNA Illumina MiSeq sequencing. The results show that bacterial dynamics were influenced by differences in the supplied water and by the material remaining attached to the pipe wall following flushing. Turbidity, metals, and phosphate were the main factors correlated with the distribution of bacteria in the samples. Coupled with the lack of inhibition of biofilm development due to residual chlorine, this suggests that limiting inorganic nutrients, rather than organic carbon, might be a viable component in treatment strategies to manage biofilms. The research also showed that repeat flushing exerted beneficial selective pressure, giving another reason for flushing being a viable advantageous biofilm management option. This work advances our understanding of microbiological processes in drinking water distribution systems and helps inform strategies to optimize asset performance. This research provides novel information regarding the dynamics of biofilm formation in real drinking water distribution systems made of different materials. This new knowledge on microbiological process in water supply systems can be used to optimize the performance of the distribution network and to guarantee safe and good-quality drinking water to consumers. Copyright © 2016 Douterelo et al.

Dynamics of Biofilm Regrowth in Drinking Water Distribution Systems

PubMed Central

Husband, S.; Loza, V.; Boxall, J.

2016-01-01

ABSTRACT The majority of biomass within water distribution systems is in the form of attached biofilm. This is known to be central to drinking water quality degradation following treatment, yet little understanding of the dynamics of these highly heterogeneous communities exists. This paper presents original information on such dynamics, with findings demonstrating patterns of material accumulation, seasonality, and influential factors. Rigorous flushing operations repeated over a 1-year period on an operational chlorinated system in the United Kingdom are presented here. Intensive monitoring and sampling were undertaken, including time-series turbidity and detailed microbial analysis using 16S rRNA Illumina MiSeq sequencing. The results show that bacterial dynamics were influenced by differences in the supplied water and by the material remaining attached to the pipe wall following flushing. Turbidity, metals, and phosphate were the main factors correlated with the distribution of bacteria in the samples. Coupled with the lack of inhibition of biofilm development due to residual chlorine, this suggests that limiting inorganic nutrients, rather than organic carbon, might be a viable component in treatment strategies to manage biofilms. The research also showed that repeat flushing exerted beneficial selective pressure, giving another reason for flushing being a viable advantageous biofilm management option. This work advances our understanding of microbiological processes in drinking water distribution systems and helps inform strategies to optimize asset performance. IMPORTANCE This research provides novel information regarding the dynamics of biofilm formation in real drinking water distribution systems made of different materials. This new knowledge on microbiological process in water supply systems can be used to optimize the performance of the distribution network and to guarantee safe and good-quality drinking water to consumers. PMID:27208119

Dynamics of spiral patterns in gas discharge detected by optical method

NASA Astrophysics Data System (ADS)

Yang, Fan; Wang, Mingyi; Liu, Shuhua

2016-09-01

The dynamics behavior of spiral patterns is investigated in gas discharge using optical method. Rich kinks of spiral patterns are obtained and the formation and evolution process is investigated. The process of pattern formation is breakdown -> hexagon -> bee comb-like -> strip -> spiral -> chaos. Spiral pattern always formed after the strip pattern. It is found that the temperature of the water electrodes plays an important role in the spiral patterns formation. When it exceeds 20°C no spiral has been obtained. The discharge current waveform and the emission spectrum of the discharge have been measured when the filaments self-organized in spiral pattern. Electron excited temperature of forming spiral pattern is calculated using intensity ratio method. It is found that the electron excited temperature of spiral pattern increase as the power supply frequency increased. Relation between wavelength and discharge parameter has been measured. It shows that the wavelength of spiral pattern increases as the discharge gap increases, and decreases as the air ratio mixed in argon increases. Accompanying measurements proved that the wavelength is approximately linear to the square root of the spiral rotating period .This work has useful reference value for studying pattern dynamics.

Analysis of dynamically stable patterns in a maze-like corridor using the Wasserstein metric.

PubMed

Ishiwata, Ryosuke; Kinukawa, Ryota; Sugiyama, Yuki

2018-04-23

The two-dimensional optimal velocity (2d-OV) model represents a dissipative system with asymmetric interactions, thus being suitable to reproduce behaviours such as pedestrian dynamics and the collective motion of living organisms. In this study, we found that particles in the 2d-OV model form optimal patterns in a maze-like corridor. Then, we estimated the stability of such patterns using the Wasserstein metric. Furthermore, we mapped these patterns into the Wasserstein metric space and represented them as points in a plane. As a result, we discovered that the stability of the dynamical patterns is strongly affected by the model sensitivity, which controls the motion of each particle. In addition, we verified the existence of two stable macroscopic patterns which were cohesive, stable, and appeared regularly over the time evolution of the model.

Regional mantle upwelling on Venus: The Beta-Atla-Themis anomaly and correlation with global tectonic patterns

NASA Technical Reports Server (NTRS)

Crumpler, L. S.; Head, J. W.; Aubele, Jayne C.

1993-01-01

The morphology and global distribution of volcanic centers and their association with other geological characteristics offers significant insight into the global patterns of geology, tectonic style, thermal state, and interior dynamics of Venus. Magellan data permit the detailed geological interpretation necessary to address questions about interior dynamics of Venus particularly as they reflect relatively physical, chemical, and thermal conditions of the interior. This paper focuses on the distribution of anomalous concentrations of volcanic centers on Venus and regional patterns of tectonic deformation as it may relate to the identification of global internal anomalies, including mantle dynamic, petrological, or thermal patterns.

Predictability of spatio-temporal patterns in a lattice of coupled FitzHugh–Nagumo oscillators

PubMed Central

Grace, Miriam; Hütt, Marc-Thorsten

2013-01-01

In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a ‘developmental path’), together with differences in cell properties (‘desynchronization’ of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh–Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or ‘spread’) of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system. PMID:23349439

A Display of Patterns of Change in Learners' Motivation: Dynamics Perspective

ERIC Educational Resources Information Center

Sel?uk, Özge; Erten, Ismail Hakki

2017-01-01

Getting to understand patterns and causes of motivational changes experienced by language learners while studying a foreign language can be of significant value. This paper aims to explore patterns of such fluctuations at the tertiary level. Through a dynamic systems perspective, this study employed Retrodictive Qualitative Modelling to generate…

Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China

PubMed Central

Liu, Jiming; Tan, Qi; Shi, Benyun

2016-01-01

Background Dengue is a serious vector-borne disease, and incidence rates have significantly increased during the past few years, particularly in 2014 in Guangzhou. The current situation is more complicated, due to various factors such as climate warming, urbanization, population increase, and human mobility. The purpose of this study is to detect dengue transmission patterns and identify the disease dispersion dynamics in Guangzhou, China. Methodology We conducted surveys in 12 districts of Guangzhou, and collected daily data of Breteau index (BI) and reported cases between September and November 2014 from the public health authority reports. Based on the available data and the Ross-Macdonald theory, we propose a dengue transmission model that systematically integrates entomologic, demographic, and environmental information. In this model, we use (1) BI data and geographic variables to evaluate the spatial heterogeneities of Aedes mosquitoes, (2) a radiation model to simulate the daily mobility of humans, and (3) a Markov chain Monte Carlo (MCMC) method to estimate the model parameters. Results/Conclusions By implementing our proposed model, we can (1) estimate the incidence rates of dengue, and trace the infection time and locations, (2) assess risk factors and evaluate the infection threat in a city, and (3) evaluate the primary diffusion process in different districts. From the results, we can see that dengue infections exhibited a spatial and temporal variation during 2014 in Guangzhou. We find that urbanization, vector activities, and human behavior play significant roles in shaping the dengue outbreak and the patterns of its spread. This study offers useful information on dengue dynamics, which can help policy makers improve control and prevention measures. PMID:27105350

Forest Conversion, Agricultural Transitions and the Influence of Multi-scale Market Factors in Southwest Cameroon

NASA Astrophysics Data System (ADS)

Ordway, E.; Lambin, E.; Asner, G. P.

2015-12-01

The changing structure of demand for commodities associated with food security and energy has had a startling impact on land use change in tropical forests in recent decades. Yet, the composition of conversion in the Congo basin remains a major uncertainty, particularly with regards to the scale of drivers of change. Owing to rapid expansion of production globally and longstanding historical production locally in the Congo basin, oil palm offers a lens through which to evaluate local land use decisions across a spectrum of small- to large-scales of production as well as interactions with regional and global supply chains. We examined the effect of global commodity crop expansion on land use change in Southwest Cameroon using a mixed-methods approach to integrate remote sensing, field surveys and socioeconomic data. Southwest Cameroon (2.5 Mha) has a long history of large- and small-scale agriculture, ranging from mixed crop subsistence agriculture to large monocrop plantations of oil palm, cocoa, and rubber. Trends and spatial patterns of forest conversion and agricultural transitions were analyzed from 2000-2015 using satellite imagery. We used economic, demographic and field survey datasets to assess how regional and global market factors and local commodity crop decisions affect land use patterns. Our results show that oil palm is a major commodity crop expanding in this region, and that conversion is occurring primarily through expansion by medium-scale producers and local elites. Results also indicate that global and regional supply chain dynamics influence local land use decision making. This research contributes new information on land use patterns and dynamics in the Congo basin, an understudied region. More specifically, results from this research contribute information on recent trends of oil palm expansion in Cameroon that will be used in national land use planning strategies.

Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm.

PubMed

Bruynseels, Koen; Santoni de Sio, Filippo; van den Hoven, Jeroen

2018-01-01

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. 'Digital Twins' in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. When applied to persons, Digital Twins are an emerging technology that builds on in silico representations of an individual that dynamically reflect molecular status, physiological status and life style over time. We use Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time. This perspective redefines the concept of 'normality' or 'health,' as a set of patterns that are regular for a particular individual , against the backdrop of patterns observed in the population. This perspective also will impact what is considered therapy and what is enhancement, as can be illustrated with the cases of the 'asymptomatic ill' and life extension via anti-aging medicine. These changes are the consequence of how meaning is derived, in case measurement data is available. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions. It can as well though be a driver for inequality, given the fact that a Digital Twin might not be an accessible technology for everyone, and given the fact that patterns identified across a population of Digital Twins can lead to segmentation and discrimination. This duality calls for governance as this emerging technology matures, including measures that ensure transparency of data usage and derived benefits, and data privacy.

Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm

PubMed Central

Bruynseels, Koen; Santoni de Sio, Filippo; van den Hoven, Jeroen

2018-01-01

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. When applied to persons, Digital Twins are an emerging technology that builds on in silico representations of an individual that dynamically reflect molecular status, physiological status and life style over time. We use Digital Twins as the hypothesis that one would be in the possession of very detailed bio-physical and lifestyle information of a person over time. This perspective redefines the concept of ‘normality’ or ‘health,’ as a set of patterns that are regular for a particular individual, against the backdrop of patterns observed in the population. This perspective also will impact what is considered therapy and what is enhancement, as can be illustrated with the cases of the ‘asymptomatic ill’ and life extension via anti-aging medicine. These changes are the consequence of how meaning is derived, in case measurement data is available. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions. It can as well though be a driver for inequality, given the fact that a Digital Twin might not be an accessible technology for everyone, and given the fact that patterns identified across a population of Digital Twins can lead to segmentation and discrimination. This duality calls for governance as this emerging technology matures, including measures that ensure transparency of data usage and derived benefits, and data privacy. PMID:29487613

High-resolution liquid patterns via three-dimensional droplet shape control.

PubMed

Raj, Rishi; Adera, Solomon; Enright, Ryan; Wang, Evelyn N

2014-09-25

Understanding liquid dynamics on surfaces can provide insight into nature's design and enable fine manipulation capability in biological, manufacturing, microfluidic and thermal management applications. Of particular interest is the ability to control the shape of the droplet contact area on the surface, which is typically circular on a smooth homogeneous surface. Here, we show the ability to tailor various droplet contact area shapes ranging from squares, rectangles, hexagons, octagons, to dodecagons via the design of the structure or chemical heterogeneity on the surface. We simultaneously obtain the necessary physical insights to develop a universal model for the three-dimensional droplet shape by characterizing the droplet side and top profiles. Furthermore, arrays of droplets with controlled shapes and high spatial resolution can be achieved using this approach. This liquid-based patterning strategy promises low-cost fabrication of integrated circuits, conductive patterns and bio-microarrays for high-density information storage and miniaturized biochips and biosensors, among others.