Sequential anaerobic/aerobic biodegradation of chloroethenes--aspects of field application.

PubMed

Tiehm, Andreas; Schmidt, Kathrin R

2011-06-01

Because of a range of different industrial activities, sites contaminated with chloroethenes are a world-wide problem. Chloroethenes can be biodegraded by reductive dechlorination under anaerobic conditions as well as by oxidation under aerobic conditions. The tendency of chloroethenes to undergo reductive dechlorination decreases with a decreasing number of chlorine substituents, whereas with less chlorine substituents chloroethenes more easily undergo oxidative degradation. There is currently a growing interest in aerobic metabolic degradation of chloroethenes, which demonstrates advantages compared to cometabolic degradation pathways. Sequential anaerobic/aerobic biodegradation can overcome the disadvantages of reductive dechlorination and leads to complete mineralization of the chlorinated pollutants. This approach shows promise for site remediation in natural settings and in engineered systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

NASA Astrophysics Data System (ADS)

Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

Random Boolean networks for autoassociative memory: Optimization and sequential learning

NASA Astrophysics Data System (ADS)

Sherrington, D.; Wong, K. Y. M.

Conventional neural networks are based on synaptic storage of information, even when the neural states are discrete and bounded. In general, the set of potential local operations is much greater. Here we discuss some aspects of the properties of networks of binary neurons with more general Boolean functions controlling the local dynamics. Two specific aspects are emphasised; (i) optimization in the presence of noise and (ii) a simple model for short-term memory exhibiting primacy and recency in the recall of sequentially taught patterns.

High speed polling protocol for multiple node network with sequential flooding of a polling message and a poll-answering message

NASA Technical Reports Server (NTRS)

Marvit, Maclen (Inventor); Kirkham, Harold (Inventor)

1995-01-01

The invention is a multiple interconnected network of intelligent message-repeating remote nodes which employs a remote node polling process performed by a master node by transmitting a polling message generically addressed to all remote nodes associated with the master node. Each remote node responds upon receipt of the generically addressed polling message by sequentially flooding the network with a poll-answering informational message and with the polling message.

The Evolution of Gene Regulatory Networks that Define Arthropod Body Plans.

PubMed

Auman, Tzach; Chipman, Ariel D

2017-09-01

Our understanding of the genetics of arthropod body plan development originally stems from work on Drosophila melanogaster from the late 1970s and onward. In Drosophila, there is a relatively detailed model for the network of gene interactions that proceeds in a sequential-hierarchical fashion to define the main features of the body plan. Over the years, we have a growing understanding of the networks involved in defining the body plan in an increasing number of arthropod species. It is now becoming possible to tease out the conserved aspects of these networks and to try to reconstruct their evolution. In this contribution, we focus on several key nodes of these networks, starting from early patterning in which the main axes are determined and the broad morphological domains of the embryo are defined, and on to later stage wherein the growth zone network is active in sequential addition of posterior segments. The pattern of conservation of networks is very patchy, with some key aspects being highly conserved in all arthropods and others being very labile. Many aspects of early axis patterning are highly conserved, as are some aspects of sequential segment generation. In contrast, regional patterning varies among different taxa, and some networks, such as the terminal patterning network, are only found in a limited range of taxa. The growth zone segmentation network is ancient and is probably plesiomorphic to all arthropods. In some insects, it has undergone significant modification to give rise to a more hardwired network that generates individual segments separately. In other insects and in most arthropods, the sequential segmentation network has undergone a significant amount of systems drift, wherein many of the genes have changed. However, it maintains a conserved underlying logic and function. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Cost-effectiveness of simultaneous versus sequential surgery in head and neck reconstruction.

PubMed

Wong, Kevin K; Enepekides, Danny J; Higgins, Kevin M

2011-02-01

To determine whether simultaneous (ablation and reconstruction overlaps by two teams) head and neck reconstruction is cost effective compared to sequentially (ablation followed by reconstruction) performed surgery. Case-controlled study. Tertiary care hospital. Oncology patients undergoing free flap reconstruction of the head and neck. A match paired comparison study was performed with a retrospective chart review examining the total time of surgery for sequential and simultaneous surgery. Nine patients were selected for both the sequential and simultaneous groups. Sequential head and neck reconstruction patients were pair matched with patients who had undergone similar oncologic ablative or reconstructive procedures performed in a simultaneous fashion. A detailed cost analysis using the microcosting method was then undertaken looking at the direct costs of the surgeons, anesthesiologist, operating room, and nursing. On average, simultaneous surgery required 3 hours 15 minutes less operating time, leading to a cost savings of approximately $1200/case when compared to sequential surgery. This represents approximately a 15% reduction in the cost of the entire operation. Simultaneous head and neck reconstruction is more cost effective when compared to sequential surgery.

Lymphoma and tuberculosis: temporal evolution of dual pathology on sequential 18F-FDG PET/CT.

PubMed

Mukherjee, Anirban; Sharma, Punit; Karunanithi, Sellam; Dhull, Varun Singh; Kumar, Rakesh

2014-08-01

Tuberculosis can often be seen in patients undergoing chemotherapy for lymphoma, especially in endemic countries. As both tuberculosis and lymphoma can lead to hypermetabolic lesions of F-FDG PET/CT, a diagnostic dilemma often ensues. We present the sequential F-FDG PET/CT images of a 22-year-old female patient with Hodgkin lymphoma who developed tuberculosis and later relapse of lymphoma. These images present the temporal evaluation of the dual pathology on F-FDG PET/CT.

Synchronization and coordination of sequences in two neural ensembles

NASA Astrophysics Data System (ADS)

Venaille, Antoine; Varona, Pablo; Rabinovich, Mikhail I.

2005-06-01

There are many types of neural networks involved in the sequential motor behavior of animals. For high species, the control and coordination of the network dynamics is a function of the higher levels of the central nervous system, in particular the cerebellum. However, in many cases, especially for invertebrates, such coordination is the result of direct synaptic connections between small circuits. We show here that even the chaotic sequential activity of small model networks can be coordinated by electrotonic synapses connecting one or several pairs of neurons that belong to two different networks. As an example, we analyzed the coordination and synchronization of the sequential activity of two statocyst model networks of the marine mollusk Clione. The statocysts are gravity sensory organs that play a key role in postural control of the animal and the generation of a complex hunting motor program. Each statocyst network was modeled by a small ensemble of neurons with Lotka-Volterra type dynamics and nonsymmetric inhibitory interactions. We studied how two such networks were synchronized by electrical coupling in the presence of an external signal which lead to winnerless competition among the neurons. We found that as a function of the number and the strength of connections between the two networks, it is possible to coordinate and synchronize the sequences that each network generates with its own chaotic dynamics. In spite of the chaoticity, the coordination of the signals is established through an activation sequence lock for those neurons that are active at a particular instant of time.

Learning Orthographic Structure With Sequential Generative Neural Networks.

PubMed

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

An exact computational method for performance analysis of sequential test algorithms for detecting network intrusions

NASA Astrophysics Data System (ADS)

Chen, Xinjia; Lacy, Fred; Carriere, Patrick

2015-05-01

Sequential test algorithms are playing increasingly important roles for quick detecting network intrusions such as portscanners. In view of the fact that such algorithms are usually analyzed based on intuitive approximation or asymptotic analysis, we develop an exact computational method for the performance analysis of such algorithms. Our method can be used to calculate the probability of false alarm and average detection time up to arbitrarily pre-specified accuracy.

Distributed Immune Systems for Wireless Network Information Assurance

DTIC Science & Technology

2010-04-26

ratio test (SPRT), where the goal is to optimize a hypothesis testing problem given a trade-off between the probability of errors and the...using cumulative sum (CUSUM) and Girshik-Rubin-Shiryaev (GRSh) statistics. In sequential versions of the problem the sequential probability ratio ...the more complicated problems, in particular those where no clear mean can be established. We developed algorithms based on the sequential probability

Bismaleimide and cyanate ester based sequential interpenetrating polymer networks for high temperature application

NASA Astrophysics Data System (ADS)

Geng, Xing

2005-07-01

A research area of high activity in connection with aerospace engineering has been the development of polymer thermosetting resins that can withstand temperature as high as 300°C while maintaining adequate toughness and providing ease of processing to enable low temperature and low cost composite fabrication methods. In order to meet such requirements, sequential interpenetrating polymer networks (IPNs) based on bismaleimide (BMI) and cyanate ester (CE) monomers were investigated. In these systems, a polycyanurate network is first formed in the presence of BMI and appropriate reactive diluent monomers and, in a second step, a network based on the BMI is created in the presence of a fully formed polycyanurate network. The materials developed can be processed at relatively low temperature (<150°C) and with the aid of electron beam (EB) curing. Of major importance to the success of this work was the identification of a reactive diluent that improves ease of processing and has tailored reactivity to allow for the controlled synthesis of CE-BMI sequential IPNs. Based on solubility and reactivity of a number of reactive diluents, N-acryloylmorpholine (AMP) was selected as a co-monomer for BMI copolymerization. A donor-acceptor reaction mechanism was suggested to explain the relative reactivity of a variety of reactive diluents towards maleimide functionality. The optimum processing parameters for the formation of the first network were determined through the study of metal catalyzed cure and hydrolysis of cyanate esters, whereas the reaction behavior for second network formation in terms of the influence of EB dose rate and temperature was elucidated through an in-situ kinetics study of maleimide and AMP copolymerization. Structure-property relationships were developed which allowed for the design of improved resin systems. In particular, an appropriate network coupler possessing cyanate ester and maleimide functionality was synthesized to link the polycyanurate first network to the BMI/AMP second network and thus form linked sequential IPNs (LIPNs). Consequently, Tg as high as 370°C was achieved and a fracture toughness of 120 J/m2 was obtained for resin systems that possess adequately low viscosity for processing using liquid molding techniques at low temperature.

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

PubMed

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

Benzimidazoles and benzoxazoles via the nucleophilic addition of anilines to nitroalkanes.

PubMed

Aksenov, Alexander V; Smirnov, Alexander N; Aksenov, Nicolai A; Bijieva, Asiyat S; Aksenova, Inna V; Rubin, Michael

2015-04-14

PPA-induced umpolung triggers efficient nucleophilic addition of unactivated anilines to nitroalkanes to produce N-hydroxyimidamides. The latter undergo sequential acid-promoted cyclocondensation with ortho-OH or ortho-NHR moieties to afford benzoxazoles and benzimidazoles, respectively.

LaRC-RP41: A Tough, High-Performance Composite Matrix

NASA Technical Reports Server (NTRS)

Pater, Ruth H.; Johnston, Norman J.; Smith, Ricky E.; Snoha, John J.; Gautreaux, Carol R.; Reddy, Rakasi M.

1991-01-01

New polymer exhibits increased toughness and resistance to microcracking. Cross-linking PMR-15 and linear LaRC-TPI combined to provide sequential semi-2-IPN designated as LaRC-RP41. Synthesized from PMR-15 imide prepolymer undergoing cross-linking in immediate presence of LaRC-TPI polyamic acid, also undergoing simultaneous imidization and linear chain extension. Potentially high-temperature matrix resin, adhesive, and molding resin. Applications include automobiles, electronics, aircraft, and aerospace structures.

Native Frames: Disentangling Sequential from Concerted Three-Body Fragmentation

NASA Astrophysics Data System (ADS)

Rajput, Jyoti; Severt, T.; Berry, Ben; Jochim, Bethany; Feizollah, Peyman; Kaderiya, Balram; Zohrabi, M.; Ablikim, U.; Ziaee, Farzaneh; Raju P., Kanaka; Rolles, D.; Rudenko, A.; Carnes, K. D.; Esry, B. D.; Ben-Itzhak, I.

2018-03-01

A key question concerning the three-body fragmentation of polyatomic molecules is the distinction of sequential and concerted mechanisms, i.e., the stepwise or simultaneous cleavage of bonds. Using laser-driven fragmentation of OCS into O++C++S+ and employing coincidence momentum imaging, we demonstrate a novel method that enables the clear separation of sequential and concerted breakup. The separation is accomplished by analyzing the three-body fragmentation in the native frame associated with each step and taking advantage of the rotation of the intermediate molecular fragment, CO2 + or CS2 + , before its unimolecular dissociation. This native-frame method works for any projectile (electrons, ions, or photons), provides details on each step of the sequential breakup, and enables the retrieval of the relevant spectra for sequential and concerted breakup separately. Specifically, this allows the determination of the branching ratio of all these processes in OCS3 + breakup. Moreover, we find that the first step of sequential breakup is tightly aligned along the laser polarization and identify the likely electronic states of the intermediate dication that undergo unimolecular dissociation in the second step. Finally, the separated concerted breakup spectra show clearly that the central carbon atom is preferentially ejected perpendicular to the laser field.

Classification and Sequential Pattern Analysis for Improving Managerial Efficiency and Providing Better Medical Service in Public Healthcare Centers

PubMed Central

Chung, Sukhoon; Rhee, Hyunsill; Suh, Yongmoo

2010-01-01

Objectives This study sought to find answers to the following questions: 1) Can we predict whether a patient will revisit a healthcare center? 2) Can we anticipate diseases of patients who revisit the center? Methods For the first question, we applied 5 classification algorithms (decision tree, artificial neural network, logistic regression, Bayesian networks, and Naïve Bayes) and the stacking-bagging method for building classification models. To solve the second question, we performed sequential pattern analysis. Results We determined: 1) In general, the most influential variables which impact whether a patient of a public healthcare center will revisit it or not are personal burden, insurance bill, period of prescription, age, systolic pressure, name of disease, and postal code. 2) The best plain classification model is dependent on the dataset. 3) Based on average of classification accuracy, the proposed stacking-bagging method outperformed all traditional classification models and our sequential pattern analysis revealed 16 sequential patterns. Conclusions Classification models and sequential patterns can help public healthcare centers plan and implement healthcare service programs and businesses that are more appropriate to local residents, encouraging them to revisit public health centers. PMID:21818426

On the origin of reproducible sequential activity in neural circuits

NASA Astrophysics Data System (ADS)

Afraimovich, V. S.; Zhigulin, V. P.; Rabinovich, M. I.

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

On the origin of reproducible sequential activity in neural circuits.

PubMed

Afraimovich, V S; Zhigulin, V P; Rabinovich, M I

2004-12-01

Robustness and reproducibility of sequential spatio-temporal responses is an essential feature of many neural circuits in sensory and motor systems of animals. The most common mathematical images of dynamical regimes in neural systems are fixed points, limit cycles, chaotic attractors, and continuous attractors (attractive manifolds of neutrally stable fixed points). These are not suitable for the description of reproducible transient sequential neural dynamics. In this paper we present the concept of a stable heteroclinic sequence (SHS), which is not an attractor. SHS opens the way for understanding and modeling of transient sequential activity in neural circuits. We show that this new mathematical object can be used to describe robust and reproducible sequential neural dynamics. Using the framework of a generalized high-dimensional Lotka-Volterra model, that describes the dynamics of firing rates in an inhibitory network, we present analytical results on the existence of the SHS in the phase space of the network. With the help of numerical simulations we confirm its robustness in presence of noise in spite of the transient nature of the corresponding trajectories. Finally, by referring to several recent neurobiological experiments, we discuss possible applications of this new concept to several problems in neuroscience.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Data Networks Reliability

DTIC Science & Technology

1988-10-03

full achievable region is achievable if there is only a bounded degree of asynchronism. E. Arikan , in a Ph.D. thesis [Ari85], extended sequential...real co-operation is required to reduce the number of transmissions to O(log log N). 14 REFERENCES [Ari85] E. Arikan , "Sequential Decoding for Multiple

THRESHOLD ELEMENTS AND THE DESIGN OF SEQUENTIAL SWITCHING NETWORKS.

DTIC Science & Technology

The report covers research performed from March 1966 to March 1967. The major topics treated are: (1) methods for finding weight- threshold vectors...that realize a given switching function in multi- threshold linear logic; (2) synthesis of sequential machines by means of shift registers and simple

Impact of remote ischaemic preconditioning on major clinical outcomes in patients undergoing cardiovascular surgery: A meta-analysis with trial sequential analysis of 32 randomised controlled trials.

PubMed

Wang, Shifei; Li, Hairui; He, Nvqin; Sun, Yili; Guo, Shengcun; Liao, Wangjun; Liao, Yulin; Chen, Yanmei; Bin, Jianping

2017-01-15

The impact of remote ischaemic preconditioning (RIPC) on major clinical outcomes in patients undergoing cardiovascular surgery remains controversial. We systematically reviewed the available evidence to evaluate the potential benefits of RIPC in such patients. PubMed, Embase, and Cochrane Library databases were searched for relevant randomised controlled trials (RCTs) conducted between January 2006 and March 2016. The pooled population of patients who underwent cardiovascular surgery was divided into the RIPC and control groups. Trial sequential analysis was applied to judge data reliability. The pooled relative risks (RRs) with 95% confidence intervals (CIs) between the groups were calculated for all-cause mortality, major adverse cardiovascular and cerebral events (MACCEs), myocardial infarction (MI), and renal failure. RIPC was not associated with improvement in all-cause mortality (RR, 1.04; 95%CI, 0.82-1.31; I 2 =26%; P>0.05) or MACCE incidence (RR, 0.90; 95%CI, 0.71-1.14; I 2 =40%; P>0.05) after cardiovascular surgery, and both results were assessed by trial sequential analysis as sufficient and conclusive. Nevertheless, RIPC was associated with a significantly lower incidence of MI (RR, 0.87; 95%CI, 0.76-1.00; I 2 =13%; P≤0.05). However, after excluding a study that had a high contribution to heterogeneity, RIPC was associated with increased rates of renal failure (RR, 1.53; 95%CI, 1.12-2.10; I 2 =5%; P≤0.05). In patients undergoing cardiovascular surgery, RIPC reduced the risk for postoperative MI, but not that for MACCEs or all-cause mortality, a discrepancy likely related to the higher rate of renal failure associated with RIPC. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

LKB1 signaling in cephalic neural crest cells is essential for vertebrate head development.

PubMed

Creuzet, Sophie E; Viallet, Jean P; Ghawitian, Maya; Torch, Sakina; Thélu, Jacques; Alrajeh, Moussab; Radu, Anca G; Bouvard, Daniel; Costagliola, Floriane; Borgne, Maïlys Le; Buchet-Poyau, Karine; Aznar, Nicolas; Buschlen, Sylvie; Hosoya, Hiroshi; Thibert, Chantal; Billaud, Marc

2016-10-15

Head development in vertebrates proceeds through a series of elaborate patterning mechanisms and cell-cell interactions involving cephalic neural crest cells (CNCC). These cells undergo extensive migration along stereotypical paths after their separation from the dorsal margins of the neural tube and they give rise to most of the craniofacial skeleton. Here, we report that the silencing of the LKB1 tumor suppressor affects the delamination of pre-migratory CNCC from the neural primordium as well as their polarization and survival, thus resulting in severe facial and brain defects. We further show that LKB1-mediated effects on the development of CNCC involve the sequential activation of the AMP-activated protein kinase (AMPK), the Rho-dependent kinase (ROCK) and the actin-based motor protein myosin II. Collectively, these results establish that the complex morphogenetic processes governing head formation critically depends on the activation of the LKB1 signaling network in CNCC. Copyright © 2016 Elsevier Inc. All rights reserved.

Sequential allosteric mechanism of ATP hydrolysis by the CCT/TRiC chaperone is revealed through Arrhenius analysis

PubMed Central

Gruber, Ranit; Levitt, Michael; Horovitz, Amnon

2017-01-01

Knowing the mechanism of allosteric switching is important for understanding how molecular machines work. The CCT/TRiC chaperonin nanomachine undergoes ATP-driven conformational changes that are crucial for its folding function. Here, we demonstrate that insight into its allosteric mechanism of ATP hydrolysis can be achieved by Arrhenius analysis. Our results show that ATP hydrolysis triggers sequential ‟conformational waves.” They also suggest that these waves start from subunits CCT6 and CCT8 (or CCT3 and CCT6) and proceed clockwise and counterclockwise, respectively. PMID:28461478

Sequential allosteric mechanism of ATP hydrolysis by the CCT/TRiC chaperone is revealed through Arrhenius analysis.

PubMed

Gruber, Ranit; Levitt, Michael; Horovitz, Amnon

2017-05-16

Knowing the mechanism of allosteric switching is important for understanding how molecular machines work. The CCT/TRiC chaperonin nanomachine undergoes ATP-driven conformational changes that are crucial for its folding function. Here, we demonstrate that insight into its allosteric mechanism of ATP hydrolysis can be achieved by Arrhenius analysis. Our results show that ATP hydrolysis triggers sequential ‟conformational waves." They also suggest that these waves start from subunits CCT6 and CCT8 (or CCT3 and CCT6) and proceed clockwise and counterclockwise, respectively.

Revascularization of Left Coronary System Using a Skeletonized Left Internal Mammary Artery　- Sequential vs. Separate Grafting.

PubMed

Ji, Qiang; Shi, YunQing; Xia, LiMin; Ma, RunHua; Shen, JinQiang; Lai, Hao; Ding, WenJun; Wang, ChunSheng

2017-12-25

To evaluate in-hospital and mid-term outcomes of sequential vs. separate grafting of in situ skeletonized left internal mammary artery (LIMA) to the left coronary system in a single-center, propensity-matched study.Methods and Results:After propensity score-matching, 120 pairs of patients undergoing first scheduled isolated coronary artery bypass grafting (CABG) with in situ skeletonized LIMA grafting to the left anterior descending artery (LAD) territory were entered into a sequential group (sequential grafting of LIMA to the diagonal artery and then to the LAD) or a control group (separate grafting of LIMA to the LAD). The in-hospital and follow-up clinical outcomes and follow-up LIMA graft patency were compared. Both propensity score-matched groups had similar in-hospital and follow-up clinical outcomes. Sequential LIMA grafting was not found to be an independent predictor of adverse events. During a follow-up period of 27.0±7.3 months, 99.1% patency for the diagonal site and 98.3% for the LAD site were determined by coronary computed tomographic angiography after sequential LIMA grafting, both of which were similar with graft patency of separate grafting of in situ skeletonized LIMA to the LAD. Revascularization of the left coronary system using a skeletonized LIMA resulted in excellent in-hospital and mid-term clinical outcomes and graft patency using sequential grafting.

Sequential associative memory with nonuniformity of the layer sizes.

PubMed

Teramae, Jun-Nosuke; Fukai, Tomoki

2007-01-01

Sequence retrieval has a fundamental importance in information processing by the brain, and has extensively been studied in neural network models. Most of the previous sequential associative memory embedded sequences of memory patterns have nearly equal sizes. It was recently shown that local cortical networks display many diverse yet repeatable precise temporal sequences of neuronal activities, termed "neuronal avalanches." Interestingly, these avalanches displayed size and lifetime distributions that obey power laws. Inspired by these experimental findings, here we consider an associative memory model of binary neurons that stores sequences of memory patterns with highly variable sizes. Our analysis includes the case where the statistics of these size variations obey the above-mentioned power laws. We study the retrieval dynamics of such memory systems by analytically deriving the equations that govern the time evolution of macroscopic order parameters. We calculate the critical sequence length beyond which the network cannot retrieve memory sequences correctly. As an application of the analysis, we show how the present variability in sequential memory patterns degrades the power-law lifetime distribution of retrieved neural activities.

On-line diagnosis of sequential systems

NASA Technical Reports Server (NTRS)

Sundstrom, R. J.

1973-01-01

A model for on-line diagnosis was investigated for discrete-time systems, and resettable sequential systems. Generalized notions of a realization are discussed along with fault tolerance and errors. Further investigation into the theory of on-line diagnosis is recommended for three levels: binary state-assigned level, logical circuit level, and the subsystem-network level.

Inverse problems in 1D hemodynamics on systemic networks: a sequential approach.

PubMed

Lombardi, D

2014-02-01

In this work, a sequential approach based on the unscented Kalman filter is applied to solve inverse problems in 1D hemodynamics, on a systemic network. For instance, the arterial stiffness is estimated by exploiting cross-sectional area and mean speed observations in several locations of the arteries. The results are compared with those ones obtained by estimating the pulse wave velocity and the Moens-Korteweg formula. In the last section, a perspective concerning the identification of the terminal models parameters and peripheral circulation (modeled by a Windkessel circuit) is presented. Copyright © 2013 John Wiley & Sons, Ltd.

A mixed study systematic review of social media in nursing and midwifery education: Protocol.

PubMed

O'Connor, Siobhan; Jolliffe, Sarah; Stanmore, Emma; Renwick, Laoise; Schmitt, Terri; Booth, Richard

2017-08-01

To synthesize evidence on the use of social media in nursing and midwifery education. Social media is one type of online platform that is being explored to determine if there is value in using interactive, digital communication tools to support how nurses and midwives learn in a variety of settings. A sequential explanatory synthesis approach will be used for this mixed study review. Five bibliographic databases; PubMed, MEDLINE, CINAHL, Scopus, and ERIC will be searched using a combination of keywords relevant to social networking and social media, nursing and midwifery, and education. The search will not be limited by year of publication. Titles, abstracts, and full papers will be screened by two independent reviewers against inclusion and exclusion criteria, with any disagreements resolved via a third reviewer. Selected studies will undergo quality assessment and data extraction. Data synthesis will occur in three sequential phases, with quantitative and qualitative data analysed separately and then integrated where possible to provide a conceptual framework illustrating learning via social media. Funding for this review was confirmed in May 2016 by Sigma Theta Tau International and the National League for Nursing. The mixed study systematic review will produce the first rigorous synthesis on the use of social media in nursing and midwifery education and will have important implications for educators as well as students. It will also highlight knowledge gaps and make recommendations on the use of this novel technology in higher and continuing education. © 2017 John Wiley & Sons Ltd.

On-line diagnosis of sequential systems, 2

NASA Technical Reports Server (NTRS)

Sundstrom, R. J.

1974-01-01

The theory and techniques applicable to the on-line diagnosis of sequential systems, were investigated. A complete model for the study of on-line diagnosis is developed. First an appropriate class of system models is formulated which can serve as a basis for a theoretical study of on-line diagnosis. Then notions of realization, fault, fault-tolerance and diagnosability are formalized which have meaningful interpretations in the the context of on-line diagnosis. The diagnosis of systems which are structurally decomposed and are represented as a network of smaller systems is studied. The fault set considered is the set of faults which only affect one component system is the network. A characterization of those networks which can be diagnosed using a purely combinational detector is achieved. A technique is given which can be used to realize any network by a network which is diagnosable in the above sense. Limits are found on the amount of redundancy involved in any such technique.

Amyloid Beta Mediates Memory Formation

ERIC Educational Resources Information Center

Garcia-Osta, Ana; Alberini, Cristina M.

2009-01-01

The amyloid precursor protein (APP) undergoes sequential cleavages to generate various polypeptides, including the amyloid [beta] (1-42) peptide (A[beta][1-42]), which is believed to play a major role in amyloid plaque formation in Alzheimer's disease (AD). Here we provide evidence that, in contrast with its pathological role when accumulated,…

Thermogravimetric and differential thermal analysis of potassium bicarbonate contaminated cellulose

Treesearch

A. Broido

1966-01-01

When samples undergo a complicated set of simultaneous and sequential reactions, as cellulose does on heating, results of thermogravimetric and differential thermal analyses are difficult to interpret. Nevertheless, careful comparison of pure and contaminated samples, pyrolyzed under identical conditions, can yield useful information. In these experiments TGA and DTA...

Analytical solution for multi-species contaminant transport in finite media with time-varying boundary conditions

USDA-ARS?s Scientific Manuscript database

Most analytical solutions available for the equations governing the advective-dispersive transport of multiple solutes undergoing sequential first-order decay reactions have been developed for infinite or semi-infinite spatial domains and steady-state boundary conditions. In this work we present an ...

Designing Robust and Resilient Tactical MANETs

DTIC Science & Technology

2014-09-25

Bounds on the Throughput Efficiency of Greedy Maximal Scheduling in Wireless Networks , IEEE/ACM Transactions on Networking , (06 2011): 0. doi: N... Wireless Sensor Networks and Effects of Long Range Dependant Data, Special IWSM Issue of Sequential Analysis, (11 2012): 0. doi: A. D. Dominguez...Bushnell, R. Poovendran. A Convex Optimization Approach for Clone Detection in Wireless Sensor Networks , Pervasive and Mobile Computing, (01 2012

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Such Stuff as Habits Are Made on: A Reply to Cooper and Shallice (2006)

ERIC Educational Resources Information Center

Botvinick, Matthew M.; Plaut, David C.

2006-01-01

The representations and mechanisms guiding everyday routine sequential action remain incompletely understood. In recent work, the authors proposed a computational model of routine sequential behavior that took the form of a recurrent neural network (M. Botvinick & D. C. Plaut, 2004). Subsequently, R. P. Cooper and T. Shallice (2006) put forth a…

Oscillations and chaos in neural networks: an exactly solvable model.

PubMed Central

Wang, L P; Pichler, E E; Ross, J

1990-01-01

We consider a randomly diluted higher-order network with noise, consisting of McCulloch-Pitts neurons that interact by Hebbian-type connections. For this model, exact dynamical equations are derived and solved for both parallel and random sequential updating algorithms. For parallel dynamics, we find a rich spectrum of different behaviors including static retrieving and oscillatory and chaotic phenomena in different parts of the parameter space. The bifurcation parameters include first- and second-order neuronal interaction coefficients and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics. Images PMID:2251287

Sequential high intensity focused ultrasound (HIFU) ablation in the treatment of benign multinodular goitre: an observational retrospective study.

PubMed

Lang, Brian H H; Woo, Yu-Cho; Chiu, Keith Wan-Hang

2018-03-19

Assessing the efficacy and safety of sequential high-intensity focused ultrasound (HIFU) ablation in a multinodular goitre (MNG) by comparing them with single HIFU ablation. One hundred and four (84.6%) patients underwent single ablation of a single nodule (group I), while 19 (15.4%) underwent sequential ablation of two relatively-dominant nodules in a MNG (group II). Extent of shrinkage per nodule [by volume reduction ratio (VRR)], pain scores (by 0-10 visual analogue scale) during and after ablation, and rate of vocal cord palsy (VCP), skin burn and nausea/vomiting were compared between the two groups. All 19 (100%) sequential ablations completed successfully. The 3- and 6-month VRR of each nodule were comparable between the two groups (p > 0.05) and in group II, the 3- and 6-month VRR between the first and second nodules were comparable (p = 0.710 and p = 0.548, respectively). Pain score was significantly higher in group II in the morning after ablation (2.29 vs 1.15, p = 0.047) and nausea/vomiting occurred significantly more frequently in group II (15.8% vs 0.0%, p = 0.012). However, VCP and skin burn were comparable (p > 0.05). Sequential ablation had comparable efficacy and safety as single ablation. However, patients undergoing sequential ablation are at higher likelihood of pain in the following morning and nausea/vomiting after ablation. • Sequential HIFU ablation is well-tolerated in patients with two dominant thyroid nodules • More pain is experienced in the morning following sequential HIFU ablation • More nausea/vomiting is experienced following sequential HIFU ablation.

Test pattern generation for ILA sequential circuits

NASA Technical Reports Server (NTRS)

Feng, YU; Frenzel, James F.; Maki, Gary K.

1993-01-01

An efficient method of generating test patterns for sequential machines implemented using one-dimensional, unilateral, iterative logic arrays (ILA's) of BTS pass transistor networks is presented. Based on a transistor level fault model, the method affords a unique opportunity for real-time fault detection with improved fault coverage. The resulting test sets are shown to be equivalent to those obtained using conventional gate level models, thus eliminating the need for additional test patterns. The proposed method advances the simplicity and ease of the test pattern generation for a special class of sequential circuitry.

Adjuvant sequential chemo and radiotherapy improves the oncological outcome in high risk endometrial cancer

PubMed Central

Signorelli, Mauro; Lissoni, Andrea Alberto; De Ponti, Elena; Grassi, Tommaso; Ponti, Serena

2015-01-01

Objective Evaluation of the impact of sequential chemoradiotherapy in high risk endometrial cancer (EC). Methods Two hundred fifty-four women with stage IB grade 3, II and III EC (2009 FIGO staging), were included in this retrospective study. Results Stage I, II, and III was 24%, 28.7%, and 47.3%, respectively. Grade 3 tumor was 53.2% and 71.3% had deep myometrial invasion. One hundred sixty-five women (65%) underwent pelvic (+/- aortic) lymphadenectomy and 58 (22.8%) had nodal metastases. Ninety-eight women (38.6%) underwent radiotherapy, 59 (23.2%) chemotherapy, 42 (16.5%) sequential chemoradiotherapy, and 55 (21.7%) were only observed. After a median follow-up of 101 months, 78 women (30.7%) relapsed and 91 women (35.8%) died. Sequential chemoradiotherapy improved survival rates in women who did not undergo nodal evaluation (disease-free survival [DFS], p=0.040; overall survival [OS], p=0.024) or pelvic (+/- aortic) lymphadenectomy (DFS, p=0.008; OS, p=0.021). Sequential chemoradiotherapy improved both DFS (p=0.015) and OS (p=0.014) in stage III, while only a trend was found for DFS (p=0.210) and OS (p=0.102) in stage I-II EC. In the multivariate analysis, only age (≤65 years) and sequential chemoradiotherapy were statistically related to the prognosis. Conclusion Sequential chemoradiotherapy improves survival rates in high risk EC compared with chemotherapy or radiotherapy alone, in particular in stage III. PMID:26197768

Cell wall invertase as a regulator in determining sequential development of endosperm and embryo through glucose signaling early in seed development.

PubMed

Wang, Lu; Liao, Shengjin; Ruan, Yong-Ling

2013-01-01

Seed development depends on coordination among embryo, endosperm and seed coat. Endosperm undergoes nuclear division soon after fertilization, whereas embryo remains quiescent for a while. Such a developmental sequence is of great importance for proper seed development. However, the underlying mechanism remains unclear. Recent results on the cellular domain- and stage-specific expression of invertase genes in cotton and Arabidopsis revealed that cell wall invertase may positively and specifically regulate nuclear division of endosperm after fertilization, thereby playing a role in determining the sequential development of endosperm and embryo, probably through glucose signaling.

The Longitudinal Effects of Network Characteristics on the Mental Health of Mothers of Children with ASD: The Mediating Role of Parent Cognitions

ERIC Educational Resources Information Center

Benson, Paul R.

2016-01-01

Employing a cohort sequential design, the effects of network characteristics on maternal cognitions (perceived social support and parenting self-efficacy) and mental health (depression and well-being) were assessed over 7 years when children with ASD of mothers in the study were age 7-14. Findings indicated that network size, network emotional…

A sequential EMT-MET mechanism drives the differentiation of human embryonic stem cells towards hepatocytes.

PubMed

Li, Qiuhong; Hutchins, Andrew P; Chen, Yong; Li, Shengbiao; Shan, Yongli; Liao, Baojian; Zheng, Dejin; Shi, Xi; Li, Yinxiong; Chan, Wai-Yee; Pan, Guangjin; Wei, Shicheng; Shu, Xiaodong; Pei, Duanqing

2017-05-03

Reprogramming has been shown to involve EMT-MET; however, its role in cell differentiation is unclear. We report here that in vitro differentiation of hESCs to hepatic lineage undergoes a sequential EMT-MET with an obligatory intermediate mesenchymal phase. Gene expression analysis reveals that Activin A-induced formation of definitive endoderm (DE) accompanies a synchronous EMT mediated by autocrine TGFβ signalling followed by a MET process. Pharmacological inhibition of TGFβ signalling blocks the EMT as well as DE formation. We then identify SNAI1 as the key EMT transcriptional factor required for the specification of DE. Genetic ablation of SNAI1 in hESCs does not affect the maintenance of pluripotency or neural differentiation, but completely disrupts the formation of DE. These results reveal a critical mesenchymal phase during the acquisition of DE, highlighting a role for sequential EMT-METs in both differentiation and reprogramming.

Networked Workstations and Parallel Processing Utilizing Functional Languages

DTIC Science & Technology

1993-03-01

program . This frees the programmer to concentrate on what the program is to do, not how the program is...traditional ’von Neumann’ architecture uses a timer based (e.g., the program counter), sequentially pro- grammed, single processor approach to problem...traditional ’von Neumann’ architecture uses a timer based (e.g., the program counter), sequentially programmed , single processor approach to

Pre-configured polyhedron based protection against multi-link failures in optical mesh networks.

PubMed

Huang, Shanguo; Guo, Bingli; Li, Xin; Zhang, Jie; Zhao, Yongli; Gu, Wanyi

2014-02-10

This paper focuses on random multi-link failures protection in optical mesh networks, instead of single, the dual or sequential failures of previous studies. Spare resource efficiency and failure robustness are major concerns in link protection strategy designing and a k-regular and k-edge connected structure is proved to be one of the optimal solutions for link protection network. Based on this, a novel pre-configured polyhedron based protection structure is proposed, and it could provide protection for both simultaneous and sequential random link failures with improved spare resource efficiency. Its performance is evaluated in terms of spare resource consumption, recovery rate and average recovery path length, as well as compared with ring based and subgraph protection under probabilistic link failure scenarios. Results show the proposed novel link protection approach has better performance than previous works.

Learning, memory, and the role of neural network architecture.

PubMed

Hermundstad, Ann M; Brown, Kevin S; Bassett, Danielle S; Carlson, Jean M

2011-06-01

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems.

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

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

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. Tomore » alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.« less

The Intelligent Management System: An Overview.

DTIC Science & Technology

1982-12-07

comprises of hundreds of subprocesses concerned with bulb grasping, positioning, heating, cooling, etc. Each is sequentially related with others in time...activity. Activity schemata can be constructed into a network to define both parallel and sequential precedence, and hierarchical to describe...The flow of work is controlled by the "operation- lineup " associated with the product being manufactured. The operation- lineup specifies the sequence of

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

NASA Astrophysics Data System (ADS)

Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

2016-10-01

Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

Methods for Selecting Phage Display Antibody Libraries.

PubMed

Jara-Acevedo, Ricardo; Diez, Paula; Gonzalez-Gonzalez, Maria; Degano, Rosa Maria; Ibarrola, Nieves; Gongora, Rafael; Orfao, Alberto; Fuentes, Manuel

2016-01-01

The selection process aims sequential enrichment of phage antibody display library in clones that recognize the target of interest or antigen as the library undergoes successive rounds of selection. In this review, selection methods most commonly used for phage display antibody libraries have been comprehensively described. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Temporal processes that contribute to nonlinearity in vegetation responses to ozone exposure and dose

Treesearch

Robert L. Heath; Allen S. Lefohn; Robert C. Musselman

2009-01-01

Ozone interacts with plant tissue through distinct temporal processes. Sequentially, plants are exposed to ambient O3 hat (1) moves through the leaf boundary layer, (2) is taken up into plant tissue primarily through stomata, and (3) undergoes chemical interaction within plant tissue, first by initiating alterations and then as part of plant...

Automatic sequential fluid handling with multilayer microfluidic sample isolated pumping

PubMed Central

Liu, Jixiao; Fu, Hai; Yang, Tianhang; Li, Songjing

2015-01-01

To sequentially handle fluids is of great significance in quantitative biology, analytical chemistry, and bioassays. However, the technological options are limited when building such microfluidic sequential processing systems, and one of the encountered challenges is the need for reliable, efficient, and mass-production available microfluidic pumping methods. Herein, we present a bubble-free and pumping-control unified liquid handling method that is compatible with large-scale manufacture, termed multilayer microfluidic sample isolated pumping (mμSIP). The core part of the mμSIP is the selective permeable membrane that isolates the fluidic layer from the pneumatic layer. The air diffusion from the fluidic channel network into the degassing pneumatic channel network leads to fluidic channel pressure variation, which further results in consistent bubble-free liquid pumping into the channels and the dead-end chambers. We characterize the mμSIP by comparing the fluidic actuation processes with different parameters and a flow rate range of 0.013 μl/s to 0.097 μl/s is observed in the experiments. As the proof of concept, we demonstrate an automatic sequential fluid handling system aiming at digital assays and immunoassays, which further proves the unified pumping-control and suggests that the mμSIP is suitable for functional microfluidic assays with minimal operations. We believe that the mμSIP technology and demonstrated automatic sequential fluid handling system would enrich the microfluidic toolbox and benefit further inventions. PMID:26487904

Situation models and memory: the effects of temporal and causal information on recall sequence.

PubMed

Brownstein, Aaron L; Read, Stephen J

2007-10-01

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.

Corrected Mean-Field Model for Random Sequential Adsorption on Random Geometric Graphs

NASA Astrophysics Data System (ADS)

Dhara, Souvik; van Leeuwaarden, Johan S. H.; Mukherjee, Debankur

2018-03-01

A notorious problem in mathematics and physics is to create a solvable model for random sequential adsorption of non-overlapping congruent spheres in the d-dimensional Euclidean space with d≥ 2 . Spheres arrive sequentially at uniformly chosen locations in space and are accepted only when there is no overlap with previously deposited spheres. Due to spatial correlations, characterizing the fraction of accepted spheres remains largely intractable. We study this fraction by taking a novel approach that compares random sequential adsorption in Euclidean space to the nearest-neighbor blocking on a sequence of clustered random graphs. This random network model can be thought of as a corrected mean-field model for the interaction graph between the attempted spheres. Using functional limit theorems, we characterize the fraction of accepted spheres and its fluctuations.

Analyzing Communication Architectures Using Commercial Off-The-Shelf (COTS) Modeling and Simulation Tools

DTIC Science & Technology

1998-06-01

4] By 2010, we should be able to change how we conduct the most intense joint operations. Instead of relying on massed forces and sequential ...not independent, sequential steps. Data probes to support the analysis phase were required to complete the logical models. This generated a need...Networks) Identify Granularity (System Level) - Establish Physical Bounds or Limits to Systems • Determine System Test Configuration and Lineup

A Preliminary Investigation of the E-Beam Induced Polymerization of Maleimide and Norbornene End-capped Polyimides

NASA Technical Reports Server (NTRS)

Palmese, Giuseppe R.; Meador, Michael A. (Technical Monitor)

2005-01-01

A research area of high activity in connection with aerospace engineering has been the development of polymer thermosetting resins that can resist temperature as high as 300 C while maintaining adequate toughness, and providing ease of processing to enable low temperature and low cost composite fabrication methods. In order to meet such requirements, sequential interpenetrating polymer networks (IPNs) based on bismaleimide (BMI) and cyanate ester (CE) monomers were investigated. In these systems, a polycyanurate network is first formed in the presence of BMI and appropriate reactive diluent monomers and in a second step, a network based on the BMI is created in the presence of a fully formed polycyanurate network. The materials developed can be processed at relatively low temperature (less than 150 C) and with the aid of electron beam (EB) curing. Of major importance to the success of this work was the identification of a reactive diluent that improves ease of processing and has tailored reactivity to allow for the controlled synthesis of CE-BMI sequential IPNs. Based on solubility and reactivity of a number of reactive diluents, N-acryloylmorpholine (AMP) was selected as a comonomer for BMI copolymerization. A donor-acceptoreaction mechanism was suggested to explain the relative reactivity of a variety of reactive diluents towards maleimide functionality. The optimum processing parameters for the formation of the first network were determined through the study of metal catalyzed cure and hydrolysis of cyanate esters, whereas the reaction behavior for second network formation in terms of the influence of EB dose rate and temperature was elucidated through an in-situ kinetics study of maleimide and AMP copolymerization. Structure-property relationships were developed which allowed for the design of improved resin systems. In particular, appropriate network coupler possessing cyanate ester and maleimide functionality was synthesized to link the polycyanurate first network to the BMI/AMP second network and thus form linked sequential IPNs (LIPNs). Consequently, Tg as high as 370 C was achieved and a fracture toughness of 120 Joules per square meters was obtained for resin systems that possess adequately low viscosity for processing using liquid molding techniques at low temperature.

Sequential events in the irreversible thermal denaturation of human brain-type creatine kinase by spectroscopic methods.

PubMed

Gao, Yan-Song; Su, Jing-Tan; Yan, Yong-Bin

2010-06-25

The non-cooperative or sequential events which occur during protein thermal denaturation are closely correlated with protein folding, stability, and physiological functions. In this research, the sequential events of human brain-type creatine kinase (hBBCK) thermal denaturation were studied by differential scanning calorimetry (DSC), CD, and intrinsic fluorescence spectroscopy. DSC experiments revealed that the thermal denaturation of hBBCK was calorimetrically irreversible. The existence of several endothermic peaks suggested that the denaturation involved stepwise conformational changes, which were further verified by the discrepancy in the transition curves obtained from various spectroscopic probes. During heating, the disruption of the active site structure occurred prior to the secondary and tertiary structural changes. The thermal unfolding and aggregation of hBBCK was found to occur through sequential events. This is quite different from that of muscle-type CK (MMCK). The results herein suggest that BBCK and MMCK undergo quite dissimilar thermal unfolding pathways, although they are highly conserved in the primary and tertiary structures. A minor difference in structure might endow the isoenzymes dissimilar local stabilities in structure, which further contribute to isoenzyme-specific thermal stabilities.

Outcome and cost analysis of bilateral sequential same-day cartilage tympanoplasty compared with bilateral staged tympanoplasty.

PubMed

Olusesi, A D; Oyeniran, O

2017-05-01

Few studies have compared bilateral same-day with staged tympanoplasty using cartilage graft materials. A prospective randomised observational study was performed of 38 chronic suppurative otitis media patients (76 ears) who were assigned to undergo bilateral sequential same-day tympanoplasty (18 patients, 36 ears) or bilateral sequential tympanoplasty performed 3 months apart (20 patients, 40 ears). Disease duration, intra-operative findings, combined duration of surgery, post-operative graft appearance at 6 weeks, post-operative complications, re-do rate and relative cost of surgery were recorded. Tympanic membrane perforations were predominantly subtotal (p = 0.36, odds ratio = 0.75). Most grafts were harvested from the conchal cartilage and fewer from the tragus (p = 0.59, odds ratio = 1.016). Types of complication, post-operative hearing gain and revision rates were similar in both patient groups. Surgical outcomes are not significantly different for same-day and bilateral cartilage tympanoplasty, but same-day surgery has the added benefit of a lower cost.

Mechanisms of palatal epithelial seam disintegration by Transforming Growth Factor (TGF)-β3

PubMed Central

Ahmed, Shaheen; Liu, Chang-Chih; Nawshad, Ali

2007-01-01

TGFβ3 signaling initiates and completes sequential phases of cellular differentiation that is required for complete disintegration of the palatal medial edge seam, that progresses between 14 to 17 embryonic days in the murine system, which is necessary in establishing confluence of the palatal stroma. Understanding the cellular mechanism of palatal MES disintegration in response to TGFβ3 signaling will result in new approaches to defining the causes of cleft palate and other facial clefts that may result from failure of seam disintegration. We have isolated MES primary cells to study the details of MES disintegration mechanism by TGFβ3 during palate development using several biochemical and genetic approaches. Our results demonstrate a novel mechanism of MES disintegration where MES, independently yet sequentially, undergoes cell cycle arrest, cell migration and apoptosis to generate immaculate palatal confluency during palatogenesis in response to robust TGFβ3 signaling. The results contribute to a missing fundamental element to our base knowledge of the diverse roles of TGFβ3 in functional and morphological changes that MES undergo during palatal seam disintegration. We believe that our findings will lead to more effective treatment of facial clefting. PMID:17698055

Syntheses, structures and redox properties of some complexes containing the Os(dppe)Cp* fragment, including [{Os(dppe)Cp*}2(mu-C triple bondCC triple bond C)].

PubMed

Bruce, Michael I; Costuas, Karine; Davin, Thomas; Halet, Jean-François; Kramarczuk, Kathy A; Low, Paul J; Nicholson, Brian K; Perkins, Gary J; Roberts, Rachel L; Skelton, Brian W; Smith, Mark E; White, Allan H

2007-12-14

The sequential conversion of [OsBr(cod)Cp*] (9) to [OsBr(dppe)Cp*] (10), [Os([=C=CH2)(dppe)Cp*]PF6 ([11]PF6), [Os(C triple bond CH)(dppe)Cp*] (12), [{Os(dppe)Cp*}2{mu-(=C=CH-CH=C=)}][PF6]2 ([13](PF6)2) and finally [{Os(dppe)Cp*}(2)(mu-C triple bond CC triple bond C)] (14) has been used to make the third member of the triad [{M(dppe)Cp*}2(mu-C triple bond CC triple bond C)] (M = Fe, Ru, Os). The molecular structures of []PF6, 12 and 14, together with those of the related osmium complexes [Os(NCMe)(dppe)Cp*]PF6 ([15]PF6) and [Os(C triple bond CPh)(dppe)Cp*] (16), have been determined by single-crystal X-ray diffraction studies. Comparison of the redox properties of 14 with those of its iron and ruthenium congeners shows that the first oxidation potential E1 varies as: Fe approximately Os < Ru. Whereas the Fe complex has been shown to undergo three sequential 1-electron oxidation processes within conventional electrochemical solvent windows, the Ru and Os compounds undergo no fewer than four sequential oxidation events giving rise to a five-membered series of redox related complexes [{M(dppe)Cp*}2(mu-C4)]n+ (n = 0, 1, 2, 3 and 4), the osmium derivatives being obtained at considerably lower potentials than the ruthenium analogues. These results are complimented by DFT and DT DFT calculations.

Semimembranosus Release for Medial Soft Tissue Balancing Does Not Weaken Knee Flexion Strength in Patients Undergoing Varus Total Knee Arthroplasty.

PubMed

Jang, Sung Won; Koh, In Jun; Kim, Man Soo; Kim, Ju Yeong; In, Yong

2016-11-01

The sequential medial release technique including semimembranosus (semiM) release is effective and safe during varus total knee arthroplasty (TKA). However, there are concerns about weakening of knee flexion strength after semiM release. We determined whether semiM release to balance the medial soft tissue decreased knee flexion strength after TKA. Fifty-nine consecutive varus knees undergoing TKA were prospectively enrolled. A 3-step sequential release protocol which consisted of release of (1) the deep medial collateral ligament (dMCL), (2) the semiM, and (3) the superficial medial collateral ligament based on medial tightness. Gap balancing was obtained after dMCL release in 31 knees. However, 28 knees required semiM release or more after dMCL release. Isometric muscle strength of the knee was compared 6 months postoperatively between the semiM release and semiM nonrelease groups. Knee stability and clinical outcomes were also compared. No differences in knee flexor or extensor peak torque were observed between the groups 6 months postoperatively (P = .322 and P = .383, respectively). No group difference was observed in medial joint opening angle on valgus stress radiographs (P = .327). No differences in the Knee Society or Western Ontario and McMaster Universities Osteoarthritis Index scores were detected between the groups (P = .840 and P = .682, respectively). These results demonstrate that semiM release as a sequential step to balance medial soft tissue in varus knees did not affect knee flexion strength after TKA. Copyright © 2016 Elsevier Inc. All rights reserved.

Anomaly Detection in Dynamic Networks

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

Turcotte, Melissa

2014-10-14

Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. Amore » second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the communication counts. In a sequential analysis, anomalous behavior is then identified from outlying behavior with respect to the fitted predictive probability models. Seasonality is again incorporated into the model and is treated as a changepoint model on the transition probabilities of a discrete time Markov process. Second stage analytics are then developed which combine anomalous edges to identify anomalous substructures in the network.« less

Speaker-dependent Multipitch Tracking Using Deep Neural Networks

DTIC Science & Technology

2015-01-01

connections through time. Studies have shown that RNNs are good at modeling sequential data like handwriting [12] and speech [26]. We plan to explore RNNs in...Schmidhuber, and S. Fernández, “Unconstrained on-line handwriting recognition with recurrent neural networks,” in Proceedings of NIPS, 2008, pp. 577–584. [13

A mathematical programming approach for sequential clustering of dynamic networks

NASA Astrophysics Data System (ADS)

Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

2016-02-01

A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

Implementation of logic functions and computations by chemical kinetics

NASA Astrophysics Data System (ADS)

Hjelmfelt, A.; Ross, J.

We review our work on the computational functions of the kinetics of chemical networks. We examine spatially homogeneous networks which are based on prototypical reactions occurring in living cells and show the construction of logic gates and sequential and parallel networks. This work motivates the study of an important biochemical pathway, glycolysis, and we demonstrate that the switch that controls the flux in the direction of glycolysis or gluconeogenesis may be described as a fuzzy AND operator. We also study a spatially inhomogeneous network which shares features of theoretical and biological neural networks.

Airborne Network Data Availability Using Peer to Peer Database Replication on a Distributed Hash Table

DTIC Science & Technology

2013-03-01

DSR Dynamic Source Routing DSSS Direct -sequence spread spectrum GUID Globally Unique ID MANET Mobile Ad-hoc Network NS3 Network Simulator 3 OLSR...networking schemes for safe maneuvering and data communication. Imagine needing to maintain an operational picture of an overall environment using a...as simple as O(n) where every node is sequentially queried to O log(n), or O(1). These schemes will be discussed with each individual DHT. Four of the

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

PubMed Central

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. PMID:28146106

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

PubMed

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-30

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

Stimulus information stored in lasting active and hidden network states is destroyed by network bursts

PubMed Central

Dranias, Mark R.; Westover, M. Brandon; Cash, Sidney; VanDongen, Antonius M. J.

2015-01-01

In both humans and animals brief synchronizing bursts of epileptiform activity known as interictal epileptiform discharges (IEDs) can, even in the absence of overt seizures, cause transient cognitive impairments (TCI) that include problems with perception or short-term memory. While no evidence from single units is available, it has been assumed that IEDs destroy information represented in neuronal networks. Cultured neuronal networks are a model for generic cortical microcircuits, and their spontaneous activity is characterized by the presence of synchronized network bursts (SNBs), which share a number of properties with IEDs, including the high degree of synchronization and their spontaneous occurrence in the absence of an external stimulus. As a model approach to understanding the processes underlying IEDs, optogenetic stimulation and multielectrode array (MEA) recordings of cultured neuronal networks were used to study whether stimulus information represented in these networks survives SNBs. When such networks are optically stimulated they encode and maintain stimulus information for as long as one second. Experiments involved recording the network response to a single stimulus and trials where two different stimuli were presented sequentially, akin to a paired pulse trial. We broke the sequential stimulus trials into encoding, delay and readout phases and found that regardless of which phase the SNB occurs, stimulus-specific information was impaired. SNBs were observed to increase the mean network firing rate, but this did not translate monotonically into increases in network entropy. It was found that the more excitable a network, the more stereotyped its response was during a network burst. These measurements speak to whether SNBs are capable of transmitting information in addition to blocking it. These results are consistent with previous reports and provide baseline predictions concerning the neural mechanisms by which IEDs might cause TCI. PMID:25755638

Energy-aware virtual network embedding in flexi-grid optical networks

NASA Astrophysics Data System (ADS)

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng; Chen, Bin

2018-01-01

Virtual network embedding (VNE) problem is to map multiple heterogeneous virtual networks (VN) on a shared substrate network, which mitigate the ossification of the substrate network. Meanwhile, energy efficiency has been widely considered in the network design. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the power increment of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low energy consumption. Numerical results show the functionality of the heuristic algorithm in a 24-node network.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

NASA Astrophysics Data System (ADS)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

A Mixed Methods Study on Teachers' Perceptions of Readiness of Higher Education Institutions to the Implementation of the K-12 Curriculum

ERIC Educational Resources Information Center

Acosta, Imee C.; Acosta, Alexander S.

2017-01-01

The Philippine Educational System is undergoing a major overhaul that shifts from a 10-year education to 12 years known as Enhanced Basic Education Curriculum or K-12. The purpose of this mixed-methods sequential explanatory study was to identify factors that determine readiness of select higher education institutions to the full implementation of…

A Systems Engineering Survey of Artificial Intelligence and Smart Sensor Networks in a Network-Centric Environment

DTIC Science & Technology

2009-09-01

problems, to better model the problem solving of computer systems. This research brought about the intertwining of AI and cognitive psychology . Much of...where symbol sequences are sequential intelligent states of the network, and must be classified as normal, abnormal , or unknown. These symbols...is associated with abnormal behavior; and abcbc is associated with unknown behavior, as it fits no known behavior. Predicted outcomes from

Structure-Property Relationships in Polycyanurate / Graphene Networks

DTIC Science & Technology

2015-12-12

Graphene Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) A. J. Guenthner, J. T. Reams, K. R. Lamison, G...Polycyanurate / Graphene Networks Andrew J. Guenthner,1 Josiah T. Reams,2 Kevin R. Lamison,2 Gregory R. Yandek,1 David D. Swanson,1 Joseph M. Mabry1...Motivation • Sequentially Prepared Graphene Types • Polycyanurate / GO Composite Preparation • Composite Morphology • Composite Mechanical and Physical

Molecular Characterization of Macrophage-Biomaterial Interactions

PubMed Central

Moore, Laura Beth; Kyriakides, Themis R.

2015-01-01

Implantation of biomaterials in vascularized tissues elicits the sequential engagement of molecular and cellular elements that constitute the foreign body response. Initial events include the non-specific adsorption of proteins to the biomaterial surface that render it adhesive for cells such as neutrophils and macrophages. The latter undergo unique activation and in some cases undergo cell-cell fusion to form foreign body giant cells that contribute to implant damage and fibrotic encapsulation. In this review, we discuss the molecular events that contribute to macrophage activation and fusion with a focus on the role of the inflammasome, signaling pathways such as JAK/STAT and NF-κB, and the putative involvement of micro RNAs in the regulation of these processes. PMID:26306446

Molecular Characterization of Macrophage-Biomaterial Interactions.

PubMed

Moore, Laura Beth; Kyriakides, Themis R

2015-01-01

Implantation of biomaterials in vascularized tissues elicits the sequential engagement of molecular and cellular elements that constitute the foreign body response. Initial events include the non-specific adsorption of proteins to the biomaterial surface that render it adhesive for cells such as neutrophils and macrophages. The latter undergo unique activation and in some cases undergo cell-cell fusion to form foreign body giant cells that contribute to implant damage and fibrotic encapsulation. In this review, we discuss the molecular events that contribute to macrophage activation and fusion with a focus on the role of the inflammasome, signaling pathways such as JAK/STAT and NF-κB, and the putative involvement of micro RNAs in the regulation of these processes.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Attitudes toward Using Social Networking Sites in Educational Settings with Underperforming Latino Youth: A Mixed Methods Study

ERIC Educational Resources Information Center

Howard, Keith E.; Curwen, Margie Sauceda; Howard, Nicol R.; Colón-Muñiz, Anaida

2015-01-01

The researchers examined the online social networking attitudes of underperforming Latino high school students in an alternative education program that uses technology as the prime venue for learning. A sequential explanatory mixed methods study was used to cross-check multiple sources of data explaining students' levels of comfort with utilizing…

An intrinsically disordered photosystem II subunit, PsbO, provides a structural template and a sensor of the hydrogen-bonding network in photosynthetic water oxidation.

PubMed

Offenbacher, Adam R; Polander, Brandon C; Barry, Bridgette A

2013-10-04

Photosystem II (PSII) is a membrane-bound enzyme that utilizes solar energy to catalyze the photooxidation of water. Molecular oxygen is evolved after four sequential light-driven oxidation reactions at the Mn4CaO5 oxygen-evolving complex, producing five sequentially oxidized states, Sn. PSII is composed of 17 membrane-spanning subunits and three extrinsic subunits, PsbP, PsbQ, and PsbO. PsbO is intrinsically disordered and plays a role in facilitation of the water oxidizing cycle. Native PsbO can be removed and substituted with recombinant PsbO, thereby restoring steady-state activity. In this report, we used reaction-induced Fourier transform infrared spectroscopy to obtain information concerning the role of PsbP, PsbQ, and PsbO during the S state cycle. Light-minus-dark difference spectra were acquired, monitoring structural changes associated with each accessible flash-induced S state transition in a highly purified plant PSII preparation (Triton X-100, octylthioglucoside). A comparison of S2 minus S1 spectra revealed that removal of PsbP and PsbQ had no significant effect on the data, whereas amide frequency and intensity changes were associated with PsbO removal. These data suggest that PsbO acts as an organizational template for the PSII reaction center. To identify any coupled conformational changes arising directly from PsbO, global (13)C-PsbO isotope editing was employed. The reaction-induced Fourier transform infrared spectra of accessible S states provide evidence that PsbO spectral contributions are temperature (263 and 277 K) and S state dependent. These experiments show that PsbO undergoes catalytically relevant structural dynamics, which are coupled over long distance to hydrogen-bonding changes at the Mn4CaO5 cluster.

Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings.

PubMed

Lu, Qing; Bi, Kun; Liu, Chu; Luo, Guoping; Tang, Hao; Yao, Zhijian

2013-10-16

Abnormal inter-regional causalities can be mapped for the objective diagnosis of various diseases. These inter-regional connectivities are usually calculated over an entire scan and used to characterize the stationary strength of the connections. However, the connectivity within networks may undergo substantial changes during a scan. In this study, we developed an objective depression recognition approach using the dynamic regional interactions that occur in response to sad facial stimuli. The whole time-period magnetoencephalography (MEG) signals from the visual cortex, amygdala, anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG) were separated into sequential time intervals. The Granger causality mapping method was used to identify the pairwise interaction pattern within each time interval. Feature selection was then undertaken within a minimum redundancy-maximum relevance (mRMR) framework. Typical classifiers were utilized to predict those patients who had depression. The overall performances of these classifiers were similar, and the highest classification accuracy rate was 87.5%. The best discriminative performance was obtained when the number of features was within a robust range. The discriminative network pattern obtained through support vector machine (SVM) analyses displayed abnormal causal connectivities that involved the amygdala during the early and late stages. These early and late connections in the amygdala appear to reveal a negative bias to coarse expression information processing and abnormal negative modulation in patients with depression, which may critically affect depression discrimination. © 2013 Elsevier B.V. All rights reserved.

Near real-time adverse drug reaction surveillance within population-based health networks: methodology considerations for data accrual.

PubMed

Avery, Taliser R; Kulldorff, Martin; Vilk, Yury; Li, Lingling; Cheetham, T Craig; Dublin, Sascha; Davis, Robert L; Liu, Liyan; Herrinton, Lisa; Brown, Jeffrey S

2013-05-01

This study describes practical considerations for implementation of near real-time medical product safety surveillance in a distributed health data network. We conducted pilot active safety surveillance comparing generic divalproex sodium to historical branded product at four health plans from April to October 2009. Outcomes reported are all-cause emergency room visits and fractures. One retrospective data extract was completed (January 2002-June 2008), followed by seven prospective monthly extracts (January 2008-November 2009). To evaluate delays in claims processing, we used three analytic approaches: near real-time sequential analysis, sequential analysis with 1.5 month delay, and nonsequential (using final retrospective data). Sequential analyses used the maximized sequential probability ratio test. Procedural and logistical barriers to active surveillance were documented. We identified 6586 new users of generic divalproex sodium and 43,960 new users of the branded product. Quality control methods identified 16 extract errors, which were corrected. Near real-time extracts captured 87.5% of emergency room visits and 50.0% of fractures, which improved to 98.3% and 68.7% respectively with 1.5 month delay. We did not identify signals for either outcome regardless of extract timeframe, and slight differences in the test statistic and relative risk estimates were found. Near real-time sequential safety surveillance is feasible, but several barriers warrant attention. Data quality review of each data extract was necessary. Although signal detection was not affected by delay in analysis, when using a historical control group differential accrual between exposure and outcomes may theoretically bias near real-time risk estimates towards the null, causing failure to detect a signal. Copyright © 2013 John Wiley & Sons, Ltd.

Tuning the Brake While Raising the Stake: Network Dynamics during Sequential Decision-Making.

PubMed

Meder, David; Haagensen, Brian Numelin; Hulme, Oliver; Morville, Tobias; Gelskov, Sofie; Herz, Damian Marc; Diomsina, Beata; Christensen, Mark Schram; Madsen, Kristoffer Hougaard; Siebner, Hartwig Roman

2016-05-11

When gathering valued goods, risk and reward are often coupled and escalate over time, for instance, during foraging, trading, or gambling. This escalating frame requires agents to continuously balance expectations of reward against those of risk. To address how the human brain dynamically computes these tradeoffs, we performed whole-brain fMRI while healthy young individuals engaged in a sequential gambling task. Participants were repeatedly confronted with the option to continue with throwing a die to accumulate monetary reward under escalating risk, or the alternative option to stop to bank the current balance. Within each gambling round, the accumulation of gains gradually increased reaction times for "continue" choices, indicating growing uncertainty in the decision to continue. Neural activity evoked by "continue" choices was associated with growing activity and connectivity of a cortico-subcortical "braking" network that positively scaled with the accumulated gains, including pre-supplementary motor area (pre-SMA), inferior frontal gyrus, caudate, and subthalamic nucleus (STN). The influence of the STN on continue-evoked activity in the pre-SMA was predicted by interindividual differences in risk-aversion attitudes expressed during the gambling task. Furthermore, activity in dorsal anterior cingulate cortex (ACC) reflected individual choice tendencies by showing increased activation when subjects made nondefault "continue" choices despite an increasing tendency to stop, but ACC activity did not change in proportion with subjective choice uncertainty. Together, the results implicate a key role of dorsal ACC, pre-SMA, inferior frontal gyrus, and STN in computing the trade-off between escalating reward and risk in sequential decision-making. Using a paradigm where subjects experienced increasing potential rewards coupled with increasing risk, this study addressed two unresolved questions in the field of decision-making: First, we investigated an "inhibitory" network of regions that has so far been investigated with externally cued action inhibition. In this study, we show that the dynamics in this network under increasingly risky decisions are predictive of subjects' risk attitudes. Second, we contribute to a currently ongoing debate about the anterior cingulate cortex's role in sequential foraging decisions by showing that its activity is related to making nondefault choices rather than to choice uncertainty. Copyright © 2016 Meder, Haagensen, et al.

Enantioselective Synthesis of Aminodiols by Sequential Rhodium-Catalysed Oxyamination/Kinetic Resolution: Expanding the Substrate Scope of Amidine-Based Catalysis.

PubMed

Guasch, Joan; Giménez-Nueno, Irene; Funes-Ardoiz, Ignacio; Bernús, Miguel; Matheu, M Isabel; Maseras, Feliu; Castillón, Sergio; Díaz, Yolanda

2018-03-26

Regio- and stereoselective oxyamination of dienes through a tandem rhodium-catalysed aziridination-nucleophilic opening affords racemic oxazolidinone derivatives, which undergo a kinetic resolution acylation process with amidine-based catalysts (ABCs) to achieve s values of up to 117. This protocol was applied to the enantioselective synthesis of sphingosine. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Initial experience of using high field strength intraoperative MRI for neurosurgical procedures.

PubMed

Raheja, Amol; Tandon, Vivek; Suri, Ashish; Sarat Chandra, P; Kale, Shashank S; Garg, Ajay; Pandey, Ravindra M; Kalaivani, Mani; Mahapatra, Ashok K; Sharma, Bhawani S

2015-08-01

We report our initial experience to optimize neurosurgical procedures using high field strength intraoperative magnetic resonance imaging (IOMRI) in 300 consecutive patients as high field strength IOMRI rapidly becomes the standard of care for neurosurgical procedures. Three sequential groups (groups A, B, C; n=100 each) were compared with respect to time management, complications and technical difficulties to assess improvement in these parameters with experience. We observed a reduction in the number of technical difficulties (p<0.001), time to induction (p<0.001) and total anesthesia time (p=0.007) in sequential groups. IOMRI was performed for neuronavigation guidance (n=252) and intraoperative validation of extent of resection (EOR; n=67). Performing IOMRI increased the EOR over and beyond the primary surgical attempt in 20.5% (29/141) and 18% (11/61) of patients undergoing glioma and pituitary surgery, respectively. Overall, EOR improved in 59.7% of patients undergoing IOMRI (40/67). Intraoperative tractography and real time navigation using re-uploaded IOMRI images (accounting for brain shift) helps in intraoperative planning to reduce complications. IOMRI is an asset to neurosurgeons, helping to augment the EOR, especially in glioma and pituitary surgery, with no significant increase in morbidity to the patient. Copyright © 2015 Elsevier Ltd. All rights reserved.

Efficient algorithm for locating and sizing series compensation devices in large power transmission grids: II. Solutions and applications

DOE PAGES

Frolov, Vladimir; Backhaus, Scott; Chertkov, Misha

2014-10-01

In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~ 2700 nodes and ~ 3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polishmore » grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements.« less

Efficient Algorithm for Locating and Sizing Series Compensation Devices in Large Transmission Grids: Solutions and Applications (PART II)

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

Frolov, Vladimir; Backhaus, Scott N.; Chertkov, Michael

2014-01-14

In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~2700 nodes and ~3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid ismore » used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements« less

UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection

PubMed Central

Sadeque, Farig; Xu, Dongfang; Bethard, Steven

2017-01-01

The 2017 CLEF eRisk pilot task focuses on automatically detecting depression as early as possible from a users’ posts to Reddit. In this paper we present the techniques employed for the University of Arizona team’s participation in this early risk detection shared task. We leveraged external information beyond the small training set, including a preexisting depression lexicon and concepts from the Unified Medical Language System as features. For prediction, we used both sequential (recurrent neural network) and non-sequential (support vector machine) models. Our models perform decently on the test data, and the recurrent neural models perform better than the non-sequential support vector machines while using the same feature sets. PMID:29075167

Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery.

PubMed

Spiegelhalter, David; Grigg, Olivia; Kinsman, Robin; Treasure, Tom

2003-02-01

To investigate the use of the risk-adjusted sequential probability ratio test in monitoring the cumulative occurrence of adverse clinical outcomes. Retrospective analysis of three longitudinal datasets. Patients aged 65 years and over under the care of Harold Shipman between 1979 and 1997, patients under 1 year of age undergoing paediatric heart surgery in Bristol Royal Infirmary between 1984 and 1995, adult patients receiving cardiac surgery from a team of cardiac surgeons in London,UK. Annual and 30-day mortality rates. Using reasonable boundaries, the procedure could have indicated an 'alarm' in Bristol after publication of the 1991 Cardiac Surgical Register, and in 1985 or 1997 for Harold Shipman depending on the data source and the comparator. The cardiac surgeons showed no significant deviation from expected performance. The risk-adjusted sequential probability test is simple to implement, can be applied in a variety of contexts, and might have been useful to detect specific instances of past divergent performance. The use of this and related techniques deserves further attention in the context of prospectively monitoring adverse clinical outcomes.

Chloroethylating nitrosoureas in cancer therapy: DNA damage, repair and cell death signaling.

PubMed

Nikolova, Teodora; Roos, Wynand P; Krämer, Oliver H; Strik, Herwig M; Kaina, Bernd

2017-08-01

Chloroethylating nitrosoureas (CNU), such as lomustine, nimustine, semustine, carmustine and fotemustine are used for the treatment of malignant gliomas, brain metastases of different origin, melanomas and Hodgkin disease. They alkylate the DNA bases and give rise to the formation of monoadducts and subsequently interstrand crosslinks (ICL). ICL are critical cytotoxic DNA lesions that link the DNA strands covalently and block DNA replication and transcription. As a result, S phase progression is inhibited and cells are triggered to undergo apoptosis and necrosis, which both contribute to the effectiveness of CNU-based cancer therapy. However, tumor cells resist chemotherapy through the repair of CNU-induced DNA damage. The suicide enzyme O 6 -methylguanine-DNA methyltransferase (MGMT) removes the precursor DNA lesion O 6 -chloroethylguanine prior to its conversion into ICL. In cells lacking MGMT, the formed ICL evoke complex enzymatic networks to accomplish their removal. Here we discuss the mechanism of ICL repair as a survival strategy of healthy and cancer cells and DNA damage signaling as a mechanism contributing to CNU-induced cell death. We also discuss therapeutic implications and strategies based on sequential and simultaneous treatment with CNU and the methylating drug temozolomide. Copyright © 2017 Elsevier B.V. All rights reserved.

Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

PubMed

Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

2017-08-01

Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

Research on aviation unsafe incidents classification with improved TF-IDF algorithm

NASA Astrophysics Data System (ADS)

Wang, Yanhua; Zhang, Zhiyuan; Huo, Weigang

2016-05-01

The text content of Aviation Safety Confidential Reports contains a large number of valuable information. Term frequency-inverse document frequency algorithm is commonly used in text analysis, but it does not take into account the sequential relationship of the words in the text and its role in semantic expression. According to the seven category labels of civil aviation unsafe incidents, aiming at solving the problems of TF-IDF algorithm, this paper improved TF-IDF algorithm based on co-occurrence network; established feature words extraction and words sequential relations for classified incidents. Aviation domain lexicon was used to improve the accuracy rate of classification. Feature words network model was designed for multi-documents unsafe incidents classification, and it was used in the experiment. Finally, the classification accuracy of improved algorithm was verified by the experiments.

Efficacy of Helicobacter pylori eradication therapies in Korea: A systematic review and network meta-analysis.

PubMed

Jung, Yoon Suk; Park, Chan Hyuk; Park, Jung Ho; Nam, Eunwoo; Lee, Hang Lak

2017-08-01

The efficacy of Helicobacter pylori eradication regimens may depend on the country where the studies were performed because of the difference in antibiotic resistance. We aimed to analyze the efficacy of H. pylori eradication regimens in Korea where clarithromycin resistance rate is high. We searched for all relevant randomized controlled trials published until November 2016 that investigated the efficacy of H. pylori eradication therapies in Korea. A network meta-analysis was performed to calculate the direct and indirect estimates of efficacy among the eradication regimens. Forty-three studies were identified through a systematic review, of which 34 studies, published since 2005, were included in the meta-analysis. Among 21 included regimens, quinolone-containing sequential therapy for 14 days (ST-Q-14) showed the highest eradication rate (91.4% [95% confidence interval [CI], 86.9%-94.4%] in the intention-to-treat [ITT] analysis). The eradication rate of the conventional triple therapy for 7 days, standard sequential therapy for 10 days, hybrid therapy for 10-14 days, and concomitant therapy for 10-14 days was 71.1% (95% CI, 68.3%-73.7%), 76.2% (95% CI, 72.8%-79.3%), 79.4% (95% CI, 75.5%-82.8%), and 78.3% (95% CI, 75.3%-80.9%), respectively, in the ITT analysis. In the network meta-analysis, ST-Q-14 showed a better comparative efficacy than the conventional triple therapy, standard sequential therapy, hybrid therapy, and concomitant therapy. In addition, tolerability of ST-Q-14 was comparable to those regimens. In Korea, ST-Q-14 showed the highest efficacy in terms of eradication and a comparable tolerability, compared to the results reported for the conventional triple therapy, standard sequential therapy, hybrid therapy, and concomitant therapy. © 2017 John Wiley & Sons Ltd.

Exploring Student Use of Social Networking Services (SNS) Surrounding Moral Development, Gender, Campus Crime, Safety, and the Clery Act: A Mixed Methods Study

ERIC Educational Resources Information Center

Baum, Haley

2017-01-01

The purpose of this explanatory sequential mixed methods study was to explore college students' use of social networking services (SNS); examining how and why they communicate about campus safety information. This study took place at Stockton University, a regional state institution in NJ. Undergraduate students took part in an online quantitative…

Abnormal Functional Activation and Connectivity in the Working Memory Network in Early-Onset Schizophrenia

ERIC Educational Resources Information Center

Kyriakopoulos, Marinos; Dima, Danai; Roiser, Jonathan P.; Corrigall, Richard; Barker, Gareth J.; Frangou, Sophia

2012-01-01

Objective: Disruption within the working memory (WM) neural network is considered an integral feature of schizophrenia. The WM network, and the dorsolateral prefrontal cortex (DLPFC) in particular, undergo significant remodeling in late adolescence. Potential interactions between developmental changes in the WM network and disease-related…

What Presidents Need To Know about the Impact of Networking.

ERIC Educational Resources Information Center

Leadership Abstracts, 1993

1993-01-01

Many colleges and universities are undergoing cultural changes as a result of extensive voice, data, and video networking. Local area networks link large portions of most campuses, and national networks have evolved from specialized services for researchers in computer-related disciplines to general utilities on many campuses. Campuswide systems…

Inverse sequential detection of parameter changes in developing time series

NASA Technical Reports Server (NTRS)

Radok, Uwe; Brown, Timothy J.

1992-01-01

Progressive values of two probabilities are obtained for parameter estimates derived from an existing set of values and from the same set enlarged by one or more new values, respectively. One probability is that of erroneously preferring the second of these estimates for the existing data ('type 1 error'), while the second probability is that of erroneously accepting their estimates for the enlarged test ('type 2 error'). A more stable combined 'no change' probability which always falls between 0.5 and 0 is derived from the (logarithmic) width of the uncertainty region of an equivalent 'inverted' sequential probability ratio test (SPRT, Wald 1945) in which the error probabilities are calculated rather than prescribed. A parameter change is indicated when the compound probability undergoes a progressive decrease. The test is explicitly formulated and exemplified for Gaussian samples.

New tracking implementation in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.

2001-01-01

As part of the Network Simplification Project, the tracking system of the Deep Space Network is being upgraded. This upgrade replaces the discrete logic sequential ranging system with a system that is based on commercial Digital Signal Processor boards. The new implementation allows both sequential and pseudo-noise types of ranging. The other major change is a modernization of the data formatting. Previously, there were several types of interfaces, delivering both intermediate data and processed data (called 'observables'). All of these interfaces were bit-packed blocks, which do not allow for easy expansion, and many of these interfaces required knowledge of the specific hardware implementations. The new interface supports four classes of data: raw (direct from the measuring equipment), derived (the observable data), interferometric (multiple antenna measurements), and filtered (data whose values depend on multiple measurements). All of the measurements are reported at the sky frequency or phase level, so that no knowledge of the actual hardware is required. The data is formatted into Standard Formatted Data Units, as defined by the Consultative Committee for Space Data Systems, so that expansion and cross-center usage is greatly enhanced.

Timed sequential chemotherapy of cytoxan-refractory multiple myeloma with cytoxan and adriamycin based on induced tumor proliferation.

PubMed

Karp, J E; Humphrey, R L; Burke, P J

1981-03-01

Malignant plasma cell proliferation and induced humoral stimulatory activity (HSA) occur in vivo at a predictable time following drug administration. Sequential sera from 11 patients with poor-risk multiple myeloma (MM) undergoing treatment with Cytoxan (CY) 2400 mq/sq m were assayed for their in vitro effects on malignant bone marrow plasma cell tritiated thymidine (3HTdR) incorporation. Peak HSA was detected day 9 following CY. Sequential changes in marrow malignant plasma cell 3HTdR-labeling indices (LI) paralleled changes in serum activity, with peak LI occurring at the time of peak HS. An in vitro model of chemotherapy demonstrated that malignant plasma cell proliferation was enhanced by HSA, as determined by 3HTdR incorporation assay, 3HTdR LI, and tumor cells counts, and that stimulated plasma cells were more sensitive to cytotoxic effects of adriamycin (ADR) than were cells cultured in autologous pretreatment serum. Based on these studies, we designed a clinical trial to treat 12 CY-refractory poor-risk patients with MM in which ADR (60 mg/sq m) was administered at the time of peak HSA and residual tumor cell LI (day 9) following initial CY, 2400 mg/m (CY1ADR9). Eight of 12 (67%) responded to timed sequential chemotherapy with a greater than 50% decrement in monoclonal protein marker and a median survival projected to be greater than 8 mo duration (range 4-21+ mo). These clinical results using timed sequential CY1ADR9 compare favorably with results obtained using ADR in nonsequential chemotherapeutic regimens.

Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay

PubMed Central

Mahoney, J. Matthew; Titiz, Ali S.; Hernan, Amanda E.; Scott, Rod C.

2016-01-01

Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation. PMID:26866597

Dealing with uncertainty in modeling intermittent water supply

NASA Astrophysics Data System (ADS)

Lieb, A. M.; Rycroft, C.; Wilkening, J.

2015-12-01

Intermittency in urban water supply affects hundreds of millions of people in cities around the world, impacting water quality and infrastructure. Building on previous work to dynamically model the transient flows in water distribution networks undergoing frequent filling and emptying, we now consider the hydraulic implications of uncertain input data. Water distribution networks undergoing intermittent supply are often poorly mapped, and household metering frequently ranges from patchy to nonexistent. In the face of uncertain pipe material, pipe slope, network connectivity, and outflow, we investigate how uncertainty affects dynamical modeling results. We furthermore identify which parameters exert the greatest influence on uncertainty, helping to prioritize data collection.

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

PubMed

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules

NASA Astrophysics Data System (ADS)

Menon, Govind; Krishnan, J.

2016-07-01

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Bridging the gap between modules in isolation and as part of networks: A systems framework for elucidating interaction and regulation of signalling modules.

PubMed

Menon, Govind; Krishnan, J

2016-07-21

While signalling and biochemical modules have been the focus of numerous studies, they are typically studied in isolation, with no examination of the effects of the ambient network. In this paper we formulate and develop a systems framework, rooted in dynamical systems, to understand such effects, by studying the interaction of signalling modules. The modules we consider are (i) basic covalent modification, (ii) monostable switches, (iii) bistable switches, (iv) adaptive modules, and (v) oscillatory modules. We systematically examine the interaction of these modules by analyzing (a) sequential interaction without shared components, (b) sequential interaction with shared components, and (c) oblique interactions. Our studies reveal that the behaviour of a module in isolation may be substantially different from that in a network, and explicitly demonstrate how the behaviour of a given module, the characteristics of the ambient network, and the possibility of shared components can result in new effects. Our global approach illuminates different aspects of the structure and functioning of modules, revealing the importance of dynamical characteristics as well as biochemical features; this provides a methodological platform for investigating the complexity of natural modules shaped by evolution, elucidating the effects of ambient networks on a module in multiple cellular contexts, and highlighting the capabilities and constraints for engineering robust synthetic modules. Overall, such a systems framework provides a platform for bridging the gap between non-linear information processing modules, in isolation and as parts of networks, and a basis for understanding new aspects of natural and engineered cellular networks.

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

DOE PAGES

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia; ...

2017-09-05

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

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

Mudunuru, Maruti Kumar; Karra, Satish; Makedonska, Nataliia

Subsurface applications, including geothermal, geological carbon sequestration, and oil and gas, typically involve maximizing either the extraction of energy or the storage of fluids. Fractures form the main pathways for flow in these systems, and locating these fractures is critical for predicting flow. However, fracture characterization is a highly uncertain process, and data from multiple sources, such as flow and geophysical are needed to reduce this uncertainty. We present a nonintrusive, sequential inversion framework for integrating data from geophysical and flow sources to constrain fracture networks in the subsurface. In this framework, we first estimate bounds on the statistics formore » the fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained using flow data. In conclusion, the efficacy of this inversion is demonstrated through a representative example.« less

Emergence of clustering in an acquaintance model without homophily

NASA Astrophysics Data System (ADS)

Bhat, Uttam; Krapivsky, P. L.; Redner, S.

2014-11-01

We introduce an agent-based acquaintance model in which social links are created by processes in which there is no explicit homophily. In spite of the homogeneous nature of the social interactions, highly-clustered social networks can arise. The crucial feature of our model is that of variable transitive interactions. Namely, when an agent introduces two unconnected friends, the rate at which a connection actually occurs between them depends on the number of their mutual acquaintances. As this transitive interaction rate is varied, the social network undergoes a dramatic clustering transition. Close to the transition, the network consists of a collection of well-defined communities. As a function of time, the network can also undergo an incomplete gelation transition, in which the gel, or giant cluster, does not constitute the entire network, even at infinite time. Some of the clustering properties of our model also arise, but in a more gradual manner, in Facebook networks. Finally, we discuss a more realistic variant of our original model in which network realizations can be constructed that quantitatively match Facebook networks.

Rural women and violence situation: access and accessibility limits to the healthcare network.

PubMed

Costa, Marta Cocco da; Silva, Ethel Bastos da; Soares, Joannie Dos Santos Fachinelli; Borth, Luana Cristina; Honnef, Fernanda

2017-07-13

To analyze the access and accessibility to the healthcare network of women dwelling in rural contexts undergoing violence situation, as seen from the professionals' speeches. A qualitative, exploratory, descriptive study with professionals from the healthcare network services about coping with violence in four municipalities in the northern region of Rio Grande do Sul. The information derived from interviews, which have been analyzed by thematic modality. (Lack of) information of women, distance, restricted access to transportation, dependence on the partner and (lack of) attention by professionals to welcome women undergoing violence situation and (non)-articulation of the network are factors that limit the access and, as a consequence, they result in the lack of confrontation of this problem. To bring closer the services which integrate the confrontation network of violence against women and to qualify professionals to welcome these situations are factors that can facilitate the access and adhesion of rural women to the services.

An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

PubMed Central

Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Yepes, Víctor

2014-01-01

Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach. PMID:24741352

On resilience studies of system detection and recovery techniques against stealthy insider attacks

NASA Astrophysics Data System (ADS)

Wei, Sixiao; Zhang, Hanlin; Chen, Genshe; Shen, Dan; Yu, Wei; Pham, Khanh D.; Blasch, Erik P.; Cruz, Jose B.

2016-05-01

With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.

An iterative approach for the optimization of pavement maintenance management at the network level.

PubMed

Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Pellicer, Eugenio; Yepes, Víctor

2014-01-01

Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach.

Thromboprophylaxis With Apixaban in Patients Undergoing Major Orthopedic Surgery: Meta-Analysis and Trial-Sequential Analysis.

PubMed

Caldeira, Daniel; Rodrigues, Filipe B; Pinto, Fausto J; Ferreira, Joaquim J; Costa, João

2017-01-01

Venous thromboembolism (VTE) is a potentially fatal complication of orthopedic surgery, and until recently, few antithrombotic compounds were available for postoperative thromboprophylaxis. The introduction of the non-vitamin K antagonists oral anticoagulants (NOAC), including apixaban, has extended the therapeutic armamentarium in this field. Therefore, estimation of NOAC net clinical benefit in comparison with the established treatment is needed to inform clinical decision making. Systematic review to assess the efficacy and safety of apixaban 2.5 mg twice a day versus low-molecular-weight heparins (LMWH) for thromboprophylaxis in patients undergoing knee or hip replacement. MEDLINE, Embase, and CENTRAL were searched from inception to September 2016, other systematic reviews, reference lists, and experts were consulted. All major orthopedic surgery randomized controlled trials comparing apixaban 2.5 mg twice daily with LMWH, reporting thrombotic and bleeding events. Two independent reviewers, using a predetermined form. The Cochrane tool to assess risk bias was used by two independent authors. RevMan software was used to estimate pooled risk ratio (RR) and 95% confidence intervals (95% CI) using random-effects meta-analysis. Trial sequential analysis (TSA) was performed in statistical significant results to evaluate whether cumulative sample size was powered for the obtained effect. Overall confidence in cumulative evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group methodology. Four studies comparing apixaban 2.5 mg twice daily with LMWH were included, with a total of 11.828 patients (55% undergoing knee and 45% hip replacement). The overall risk of bias across studies was low. In comparison with LMWH (all regimens), apixaban showed a significantly lower risk of VTE events and overall mortality combined (RR: 0.63, 95% CI: 0.42-0.95, I 2 = 84%, n = 8346), but not of major VTE events (RR: 0.62, 95% CI: 0.32-1.19, I 2 = 63%, n = 9493), or of symptomatic VTE events and VTE-related mortality combined (RR: 1.14, 95% CI: 0.68-1.90, I 2 = 0%, n = 11 879). Trial sequential analysis showed that the risk reduction obtained for VTE and mortality was based on underpowered cumulative sample size and effect dimension. Subgroup analysis according to LMWH regimens showed that apixaban reduced the risk of VTE events and overall mortality, and major VTE events, when compared with LMWH once daily, without differences between apixaban and LMWH twice daily. There is low to moderate evidence that in patients undergoing knee or hip replacement, apixaban seems equally effective and safe to LMWH twice a day. When compared with LMWH once a day, apixaban seems a superior thromboprophylaxis option. However, the results are underpowered which precludes definite answers regarding the true net clinical benefit of apixaban versus LMWH in this clinical context.

Energy-aware virtual network embedding in flexi-grid networks.

PubMed

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Genomic profiling of multiple sequentially acquired tumor metastatic sites from an “exceptional responder” lung adenocarcinoma patient reveals extensive genomic heterogeneity and novel somatic variants driving treatment response. | Center for Cancer Research

Cancer.gov

Biswas et al. describe an “exceptional responder” lung adenocarcinoma patient who survived with metastatic lung adenocarcinoma for 7 years while undergoing single or combination ERBB2-directed therapies. Whole-genome, whole-exome, and high-coverage ion-torrent targeted sequencing were used to demonstrate extreme genomic heterogeneity between the lung and lymph node metastatic

Serial-to-parallel color-TV converter

NASA Technical Reports Server (NTRS)

Doak, T. W.; Merwin, R. B.; Zuckswert, S. E.; Sepper, W.

1976-01-01

Solid analog-to-digital converter eliminates flicker and problems with time base stability and gain variation in sequential color TV cameras. Device includes 3-bit delta modulator; two-field memory; timing, switching, and sync network; and three 3-bit delta demodulators

The role of social and cognitive processes in the relationship between fear network and psychological distress among parents of children undergoing hematopoietic stem cell transplantation.

PubMed

Virtue, Shannon Myers; Manne, Sharon; Mee, Laura; Bartell, Abraham; Sands, Stephen; Ohman-Strickland, Pamela; Gajda, Tina Marie

2014-09-01

The current study examined whether cognitive and social processing variables mediated the relationship between fear network and depression among parents of children undergoing hematopoietic stem cell transplant (HSCT). Parents whose children were initiating HSCT (N = 179) completed survey measures including fear network, Beck Depression Inventory, cognitive processing variables (positive reappraisal and self-blame) and social processing variables (emotional support and holding back from sharing concerns). Fear network was positively correlated with depression (p < .001). Self-blame and holding back emerged as individual partial mediators in the relationship between fear network and depression. Together they accounted for 34.3% of the variance in the relationship between fear network and depression. Positive reappraisal and emotional support did not have significant mediating effects. Social and cognitive processes, specifically self-blame and holding back from sharing concerns, play a negative role in parents' psychological adaptation to fears surrounding a child's HSCT.

The Role of Social and Cognitive Processes in the Relationship between Fear Network and Psychological Distress among Parents of Children Undergoing Hematopoietic Stem Cell Transplantation

PubMed Central

Virtue, Shannon Myers; Manne, Sharon; Mee, Laura; Bartell, Abraham; Sands, Stephen; Ohman-Strickland, Pamela; Gajda, Tina Marie

2014-01-01

The current study examined whether cognitive and social processing variables mediated the relationship between fear network and depression among parents of children undergoing hematopoietic stem cell transplant (HSCT). Parents whose children were initiating HSCT (N = 179) completed survey measures including fear network, Beck Depression Inventory (BDI), cognitive processing variables (positive reappraisal and self-blame) and social processing variables (emotional support and holding back from sharing concerns). Fear network was positively correlated with depression (p < .001). Self-blame and holding back emerged as individual partial mediators in the relationship between fear network and depression. Together they accounted for 34.3% of the variance in the relationship between fear network and depression. Positive reappraisal and emotional support did not have significant mediating effects. Social and cognitive processes, specifically self-blame and holding back from sharing concerns, play a negative role in parents’ psychological adaptation to fears surrounding a child’s HSCT. PMID:25081956

Modulation of Limbic and Prefrontal Connectivity by Electroconvulsive Therapy in Treatment-resistant Depression: A Preliminary Study.

PubMed

Cano, Marta; Cardoner, Narcís; Urretavizcaya, Mikel; Martínez-Zalacaín, Ignacio; Goldberg, Ximena; Via, Esther; Contreras-Rodríguez, Oren; Camprodon, Joan; de Arriba-Arnau, Aida; Hernández-Ribas, Rosa; Pujol, Jesús; Soriano-Mas, Carles; Menchón, José M

2016-01-01

Although current models of depression suggest that a sequential modulation of limbic and prefrontal connectivity is needed for illness recovery, neuroimaging studies of electroconvulsive therapy (ECT) have focused on assessing functional connectivity (FC) before and after an ECT course, without characterizing functional changes occurring at early treatment phases. To assess sequential changes in limbic and prefrontal FC during the course of ECT and their impact on clinical response. Longitudinal intralimbic and limbic-prefrontal networks connectivity study. We assessed 15 patients with treatment-resistant depression at four different time-points throughout the entire course of an ECT protocol and 10 healthy participants at two functional neuroimaging examinations. Furthermore, a path analysis to test direct and indirect predictive effects of limbic and prefrontal FC changes on clinical response measured with the Hamilton Rating Scale for Depression was also performed. An early significant intralimbic FC decrease significantly predicted a later increase in limbic-prefrontal FC, which in turn significantly predicted clinical improvement at the end of an ECT course. Our data support that treatment response involves sequential changes in FC within regions of the intralimbic and limbic-prefrontal networks. This approach may help in identifying potential early biomarkers of treatment response. Copyright © 2015 Elsevier Inc. All rights reserved.

Efficient partitioning and assignment on programs for multiprocessor execution

NASA Technical Reports Server (NTRS)

Standley, Hilda M.

1993-01-01

The general problem studied is that of segmenting or partitioning programs for distribution across a multiprocessor system. Efficient partitioning and the assignment of program elements are of great importance since the time consumed in this overhead activity may easily dominate the computation, effectively eliminating any gains made by the use of the parallelism. In this study, the partitioning of sequentially structured programs (written in FORTRAN) is evaluated. Heuristics, developed for similar applications are examined. Finally, a model for queueing networks with finite queues is developed which may be used to analyze multiprocessor system architectures with a shared memory approach to the problem of partitioning. The properties of sequentially written programs form obstacles to large scale (at the procedure or subroutine level) parallelization. Data dependencies of even the minutest nature, reflecting the sequential development of the program, severely limit parallelism. The design of heuristic algorithms is tied to the experience gained in the parallel splitting. Parallelism obtained through the physical separation of data has seen some success, especially at the data element level. Data parallelism on a grander scale requires models that accurately reflect the effects of blocking caused by finite queues. A model for the approximation of the performance of finite queueing networks is developed. This model makes use of the decomposition approach combined with the efficiency of product form solutions.

Parallel discrete event simulation using shared memory

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1988-01-01

With traditional event-list techniques, evaluating a detailed discrete-event simulation-model can often require hours or even days of computation time. By eliminating the event list and maintaining only sufficient synchronization to ensure causality, parallel simulation can potentially provide speedups that are linear in the numbers of processors. A set of shared-memory experiments, using the Chandy-Misra distributed-simulation algorithm, to simulate networks of queues is presented. Parameters of the study include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential-simulation of most queueing network models.

Cooperative working of bacterial chromosome replication proteins generated by a reconstituted protein expression system

PubMed Central

Fujiwara, Kei; Katayama, Tsutomu; Nomura, Shin-ichiro M.

2013-01-01

Replication of all living cells relies on the multirounds flow of the central dogma. Especially, expression of DNA replication proteins is a key step to circulate the processes of the central dogma. Here we achieved the entire sequential transcription–translation–replication process by autonomous expression of chromosomal DNA replication machineries from a reconstituted transcription–translation system (PURE system). We found that low temperature is essential to express a complex protein, DNA polymerase III, in a single tube using the PURE system. Addition of the 13 genes, encoding initiator, DNA helicase, helicase loader, RNA primase and DNA polymerase III to the PURE system gave rise to a DNA replication system by a coupling manner. An artificial genetic circuit demonstrated that the DNA produced as a result of the replication is able to provide genetic information for proteins, indicating the in vitro central dogma can sequentially undergo two rounds. PMID:23737447

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Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-04

... ability of the Network to respond to a biological or chemical terrorism event. The sensitivity of all... Project Laboratory Response Network (LRN)--Existing Data Collection in use without an OMB Control Number... Disease Control and Prevention (CDC). Background and Brief Description The Laboratory Response Network...

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2004-01-01

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

Microcomputer Network for Computerized Adaptive Testing (CAT)

DTIC Science & Technology

1984-03-01

PRDC TR 84-33 \\Q.�d-33- \\ MICROCOMPUTER NETWOJlt FOR COMPUTERIZED ADAPTIVE TESTING ( CAT ) Baldwin Quan Thomas A . Park Gary Sandahl John H...ACCEIIION NO NPRDC TR 84-33 4. TITLE (-d Sul>tlllo) MICROCOMP UTER NETWORK FOR COMPUTERIZED ADA PTIVE TESTING ( CAT ) 1. Q B. uan T. A . Park...adaptive testing ( CAT ) Bayesian sequential testing 20. ABSTitACT (Continuo on ro•••• aide II noco .. _, _., ld-tlly ,.,. t.loclt _._.) DO Computerized

Carbon Nanotube Growth Rate Regression using Support Vector Machines and Artificial Neural Networks

DTIC Science & Technology

2014-03-27

intensity D peak. Reprinted with permission from [38]. The SVM classifier is trained using custom written Java code leveraging the Sequential Minimal...Society Encog is a machine learning framework for Java , C++ and .Net applications that supports Bayesian Networks, Hidden Markov Models, SVMs and ANNs [13...SVM classifiers are trained using Weka libraries and leveraging custom written Java code. The data set is created as an Attribute Relationship File

The Modeling, Simulation and Comparison of Interconnection Networks for Parallel Processing.

DTIC Science & Technology

1987-12-01

performs better at a lower hardware cost than do the single stage cube and mesh networks. As a result, the designer of a paralll pro- cessing system is...attempted, and in most cases succeeded, in designing and implementing faster. more powerful systems. Due to design innovations and technological advances...largely to the computational complexity of the algorithms executed. In the von Neumann machine, instructions must be executed in a sequential manner. Design

Automatic transducer switching provides accurate wide range measurement of pressure differential

NASA Technical Reports Server (NTRS)

Yoder, S. K.

1967-01-01

Automatic pressure transducer switching network sequentially selects any one of a number of limited-range transducers as gas pressure rises or falls, extending the range of measurement and lessening the chances of damage due to high pressure.

Reorganization of large-scale cognitive networks during automation of imagination of a complex sequential movement.

PubMed

Sauvage, C; De Greef, N; Manto, M; Jissendi, P; Nioche, C; Habas, C

2015-04-01

We investigated the functional reconfiguration of the cerebral networks involved in imagination of sequential movements of the left foot, both performed at regular and fast speed after mental imagery training. Thirty-five volunteers were scanned with a 3T MRI while they imagined a sequence of ankle movements (dorsiflexion, plantar flexion, varus and valgus) before and after mental practice. Subjects were distributed in two groups: the first group executed regular movements whereas the second group made fast movements. We applied the general linear model (GLM) and model-free, exploratory tensorial independent component analytic (TICA) approaches to identify plastic post-training effects on brain activation. GLM showed that post-training imagination of movement was accompanied by a dual effect: a specific recruitment of a medial prefronto-cingulo-parietal circuit reminiscent of the default-mode network, with the left putamen, and a decreased activity of a lateral fronto-parietal network. Training-related subcortical changes only consisted in an increased activity in the left striatum. Unexpectedly, no difference was observed in the cerebellum. TICA also revealed involvement of the left executive network, and of the dorsal control executive network but no significant differences were found between pre- and post-training phases. Therefore, repetitive motor mental imagery induced specific putamen (motor rehearsal) recruitment that one previously observed during learning of overt movements, and, simultaneously, a specific shift of activity from the dorsolateral prefrontal cortex (attention, working memory) to the medial posterior parietal and cingulate cortices (mental imagery and memory rehearsal). Our data complement and confirm the notion that differential and coupled recruitment of cognitive networks can constitute a neural marker of training effects. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

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... Project Data Calls for the Laboratory Response Network--Existing collection in use without an OMB Control... Response Network (LRN) was established by the Department of Health and Human Services, Centers for Disease... LRN's mission is to maintain an integrated national and international network of laboratories that can...

[Revascularization of left anterior descending artery area using a skeletonized left internal mammary artery: a comparison between sequential and separate grafting].

PubMed

Shen, J Q; Ji, Q; Ding, W J; Xia, L M; Wei, L; Wang, C S

2018-03-13

Objective: To evaluate in-hospital and mid-term outcomes of sequential versus separate grafting of in situ skeletonized left internal mammary artery (LIMA) to the left coronary system in a single-center, propensity-matched study. Methods: After propensity score matching, 120 pairs of patients undergoing first, scheduled, isolated coronary artery bypass grafting (CABG) with in situ skeletonized LIMA grafting to the left anterior descending artery (LAD) territory were entered into a sequential group (sequential grafting of LIMA to the diagonal artery and then to the LAD) or a control group (separate grafting of LIMA to the LAD). The in-hospital and follow-up clinical outcomes and follow-up LIMA graft patency were compared. Results: The two propensity score-matched groups had similar in-hospital and follow-up clinical outcomes. The number of bypass conduits ranged from 3 to 6 (with a mean of 3.5), and 91.3%(219/240)of the included patients received off-pump CABG surgery. No significant differences were found between the two propensity score-matched groups in the in-hospital outcomes, including in-hospital death and the incidence of complications associated with CABG (prolonged ventilation, peroperative stroke, re-operation before discharge, and deep sternal wound infection). During follow-up, 9 patients (4 patients from the sequential group and 5 patients from the control group) died, and the all-cause mortality rate was 3.9%. No significant difference was found in the all-cause mortality rate between the 2 groups[3.4% (4/116) vs 4.3% (5/115), P =0.748]. During follow-up period, 99.1% (115/116) patency for the diagonal site and 98.3% (114/116) for the LAD site were determined by coronary computed tomographic angiography after sequential LIMA grafting, both of which were similar with graft patency of separate grafting of in situ skeletonized LIMA to the LAD. Conclusions: Revascularization of the left coronary system using a skeletonized LIMA resulted in excellent in-hospital and mid-term clinical outcomes and graft patency using sequential grafting.

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

2016-06-01

While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

Long-term results and quality of life of patients undergoing sequential surgical treatment for severe acute pancreatitis complicated by infected pancreatic necrosis.

PubMed

Cinquepalmi, Lorenza; Boni, Luigi; Dionigi, Gianlorenzo; Rovera, Francesca; Diurni, Mario; Benevento, Angelo; Dionigi, Renzo

2006-01-01

Infected pancreatic necrosis (IPN) is one of the most severe complications of acute pancreatitis (AP). Sequential surgical debridement represents one of the most effective treatments in terms of morbidity and mortality. The aim of this paper is to describe the quality of life and long-term results (e.g., nutritional, muscular, and pancreatic function) of patients treated by sequential necrosectomy at the Department of Surgery of the University of Insubria (Varese, Italy). Data were collected on patients undergoing sequential surgical debridement as treatment for IPN. The severity of AP was evaluated using the Ranson criteria, the Acute Physiology and Chronic Health Evaluation (APACHE II) Score, and the Sepsis Score, as well as the extent of necrosis. The surgical approach was through a midline or subcostal laparotomy, followed by exploration of the peritoneal cavity, wide debridement, and peritoneal lavage. The abdomen was either left open or closed partially with a surgical zipper, with multiple re-laparotomies scheduled until debridement of necrotic tissue was complete. The long-term evaluation focused on late morbidity, performance status, and abdominal wall function. In the majority of patients (68%), mixed flora were isolated. Pseudomonas aeruginosa was the microorganism identified most commonly (59%), often associated with Candida albicans or C. glabrata. The mean total hospital stay was 71+/-38 days (range 13-146 days), of which 24+/-19 days (range 0-66 days) were in the intensive care unit. Eight patients died, the deaths being caused by multiple organ dysfunction syndrome in seven patients and hemorrhage from the splenic artery in one. Normal exocrine and endocrine pancreatic function was observed in 28 patients (88%). At discharge, four patients had steatorrhea, which was temporary. Eight patients (23%) developed pancreatic pseudocysts, and in six, cystogastostomy was performed. Most patients (29/32, 91%) developed a post-operative hernia, but only five required surgical repair. All patients had a Short Form (SF)-36 score>60%, and 20 of the 32 patients (68%) had scores>70-80% (good quality of life). The worst scores were related to alcoholic pancreatitis. The degree of pancreatic failure (exocrine and endocrine function) is not related to the amount of pancreatic necrosis. Even with a need for repeated laparotomy and multiple surgical procedures, the abdominal wall capacity as well as long-term quality of life remain excellent.

Emergent Oscillations in Networks of Stochastic Spiking Neurons

PubMed Central

van Drongelen, Wim; Cowan, Jack D.

2011-01-01

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

Estimating the residual axial load capacity of flexure-dominated reinforced concrete bridge columns.

DOT National Transportation Integrated Search

2014-08-01

Extreme events such as earthquakes have the potential to damage hundreds, if not thousands, of bridges on a : transportation network. Following an earthquake, the damaged bridges are inspected by engineers sequentially to : decide whether or not to c...

Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

PubMed

Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

2016-11-15

There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects. Copyright © 2016 Elsevier Inc. All rights reserved.

Attractors in complex networks

NASA Astrophysics Data System (ADS)

Rodrigues, Alexandre A. P.

2017-10-01

In the framework of the generalized Lotka-Volterra model, solutions representing multispecies sequential competition can be predictable with high probability. In this paper, we show that it occurs because the corresponding "heteroclinic channel" forms part of an attractor. We prove that, generically, in an attracting heteroclinic network involving a finite number of hyperbolic and non-resonant saddle-equilibria whose linearization has only real eigenvalues, the connections corresponding to the most positive expanding eigenvalues form part of an attractor (observable in numerical simulations).

Attractors in complex networks.

PubMed

Rodrigues, Alexandre A P

2017-10-01

In the framework of the generalized Lotka-Volterra model, solutions representing multispecies sequential competition can be predictable with high probability. In this paper, we show that it occurs because the corresponding "heteroclinic channel" forms part of an attractor. We prove that, generically, in an attracting heteroclinic network involving a finite number of hyperbolic and non-resonant saddle-equilibria whose linearization has only real eigenvalues, the connections corresponding to the most positive expanding eigenvalues form part of an attractor (observable in numerical simulations).

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

An Intrinsically Disordered Photosystem II Subunit, PsbO, Provides a Structural Template and a Sensor of the Hydrogen-bonding Network in Photosynthetic Water Oxidation*

PubMed Central

Offenbacher, Adam R.; Polander, Brandon C.; Barry, Bridgette A.

2013-01-01

Photosystem II (PSII) is a membrane-bound enzyme that utilizes solar energy to catalyze the photooxidation of water. Molecular oxygen is evolved after four sequential light-driven oxidation reactions at the Mn4CaO5 oxygen-evolving complex, producing five sequentially oxidized states, Sn. PSII is composed of 17 membrane-spanning subunits and three extrinsic subunits, PsbP, PsbQ, and PsbO. PsbO is intrinsically disordered and plays a role in facilitation of the water oxidizing cycle. Native PsbO can be removed and substituted with recombinant PsbO, thereby restoring steady-state activity. In this report, we used reaction-induced Fourier transform infrared spectroscopy to obtain information concerning the role of PsbP, PsbQ, and PsbO during the S state cycle. Light-minus-dark difference spectra were acquired, monitoring structural changes associated with each accessible flash-induced S state transition in a highly purified plant PSII preparation (Triton X-100, octylthioglucoside). A comparison of S2 minus S1 spectra revealed that removal of PsbP and PsbQ had no significant effect on the data, whereas amide frequency and intensity changes were associated with PsbO removal. These data suggest that PsbO acts as an organizational template for the PSII reaction center. To identify any coupled conformational changes arising directly from PsbO, global 13C-PsbO isotope editing was employed. The reaction-induced Fourier transform infrared spectra of accessible S states provide evidence that PsbO spectral contributions are temperature (263 and 277 K) and S state dependent. These experiments show that PsbO undergoes catalytically relevant structural dynamics, which are coupled over long distance to hydrogen-bonding changes at the Mn4CaO5 cluster. PMID:23940038

On effectiveness of network sensor-based defense framework

NASA Astrophysics Data System (ADS)

Zhang, Difan; Zhang, Hanlin; Ge, Linqiang; Yu, Wei; Lu, Chao; Chen, Genshe; Pham, Khanh

2012-06-01

Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring, detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework, we also implement the statistical-based detection and sequential testing-based detection techniques and compare their respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed framework for combating cyber attacks.

Endemic infections are always possible on regular networks

NASA Astrophysics Data System (ADS)

Del Genio, Charo I.; House, Thomas

2013-10-01

We study the dependence of the largest component in regular networks on the clustering coefficient, showing that its size changes smoothly without undergoing a phase transition. We explain this behavior via an analytical approach based on the network structure, and provide an exact equation describing the numerical results. Our work indicates that intrinsic structural properties always allow the spread of epidemics on regular networks.

Evolving network simulation study. From regular lattice to scale free network

NASA Astrophysics Data System (ADS)

Makowiec, D.

2005-12-01

The Watts-Strogatz algorithm of transferring the square lattice to a small world network is modified by introducing preferential rewiring constrained by connectivity demand. The evolution of the network is two-step: sequential preferential rewiring of edges controlled by p and updating the information about changes done. The evolving system self-organizes into stationary states. The topological transition in the graph structure is noticed with respect to p. Leafy phase a graph formed by multiple connected vertices (graph skeleton) with plenty of leaves attached to each skeleton vertex emerges when p is small enough to pretend asynchronous evolution. Tangling phase where edges of a graph circulate frequently among low degree vertices occurs when p is large. There exist conditions at which the resulting stationary network ensemble provides networks which degree distribution exhibit power-law decay in large interval of degrees.

Neural networks for vertical microcode compaction

NASA Astrophysics Data System (ADS)

Chu, Pong P.

1992-09-01

Neural networks provide an alternative way to solve complex optimization problems. Instead of performing a program of instructions sequentially as in a traditional computer, neural network model explores many competing hypotheses simultaneously using its massively parallel net. The paper shows how to use the neural network approach to perform vertical micro-code compaction for a micro-programmed control unit. The compaction procedure includes two basic steps. The first step determines the compatibility classes and the second step selects a minimal subset to cover the control signals. Since the selection process is an NP- complete problem, to find an optimal solution is impractical. In this study, we employ a customized neural network to obtain the minimal subset. We first formalize this problem, and then define an `energy function' and map it to a two-layer fully connected neural network. The modified network has two types of neurons and can always obtain a valid solution.

Efficacy of chlorhexidine, dexpanthenol, allantoin and chitosan gel in comparison with bicarbonate oral rinse in controlling post-interventional inflammation, pain and cicatrization in subjects undergoing dental surgery.

PubMed

Lopez-Lopez, Jose; Lope-Lopez, Jose; Jan-Pallí, Enric; lez-Navarro, Beatriz Gonzá; González-Navarro, Beatriz; Jané-Salas, Enric; Estrugo-Devesa, Albert; Milani, Massimo

2015-12-01

Reducing post-interventional inflammation and pain in odontostomatological surgery procedures, such as tooth extractions, implants or oral biopsies is a relevant clinical goal. Chlorhexidine oral rinse is commonly used with this aim. Recently a new product containing chlorhexidine, dexpanthenol, allantoin and chitosan (Bexident Post [BP]) in a gel formulation has been developed. We evaluated the efficacy of BP in controlling postsurgical inflammation and pain and in promoting cicatrization in subjects undergoing molar extractions. We conducted a prospective sequential cross-over, randomized controlled study in patients undergoing surgical removal of at least two impacted mandibular third molars (teeth numbers 38 and 48) (numbers 17 and 32 in the Universal Tooth Numbering System), in two separate sessions, to determine the effect of BP in comparison with bicarbonate (BC) oral rinse (one spoonful in 200 ml of water), both used three times daily. Each subject utilized both products in a randomized sequential manner after each tooth extraction. Primary outcomes of the study were post-procedure pain and inflammation. Secondary outcomes were analgesic pill rescue use (metamizole 1 cap every 8 hours if needed) and an assessor-blinded evaluation of cicatrization with a semi-quantitative scale (good, satisfactory and insufficient). Post-procedure pain was assessed 6 hours after tooth extraction and for seven consecutive days by means of a 10 cm visual analogue scale (VAS) (from 0: no pain to 10: extreme pain). The extent of inflammation was evaluated through metric measurements of facial perimeter using standardized anatomical reference points. A total of 47 patients (22 men and 25 women; mean age 34 years) were enrolled with a total of 94 molars extracted. Nineteen subjects applied BC as the first sequential treatment and 28 BP as the first. Before surgery no mean differences in the two treatments in inflammation measurements were observed. After surgery mean VAS pain score was similar between the two treatments in the first 6 hours (VAS score = 6.5). A marked progressive reduction in pain intensity with the use of BP was observed throughout the treatment period in comparison with BC (7 day mean scores 3.7 vs. 5.3; p = 0.0001). BP was superior to BC in reducing inflammation with -50% of the inflammation-related measurement (6 mm vs. 12 mm; p = 0.0001). Analgesic pill consumption was lower with BP in comparison with BC (13 pills vs. 24; p < 0.05). Cicatrization was scored 'good' in a higher percentage of subjects during BP use (64%) in comparison with the BC group (13%) (p = 0.0001). No serious side effects were reported with either treatment regimen. In this trial BP performed better than BC in controlling pain and inflammation in subjects undergoing dental surgery, reducing the consumption of analgesics and favoring better cicatrization.

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro

PubMed Central

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P. C.; Livesey, Frederick J.

2015-01-01

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. PMID:26395144

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

PubMed

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

2015-09-15

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (<10) of presynaptic inputs, whereas a small set of hub-like neurons have large numbers of synaptic connections (>40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

On the degelation of networks – Case of the radiochemical degradation of methyl methacrylate – ethylene glycol dimethacrylate copolymers

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

Richaud, Emmanuel; Gilormini, Pierre; Verdu, Jacques

2016-05-18

Methyl methacrylate networks were synthetized and submitted to radiochemical degradation. Ageing was monitored by means of sol-gel analysis and glass transition temperature measurements. Networks were shown to undergo exclusively chain scission process leading to the degelation of network. The critical conversion degree corresponding to degelation (loss of all elastically active chains) is discussed regarding a statistical theory.

Mining of high utility-probability sequential patterns from uncertain databases

PubMed Central

Zhang, Binbin; Fournier-Viger, Philippe; Li, Ting

2017-01-01

High-utility sequential pattern mining (HUSPM) has become an important issue in the field of data mining. Several HUSPM algorithms have been designed to mine high-utility sequential patterns (HUPSPs). They have been applied in several real-life situations such as for consumer behavior analysis and event detection in sensor networks. Nonetheless, most studies on HUSPM have focused on mining HUPSPs in precise data. But in real-life, uncertainty is an important factor as data is collected using various types of sensors that are more or less accurate. Hence, data collected in a real-life database can be annotated with existing probabilities. This paper presents a novel pattern mining framework called high utility-probability sequential pattern mining (HUPSPM) for mining high utility-probability sequential patterns (HUPSPs) in uncertain sequence databases. A baseline algorithm with three optional pruning strategies is presented to mine HUPSPs. Moroever, to speed up the mining process, a projection mechanism is designed to create a database projection for each processed sequence, which is smaller than the original database. Thus, the number of unpromising candidates can be greatly reduced, as well as the execution time for mining HUPSPs. Substantial experiments both on real-life and synthetic datasets show that the designed algorithm performs well in terms of runtime, number of candidates, memory usage, and scalability for different minimum utility and minimum probability thresholds. PMID:28742847

Population structuring of multi-copy, antigen-encoding genes in Plasmodium falciparum

PubMed Central

Artzy-Randrup, Yael; Rorick, Mary M; Day, Karen; Chen, Donald; Dobson, Andrew P; Pascual, Mercedes

2012-01-01

The coexistence of multiple independently circulating strains in pathogen populations that undergo sexual recombination is a central question of epidemiology with profound implications for control. An agent-based model is developed that extends earlier ‘strain theory’ by addressing the var gene family of Plasmodium falciparum. The model explicitly considers the extensive diversity of multi-copy genes that undergo antigenic variation via sequential, mutually exclusive expression. It tracks the dynamics of all unique var repertoires in a population of hosts, and shows that even under high levels of sexual recombination, strain competition mediated through cross-immunity structures the parasite population into a subset of coexisting dominant repertoires of var genes whose degree of antigenic overlap depends on transmission intensity. Empirical comparison of patterns of genetic variation at antigenic and neutral sites supports this role for immune selection in structuring parasite diversity. DOI: http://dx.doi.org/10.7554/eLife.00093.001 PMID:23251784

Learning Orthographic Structure with Sequential Generative Neural Networks

ERIC Educational Resources Information Center

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-01-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

The Use of Tailored Testing with Instructional Programs. Final Report.

ERIC Educational Resources Information Center

Reckase, Mark D.

A computerized testing system was implemented in conjunction with the Radar Technician Training Course at the Naval Training Center, Great Lakes, Illinois. The feasibility of the system and students' attitudes toward it were examined. The system, a multilevel, microprocessor-based computer network, administered tests in a sequential, fixed length…

Endogenous Sequential Cortical Activity Evoked by Visual Stimuli

PubMed Central

Miller, Jae-eun Kang; Hamm, Jordan P.; Jackson, Jesse; Yuste, Rafael

2015-01-01

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time. PMID:26063915

Cardinality enhancement utilizing Sequential Algorithm (SeQ) code in OCDMA system

NASA Astrophysics Data System (ADS)

Fazlina, C. A. S.; Rashidi, C. B. M.; Rahman, A. K.; Aljunid, S. A.

2017-11-01

Optical Code Division Multiple Access (OCDMA) has been important with increasing demand for high capacity and speed for communication in optical networks because of OCDMA technique high efficiency that can be achieved, hence fibre bandwidth is fully used. In this paper we will focus on Sequential Algorithm (SeQ) code with AND detection technique using Optisystem design tool. The result revealed SeQ code capable to eliminate Multiple Access Interference (MAI) and improve Bit Error Rate (BER), Phase Induced Intensity Noise (PIIN) and orthogonally between users in the system. From the results, SeQ shows good performance of BER and capable to accommodate 190 numbers of simultaneous users contrast with existing code. Thus, SeQ code have enhanced the system about 36% and 111% of FCC and DCS code. In addition, SeQ have good BER performance 10-25 at 155 Mbps in comparison with 622 Mbps, 1 Gbps and 2 Gbps bit rate. From the plot graph, 155 Mbps bit rate is suitable enough speed for FTTH and LAN networks. Resolution can be made based on the superior performance of SeQ code. Thus, these codes will give an opportunity in OCDMA system for better quality of service in an optical access network for future generation's usage

Parallel discrete event simulation: A shared memory approach

NASA Technical Reports Server (NTRS)

Reed, Daniel A.; Malony, Allen D.; Mccredie, Bradley D.

1987-01-01

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models.

Exploring the evolution of node neighborhoods in Dynamic Networks

NASA Astrophysics Data System (ADS)

Orman, Günce Keziban; Labatut, Vincent; Naskali, Ahmet Teoman

2017-09-01

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of neighborhood event, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes.

Dysphagia after sequential chemoradiation therapy for advanced head and neck cancer.

PubMed

Goguen, Laura A; Posner, Marshall R; Norris, Charles M; Tishler, Roy B; Wirth, Lori J; Annino, Donald J; Gagne, Adele; Sullivan, Christopher A; Sammartino, Daniel E; Haddad, Robert I

2006-06-01

Assess impact of sequential chemoradiation therapy (SCRT) for advanced head and neck cancer (HNCA) on swallowing, nutrition, and quality of life. Prospective cohort study of 59 patients undergoing SCRT for advanced head and neck cancer. Follow-up median was 47.5 months. Regional Cancer Center. Median time to gastrostomy tube removal was 21 weeks. Eighteen of 23 patients who underwent modified barium swallow demonstrated aspiration; none developed pneumonia. Six of 7 with pharyngoesophageal stricture underwent successful dilatation. Functional Assessment of Cancer Therapy-Head and Neck Scale questionnaires at median 6 months after treatment revealed "somewhat" satisfaction with swallowing. At the time of analysis, 97% have the gastronomy tube removed and take soft/regular diet. Early after treatment dysphagia adversely affected weight, modified barium swallow results, and quality of life. Diligent swallow therapy, and dilation as needed, allowed nearly all patients to have their gastronomy tubes removed and return to a soft/regular diet. Dysphagia is significant after SCRT but generally slowly recovers 6 to 12 months after SCRT. C-4.

Allosteric substrate switching in a voltage-sensing lipid phosphatase.

PubMed

Grimm, Sasha S; Isacoff, Ehud Y

2016-04-01

Allostery provides a critical control over enzyme activity, biasing the catalytic site between inactive and active states. We found that the Ciona intestinalis voltage-sensing phosphatase (Ci-VSP), which modifies phosphoinositide signaling lipids (PIPs), has not one but two sequential active states with distinct substrate specificities, whose occupancy is allosterically controlled by sequential conformations of the voltage-sensing domain (VSD). Using fast fluorescence resonance energy transfer (FRET) reporters of PIPs to monitor enzyme activity and voltage-clamp fluorometry to monitor conformational changes in the VSD, we found that Ci-VSP switches from inactive to a PIP3-preferring active state when the VSD undergoes an initial voltage-sensing motion and then into a second PIP2-preferring active state when the VSD activates fully. This two-step allosteric control over a dual-specificity enzyme enables voltage to shape PIP concentrations in time, and provides a mechanism for the complex modulation of PIP-regulated ion channels, transporters, cell motility, endocytosis and exocytosis.

Allosteric substrate switching in a voltage sensing lipid phosphatase

PubMed Central

Grimm, Sasha S.; Isacoff, Ehud Y.

2016-01-01

Allostery provides a critical control over enzyme activity, biasing the catalytic site between inactive and active states. We find the Ciona intestinalis voltage-sensing phosphatase (Ci-VSP), which modifies phosphoinositide signaling lipids (PIPs), to have not one but two sequential active states with distinct substrate specificities, whose occupancy is allosterically controlled by sequential conformations of the voltage sensing domain (VSD). Using fast FRET reporters of PIPs to monitor enzyme activity and voltage clamp fluorometry to monitor conformational changes in the VSD, we find that Ci-VSP switches from inactive to a PIP3-preferring active state when the VSD undergoes an initial voltage sensing motion and then into a second PIP2-preferring active state when the VSD activates fully. This novel 2-step allosteric control over a dual specificity enzyme enables voltage to shape PIP concentrations in time, and provides a mechanism for the complex modulation of PIP-regulated ion channels, transporters, cell motility and endo/exocytosis. PMID:26878552

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Developing neuronal networks: Self-organized criticality predicts the future

NASA Astrophysics Data System (ADS)

Pu, Jiangbo; Gong, Hui; Li, Xiangning; Luo, Qingming

2013-01-01

Self-organized criticality emerged in neural activity is one of the key concepts to describe the formation and the function of developing neuronal networks. The relationship between critical dynamics and neural development is both theoretically and experimentally appealing. However, whereas it is well-known that cortical networks exhibit a rich repertoire of activity patterns at different stages during in vitro maturation, dynamical activity patterns through the entire neural development still remains unclear. Here we show that a series of metastable network states emerged in the developing and ``aging'' process of hippocampal networks cultured from dissociated rat neurons. The unidirectional sequence of state transitions could be only observed in networks showing power-law scaling of distributed neuronal avalanches. Our data suggest that self-organized criticality may guide spontaneous activity into a sequential succession of homeostatically-regulated transient patterns during development, which may help to predict the tendency of neural development at early ages in the future.

Cell Assembly Dynamics of Sparsely-Connected Inhibitory Networks: A Simple Model for the Collective Activity of Striatal Projection Neurons.

PubMed

Angulo-Garcia, David; Berke, Joshua D; Torcini, Alessandro

2016-02-01

Striatal projection neurons form a sparsely-connected inhibitory network, and this arrangement may be essential for the appropriate temporal organization of behavior. Here we show that a simplified, sparse inhibitory network of Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal population activity, as observed in brain slices. In particular we develop a new metric to determine the conditions under which sparse inhibitory networks form anti-correlated cell assemblies with time-varying activity of individual cells. We find that under these conditions the network displays an input-specific sequence of cell assembly switching, that effectively discriminates similar inputs. Our results support the proposal that GABAergic connections between striatal projection neurons allow stimulus-selective, temporally-extended sequential activation of cell assemblies. Furthermore, we help to show how altered intrastriatal GABAergic signaling may produce aberrant network-level information processing in disorders such as Parkinson's and Huntington's diseases.

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

[Introduction of computerized anesthesia-recording systems and construction of comprehensive medical information network for patients undergoing surgery in the University of Tokyo Hospital].

PubMed

Kitamura, Takayuki; Hoshimoto, Hiroyuki; Yamada, Yoshitsugu

2009-10-01

The computerized anesthesia-recording systems are expensive and the introduction of the systems takes time and requires huge effort. Generally speaking, the efficacy of the computerized anesthesia-recording systems on the anesthetic managements is focused on the ability to automatically input data from the monitors to the anesthetic records, and tends to be underestimated. However, once the computerized anesthesia-recording systems are integrated into the medical information network, several features, which definitely contribute to improve the quality of the anesthetic management, can be developed; for example, to prevent misidentification of patients, to prevent mistakes related to blood transfusion, and to protect patients' personal information. Here we describe our experiences of the introduction of the computerized anesthesia-recording systems and the construction of the comprehensive medical information network for patients undergoing surgery in The University of Tokyo Hospital. We also discuss possible efficacy of the comprehensive medical information network for patients during surgery under anesthetic managements.

Fragmenting networks by targeting collective influencers at a mesoscopic level.

PubMed

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-25

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

NASA Astrophysics Data System (ADS)

Kobayashi, Teruyoshi; Masuda, Naoki

2016-11-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure.

Fragmenting networks by targeting collective influencers at a mesoscopic level

PubMed Central

Kobayashi, Teruyoshi; Masuda, Naoki

2016-01-01

A practical approach to protecting networks against epidemic processes such as spreading of infectious diseases, malware, and harmful viral information is to remove some influential nodes beforehand to fragment the network into small components. Because determining the optimal order to remove nodes is a computationally hard problem, various approximate algorithms have been proposed to efficiently fragment networks by sequential node removal. Morone and Makse proposed an algorithm employing the non-backtracking matrix of given networks, which outperforms various existing algorithms. In fact, many empirical networks have community structure, compromising the assumption of local tree-like structure on which the original algorithm is based. We develop an immunization algorithm by synergistically combining the Morone-Makse algorithm and coarse graining of the network in which we regard a community as a supernode. In this way, we aim to identify nodes that connect different communities at a reasonable computational cost. The proposed algorithm works more efficiently than the Morone-Makse and other algorithms on networks with community structure. PMID:27886251

Unsupervised Sequential Outlier Detection With Deep Architectures.

PubMed

Lu, Weining; Cheng, Yu; Xiao, Cao; Chang, Shiyu; Huang, Shuai; Liang, Bin; Huang, Thomas

2017-09-01

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

Deformation behavior and mechanical analysis of vertically aligned carbon nanotube (VACNT) bundles

NASA Astrophysics Data System (ADS)

Hutchens, Shelby B.

Vertically aligned carbon nanotubes (VACNTs) serve as integral components in a variety of applications including MEMS devices, energy absorbing materials, dry adhesives, light absorbing coatings, and electron emitters, all of which require structural robustness. It is only through an understanding of VACNT's structural mechanical response and local constitutive stress-strain relationship that future advancements through rational design may take place. Even for applications in which the structural response is not central to device performance, VACNTs must be sufficiently robust and therefore knowledge of their microstructure-property relationship is essential. This thesis first describes the results of in situ uniaxial compression experiments of 50 micron diameter cylindrical bundles of these complex, hierarchical materials as they undergo unusual deformation behavior. Most notably they deform via a series of localized folding events, originating near the bundle base, which propagate laterally and collapse sequentially from bottom to top. This deformation mechanism accompanies an overall foam-like stress-strain response having elastic, plateau, and densification regimes with the addition of undulations in the stress throughout the plateau regime that correspond to the sequential folding events. Microstructural observations indicate the presence of a strength gradient, due to a gradient in both tube density and alignment along the bundle height, which is found to play a key role in both the sequential deformation process and the overall stress-strain response. Using the complicated structural response as both motivation and confirmation, a finite element model based on a viscoplastic solid is proposed. This model is characterized by a flow stress relation that contains an initial peak followed by strong softening and successive hardening. Analysis of this constitutive relation results in capture of the sequential buckling phenomenon and a strength gradient effect. This combination of experimental and modeling approaches motivates discussion of the particular microstructural mechanisms and local material behavior that govern the non-trivial energy absorption via sequential, localized buckle formation in the VACNT bundles.

Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch

PubMed Central

Lou, Chunbo; Liu, Xili; Ni, Ming; Huang, Yiqi; Huang, Qiushi; Huang, Longwen; Jiang, Lingli; Lu, Dan; Wang, Mingcong; Liu, Chang; Chen, Daizhuo; Chen, Chongyi; Chen, Xiaoyue; Yang, Le; Ma, Haisu; Chen, Jianguo; Ouyang, Qi

2010-01-01

Design and synthesis of basic functional circuits are the fundamental tasks of synthetic biologists. Before it is possible to engineer higher-order genetic networks that can perform complex functions, a toolkit of basic devices must be developed. Among those devices, sequential logic circuits are expected to be the foundation of the genetic information-processing systems. In this study, we report the design and construction of a genetic sequential logic circuit in Escherichia coli. It can generate different outputs in response to the same input signal on the basis of its internal state, and ‘memorize' the output. The circuit is composed of two parts: (1) a bistable switch memory module and (2) a double-repressed promoter NOR gate module. The two modules were individually rationally designed, and they were coupled together by fine-tuning the interconnecting parts through directed evolution. After fine-tuning, the circuit could be repeatedly, alternatively triggered by the same input signal; it functions as a push-on push-off switch. PMID:20212522

Synthesizing a novel genetic sequential logic circuit: a push-on push-off switch.

PubMed

Lou, Chunbo; Liu, Xili; Ni, Ming; Huang, Yiqi; Huang, Qiushi; Huang, Longwen; Jiang, Lingli; Lu, Dan; Wang, Mingcong; Liu, Chang; Chen, Daizhuo; Chen, Chongyi; Chen, Xiaoyue; Yang, Le; Ma, Haisu; Chen, Jianguo; Ouyang, Qi

2010-01-01

Design and synthesis of basic functional circuits are the fundamental tasks of synthetic biologists. Before it is possible to engineer higher-order genetic networks that can perform complex functions, a toolkit of basic devices must be developed. Among those devices, sequential logic circuits are expected to be the foundation of the genetic information-processing systems. In this study, we report the design and construction of a genetic sequential logic circuit in Escherichia coli. It can generate different outputs in response to the same input signal on the basis of its internal state, and 'memorize' the output. The circuit is composed of two parts: (1) a bistable switch memory module and (2) a double-repressed promoter NOR gate module. The two modules were individually rationally designed, and they were coupled together by fine-tuning the interconnecting parts through directed evolution. After fine-tuning, the circuit could be repeatedly, alternatively triggered by the same input signal; it functions as a push-on push-off switch.

Combined techniques for characterising pasta structure reveals how the gluten network slows enzymic digestion rate.

PubMed

Zou, Wei; Sissons, Mike; Gidley, Michael J; Gilbert, Robert G; Warren, Frederick J

2015-12-01

The aim of the present study is to characterise the influence of gluten structure on the kinetics of starch hydrolysis in pasta. Spaghetti and powdered pasta were prepared from three different cultivars of durum semolina, and starch was also purified from each cultivar. Digestion kinetic parameters were obtained through logarithm-of-slope analysis, allowing identification of sequential digestion steps. Purified starch and semolina were digested following a single first-order rate constant, while pasta and powdered pasta followed two sequential first-order rate constants. Rate coefficients were altered by pepsin hydrolysis. Confocal microscopy revealed that, following cooking, starch granules were completely swollen for starch, semolina and pasta powder samples. In pasta, they were completely swollen in the external regions, partially swollen in the intermediate region and almost intact in the pasta strand centre. Gluten entrapment accounts for sequential kinetic steps in starch digestion of pasta; the compact microstructure of pasta also reduces digestion rates. Copyright © 2015 Elsevier Ltd. All rights reserved.

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

75 FR 69673 - Agency Forms Undergoing Paperwork Reduction Act Review

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-15

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention [30-Day 11-0636... Project Centers for Disease Control and Prevention (CDC) Secure Communications Network (Epi-X) (OMB No... Secure Communication Network (Epi-X))--Revision--Office of Public Health Preparedness and Response (OPHPR...

Memory Retrieval Time and Memory Capacity of the CA3 Network: Role of Gamma Frequency Oscillations

ERIC Educational Resources Information Center

de Almeida, Licurgo; Idiart, Marco; Lisman, John E.

2007-01-01

The existence of recurrent synaptic connections in CA3 led to the hypothesis that CA3 is an autoassociative network similar to the Hopfield networks studied by theorists. CA3 undergoes gamma frequency periodic inhibition that prevents a persistent attractor state. This argues against the analogy to Hopfield nets, in which an attractor state can be…

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

The privacy preserving data mining technique researches have gained much attention in recent years. For data stream systems, wireless networks and mobile devices, the related stream data mining techniques research is still in its' early stage. In this paper, an data mining algorithm dealing with privacy preserving problem in data stream is presented.

The Philosophical and Pedagogical Underpinnings of Active Learning in Engineering Education

ERIC Educational Resources Information Center

Christie, Michael; de Graaff, Erik

2017-01-01

In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving…

Getting Together: Social Contact Frequency across the Life Span

ERIC Educational Resources Information Center

Sander, Julia; Schupp, Jürgen; Richter, David

2017-01-01

Frequent social interactions are strongly linked to positive affect, longevity, and good health. Although there has been extensive research on changes in the size of social networks over time, little attention has been given to the development of contact frequency across the life span. In this cohort-sequential longitudinal study, we examined…

Optimization of Multiple Related Negotiation through Multi-Negotiation Network

NASA Astrophysics Data System (ADS)

Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Objective and Subjective Measures of Simultaneous vs Sequential Bilateral Cochlear Implants in Adults: A Randomized Clinical Trial.

PubMed

Kraaijenga, Véronique J C; Ramakers, Geerte G J; Smulders, Yvette E; van Zon, Alice; Stegeman, Inge; Smit, Adriana L; Stokroos, Robert J; Hendrice, Nadia; Free, Rolien H; Maat, Bert; Frijns, Johan H M; Briaire, Jeroen J; Mylanus, E A M; Huinck, Wendy J; Van Zanten, Gijsbert A; Grolman, Wilko

2017-09-01

To date, no randomized clinical trial on the comparison between simultaneous and sequential bilateral cochlear implants (BiCIs) has been performed. To investigate the hearing capabilities and the self-reported benefits of simultaneous BiCIs compared with those of sequential BiCIs. A multicenter randomized clinical trial was conducted between January 12, 2010, and September 2, 2012, at 5 tertiary referral centers among 40 participants eligible for BiCIs. Main inclusion criteria were postlingual severe to profound hearing loss, age 18 to 70 years, and a maximum duration of 10 years without hearing aid use in both ears. Data analysis was conducted from May 24 to June 12, 2016. The simultaneous BiCI group received 2 cochlear implants during 1 surgical procedure. The sequential BiCI group received 2 cochlear implants with an interval of 2 years between implants. First, the results 1 year after receiving simultaneous BiCIs were compared with the results 1 year after receiving sequential BiCIs. Second, the results of 3 years of follow-up for both groups were compared separately. The primary outcome measure was speech intelligibility in noise from straight ahead. Secondary outcome measures were speech intelligibility in noise from spatially separated sources, speech intelligibility in silence, localization capabilities, and self-reported benefits assessed with various hearing and quality of life questionnaires. Nineteen participants were randomized to receive simultaneous BiCIs (11 women and 8 men; median age, 52 years [interquartile range, 36-63 years]), and another 19 participants were randomized to undergo sequential BiCIs (8 women and 11 men; median age, 54 years [interquartile range, 43-64 years]). Three patients did not receive a second cochlear implant and were unavailable for follow-up. Comparable results were found 1 year after simultaneous or sequential BiCIs for speech intelligibility in noise from straight ahead (difference, 0.9 dB [95% CI, -3.1 to 4.4 dB]) and all secondary outcome measures except for localization with a 30° angle between loudspeakers (difference, -10% [95% CI, -20.1% to 0.0%]). In the sequential BiCI group, all participants performed significantly better after the BiCIs on speech intelligibility in noise from spatially separated sources and on all localization tests, which was consistent with most of the participants' self-reported hearing capabilities. Speech intelligibility-in-noise results improved in the simultaneous BiCI group up to 3 years following the BiCIs. This study shows comparable objective and subjective hearing results 1 year after receiving simultaneous BiCIs and sequential BiCIs with an interval of 2 years between implants. It also shows a significant benefit of sequential BiCIs over a unilateral cochlear implant. Until 3 years after receiving simultaneous BiCIs, speech intelligibility in noise significantly improved compared with previous years. trialregister.nl Identifier: NTR1722.

Design of Neural Networks for Fast Convergence and Accuracy

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1998-01-01

A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Solute transport with multisegment, equilibrium-controlled, classical reactions: Problem solvability and feed forward method's applicability for complex segments of at most binary participants

USGS Publications Warehouse

Rubin, Jacob

1992-01-01

The feed forward (FF) method derives efficient operational equations for simulating transport of reacting solutes. It has been shown to be applicable in the presence of networks with any number of homogeneous and/or heterogeneous, classical reaction segments that consist of three, at most binary participants. Using a sequential (network type after network type) exploration approach and, independently, theoretical explanations, it is demonstrated for networks with classical reaction segments containing more than three, at most binary participants that if any one of such networks leads to a solvable transport problem then the FF method is applicable. Ways of helping to avoid networks that produce problem insolvability are developed and demonstrated. A previously suggested algebraic, matrix rank procedure has been adapted and augmented to serve as the main, easy-to-apply solvability test for already postulated networks. Four network conditions that often generate insolvability have been identified and studied. Their early detection during network formulation may help to avoid postulation of insolvable networks.

Crack deflection in brittle media with heterogeneous interfaces and its application in shale fracking

NASA Astrophysics Data System (ADS)

Zeng, Xiaguang; Wei, Yujie

Driven by the rapid progress in exploiting unconventional energy resources such as shale gas, there is growing interest in hydraulic fracture of brittle yet heterogeneous shales. In particular, how hydraulic cracks interact with natural weak zones in sedimentary rocks to form permeable cracking networks is of significance in engineering practice. Such a process is typically influenced by crack deflection, material anisotropy, crack-surface friction, crustal stresses, and so on. In this work, we extend the He-Hutchinson theory (He and Hutchinson, 1989) to give the closed-form formulae of the strain energy release rate of a hydraulic crack with arbitrary angles with respect to the crustal stress. The critical conditions in which the hydraulic crack deflects into weak interfaces and exhibits a dependence on crack-surface friction and crustal stress anisotropy are given in explicit formulae. We reveal analytically that, with increasing pressure, hydraulic fracture in shales may sequentially undergo friction locking, mode II fracture, and mixed mode fracture. Mode II fracture dominates the hydraulic fracturing process and the impinging angle between the hydraulic crack and the weak interface is the determining factor that accounts for crack deflection; the lower friction coefficient between cracked planes and the greater crustal stress difference favor hydraulic fracturing. In addition to shale fracking, the analytical solution of crack deflection could be used in failure analysis of other brittle media.

Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

NASA Astrophysics Data System (ADS)

Ndaw, Joseph D.; Faye, Andre; Maïga, Amadou S.

2017-05-01

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-DOA) with ANN models. In this paper we present a fast artificial neural network approach for 2D-DOA estimation with reduced training sets sizes. We exploit the symmetry properties of Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.

Social Networks, Support, and Psychosocial Functioning among American Indian Women in Treatment

ERIC Educational Resources Information Center

Chong, Jenny; Lopez, Darlene

2005-01-01

The relationship of social networks and social support to the psychosocial functioning (self-efficacy, self-esteem, anxiety, depression, and hostility) of 159 American Indian women undergoing residential substance abuse treatment at Native American Connections was assessed. Social support and active participation by clients' families during…

A case of a large Chiari network mimicking a right atrial thrombus.

PubMed

Erdogan, Sevinc Bayer; Akansel, Serdar; Sargın, Murat; Mete, Muge Evren Tasdemir; Arslanhan, Gokhan; Aka, Serap Aykut

2017-01-01

The Chiari network is described as a reticulated network of fibers connected to the Eustachian valve identified as the embryological remnant of the right valve of the sinus venosus. It is an incidental finding without any significant pathophysiological consequences. However, the presence of the Chiari network in the right atrium obliges the physician to differentiate from other right atrial pathologies. We present a case of a large Chiari network mimicking a right atrial thrombus with incidental finding in a 76-year-old man undergoing coronary artery bypass surgery.

Properties of true quaternary fission of nuclei with allowance for its multistep and sequential character

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

Kadmensky, S. G., E-mail: kadmensky@phys.vsu.ru; Titova, L. V.; Bulychev, A. O.

An analysis of basicmechanisms of binary and ternary fission of nuclei led to the conclusion that true ternary and quaternary fission of nuclei has a sequential two-step (three-step) character, where, at the first step, a fissile nucleus emits a third light particle (third and fourth light particles) under shakeup effects associated with a nonadiabatic character of its collective deformation motion, whereupon the residual nucleus undergoes fission to two fission fragments. Owing to this, the formulas derived earlier for the widths with respect to sequential two- and three-step decays of nuclei in constructing the theory of two-step twoproton decays and multistepmore » decays in chains of genetically related nuclei could be used to describe the relative yields and angular and energy distributions of third and fourth light particles emitted in (α, α), (t, t), and (α, t) pairs upon the true quaternary spontaneous fission of {sup 252}Cf and thermal-neutron-induced fission of {sup 235}U and {sup 233}U target nuclei. Mechanisms that explain a sharp decrease in the yield of particles appearing second in time and entering into the composition of light-particle pairs that originate from true quaternary fission of nuclei in relation to the yields of analogous particles in true ternary fission of nuclei are proposed.« less

Effects of pretransplant sarcopenia and sequential changes in sarcopenic parameters after living donor liver transplantation.

PubMed

Kaido, Toshimi; Tamai, Yumiko; Hamaguchi, Yuhei; Okumura, Shinya; Kobayashi, Atsushi; Shirai, Hisaya; Yagi, Shintaro; Kamo, Naoko; Hammad, Ahmed; Inagaki, Nobuya; Uemoto, Shinji

2017-01-01

Sarcopenia is characterized by muscle mass depletion and decrease in muscle power or physical activity. We previously reported that low skeletal muscle mass (SMM) is closely involved with posttransplant mortality in patients undergoing living donor liver transplantation (LDLT). The aim of this study was to prospectively investigate the effects of pretransplant sarcopenia on survival and examine sequential changes in sarcopenic parameters after LDLT. Sarcopenia was defined by measuring SMM using a multifrequency body composition analyzer and assessing grip strength (GS) in 72 adults who underwent LDLT at Kyoto University Hospital between January 2013 and October 2015. The effects of pretransplant sarcopenia on short-term survival and sequential changes in SMM and GS were prospectively analyzed. Of 72 patients, 10 (14%) were defined as having pretransplant sarcopenia. Overall survival rates were significantly lower in patients with sarcopenia (n = 10) than those without sarcopenia (n = 62; P < 0.001). SMM worsened after LDLT and did not return to preoperative levels until 1 y after LDLT. In contrast, GS returned to preoperative levels at 6 mo after LDLT, following sharp decrease at 1 mo after LDLT. This prospective study confirmed that pretransplant sarcopenia is closely associated with short-term survival after LDLT and that GS recovers before SMM. Copyright © 2016 Elsevier Inc. All rights reserved.

Outcome of heart-lung and bilateral sequential lung transplantation for cystic fibrosis: a UK national study.

PubMed

Ganesh, J S; Rogers, C A; Bonser, R S; Banner, N R

2005-06-01

Cystic fibrosis (CF) patients requiring transplantation for respiratory failure may undergo either heart-lung (HLT) or bilateral sequential lung (BSLT) transplantation. The choice of operation varies between surgeons, centres and countries. The current authors investigated whether operation type influenced outcome in adult CF patients transplanted in the UK between July 1995 and June 2002. Propensity scores for receipt of BSLT versus HLT were derived using logistic regression. Cox regression was used to compare survival. In total, 88 BSLTs and 93 HLTs were identified. Patient characteristics were similar overall, but HLT recipients were more likely to be on long-term oxygen therapy and to have had prior resuscitation. There were 72 deaths (29 BSLT and 43 HLT) within 4 yrs. There was a trend towards higher unadjusted survival following BSLT, but, after adjustment, no difference was found (hazard ratio = 0.77; 95% confidence interval 0.29-2.06). Time to the first rejection episode and infection rates were also similar. A total of 82% of hearts from HLT recipients were used as domino heart transplants. In conclusion, after adjusting for comorbidity, donor factors and ischaemia time, it was found that heart-lung and bilateral sequential lung transplantation achieved a similar outcome. The use of domino heart transplantation ameliorated the impact of heart-lung transplantation on total organ availability.

Adverse Outcomes in Infantile Bilateral Developmental Dysplasia of the Hip.

PubMed

Morbi, Abigail H M; Carsi, Belen; Gorianinov, Vitalli; Clarke, Nicholas M P

2015-01-01

It is believed that bilateral developmental dysplasia of the hip (DDH) has poorer outcomes with higher rates of avascular necrosis (AVN) and reintervention, compared with unilateral DDH. However, there is limited evidence in the literature, with few studies looking specifically at bilateral cases. A retrospective review of 36 patients (72 hips) with >4 years of follow-up. Patient population included surgically treated DDH including late presentations and failures of conservative treatment. The dislocated hips underwent either simultaneous closed or 1 open and 1 closed, or sequential open reduction. AVN and secondary procedures were used as endpoints for analysis as well as clinical and radiologic outcomes. At the last follow-up, 33% of hips had radiologic signs of AVN. Those hips that had no ossific nucleus (ON) at the time of surgery had an odds ratio of developing AVN of 3.05 and a statistically significant association between the 2 variables, whereas open/closed or simultaneous/sequential reduction did not increase the risk for AVN. In addition, 45.8% of those hips required further surgery. The estimated odds ratio of needing additional surgery after simultaneous reduction was 4.04. Clinically, 79.2% of the hips were graded as McKay I, whereas radiologically only 38.8% were Severin I. The AVN rate in bilateral DDH treated surgically is greater than the rate noted in unilateral cases from the same institution undergoing identical protocols. There was no difference in AVN rates between simultaneous and sequential or between the first and second hip to be sequentially reduced. Presence of ON decreases the risk for AVN, suggesting that in bilateral cases, awaiting the appearance of the ON is an important tool to reduce the incidence of AVN. IV.

Distal end side-to-side anastomoses of sequential vein graft to small target coronary arteries improve intraoperative graft flow

PubMed Central

2014-01-01

Background End-to-side anastomoses to connect the distal end of the great saphenous vein (GSV) to small target coronary arteries are commonly performed in sequential coronary artery bypass grafting (CABG). However, the oversize diameter ratio between the GSV and small target vessels at end-to-side anastomoses might induce adverse hemodynamic condition. The purpose of this study was to describe a distal end side-to-side anastomosis technique and retrospectively compare the effect of distal end side-to-side versus end-to-side anastomosis on graft flow characteristics. Methods We performed side-to-side anastomoses to connect the distal end of the GSV to small target vessels on 30 patients undergoing off-pump sequential CABG in our hospital between October 2012 and July 2013. Among the 30 patients, end-to-side anastomoses at the distal end of the GSV were initially performed on 14 patients; however, due to poor graft flow, those anastomoses were revised into side-to-side anastomoses. We retrospectively compared the intraoperative graft flow characteristics of the end-to-side versus side-to-side anastomoses in the 14 patients. The patient outcomes were also evaluated. Results We found that the side-to-side anastomosis reconstruction improved intraoperative flow and reduced pulsatility index in all the 14 patients significantly. The 16 patients who had the distal end side-to-side anastomoses performed directly also exhibited satisfactory intraoperative graft flow. Three-month postoperative outcomes for all the patients were satisfactory. Conclusions Side-to-side anastomosis at the distal end of sequential vein grafts might be a promising strategy to connect small target coronary arteries to the GSV. PMID:24884776

Parents' Verbal and Nonverbal Caring Behaviors and Child Distress During Cancer-Related Port Access Procedures: A Time-Window Sequential Analysis.

PubMed

Bai, Jinbing; Harper, Felicity W K; Penner, Louis A; Swanson, Kristen; Santacroce, Sheila J

2017-11-01

To study the relationship between parental verbal and nonverbal caring behaviors and child distress during cancer-related port access placement using correlational and time-window sequential analyses.
. Longitudinal, observational design.
. Children's Hospital of Michigan and St. Jude Children's Research Hospital.
. 43 child-parent dyads, each with two or three video recordings of the child undergoing cancer-related port placement.
. Two trained raters coded parent interaction behaviors and child distress using the Parent Caring Response Scoring System and Karmanos Child Coping and Distress Scale, respectively. Mixed modeling with generalized estimating equations examined the associations between parent interaction behaviors and parent distress, child distress, and child cooperation reported by multiple raters. Time-window sequential analyses were performed to investigate the temporal relationships in parent-child interactions within a five-second window.
. Parent caring behaviors, child distress, and child cooperation.
. Parent caring interaction behaviors were significantly correlated with parent distress, child distress, and child cooperation during repeated cancer port accessing. Sequential analyses showed that children were significantly less likely to display behavioral and verbal distress following parent caring behaviors than at any other time. If a child is already distressed, parent verbal and nonverbal caring behaviors can significantly reduce child behavioral and verbal distress.
. Parent caring behaviors, particularly the rarely studied nonverbal behaviors (e.g., eye contact, distance close to touch, supporting/allowing), can reduce the child's distress during cancer port accessing procedures.
. Studying parent-child interactions during painful cancer-related procedures can provide evidence to develop nursing interventions to support parents in caring for their child during painful procedures.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

PubMed

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

DDN (Defense Data Network) Protocol Handbook. Volume 1. DoD Military Standard Protocols

DTIC Science & Technology

1985-12-01

official Military Standard communication protocols in use on the DDN are included, as are several ARPANET (Advanced Research Projects Agency Network... research protocols which are currently in use, and some protocols currently undergoing review. Tutorial information and auxiliary documents are also...compatible with DoD needs, by researchers wishing to improve the protocols, and by impleroentors of local area networks (LANs) wishing their

Following the Footsteps of Chlamydial Gene Regulation

PubMed Central

Domman, D.; Horn, M.

2015-01-01

Regulation of gene expression ensures an organism responds to stimuli and undergoes proper development. Although the regulatory networks in bacteria have been investigated in model microorganisms, nearly nothing is known about the evolution and plasticity of these networks in obligate, intracellular bacteria. The phylum Chlamydiae contains a vast array of host-associated microbes, including several human pathogens. The Chlamydiae are unique among obligate, intracellular bacteria as they undergo a complex biphasic developmental cycle in which large swaths of genes are temporally regulated. Coupled with the low number of transcription factors, these organisms offer a model to study the evolution of regulatory networks in intracellular organisms. We provide the first comprehensive analysis exploring the diversity and evolution of regulatory networks across the phylum. We utilized a comparative genomics approach to construct predicted coregulatory networks, which unveiled genus- and family-specific regulatory motifs and architectures, most notably those of virulence-associated genes. Surprisingly, our analysis suggests that few regulatory components are conserved across the phylum, and those that are conserved are involved in the exploitation of the intracellular niche. Our study thus lends insight into a component of chlamydial evolution that has otherwise remained largely unexplored. PMID:26424812

A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer.

PubMed

Júnez-Ferreira, H E; Herrera, G S

2013-04-01

This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

Random sequential renormalization and agglomerative percolation in networks: application to Erdös-Rényi and scale-free graphs.

PubMed

Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

2011-12-01

We study the statistical behavior under random sequential renormalization (RSR) of several network models including Erdös-Rényi (ER) graphs, scale-free networks, and an annealed model related to ER graphs. In RSR the network is locally coarse grained by choosing at each renormalization step a node at random and joining it to all its neighbors. Compared to previous (quasi-)parallel renormalization methods [Song et al., Nature (London) 433, 392 (2005)], RSR allows a more fine-grained analysis of the renormalization group (RG) flow and unravels new features that were not discussed in the previous analyses. In particular, we find that all networks exhibit a second-order transition in their RG flow. This phase transition is associated with the emergence of a giant hub and can be viewed as a new variant of percolation, called agglomerative percolation. We claim that this transition exists also in previous graph renormalization schemes and explains some of the scaling behavior seen there. For critical trees it happens as N/N(0) → 0 in the limit of large systems (where N(0) is the initial size of the graph and N its size at a given RSR step). In contrast, it happens at finite N/N(0) in sparse ER graphs and in the annealed model, while it happens for N/N(0) → 1 on scale-free networks. Critical exponents seem to depend on the type of the graph but not on the average degree and obey usual scaling relations for percolation phenomena. For the annealed model they agree with the exponents obtained from a mean-field theory. At late times, the networks exhibit a starlike structure in agreement with the results of Radicchi et al. [Phys. Rev. Lett. 101, 148701 (2008)]. While degree distributions are of main interest when regarding the scheme as network renormalization, mass distributions (which are more relevant when considering "supernodes" as clusters) are much easier to study using the fast Newman-Ziff algorithm for percolation, allowing us to obtain very high statistics.

Directed functional connectivity matures with motor learning in a cortical pattern generator.

PubMed

Day, Nancy F; Terleski, Kyle L; Nykamp, Duane Q; Nick, Teresa A

2013-02-01

Sequential motor skills may be encoded by feedforward networks that consist of groups of neurons that fire in sequence (Abeles 1991; Long et al. 2010). However, there has been no evidence of an anatomic map of activation sequence in motor control circuits, which would be potentially detectable as directed functional connectivity of coactive neuron groups. The proposed pattern generator for birdsong, the HVC (Long and Fee 2008; Vu et al. 1994), contains axons that are preferentially oriented in the rostrocaudal axis (Nottebohm et al. 1982; Stauffer et al. 2012). We used four-tetrode recordings to assess the activity of ensembles of single neurons along the rostrocaudal HVC axis in anesthetized zebra finches. We found an axial, polarized neural network in which sequential activity is directionally organized along the rostrocaudal axis in adult males, who produce a stereotyped song. Principal neurons fired in rostrocaudal order and with interneurons that were rostral to them, suggesting that groups of excitatory neurons fire at the leading edge of travelling waves of inhibition. Consistent with the synchronization of neurons by caudally travelling waves of inhibition, the activity of interneurons was more coherent in the orthogonal mediolateral axis than in the rostrocaudal axis. If directed functional connectivity within the HVC is important for stereotyped, learned song, then it may be lacking in juveniles, which sing a highly variable song. Indeed, we found little evidence for network directionality in juveniles. These data indicate that a functionally directed network within the HVC matures during sensorimotor learning and may underlie vocal patterning.

Directed functional connectivity matures with motor learning in a cortical pattern generator

PubMed Central

Day, Nancy F.; Terleski, Kyle L.; Nykamp, Duane Q.

2013-01-01

Sequential motor skills may be encoded by feedforward networks that consist of groups of neurons that fire in sequence (Abeles 1991; Long et al. 2010). However, there has been no evidence of an anatomic map of activation sequence in motor control circuits, which would be potentially detectable as directed functional connectivity of coactive neuron groups. The proposed pattern generator for birdsong, the HVC (Long and Fee 2008; Vu et al. 1994), contains axons that are preferentially oriented in the rostrocaudal axis (Nottebohm et al. 1982; Stauffer et al. 2012). We used four-tetrode recordings to assess the activity of ensembles of single neurons along the rostrocaudal HVC axis in anesthetized zebra finches. We found an axial, polarized neural network in which sequential activity is directionally organized along the rostrocaudal axis in adult males, who produce a stereotyped song. Principal neurons fired in rostrocaudal order and with interneurons that were rostral to them, suggesting that groups of excitatory neurons fire at the leading edge of travelling waves of inhibition. Consistent with the synchronization of neurons by caudally travelling waves of inhibition, the activity of interneurons was more coherent in the orthogonal mediolateral axis than in the rostrocaudal axis. If directed functional connectivity within the HVC is important for stereotyped, learned song, then it may be lacking in juveniles, which sing a highly variable song. Indeed, we found little evidence for network directionality in juveniles. These data indicate that a functionally directed network within the HVC matures during sensorimotor learning and may underlie vocal patterning. PMID:23175804

Programming Cell Adhesion for On-Chip Sequential Boolean Logic Functions.

PubMed

Qu, Xiangmeng; Wang, Shaopeng; Ge, Zhilei; Wang, Jianbang; Yao, Guangbao; Li, Jiang; Zuo, Xiaolei; Shi, Jiye; Song, Shiping; Wang, Lihua; Li, Li; Pei, Hao; Fan, Chunhai

2017-08-02

Programmable remodelling of cell surfaces enables high-precision regulation of cell behavior. In this work, we developed in vitro constructed DNA-based chemical reaction networks (CRNs) to program on-chip cell adhesion. We found that the RGD-functionalized DNA CRNs are entirely noninvasive when interfaced with the fluidic mosaic membrane of living cells. DNA toehold with different lengths could tunably alter the release kinetics of cells, which shows rapid release in minutes with the use of a 6-base toehold. We further demonstrated the realization of Boolean logic functions by using DNA strand displacement reactions, which include multi-input and sequential cell logic gates (AND, OR, XOR, and AND-OR). This study provides a highly generic tool for self-organization of biological systems.

Sequential ranging integration times in the presence of CW interference in the ranging channel

NASA Technical Reports Server (NTRS)

Mathur, Ashok; Nguyen, Tien

1986-01-01

The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, is used primarily for communication with interplanetary spacecraft. The high sensitivity required to achieve planetary communications makes the DSN very susceptible to radio-frequency interference (RFI). In this paper, an analytical model is presented of the performance degradation of the DSN sequential ranging subsystem in the presence of downlink CW interference in the ranging channel. A trade-off between the ranging component integration times and the ranging signal-to-noise ratio to achieve a desired level of range measurement accuracy and the probability of error in the code components is also presented. Numerical results presented illustrate the required trade-offs under various interference conditions.

Enantio- and Diastereoselective Cyclopropanation of 1-Alkenylboronates: Synthesis of 1-Boryl-2,3-Disubstituted Cyclopropanes.

PubMed

Carreras, Javier; Caballero, Ana; Pérez, Pedro J

2018-02-23

A novel, highly enantio- and diastereoselective synthesis of 1-boryl-2,3-disubstituted cyclopropanes has been developed by means of the cyclopropanation of alkenylboronates with ethyl diazoacetate in the presence of catalytic amounts of a chiral copper(I) complex. The products can also be directly accessed from alkynes through an operationally simple, sequential hydroboration-cyclopropanation protocol. The resulting enantioenriched 1-boryl-2,3-disubstituted cyclopropanes are versatile synthetic intermediates that undergo further transformations at the carbon-boron bond. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A simple synthesis of N-perfluoroacylated and N-acylated glycals of neuraminic acid with a cyclic aminic substituent at the 4α position as possible inhibitors of sialidases.

PubMed

Rota, Paola; Allevi, Pietro; Agnolin, Irene S; Mattina, Roberto; Papini, Nadia; Anastasia, Mario

2012-04-14

A simple protocol for the synthesis of N-perfluoroacylated and N-acylated glycals of neuraminic acid, with a secondary cyclic amine (morpholine or piperidine) at the 4α position, has been set-up, starting from peracetylated N-acetylneuraminic acid methyl ester that undergoes, sequentially to its direct N-transacylation followed by a C-4 amination, a β-elimination, and a selective hydrolysis of the ester functions, without affecting the sensitive perfluorinated amide. This journal is © The Royal Society of Chemistry 2012

Linear Algebra and Sequential Importance Sampling for Network Reliability

DTIC Science & Technology

2011-12-01

first test case is an Erdős- Renyi graph with 100 vertices and 150 edges. Figure 1 depicts the relative variance of the three Algorithms: Algorithm TOP...e va ria nc e Figure 1: Relative variance of various algorithms on Erdős Renyi graph, 100 vertices 250 edges. Key: Solid = TOP-DOWN algorithm

Optogenetic dissection reveals multiple rhythmogenic modules underlying locomotion

PubMed Central

Hägglund, Martin; Dougherty, Kimberly J.; Borgius, Lotta; Itohara, Shigeyoshi; Iwasato, Takuji; Kiehn, Ole

2013-01-01

Neural networks in the spinal cord known as central pattern generators produce the sequential activation of muscles needed for locomotion. The overall locomotor network architectures in limbed vertebrates have been much debated, and no consensus exists as to how they are structured. Here, we use optogenetics to dissect the excitatory and inhibitory neuronal populations and probe the organization of the mammalian central pattern generator. We find that locomotor-like rhythmic bursting can be induced unilaterally or independently in flexor or extensor networks. Furthermore, we show that individual flexor motor neuron pools can be recruited into bursting without any activity in other nearby flexor motor neuron pools. Our experiments differentiate among several proposed models for rhythm generation in the vertebrates and show that the basic structure underlying the locomotor network has a distributed organization with many intrinsically rhythmogenic modules. PMID:23798384

Extended target recognition in cognitive radar networks.

PubMed

Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

2010-01-01

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Force measurements in stiff, 3D, opaque granular materials

NASA Astrophysics Data System (ADS)

Hurley, Ryan C.; Hall, Stephen A.; Andrade, José E.; Wright, Jonathan

2017-06-01

We present results from two experiments that provide the first quantification of inter-particle force networks in stiff, 3D, opaque granular materials. Force vectors between all grains were determined using a mathematical optimization technique that seeks to satisfy grain equilibrium and strain measurements. Quantities needed in the optimization - the spatial location of the inter-particle contact network and tensor grain strains - were found using 3D X-ray diffraction and X-ray computed tomography. The statistics of the force networks are consistent with those found in past simulations and 2D experiments. In particular, we observe an exponential decay of normal forces above the mean and a partition of forces into strong and weak networks. In the first experiment, involving 77 single-crystal quartz grains, we also report on the temporal correlation of the force network across two sequential load cycles. In the second experiment, involving 1099 single-crystal ruby grains, we characterize force network statistics at low levels of compression.

Modeling Current-Voltage Charateristics of Proteorhodopsin and Bacteriorhodopsin: Towards an Optoelectronics Based on Proteins.

PubMed

Alfinito, Eleonora; Reggiani, Lino

2016-10-01

Current-voltage characteristics of metal-protein-metal structures made of proteorhodopsin and bacteriorhodopsin are modeled by using a percolation-like approach. Starting from the tertiary structure pertaining to the single protein, an analogous resistance network is created. Charge transfer inside the network is described as a sequential tunneling mechanism and the current is calculated for each value of the given voltage. The theory is validated with available experiments, in dark and light. The role of the tertiary structure of the single protein and of the mechanisms responsible for the photo-activity is discussed.

Self-organized emergence of multilayer structure and chimera states in dynamical networks with adaptive couplings

NASA Astrophysics Data System (ADS)

Kasatkin, D. V.; Yanchuk, S.; Schöll, E.; Nekorkin, V. I.

2017-12-01

We report the phenomenon of self-organized emergence of hierarchical multilayered structures and chimera states in dynamical networks with adaptive couplings. This process is characterized by a sequential formation of subnetworks (layers) of densely coupled elements, the size of which is ordered in a hierarchical way, and which are weakly coupled between each other. We show that the hierarchical structure causes the decoupling of the subnetworks. Each layer can exhibit either a two-cluster state, a periodic traveling wave, or an incoherent state, and these states can coexist on different scales of subnetwork sizes.

Adaptive web sampling.

PubMed

Thompson, Steven K

2006-12-01

A flexible class of adaptive sampling designs is introduced for sampling in network and spatial settings. In the designs, selections are made sequentially with a mixture distribution based on an active set that changes as the sampling progresses, using network or spatial relationships as well as sample values. The new designs have certain advantages compared with previously existing adaptive and link-tracing designs, including control over sample sizes and of the proportion of effort allocated to adaptive selections. Efficient inference involves averaging over sample paths consistent with the minimal sufficient statistic. A Markov chain resampling method makes the inference computationally feasible. The designs are evaluated in network and spatial settings using two empirical populations: a hidden human population at high risk for HIV/AIDS and an unevenly distributed bird population.

Method for star identification using neural networks

NASA Astrophysics Data System (ADS)

Lindsey, Clark S.; Lindblad, Thomas; Eide, Age J.

1997-04-01

Identification of star constellations with an onboard star tracker provides the highest precision of all attitude determination techniques for spacecraft. A method for identification of star constellations inspired by neural network (NNW) techniques is presented. It compares feature vectors derived from histograms of distances to multiple stars around the unknown star. The NNW method appears most robust with respect to position noise and would require a smaller database than conventional methods, especially for small fields of view. The neural network method is quite slow when performed on a sequential (serial) processor, but would provide very high speed if implemented in special hardware. Such hardware solutions could also yield lower low weight and low power consumption, both important features for small satellites.

Spontaneous network activity and synaptic development

PubMed Central

Kerschensteiner, Daniel

2014-01-01

Throughout development, the nervous system produces patterned spontaneous activity. Research over the last two decades has revealed a core group of mechanisms that mediate spontaneous activity in diverse circuits. Many circuits engage several of these mechanisms sequentially to accommodate developmental changes in connectivity. In addition to shared mechanisms, activity propagates through developing circuits and neuronal pathways (i.e. linked circuits in different brain areas) in stereotypic patterns. Increasing evidence suggests that spontaneous network activity shapes synaptic development in vivo. Variations in activity-dependent plasticity may explain how similar mechanisms and patterns of activity can be employed to establish diverse circuits. Here, I will review common mechanisms and patterns of spontaneous activity in emerging neural networks and discuss recent insights into their contribution to synaptic development. PMID:24280071

Creation and perturbation of planar networks of chemical oscillators

PubMed Central

Tompkins, Nathan; Cambria, Matthew Carl; Wang, Adam L.; Heymann, Michael; Fraden, Seth

2015-01-01

Methods for creating custom planar networks of diffusively coupled chemical oscillators and perturbing individual oscillators within the network are presented. The oscillators consist of the Belousov-Zhabotinsky (BZ) reaction contained in an emulsion. Networks of drops of the BZ reaction are created with either Dirichlet (constant-concentration) or Neumann (no-flux) boundary conditions in a custom planar configuration using programmable illumination for the perturbations. The differences between the observed network dynamics for each boundary condition are described. Using light, we demonstrate the ability to control the initial conditions of the network and to cause individual oscillators within the network to undergo sustained period elongation or a one-time phase delay. PMID:26117136

[Bilateral cochlear implants].

PubMed

Müller, J

2017-07-01

Cochlear implants (CI) are standard for the hearing rehabilitation of severe to profound deafness. Nowadays, if bilaterally indicated, bilateral implantation is usually recommended (in accordance with German guidelines). Bilateral implantation enables better speech discrimination in quiet and in noise, and restores directional and spatial hearing. Children with bilateral CI are able to undergo hearing-based hearing and speech development. Within the scope of their individual possibilities, bilaterally implanted children develop faster than children with unilateral CI and attain, e.g., a larger vocabulary within a certain time interval. Only bilateral implantation allows "binaural hearing," with all the benefits that people with normal hearing profit from, namely: better speech discrimination in quiet and in noise, as well as directional and spatial hearing. Naturally, the developments take time. Binaural CI users benefit from the same effects as normal hearing persons: head shadow effect, squelch effect, and summation and redundancy effects. Sequential CI fitting is not necessarily disadvantageous-both simultaneously and sequentially fitted patients benefit in a similar way. For children, earliest possible fitting and shortest possible interval between the two surgeries seems to positively influence the outcome if bilateral CI are indicated.

Spatiotemporal transcriptome provides insights into early fruit development of tomato (Solanum lycopersicum).

PubMed

Zhang, Shuaibin; Xu, Meng; Qiu, Zhengkun; Wang, Ketao; Du, Yongchen; Gu, Lianfeng; Cui, Xia

2016-03-18

Early fruit development is crucial for crop production in tomato. After fertilization, the ovary undergoes cell division and cell expansion before maturation. Although the roles of regulatory signals such as hormone and carbohydrate during early fruit development have been studied, the spatial distribution and the sequential initiation of these regulatory signals still need to be explored. Using the tomato cultivar 'Moneymaker', we analyzed the transcriptome of the ovule and the ovary wall/pericarp dissected from four different stages of the early developing fruits by stereoscope. These datasets give us the whole picture about the spatial and temporal signal distribution in early development of ovule and pericarp. Our results indicate that the hormone signal was initiated in both ovule and pericarp after fertilization. After that, different signals were activated in ovule and pericarp due to their distinct developmental processes. Our study provides spatiotemporal regulatory landscape of gene expression with sequential information which was not studied by previous work and further strengthens the comprehension of the regulatory and metabolic events controlling early fruit development.

Unusual behavior in magnesium-copper cluster matter produced by helium droplet mediated deposition.

PubMed

Emery, S B; Xin, Y; Ridge, C J; Buszek, R J; Boatz, J A; Boyle, J M; Little, B K; Lindsay, C M

2015-02-28

We demonstrate the ability to produce core-shell nanoclusters of materials that typically undergo intermetallic reactions using helium droplet mediated deposition. Composite structures of magnesium and copper were produced by sequential condensation of metal vapors inside the 0.4 K helium droplet baths and then gently deposited onto a substrate for analysis. Upon deposition, the individual clusters, with diameters ∼5 nm, form a cluster material which was subsequently characterized using scanning and transmission electron microscopies. Results of this analysis reveal the following about the deposited cluster material: it is in the un-alloyed chemical state, it maintains a stable core-shell 5 nm structure at sub-monolayer quantities, and it aggregates into unreacted structures of ∼75 nm during further deposition. Surprisingly, high angle annular dark field scanning transmission electron microscopy images revealed that the copper appears to displace the magnesium at the core of the composite cluster despite magnesium being the initially condensed species within the droplet. This phenomenon was studied further using preliminary density functional theory which revealed that copper atoms, when added sequentially to magnesium clusters, penetrate into the magnesium cores.

Anticipating conflict facilitates controlled stimulus-response selection

PubMed Central

Correa, Ángel; Rao, Anling; Nobre, Anna C.

2014-01-01

Cognitive control can be triggered in reaction to previous conflict, as suggested by the finding of sequential effects in conflict tasks. Can control also be triggered proactively by presenting cues predicting conflict (‘proactive control’)? We exploited the high temporal resolution of event-related potentials (ERPs) and controlled for sequential effects to ask whether proactive control based on anticipating conflict modulates neural activity related to cognitive control, as may be predicted from the conflict-monitoring model. ERPs associated with conflict detection (N2) were measured during a cued flanker task. Symbolic cues were either informative or neutral with respect to whether the target involved conflicting or congruent responses. Sequential effects were controlled by analysing the congruency of the previous trial. The results showed that cuing conflict facilitated conflict resolution and reduced the N2 latency. Other potentials (frontal N1 and P3) were also modulated by cuing conflict. Cuing effects were most evident after congruent than after incongruent trials. This interaction between cuing and sequential effects suggests neural overlap between the control networks triggered by proactive and reactive signals. This finding clarifies why previous neuroimaging studies, in which reactive sequential effects were not controlled, have rarely found anticipatory effects upon conflict-related activity. Finally, the high temporal resolution of ERPs was critical to reveal a temporal modulation of conflict detection by proactive control. This novel finding suggests that anticipating conflict speeds up conflict detection and resolution. Recent research suggests that this anticipatory mechanism may be mediated by pre-activation of the ACC during the preparatory interval. PMID:18823248

Quasispecies in population of compositional assemblies.

PubMed

Gross, Renan; Fouxon, Itzhak; Lancet, Doron; Markovitch, Omer

2014-12-30

The quasispecies model refers to information carriers that undergo self-replication with errors. A quasispecies is a steady-state population of biopolymer sequence variants generated by mutations from a master sequence. A quasispecies error threshold is a minimal replication accuracy below which the population structure breaks down. Theory and experimentation of this model often refer to biopolymers, e.g. RNA molecules or viral genomes, while its prebiotic context is often associated with an RNA world scenario. Here, we study the possibility that compositional entities which code for compositional information, intrinsically different from biopolymers coding for sequential information, could show quasispecies dynamics. We employed a chemistry-based model, graded autocatalysis replication domain (GARD), which simulates the network dynamics within compositional molecular assemblies. In GARD, a compotype represents a population of similar assemblies that constitute a quasi-stationary state in compositional space. A compotype's center-of-mass is found to be analogous to a master sequence for a sequential quasispecies. Using single-cycle GARD dynamics, we measured the quasispecies transition matrix (Q) for the probabilities of transition from one center-of-mass Euclidean distance to another. Similarly, the quasispecies' growth rate vector (A) was obtained. This allowed computing a steady state distribution of distances to the center of mass, as derived from the quasispecies equation. In parallel, a steady state distribution was obtained via the GARD equation kinetics. Rewardingly, a significant correlation was observed between the distributions obtained by these two methods. This was only seen for distances to the compotype center-of-mass, and not to randomly selected compositions. A similar correspondence was found when comparing the quasispecies time dependent dynamics towards steady state. Further, changing the error rate by modifying basal assembly joining rate of GARD kinetics was found to display an error catastrophe, similar to the standard quasispecies model. Additional augmentation of compositional mutations leads to the complete disappearance of the master-like composition. Our results show that compositional assemblies, as simulated by the GARD formalism, portray significant attributes of quasispecies dynamics. This expands the applicability of the quasispecies model beyond sequence-based entities, and potentially enhances validity of GARD as a model for prebiotic evolution.

Network Analysis Reveals a Common Host-Pathogen Interaction Pattern in Arabidopsis Immune Responses.

PubMed

Li, Hong; Zhou, Yuan; Zhang, Ziding

2017-01-01

Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein-protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs). We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant-pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

Smoluchowski Equation for Networks: Merger Induced Intermittent Giant Node Formation and Degree Gap

NASA Astrophysics Data System (ADS)

Goto, Hayato; Viegas, Eduardo; Jensen, Henrik Jeldtoft; Takayasu, Hideki; Takayasu, Misako

2018-06-01

The dynamical phase diagram of a network undergoing annihilation, creation, and coagulation of nodes is found to exhibit two regimes controlled by the combined effect of preferential attachment for initiator and target nodes during coagulation and for link assignment to new nodes. The first regime exhibits smooth dynamics and power law degree distributions. In the second regime, giant degree nodes and gaps in the degree distribution are formed intermittently. Data for the Japanese firm network in 1994 and 2014 suggests that this network is moving towards the intermittent switching region.

A High-Performance Recycling Solution for Polystyrene Achieved by the Synthesis of Renewable Poly(thioether) Networks Derived from d-Limonene

PubMed Central

Nash, Landon D.; Rodriguez, Jennifer N.; Lonnecker, Alexander T.; Raymond, Jeffery E.; Wilson, Thomas S.; Wooley, Karen L.; Maitland, Duncan J.

2014-01-01

Nanocomposite polymers have been prepared using a new sustainable materials synthesis process in which d-Limonene functions simultaneously both as a solvent for recycling polystyrene (PS) waste and as a monomer that undergoes UV-catalyzed thiol-ene polymerization reactions with polythiol co-monomers to afford polymeric products comprised of precipitated PS phases dispersed throughout elastomeric poly(thioether) networks. These blended networks exhibit mechanical properties that greatly exceed those of either polystyrene or the poly(thioether) network homopolymers alone. PMID:24249666

A nonaffine network model for elastomers undergoing finite deformations

NASA Astrophysics Data System (ADS)

Davidson, Jacob D.; Goulbourne, N. C.

2013-08-01

In this work, we construct a new physics-based model of rubber elasticity to capture the strain softening, strain hardening, and deformation-state dependent response of rubber materials undergoing finite deformations. This model is unique in its ability to capture large-stretch mechanical behavior with parameters that are connected to the polymer chemistry and can also be easily identified with the important characteristics of the macroscopic stress-stretch response. The microscopic picture consists of two components: a crosslinked network of Langevin chains and an entangled network with chains confined to a nonaffine tube. These represent, respectively, changes in entropy due to thermally averaged chain conformations and changes in entropy due to the magnitude of these conformational fluctuations. A simple analytical form for the strain energy density is obtained using Rubinstein and Panyukov's single-chain description of network behavior. The model only depends on three parameters that together define the initial modulus, extent of strain softening, and the onset of strain hardening. Fits to large stretch data for natural rubber, silicone rubber, VHB 4905 (polyacrylate rubber), and b186 rubber (a carbon black-filled rubber) are presented, and a comparison is made with other similar constitutive models of large-stretch rubber elasticity. We demonstrate that the proposed model provides a complete description of elastomers undergoing large deformations for different applied loading configurations. Moreover, since the strain energy is obtained using a clear set of physical assumptions, this model may be tested and used to interpret the results of computer simulation and experiments on polymers of known microscopic structure.

Optimal Achievable Encoding for Brain Machine Interface

DTIC Science & Technology

2017-12-22

dictionary-based encoding approach to translate a visual image into sequential patterns of electrical stimulation in real time , in a manner that...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and...networks, and by applying linear decoding to complete recorded populations of retinal ganglion cells for the first time . Third, we developed a greedy

Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

PubMed

Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

2017-03-01

We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

Spiking neural network model for memorizing sequences with forward and backward recall.

PubMed

Borisyuk, Roman; Chik, David; Kazanovich, Yakov; da Silva Gomes, João

2013-06-01

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Tsakmalis, Anestis; Chatzinotas, Symeon; Ottersten, Bjorn

2018-02-01

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy.

PubMed

Amidi, Ali; Hosseini, S M Hadi; Leemans, Alexander; Kesler, Shelli R; Agerbæk, Mads; Wu, Lisa M; Zachariae, Robert

2017-12-01

Cisplatin-based chemotherapy may have neurotoxic effects within the central nervous system. The aims of this study were 1) to longitudinally investigate the impact of cisplatin-based chemotherapy on whole-brain networks in testicular cancer patients undergoing treatment and 2) to explore whether possible changes are related to decline in cognitive functioning. Sixty-four newly orchiectomized TC patients underwent structural magnetic resonance imaging (T1-weighted and diffusion-weighted imaging) and cognitive testing at baseline prior to further treatment and again at a six-month follow-up. At follow-up, 22 participants had received cisplatin-based chemotherapy (CT) while 42 were in active surveillance (S). Brain structural networks were constructed for each participant, and network properties were investigated using graph theory and longitudinally compared across groups. Cognitive functioning was evaluated using standardized neuropsychological tests. All statistical tests were two-sided. Compared with the S group, the CT group demonstrated altered global and local brain network properties from baseline to follow-up as evidenced by decreases in important brain network properties such as small-worldness (P = .04), network clustering (P = .04), and local efficiency (P = .02). In the CT group, poorer overall cognitive performance was associated with decreased small-worldness (r = -0.46, P = .04) and local efficiency (r = -0.51, P = .02), and verbal fluency was associated with decreased local efficiency (r = -0.55, P = .008). Brain structural networks may be disrupted following treatment with cisplatin-based chemotherapy. Impaired brain networks may underlie poorer performance over time on both specific and nonspecific cognitive functions in patients undergoing chemotherapy. To the best of our knowledge, this is the first study to longitudinally investigate changes in structural brain networks in a cancer population, providing novel insights regarding the neurobiological mechanisms of cancer-related cognitive impairment.

Stereocontrolled Alkylative Construction of Quaternary Carbon Centers

PubMed Central

Kummer, David A.; Chain, William J.; Morales, Marvin R.; Quiroga, Olga; Myers, Andrew G.

2009-01-01

Protocols for the stereodefined formation of α,α-disubstituted enolates of pseudoephedrine amides are presented followed by the implementation of these in diastereoselective alkylation reactions. Direct alkylation of α,α-disubstituted pseudoephedrine amide substrates is demonstrated to be both efficient and diastereoselective across a range of substrates, as exemplified by alkylation of the diastereomeric pseudoephedrine α-methylbutyramides, where both substrates are found to undergo stereospecific replacement of the α-C-H bond with α-C-alkyl, with retention of stereochemistry. This is shown to arise by sequential stereospecific enolization and alkylation reactions, with the alkyl halide attacking a common π-face of the E- and Z-enolates, proposed to be that opposite the pseudoephedrine alkoxide side-chain. Pseudoephedrine α-phenylbutyramides are found to undergo highly stereoselective but not stereospecific α-alkylation reactions, which evidence suggests is due to facile enolate isomerization. Also, we show that α, α-disubstituted pseudoephedrine amide enolates can be generated in a highly stereocontrolled fashion by conjugate addition of an alkyllithium reagent to the s-cis-conformer of an α-alkyl-α,β-unsaturated pseudoephedrine amide, providing α,α-disubstituted enolate substrates that undergo alkylation in the same sense as those formed by direct deprotonation. Methods are presented to transform the α-quaternary pseudoephedrine amide products into optically active carboxylic acids, ketones, primary alcohols, and aldehydes. PMID:18788739

Mean-field crack networks on desiccated films and their applications: Girl with a Pearl Earring.

PubMed

Flores, J C

2017-02-15

Usual requirements for bulk and fissure energies are considered in obtaining the interdependence among external stress, thickness and area of crack polygons in desiccated films. The average area of crack polygons increases with thickness as a power-law of 4/3. The sequential fragmentation process is characterized by a topological factor related to a scaling finite procedure. Non-sequential overly tensioned (prompt) fragmentation is briefly discussed. Vermeer's painting, Girl with a Pearl Earring, is considered explicitly by using computational image tools and simple experiments and applying the proposed theoretical analysis. In particular, concerning the source of lightened effects on the girl's face, the left/right thickness layer ratio (≈1.34) and the stress ratio (≈1.102) are evaluated. Other master paintings are briefly considered.

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Design of neural networks for fast convergence and accuracy: dynamics and control.

PubMed

Maghami, P G; Sparks, D R

2000-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Sequential detection of temporal communities by estrangement confinement.

PubMed

Kawadia, Vikas; Sreenivasan, Sameet

2012-01-01

Temporal communities are the result of a consistent partitioning of nodes across multiple snapshots of an evolving network, and they provide insights into how dense clusters in a network emerge, combine, split and decay over time. To reliably detect temporal communities we need to not only find a good community partition in a given snapshot but also ensure that it bears some similarity to the partition(s) found in the previous snapshot(s), a particularly difficult task given the extreme sensitivity of community structure yielded by current methods to changes in the network structure. Here, motivated by the inertia of inter-node relationships, we present a new measure of partition distance called estrangement, and show that constraining estrangement enables one to find meaningful temporal communities at various degrees of temporal smoothness in diverse real-world datasets. Estrangement confinement thus provides a principled approach to uncovering temporal communities in evolving networks.

Selective impairment of attention networks during propofol anesthesia after gynecological surgery in middle-aged women.

PubMed

Chen, Chen; Xu, Guang-hong; Li, Yuan-hai; Tang, Wei-xiang; Wang, Kai

2016-04-15

Postoperative cognitive dysfunction is a common complication of anesthesia and surgery. Attention networks are essential components of cognitive function and are subject to impairment after anesthesia and surgery. It is not known whether such impairment represents a global attention deficit or relates to a specific attention network. We used an Attention Network Task (ANT) to examine the efficiency of the alerting, orienting, and executive control attention networks in middle-aged women (40-60 years) undergoing gynecologic surgery. A matched group of medical inpatients were recruited as a control. Fifty female patients undergoing gynecologic surgery (observation group) and 50 female medical inpatients (control group) participated in this study. Preoperatively patients were administered a mini-mental state examination as a screening method. The preoperative efficiencies of three attention networks in an attention network test were compared to the 1st and 5th post-operative days. The control group did not have any significant attention network impairments. On the 1st postoperative day, significant impairment was shown in the alerting (p=0.003 vs. control group, p=0.015 vs. baseline), orienting (p<0.001 vs. both baseline level and control group), and executive control networks (p=0.007 vs. control group, p=0.002 vs. baseline) of the observation group. By the 5th postoperative day, the alerting network efficiency had recovered to preoperative levels (p=0.464 vs. baseline) and the orienting network efficiency had recovered partially (p=0.031 vs. 1st post-operative day), but not to preoperative levels (p=0.01 vs. baseline). The executive control network did not recover by the 5th postoperative day (p=0.001 vs. baseline, p=0.680 vs. 1st post-operative day). Attention networks of middle-aged women show a varying degree of significant impairment and differing levels of recovery after surgery and propofol anesthetic. Copyright © 2016 Elsevier B.V. All rights reserved.

Robustness of networks formed from interdependent correlated networks under intentional attacks

NASA Astrophysics Data System (ADS)

Liu, Long; Meng, Ke; Dong, Zhaoyang

2018-02-01

We study the problem of intentional attacks targeting to interdependent networks generated with known degree distribution (in-degree oriented model) or distribution of interlinks (out-degree oriented model). In both models, each node's degree is correlated with the number of its links that connect to the other network. For both models, varying the correlation coefficient has a significant effect on the robustness of a system undergoing random attacks or attacks targeting nodes with low degree. For a system with an assortative relationship between in-degree and out-degree, reducing the broadness of networks' degree distributions can increase the resistance of systems against intentional attacks.

International collaboration: a retrospective study examining the survival of Irish citizens following lung transplantation in both the UK and Ireland.

PubMed

Adamali, Huzaifa I; Judge, Eoin P; Healy, David; Nolke, Lars; Redmond, Karen C; Bartosik, Waldemar; McCarthy, Jim; Egan, Jim J

2012-01-01

Prior to 2005, Irish citizens had exclusively availed of lung transplantation services in the UK. Since 2005, lung transplantation has been available to these patients in both the UK and Ireland. We aimed to evaluate the outcomes of Irish patients undergoing lung transplantation in both the UK and Ireland. We retrospectively examined the outcome of Irish patients transplanted in the UK and Ireland. Lung allocation score (LAS) was used as a marker of disease severity. A total of 134 patients have undergone transplantation. 102 patients underwent transplantation in the UK and 32 patients in Ireland. In total, 52% were patients with cystic fibrosis, 19% had emphysema and 15% had idiopathic pulmonary fibrosis. In Ireland, 44% of the patients suffered from idiopathic pulmonary fibrosis, 31% had emphysema and 16% had cystic fibrosis. A total of 96 double sequential transplants and 38 single transplants have been performed. LAS of all patients undergoing lung transplantation was 37.8 (±1.02). The mean LAS for patients undergoing lung transplantation in Ireland was 44.7 (±3.1), and 35 (±0.4) for patients undergoing lung transplantation in the UK (p<0.05). The 5-year survival of all Irish citizens who had undergone lung transplantation was 73%. The 5-year survival of Irish patients transplanted in the UK was 69% and in Ireland was 91% and 73% at 5.01 years. International collaboration can be achieved, as evidenced by the favourable outcomes seen in Irish citizens who undergo lung transplantation in both the UK and Ireland. Irish citizens undergoing lung transplantation in Ireland have a higher LAS score. Despite excellent outcomes, an intention-to-treat analysis of the treatment utility (transplant) indicates the limited effectiveness of lung transplantation in Ireland and emphasises the need for increased rates of lung transplantation.

Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks

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

Ramirez Aviles, Camila A.; Rao, Nageswara S.

We consider the problem of inferring the operational state of a reactor facility by using measurements from a radiation sensor network, which is deployed around the facility’s ventilation stack. The radiation emissions from the stack decay with distance, and the corresponding measurements are inherently random with parameters determined by radiation intensity levels at the sensor locations. We fuse measurements from network sensors to estimate the intensity at the stack, and use this estimate in a one-sided Sequential Probability Ratio Test (SPRT) to infer the on/off state of the reactor facility. We demonstrate the superior performance of this method over conventionalmore » majority vote fusers and individual sensors using (i) test measurements from a network of NaI sensors, and (ii) emulated measurements using radioactive effluents collected at a reactor facility stack. We analytically quantify the performance improvements of individual sensors and their networks with adaptive thresholds over those with fixed ones, by using the packing number of the radiation intensity space.« less

Pattern classification by memristive crossbar circuits using ex situ and in situ training.

PubMed

Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B

2013-01-01

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

Pattern classification by memristive crossbar circuits using ex situ and in situ training

NASA Astrophysics Data System (ADS)

Alibart, Fabien; Zamanidoost, Elham; Strukov, Dmitri B.

2013-06-01

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

On-line diagnosis of sequential systems, 3

NASA Technical Reports Server (NTRS)

Sundstrom, R. J.

1975-01-01

A formal model is introduced which can serve as the basis for a theoretical investigation of on-line diagnosis. Within this model a fault of a system S is considered to be a transformation of S into another system S prime at some time tau. The resulting faulty system is taken to be the system which looks like S up to time tau and like S prime thereafter. The on-line diagnosis of systems which are structurally decomposed and represented as a network of smaller systems is also investigated. The fault set considered is the set of unrestricted component faults; namely, the set of faults which only affect one component of the network. A characterization of networks which can be diagnosed using a combinational detector is obtained. It is further shown that any network can be made diagnosable in the above sense through the addition of one component. In addition, a lower bound is obtained on the complexity of any component, the addition of which is sufficient to make a particular network combinationally diagnosable.

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.

PubMed

Tauste Campo, Adrià; Principe, Alessandro; Ley, Miguel; Rocamora, Rodrigo; Deco, Gustavo

2018-04-01

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

Mechanism of the reset process in bipolar-resistance-switching Ta/TaOx/Pt capacitors based on observation of the capacitance and resistance

NASA Astrophysics Data System (ADS)

Na, Sang-Chul; Kim, Jae-Jun; Chul Chun, Min; Hee Jin, Da; Ahn, Seung-Eon; Soo Kang, Bo

2014-03-01

The capacitance (C) and the resistance (R) were measured at various states as the reset process progressed in bipolar-resistance-switching Ta/TaOx/Pt thin film capacitors. The reset process was found to undergo three sequential stages where C and R showed different behavior: increasing C and constant R before an abrupt reset transition, the rapid increase of both C and R upon transition, and saturated C thereafter. These behaviors can be explained in terms of the annihilation of the oxygen vacancies followed by rupture of the conducting channels.

Evidence-based assessment of proton-pump inhibitors in Helicobacter pylori eradication: a systematic review.

PubMed

Nagaraja, Vinayak; Eslick, Guy D

2014-10-28

Peptic ulcer disease continues to be issue especially due to its high prevalence in the developing world. Helicobacter pylori (H. pylori) infection associated duodenal ulcers should undergo eradication therapy. There are many regimens offered for H. pylori eradication which include triple, quadruple, or sequential therapy regimens. The central aim of this systematic review is to evaluate the evidence for H. pylori therapy from a meta-analytical outlook. The consequence of the dose, type of proton-pump inhibitor, and the length of the treatment will be debated. The most important risk factor for eradication failure is resistance to clarithromycin and metronidazole.

Visualizing non-equilibrium lithiation of spinel oxide via in situ transmission electron microscopy

DOE PAGES

He, Kai; Zhang, Sen; Li, Jing; ...

2016-05-09

In this study, spinel transition metal oxides are an important class of materials that are being considered as electrodes for lithium-ion batteries, due to their low cost and high theoretical capacity. The lithiation of these compounds is known to undergo a two-step reaction, whereby intercalation and conversion occur in a sequential fashion. These two reactions are known to have distinct reaction dynamics, but it is unclear how the kinetics of these processes affect the overall electrochemical response. Here, we explore the lithiation of nanosized magnetite (F e3O 4) by employing a new strain-sensitive, bright-field scanning transmission electron microscopy approach.

Structure of a randomly grown 2-d network.

PubMed

Ajazi, Fioralba; Napolitano, George M; Turova, Tatyana; Zaurbek, Izbassar

2015-10-01

We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Neuronal network model of interictal and recurrent ictal activity

NASA Astrophysics Data System (ADS)

Lopes, M. A.; Lee, K.-E.; Goltsev, A. V.

2017-12-01

We propose a neuronal network model which undergoes a saddle node on an invariant circle bifurcation as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.

Fibrin network changes in neonates after cardiopulmonary bypass

PubMed Central

Brown, Ashley C.; Hannan, Riley; Timmins, Lucas H.; Fernandez, Janet D.; Barker, Thomas H.; Guzzetta, Nina A.

2016-01-01

Background Quantitative and qualitative differences exist between the hemostatic systems of neonates and adults, among them the presence of ‘fetal’ fibrinogen, a qualitatively dysfunctional form of fibrinogen that exists until one year of age. The consequences of ‘fetal’ fibrinogen on clot structure in neonates, particularly in the context of surgical associated bleeding, have not been well characterized. Here we examine the sequential changes in clotting components and resultant clot structure in a small sample of neonates undergoing cardiac surgery and cardiopulmonary bypass (CPB). Methods Blood samples were collected from neonates (n=10) before surgery, immediately after CPB and following the transfusion of cryoprecipitate (i.e. adult fibrinogen component). Clots were formed from patient samples or purified neonatal and adult fibrinogen. Clot structure was analyzed using confocal microscopy. Results Clots formed from plasma obtained after CPB and after transfusion were more porous than baseline clots. Analysis of clots formed from purified neonatal and adult fibrinogen, demonstrated that at equivalent fibrinogen concentrations, neonatal clots lack three-dimensional structure while adult clots were denser with significant three-dimensional structure. Clots formed from a combination of purified neonatal and adult fibrinogen were less homogenous than those formed from either purified adult or neonatal fibrinogen. Conclusions Our results confirm that significant differences exist in clot structure between neonates and adults, and that neonatal and adult fibrinogen may not integrate well. These findings suggest that differential treatment strategies for neonates should be pursued to reduce the demonstrated morbidity of blood product transfusion. PMID:26914227

Recursive Bayesian recurrent neural networks for time-series modeling.

PubMed

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Self-adaptive tensor network states with multi-site correlators

NASA Astrophysics Data System (ADS)

Kovyrshin, Arseny; Reiher, Markus

2017-12-01

We introduce the concept of self-adaptive tensor network states (SATNSs) based on multi-site correlators. The SATNS ansatz gradually extends its variational space incorporating the most important next-order correlators into the ansatz for the wave function. The selection of these correlators is guided by entanglement-entropy measures from quantum information theory. By sequentially introducing variational parameters and adjusting them to the system under study, the SATNS ansatz achieves keeping their number significantly smaller than the total number of full-configuration interaction parameters. The SATNS ansatz is studied for manganocene in its lowest-energy sextet and doublet states; the latter of which is known to be difficult to describe. It is shown that the SATNS parametrization solves the convergence issues found for previous correlator-based tensor network states.

Observation Uncertainty in Gaussian Sensor Networks

DTIC Science & Technology

2006-01-23

Ziv , J., and Lempel , A. A universal algorithm for sequential data compression . IEEE Transactions on Information Theory 23, 3 (1977), 337–343. 73 ...using the Lempel - Ziv algorithm [42], context-tree weighting [41], or the Burrows-Wheeler Trans- form [4], [15], for example. These source codes will...and Computation (Monticello, IL, September 2004). [4] Burrows, M., and Wheeler, D. A block sorting lossless data compression algorithm . Tech.

Epidemic Threshold in Structured Scale-Free Networks

NASA Astrophysics Data System (ADS)

EguíLuz, VíCtor M.; Klemm, Konstantin

2002-08-01

We analyze the spreading of viruses in scale-free networks with high clustering and degree correlations, as found in the Internet graph. For the susceptible-infected-susceptible model of epidemics the prevalence undergoes a phase transition at a finite threshold of the transmission probability. Comparing with the absence of a finite threshold in networks with purely random wiring, our result suggests that high clustering (modularity) and degree correlations protect scale-free networks against the spreading of viruses. We introduce and verify a quantitative description of the epidemic threshold based on the connectivity of the neighborhoods of the hubs.

Resilience of networks formed of interdependent modular networks

NASA Astrophysics Data System (ADS)

Shekhtman, Louis M.; Shai, Saray; Havlin, Shlomo

2015-12-01

Many infrastructure networks have a modular structure and are also interdependent with other infrastructures. While significant research has explored the resilience of interdependent networks, there has been no analysis of the effects of modularity. Here we develop a theoretical framework for attacks on interdependent modular networks and support our results through simulations. We focus, for simplicity, on the case where each network has the same number of communities and the dependency links are restricted to be between pairs of communities of different networks. This is particularly realistic for modeling infrastructure across cities. Each city has its own infrastructures and different infrastructures are dependent only within the city. However, each infrastructure is connected within and between cities. For example, a power grid will connect many cities as will a communication network, yet a power station and communication tower that are interdependent will likely be in the same city. It has previously been shown that single networks are very susceptible to the failure of the interconnected nodes (between communities) (Shai et al 2014 arXiv:1404.4748) and that attacks on these nodes are even more crippling than attacks based on betweenness (da Cunha et al 2015 arXiv:1502.00353). In our example of cities these nodes have long range links which are more likely to fail. For both treelike and looplike interdependent modular networks we find distinct regimes depending on the number of modules, m. (i) In the case where there are fewer modules with strong intraconnections, the system first separates into modules in an abrupt first-order transition and then each module undergoes a second percolation transition. (ii) When there are more modules with many interconnections between them, the system undergoes a single transition. Overall, we find that modular structure can significantly influence the type of transitions observed in interdependent networks and should be considered in attempts to make interdependent networks more resilient.

Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

NASA Astrophysics Data System (ADS)

Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

2018-04-01

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

Spatial spreading of infectious disease via local and national mobility networks in South Korea

NASA Astrophysics Data System (ADS)

Kwon, Okyu; Son, Woo-Sik

2017-12-01

We study the spread of infectious disease based on local- and national-scale mobility networks. We construct a local mobility network using data on urban bus services to estimate local-scale movement of people. We also construct a national mobility network from orientation-destination data of vehicular traffic between highway tollgates to evaluate national-scale movement of people. A metapopulation model is used to simulate the spread of epidemics. Thus, the number of infected people is simulated using a susceptible-infectious-recovered (SIR) model within the administrative division, and inter-division spread of infected people is determined through local and national mobility networks. In this paper, we consider two scenarios for epidemic spread. In the first, the infectious disease only spreads through local-scale movement of people, that is, the local mobility network. In the second, it spreads via both local and national mobility networks. For the former, the simulation results show infected people sequentially spread to neighboring divisions. Yet for the latter, we observe a faster spreading pattern to distant divisions. Thus, we confirm the national mobility network enhances synchronization among the incidence profiles of all administrative divisions.

A hierarchical clustering scheme approach to assessment of IP-network traffic using detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Takuma, Takehisa; Masugi, Masao

2009-03-01

This paper presents an approach to the assessment of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, we use a hierarchical clustering scheme, which provides a way to classify high-dimensional data with a tree-like structure. Also, in the LRD-based analysis, we employ detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. Based on sequential measurements of IP-network traffic at two locations, this paper derives corresponding values for the LRD-related parameter α that reflects the degree of LRD of measured data. In performing the hierarchical clustering scheme, we use three parameters: the α value, average throughput, and the proportion of network traffic that exceeds 80% of network bandwidth for each measured data set. We visually confirm that the traffic data can be classified in accordance with the network traffic properties, resulting in that the combined depiction of the LRD and other factors can give us an effective assessment of network conditions at different times.

Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network.

PubMed

Ito, Hidekatsu; Minoshima, Wataru; Kudoh, Suguru N

2015-08-01

To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

Youth's social network structures and peer influences: study protocol MyMovez project - Phase I.

PubMed

Bevelander, Kirsten E; Smit, Crystal R; van Woudenberg, Thabo J; Buijs, Laura; Burk, William J; Buijzen, Moniek

2018-04-16

Youth are an important target group for social network interventions, because they are particularly susceptible to the adaptation of healthy and unhealthy habits and behaviors of others. They are surrounded by 'social influence agents' (i.e., role models such as family, friends and peers) that co-determine their dietary intake and physical activity. However, there is a lack of systematic and comprehensive research on the implementation of a social network approach in health campaigns. The MyMovez research project aims to fill this gap by developing a method for effective social network campaign implementation. This protocol paper describes the design and methods of Phase I of the MyMovez project, aiming to unravel youth's social network structures in combination with individual, psychosocial, and environmental factors related to energy intake and expenditure. In addition, the Wearable Lab is developed to enable an attractive and state-of-the-art way of collecting data and online campaign implementation via social networks. Phase I of the MyMovez project consists of a large-scale cross-sequential cohort study (N = 953; 8-12 and 12-15 y/o). In five waves during a 3-year period (2016-2018), data are collected about youth's social network exposure, media consumption, socialization experiences, psychological determinants of behavior, physical environment, dietary intake (snacking and drinking behavior) and physical activity using the Wearable Lab. The Wearable Lab exists of a smartphone-based research application (app) connected to an activity tracking bracelet, that is developed throughout the duration of the project. It generates peer- and self-reported (e.g., sociometric data and surveys) and experience sampling data, social network beacon data, real-time physical activity data (i.e., steps and cycling), location information, photos and chat conversation data from the app's social media platform Social Buzz. The MyMovez project - Phase I is an innovative cross-sequential research project that investigates how social influences co-determine youth's energy intake and expenditure. This project utilizes advanced research technologies (Wearable Lab) that provide unique opportunities to better understand the underlying processes that impact youths' health-related behaviors. The project is theoretically and methodologically pioneering and produces a unique and useful method for successfully implementing and improving health campaigns.

Learning and coding in biological neural networks

NASA Astrophysics Data System (ADS)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and theoretical results on the scalability of this rule show that learning with stochastic gradient ascent may be adequately fast to explain learning in the bird. Finally, we address the more general issue of the scalability of stochastic gradient learning on quadratic cost surfaces in linear systems, as a function of system size and task characteristics, by deriving analytical expressions for the learning curves.

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Neuro-evolutionary computing paradigm for Painlevé equation-II in nonlinear optics

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Ahmad, Sufyan; Awais, Muhammad; Ul Islam Ahmad, Siraj; Asif Zahoor Raja, Muhammad

2018-05-01

The aim of this study is to investigate the numerical treatment of the Painlevé equation-II arising in physical models of nonlinear optics through artificial intelligence procedures by incorporating a single layer structure of neural networks optimized with genetic algorithms, sequential quadratic programming and active set techniques. We constructed a mathematical model for the nonlinear Painlevé equation-II with the help of networks by defining an error-based cost function in mean square sense. The performance of the proposed technique is validated through statistical analyses by means of the one-way ANOVA test conducted on a dataset generated by a large number of independent runs.

Energy optimization in mobile sensor networks

NASA Astrophysics Data System (ADS)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

PubMed

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Recurrent Neural Network for Computing the Drazin Inverse.

PubMed

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities.

PubMed

Matsubara, Takashi; Torikai, Hiroyuki

2016-04-01

Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.

Quantitative EEG and Low-Resolution Electromagnetic Tomography (LORETA) Imaging of Patients Undergoing Methadone Treatment for Opiate Addiction.

PubMed

Wang, Grace Y; Kydd, Robert R; Russell, Bruce R

2016-07-01

Methadone maintenance treatment (MMT) has been used as a treatment for opiate dependence since the mid-1960s. Evidence suggests that methadone binds to mu opiate receptors as do other opiates and induces changes in neurophysiological function. However, little is known, about how neural activity within the higher frequency gamma band (>30 Hz) while at rest changes in those stabilized on MMT despite its association with the excitation-inhibition balance within pyramidal-interneuron networks. Our study investigated differences in resting gamma power (37-41 Hz) between patients undergoing MMT for opiate dependence, illicit opiate users, and healthy controls subjects. Electroencephalographic data were recorded from 26 sites according to the international 10-20 system. Compared with the healthy controls subjects, people either undergoing MMT (mean difference [MD] = 0.32, 95% CI = 0.09-0.55, P < .01) or currently using illicit opiates (MD = 0.31, 95% CI = 0.06-0.56, P = .01) exhibited significant increased gamma power. The sLORETA (standardized low-resolution electromagnetic tomography) between-group comparison revealed dysfunctional neuronal activity in the occipital, parietal, and frontal lobes in the patients undergoing MMT. A more severe profile of dysfunction was observed in those using illicit opiates. Our findings suggest that long-term exposure to opioids is associated with disrupted resting state network, which may be reduced after MMT. © EEG and Clinical Neuroscience Society (ECNS) 2015.

Decoding of finger trajectory from ECoG using deep learning.

PubMed

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Decoding of finger trajectory from ECoG using deep learning

NASA Astrophysics Data System (ADS)

Xie, Ziqian; Schwartz, Odelia; Prasad, Abhishek

2018-06-01

Objective. Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs. Approach. We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal. Main results. We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state. Significance. This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.

Postoperative seizure freedom does not normalize altered connectivity in temporal lobe epilepsy.

PubMed

Maccotta, Luigi; Lopez, Mayra A; Adeyemo, Babatunde; Ances, Beau M; Day, Brian K; Eisenman, Lawrence N; Dowling, Joshua L; Leuthardt, Eric C; Schlaggar, Bradley L; Hogan, Robert Edward

2017-11-01

Specific changes in the functional connectivity of brain networks occur in patients with epilepsy. Yet whether such changes reflect a stable disease effect or one that is a function of active seizure burden remains unclear. Here, we longitudinally assessed the connectivity of canonical cognitive functional networks in patients with intractable temporal lobe epilepsy (TLE), both before and after patients underwent epilepsy surgery and achieved seizure freedom. Seventeen patients with intractable TLE who underwent epilepsy surgery with Engel class I outcome and 17 matched healthy controls took part in the study. The functional connectivity of a set of cognitive functional networks derived from typical cognitive tasks was assessed in patients, preoperatively and postoperatively, as well as in controls, using stringent methods of artifact reduction. Preoperatively, functional networks in TLE patients differed significantly from healthy controls, with differences that largely, but not exclusively, involved the default mode and temporal/auditory subnetworks. However, undergoing epilepsy surgery and achieving seizure freedom did not lead to significant changes in network connectivity, with postoperative functional network abnormalities closely mirroring the preoperative state. This result argues for a stable chronic effect of the disease on brain connectivity, with changes that are largely "burned in" by the time a patient with intractable TLE undergoes epilepsy surgery, which typically occurs years after the initial diagnosis. The result has potential implications for the treatment of intractable epilepsy, suggesting that delaying surgical intervention that may achieve seizure freedom may lead to functional network changes that are no longer reversible by the time of epilepsy surgery. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Acetylcholine-modulated plasticity in reward-driven navigation: a computational study.

PubMed

Zannone, Sara; Brzosko, Zuzanna; Paulsen, Ole; Clopath, Claudia

2018-06-21

Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike-Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules.

Mind-to-mind heteroclinic coordination: Model of sequential episodic memory initiation.

PubMed

Afraimovich, V S; Zaks, M A; Rabinovich, M I

2018-05-01

Retrieval of episodic memory is a dynamical process in the large scale brain networks. In social groups, the neural patterns, associated with specific events directly experienced by single members, are encoded, recalled, and shared by all participants. Here, we construct and study the dynamical model for the formation and maintaining of episodic memory in small ensembles of interacting minds. We prove that the unconventional dynamical attractor of this process-the nonsmooth heteroclinic torus-is structurally stable within the Lotka-Volterra-like sets of equations. Dynamics on this torus combines the absence of chaos with asymptotic instability of every separate trajectory; its adequate quantitative characteristics are length-related Lyapunov exponents. Variation of the coupling strength between the participants results in different types of sequential switching between metastable states; we interpret them as stages in formation and modification of the episodic memory.

Mind-to-mind heteroclinic coordination: Model of sequential episodic memory initiation

NASA Astrophysics Data System (ADS)

Afraimovich, V. S.; Zaks, M. A.; Rabinovich, M. I.

2018-05-01

Retrieval of episodic memory is a dynamical process in the large scale brain networks. In social groups, the neural patterns, associated with specific events directly experienced by single members, are encoded, recalled, and shared by all participants. Here, we construct and study the dynamical model for the formation and maintaining of episodic memory in small ensembles of interacting minds. We prove that the unconventional dynamical attractor of this process—the nonsmooth heteroclinic torus—is structurally stable within the Lotka-Volterra-like sets of equations. Dynamics on this torus combines the absence of chaos with asymptotic instability of every separate trajectory; its adequate quantitative characteristics are length-related Lyapunov exponents. Variation of the coupling strength between the participants results in different types of sequential switching between metastable states; we interpret them as stages in formation and modification of the episodic memory.

Parallel solution of closely coupled systems

NASA Technical Reports Server (NTRS)

Utku, S.; Salama, M.

1986-01-01

The odd-even permutation and associated unitary transformations for reordering the matrix coefficient A are employed as means of breaking the strong seriality which is characteristic of closely coupled systems. The nested dissection technique is also reviewed, and the equivalence between reordering A and dissecting its network is established. The effect of transforming A with odd-even permutation on its topology and the topology of its Cholesky factors is discussed. This leads to the construction of directed graphs showing the computational steps required for factoring A, their precedence relationships and their sequential and concurrent assignment to the available processors. Expressions for the speed-up and efficiency of using N processors in parallel relative to the sequential use of a single processor are derived from the directed graph. Similar expressions are also derived when the number of available processors is fewer than required.

Cascading failure in scale-free networks with tunable clustering

NASA Astrophysics Data System (ADS)

Zhang, Xue-Jun; Gu, Bo; Guan, Xiang-Min; Zhu, Yan-Bo; Lv, Ren-Li

2016-02-01

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

Sequential multiple-assignment randomized trial design of neurobehavioral treatment for patients with metastatic malignant melanoma undergoing high-dose interferon-alpha therapy.

PubMed

Auyeung, S Freda; Long, Qi; Royster, Erica Bruce; Murthy, Smitha; McNutt, Marcia D; Lawson, David; Miller, Andrew; Manatunga, Amita; Musselman, Dominique L

2009-10-01

Interferon-alpha therapy, which is used to treat metastatic malignant melanoma, can cause patients to develop two distinct neurobehavioral symptom complexes: a mood syndrome and a neurovegetative syndrome. Interferon-alpha effects on serotonin metabolism appear to contribute to the mood and anxiety syndrome, while the neurovegetative syndrome appears to be related to interferon-alpha effects on dopamine. Our goal is to propose a design for utilizing a sequential, multiple assignment, randomized trial design for patients with malignant melanoma to test the relative efficacy of drugs that target serotonin versus dopamine metabolism during 4 weeks of intravenous, then 8 weeks of subcutaneous, interferon-alpha therapy. Patients will be offered participation in a double-blinded, randomized, controlled, 14-week trial involving two treatment phases. During the first month of intravenous interferon-alpha therapy, we will test the hypotheses that escitalopram will be more effective in reducing depressed mood, anxiety, and irritability, whereas methylphenidate will be more effective in diminishing interferon-alpha-induced neurovegetative symptoms, such as fatigue and psychomotor slowing. During the next 8 weeks of subcutaneous interferon therapy, participants whose symptoms do not improve significantly will be randomized to the alternate agent alone versus escitalopram and methylphenidate together. We present a prototype for a single-center, sequential, multiple assignment, randomized trial, which seeks to determine the efficacy of sequenced and targeted treatment for the two distinct symptom complexes suffered by patients treated with interferon-alpha. Because we cannot completely control for external factors, a relevant question is whether or not 'short-term' neuropsychiatric interventions can increase the number of interferon-alpha doses tolerated and improve long-term survival. This sequential, multiple assignment, randomized trial proposes a framework for developing optimal treatment strategies; however, additional studies are needed to determine the best strategy for treating or preventing neurobehavioral symptoms induced by the immunotherapy interferon-alpha.

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model

PubMed Central

Gönner, Lorenz; Vitay, Julien; Hamker, Fred H.

2017-01-01

Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion. To address these questions, we propose a model of spatial learning and sequence generation as interdependent processes, integrating cortical contextual coding, synaptic plasticity and neuromodulatory mechanisms into a map-based approach. Following goal learning, sequential activity emerges from continuous attractor network dynamics biased by goal memory inputs. We apply Bayesian decoding on the resulting spike trains, allowing a direct comparison with experimental data. Simulations show that this model (1) explains the generation of never-experienced sequence trajectories in familiar environments, without requiring virtual self-motion signals, (2) accounts for the bias in place-cell sequences toward goal locations, (3) highlights their utility in flexible route planning, and (4) provides specific testable predictions. PMID:29075187

Information transfer network of global market indices

NASA Astrophysics Data System (ADS)

Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

2015-07-01

We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

Dissociating hippocampal and striatal contributions to sequential prediction learning

PubMed Central

Bornstein, Aaron M.; Daw, Nathaniel D.

2011-01-01

Behavior may be generated on the basis of many different kinds of learned contingencies. For instance, responses could be guided by the direct association between a stimulus and response, or by sequential stimulus-stimulus relationships (as in model-based reinforcement learning or goal-directed actions). However, the neural architecture underlying sequential predictive learning is not well-understood, in part because it is difficult to isolate its effect on choice behavior. To track such learning more directly, we examined reaction times (RTs) in a probabilistic sequential picture identification task. We used computational learning models to isolate trial-by-trial effects of two distinct learning processes in behavior, and used these as signatures to analyze the separate neural substrates of each process. RTs were best explained via the combination of two delta rule learning processes with different learning rates. To examine neural manifestations of these learning processes, we used functional magnetic resonance imaging to seek correlates of timeseries related to expectancy or surprise. We observed such correlates in two regions, hippocampus and striatum. By estimating the learning rates best explaining each signal, we verified that they were uniquely associated with one of the two distinct processes identified behaviorally. These differential correlates suggest that complementary anticipatory functions drive each region's effect on behavior. Our results provide novel insights as to the quantitative computational distinctions between medial temporal and basal ganglia learning networks and enable experiments that exploit trial-by-trial measurement of the unique contributions of both hippocampus and striatum to response behavior. PMID:22487032

ProperCAD: A portable object-oriented parallel environment for VLSI CAD

NASA Technical Reports Server (NTRS)

Ramkumar, Balkrishna; Banerjee, Prithviraj

1993-01-01

Most parallel algorithms for VLSI CAD proposed to date have one important drawback: they work efficiently only on machines that they were designed for. As a result, algorithms designed to date are dependent on the architecture for which they are developed and do not port easily to other parallel architectures. A new project under way to address this problem is described. A Portable object-oriented parallel environment for CAD algorithms (ProperCAD) is being developed. The objectives of this research are (1) to develop new parallel algorithms that run in a portable object-oriented environment (CAD algorithms using a general purpose platform for portable parallel programming called CARM is being developed and a C++ environment that is truly object-oriented and specialized for CAD applications is also being developed); and (2) to design the parallel algorithms around a good sequential algorithm with a well-defined parallel-sequential interface (permitting the parallel algorithm to benefit from future developments in sequential algorithms). One CAD application that has been implemented as part of the ProperCAD project, flat VLSI circuit extraction, is described. The algorithm, its implementation, and its performance on a range of parallel machines are discussed in detail. It currently runs on an Encore Multimax, a Sequent Symmetry, Intel iPSC/2 and i860 hypercubes, a NCUBE 2 hypercube, and a network of Sun Sparc workstations. Performance data for other applications that were developed are provided: namely test pattern generation for sequential circuits, parallel logic synthesis, and standard cell placement.

An Assessment of ELINT Exploitation for Situational Awareness Visualisations on Operator Situational Awareness

DTIC Science & Technology

2006-10-01

C L A S I F I C A T I O N An Assessment of ELINT Exploitation for Situational Awareness Visualisations on Operator Situational...environment. The An Assessment of ELINT Exploitation for Situational Awareness Visualisations on Operator Situational Awareness EXECUTIVE SUMMARY...1. ELEXSA’s Process of Sequential Enrichment. 2 Figure 2. Sample ELEXSA Visualisation . 3 Figure 3. Example Llama/Cheetah Network Layout. 5

Orbit Determination Using Vinti’s Solution

DTIC Science & Technology

2016-09-15

Surveillance Network STK Systems Tool Kit TBP Two Body Problem TLE Two-line Element Set xv Acronym Definition UKF Unscented Kalman Filter WPAFB Wright...simplicity, stability, and speed. On the other hand, Kalman filters would be best suited for sequential estimation of stochastic or random components of a...be likened to how an Unscented Kalman Filter samples a system’s nonlinearities directly, avoiding linearizing the dynamics in the partials matrices

Princeton VLSI Project.

DTIC Science & Technology

1984-01-01

software, or using a local area network of personal computers. Since the hardware is not designed around the algorithms, improvements in the sequential...Hall, 1982. [4] Robinson, RW., and N.C. Wormald. Numbers of Cubic Graphs. Journa [5] Lane, T. Carnegie-Mellon University. Personal communication, 1/31...34Amdahl’s constant." It is not entirely clear why commercial machines have stayed close to this value, but market forces appear to have played an

Structured Representations in the Control of Behavior Cannot Be So Easily Dismissed: A Reply to Botvinick and Plaut (2006)

ERIC Educational Resources Information Center

Cooper, Richard P.; Shallice, Tim

2006-01-01

M. Botvinick and D. C. Plaut (see record 2006-12689-009) argued that many of the criticisms of their earlier simple recurrent network (SRN) model of routine sequential action raised by R. P. Cooper and T. Shallice (see record 2006-12689-008) were criticisms of the specific implementation rather than criticisms of the underlying theory. Cooper and…

On-Line Data Reconstruction in Redundant Disk Arrays.

DTIC Science & Technology

1994-05-01

each sale, - file servers that support a large number of clients with differing work schedules , and * automated teller networks in banking systems...24KB Head scheduling : FIFO User data layout: Sequential in address space of array Disk spindles: Synchronized Table 2.2: Default array parameters for...package and a set of scheduling and queueing routines. 2.3.3. Default workload This dissertation reports on many performance evaluations. In order to

Central Metabolic Responses to Ozone and Herbivory Affect Photosynthesis and Stomatal Closure1[OPEN

PubMed Central

Khaling, Eliezer; Lassueur, Steve

2016-01-01

Plants have evolved adaptive mechanisms that allow them to tolerate a continuous range of abiotic and biotic stressors. Tropospheric ozone (O3), a global anthropogenic pollutant, directly affects living organisms and ecosystems, including plant-herbivore interactions. In this study, we investigate the stress responses of Brassica nigra (wild black mustard) exposed consecutively to O3 and the specialist herbivore Pieris brassicae. Transcriptomics and metabolomics data were evaluated using multivariate, correlation, and network analyses for the O3 and herbivory responses. O3 stress symptoms resembled those of senescence and phosphate starvation, while a sequential shift from O3 to herbivory induced characteristic plant defense responses, including a decrease in central metabolism, induction of the jasmonic acid/ethylene pathways, and emission of volatiles. Omics network and pathway analyses predicted a link between glycerol and central energy metabolism that influences the osmotic stress response and stomatal closure. Further physiological measurements confirmed that while O3 stress inhibited photosynthesis and carbon assimilation, sequential herbivory counteracted the initial responses induced by O3, resulting in a phenotype similar to that observed after herbivory alone. This study clarifies the consequences of multiple stress interactions on a plant metabolic system and also illustrates how omics data can be integrated to generate new hypotheses in ecology and plant physiology. PMID:27758847

Sequential analysis of sperm functional aspects involved in fertilisation: a pilot study.

PubMed

Abu, D A H; Franken, D R; Hoffman, B; Henkel, R

2012-05-01

The development of diagnostic techniques in andrology as a second level of approach to the diagnosis of male factor infertility has enthused the focus of researchers on the development of a sequential diagnostic programme for these men. Semen samples of 78 men form couples undergoing in vitro fertilisation therapy were used in the study. The semen samples were used to test sperm functional aspects known to interfere with fertilisation. These tests included semen profile, DNA integrity, apoptosis, chromatin packaging, acridin orange staining, zona binding capacity, zona-induced acrosome reaction (AR). Results were correlated with fertilisation outcome. Statistical analyses of the recorded data were carried out using a logistic regression analysis model on all sperm functional tests. A negative and significant association with the fertilisation rates was recorded for DNA damage (r = -0.56; P ≤ 0.0005). A positive significant correlation was recorded between fertilisation rates and sperm with normal DNA (r = -0.57, P ≤ 0.0004), and zona-induced AR (r = 0.33, P ≤ 0.002). Diagnostic andrology can be regarded as a mandatory part of the male factor patient's work-up schedule to assist clinicians with the most suitable therapeutic modality to follow. © 2011 Blackwell Verlag GmbH.

Unusual behavior in magnesium-copper cluster matter produced by helium droplet mediated deposition

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

Emery, S. B., E-mail: samuel.emery@navy.mil; Little, B. K.; Air Force Research Laboratory, Munitions Directorate, 2306 Perimeter Rd., Eglin AFB, Florida 32542

2015-02-28

We demonstrate the ability to produce core-shell nanoclusters of materials that typically undergo intermetallic reactions using helium droplet mediated deposition. Composite structures of magnesium and copper were produced by sequential condensation of metal vapors inside the 0.4 K helium droplet baths and then gently deposited onto a substrate for analysis. Upon deposition, the individual clusters, with diameters ∼5 nm, form a cluster material which was subsequently characterized using scanning and transmission electron microscopies. Results of this analysis reveal the following about the deposited cluster material: it is in the un-alloyed chemical state, it maintains a stable core-shell 5 nm structuremore » at sub-monolayer quantities, and it aggregates into unreacted structures of ∼75 nm during further deposition. Surprisingly, high angle annular dark field scanning transmission electron microscopy images revealed that the copper appears to displace the magnesium at the core of the composite cluster despite magnesium being the initially condensed species within the droplet. This phenomenon was studied further using preliminary density functional theory which revealed that copper atoms, when added sequentially to magnesium clusters, penetrate into the magnesium cores.« less

Bio-inspired computational heuristics to study Lane-Emden systems arising in astrophysics model.

PubMed

Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Bilal, Muhammad; Ashraf, Farooq

2016-01-01

This study reports novel hybrid computational methods for the solutions of nonlinear singular Lane-Emden type differential equation arising in astrophysics models by exploiting the strength of unsupervised neural network models and stochastic optimization techniques. In the scheme the neural network, sub-part of large field called soft computing, is exploited for modelling of the equation in an unsupervised manner. The proposed approximated solutions of higher order ordinary differential equation are calculated with the weights of neural networks trained with genetic algorithm, and pattern search hybrid with sequential quadratic programming for rapid local convergence. The results of proposed solvers for solving the nonlinear singular systems are in good agreements with the standard solutions. Accuracy and convergence the design schemes are demonstrated by the results of statistical performance measures based on the sufficient large number of independent runs.

Emp is a component of the nuclear matrix of mammalian cells and undergoes dynamic rearrangements during cell division

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

Bala, Shashi; Kumar, Ajay; Soni, Shivani

2006-04-21

Emp, originally detected in erythroblastic islands, is expressed in numerous cell types and tissues suggesting a functionality not limited to hematopoiesis. To study the function of Emp in non-hematopoietic cells, an epitope-tagged recombinant human Emp was expressed in HEK cells. Preliminary studies revealed that Emp partitioned into both the nuclear and Triton X-100-insoluble cytoskeletal fractions in approximately a 4:1 ratio. In this study, we report investigations of Emp in the nucleus. Sequential extractions of interphase nuclei showed that recombinant Emp was present predominantly in the nuclear matrix. Immunofluorescence microscopy showed that Emp was present in typical nuclear speckles enriched withmore » the spliceosome assembly factor SC35 and partially co-localized with actin staining. Coimmunoprecipitation and GST-pull-down assays confirmed the apparent close association of Emp with nuclear actin. During mitosis, Emp was detected at the mitotic spindle/spindle poles, as well as in the contractile ring during cytokinesis. These results suggest that Emp undergoes dynamic rearrangements within the nuclear architecture that are correlated with cell division.« less

Emp is a component of the nuclear matrix of mammalian cells and undergoes dynamic rearrangements during cell division.

PubMed

Bala, Shashi; Kumar, Ajay; Soni, Shivani; Sinha, Sudha; Hanspal, Manjit

2006-04-21

Emp, originally detected in erythroblastic islands, is expressed in numerous cell types and tissues suggesting a functionality not limited to hematopoiesis. To study the function of Emp in non-hematopoietic cells, an epitope-tagged recombinant human Emp was expressed in HEK cells. Preliminary studies revealed that Emp partitioned into both the nuclear and Triton X-100-insoluble cytoskeletal fractions in approximately a 4:1 ratio. In this study, we report investigations of Emp in the nucleus. Sequential extractions of interphase nuclei showed that recombinant Emp was present predominantly in the nuclear matrix. Immunofluorescence microscopy showed that Emp was present in typical nuclear speckles enriched with the spliceosome assembly factor SC35 and partially co-localized with actin staining. Coimmunoprecipitation and GST-pull-down assays confirmed the apparent close association of Emp with nuclear actin. During mitosis, Emp was detected at the mitotic spindle/spindle poles, as well as in the contractile ring during cytokinesis. These results suggest that Emp undergoes dynamic rearrangements within the nuclear architecture that are correlated with cell division.

Difference in vascular patterns between transosseous-equivalent and transosseous rotator cuff repair.

PubMed

Urita, Atsushi; Funakoshi, Tadanao; Horie, Tatsunori; Nishida, Mutsumi; Iwasaki, Norimasa

2017-01-01

Vascularity is the important factor of biologic healing of the repaired tissue. The purpose of this study was to clarify sequential vascular patterns of repaired rotator cuff by suture techniques. We randomized 21 shoulders in 20 patients undergoing arthroscopic rotator cuff repair into 2 groups: transosseous-equivalent repair (TOE group, n = 10) and transosseous repair (TO group, n = 11). Blood flow in 4 regions inside the cuff (lateral articular, lateral bursal, medial articular, and medial bursal), in the knotless suture anchor in the TOE group, and in the bone tunnel in the TO group was measured using contrast-enhanced ultrasound at 1 month, 2 months, 3 months, and 6 months postoperatively. The sequential vascular pattern inside the repaired rotator cuff was different between groups. The blood flow in the lateral articular area at 1 month, 2 months, and 3 months (P = .002, .005, and .025) and that in the lateral bursal area at 2 months (P = .031) in the TO group were significantly greater than those in the TOE group postoperatively. Blood flow was significantly greater for the bone tunnels in the TO group than for the knotless suture anchor in the TOE group at 1 month and 2 months postoperatively (P = .041 and .009). This study clarified that the sequential vascular pattern inside the repaired rotator cuff depends on the suture technique used. Bone tunnels through the footprint may contribute to biologic healing by increasing blood flow in the repaired rotator cuff. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Skin Preparation for Prevention of Surgical Site Infection After Cesarean Delivery: A Randomized Controlled Trial.

PubMed

Ngai, Ivan M; Van Arsdale, Anne; Govindappagari, Shravya; Judge, Nancy E; Neto, Nicole K; Bernstein, Jeffrey; Bernstein, Peter S; Garry, David J

2015-12-01

To compare chlorhexidine with alcohol, povidone-iodine with alcohol, and both applied sequentially to estimate their relative effectiveness in prevention of surgical site infections after cesarean delivery. Women undergoing nonemergent cesarean birth at greater than 37 0/7 weeks of gestation were randomly allocated to one of three antiseptic skin preparations: povidone-iodine with alcohol, chlorhexidine with alcohol, or the sequential combination of both solutions. The primary outcome was surgical site infection reported within the first 30 days postpartum. Based on a surgical site infection rate of 12%, an anticipated 50% reduction for the combination group relative to either single skin preparation group, with a power of 0.90 and an α of 0.05, 430 women per group were needed to detect a difference. From January 2013 to July 2014, 1,404 women were randomly assigned to one of three groups: povidone-iodine with alcohol (n=463), chlorhexidine with alcohol (n=474), or both (n=467). The groups were similar with respect to demographics, medical disorders, indication for cesarean delivery, operative time, and blood loss. The overall rate of surgical site infection-4.3%-was lower than anticipated. The skin preparation groups had similar surgical site infection rates: povidone-iodine 4.6%, chlorhexidine with alcohol 4.5%, and sequential 3.9% (P=.85). The skin preparation techniques resulted in similar rates of surgical site infections. Our study provides no support for any particular method of skin preparation before cesarean delivery. ClinicalTrials.gov, www.clinicaltrials.gov, NCT01870583. I.

Sequential vs simultaneous revascularization in patients undergoing liver transplantation: A meta-analysis.

PubMed

Wang, Jia-Zhong; Liu, Yang; Wang, Jin-Long; Lu, Le; Zhang, Ya-Fei; Lu, Hong-Wei; Li, Yi-Ming

2015-06-14

We undertook this meta-analysis to investigate the relationship between revascularization and outcomes after liver transplantation. A literature search was performed using MeSH and key words. The quality of the included studies was assessed using the Jadad Score and the Newcastle-Ottawa Scale. Heterogeneity was evaluated by the χ(2) and I (2) tests. The risk of publication bias was assessed using a funnel plot and Egger's test, and the risk of bias was assessed using a domain-based assessment tool. A sensitivity analysis was conducted by reanalyzing the data using different statistical approaches. Six studies with a total of 467 patients were included. Ischemic-type biliary lesions were significantly reduced in the simultaneous revascularization group compared with the sequential revascularization group (OR = 4.97, 95%CI: 2.45-10.07; P < 0.00001), and intensive care unit (ICU) days were decreased (MD = 2.00, 95%CI: 0.55-3.45; P = 0.007) in the simultaneous revascularization group. Although warm ischemia time was prolonged in simultaneous revascularization group (MD = -25.84, 95%CI: -29.28-22.40; P < 0.00001), there were no significant differences in other outcomes between sequential and simultaneous revascularization groups. Assessment of the risk of bias showed that the methods of random sequence generation and blinding might have been a source of bias. The sensitivity analysis strengthened the reliability of the results of this meta-analysis. The results of this study indicate that simultaneous revascularization in liver transplantation may reduce the incidence of ischemic-type biliary lesions and length of stay of patients in the ICU.

Predicting the Necessity for Extracorporeal Circulation During Lung Transplantation: A Feasibility Study.

PubMed

Hinske, Ludwig Christian; Hoechter, Dominik Johannes; Schröeer, Eva; Kneidinger, Nikolaus; Schramm, René; Preissler, Gerhard; Tomasi, Roland; Sisic, Alma; Frey, Lorenz; von Dossow, Vera; Scheiermann, Patrick

2017-06-01

The factors leading to the implementation of unplanned extracorporeal circulation during lung transplantation are poorly defined. Consequently, the authors aimed to identify patients at risk for unplanned extracorporeal circulation during lung transplantation. Retrospective data analysis. Single-center university hospital. A development data set of 170 consecutive patients and an independent validation cohort of 52 patients undergoing lung transplantation. The authors investigated a cohort of 170 consecutive patients undergoing single or sequential bilateral lung transplantation without a priori indication for extracorporeal circulation and evaluated the predictive capability of distinct preoperative and intraoperative variables by using automated model building techniques at three clinically relevant time points (preoperatively, after endotracheal intubation, and after establishing single-lung ventilation). Preoperative mean pulmonary arterial pressure was the strongest predictor for unplanned extracorporeal circulation. A logistic regression model based on preoperative mean pulmonary arterial pressure and lung allocation score achieved an area under the receiver operating characteristic curve of 0.85. Consequently, the authors developed a novel 3-point scoring system based on preoperative mean pulmonary arterial pressure and lung allocation score, which identified patients at risk for unplanned extracorporeal circulation and validated this score in an independent cohort of 52 patients undergoing lung transplantation. The authors showed that patients at risk for unplanned extracorporeal circulation during lung transplantation could be identified by their novel 3-point score. Copyright © 2017 Elsevier Inc. All rights reserved.

Microfabrication of channel arrays promotes vessel-like network formation in cardiac cell construct and vascularization in vivo.

PubMed

Zieber, Liran; Or, Shira; Ruvinov, Emil; Cohen, Smadar

2014-06-01

Pre-vascularization is important for the reconstruction of dense and metabolically active myocardial tissue and its integration with the host myocardium after implantation. Herein, we demonstrate that the fabrication of micro-channels in alginate scaffold combined with the presentation of adhesion peptides and an angiogenic growth factor promote vessel-like networks in the construct, both in vitro and in vivo. Using a CO2 laser engraving system, 200 µm diameter channels were formed from top to bottom of the 2 mm thick alginate scaffold, with a channel-to-channel distance of 400 µm. Cells were seeded in a sequential manner onto the scaffolds: first, human umbilical vascular endothelial cells (HUVECs) were seeded and cultured for three days, then neonatal rat cardiomyocytes (CMs) and cardiofibroblasts were added at a final cell ratio of 50:35:15, respectively, and the constructs were cultivated for an additional seven days. A vessel-like network was formed within the cell constructs, wherein HUVECs were organized around the channels in a multilayer manner, while the CMs were located in-between the channels and exhibited the characteristic morphological features of a mature cardiac fiber. Acellular scaffolds with the affinity-bound basic fibroblast growth factor were implanted subcutaneously in mice. Increased cell penetration into the channeled scaffold and greater vessel density were found in comparison with the nonchanneled scaffolds. Our results thus point to the importance of micro-channels as a major structural promoter of vascularization in scaffolds, in conjunction with the sequential preculture of ECs and angiogenic factor presentation.

Sequential neural processes in abacus mental addition: an EEG and FMRI case study.

PubMed

Ku, Yixuan; Hong, Bo; Zhou, Wenjing; Bodner, Mark; Zhou, Yong-Di

2012-01-01

Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.

Embedding memories in colloidal gels though oscillatory shear

NASA Astrophysics Data System (ADS)

Schwen, Eric; Ramaswamay, Meera; Jan, Linda; Cheng, Chieh-Min; Cohen, Itai

While gels are ubiquitous in applications from food products to filtration, their mechanical properties are usually determined by self-assembly. We use oscillatory shear to train colloidal gels, embedding memories of the training protocol in rheological responses such as the yield strain and the gel network structures. When our gels undergo shear, the particles are forced to rearrange until they organize into structures that can locally undergo reversible shear cycles. We utilize a high-speed confocal microscope and a shear cell to image a colloidal gel while simultaneously straining the gel and measuring its shear stresses. By comparing stroboscopic images of the gel, we quantify the decrease in particle rearrangement as the gel develops reversible structures. We analyze and construct a model for the rates at which different regions in the gel approach reversible configurations. Through characterizing the gel network, we determine the structural origins of these shear training memories in gels. These results may allow us to use shear training protocols to produce gels with controllable yield strains and to better understand changes in the microstructure and rheology of gels that undergo repeated shear through mixing or flowing. This research was supported in part by NSF CBET 1509308 and Xerox Corporation.

Autonomous molecular cascades for evaluation of cell surfaces

NASA Astrophysics Data System (ADS)

Rudchenko, Maria; Taylor, Steven; Pallavi, Payal; Dechkovskaia, Alesia; Khan, Safana; Butler, Vincent P., Jr.; Rudchenko, Sergei; Stojanovic, Milan N.

2013-08-01

Molecular automata are mixtures of molecules that undergo precisely defined structural changes in response to sequential interactions with inputs. Previously studied nucleic acid-based automata include game-playing molecular devices (MAYA automata) and finite-state automata for the analysis of nucleic acids, with the latter inspiring circuits for the analysis of RNA species inside cells. Here, we describe automata based on strand-displacement cascades directed by antibodies that can analyse cells by using their surface markers as inputs. The final output of a molecular automaton that successfully completes its analysis is the presence of a unique molecular tag on the cell surface of a specific subpopulation of lymphocytes within human blood cells.

Exploring first-order phase transitions with population annealing

NASA Astrophysics Data System (ADS)

Barash, Lev Yu.; Weigel, Martin; Shchur, Lev N.; Janke, Wolfhard

2017-03-01

Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with q > 4, where it undergoes a first-order transition.

Expedient synthesis of fused azepine derivatives using a sequential rhodium(II)-catalyzed cyclopropanation/1-aza-Cope rearrangement of dienyltriazoles.

PubMed

Schultz, Erica E; Lindsay, Vincent N G; Sarpong, Richmond

2014-09-08

A general method for the formation of fused dihydroazepine derivatives from 1-sulfonyl-1,2,3-triazoles bearing a tethered diene is reported. The process involves an intramolecular cyclopropanation of an α-imino rhodium(II) carbenoid, leading to a transient 1-imino-2-vinylcyclopropane intermediate which rapidly undergoes a 1-aza-Cope rearrangement to generate fused dihydroazepine derivatives in moderate to excellent yields. The reaction proceeds with similar efficiency on gram scale. The use of catalyst-free conditions leads to the formation of a novel [4.4.0] bicyclic heterocycle. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of human locomotion.

PubMed

Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

2012-10-01

Neural control of locomotion in human adults involves the generation of a small set of basic patterned commands directed to the leg muscles. The commands are generated sequentially in time during each step by neural networks located in the spinal cord, called Central Pattern Generators. This review outlines recent advances in understanding how motor commands are expressed at different stages of human development. Similar commands are found in several other vertebrates, indicating that locomotion development follows common principles of organization of the control networks. Movements show a high degree of flexibility at all stages of development, which is instrumental for learning and exploration of variable interactions with the environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Range Measurement as Practiced in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.; Kinman, Peter W.

2007-01-01

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

Mining the Temporal Dimension of the Information Propagation

NASA Astrophysics Data System (ADS)

Berlingerio, Michele; Coscia, Michele; Giannotti, Fosca

In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining area has increased very fast. Among the possible related topics, the study of the information propagation in a network attracted the interest of many researchers, also from the industrial world. However, only a few answers to the questions “How does the information propagates over a network, why and how fast?” have been discovered so far. On the other hand, these answers are of large interest, since they help in the tasks of finding experts in a network, assessing viral marketing strategies, identifying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a network with the help of two different techniques: TAS (Temporally Annotated Sequences) mining, aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data, and Graph Mining, which is helpful for locally analyzing the nodes of the networks with their properties. Finally we show preliminary results done in the direction of mining the information propagation over a network, performed on two well known email datasets, that show the power of the combination of these two approaches.

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Elasticity and photoelasticity relationships for polyethylene terephthalate fiber networks by molecular simulation

NASA Astrophysics Data System (ADS)

Nayak, Kapileswar; Das, Sushanta; Nanavati, Hemant

2008-01-01

We present a framework for the development of elasticity and photoelasticity relationships for polyethylene terephthalate fiber networks, incorporating aspects of the primary molecular structure. Semicrystalline polymeric fiber networks are modeled as sequentially arranged crystalline and amorphous regions. Rotational isomeric states-Monte Carlo simulations of amorphous chains of up to 360 bonds (degree of polymerization, DP =60), confined between and bridging infinite impenetrable crystalline walls, have been characterized by Ω, the probability density of the intercrystal separation h, and Δβ, the polarizability anisotropy. lnΩ and Δβ have been modeled as functions of h, yielding the chain deformation relationships. The development has been extended to the fiber network to yield the photoelasticity relationships. We execute our framework by fitting to experimental stress-elongation data and employing the single fitted parameter to directly predict the birefringence-elongation behavior, without any further fitting. Incorporating the effect of strain-induced crystallization into the framework makes it physically more meaningful and yields accurate predictions of the birefringence-elongation behavior.

MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters.

PubMed

Gonzalez-Dominguez, Jorge; Martin, Maria J

2017-10-10

In this work we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. Source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

PubMed

Berlow, Noah; Pal, Ranadip

2011-01-01

Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

Clique of Functional Hubs Orchestrates Population Bursts in Developmentally Regulated Neural Networks

PubMed Central

Luccioli, Stefano; Ben-Jacob, Eshel; Barzilai, Ari; Bonifazi, Paolo; Torcini, Alessandro

2014-01-01

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. PMID:25255443

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

Rao, Nageswara S.; Ramirez Aviles, Camila A.

We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility’s ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor’s location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i)more » test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.« less

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

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

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Decoding the Regulatory Network for Blood Development from Single-Cell Gene Expression Measurements

PubMed Central

Haghverdi, Laleh; Lilly, Andrew J.; Tanaka, Yosuke; Wilkinson, Adam C.; Buettner, Florian; Macaulay, Iain C.; Jawaid, Wajid; Diamanti, Evangelia; Nishikawa, Shin-Ichi; Piterman, Nir; Kouskoff, Valerie; Theis, Fabian J.; Fisher, Jasmin; Göttgens, Berthold

2015-01-01

Here we report the use of diffusion maps and network synthesis from state transition graphs to better understand developmental pathways from single cell gene expression profiling. We map the progression of mesoderm towards blood in the mouse by single-cell expression analysis of 3,934 cells, capturing cells with blood-forming potential at four sequential developmental stages. By adapting the diffusion plot methodology for dimensionality reduction to single-cell data, we reconstruct the developmental journey to blood at single-cell resolution. Using transitions between individual cellular states as input, we develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model that recapitulates blood development. Model predictions were validated by showing that Sox7 inhibits primitive erythropoiesis, and that Sox and Hox factors control early expression of Erg. We therefore demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that control organogenesis. PMID:25664528

Sequential visibility-graph motifs

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Lacasa, Lucas

2016-04-01

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

Power-Aware Compiler Controllable Chip Multiprocessor

NASA Astrophysics Data System (ADS)

Shikano, Hiroaki; Shirako, Jun; Wada, Yasutaka; Kimura, Keiji; Kasahara, Hironori

A power-aware compiler controllable chip multiprocessor (CMP) is presented and its performance and power consumption are evaluated with the optimally scheduled advanced multiprocessor (OSCAR) parallelizing compiler. The CMP is equipped with power control registers that change clock frequency and power supply voltage to functional units including processor cores, memories, and an interconnection network. The OSCAR compiler carries out coarse-grain task parallelization of programs and reduces power consumption using architectural power control support and the compiler's power saving scheme. The performance evaluation shows that MPEG-2 encoding on the proposed CMP with four CPUs results in 82.6% power reduction in real-time execution mode with a deadline constraint on its sequential execution time. Furthermore, MP3 encoding on a heterogeneous CMP with four CPUs and four accelerators results in 53.9% power reduction at 21.1-fold speed-up in performance against its sequential execution in the fastest execution mode.

Short-term prediction of chaotic time series by using RBF network with regression weights.

PubMed

Rojas, I; Gonzalez, J; Cañas, A; Diaz, A F; Rojas, F J; Rodriguez, M

2000-10-01

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

Effect of hydro mechanical coupling on natural fracture network formation in sedimentary basins

NASA Astrophysics Data System (ADS)

Ouraga, Zady; Guy, Nicolas; Pouya, Amade

2018-05-01

In sedimentary basin context, numerous phenomena, depending on the geological time span, can result in natural fracture network formation. In this paper, fracture network and dynamic fracture spacing triggered by significant sedimentation rate are studied considering mode I fracture propagation using a coupled hydro-mechanical numerical methods. The focus is put on synthetic geological structure under a constant sedimentation rate on its top. This model contains vertical fracture network initially closed and homogeneously distributed. The fractures are modelled with cohesive zone model undergoing damage and the flow is described by Poiseuille's law. The effect of the behaviour of the rock is studied and the analysis leads to a pattern of fracture network and fracture spacing in the geological layer.

Conducting-insulating transition in adiabatic memristive networks

NASA Astrophysics Data System (ADS)

Sheldon, Forrest C.; Di Ventra, Massimiliano

2017-01-01

The development of neuromorphic systems based on memristive elements—resistors with memory—requires a fundamental understanding of their collective dynamics when organized in networks. Here, we study an experimentally inspired model of two-dimensional disordered memristive networks subject to a slowly ramped voltage and show that they undergo a discontinuous transition in the conductivity for sufficiently high values of memory, as quantified by the memristive ON-OFF ratio. We investigate the consequences of this transition for the memristive current-voltage characteristics both through simulation and theory, and demonstrate the role of current-voltage duality in relating forward and reverse switching processes. Our work sheds considerable light on the statistical properties of memristive networks that are presently studied both for unconventional computing and as models of neural networks.

Mu-2 ranging

NASA Technical Reports Server (NTRS)

Martin, W. L.; Zygielbaum, A. I.

1977-01-01

The Mu-II Dual-Channel Sequential Ranging System designed as a model for future Deep Space Network ranging equipment is described. A list of design objectives is followed by a theoretical explanation of the digital demodulation techniques first employed in this machine. Hardware and software implementation are discussed, together with the details relating to the construction of the device. Two appendixes are included relating to the programming and operation of this equipment to yield the maximum scientific data.

A Guide for Collecting Seismic, Acoustic, and Magnetic Data for Multiple Uses

DTIC Science & Technology

1975-01-01

time, simul- (- taneously for the analog technique or sequentially for the digital tech- nique. Both methods require that precision timing networks be...with a precise voltage proportional to the sensitivity of the magnetometer . Whenever any electronic equip- ment affecting cal.bration has to be replaced...described as precisely as possible, including (but not limited to) the following: a. Name of source b. Continuous or transient c. Distance from geophone

An Analysis on the Use of Educational Social Networking Sites in the Course Activities of Geography Department Students: Edmodo Sample

ERIC Educational Resources Information Center

Teyfur, Emine; Özkan, Adem; Teyfur, Mehmet

2017-01-01

The aim of this study was to examine the views of the students of Geography Department on the use of ESNS Edmodo in the course activities. Sequential explanatory design in mixed methods research designs was used in the study. This study was conducted with a total of 41 second grade students who take Europe Geography class and study in the…

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

PubMed Central

Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

2014-01-01

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs. PMID:25350506

DCS-Neural-Network Program for Aircraft Control and Testing

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Wasted research when systematic reviews fail to provide a complete and up-to-date evidence synthesis: the example of lung cancer.

PubMed

Créquit, Perrine; Trinquart, Ludovic; Yavchitz, Amélie; Ravaud, Philippe

2016-01-20

Multiple treatments are frequently available for a given condition, and clinicians and patients need a comprehensive, up-to-date synthesis of evidence for all competing treatments. We aimed to quantify the waste of research related to the failure of systematic reviews to provide a complete and up-to-date evidence synthesis over time. We performed a series of systematic overviews and networks of randomized trials assessing the gap between evidence covered by systematic reviews and available trials of second-line treatments for advanced non-small cell lung cancer. We searched the Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, MEDLINE, EMBASE, and other resources sequentially by year from 2009 to March 2, 2015. We sequentially compared the amount of evidence missing from systematic reviews to the randomized evidence available for inclusion each year. We constructed cumulative networks of randomized evidence over time and evaluated the proportion of trials, patients, treatments, and treatment comparisons not covered by systematic reviews on December 31 each year from 2009 to 2015. We identified 77 trials (28,636 patients) assessing 47 treatments with 54 comparisons and 29 systematic reviews (13 published after 2013). From 2009 to 2015, the evidence covered by existing systematic reviews was consistently incomplete: 45 % to 70 % of trials; 30 % to 58 % of patients; 40 % to 66 % of treatments; and 38 % to 71 % of comparisons were missing. In the cumulative networks of randomized evidence, 10 % to 17 % of treatment comparisons were partially covered by systematic reviews and 55 % to 85 % were partially or not covered. We illustrate how systematic reviews of a given condition provide a fragmented, out-of-date panorama of the evidence for all treatments. This waste of research might be reduced by the development of live cumulative network meta-analyses.

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

NASA Astrophysics Data System (ADS)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

MZmine 2 Data-Preprocessing To Enhance Molecular Networking Reliability.

PubMed

Olivon, Florent; Grelier, Gwendal; Roussi, Fanny; Litaudon, Marc; Touboul, David

2017-08-01

Molecular networking is becoming more and more popular into the metabolomic community to organize tandem mass spectrometry (MS 2 ) data. Even though this approach allows the treatment and comparison of large data sets, several drawbacks related to the MS-Cluster tool routinely used on the Global Natural Product Social Molecular Networking platform (GNPS) limit its potential. MS-Cluster cannot distinguish between chromatography well-resolved isomers as retention times are not taken into account. Annotation with predicted chemical formulas is also not implemented and semiquantification is only based on the number of MS 2 scans. We propose to introduce a data-preprocessing workflow including the preliminary data treatment by MZmine 2 followed by a homemade Python script freely available to the community that clears the major previously mentioned GNPS drawbacks. The efficiency of this workflow is exemplified with the analysis of six fractions of increasing polarities obtained from a sequential supercritical CO 2 extraction of Stillingia lineata leaves.

Social influence in small-world networks

NASA Astrophysics Data System (ADS)

Sun, Kai; Mao, Xiao-Ming; Ouyang, Qi

2002-12-01

We report on our numerical studies of the Axelrod model for social influence in small-world networks. Our simulation results show that the topology of the network has a crucial effect on the evolution of cultures. As the randomness of the network increases, the system undergoes a transition from a highly fragmented phase to a uniform phase. We also find that the power-law distribution at the transition point, reported by Castellano et al, is not a critical phenomenon; it exists not only at the onset of transition but also for almost any control parameters. All these power-law distributions are stable against perturbations. A mean-field theory is developed to explain these phenomena.

A multi-signal fluorescent probe for simultaneously distinguishing and sequentially sensing cysteine/homocysteine, glutathione, and hydrogen sulfide in living cells† †Electronic supplementary information (ESI) available: Experimental details for chemical synthesis of all compounds, chemical structure characterization, supplementary spectra of probe, and fluorescence imaging methods and data. See DOI: 10.1039/c7sc00423k Click here for additional data file.

PubMed Central

He, Longwei; Yang, Xueling; Xu, Kaixin; Kong, Xiuqi

2017-01-01

Biothiols, which have a close network of generation and metabolic pathways among them, are essential reactive sulfur species (RSS) in the cells and play vital roles in human physiology. However, biothiols possess highly similar chemical structures and properties, resulting in it being an enormous challenge to simultaneously discriminate them from each other. Herein, we develop a unique fluorescent probe (HMN) for not only simultaneously distinguishing Cys/Hcy, GSH, and H2S from each other, but also sequentially sensing Cys/Hcy/GSH and H2S using a multi-channel fluorescence mode for the first time. When responding to the respective biothiols, the robust probe exhibits multiple sets of fluorescence signals at three distinct emission bands (blue-green-red). The new probe can also sense H2S at different concentration levels with changes of fluorescence at the blue and red emission bands. In addition, the novel probe HMN is able to discriminate and sequentially sense biothiols in biological environments via three-color fluorescence imaging. We expect that the development of the robust probe HMN will provide a powerful strategy to design fluorescent probes for the discrimination and sequential detection of biothiols, and offer a promising tool for exploring the interrelated roles of biothiols in various physiological and pathological conditions. PMID:28989659

Computational time reduction for sequential batch solutions in GNSS precise point positioning technique

NASA Astrophysics Data System (ADS)

Martín Furones, Angel; Anquela Julián, Ana Belén; Dimas-Pages, Alejandro; Cos-Gayón, Fernando

2017-08-01

Precise point positioning (PPP) is a well established Global Navigation Satellite System (GNSS) technique that only requires information from the receiver (or rover) to obtain high-precision position coordinates. This is a very interesting and promising technique because eliminates the need for a reference station near the rover receiver or a network of reference stations, thus reducing the cost of a GNSS survey. From a computational perspective, there are two ways to solve the system of observation equations produced by static PPP either in a single step (so-called batch adjustment) or with a sequential adjustment/filter. The results of each should be the same if they are both well implemented. However, if a sequential solution (that is, not only the final coordinates, but also those observed in previous GNSS epochs), is needed, as for convergence studies, finding a batch solution becomes a very time consuming task owing to the need for matrix inversion that accumulates with each consecutive epoch. This is not a problem for the filter solution, which uses information computed in the previous epoch for the solution of the current epoch. Thus filter implementations need extra considerations of user dynamics and parameter state variations between observation epochs with appropriate stochastic update parameter variances from epoch to epoch. These filtering considerations are not needed in batch adjustment, which makes it attractive. The main objective of this research is to significantly reduce the computation time required to obtain sequential results using batch adjustment. The new method we implemented in the adjustment process led to a mean reduction in computational time by 45%.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

PubMed Central

Cao, Youfang; Liang, Jie

2013-01-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape. PMID:23862966

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method

NASA Astrophysics Data System (ADS)

Cao, Youfang; Liang, Jie

2013-07-01

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.

PubMed

Cao, Youfang; Liang, Jie

2013-07-14

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.

Mitochondrial network complexity emerges from fission/fusion dynamics.

PubMed

Zamponi, Nahuel; Zamponi, Emiliano; Cannas, Sergio A; Billoni, Orlando V; Helguera, Pablo R; Chialvo, Dante R

2018-01-10

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

78 FR 16269 - Agency Forms Undergoing Paperwork Reduction Act Review

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-14

...-0017, Expiration 03/31/ 2013)--Revision--Scientific Education and Professional Development Program... activities to professionals worldwide. Employees of hospitals, universities, medical centers, laboratories... Continuing Education Online New Participant Registration Form and the National Laboratory Training Network...

Multitarget tracking in cluttered environment for a multistatic passive radar system under the DAB/DVB network

NASA Astrophysics Data System (ADS)

Shi, Yi Fang; Park, Seung Hyo; Song, Taek Lyul

2017-12-01

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with illuminators of opportunity faces two main challenges: the first challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional association ambiguity between the measurements and targets, which introduces a significantly complex three-dimensional (3-D) data association problem among the target-measurement illuminator, this is because all the illuminators transmit the same carrier frequency signals and signals transmitted by different illuminators but reflected via the same target become indistinguishable; the other challenge is that only the bistatic range and range-rate measurements are available while the angle information is unavailable or of very poor quality. In this paper, the authors propose a new target tracking algorithm directly in three-dimensional (3-D) Cartesian coordinates with the capability of track management using the probability of target existence as a track quality measure. The proposed algorithm is termed sequential processing-joint integrated probabilistic data association (SP-JIPDA), which applies the modified sequential processing technique to resolve the additional association ambiguity between measurements and illuminators. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. For reasons of fair comparison, the existing modified joint probabilistic data association (MJPDA) algorithm that addresses the 3-D data association problem via "supertargets" using gate grouping and provides tracks directly in 3-D Cartesian coordinates, is enhanced by incorporating the probability of target existence as an effective track quality measure for track management. Both algorithms deal with nonlinear observations using the extended Kalman filtering. A simulation study is performed to verify the superiority of the proposed SP-JIPDA algorithm over the MJIPDA in this multistatic passive radar system.

Joint Data Assimilation and Parameter Calibration in on-line groundwater modelling using Sequential Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Ramgraber, M.; Schirmer, M.

2017-12-01

As computational power grows and wireless sensor networks find their way into common practice, it becomes increasingly feasible to pursue on-line numerical groundwater modelling. The reconciliation of model predictions with sensor measurements often necessitates the application of Sequential Monte Carlo (SMC) techniques, most prominently represented by the Ensemble Kalman Filter. In the pursuit of on-line predictions it seems advantageous to transcend the scope of pure data assimilation and incorporate on-line parameter calibration as well. Unfortunately, the interplay between shifting model parameters and transient states is non-trivial. Several recent publications (e.g. Chopin et al., 2013, Kantas et al., 2015) in the field of statistics discuss potential algorithms addressing this issue. However, most of these are computationally intractable for on-line application. In this study, we investigate to what extent compromises between mathematical rigour and computational restrictions can be made within the framework of on-line numerical modelling of groundwater. Preliminary studies are conducted in a synthetic setting, with the goal of transferring the conclusions drawn into application in a real-world setting. To this end, a wireless sensor network has been established in the valley aquifer around Fehraltorf, characterized by a highly dynamic groundwater system and located about 20 km to the East of Zürich, Switzerland. By providing continuous probabilistic estimates of the state and parameter distribution, a steady base for branched-off predictive scenario modelling could be established, providing water authorities with advanced tools for assessing the impact of groundwater management practices. Chopin, N., Jacob, P.E. and Papaspiliopoulos, O. (2013): SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3), p. 397-426. Kantas, N., Doucet, A., Singh, S.S., Maciejowski, J., and Chopin, N. (2015): On Particle Methods for Parameter Estimation in State-Space Models. Statistical Science, 30 (3), p. 328.-351.

Differential proteomic analysis reveals sequential heat stress-responsive regulatory network in radish (Raphanus sativus L.) taproot.

PubMed

Wang, Ronghua; Mei, Yi; Xu, Liang; Zhu, Xianwen; Wang, Yan; Guo, Jun; Liu, Liwang

2018-05-01

Differential abundance protein species (DAPS) involved in reducing damage and enhancing thermotolerance in radish were firstly identified. Proteomic analysis and omics association analysis revealed a HS-responsive regulatory network in radish. Heat stress (HS) is a major destructive factor influencing radish production and supply in summer, for radish is a cool season vegetable crop being susceptible to high temperature. In this study, the proteome changes of radish taproots under 40 °C treatment at 0 h (Control), 12 h (Heat12) and 24 h (Heat24) were analyzed using iTRAQ (Isobaric Tag for Relative and Absolute Quantification) approach. In total, 2258 DAPS representing 1542 differentially accumulated uniprotein species which respond to HS were identified. A total of 604, 910 and 744 DAPS was detected in comparison of Control vs. Heat12, Control vs. Heat24, and Heat12 vs. Heat24, respectively. Gene ontology and pathway analysis showed that annexin, ubiquitin-conjugating enzyme, ATP synthase, heat shock protein (HSP) and other stress-related proteins were predominately enriched in signal transduction, stress and defense pathways, photosynthesis and energy metabolic pathways, working cooperatively to reduce stress-induced damage in radish. Based on iTRAQ combined with the transcriptomics analysis, a schematic model of a sequential HS-responsive regulatory network was proposed. The initial sensing of HS occurred at the plasma membrane, and then key components of stress signal transduction triggered heat-responsive genes in the plant protective metabolism to re-establish homeostasis and enhance thermotolerance. These results provide new insights into characteristics of HS-responsive DAPS and facilitate dissecting the molecular mechanisms underlying heat tolerance in radish and other root crops.

Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity

PubMed Central

Luongo, Francisco J.; Zimmerman, Chris A.; Horn, Meryl E.

2016-01-01

Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization—they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity. PMID:26888108

Effectiveness of oral hydration in preventing contrast-induced acute kidney injury in patients undergoing coronary angiography or intervention: a pairwise and network meta-analysis.

PubMed

Zhang, Weidai; Zhang, Jiawei; Yang, Baojun; Wu, Kefei; Lin, Hanfei; Wang, Yanping; Zhou, Lihong; Wang, Huatao; Zeng, Chujuan; Chen, Xiao; Wang, Zhixing; Zhu, Junxing; Songming, Chen

2018-06-01

The effectiveness of oral hydration in preventing contrast-induced acute kidney injury (CI-AKI) in patients undergoing coronary angiography or intervention has not been well established. This study aims to evaluate the efficacy of oral hydration compared with intravenous hydration and other frequently used hydration strategies. PubMed, Embase, Web of Science, and the Cochrane central register of controlled trials were searched from inception to 8 October 2017. To be eligible for analysis, studies had to evaluate the relative efficacy of different prophylactic hydration strategies. We selected and assessed the studies that fulfilled the inclusion criteria and carried out a pairwise and network meta-analysis using RevMan5.2 and Aggregate Data Drug Information System 1.16.8 software. A total of four studies (538 participants) were included in our pairwise meta-analysis and 1754 participants from eight studies with four frequently used hydration strategies were included in a network meta-analysis. Pairwise meta-analysis indicated that oral hydration was as effective as intravenous hydration for the prevention of CI-AKI (5.88 vs. 8.43%; odds ratio: 0.73; 95% confidence interval: 0.36-1.47; P>0.05), with no significant heterogeneity between studies. Network meta-analysis showed that there was no significant difference in the prevention of CI-AKI. However, the rank probability plot suggested that oral plus intravenous hydration had a higher probability (51%) of being the best strategy, followed by diuretic plus intravenous hydration (39%) and oral hydration alone (10%). Intravenous hydration alone was the strategy with the highest probability (70%) of being the worst hydration strategy. Our study shows that oral hydration is not inferior to intravenous hydration for the prevention of CI-AKI in patients with normal or mild-to-moderate renal dysfunction undergoing coronary angiography or intervention.

Effectiveness of a myocardial infarction protocol in reducing door-to-ballon time.

PubMed

Correia, Luis Cláudio Lemos; Brito, Mariana; Kalil, Felipe; Sabino, Michael; Garcia, Guilherme; Ferreira, Felipe; Matos, Iracy; Jacobs, Peter; Ronzoni, Liliana; Noya-Rabelo, Márcia

2013-07-01

An adequate door-to-balloon time (<120 minutes) is the necessary condition for the efficacy of primary angioplasty in infarction to translate into effectiveness. To describe the effectiveness of a quality of care protocol in reducing the door-to-balloon time. Between May 2010 and August 2012, all individuals undergoing primary angioplasty in our hospital were analyzed. The door time was electronically recorded at the moment the patient took a number to be evaluated in the emergency room, which occurred prior to filling the check-in forms and to the triage. The balloon time was defined as the beginning of artery opening (introduction of the first device). The first 5 months of monitoring corresponded to the period of pre-implementation of the protocol. The protocol comprised the definition of a flowchart of actions from patient arrival at the hospital, the team's awareness raising in relation to the prioritization of time, and provision of a periodic feedback on the results and possible inadequacies. A total of 50 individuals were assessed. They were divided into five groups of 10 sequential patients (one group pre- and four groups post-protocol). The door-to-balloon time regarding the 10 cases recorded before protocol implementation was 200 ± 77 minutes. After protocol implementation, there was a progressive reduction of the door-to-balloon time to 142±78 minutes in the first 10 patients, then to 150±50 minutes, 131±37 minutes and, finally, 116±29 minutes in the three sequential groups of 10 patients, respectively. Linear regression between sequential patients and the door-to-balloon time (r = - 0.41) showed a regression coefficient of - 1.74 minutes. The protocol implementation proved effective in the reduction of the door-to-balloon time.

Combination of Pre-Treatment DWI-Signal Intensity and S-1 Treatment: A Predictor of Survival in Patients with Locally Advanced Pancreatic Cancer Receiving Stereotactic Body Radiation Therapy and Sequential S-1.

PubMed

Zhang, Yu; Zhu, Xiaofei; Liu, Ri; Wang, Xianglian; Sun, Gaofeng; Song, Jiaqi; Lu, Jianping; Zhang, Huojun

2018-04-01

To identify whether the combination of pre-treatment radiological and clinical factors can predict the overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiation and sequential S-1 (a prodrug of 5-FU combined with two modulators) therapy with improved accuracy compared with that of established clinical and radiologic risk models. Patients admitted with LAPC underwent diffusion weighted imaging (DWI) scan at 3.0-T (b = 600 s/mm 2 ). The mean signal intensity (SI b = 600) of region-of-interest (ROI) was measured. The Log-rank test was done for tumor location, biliary stent, S-1, and other treatments and the Cox regression analysis was done to identify independent prognostic factors for OS. Prediction error curves (PEC) were used to assess potential errors in prediction of survival. The accuracy of prediction was evaluated by Integrated Brier Score (IBS) and C index. 41 patients were included in this study. The median OS was 11.7 months (2.8-23.23 months). The 1-year OS was 46%. Multivariate analysis showed that pre-treatment SI b = 600 value and administration of S-1 were independent predictors for OS. The performance of pre-treatment SI b = 600 and S-1 treatment in combination was better than that of SI b = 600 or S-1 treatment alone. The combination of pre-treatment SI b = 600 and S-1 treatment could predict the OS in patients with LAPC undergoing SBRT and sequential S-1 therapy with improved accuracy compared with that of established clinical and radiologic risk models. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Sequential vs simultaneous revascularization in patients undergoing liver transplantation: A meta-analysis

PubMed Central

Wang, Jia-Zhong; Liu, Yang; Wang, Jin-Long; Lu, Le; Zhang, Ya-Fei; Lu, Hong-Wei; Li, Yi-Ming

2015-01-01

AIM: We undertook this meta-analysis to investigate the relationship between revascularization and outcomes after liver transplantation. METHODS: A literature search was performed using MeSH and key words. The quality of the included studies was assessed using the Jadad Score and the Newcastle-Ottawa Scale. Heterogeneity was evaluated by the χ2 and I2 tests. The risk of publication bias was assessed using a funnel plot and Egger’s test, and the risk of bias was assessed using a domain-based assessment tool. A sensitivity analysis was conducted by reanalyzing the data using different statistical approaches. RESULTS: Six studies with a total of 467 patients were included. Ischemic-type biliary lesions were significantly reduced in the simultaneous revascularization group compared with the sequential revascularization group (OR = 4.97, 95%CI: 2.45-10.07; P < 0.00001), and intensive care unit (ICU) days were decreased (MD = 2.00, 95%CI: 0.55-3.45; P = 0.007) in the simultaneous revascularization group. Although warm ischemia time was prolonged in simultaneous revascularization group (MD = -25.84, 95%CI: -29.28-22.40; P < 0.00001), there were no significant differences in other outcomes between sequential and simultaneous revascularization groups. Assessment of the risk of bias showed that the methods of random sequence generation and blinding might have been a source of bias. The sensitivity analysis strengthened the reliability of the results of this meta-analysis. CONCLUSION: The results of this study indicate that simultaneous revascularization in liver transplantation may reduce the incidence of ischemic-type biliary lesions and length of stay of patients in the ICU. PMID:26078582

Communication latencies of wireless devices suitable for time-critical messaging to anesthesia providers.

PubMed

Epstein, Richard H; Dexter, Franklin; Rothman, Brian

2013-04-01

Rapid and reliable methods of text communication to mobile anesthesia care providers are important to patient care and to efficient operating room management. Anesthesia departments are implementing automated methods to send text messages to mobile devices for abnormal vital signs, clinical recommendations, quality of care, and compliance or billing issues. The most time-critical communications determine maximum acceptable latencies. We studied the reliability of several alphanumeric messaging systems to identify an appropriate technology for such use. Latencies between message initiation and delivery to 3 alphanumeric paging devices were measured over weeks. Two devices used Internet pathways outside the hospital's local network with an external paging vendor (SkyTel). The third device used only the internal hospital network (Zetron). Sequential cell phone text page latencies were examined for lag-1 autocorrelation using the runs test, with results binned by hour and by day. Message latencies subsequently were batched in successive 1-week bins for calculation of the mean and 99th percentiles of latencies. We defined acceptance criteria as a mean latency <30 seconds and no more than 1 in 200 pages (0.5%) having a latency longer than 100 seconds. Cell phone texting was used as a positive control to assure that the analysis was appropriate, because such devices have (known) poor reliability during high network activity. There was substantial correlation among latencies for sequential cell phone text messages when binned by hours (P < 0.0001), but not by days (P = 0.61). The 2 devices using Internet pathways outside the hospital's network demonstrated unacceptable performance, with 1.3% and 33% of latencies exceeding 100 seconds, respectively. The device dependent only on the internal network had a mean latency of 8 seconds, with 100% of 40,200 pages having latencies <100 seconds. The findings suggest that the network used was the deciding factor. Developers of anesthesia communication systems need to measure latencies of proposed communication pathways and devices used to deliver urgent messages to mobile users. Similar evaluation is relevant for text pagers used on an ad hoc basis for delivery of time-critical notifications. Testing over a period of hours to days is adequate only for disqualification of a candidate paging system, because acceptable results are not necessarily indicative of long-term performance. Rather, weeks of testing are required, with appropriate batching of pages for analysis.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

500 MHz narrowband beam position monitor electronics for electron synchrotrons

NASA Astrophysics Data System (ADS)

Mohos, I.; Dietrich, J.

1998-12-01

Narrowband beam position monitor electronics were developed in the Forschungszentrum Jülich-IKP for the orbit measurement equipment used at ELSA Bonn. The equipment uses 32 monitor chambers, each with four capacitive button electrodes. The monitor electronics, consisting of an rf signal processing module (BPM-RF) and a data acquisition and control module (BPM-DAQ), sequentially process and measure the monitor signals and deliver calculated horizontal and vertical beam position data via a serial network.

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

DOE PAGES

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles; ...

2018-05-05

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

Randomness in the network inhibits cooperation based on the bounded rational collective altruistic decision

NASA Astrophysics Data System (ADS)

Ohdaira, Tetsushi

2014-07-01

Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.

Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

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

Gawande, Nitin A.; Daily, Jeff A.; Siegel, Charles

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors—including NVIDIA, Intel, AMD, and IBM—have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. Here, this article provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consistsmore » of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling—sometimes encouraged by restricted GPU memory—NVLink is less important.« less

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

NASA Astrophysics Data System (ADS)

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-01

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Thin, Soft, Skin-Mounted Microfluidic Networks with Capillary Bursting Valves for Chrono-Sampling of Sweat.

PubMed

Choi, Jungil; Kang, Daeshik; Han, Seungyong; Kim, Sung Bong; Rogers, John A

2017-03-01

Systems for time sequential capture of microliter volumes of sweat released from targeted regions of the skin offer the potential to enable analysis of temporal variations in electrolyte balance and biomarker concentration throughout a period of interest. Current methods that rely on absorbent pads taped to the skin do not offer the ease of use in sweat capture needed for quantitative tracking; emerging classes of electronic wearable sweat analysis systems do not directly manage sweat-induced fluid flows for sample isolation. Here, a thin, soft, "skin-like" microfluidic platform is introduced that bonds to the skin to allow for collection and storage of sweat in an interconnected set of microreservoirs. Pressure induced by the sweat glands drives flow through a network of microchannels that incorporates capillary bursting valves designed to open at different pressures, for the purpose of passively guiding sweat through the system in sequential fashion. A representative device recovers 1.8 µL volumes of sweat each from 0.8 min of sweating into a set of separate microreservoirs, collected from 0.03 cm 2 area of skin with approximately five glands, corresponding to a sweat rate of 0.60 µL min -1 per gland. Human studies demonstrate applications in the accurate chemical analysis of lactate, sodium, and potassium concentrations and their temporal variations. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Differentially-charged and sequentially-switched square-wave pulse forming network

DOEpatents

North, George G. [Stockton, CA; Vogilin, George E. [Livermore, CA

1980-04-01

A pulse forming network for delivering a high-energy square-wave pulse to a load, including a series of inductive-capacitive sections wherein the capacitors are differentially charged higher further from the load. Each charged capacitor is isolated from adjacent sections and the load by means of a normally open switch at the output of each section. The switch between the load and the closest section to the load is closed to begin discharge of the capacitor in that section into the load. During discharge of each capacitor, the voltage thereacross falls to a predetermined potential with respect to the potential across the capacitor in the next adjacent section further from the load. When this potential is reached, it is used to close the switch in the adjacent section further from the load and thereby apply the charge in that section to the load through the adjacent section toward the load. Each successive section further from the load is sequentially switched in this manner to continuously and evenly supply energy to the load over the period of the pulse, with the differentially charged capacitors providing higher potentials away from the load to compensate for the voltage drop across the resistance of each inductor. This arrangement is low in cost and yet provides a high-energy pulse in an acceptable square-wave form.

Differentially-charged and sequentially-switched square-wave pulse forming network

DOEpatents

North, G.G.; Vogilin, G.E.

1980-04-01

Disclosed is a pulse forming network for delivering a high-energy square-wave pulse to a load, including a series of inductive-capacitive sections wherein the capacitors are differentially charged higher further from the load. Each charged capacitor is isolated from adjacent sections and the load by means of a normally open switch at the output of each section. The switch between the load and the closest section to the load is closed to begin discharge of the capacitor in that section into the load. During discharge of each capacitor, the voltage thereacross falls to a predetermined potential with respect to the potential across the capacitor in the next adjacent section further from the load. When this potential is reached, it is used to close the switch in the adjacent section further from the load and thereby apply the charge in that section to the load through the adjacent section toward the load. Each successive section further from the load is sequentially switched in this manner to continuously and evenly supply energy to the load over the period of the pulse, with the differentially charged capacitors providing higher potentials away from the load to compensate for the voltage drop across the resistance of each inductor. This arrangement is low in cost and yet provides a high-energy pulse in an acceptable square-wave form. 5 figs.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

PubMed

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Physical properties of sequential interpenetrating polymer networks produced from canola oil-based polyurethane and poly(methyl methacrylate).

PubMed

Kong, Xiaohua; Narine, Suresh S

2008-05-01

Sequential interpenetrating polymer networks (IPNs) were prepared using polyurethane (PUR) synthesized from canola oil-based polyol with terminal primary functional groups and poly(methyl methacrylate) (PMMA). The properties of the material were evaluated by dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), and modulated differential scanning calorimetry (MDSC), as well as tensile properties measurements. The morphology of the IPNs was investigated using scanning electron microscopy (SEM) and MDSC. A five-phase morphology, that is, sol phase, PUR-rich phase, PUR-rich interphase, PMMA-rich interphase, and PMMA-rich phase, was observed for all the IPNs by applying a new quantitative method based on the measurement of the differential of reversing heat capacity versus temperature from MDSC, although not confirmed by SEM, most likely due to resolution restrictions. NCO/OH molar ratios (cross-linking density) and compositional variations of PUR/PMMA both affected the thermal properties and phase behaviors of the IPNs. Higher degrees of mixing occurred for the IPN with higher NCO/OH molar ratio (2.0/1.0) at PUR concentration of 25 wt %, whereas for the IPN with lower NCO/OH molar ratio (1.6/1.0), higher degrees of mixing occurred at PUR concentration of 35 wt %. The mechanical properties of the IPNs were superior to those of the constituent polymers due to the finely divided rubber and plastic combination structures in these IPNs.

A Novel Data-Driven Approach to Preoperative Mapping of Functional Cortex Using Resting-State Functional Magnetic Resonance Imaging

PubMed Central

Mitchell, Timothy J.; Hacker, Carl D.; Breshears, Jonathan D.; Szrama, Nick P.; Sharma, Mohit; Bundy, David T.; Pahwa, Mrinal; Corbetta, Maurizio; Snyder, Abraham Z.; Shimony, Joshua S.

2013-01-01

BACKGROUND: Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping. OBJECTIVE: To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks. METHODS: This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy. RESULTS: The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively. CONCLUSION: A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions. ABBREVIATIONS: AUC, area under the curve BA, Brodmann area BOLD, blood oxygen level dependent ECS, electrocortical stimulation fMRI, functional magnetic resonance imaging ICA, independent component analysis MLP, multilayer perceptron MP-RAGE, magnetization-prepared rapid gradient echo ROC, receiver-operating characteristic rs-fMRI, resting-state functional magnetic resonance imaging RSN, resting-state network PMID:24264234

ARCH: Adaptive recurrent-convolutional hybrid networks for long-term action recognition

PubMed Central

Xin, Miao; Zhang, Hong; Wang, Helong; Sun, Mingui; Yuan, Ding

2017-01-01

Recognition of human actions from digital video is a challenging task due to complex interfering factors in uncontrolled realistic environments. In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities. Utilizing a cognitive-based data reduction method and a hybrid “network upon networks” architecture, we extract human action representations which are robust against spatial and temporal interferences and adaptive to variations in both action speed and duration. We evaluated our method on the UCF101 and other three challenging datasets. Our results demonstrated a superior performance of the proposed algorithm in human action recognition. PMID:29290647

Fate of Zinc and Silver Engineered Nanoparticles in Sewerage Networks

EPA Science Inventory

Engineered zinc oxide (ZnO) and silver (Ag) nanoparticles (NPs) used in consumer products are largely released into the environment through the wastewater stream. Limited information is available regarding the transformations they undergo during their transit through sewerage sy...

Evaluation of the effect of nurse education on patient-reported foot checks and foot care behaviour of people with diabetes receiving haemodialysis.

PubMed

Brand, S L; Musgrove, A; Jeffcoate, W J; Lincoln, N B

2016-02-01

To assess whether a programme of nurse education increased the frequency with which nurses conducted foot checks on people with diabetes undergoing haemodialysis and to evaluate whether this influenced self-reported foot care behaviour. A non-randomized stepped-wedge design was used to evaluate a nurse education programme implemented in four UK National Health Service dialysis units. People with diabetes undergoing haemodialysis were invited to complete a questionnaire on the frequency of foot examination by health professionals, on the presence of foot problems and on their own foot care behaviour, using the Nottingham Assessment of Functional Foot-care (NAFF). An education session for nurses, including procedures for foot examination, was conducted sequentially in each of four haemodialysis units. The questionnaire was repeated at 2-monthly intervals. The education session resulted in a significant increase in the reported number of foot examinations by nurses (P = 0.007). There was also a significant improvement in reported foot care behaviour (P < 0.001), but this occurred between the first and second 2-monthly assessments and was unrelated to the timing of the intervention. A single education session can improve the routine checking of the feet of people with diabetes undergoing haemodialysis. The administration of the Nottingham Assessment of Functional Foot-care questionnaire was associated with improved self-reported foot care behaviour, reflecting greater awareness of risk in this population. © 2015 The Authors. Diabetic Medicine © 2015 Diabetes UK.

Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network.

PubMed

Ziada, A M; Lisle, T C; Snow, P B; Levine, R F; Miller, G; Crawford, E D

2001-04-15

The advent of advanced computing techniques has provided the opportunity to analyze clinical data using artificial intelligence techniques. This study was designed to determine whether a neural network could be developed using preoperative prognostic indicators to predict the pathologic stage and time of biochemical failure for patients who undergo radical prostatectomy. The preoperative information included TNM stage, prostate size, prostate specific antigen (PSA) level, biopsy results (Gleason score and percentage of positive biopsy), as well as patient age. All 309 patients underwent radical prostatectomy at the University of Colorado Health Sciences Center. The data from all patients were used to train a multilayer perceptron artificial neural network. The failure rate was defined as a rise in the PSA level > 0.2 ng/mL. The biochemical failure rate in the data base used was 14.2%. Univariate and multivariate analyses were performed to validate the results. The neural network statistics for the validation set showed a sensitivity and specificity of 79% and 81%, respectively, for the prediction of pathologic stage with an overall accuracy of 80% compared with an overall accuracy of 67% using the multivariate regression analysis. The sensitivity and specificity for the prediction of failure were 67% and 85%, respectively, demonstrating a high confidence in predicting failure. The overall accuracy rates for the artificial neural network and the multivariate analysis were similar. Neural networks can offer a convenient vehicle for clinicians to assess the preoperative risk of disease progression for patients who are about to undergo radical prostatectomy. Continued investigation of this approach with larger data sets seems warranted. Copyright 2001 American Cancer Society.

Formation of fivefold deformation twins in nanocrystalline face-centered-cubic copper based on molecular dynamics simulations

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

Cao, A. J.; Wei, Y. G.

2006-07-24

Fivefold deformation twins were reported recently to be observed in the experiment of the nanocrystalline face-centered-cubic metals and alloys. However, they were not predicted previously based on the molecular dynamics (MD) simulations and the reason was thought to be a uniaxial tension considered in the simulations. In the present investigation, through introducing pretwins in grain regions, using the MD simulations, the authors predict out the fivefold deformation twins in the grain regions of the nanocrystal grain cell, which undergoes a uniaxial tension. It is shown in their simulation results that series of Shockley partial dislocations emitted from grain boundaries providemore » sequential twining mechanism, which results in fivefold deformation twins.« less

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

PubMed

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Differential receipt of sentinel lymph node biopsy within practice-based research networks

PubMed Central

Meyer, Anne-Marie; Reeder-Hayes, Katherine E.; Liu, Huan; Wheeler, Stephanie B.; Penn, Dolly; Weiner, Bryan J.; Carpenter, William R.

2013-01-01

Background Provider-based research networks (PBRNs) are promising for accelerating not only research, but also dissemination of research-based evidence into broader community practice. Sentinel lymph node biopsy (SLNB) is an innovation in breast cancer care associated with equivalent survival and lower morbidity, as compared to standard axillary lymph node dissection. We examined the diffusion of SLNB into practice and whether affiliation with the Community Clinical Oncology Program (CCOP), a cancer-focused PBRN, was associated with more rapid uptake of SLNB. Research Design Surveillance Epidemiology and End Results(SEER)-Medicare data were used to study women diagnosed with stage I or II breast cancer in the years 2000 to 2005 and undergoing breast conserving surgery with axillary staging (n=6,226). The primary outcome was undergoing SLNB. CCOP affiliation of the surgical physician was ascertained from NCI records. Multivariable generalized linear modeling with generalized estimating equations was used to measure association between CCOP exposure and undergoing SLNB, controlling for potential confounders. Results Women treated by a CCOP physician had significantly higher odds of receiving SLNB compared to women treated by a non-CCOP physician (OR 2.68; 95% CI 1.35, 5.34). The magnitude of this association was larger than that observed among patients treated by physicians operating in medical school-affiliated hospitals (OR 1.76; 95% CI 1.30–2.39). Conclusion Women treated by CCOP-affiliated physicians were more likely to undergo SLNB irrespective of the hospital’s medical school affiliation, suggesting that the CCOP PBRN may play a role in the rapid adoption of research-based innovation in community practice. PMID:23942221

Ordered alternating binary polymer nanodroplet array by sequential spin dewetting.

PubMed

Bhandaru, Nandini; Das, Anuja; Salunke, Namrata; Mukherjee, Rabibrata

2014-12-10

We report a facile technique for fabricating an ordered array of nearly equal-sized mesoscale polymer droplets of two constituent polymers (polystyrene, PS and poly(methyl methacrylate), PMMA) arranged in an alternating manner on a topographically patterned substrate. The self-organized array of binary polymers is realized by sequential spin dewetting. First, a dilute solution of PMMA is spin-dewetted on a patterned substrate, resulting in an array of isolated PMMA droplets arranged along the substrate grooves due to self-organization during spin coating itself. The sample is then silanized with octadecyltrichlorosilane (OTS), and subsequently, a dilute solution of PS is spin-coated on to it, which also undergoes spin dewetting. The spin-dewetted PS drops having a size nearly equal to the pre-existing PMMA droplets position themselves between two adjacent PMMA drops under appropriate conditions, forming an alternating binary polymer droplet array. The alternating array formation takes place for a narrow range of solution concentration for both the polymers and depends on the geometry of the substrate. The size of the droplets depends on the extent of confinement, and droplets as small as 100 nm can be obtained by this method, on a suitable template. The findings open up the possibility of creating novel surfaces having ordered multimaterial domains with a potential multifunctional capability.

Sequential compression device with thigh-high sleeves supports mean arterial pressure during Caesarean section under spinal anaesthesia.

PubMed

Adsumelli, R S N; Steinberg, E S; Schabel, J E; Saunders, T A; Poppers, P J

2003-11-01

This study investigated the use of a Sequential Compression Device (SCD) with thigh-high sleeves and a preset pressure of 50 mm Hg that recruits blood from the lower limbs intermittently, as a method to prevent spinal hypotension during elective Caesarean section. Possible association of arterial pressure changes with maternal, fetal, haemodynamic, and anaesthetic factors were studied. Fifty healthy parturients undergoing elective Caesarean section under spinal anaesthesia were randomly assigned to either SCD (n=25) or control (n=25) groups. A standardized protocol for pre-hydration and anaesthetic technique was followed. Hypotension was defined as a decrease in any mean arterial pressure (MAP) measurement by more than 20% of the baseline MAP. Systolic (SAP), MAP and diastolic (DAP) arterial pressure, pulse pressure (PP), and heart rate (HR) were noted at baseline and every minute after the spinal block until delivery. A greater than 20% decrease in MAP occurred in 52% of patients in the SCD group vs 92% in the control group (P=0.004, odds ratio 0.094, 95% CI 0.018-0.488). There were no significant differences in SAP, DAP, HR, and PP between the groups. SCD use in conjunction with vasopressor significantly reduced the incidence of a 20% reduction of MAP.

Myocardial Iron Loading Assessment by Automatic Left Ventricle Segmentation with Morphological Operations and Geodesic Active Contour on T2* images

NASA Astrophysics Data System (ADS)

Luo, Yun-Gang; Ko, Jacky Kl; Shi, Lin; Guan, Yuefeng; Li, Linong; Qin, Jing; Heng, Pheng-Ann; Chu, Winnie Cw; Wang, Defeng

2015-07-01

Myocardial iron loading thalassemia patients could be identified using T2* magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2* mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2* analysis indicated that regions with intensity lower than 20 ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.

Chromatin remodeling agent trichostatin A: a key-factor in the hepatic differentiation of human mesenchymal stem cells derived of adult bone marrow

PubMed Central

Snykers, Sarah; Vanhaecke, Tamara; De Becker, Ann; Papeleu, Peggy; Vinken, Mathieu; Van Riet, Ivan; Rogiers, Vera

2007-01-01

Background The capability of human mesenchymal stem cells (hMSC) derived of adult bone marrow to undergo in vitro hepatic differentiation was investigated. Results Exposure of hMSC to a cocktail of hepatogenic factors [(fibroblast growth factor-4 (FGF-4), hepatocyte growth factor (HGF), insulin-transferrin-sodium-selenite (ITS) and dexamethasone)] failed to induce hepatic differentiation. Sequential exposure to these factors (FGF-4, followed by HGF, followed by HGF+ITS+dexamethasone), however, resembling the order of secretion during liver embryogenesis, induced both glycogen-storage and cytokeratin (CK)18 expression. Additional exposure of the cells to trichostatin A (TSA) considerably improved endodermal differentiation, as evidenced by acquisition of an epithelial morphology, chronological expression of hepatic proteins, including hepatocyte-nuclear factor (HNF)-3β, alpha-fetoprotein (AFP), CK18, albumin (ALB), HNF1α, multidrug resistance-associated protein (MRP)2 and CCAAT-enhancer binding protein (C/EBP)α, and functional maturation, i.e. upregulated ALB secretion, urea production and inducible cytochrome P450 (CYP)-dependent activity. Conclusion hMSC are able to undergo mesenchymal-to-epithelial transition. TSA is hereby essential to promote differentiation of hMSC towards functional hepatocyte-like cells. PMID:17407549

Vibration energy harvesting based monitoring of an operational bridge undergoing forced vibration and train passage

NASA Astrophysics Data System (ADS)

Cahill, Paul; Hazra, Budhaditya; Karoumi, Raid; Mathewson, Alan; Pakrashi, Vikram

2018-06-01

The application of energy harvesting technology for monitoring civil infrastructure is a bourgeoning topic of interest. The ability of kinetic energy harvesters to scavenge ambient vibration energy can be useful for large civil infrastructure under operational conditions, particularly for bridge structures. The experimental integration of such harvesters with full scale structures and the subsequent use of the harvested energy directly for the purposes of structural health monitoring shows promise. This paper presents the first experimental deployment of piezoelectric vibration energy harvesting devices for monitoring a full-scale bridge undergoing forced dynamic vibrations under operational conditions using energy harvesting signatures against time. The calibration of the harvesters is presented, along with details of the host bridge structure and the dynamic assessment procedures. The measured responses of the harvesters from the tests are presented and the use the harvesters for the purposes of structural health monitoring (SHM) is investigated using empirical mode decomposition analysis, following a bespoke data cleaning approach. Finally, the use of sequential Karhunen Loeve transforms to detect train passages during the dynamic assessment is presented. This study is expected to further develop interest in energy-harvesting based monitoring of large infrastructure for both research and commercial purposes.

Frontal parietal control network regulates the anti-correlated default and dorsal attention networks.

PubMed

Gao, Wei; Lin, Weili

2012-01-01

Recent reports demonstrate the anti-correlated behaviors between the default (DF) and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks-FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. Copyright © 2011 Wiley Periodicals, Inc.

Complex quantum network geometries: Evolution and phase transitions

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Complex quantum network geometries: Evolution and phase transitions.

PubMed

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion.

PubMed

Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E; Gur, Ruben C; Satterthwaite, Theodore D; Davatzikos, Christos

2017-03-28

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

A customized MILP approach to the synthesis of heat recovery networks reaching specified topology targets

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

Galli, M.R.; Cerda, J.

1998-06-01

A mathematical representation of a heat-exchanger network structure that explicitly accounts for the relative location of heat-transfer units, splitters, and mixers is presented. It is the basis of a mixed-integer linear programming sequential approach to the synthesis of heat-exchanger networks that allows the designer to specify beforehand some desired topology features as further design targets. Such structural information stands for additional problem data to be considered in the problem formulation, thus enhancing the involvement of the design engineer in the synthesis task. The topology constraints are expressed in terms of (1) the equipment items (heat exchangers, splitters, and mixers) thatmore » could be incorporated into the network, (2) the feasible neighbors for every potential unit, and (3) the heat matches, if any, with which a heat exchanger can be accomplished in parallel over any process stream. Moreover, the number and types of splitters being arranged over either a particular stream or the whole network can also be restrained. The new approach has been successfully applied to the solution of five example problems at each of which a wide variety of structural design restrictions were specified.« less

Radionuclide Gas Transport through Nuclear Explosion-Generated Fracture Networks

PubMed Central

Jordan, Amy B.; Stauffer, Philip H.; Knight, Earl E.; Rougier, Esteban; Anderson, Dale N.

2015-01-01

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gas breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. Seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable. PMID:26676058

Radionuclide Gas Transport through Nuclear Explosion-Generated Fracture Networks.

PubMed

Jordan, Amy B; Stauffer, Philip H; Knight, Earl E; Rougier, Esteban; Anderson, Dale N

2015-12-17

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gas breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. Seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable.

PREMER: a Tool to Infer Biological Networks.

PubMed

Villaverde, Alejandro F; Becker, Kolja; Banga, Julio R

2017-10-04

Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems. The computational cost of such calculations can be alleviated by the use of compiled programs and parallelization. To this end we have developed PREMER (Parallel Reverse Engineering with Mutual information & Entropy Reduction), a software toolbox that can run in parallel and sequential environments. It uses information theoretic criteria to recover network topology and determine the strength and causality of interactions, and allows incorporating prior knowledge, imputing missing data, and correcting outliers. PREMER is a free, open source software tool that does not require any commercial software. Its core algorithms are programmed in FORTRAN 90 and implement OpenMP directives. It has user interfaces in Python and MATLAB/Octave, and runs on Windows, Linux and OSX (https://sites.google.com/site/premertoolbox/).

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

Percolation in real interdependent networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2015-07-01

The function of a real network depends not only on the reliability of its own components, but is affected also by the simultaneous operation of other real networks coupled with it. Whereas theoretical methods of direct applicability to real isolated networks exist, the frameworks developed so far in percolation theory for interdependent network layers are of little help in practical contexts, as they are suited only for special models in the limit of infinite size. Here, we introduce a set of heuristic equations that takes as inputs the adjacency matrices of the layers to draw the entire phase diagram for the interconnected network. We demonstrate that percolation transitions in interdependent networks can be understood by decomposing these systems into uncoupled graphs: the intersection among the layers, and the remainders of the layers. When the intersection dominates the remainders, an interconnected network undergoes a smooth percolation transition. Conversely, if the intersection is dominated by the contribution of the remainders, the transition becomes abrupt even in small networks. We provide examples of real systems that have developed interdependent networks sharing cores of `high quality’ edges to prevent catastrophic failures.

Relaxation dynamics of maximally clustered networks

NASA Astrophysics Data System (ADS)

Klaise, Janis; Johnson, Samuel

2018-01-01

We study the relaxation dynamics of fully clustered networks (maximal number of triangles) to an unclustered state under two different edge dynamics—the double-edge swap, corresponding to degree-preserving randomization of the configuration model, and single edge replacement, corresponding to full randomization of the Erdős-Rényi random graph. We derive expressions for the time evolution of the degree distribution, edge multiplicity distribution and clustering coefficient. We show that under both dynamics networks undergo a continuous phase transition in which a giant connected component is formed. We calculate the position of the phase transition analytically using the Erdős-Rényi phenomenology.

Adaptive oscillator networks with conserved overall coupling: Sequential firing and near-synchronized states

NASA Astrophysics Data System (ADS)

Picallo, Clara B.; Riecke, Hermann

2011-03-01

Motivated by recent observations in neuronal systems we investigate all-to-all networks of nonidentical oscillators with adaptive coupling. The adaptation models spike-timing-dependent plasticity in which the sum of the weights of all incoming links is conserved. We find multiple phase-locked states that fall into two classes: near-synchronized states and splay states. Among the near-synchronized states are states that oscillate with a frequency that depends only very weakly on the coupling strength and is essentially given by the frequency of one of the oscillators, which is, however, neither the fastest nor the slowest oscillator. In sufficiently large networks the adaptive coupling is found to develop effective network topologies dominated by one or two loops. This results in a multitude of stable splay states, which differ in their firing sequences. With increasing coupling strength their frequency increases linearly and the oscillators become less synchronized. The essential features of the two classes of states are captured analytically in perturbation analyses of the extended Kuramoto model used in the simulations.

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

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

Gupta, Chinmaya; López, José Manuel; Azencott, Robert

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemicalmore » Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.« less

Performance of a Protected Wireless Sensor Network in a Fire. Analysis of Fire Spread and Data Transmission

PubMed Central

Antoine-Santoni, Thierry; Santucci, Jean-François; de Gentili, Emmanuelle; Silvani, Xavier; Morandini, Frederic

2009-01-01

The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m2. PMID:22454563

Performance of a protected wireless sensor network in a fire. Analysis of fire spread and data transmission.

PubMed

Antoine-Santoni, Thierry; Santucci, Jean-François; de Gentili, Emmanuelle; Silvani, Xavier; Morandini, Frederic

2009-01-01

The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m(2).

CA1 cell activity sequences emerge after reorganization of network correlation structure during associative learning

PubMed Central

Modi, Mehrab N; Dhawale, Ashesh K; Bhalla, Upinder S

2014-01-01

Animals can learn causal relationships between pairs of stimuli separated in time and this ability depends on the hippocampus. Such learning is believed to emerge from alterations in network connectivity, but large-scale connectivity is difficult to measure directly, especially during learning. Here, we show that area CA1 cells converge to time-locked firing sequences that bridge the two stimuli paired during training, and this phenomenon is coupled to a reorganization of network correlations. Using two-photon calcium imaging of mouse hippocampal neurons we find that co-time-tuned neurons exhibit enhanced spontaneous activity correlations that increase just prior to learning. While time-tuned cells are not spatially organized, spontaneously correlated cells do fall into distinct spatial clusters that change as a result of learning. We propose that the spatial re-organization of correlation clusters reflects global network connectivity changes that are responsible for the emergence of the sequentially-timed activity of cell-groups underlying the learned behavior. DOI: http://dx.doi.org/10.7554/eLife.01982.001 PMID:24668171

Dynamical Origin of the Effective Storage Capacity in the Brain's Working Memory

NASA Astrophysics Data System (ADS)

Bick, Christian; Rabinovich, Mikhail I.

2009-11-01

The capacity of working memory (WM), a short-term buffer for information in the brain, is limited. We suggest a model for sequential WM that is based upon winnerless competition amongst representations of available informational items. Analytical results for the underlying mathematical model relate WM capacity and relative lateral inhibition in the corresponding neural network. This implies an upper bound for WM capacity, which is, under reasonable neurobiological assumptions, close to the “magical number seven.”

Sequential neural text compression.

PubMed

Schmidhuber, J; Heil, S

1996-01-01

The purpose of this paper is to show that neural networks may be promising tools for data compression without loss of information. We combine predictive neural nets and statistical coding techniques to compress text files. We apply our methods to certain short newspaper articles and obtain compression ratios exceeding those of the widely used Lempel-Ziv algorithms (which build the basis of the UNIX functions "compress" and "gzip"). The main disadvantage of our methods is that they are about three orders of magnitude slower than standard methods.

Connectionist Models: Proceedings of the Summer School Held in San Diego, California on 1990

DTIC Science & Technology

1990-01-01

modes: control network continues activation spreading based There is the sequential version and the parallel version on the actual inputs instead of...ent). 2. Execute all motoric actions based on activations of r a ent.The parallel version of the algorithm is local in time, units in A. Update the...a- movements that help o recognize an entering person.) tions like ’move focus left’, ’rotate focus’ are based on the activations of the C’s output

Diffusion in the system K2O-SrO-SiO2. II - Cation self-diffusion coefficients.

NASA Technical Reports Server (NTRS)

Varshneya, A. K.; Cooper, A. R.

1972-01-01

The self-diffusion coefficients were measured by introducing a slab of glass previously irradiated in a reactor between two slabs of unirradiated glass. By heating the specimens, etching them sequentially and determining the radioactivity, self-diffusion coefficients for K and Sr were measured. It is pointed out that the results obtained in the investigations appear to support the proposal that the network of the base glass predominantly controls the activation energy for the diffusion of ions.

A Methodology and Software Environment for Testing Process Model’s Sequential Predictions with Protocols

DTIC Science & Technology

1992-12-21

in preparation). Foundations of artificial intelligence. Cambridge, MA: MIT Press. O’Reilly, R. C. (1991). X3DNet: An X- Based Neural Network ...2.2.3 Trace based protocol analysis 19 2.2A Summary of important data features 21 2.3 Tools related to process model testing 23 2.3.1 Tools for building...algorithm 57 3. Requirements for testing process models using trace based protocol 59 analysis 3.1 Definition of trace based protocol analysis (TBPA) 59

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Engineering anastomosis between living capillary networks and endothelial cell-lined microfluidic channels.

PubMed

Wang, Xiaolin; Phan, Duc T T; Sobrino, Agua; George, Steven C; Hughes, Christopher C W; Lee, Abraham P

2016-01-21

This paper reports a method for generating an intact and perfusable microvascular network that connects to microfluidic channels without appreciable leakage. This platform incorporates different stages of vascular development including vasculogenesis, endothelial cell (EC) lining, sprouting angiogenesis, and anastomosis in sequential order. After formation of a capillary network inside the tissue chamber via vasculogenesis, the adjacent microfluidic channels are lined with a monolayer of ECs, which then serve as the high-pressure input ("artery") and low pressure output ("vein") conduits. To promote a tight interconnection between the artery/vein and the capillary network, sprouting angiogenesis is induced, which promotes anastomosis of the vasculature inside the tissue chamber with the EC lining along the microfluidic channels. Flow of fluorescent microparticles confirms the perfusability of the lumenized microvascular network, and minimal leakage of 70 kDa FITC-dextran confirms physiologic tightness of the EC junctions and completeness of the interconnections between artery/vein and the capillary network. This versatile device design and its robust construction methodology establish a physiological transport model of interconnected perfused vessels from artery to vascularized tissue to vein. The system has utility in a wide range of organ-on-a-chip applications as it enables the physiological vascular interconnection of multiple on-chip tissue constructs that can serve as disease models for drug screening.

Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.

PubMed

Wang, Qian; Lou, Zhifeng; Zhai, Liansuo; Zhao, Haibin

2017-06-01

To identify significant biomarkers for detection of pneumococcal meningitis based on ego network. Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis. By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively. The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

Frontal Parietal Control Network Regulates the Anti-Correlated Default and Dorsal Attention Networks

PubMed Central

Gao, Wei; Lin, Weili

2011-01-01

Recent reports demonstrate the anti-correlated behaviors between the default and the dorsal attention (DA) networks. We aimed to investigate the roles of the frontal parietal control (FPC) network in regulating the two anti-correlated networks through three experimental conditions, including resting, continuous self-paced/attended sequential finger tapping (FT), and natural movie watching (MW), respectively. The two goal-directed tasks were chosen to engage either one of the two competing networks—FT for DA whereas MW for default. We hypothesized that FPC will selectively augment/suppress either network depending on how the task targets the specific network; FPC will positively correlate with the target network, but negatively correlate with the network anti-correlated with the target network. We further hypothesized that significant causal links from FPC to both DA and DF are present during all three experimental conditions, supporting the initiative regulating role of FPC over the two opposing systems. Consistent with our hypotheses, FPC exhibited a significantly higher positive correlation with DA (P = 0.0095) whereas significantly more negative correlation with default (P = 0.0025) during FT when compared to resting. Completely opposite to that observed during FT, the FPC was significantly anti-correlated with DA (P = 2.1e-6) whereas positively correlated with default (P = 0.0035) during MW. Furthermore, extensive causal links from FPC to both DA and DF were observed across all three experimental states. Together, our results strongly support the notion that the FPC regulates the anti-correlated default and DA networks. PMID:21391263

Structural and Functional Plasticity in the Maternal Brain Circuitry

ERIC Educational Resources Information Center

Pereira, Mariana

2016-01-01

Parenting recruits a distributed network of brain structures (and neuromodulators) that coordinates caregiving responses attuned to the young's affect, needs, and developmental stage. Many of these structures and connections undergo significant structural and functional plasticity, mediated by the interplay between maternal hormones and social…

Adaptation in constitutional dynamic libraries and networks, switching between orthogonal metalloselection and photoselection processes.

PubMed

Vantomme, Ghislaine; Jiang, Shimei; Lehn, Jean-Marie

2014-07-02

Constitutional dynamic libraries of hydrazones (a)A(b)B and acylhydrazones (a)A(c)C undergo reorganization and adaptation in response to a chemical effector (metal cations) or a physical stimulus (light). The set of hydrazones [(1)A(1)B, (1)A(2)B, (2)A(1)B, (2)A(2)B] undergoes metalloselection on addition of zinc cations which drive the amplification of Zn((1)A(2)B)2 by selection of the fittest component (1)A(2)B. The set of acylhydrazones [E-(1)A(1)C, (1)A(2)C, (2)A(1)C, (2)A(2)C] undergoes photoselection by irradiation of the system, which causes photoisomerization of E-(1)A(1)C into Z-(1)A(1)C with amplification of the latter. The set of acyl hydrazones [E-(1)A(1)C, (1)A(3)C, (2)A(1)C, (2)A(3)C] undergoes a dual adaptation via component exchange and selection in response to two orthogonal external agents: a chemical effector, metal cations, and a physical stimulus, light irradiation. Metalloselection takes place on addition of zinc cations which drive the amplification of Zn((1)A(3)C)2 by selection of the fittest constituent (1)A(3)C. Photoselection is obtained on irradiation of the acylhydrazones that leads to photoisomerization from E-(1)A(1)C to Z-(1)A(1)C configuration with amplification of the latter. These changes may be represented by square constitutional dynamic networks that display up-regulation of the pairs of agonists ((1)A(2)B, (2)A(1)B), (Z-(1)A(1)C, (2)A(2)C), ((1)A(3)C, (2)A(1)C), (Z-(1)A(1)C, (2)A(3)C) and the simultaneous down-regulation of the pairs of antagonists ((1)A(1)B, (2)A(2)B), ((1)A(2)C, (2)A(1)C), (E-(1)A(1)C, (2)A(3)C), ((1)A(3)C, (2)A(1)C). The orthogonal dual adaptation undergone by the set of acylhydrazones amounts to a network switching process.

Modeling non-homologous end joining.

PubMed

Li, Yongfeng; Cucinotta, Francis A

2011-08-21

Non-homologous end joining (NHEJ) is an important DNA repair pathway for DNA double-strand breaks. Several proteins, including Ku, DNA-PKcs, Artemis, XRCC4/Ligase IV and XLF, are involved in the NHEJ for the DNA damage detection, DNA free end processing and ligation. The classical model of NHEJ is a sequential model in which DNA-PKcs is first recruited by the Ku bound DNA prior to any other repair proteins. Recent experimental study (McElhinny et al., 2000; Costantini et al., 2007; Mari et al., 2006; Yano and Chen, 2008) suggested that the recruitment ordering is not crucial. In this work, by proposing a mathematical model in terms of biochemical reaction network and performing stability and related analysis, we demonstrate theoretically that if DSB repair pathway independent of DNA-PKcs exists, then the classical sequential model and new two-phase model are essentially indistinguishable in the sense that DSB can be repaired thoroughly in both models when the repair proteins are sufficient. Published by Elsevier Ltd.

Inferring Interaction Force from Visual Information without Using Physical Force Sensors.

PubMed

Hwang, Wonjun; Lim, Soo-Chul

2017-10-26

In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

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.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Binary tree eigen solver in finite element analysis

NASA Technical Reports Server (NTRS)

Akl, F. A.; Janetzke, D. C.; Kiraly, L. J.

1993-01-01

This paper presents a transputer-based binary tree eigensolver for the solution of the generalized eigenproblem in linear elastic finite element analysis. The algorithm is based on the method of recursive doubling, which parallel implementation of a number of associative operations on an arbitrary set having N elements is of the order of o(log2N), compared to (N-1) steps if implemented sequentially. The hardware used in the implementation of the binary tree consists of 32 transputers. The algorithm is written in OCCAM which is a high-level language developed with the transputers to address parallel programming constructs and to provide the communications between processors. The algorithm can be replicated to match the size of the binary tree transputer network. Parallel and sequential finite element analysis programs have been developed to solve for the set of the least-order eigenpairs using the modified subspace method. The speed-up obtained for a typical analysis problem indicates close agreement with the theoretical prediction given by the method of recursive doubling.

Photo-induced Mass Transport through Polymer Networks

NASA Astrophysics Data System (ADS)

Meng, Yuan; Anthamatten, Mitchell

2014-03-01

Among adaptable materials, photo-responsive polymers are especially attractive as they allow for spatiotemporal stimuli and response. We have recently developed a macromolecular network capable of photo-induced mass transport of covalently bound species. The system comprises of crosslinked chains that form an elastic network and photosensitive fluorescent arms that become mobile upon irradiation. We form loosely crosslinked polymer networks by Michael-Addition between multifunctional thiols and small molecule containing acrylate end-groups. The arms are connected to the network by allyl sulfide, that undergoes addition-fragmentation chain transfer (AFCT) in the presence of free radicals, releasing diffusible fluorophore. The networks are loaded with photoinitiator to allow for spatial modulation of the AFCT reactions. FRAP experiments within bulk elastomers are conducted to establish correlations between the fluorophore's diffusion coefficient and experimental variables such as network architecture, temperature and UV intensity. Photo-induced mass transport between two contacted films is demonstrated, and release of fluorophore into a solvent is investigated. Spatial and temporal control of mass transport could benefit drug release, printing, and sensing applications.

Altering the swelling pressures within in vitro engineered cartilage is predicted to modulate the configuration of the collagen network and hence improve tissue mechanical properties.

PubMed

Nagel, Thomas; Kelly, Daniel J

2013-06-01

Prestress in the collagen network has a significant impact on the material properties of cartilaginous tissues. It is closely related to the recruitment configuration of the collagen network which defines the transition from lax collagen fibres to uncrimped, load-bearing collagen fibres. This recruitment configuration can change in response to alterations in the external environmental conditions. In this study, the influence of changes in external salt concentration or sequential proteoglycan digestion on the configuration of the collagen network of tissue engineered cartilage is investigated using a previously developed computational model. Collagen synthesis and network assembly are assumed to occur in the tissue configuration present during in vitro culture. The model assumes that if this configuration is more compact due to changes in tissue swelling, the collagen network will adapt by lowering its recruitment stretch. When returned to normal physiological conditions, these tissues will then have a higher prestress in the collagen network. Based on these assumptions, the model demonstrates that proteoglycan digestion at discrete time points during culture as well as culture in a hypertonic medium can improve the functionality of tissue engineered cartilage, while culture in hypotonic solution is detrimental to the apparent mechanical properties of the graft. Copyright © 2013 Elsevier Ltd. All rights reserved.

NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities.

PubMed

da Rocha, Edroaldo Lummertz; Ung, Choong Yong; McGehee, Cordelia D; Correia, Cristina; Li, Hu

2016-06-02

The sequential chain of interactions altering the binary state of a biomolecule represents the 'information flow' within a cellular network that determines phenotypic properties. Given the lack of computational tools to dissect context-dependent networks and gene activities, we developed NetDecoder, a network biology platform that models context-dependent information flows using pairwise phenotypic comparative analyses of protein-protein interactions. Using breast cancer, dyslipidemia and Alzheimer's disease as case studies, we demonstrate NetDecoder dissects subnetworks to identify key players significantly impacting cell behaviour specific to a given disease context. We further show genes residing in disease-specific subnetworks are enriched in disease-related signalling pathways and information flow profiles, which drive the resulting disease phenotypes. We also devise a novel scoring scheme to quantify key genes-network routers, which influence many genes, key targets, which are influenced by many genes, and high impact genes, which experience a significant change in regulation. We show the robustness of our results against parameter changes. Our network biology platform includes freely available source code (http://www.NetDecoder.org) for researchers to explore genome-wide context-dependent information flow profiles and key genes, given a set of genes of particular interest and transcriptome data. More importantly, NetDecoder will enable researchers to uncover context-dependent drug targets. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Role of the evening eastward electric field and the seed perturbations in the sequential occurrence of plasma bubble

NASA Astrophysics Data System (ADS)

Abadi, P.; Otsuka, Y.; Shiokawa, K.; Yamamoto, M.; M Buhari, S.; Abdullah, M.

2017-12-01

We investigate the 3-m ionospheric irregularities and the height variation of equatorial F-region observed by the Equatorial Atmosphere Radar (EAR) at Kototabang (100.3°E, 0.2°S, dip. Lat.: 10.1°S) in Indonesia and ionosondes at Chumphon (99.3°E, 10.7°N, dip. Lat.: 3°N) in Thailand and at Bac Lieu (105.7°E, 9.3°N, dip. Lat.; 1.5°N) in Vietnam, respectively, during March-April from 2011 to 2014. We aim to disclose the relation between pre-reversal enhancement (PRE) of evening eastward electric field and the sequential occurrence of the equatorial plasma bubble (EPB) in the period of 19-22 LT. In summary, (i) we found that the zonal spacing between consecutive EPBs ranges from less than 100 km up to 800 km with a maximum occurrence around 100-300 km as shown in Figure 1(a), and this result is consistent with the previous study [e.g. Makela et al., 2010]; (ii) the probability of the sequential occurrence of the EPB enhances with the increase of PRE strength (see Figure 1(b)); and (iii) Figure 1(c) shows that the zonal spacing between consecutive EPBs is less than 300 km for the weaker PRE (<30 m/s), whereas the zonal spacing is more varied for the stronger PRE (≥30 m/s). Our results remark that the PRE strength is a prominent factor of the sequential occurrence of the EPB. However, we also consider another factor, namely the zonal structure of seed perturbation modulated by gravity wave (GW), and the zonal spacing between consecutive EPBs may fit with the wavelength of the zonal structure of seed perturbation. We particularly attribute the result (iii) to the effects of PRE and seed perturbation on the sequential occurrence of the EPB, that is, we suggest that the weaker PRE could cause the sequential occurrence of the EPB when the zonal structure of seed perturbation has a shorter wavelength. We, however, need a further investigation for confirming the periodic seeding mechanism, and we will use a network of GPS receivers in the western part of Southeast Asia to analyze the zonal wavy structure in the TEC as a manifestation of the seed perturbations.

Global Synchronization of Multiple Recurrent Neural Networks With Time Delays via Impulsive Interactions.

PubMed

Yang, Shaofu; Guo, Zhenyuan; Wang, Jun

2017-07-01

In this paper, new results on the global synchronization of multiple recurrent neural networks (NNs) with time delays via impulsive interactions are presented. Impulsive interaction means that a number of NNs communicate with each other at impulse instants only, while they are independent at the remaining time. The communication topology among NNs is not required to be always connected and can switch ON and OFF at different impulse instants. By using the concept of sequential connectivity and the properties of stochastic matrices, a set of sufficient conditions depending on time delays is derived to ascertain global synchronization of multiple continuous-time recurrent NNs. In addition, a counterpart on the global synchronization of multiple discrete-time NNs is also discussed. Finally, two examples are presented to illustrate the results.

Phenotypic Checkpoints Regulate Neuronal Development

PubMed Central

Ben-Ari, Yehezkel; Spitzer, Nicholas C.

2010-01-01

Nervous system development proceeds by sequential gene expression mediated by cascades of transcription factors in parallel with sequences of patterned network activity driven by receptors and ion channels. These sequences are cell type- and developmental stage-dependent and modulated by paracrine actions of substances released by neurons and glia. How and to what extent these sequences interact to enable neuronal network development is not understood. Recent evidence demonstrates that CNS development requires intermediate stages of differentiation providing functional feedback that influences gene expression. We suggest that embryonic neuronal functions constitute a series of phenotypic checkpoint signatures; neurons failing to express these functions are delayed or developmentally arrested. Such checkpoints are likely to be a general feature of neuronal development and may constitute presymptomatic signatures of neurological disorders when they go awry. PMID:20864191

A continuous-time neural model for sequential action.

PubMed

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Sequence of deep-focus earthquakes beneath the Bonin Islands identified by the NIED nationwide dense seismic networks Hi-net and F-net

NASA Astrophysics Data System (ADS)

Takemura, Shunsuke; Saito, Tatsuhiko; Shiomi, Katsuhiko

2017-03-01

An M 6.8 ( Mw 6.5) deep-focus earthquake occurred beneath the Bonin Islands at 21:18 (JST) on June 23, 2015. Observed high-frequency (>1 Hz) seismograms across Japan, which contain several sets of P- and S-wave arrivals for the 10 min after the origin time, indicate that moderate-to-large earthquakes occurred sequentially around Japan. Snapshots of the seismic energy propagation illustrate that after one deep-focus earthquake occurred beneath the Sea of Japan, two deep-focus earthquakes occurred sequentially after the first ( Mw 6.5) event beneath the Bonin Islands in the next 4 min. The United States Geological Survey catalog includes three Bonin deep-focus earthquakes with similar hypocenter locations, but their estimated magnitudes are inconsistent with seismograms from across Japan. The maximum-amplitude patterns of the latter two earthquakes were similar to that of the first Bonin earthquake, which indicates similar locations and mechanisms. Furthermore, based on the ratios of the S-wave amplitudes to that of the first event, the magnitudes of the latter events are estimated as M 6.5 ± 0.02 and M 5.8 ± 0.02, respectively. Three magnitude-6-class earthquakes occurred sequentially within 4 min in the Pacific slab at 480 km depth, where complex heterogeneities exist within the slab.[Figure not available: see fulltext.

Optical Tracking Data Validation and Orbit Estimation for Sparse Observations of Satellites by the OWL-Net.

PubMed

Choi, Jin; Jo, Jung Hyun; Yim, Hong-Suh; Choi, Eun-Jung; Cho, Sungki; Park, Jang-Hyun

2018-06-07

An Optical Wide-field patroL-Network (OWL-Net) has been developed for maintaining Korean low Earth orbit (LEO) satellites' orbital ephemeris. The OWL-Net consists of five optical tracking stations. Brightness signals of reflected sunlight of the targets were detected by a charged coupled device (CCD). A chopper system was adopted for fast astrometric data sampling, maximum 50 Hz, within a short observation time. The astrometric accuracy of the optical observation data was validated with precise orbital ephemeris such as Consolidated Prediction File (CPF) data and precise orbit determination result with onboard Global Positioning System (GPS) data from the target satellite. In the optical observation simulation of the OWL-Net for 2017, an average observation span for a single arc of 11 LEO observation targets was about 5 min, while an average optical observation separation time was 5 h. We estimated the position and velocity with an atmospheric drag coefficient of LEO observation targets using a sequential-batch orbit estimation technique after multi-arc batch orbit estimation. Post-fit residuals for the multi-arc batch orbit estimation and sequential-batch orbit estimation were analyzed for the optical measurements and reference orbit (CPF and GPS data). The post-fit residuals with reference show few tens-of-meters errors for in-track direction for multi-arc batch and sequential-batch orbit estimation results.

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

PubMed

Khader, Patrick H; Pachur, Thorsten; Weber, Lilian A E; Jost, Kerstin

2016-01-01

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

The Neural Representation of Prospective Choice during Spatial Planning and Decisions

PubMed Central

Kaplan, Raphael; Koster, Raphael; Penny, William D.; Burgess, Neil; Friston, Karl J.

2017-01-01

We are remarkably adept at inferring the consequences of our actions, yet the neuronal mechanisms that allow us to plan a sequence of novel choices remain unclear. We used functional magnetic resonance imaging (fMRI) to investigate how the human brain plans the shortest path to a goal in novel mazes with one (shallow maze) or two (deep maze) choice points. We observed two distinct anterior prefrontal responses to demanding choices at the second choice point: one in rostrodorsal medial prefrontal cortex (rd-mPFC)/superior frontal gyrus (SFG) that was also sensitive to (deactivated by) demanding initial choices and another in lateral frontopolar cortex (lFPC), which was only engaged by demanding choices at the second choice point. Furthermore, we identified hippocampal responses during planning that correlated with subsequent choice accuracy and response time, particularly in mazes affording sequential choices. Psychophysiological interaction (PPI) analyses showed that coupling between the hippocampus and rd-mPFC increases during sequential (deep versus shallow) planning and is higher before correct versus incorrect choices. In short, using a naturalistic spatial planning paradigm, we reveal how the human brain represents sequential choices during planning without extensive training. Our data highlight a network centred on the cortical midline and hippocampus that allows us to make prospective choices while maintaining initial choices during planning in novel environments. PMID:28081125

Diffusion, capture and recycling of SCAR/WAVE and Arp2/3 complexes observed in cells by single-molecule imaging.

PubMed

Millius, Arthur; Watanabe, Naoki; Weiner, Orion D

2012-03-01

The SCAR/WAVE complex drives lamellipodium formation by enhancing actin nucleation by the Arp2/3 complex. Phosphoinositides and Rac activate the SCAR/WAVE complex, but how SCAR/WAVE and Arp2/3 complexes converge at sites of nucleation is unknown. We analyzed the single-molecule dynamics of WAVE2 and p40 (subunits of the SCAR/WAVE and Arp2/3 complexes, respectively) in XTC cells. We observed lateral diffusion of both proteins and captured the transition of p40 from diffusion to network incorporation. These results suggest that a diffusive 2D search facilitates binding of the Arp2/3 complex to actin filaments necessary for nucleation. After nucleation, the Arp2/3 complex integrates into the actin network and undergoes retrograde flow, which results in its broad distribution throughout the lamellipodium. By contrast, the SCAR/WAVE complex is more restricted to the cell periphery. However, with single-molecule imaging, we also observed WAVE2 molecules undergoing retrograde motion. WAVE2 and p40 have nearly identical speeds, lifetimes and sites of network incorporation. Inhibition of actin retrograde flow does not prevent WAVE2 association and disassociation with the membrane but does inhibit WAVE2 removal from the actin cortex. Our results suggest that membrane binding and diffusion expedites the recruitment of nucleation factors to a nucleation site independent of actin assembly, but after network incorporation, ongoing actin polymerization facilitates recycling of SCAR/WAVE and Arp2/3 complexes.

Diffusion, capture and recycling of SCAR/WAVE and Arp2/3 complexes observed in cells by single-molecule imaging

PubMed Central

Millius, Arthur; Watanabe, Naoki; Weiner, Orion D.

2012-01-01

The SCAR/WAVE complex drives lamellipodium formation by enhancing actin nucleation by the Arp2/3 complex. Phosphoinositides and Rac activate the SCAR/WAVE complex, but how SCAR/WAVE and Arp2/3 complexes converge at sites of nucleation is unknown. We analyzed the single-molecule dynamics of WAVE2 and p40 (subunits of the SCAR/WAVE and Arp2/3 complexes, respectively) in XTC cells. We observed lateral diffusion of both proteins and captured the transition of p40 from diffusion to network incorporation. These results suggest that a diffusive 2D search facilitates binding of the Arp2/3 complex to actin filaments necessary for nucleation. After nucleation, the Arp2/3 complex integrates into the actin network and undergoes retrograde flow, which results in its broad distribution throughout the lamellipodium. By contrast, the SCAR/WAVE complex is more restricted to the cell periphery. However, with single-molecule imaging, we also observed WAVE2 molecules undergoing retrograde motion. WAVE2 and p40 have nearly identical speeds, lifetimes and sites of network incorporation. Inhibition of actin retrograde flow does not prevent WAVE2 association and disassociation with the membrane but does inhibit WAVE2 removal from the actin cortex. Our results suggest that membrane binding and diffusion expedites the recruitment of nucleation factors to a nucleation site independent of actin assembly, but after network incorporation, ongoing actin polymerization facilitates recycling of SCAR/WAVE and Arp2/3 complexes. PMID:22349699

Basal melting of snow on early Mars: A possible origin of some valley networks

USGS Publications Warehouse

Carr, M.H.; Head, J. W.

2003-01-01

Valley networks appear to be cut by liquid water, yet simulations suggest that early Mars could not have been warmed enough by a CO2-H2O greenhouse to permit rainfall. The vulnerability of an early atmosphere to impact erosion, the likely rapid scavenging of CO2 from the atmosphere by weathering, and the lack of detection of weathering products all support a cold early Mars. We explore the hypothesis that valley networks could have formed as a result of basal melting of thick snow and ice deposits. Depending on the heat flow, an early snowpack a few hundred meters to a few kilometers thick could undergo basal melting, providing water to cut valley networks. Copyright 2003 by the American Geophysical Union.

Mechanism study of biopolymer hair as a coupled thermo-water responsive smart material

NASA Astrophysics Data System (ADS)

Xiao, Xueliang; Zhou, Hongtao; Qian, Kun

2017-03-01

Animal hairs existing broadly in nature are found to be effectively responsive to stimuli of heat and water in sequence for shape deformation and recovery, namely, coupled shape memory function (CSMF). In the paper, the ability of thermo-water sensitive CSMF was first time investigated for animal hairs, the structural and molecular networks for net-points and switches were therefrom identified. Experimentally, animal hair manifested a high ability of shape fixation in thermal processing and good shape recovery by water stimulus. Characterizations of two stimuli (heating and hydration) were performed systematically on hair’s deformation, recovery, viscoelasticity and chemical components (crystalline phase, key bonds inamorphous area). The variations of related chemical components in molecular networks were also explored. A hybrid structural network model was thereafter proposed to interpret the thermo-water sensitive CSMF of hair. This study of two-sequential-stimuli CSMF is original and inspired to explore more complex functions of other smart natural materials and expected to make much smarter synthetic polymers.

EM connectomics reveals axonal target variation in a sequence-generating network

PubMed Central

Narayanan, Rajeevan T; Svara, Fabian; Egger, Robert; Oberlaender, Marcel; Denk, Winfried; Long, Michael A

2017-01-01

The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that generates sequences underlying the temporal progression of the song. We combined serial block-face electron microscopy with light microscopy to determine the cell types targeted by HVC(RA) neurons, which control song timing. Close to their soma, axons almost exclusively targeted inhibitory interneurons, consistent with what had been found with electrical recordings from pairs of cells. Conversely, far from the soma the targets were mostly other excitatory neurons, about half of these being other HVC(RA) cells. Both observations are consistent with the notion that the neural sequences that pace the song are generated by global synaptic chains in HVC embedded within local inhibitory networks. DOI: http://dx.doi.org/10.7554/eLife.24364.001 PMID:28346140

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

PubMed

Yun, Sangdoo; Choi, Jongwon; Yoo, Youngjoon; Yun, Kimin; Choi, Jin Young

2018-06-01

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

Interactive social contagions and co-infections on complex networks

NASA Astrophysics Data System (ADS)

Liu, Quan-Hui; Zhong, Lin-Feng; Wang, Wei; Zhou, Tao; Eugene Stanley, H.

2018-01-01

What we are learning about the ubiquitous interactions among multiple social contagion processes on complex networks challenges existing theoretical methods. We propose an interactive social behavior spreading model, in which two behaviors sequentially spread on a complex network, one following the other. Adopting the first behavior has either a synergistic or an inhibiting effect on the spread of the second behavior. We find that the inhibiting effect of the first behavior can cause the continuous phase transition of the second behavior spreading to become discontinuous. This discontinuous phase transition of the second behavior can also become a continuous one when the effect of adopting the first behavior becomes synergistic. This synergy allows the second behavior to be more easily adopted and enlarges the co-existence region of both behaviors. We establish an edge-based compartmental method, and our theoretical predictions match well with the simulation results. Our findings provide helpful insights into better understanding the spread of interactive social behavior in human society.

Clinical impact of an inter-hospital transfer strategy in patients with ST-elevation myocardial infarction undergoing primary angioplasty: the Emilia-Romagna ST-segment elevation acute myocardial infarction network.

PubMed

Manari, Antonio; Ortolani, Paolo; Guastaroba, Paolo; Casella, Gianni; Vignali, Luigi; Varani, Elisabetta; Piovaccari, Giancarlo; Guiducci, Vincenzo; Percoco, Gianfranco; Tondi, Stefano; Passerini, Francesco; Santarelli, Andrea; Marzocchi, Antonio

2008-08-01

This study sought to evaluate the impact of an inter-hospital transfer strategy on treatment times and in-hospital and 1 year cardiac mortality of patients with ST-segment elevation acute myocardial infarction (STEMI) undergoing primary percutaneous intervention (p-PCI) in the Italian region of Emilia-Romagna, where an efficient region-wide system for reperfusion has been established. 3296 patients with STEMI, undergoing on-site p-PCI (2444 patients) (OS group) or p-PCI after inter-hospital transfer (852 patients) (T group) between 1 January 2004 and 30 June 2006 in the Italian region of Emilia-Romagna, were considered. During the study period, the number of patients undergoing p-PCI increased both for patients admitted to interventional centres and for those admitted to peripheral hospitals. At the same time, the proportion of patients with STEMI initially admitted to peripheral hospitals and not transferred and the door-to-balloon time delays of transfer patients decreased. In spite of longer door-to-balloon delay in the transfer group [112 min (86-147) vs. 71 min (46-104)], in-hospital cardiac mortality (OS 7.0 vs. T 5.4%, P = 0.10) did not significantly differ between the two groups. After multivariable adjustment, the transfer strategy was not associated with increased risk of in-hospital [odds ratio 0.956; 95% confidence interval (CI) 0.633-1.442] and 1 year (hazard ratio 0.817; 95% CI 0.617-1.085) cardiac mortality. This study, concerning an established STEMI regional network, suggests that a strategy of inter-hospital transfer for p-PCI, when supported by an organized system of care, may be applied with rapid reperfusion times and favourable short- and long-term clinical outcomes.

Incorporation of spatial interactions in location networks to identify critical geo-referenced routes for assessing disease control measures on a large-scale campus.

PubMed

Wen, Tzai-Hung; Chin, Wei Chien Benny

2015-04-14

Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission.

Puzzles in modern biology. V. Why are genomes overwired?

PubMed

Frank, Steven A

2017-01-01

Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

PubMed Central

Monner, Derek; Reggia, James A.

2011-01-01

The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

Associative memory for online learning in noisy environments using self-organizing incremental neural network.

PubMed

Sudo, Akihito; Sato, Akihiro; Hasegawa, Osamu

2009-06-01

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.

Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.

PubMed

Bohn, Justin; Eddings, Wesley; Schneeweiss, Sebastian

2017-03-15

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemiologic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed health-care databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Automatic tuned MRI RF coil for multinuclear imaging of small animals at 3T.

PubMed

Muftuler, L Tugan; Gulsen, Gultekin; Sezen, Kumsal D; Nalcioglu, Orhan

2002-03-01

We have developed an MRI RF coil whose tuning can be adjusted automatically between 120 and 128 MHz for sequential spectroscopic imaging of hydrogen and fluorine nuclei at field strength 3 T. Variable capacitance (varactor) diodes were placed on each rung of an eight-leg low-pass birdcage coil to change the tuning frequency of the coil. The diode junction capacitance can be controlled by the amount of applied reverse bias voltage. Impedance matching was also done automatically by another pair of varactor diodes to obtain the maximum SNR at each frequency. The same bias voltage was applied to the tuning varactors on all rungs to avoid perturbations in the coil. A network analyzer was used to monitor matching and tuning of the coil. A Pentium PC controlled the analyzer through the GPIB bus. A code written in LABVIEW was used to communicate with the network analyzer and adjust the bias voltages of the varactors via D/A converters. Serially programmed D/A converter devices were used to apply the bias voltages to the varactors. Isolation amplifiers were used together with RF choke inductors to provide isolation between the RF coil and the DC bias lines. We acquired proton and fluorine images sequentially from a multicompartment phantom using the designed coil. Good matching and tuning were obtained at both resonance frequencies. The tuning and matching of the coil were changed from one resonance frequency to the other within 60 s. (c) 2002 Elsevier Science (USA).

Current progress and technical challenges of flexible liquid crystal displays

NASA Astrophysics Data System (ADS)

Fujikake, Hideo; Sato, Hiroto

2009-02-01

We focused on several technical approaches to flexible liquid crystal (LC) display in this report. We have been developing flexible displays using plastic film substrates based on polymer-dispersed LC technology with molecular alignment control. In our representative devices, molecular-aligned polymer walls keep plastic-substrate gap constant without LC alignment disorder, and aligned polymer networks create monostable switching of fast-response ferroelectric LC (FLC) for grayscale capability. In the fabrication process, a high-viscosity FLC/monomer solution was printed, sandwiched and pressed between plastic substrates. Then the polymer walls and networks were sequentially formed based on photo-polymerization-induced phase separation in the nematic phase by two exposure processes of patterned and uniform ultraviolet light. The two flexible backlight films of direct illumination and light-guide methods using small three-primary-color light-emitting diodes were fabricated to obtain high-visibility display images. The fabricated flexible FLC panels were driven by external transistor arrays, internal organic thin film transistor (TFT) arrays, and poly-Si TFT arrays. We achieved full-color moving-image displays using the flexible FLC panel and the flexible backlight film based on field-sequential-color driving technique. Otherwise, for backlight-free flexible LC displays, flexible reflective devices of twisted guest-host nematic LC and cholesteric LC were discussed with molecular-aligned polymer walls. Singlesubstrate device structure and fabrication method using self-standing polymer-stabilized nematic LC film and polymer ceiling layer were also proposed for obtaining LC devices with excellent flexibility.

Sequential interpenetrating polymer networks produced from vegetable oil based polyurethane and poly(methyl methacrylate).

PubMed

Kong, Xiaohua; Narine, Suresh S

2008-08-01

Sequential interpenetrating polymer networks (IPNs) were prepared using polyurethane produced from a canola oil based polyol with primary terminal functional groups and poly(methyl methacrylate) (PMMA). The properties of the material were studied and compared to the IPNs made from commercial castor oil using dynamic mechanical analysis, differential scanning calorimetry, as well as tensile measurements. The morphology of the IPNs was investigated using scanning electron microscopy and transmission electron microscopy. The chemical diversity of the starting materials allowed the evaluation of the effects of dangling chains and graftings on the properties of the IPNs. The polymerization process of canola oil based IPNs was accelerated because of the utilization of polyol with primary functional groups, which efficiently lessened the effect of dangling chains and yielded a higher degree of phase mixing. The mechanical properties of canola oil based IPNs containing more than 75 wt % PMMA were comparable to the corresponding castor oil based IPNs; both were superior to those of the constituent polymers due to the finely divided rubber and plastic combination structures in these IPNs. However, when PMMA content was less than 65 wt %, canola oil based IPNs exhibited a typical mechanical behavior of rigid plastics, whereas castor oil based IPNs showed a typical mechanical behavior of soft rubber. It is proposed that these new IPN materials with high performance prepared from alternative renewable resources can prove to be valuable substitutes for existing materials in various applications.

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological function.

Connecting macroscopic dynamics with microscopic properties in active microtubule network contraction

NASA Astrophysics Data System (ADS)

Foster, Peter J.; Yan, Wen; Fürthauer, Sebastian; Shelley, Michael J.; Needleman, Daniel J.

2017-12-01

The cellular cytoskeleton is an active material, driven out of equilibrium by molecular motor proteins. It is not understood how the collective behaviors of cytoskeletal networks emerge from the properties of the network’s constituent motor proteins and filaments. Here we present experimental results on networks of stabilized microtubules in Xenopus oocyte extracts, which undergo spontaneous bulk contraction driven by the motor protein dynein, and investigate the effects of varying the initial microtubule density and length distribution. We find that networks contract to a similar final density, irrespective of the length of microtubules or their initial density, but that the contraction timescale varies with the average microtubule length. To gain insight into why this microscopic property influences the macroscopic network contraction time, we developed simulations where microtubules and motors are explicitly represented. The simulations qualitatively recapitulate the variation of contraction timescale with microtubule length, and allowed stress contributions from different sources to be estimated and decoupled.

Developmental Self-Construction and -Configuration of Functional Neocortical Neuronal Networks

PubMed Central

Bauer, Roman; Zubler, Frédéric; Pfister, Sabina; Hauri, Andreas; Pfeiffer, Michael; Muir, Dylan R.; Douglas, Rodney J.

2014-01-01

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative (‘winner-take-all’, WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data. PMID:25474693

Ni-Catalyzed Carbon-Carbon Bond-Forming Reductive Amination.

PubMed

Heinz, Christoph; Lutz, J Patrick; Simmons, Eric M; Miller, Michael M; Ewing, William R; Doyle, Abigail G

2018-02-14

This report describes a three-component, Ni-catalyzed reductive coupling that enables the convergent synthesis of tertiary benzhydryl amines, which are challenging to access by traditional reductive amination methodologies. The reaction makes use of iminium ions generated in situ from the condensation of secondary N-trimethylsilyl amines with benzaldehydes, and these species undergo reaction with several distinct classes of organic electrophiles. The synthetic value of this process is demonstrated by a single-step synthesis of antimigraine drug flunarizine (Sibelium) and high yielding derivatization of paroxetine (Paxil) and metoprolol (Lopressor). Mechanistic investigations support a sequential oxidative addition mechanism rather than a pathway proceeding via α-amino radical formation. Accordingly, application of catalytic conditions to an intramolecular reductive coupling is demonstrated for the synthesis of endo- and exocyclic benzhydryl amines.

Current concepts of molecular events during bovine and porcine spermatozoa capacitation.

PubMed

Vadnais, Melissa L; Galantino-Homer, Hannah L; Althouse, Gary C

2007-01-01

Spermatozoa are required to undergo the processes of capacitation before they obtain fertilizing ability. The molecular changes of capacitation are still not fully understood. However, it is accepted that capacitation is a sequential process involving numerous physiological changes including destabilization of the plasma membrane, alterations of intracellular ion concentrations and membrane potential, and protein phosphorylation. There are no known morphological changes that occur to the spermatozoon during capacitation. The purpose of this review is to summarize current evidence on the molecular aspects of capacitation both in vivo and in vitro in bovine and porcine spermatozoa. For the purpose of this review, the process of sperm capacitation will encompass maturational events that occur following ejaculation up to binding to the zona pellucida, that triggers acrosomal exocytosis and initiates fertilization.

Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

PubMed Central

Bi, Zedong; Zhou, Changsong

2016-01-01

Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

Epidemic spreading on adaptively weighted scale-free networks.

PubMed

Sun, Mengfeng; Zhang, Haifeng; Kang, Huiyan; Zhu, Guanghu; Fu, Xinchu

2017-04-01

We introduce three modified SIS models on scale-free networks that take into account variable population size, nonlinear infectivity, adaptive weights, behavior inertia and time delay, so as to better characterize the actual spread of epidemics. We develop new mathematical methods and techniques to study the dynamics of the models, including the basic reproduction number, and the global asymptotic stability of the disease-free and endemic equilibria. We show the disease-free equilibrium cannot undergo a Hopf bifurcation. We further analyze the effects of local information of diseases and various immunization schemes on epidemic dynamics. We also perform some stochastic network simulations which yield quantitative agreement with the deterministic mean-field approach.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

DNA-Enabled Integrated Molecular Systems for Computation and Sensing

DTIC Science & Technology

2014-05-21

nanostructures to create nanophotonic networks that undergo nonradiative , near-field energy transfer. This process is known as resonance energy transfer (RET...promoting A to A*. The A* species can then decay nonradiatively or emit a photon of energy hν2.When chromophores are too far away, they cannot efficiently

Execute-Only Attacks against Execute-Only Defenses

DTIC Science & Technology

2016-02-18

and network cards , do not undergo translation by the MMU and are unaffected by EPT permission. The idea of exploiting systems via DMA is well studied... dump . There are two ways an attacker can gain access to a file opened using O_DIRECT. In the most straightforward scenario, the victim process may

The Typical Developmental Trajectory of Social and Executive Functions in Late Adolescence and Early Adulthood

ERIC Educational Resources Information Center

Taylor, Sophie Jane; Barker, Lynne Ann; Heavey, Lisa; McHale, Sue

2013-01-01

Executive functions and social cognition develop through childhood into adolescence and early adulthood and are important for adaptive goal-oriented behavior (Apperly, Samson, & Humphreys, 2009; Blakemore & Choudhury, 2006). These functions are attributed to frontal networks known to undergo protracted maturation into early adulthood…

The Ribosome Shape Directs mRNA Translocation through Entrance and Exit Dynamics

USDA-ARS?s Scientific Manuscript database

The protein-synthesizing ribosome undergoes large motions to effect the translocation of tRNAs (transfer ribonucleic acids) and mRNA (messenger ribonucleic acid); here the domain motions of this system are explored with a coarse-grained elastic network model using normal mode analysis. Crystal struc...

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band.

PubMed

Khan, Sheraz; Hashmi, Javeria A; Mamashli, Fahimeh; Michmizos, Konstantinos; Kitzbichler, Manfred G; Bharadwaj, Hari; Bekhti, Yousra; Ganesan, Santosh; Garel, Keri-Lee A; Whitfield-Gabrieli, Susan; Gollub, Randy L; Kong, Jian; Vaina, Lucia M; Rana, Kunjan D; Stufflebeam, Steven M; Hämäläinen, Matti S; Kenet, Tal

2018-07-01

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development. Copyright © 2018. Published by Elsevier Inc.

Percolation of networks with directed dependency links

NASA Astrophysics Data System (ADS)

Niu, Dunbiao; Yuan, Xin; Du, Minhui; Stanley, H. Eugene; Hu, Yanqing

2016-04-01

The self-consistent probabilistic approach has proven itself powerful in studying the percolation behavior of interdependent or multiplex networks without tracking the percolation process through each cascading step. In order to understand how directed dependency links impact criticality, we employ this approach to study the percolation properties of networks with both undirected connectivity links and directed dependency links. We find that when a random network with a given degree distribution undergoes a second-order phase transition, the critical point and the unstable regime surrounding the second-order phase transition regime are determined by the proportion of nodes that do not depend on any other nodes. Moreover, we also find that the triple point and the boundary between first- and second-order transitions are determined by the proportion of nodes that depend on no more than one node. This implies that it is maybe general for multiplex network systems, some important properties of phase transitions can be determined only by a few parameters. We illustrate our findings using Erdős-Rényi networks.

A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

PubMed Central

Sweeney, Yann; Hellgren Kotaleski, Jeanette; Hennig, Matthias H.

2015-01-01

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes. PMID:26158556

APINetworks: A general API for the treatment of complex networks in arbitrary computational environments

NASA Astrophysics Data System (ADS)

Niño, Alfonso; Muñoz-Caro, Camelia; Reyes, Sebastián

2015-11-01

The last decade witnessed a great development of the structural and dynamic study of complex systems described as a network of elements. Therefore, systems can be described as a set of, possibly, heterogeneous entities or agents (the network nodes) interacting in, possibly, different ways (defining the network edges). In this context, it is of practical interest to model and handle not only static and homogeneous networks but also dynamic, heterogeneous ones. Depending on the size and type of the problem, these networks may require different computational approaches involving sequential, parallel or distributed systems with or without the use of disk-based data structures. In this work, we develop an Application Programming Interface (APINetworks) for the modeling and treatment of general networks in arbitrary computational environments. To minimize dependency between components, we decouple the network structure from its function using different packages for grouping sets of related tasks. The structural package, the one in charge of building and handling the network structure, is the core element of the system. In this work, we focus in this API structural component. We apply an object-oriented approach that makes use of inheritance and polymorphism. In this way, we can model static and dynamic networks with heterogeneous elements in the nodes and heterogeneous interactions in the edges. In addition, this approach permits a unified treatment of different computational environments. Tests performed on a C++11 version of the structural package show that, on current standard computers, the system can handle, in main memory, directed and undirected linear networks formed by tens of millions of nodes and edges. Our results compare favorably to those of existing tools.

Effects of restricting perioperative use of intravenous chloride on kidney injury in patients undergoing cardiac surgery: the LICRA pragmatic controlled clinical trial.

PubMed

McIlroy, David; Murphy, Deirdre; Kasza, Jessica; Bhatia, Dhiraj; Wutzlhofer, Lisa; Marasco, Silvana

2017-06-01

The administration of chloride-rich intravenous (IV) fluid and hyperchloraemia have been associated with perioperative renal injury. The aim of this study was to determine whether a comprehensive perioperative protocol for the administration of chloride-limited IV fluid would reduce perioperative renal injury in adults undergoing cardiac surgery. From February 2014 through to December 2015, all adult patients undergoing cardiac surgery within a single academic medical center received IV fluid according to the study protocol. The perioperative protocol governed all fluid administration from commencement of anesthesia through to discharge from the intensive care unit and varied over four sequential periods, each lasting 5 months. In periods 1 and 4 a chloride-rich strategy, consisting of 0.9% saline and 4% albumin, was adopted; in periods 2 and 3, a chloride-limited strategy, consisting of a buffered salt solution and 20% albumin, was used. Co-primary outcomes were peak delta serum creatinine (∆S Cr ) within 5 days after the operation and KDIGO-defined stage 2 or stage 3 acute kidney injury (AKI) within 5 days after the operation. We enrolled and analysed data from 1136 patients, with 569 patients assigned to a chloride-rich fluid strategy and 567 to a chloride-limited one. Compared with a chloride-limited strategy and adjusted for prespecified covariates, there was no association between a chloride-rich perioperative fluid strategy and either peak ∆S Cr , transformed to satisfy the assumptions of multivariable linear regression [regression coefficient 0.03, 95% confidence interval (CI) -0.03 to 0.08); p = 0.39], or stage 2 or 3 AKI (adjusted odds ratio 0.97, 95% CI 0.65-1.47; p = 0.90]. A perioperative fluid strategy to restrict IV chloride administration was not associated with an altered incidence of AKI or other metrics of renal injury in adult patients undergoing cardiac surgery. Clinicaltrials.gov Identifier: NCT02020538.

Radionuclide gas transport through nuclear explosion-generated fracture networks

DOE PAGES

Jordan, Amy B.; Stauffer, Philip H.; Knight, Earl E.; ...

2015-12-17

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gasmore » breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. In conclusion, seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable.« less

Radionuclide gas transport through nuclear explosion-generated fracture networks

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

Jordan, Amy B.; Stauffer, Philip H.; Knight, Earl E.

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gasmore » breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. In conclusion, seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable.« less

The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks

PubMed Central

Orban, Pierre; Doyon, Julien; Petrides, Michael; Mennes, Maarten; Hoge, Richard; Bellec, Pierre

2015-01-01

Functional magnetic resonance imaging can measure distributed and subtle variations in brain responses associated with task performance. However, it is unclear whether the rich variety of responses observed across the brain is functionally meaningful and consistent across individuals. Here, we used a multivariate clustering approach that grouped brain regions into clusters based on the similarity of their task-evoked temporal responses at the individual level, and then established the spatial consistency of these individual clusters at the group level. We observed a stable pseudohierarchy of task-evoked networks in the context of a delayed sequential motor task, where the fractionation of networks was driven by a gradient of involvement in motor sequence preparation versus execution. In line with theories about higher-level cognitive functioning, this gradient evolved in a rostro-caudal manner in the frontal lobe. In addition, parcellations in the cerebellum and basal ganglia matched with known anatomical territories and fiber pathways with the cerebral cortex. These findings demonstrate that subtle variations in brain responses associated with task performance are systematic enough across subjects to define a pseudohierarchy of task-evoked networks. Such networks capture meaningful functional features of brain organization as shaped by a given cognitive context. PMID:24729172

Tricolor emission of a fluorescent heteroditopic ligand over a concentration gradient of zinc(II) ions.

PubMed

Sreenath, Kesavapillai; Clark, Ronald J; Zhu, Lei

2012-09-21

The internal charge transfer (ICT) type fluoroionophore arylvinyl-bipy (bipy = 2,2'-bipyridyl) is covalently tethered to the spirolactam form of rhodamine to afford fluorescent heteroditopic ligand 4. Compound 4 can be excited in the visible region, the emission of which undergoes sequential bathochromic shifts over an increasing concentration gradient of Zn(ClO(4))(2) in acetonitrile. Coordination of Zn(2+) stabilizes the ICT excited state of the arylvinyl-bipy component of 4, leading to the first emission color shift from blue to green. At sufficiently high concentrations of Zn(ClO(4))(2), the nonfluorescent spirolactam component of 4 is transformed to the fluorescent rhodamine, which turns the emission color from green to orange via intramolecular fluorescence resonance energy transfer (FRET) from the Zn(2+)-bound arylvinyl-bipy fluorophore to rhodamine. While this work offers a new design of ratiometric chemosensors, in which sequential analyte-induced emission band shifts result in the sampling of multiple colors at different concentration ranges (i.e., from blue to green to orange as [Zn(2+)] increases in the current case), it also reveals the nuances of rhodamine spirolactam chemistry that have not been sufficiently addressed in the published literature. These issues include the ability of rhodamine spirolactam as a fluorescence quencher via electron transfer, and the slow kinetics of spirolactam ring-opening effected by Zn(2+) coordination under pH neutral aqueous conditions.

Modular verification of chemical reaction network encodings via serializability analysis

PubMed Central

Lakin, Matthew R.; Stefanovic, Darko; Phillips, Andrew

2015-01-01

Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a “commit reaction” that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of “extra tolerance”, which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited. PMID:27325906

Application of machine learning methods for traffic signs recognition

NASA Astrophysics Data System (ADS)

Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

2018-02-01

This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

Creating insight when the literature is absent: the circle of advisors.

PubMed

Batcheller, Joyce; Yoder-Wise, Patricia S

2011-01-01

When changes happen as rapidly as they do today, the literature is often absent. Although related literature may be available to substantiate a direction to take when faced with some issue to resolve, that literature may be vague in terms of its applicability within health care. The idea of a circle of advisors was instituted to gain insight from experts who had faced similar challenges and often had extensive networks of shared experiences. The use of a sequential dialog identified specific talents to be developed in a chief nurse executive enculturation program.

II. Inhibited Diffusion Driven Surface Transmutations

NASA Astrophysics Data System (ADS)

Chubb, Talbot A.

2006-02-01

This paper is the second of a set of three papers dealing with the role of coherent partitioning as a common element in Low Energy Nuclear Reactions (LENR), by which is meant cold-fusion related processes. This paper discusses the first step in a sequence of four steps that seem to be necessary to explain Iwamura 2-α-addition surface transmutations. Three concepts are examined: salt-metal interface states, sequential tunneling that transitions D+ ions from localized interstitial to Bloch form, and the general applicability of 2-dimensional vs. 3-dimensional symmetry hosting networks.

Wavelength-division and spatial multiplexing using tandem interferometers for Bragg grating sensor networks

NASA Astrophysics Data System (ADS)

Kalli, K.; Brady, G. P.; Webb, D. J.; Jackson, D. A.; Zhang, L.; Bennion, I.

1995-12-01

We present a new method for the interrogation of large arrays of Bragg grating sensors. Eight gratings operating between the wavelengths of 1533 and 1555 nm have been demultiplexed. An unbalanced Mach-Zehnder interferometer illuminated by a single low-coherence source provides a high-phase-resolution output for each sensor, the outputs of which are sequentially selected in wavelength by a tunable Fabry-Perot interferometer. The minimum detectable strain measured was 90 n 3 / \\radical Hz \\end-radical at 7 Hz for a wavelength of 1535 nm.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, Daniel L.; Reginato, Louis L.

1988-01-01

An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .gtoreq.0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, Daniel L.; Reginato, Louis L.

1987-01-01

An electron beam accelerator comprising an electron beam generator-injector to produce a focused beam of .gtoreq.0.1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electrons by about 0.1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially 0.1-1 MeV maximum energy over a time duration of .ltoreq.1 .mu.sec.

Electron beam accelerator with magnetic pulse compression and accelerator switching

DOEpatents

Birx, D.L.; Reginato, L.L.

1984-03-22

An electron beam accelerator is described comprising an electron beam generator-injector to produce a focused beam of greater than or equal to .1 MeV energy electrons; a plurality of substantially identical, aligned accelerator modules to sequentially receive and increase the kinetic energies of the beam electron by about .1-1 MeV per module. Each accelerator module includes a pulse-forming network that delivers a voltage pulse to the module of substantially .1-1 MeV maximum energy over a time duration of less than or equal to 1 ..mu..sec.

Evolution of Controllability in Interbank Networks

NASA Astrophysics Data System (ADS)

Delpini, Danilo; Battiston, Stefano; Riccaboni, Massimo; Gabbi, Giampaolo; Pammolli, Fabio; Caldarelli, Guido

2013-04-01

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected ``hub'' institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies.

Network visualization of conformational sampling during molecular dynamics simulation.

PubMed

Ahlstrom, Logan S; Baker, Joseph Lee; Ehrlich, Kent; Campbell, Zachary T; Patel, Sunita; Vorontsov, Ivan I; Tama, Florence; Miyashita, Osamu

2013-11-01

Effective data reduction methods are necessary for uncovering the inherent conformational relationships present in large molecular dynamics (MD) trajectories. Clustering algorithms provide a means to interpret the conformational sampling of molecules during simulation by grouping trajectory snapshots into a few subgroups, or clusters, but the relationships between the individual clusters may not be readily understood. Here we show that network analysis can be used to visualize the dominant conformational states explored during simulation as well as the connectivity between them, providing a more coherent description of conformational space than traditional clustering techniques alone. We compare the results of network visualization against 11 clustering algorithms and principal component conformer plots. Several MD simulations of proteins undergoing different conformational changes demonstrate the effectiveness of networks in reaching functional conclusions. Copyright © 2013 Elsevier Inc. All rights reserved.

Nonelective surgery at night and in-hospital mortality: Prospective observational data from the European Surgical Outcomes Study.

PubMed

van Zaane, Bas; van Klei, Wilton A; Buhre, Wolfgang F; Bauer, Peter; Boerma, E Christiaan; Hoeft, Andreas; Metnitz, Philipp; Moreno, Rui P; Pearse, Rupert; Pelosi, Paolo; Sander, Michael; Vallet, Benoit; Pettilä, Ville; Vincent, Jean-Louis; Rhodes, Andrew

2015-07-01

Evidence suggests that sleep deprivation associated with night-time working may adversely affect performance resulting in a reduction in the safety of surgery and anaesthesia. Our primary objective was to evaluate an association between nonelective night-time surgery and in-hospital mortality. We hypothesised that urgent surgery performed during the night was associated with higher in-hospital mortality and also an increase in the duration of hospital stay and the number of admissions to critical care. A prospective cohort study. This is a secondary analysis of a large database related to perioperative care and outcome (European Surgical Outcome Study). Four hundred and ninety-eight hospitals in 28 European countries. Men and women older than 16 years who underwent nonelective, noncardiac surgery were included according to time of the procedure. None. Primary outcome was in-hospital mortality; the secondary outcome was the duration of hospital stay and critical care admission. Eleven thousand two hundred and ninety patients undergoing urgent surgery were included in the analysis with 636 in-hospital deaths (5.6%). Crude mortality odds ratios (ORs) increased sequentially from daytime [426 deaths (5.3%)] to evening [150 deaths (6.0%), OR 1.14; 95% confidence interval 0.94 to 1.38] to night-time [60 deaths (8.3%), OR 1.62; 95% confidence interval 1.22 to 2.14]. Following adjustment for confounding factors, surgery during the evening (OR 1.09; 95% confidence interval 0.91 to 1.31) and night (OR 1.20; 95% confidence interval 0.9 to 1.6) was not associated with an increased risk of postoperative death. Admittance rate to an ICU increased sequentially from daytime [891 (11.1%)], to evening [347 (13.8%)] to night time [149 (20.6%)]. In patients undergoing nonelective urgent noncardiac surgery, in-hospital mortality was associated with well known risk factors related to patients and surgery, but we did not identify any relationship with the time of day at which the procedure was performed. Clinicaltrials.gov identifier: NCT01203605.

Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Bandyopadhyay, Goutami

2007-01-01

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.

Auto-Associative Recurrent Neural Networks and Long Term Dependencies in Novelty Detection for Audio Surveillance Applications

NASA Astrophysics Data System (ADS)

Rossi, A.; Montefoschi, F.; Rizzo, A.; Diligenti, M.; Festucci, C.

2017-10-01

Machine Learning applied to Automatic Audio Surveillance has been attracting increasing attention in recent years. In spite of several investigations based on a large number of different approaches, little attention had been paid to the environmental temporal evolution of the input signal. In this work, we propose an exploration in this direction comparing the temporal correlations extracted at the feature level with the one learned by a representational structure. To this aim we analysed the prediction performances of a Recurrent Neural Network architecture varying the length of the processed input sequence and the size of the time window used in the feature extraction. Results corroborated the hypothesis that sequential models work better when dealing with data characterized by temporal order. However, so far the optimization of the temporal dimension remains an open issue.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

Connections of intermediate filaments with the nuclear lamina and the cell periphery.

PubMed

Katsuma, Y; Swierenga, S H; Marceau, N; French, S W

1987-01-01

We investigated the relationship between intermediate filaments (IFs) and other detergent- and nuclease-resistant filamentous structures of cultured liver epithelial cells (T51B cell line) using whole mount unembedded preparations which were sequentially extracted with Triton X-100 and nucleases. Immunogold labelling and stereoscopic observation facilitated the examination of each filamentous structure and their three-dimensional relationships to each other. After solubilizing phospholipid, nucleic acid and soluble cellular protein, the resulting cytoskeleton preparation consisted of a network of cytokeratin and vimentin IFs linked by 3 nm filaments. The IFs were anchored to and determined the position of the nuclear lamina filaments (NLF) network and the centrioles. The NLF was composed of the nuclear lamina filaments measuring 3-6 nm in diameter which radiated from and anchored to the skeleton of the nuclear pores. The IFs located in the nuclear region appeared to be interwoven with the NLF. At the cell surface, the IFs seemed to be attached to the putative actin filament network. They formed a focally interrupted plexus-like structure at the cell periphery. Fragments of vimentin filaments were found among the filamentous network located at the cell surface, and some filaments terminated blindly there.

Maximizing the Spread of Influence via Generalized Degree Discount.

PubMed

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods.

Maximizing the Spread of Influence via Generalized Degree Discount

PubMed Central

Wang, Xiaojie; Zhang, Xue; Zhao, Chengli; Yi, Dongyun

2016-01-01

It is a crucial and fundamental issue to identify a small subset of influential spreaders that can control the spreading process in networks. In previous studies, a degree-based heuristic called DegreeDiscount has been shown to effectively identify multiple influential spreaders and has severed as a benchmark method. However, the basic assumption of DegreeDiscount is not adequate, because it treats all the nodes equally without any differences. To consider a general situation in real world networks, a novel heuristic method named GeneralizedDegreeDiscount is proposed in this paper as an effective extension of original method. In our method, the status of a node is defined as a probability of not being influenced by any of its neighbors, and an index generalized discounted degree of one node is presented to measure the expected number of nodes it can influence. Then the spreaders are selected sequentially upon its generalized discounted degree in current network. Empirical experiments are conducted on four real networks, and the results show that the spreaders identified by our approach are more influential than several benchmark methods. Finally, we analyze the relationship between our method and three common degree-based methods. PMID:27732681

Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

PubMed Central

Demongeot, Jacques; Ben Amor, Hedi; Elena, Adrien; Gillois, Pierre; Noual, Mathilde; Sené, Sylvain

2009-01-01

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control. PMID:20057955

Auxetic metamaterials from disordered networks

NASA Astrophysics Data System (ADS)

Reid, Daniel R.; Pashine, Nidhi; Wozniak, Justin M.; Jaeger, Heinrich M.; Liu, Andrea J.; Nagel, Sidney R.; de Pablo, Juan J.

2018-02-01

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as Poisson’s ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and in the nonlinear regimes. In particular, it is shown that Poisson’s ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting 2D sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks’ susceptibility to tuning to design networks that possess a distribution of stiffer and more compliant bonds and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of unique mechanical metamaterials.

Anatomy and histology as socially networked learning environments: some preliminary findings.

PubMed

Hafferty, Frederic W; Castellani, Brian; Hafferty, Philip K; Pawlina, Wojciech

2013-09-01

An exploratory study to better understand the "networked" life of the medical school as a learning environment. In a recent academic year, the authors gathered data during two six-week blocks of a sequential histology and anatomy course at a U.S. medical college. An eight-item questionnaire captured different dimensions of student interactions. The student cohort/network was 48 first-year medical students. Using social network analysis (SNA), the authors focused on (1) the initial structure and the evolution of informal class networks over time, (2) how informal class networks compare to formal in-class small-group assignments in influencing student information gathering, and (3) how peer assignment of professionalism role model status is shaped more by informal than formal ties. In examining these latter two issues, the authors explored not only how formal group assignment persisted over time but also how it functioned to prevent the tendency for groupings based on gender or ethnicity. The study revealed an evolving dynamic between the formal small-group learning structure of the course blocks and the emergence of informal student networks. For example, whereas formal group membership did influence in-class questions and did prevent formation of groups of like gender and ethnicity, outside-class questions and professionalism were influenced more by informal group ties where gender and, to a much lesser extent, ethnicity influence student information gathering. The richness of these preliminary findings suggests that SNA may be a useful tool in examining an array of medical student learning encounters.

Flory-Stockmayer analysis on reprocessable polymer networks

NASA Astrophysics Data System (ADS)

Li, Lingqiao; Chen, Xi; Jin, Kailong; Torkelson, John

Reprocessable polymer networks can undergo structure rearrangement through dynamic chemistries under proper conditions, making them a promising candidate for recyclable crosslinked materials, e.g. tires. This research field has been focusing on various chemistries. However, there has been lacking of an essential physical theory explaining the relationship between abundancy of dynamic linkages and reprocessability. Based on the classical Flory-Stockmayer analysis on network gelation, we developed a similar analysis on reprocessable polymer networks to quantitatively predict the critical condition for reprocessability. Our theory indicates that it is unnecessary for all bonds to be dynamic to make the resulting network reprocessable. As long as there is no percolated permanent network in the system, the material can fully rearrange. To experimentally validate our theory, we used a thiol-epoxy network model system with various dynamic linkage compositions. The stress relaxation behavior of resulting materials supports our theoretical prediction: only 50 % of linkages between crosslinks need to be dynamic for a tri-arm network to be reprocessable. Therefore, this analysis provides the first fundamental theoretical platform for designing and evaluating reprocessable polymer networks. We thank McCormick Research Catalyst Award Fund and ISEN cluster fellowship (L. L.) for funding support.

Primary percutaneous coronary intervention by magnetic navigation compared with conventional wire technique.

PubMed

Patterson, Mark S; Dirksen, Maurits T; Ijsselmuiden, Alexander J; Amoroso, Giovanni; Slagboom, Ton; Laarman, Gerrit-Jan; Schultz, Carl; van Domburg, Ron T; Serruys, Patrick W; Kiemeneij, Ferdinand

2011-06-01

Aims Comparison of magnetic guidewire navigation in percutaneous coronary intervention (MPCI) vs. conventional percutaneous coronary intervention (CPCI) for the treatment of acute myocardial infarction. Methods and results We compared 65 sequential patients (mean age 61 ± 15 years) undergoing primary MPCI with those of 405 patients undergoing CPCI (mean age 61 ± 13 years). The major endpoint was contrast media use. Technical success and procedural outcomes were evaluated. Clinical demographics and angiographic characteristics of the two groups were similar, except for fewer patients with previous coronary artery bypass grafting (CABG) and hypertension in the CPCI group and fewer patients with diabetes in the MPCI group. The technical success rate was high in both the MPCI and CPCI groups (95.4 vs. 98%). There was significantly less contrast media usage in the MPCI compared with the CPCI group, median reduction of contrast media of 30 mL with an OR = 0.41 (0.21-0.81). Fluoroscopy times were significantly reduced for MPCI compared with CPCI, median reduction of 7.2 min with an OR = 0.42 (0.20-0.79). Conclusion This comparison indicates the feasibility and non-inferiority of magnetic navigation in performing primary PCI and suggests the possibility of reductions in contrast media use and fluoroscopy time compared with CPCI.