Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

Science.gov (United States)

Cai, Zhaohui; Bresell, Anders; Steinberg, Mark H; Silberg, Debra G; Furlong, Stephen T

2012-01-01

The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information. Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results. Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy's law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests. It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

Energy Technology Data Exchange (ETDEWEB)

Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)

2016-06-15

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

Machine learning approaches for the prediction of signal peptides and otherprotein sorting signals

DEFF Research Database (Denmark)

Nielsen, Henrik; Brunak, Søren; von Heijne, Gunnar

1999-01-01

Prediction of protein sorting signals from the sequence of amino acids has great importance in the field of proteomics today. Recently,the growth of protein databases, combined with machine learning approaches, such as neural networks and hidden Markov models, havemade it possible to achieve...

Signal extraction and wave field separation in tunnel seismic prediction by independent component analysis

Science.gov (United States)

Yue, Y.; Jiang, T.; Zhou, Q.

2017-12-01

In order to ensure the rationality and the safety of tunnel excavation, the advanced geological prediction has been become an indispensable step in tunneling. However, the extraction of signal and the separation of P and S waves directly influence the accuracy of geological prediction. Generally, the raw data collected in TSP system is low quality because of the numerous disturb factors in tunnel projects, such as the power interference and machine vibration interference. It's difficult for traditional method (band-pass filtering) to remove interference effectively as well as bring little loss to signal. The power interference, machine vibration interference and the signal are original variables and x, y, z component as observation signals, each component of the representation is a linear combination of the original variables, which satisfy applicable conditions of independent component analysis (ICA). We perform finite-difference simulations of elastic wave propagation to synthetic a tunnel seismic reflection record. The method of ICA was adopted to process the three-component data, and the results show that extract the estimates of signal and the signals are highly correlated (the coefficient correlation is up to more than 0.93). In addition, the estimates of interference that separated from ICA and the interference signals are also highly correlated, and the coefficient correlation is up to more than 0.99. Thus, simulation results showed that the ICA is an ideal method for extracting high quality data from mixed signals. For the separation of P and S waves, the conventional separation techniques are based on physical characteristics of wave propagation, which require knowledge of the near-surface P and S waves velocities and density. Whereas the ICA approach is entirely based on statistical differences between P and S waves, and the statistical technique does not require a priori information. The concrete results of the wave field separation will be presented in

Skill forecasting from different wind power ensemble prediction methods

International Nuclear Information System (INIS)

Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

2007-01-01

This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

Predictive model identifies key network regulators of cardiomyocyte mechano-signaling.

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Philip M Tan

2017-11-01

Full Text Available Mechanical strain is a potent stimulus for growth and remodeling in cells. Although many pathways have been implicated in stretch-induced remodeling, the control structures by which signals from distinct mechano-sensors are integrated to modulate hypertrophy and gene expression in cardiomyocytes remain unclear. Here, we constructed and validated a predictive computational model of the cardiac mechano-signaling network in order to elucidate the mechanisms underlying signal integration. The model identifies calcium, actin, Ras, Raf1, PI3K, and JAK as key regulators of cardiac mechano-signaling and characterizes crosstalk logic imparting differential control of transcription by AT1R, integrins, and calcium channels. We find that while these regulators maintain mostly independent control over distinct groups of transcription factors, synergy between multiple pathways is necessary to activate all the transcription factors necessary for gene transcription and hypertrophy. We also identify a PKG-dependent mechanism by which valsartan/sacubitril, a combination drug recently approved for treating heart failure, inhibits stretch-induced hypertrophy, and predict further efficacious pairs of drug targets in the network through a network-wide combinatorial search.

Analysis of Arm Movement Prediction by Using the Electroencephalography Signal

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Reza Darmakusuma

2016-04-01

Full Text Available Various technological approaches have been developed in order to help those people who are unfortunateenough to be afflicted with different types of paralysis which limit them in performing their daily life activitiesindependently. One of the proposed technologies is the Brain-Computer Interface (BCI. The BCI system uses electroencephalography (EEG which is generated by the subject’s mental activityas input, and converts it into commands. Some previous experiments have shown the capability of the BCI system to predict the movement intention before the actual movement is onset. Thus research has predicted the movement by discriminating between data in the “rest” condition, wherethere is no movement intention, with “pre-movement” condition, where movement intention is detected before actual movement occurs. This experiment, however, was done to analyze the system for which machine learning was applied to data obtained in a continuous time interval, between 3 seconds before the movement was detected until 1 second after the actual movement was onset. This experiment shows that the system can discriminate the “pre-movement” condition and “rest” condition by using the EEG signal in 7-30 Hzwhere the Mu and Beta rhythm can be discovered with an average True Positive Rate (TPR value of 0.64 ± 0.11 and an average False Positive Rate (FPR of 0.17 ± 0.08. This experiment also shows that by using EEG signals obtained nearing the movement onset, the system has higher TPR or a detection rate in predicting the movement intention.

Analytical prediction of digital signal crosstalk of FCC

Science.gov (United States)

Belleisle, A. P.

1972-01-01

The results are presented of study effort whose aim was the development of accurate means of analyzing and predicting signal cross-talk in multi-wire digital data cables. A complete analytical model is developed n + 1 wire systems of uniform transmission lines with arbitrary linear boundary conditions. In addition, a minimum set of parameter measurements required for the application of the model are presented. Comparisons between cross-talk predicted by this model and actual measured cross-talk are shown for a six conductor ribbon cable.

An imperfect dopaminergic error signal can drive temporal-difference learning.

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Wiebke Potjans

2011-05-01

Full Text Available An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards.

Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Science.gov (United States)

Zhang, Tinghe; Mao, Zijing; Xu, Xiaojing; Zhang, Lin; Pack, Daniel J.; Dong, Bing; Huang, Yufei

2018-01-01

Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest R2 (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures. PMID:29690601

The application of sparse linear prediction dictionary to compressive sensing in speech signals

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YOU Hanxu

2016-04-01

Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.

Attributing Predictable Signals at Subseasonal Timescales

Science.gov (United States)

Shelly, A.; Norton, W.; Rowlands, D.; Beech-Brandt, J.

2016-12-01

Subseasonal forecasts offer significant economic value in the management of energy infrastructure and through the associated financial markets. Models are now accurate enough to provide, for some occasions, good forecasts in the subseasonal range. However, it is often not clear what the drivers of these subseasonal signals are and if the forecasts could be more accurate with better representation of physical processes. Also what are the limits of predictability in the subseasonal range? To address these questions, we have run the ECMWF monthly forecast system over the 2015/16 winter with a set of 6 week ensemble integrations initialised every week over the period. In these experiments, we have relaxed the band 15N to 15S to reanalysis fields. Hence, we have a set of forecasts where the tropics is constrained to actual events and we can analyse the changes in predictability in middle latitudes - in particular in regions of high energy consumption like North America and Europe. Not surprisingly, the forecast of some periods are significantly improved while others show no improvement. We discuss events/patterns that have extended range predictability and also the tropical forecast errors which prevent the potential predictability in middle latitudes from being realised.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

Directory of Open Access Journals (Sweden)

Cai Z

2012-11-01

Full Text Available Zhaohui Cai,1,* Anders Bresell,2,* Mark H Steinberg,1 Debra G Silberg,1 Stephen T Furlong11AstraZeneca Pharmaceuticals, Wilmington, DE, USA; 2AstraZeneca Pharmaceuticals, Södertälje, Sweden*These authors contributed equally to this workPurpose: The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline information.Patients and methods: Based on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results.Results: Predictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests.Conclusion: It is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.Keywords: bilirubin, Hy’s Law, ALT, GGT, baseline, prediction

Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts.

Science.gov (United States)

Luo, Jing-Jia; Masson, Sebastien; Behera, Swadhin; Shingu, Satoru; Yamagata, Toshio

2005-11-01

Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured.Hindcast results for the period 1982 2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9 12-month lead times.In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7 12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

The effect of hearing aid signal-processing schemes on acceptable noise levels: perception and prediction.

Science.gov (United States)

Wu, Yu-Hsiang; Stangl, Elizabeth

2013-01-01

The acceptable noise level (ANL) test determines the maximum noise level that an individual is willing to accept while listening to speech. The first objective of the present study was to systematically investigate the effect of wide dynamic range compression processing (WDRC), and its combined effect with digital noise reduction (DNR) and directional processing (DIR), on ANL. Because ANL represents the lowest signal-to-noise ratio (SNR) that a listener is willing to accept, the second objective was to examine whether the hearing aid output SNR could predict aided ANL across different combinations of hearing aid signal-processing schemes. Twenty-five adults with sensorineural hearing loss participated in the study. ANL was measured monaurally in two unaided and seven aided conditions, in which the status of the hearing aid processing schemes (enabled or disabled) and the location of noise (front or rear) were manipulated. The hearing aid output SNR was measured for each listener in each condition using a phase-inversion technique. The aided ANL was predicted by unaided ANL and hearing aid output SNR, under the assumption that the lowest acceptable SNR at the listener's eardrum is a constant across different ANL test conditions. Study results revealed that, on average, WDRC increased (worsened) ANL by 1.5 dB, while DNR and DIR decreased (improved) ANL by 1.1 and 2.8 dB, respectively. Because the effects of WDRC and DNR on ANL were opposite in direction but similar in magnitude, the ANL of linear/DNR-off was not significantly different from that of WDRC/DNR-on. The results further indicated that the pattern of ANL change across different aided conditions was consistent with the pattern of hearing aid output SNR change created by processing schemes. Compared with linear processing, WDRC creates a noisier sound image and makes listeners less willing to accept noise. However, this negative effect on noise acceptance can be offset by DNR, regardless of microphone mode

Evaluation of secretion prediction highlights differing approaches needed for oomycete and fungal effectors

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Jana eSperschneider

2015-12-01

Full Text Available The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen’s advantage. Proteinaceous effectors are synthesised intracellularly and must be externalised to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localisation predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.

ARRIVAL TIME DIFFERENCES BETWEEN GRAVITATIONAL WAVES AND ELECTROMAGNETIC SIGNALS DUE TO GRAVITATIONAL LENSING

Energy Technology Data Exchange (ETDEWEB)

Takahashi, Ryuichi [Faculty of Science and Technology, Hirosaki University, 3 Bunkyo-cho, Hirosaki, Aomori 036-8561 (Japan)

2017-01-20

In this study we demonstrate that general relativity predicts arrival time differences between gravitational wave (GW) and electromagnetic (EM) signals caused by the wave effects in gravitational lensing. The GW signals can arrive earlier than the EM signals in some cases if the GW/EM signals have passed through a lens, even if both signals were emitted simultaneously by a source. GW wavelengths are much larger than EM wavelengths; therefore, the propagation of the GWs does not follow the laws of geometrical optics, including the Shapiro time delay, if the lens mass is less than approximately 10{sup 5} M {sub ⊙}( f /Hz){sup −1}, where f is the GW frequency. The arrival time difference can reach ∼0.1 s ( f /Hz){sup −1} if the signals have passed by a lens of mass ∼8000 M {sub ⊙}( f /Hz){sup −1} with the impact parameter smaller than the Einstein radius; therefore, it is more prominent for lower GW frequencies. For example, when a distant supermassive black hole binary (SMBHB) in a galactic center is lensed by an intervening galaxy, the time lag becomes of the order of 10 days. Future pulsar timing arrays including the Square Kilometre Array and X-ray detectors may detect several time lags by measuring the orbital phase differences between the GW/EM signals in the SMBHBs. Gravitational lensing imprints a characteristic modulation on a chirp waveform; therefore, we can deduce whether a measured arrival time lag arises from intrinsic source properties or gravitational lensing. Determination of arrival time differences would be extremely useful in multimessenger observations and tests of general relativity.

Prediction of lipoprotein signal peptides in Gram-negative bacteria

DEFF Research Database (Denmark)

Juncker, Agnieszka; Willenbrock, Hanni; Von Heijne, G.

2003-01-01

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor ...

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

Science.gov (United States)

Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

2015-01-01

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

Science.gov (United States)

Hu, Peizhen; Chung, Leland W K; Berel, Dror; Frierson, Henry F; Yang, Hua; Liu, Chunyan; Wang, Ruoxiang; Li, Qinlong; Rogatko, Andre; Zhau, Haiyen E

2013-01-01

We reported (PLoS One 6 (12):e28670, 2011) that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC) tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE) tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1) expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

Science.gov (United States)

Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

2010-08-01

We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

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Shandilya Sharad

2012-10-01

Full Text Available Abstract Background Ventricular Fibrillation (VF is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2, using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA technique. Methods A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold. Results The integrative model performs real-time, short-term (7.8 second analysis of the Electrocardiogram (ECG. For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC Area Under the Curve (AUC of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64

Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

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Neto Armando

2012-11-01

Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

Expression profiling associates blood and brain glucocorticoid receptor signaling with trauma-related individual differences in both sexes.

Science.gov (United States)

Daskalakis, Nikolaos P; Cohen, Hagit; Cai, Guiqing; Buxbaum, Joseph D; Yehuda, Rachel

2014-09-16

Delineating the molecular basis of individual differences in the stress response is critical to understanding the pathophysiology and treatment of posttraumatic stress disorder (PTSD). In this study, 7 d after predator-scent-stress (PSS) exposure, male and female rats were classified into vulnerable (i.e., "PTSD-like") and resilient (i.e., minimally affected) phenotypes on the basis of their performance on a variety of behavioral measures. Genome-wide expression profiling in blood and two limbic brain regions (amygdala and hippocampus), followed by quantitative PCR validation, was performed in these two groups of animals, as well as in an unexposed control group. Differentially expressed genes were identified in blood and brain associated with PSS-exposure and with distinct behavioral profiles postexposure. There was a small but significant between-tissue overlap (4-21%) for the genes associated with exposure-related individual differences, indicating convergent gene expression in both sexes. To uncover convergent signaling pathways across tissue and sex, upstream activated/deactivated transcription factors were first predicted for each tissue and then the respective pathways were identified. Glucocorticoid receptor (GR) signaling was the only convergent pathway associated with individual differences when using the most stringent statistical threshold. Corticosterone treatment 1 h after PSS-exposure prevented anxiety and hyperarousal 7 d later in both sexes, confirming the GR involvement in the PSS behavioral response. In conclusion, genes and pathways associated with extreme differences in the traumatic stress behavioral response can be distinguished from those associated with trauma exposure. Blood-based biomarkers can predict aspects of brain signaling. GR signaling is a convergent signaling pathway, associated with trauma-related individual differences in both sexes.

SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

Science.gov (United States)

Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

2017-01-01

Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

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Peizhen Hu

Full Text Available We reported (PLoS One 6 (12:e28670, 2011 that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1 expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks

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Chentao Wen

2016-11-01

Full Text Available To ensure survival, animals must update the internal representations of their environment in a trial-and-error fashion. Psychological studies of associative learning and neurophysiological analyses of dopaminergic neurons have suggested that this updating process involves the temporal-difference (TD method in the basal ganglia network. However, the way in which the component variables of the TD method are implemented at the neuronal level is unclear. To investigate the underlying neural mechanisms, we trained domestic chicks to associate color cues with food rewards. We recorded neuronal activities from the medial striatum or tegmentum in a freely behaving condition and examined how reward omission changed neuronal firing. To compare neuronal activities with the signals assumed in the TD method, we simulated the behavioral task in the form of a finite sequence composed of discrete steps of time. The three signals assumed in the simulated task were the prediction signal, the target signal for updating, and the TD-error signal. In both the medial striatum and tegmentum, the majority of recorded neurons were categorized into three types according to their fitness for three models, though these neurons tended to form a continuum spectrum without distinct differences in the firing rate. Specifically, two types of striatal neurons successfully mimicked the target signal and the prediction signal. A linear summation of these two types of striatum neurons was a good fit for the activity of one type of tegmental neurons mimicking the TD-error signal. The present study thus demonstrates that the striatum and tegmentum can convey the signals critically required for the TD method. Based on the theoretical and neurophysiological studies, together with tract-tracing data, we propose a novel model to explain how the convergence of signals represented in the striatum could lead to the computation of TD error in tegmental dopaminergic neurons.

Predictable and Predictive Emotions:Explaining Cheap Signals and Trust Re-Extension

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Eric eSchniter

2014-11-01

Full Text Available Despite normative predictions from economics and biology, unrelated strangers will often develop the trust necessary to reap gains from one-shot economic exchange opportunities. This appears to be especially true when declared intentions and emotions can be cheaply communicated. Perhaps even more puzzling to economists and biologists is the observation that anonymous and unrelated individuals, known to have breached trust, often make effective use of cheap signals, such as promises and apologies, to encourage trust re-extension. We used a pair of trust games with one-way communication and emotion surveys to investigate the role of emotions in regulating the propensity to message, apologize, re-extend trust, and demonstrate trustworthiness. This design allowed us to observe the endogenous emergence and natural distribution of trust-relevant behaviors, remedial strategies used by promise-breakers, their effects on behavior, and subsequent outcomes. We found that emotions triggered by interaction outcomes are predictable and also predict subsequent apology and trust re-extension. The role of emotions in behavioral regulation helps explain why messages are produced, when they can be trusted, and when trust will be re-extended.

Predictable and predictive emotions: explaining cheap signals and trust re-extension.

Science.gov (United States)

Schniter, Eric; Sheremeta, Roman M

2014-01-01

Despite normative predictions from economics and biology, unrelated strangers will often develop the trust necessary to reap gains from one-shot economic exchange opportunities. This appears to be especially true when declared intentions and emotions can be cheaply communicated. Perhaps even more puzzling to economists and biologists is the observation that anonymous and unrelated individuals, known to have breached trust, often make effective use of cheap signals, such as promises and apologies, to encourage trust re-extension. We used a pair of trust games with one-way communication and an emotion survey to investigate the role of emotions in regulating the propensity to message, apologize, re-extend trust, and demonstrate trustworthiness. This design allowed us to observe the endogenous emergence and natural distribution of trust-relevant behaviors, remedial strategies used by promise-breakers, their effects on behavior, and subsequent outcomes. We found that emotions triggered by interaction outcomes are predictable and also predict subsequent apology and trust re-extension. The role of emotions in behavioral regulation helps explain why messages are produced, when they can be trusted, and when trust will be re-extended.

Predict or classify: The deceptive role of time-locking in brain signal classification

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Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Predictions for optimal mitigation of paracrine inhibitory signalling in haemopoietic stem cell cultures.

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Berry, Joseph D; Godara, Pankaj; Liovic, Petar; Haylock, David N

2015-04-16

Recent studies in the literature have highlighted the critical role played by cell signalling in determining haemopoietic stem cell (HSC) fate within ex vivo culture systems. Stimulatory signals can enhance proliferation and promote differentiation, whilst inhibitory signals can significantly limit culture output. Numerical models of various mitigation strategies are presented and applied to determine effectiveness of these strategies toward mitigation of paracrine inhibitory signalling inherent in these culture systems. The strategies assessed include mixing, media-exchange, fed-batch and perfusion. The models predict that significant spatial concentration gradients exist in typical cell cultures, with important consequences for subsequent cell expansion. Media exchange is shown to be the most effective mitigation strategy, but remains labour intensive and difficult to scale-up for large culture systems. The fed-batch strategy is only effective at very small Peclet number, and its effect is diminished as the cell culture volume grows. Conversely, mixing is effective at high Peclet number, and ineffective at low Peclet number. The models predict that cell expansion in fed-batch cultures becomes independent of increasing dilution rate, consistent with experimental results previously reported in the literature. In contrast, the models predict that increasing the flow rate in perfused cultures will lead to increased cell expansion, indicating the suitability of perfusion for use as an automated, tunable strategy. The effect of initial cell seeding density is also investigated, with the model showing that perfusion outperforms dilution for all densities considered. The models predict that the impact of inhibitory signalling in HSC cultures can be mitigated against using media manipulation strategies, with the optimal strategy dependent upon the protein diffusion time-scale relative to the media manipulation time-scale. The key messages from this study can be applied to

Research on the Wire Network Signal Prediction Based on the Improved NNARX Model

Science.gov (United States)

Zhang, Zipeng; Fan, Tao; Wang, Shuqing

It is difficult to obtain accurately the wire net signal of power system's high voltage power transmission lines in the process of monitoring and repairing. In order to solve this problem, the signal measured in remote substation or laboratory is employed to make multipoint prediction to gain the needed data. But, the obtained power grid frequency signal is delay. In order to solve the problem, an improved NNARX network which can predict frequency signal based on multi-point data collected by remote substation PMU is describes in this paper. As the error curved surface of the NNARX network is more complicated, this paper uses L-M algorithm to train the network. The result of the simulation shows that the NNARX network has preferable predication performance which provides accurate real time data for field testing and maintenance.

Multimodal signalling in estrildid finches: song, dance and colour are associated with different ecological and life-history traits.

Science.gov (United States)

Gomes, A C R; Funghi, C; Soma, M; Sorenson, M D; Cardoso, G C

2017-07-01

Sexual traits (e.g. visual ornaments, acoustic signals, courtship behaviour) are often displayed together as multimodal signals. Some hypotheses predict joint evolution of different sexual signals (e.g. to increase the efficiency of communication) or that different signals trade off with each other (e.g. due to limited resources). Alternatively, multiple signals may evolve independently for different functions, or to communicate different information (multiple message hypothesis). We evaluated these hypotheses with a comparative study in the family Estrildidae, one of the largest songbird radiations, and one that includes many model species for research in sexual selection and communication. We found little evidence for either joint evolution or trade-offs between song and colour ornamentation. Some negative correlations between dance repertoire and song traits may suggest a functional compromise, but generally courtship dance also evolved independently from other signals. Instead of correlated evolution, we found that song, dance and colour are each related to different socio-ecological traits. Song complexity evolved together with ecological generalism, song performance with investment in reproduction, dance with commonness and habitat type, whereas colour ornamentation was shown previously to correlate mostly with gregariousness. We conclude that multimodal signals evolve in response to various socio-ecological traits, suggesting the accumulation of distinct signalling functions. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

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Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Different protein-protein interface patterns predicted by different machine learning methods.

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Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Real-Time Prediction of Temperature Elevation During Robotic Bone Drilling Using the Torque Signal.

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Feldmann, Arne; Gavaghan, Kate; Stebinger, Manuel; Williamson, Tom; Weber, Stefan; Zysset, Philippe

2017-09-01

Bone drilling is a surgical procedure commonly required in many surgical fields, particularly orthopedics, dentistry and head and neck surgeries. While the long-term effects of thermal bone necrosis are unknown, the thermal damage to nerves in spinal or otolaryngological surgeries might lead to partial paralysis. Previous models to predict the temperature elevation have been suggested, but were not validated or have the disadvantages of computation time and complexity which does not allow real time predictions. Within this study, an analytical temperature prediction model is proposed which uses the torque signal of the drilling process to model the heat production of the drill bit. A simple Green's disk source function is used to solve the three dimensional heat equation along the drilling axis. Additionally, an extensive experimental study was carried out to validate the model. A custom CNC-setup with a load cell and a thermal camera was used to measure the axial drilling torque and force as well as temperature elevations. Bones with different sets of bone volume fraction were drilled with two drill bits ([Formula: see text]1.8 mm and [Formula: see text]2.5 mm) and repeated eight times. The model was calibrated with 5 of 40 measurements and successfully validated with the rest of the data ([Formula: see text]C). It was also found that the temperature elevation can be predicted using only the torque signal of the drilling process. In the future, the model could be used to monitor and control the drilling process of surgeries close to vulnerable structures.

The Prediction of Metal Slopping in LD Converter on Base an Acoustic Signal

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Kostúr, K.

2006-01-01

Full Text Available The negative influences of slopping in a BOF are pollution to the environment. They give lower yield and cause equipment damage. The prediction of these phenomena is based on information processing from the measuring microphone. The change of frequency in certain range is done by a signal for the prediction of slopping. In this paper two methods for prediction of slopping are described. The first method is based on measuring and processing of sound emitted from the vessel during the blow. The second method utilizes Fourier’s transformation for processing of acoustic signal from sonic meter. The success rate of prediction has been evaluated by help of five criterions. It is possible to forecast the slopping on selected frequency (band. It is the essence of the second method, because this method has high success (criterion K1. Note, that criterion K5 defines acknowledgment of duration slopping. This criterion has the highest value.

Human Splicing Finder: an online bioinformatics tool to predict splicing signals.

Science.gov (United States)

Desmet, François-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Béroud, Gwenaëlle; Claustres, Mireille; Béroud, Christophe

2009-05-01

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-beta Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5' and 3' splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

Energy Technology Data Exchange (ETDEWEB)

Zhong, Haoti; Miller, David J. [The Pennsylvania State University, Department of Electrical Engineering, University Park, PA (United States); Urish, Kenneth L. [Magee Womens Hospital of the University of Pittsburgh Medical Center, The Bone and Joint Center, Pittsburgh, PA (United States); University of Pittsburgh School of Medicine, Department of Orthopaedic Surgery, Pittsburgh, PA (United States)

2016-07-15

The aim of this work is to use quantitative magnetic resonance imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps might predict symptomatic OA progression. Patients were selected from the Osteoarthritis Initiative (OAI), a prospective cohort. Two groups were identified: a symptomatic OA progression group and a control group. At baseline, both groups were asymptomatic (Western Ontario and McMaster Universities Arthritis [WOMAC] pain score total <10) with no radiographic evidence of OA (Kellgren-Lawrence [KL] score ≤ 1). The OA progression group (n = 103) had a change in total WOMAC score greater than 10 by the 3-year follow-up. The control group (n = 79) remained asymptomatic, with a change in total WOMAC score less than 10 at the 3-year follow-up. A classifier was designed to predict OA progression in an independent population based on T2 map cartilage signal variation. The classifier was designed using a nearest neighbor classification based on a Gaussian Mixture Model log-likelihood fit of T2 map cartilage voxel intensities. The use of T2 map signal variation to predict symptomatic OA progression in asymptomatic individuals achieved a specificity of 89.3 %, a sensitivity of 77.2 %, and an overall accuracy rate of 84.2 %. T2 map signal variation can predict symptomatic knee OA progression in asymptomatic individuals, serving as a possible early OA imaging biomarker. (orig.)

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals.

Science.gov (United States)

Bamatraf, Saeed; Hussain, Muhammad; Aboalsamh, Hatim; Qazi, Emad-Ul-Haq; Malik, Amir Saeed; Amin, Hafeez Ullah; Mathkour, Hassan; Muhammad, Ghulam; Imran, Hafiz Muhammad

2016-01-01

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM.

Detecting and Predicting Muscle Fatigue during Typing By SEMG Signal Processing and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Elham Ghoochani

2011-03-01

Full Text Available Introduction: Repetitive strain injuries are one of the most prevalent problems in occupational diseases. Repetition, vibration and bad postures of the extremities are physical risk factors related to work that can cause chronic musculoskeletal disorders. Repetitive work on a computer with low level contraction requires the posture to be maintained for a long time, which can cause muscle fatigue. Muscle fatigue in shoulders and neck is one of the most prevalent problems reported with computer users especially during typing. Surface electromyography (SEMG signals are used for detecting muscle fatigue as a non-invasive method. Material and Methods: Nine healthy females volunteered for signal recoding during typing. EMG signals were recorded from the trapezius muscle, which is subjected to muscle fatigue during typing.  After signal analysis and feature extraction, detecting and predicting muscle fatigue was performed by using the MLP artificial neural network. Results: Recorded signals were analyzed in time and frequency domains for feature extraction. Results of classification showed that the MLP neural network can detect and predict muscle fatigue during typing with 80.79 % ± 1.04% accuracy. Conclusion: Intelligent classification and prediction of muscle fatigue can have many applications in human factors engineering (ergonomics, rehabilitation engineering and biofeedback equipment for mitigating the injuries of repetitive works.

Transethnic differences in GWAS signals: A simulation study.

Science.gov (United States)

Zanetti, Daniela; Weale, Michael E

2018-05-07

Genome-wide association studies (GWASs) have allowed researchers to identify thousands of single nucleotide polymorphisms (SNPs) and other variants associated with particular complex traits. Previous studies have reported differences in the strength and even the direction of GWAS signals across different populations. These differences could be due to a combination of (1) lack of power, (2) allele frequency differences, (3) linkage disequilibrium (LD) differences, and (4) true differences in causal variant effect sizes. To determine whether properties (1)-(3) on their own might be sufficient to explain the patterns previously noted in strong GWAS signals, we simulated case-control data of European, Asian and African ancestry, applying realistic allele frequencies and LD from 1000 Genomes data but enforcing equal causal effect sizes across populations. Much of the observed differences in strong GWAS signals could indeed be accounted for by allele frequency and LD differences, enhanced by the Euro-centric SNP bias and lower SNP coverage found in older GWAS panels. While we cannot rule out a role for true transethnic effect size differences, our results suggest that strong causal effects may be largely shared among human populations, motivating the use of transethnic data for fine-mapping. © 2018 John Wiley & Sons Ltd/University College London.

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal

International Nuclear Information System (INIS)

Mohebbi, Maryam; Ghassemian, Hassan

2011-01-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L max  ), average length of the diagonal lines (L mean ), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches

Intracellular signaling entropy can be a biomarker for predicting the development of cervical intraepithelial neoplasia.

Directory of Open Access Journals (Sweden)

Masakazu Sato

Full Text Available While the mortality rates for cervical cancer have been drastically reduced after the introduction of the Pap smear test, it still is one of the leading causes of death in women worldwide. Additionally, studies that appropriately evaluate the risk of developing cervical lesions are needed. Therefore, we investigated whether intracellular signaling entropy, which is measured with microarray data, could be useful for predicting the risks of developing cervical lesions. We used three datasets, GSE63514 (histology, GSE27678 (cytology and GSE75132 (cytology, a prospective study. From the data in GSE63514, the entropy rate was significantly increased with disease progression (normal < cervical intraepithelial neoplasia, CIN < cancer (Kruskal-Wallis test, p < 0.0001. From the data in GSE27678, similar results (normal < low-grade squamous intraepithelial lesions, LSILs < high-grade squamous intraepithelial lesions, HSILs ≤ cancer were obtained (Kruskal-Wallis test, p < 0.001. From the data in GSE75132, the entropy rate tended to be higher in the HPV-persistent groups than the HPV-negative group. The group that was destined to progress to CIN 3 or higher had a tendency to have a higher entropy rate than the HPV16-positive without progression group. In conclusion, signaling entropy was suggested to be different for different lesion statuses and could be a useful biomarker for predicting the development of cervical intraepithelial neoplasia.

Multimodal signalling in estrildid finches

DEFF Research Database (Denmark)

Gomes, A. C. R.; Funghi, C.; Soma, M.

2017-01-01

Sexual traits (e.g. visual ornaments, acoustic signals, courtship behaviour) are often displayed together as multimodal signals. Some hypotheses predict joint evolution of different sexual signals (e.g. to increase the efficiency of communication) or that different signals trade off with each other...... (e.g. due to limited resources). Alternatively, multiple signals may evolve independently for different functions, or to communicate different information (multiple message hypothesis). We evaluated these hypotheses with a comparative study in the family Estrildidae, one of the largest songbird...... compromise, but generally courtship dance also evolved independently from other signals. Instead of correlated evolution, we found that song, dance and colour are each related to different socio-ecological traits. Song complexity evolved together with ecological generalism, song performance with investment...

Tracking Neuronal Connectivity from Electric Brain Signals to Predict Performance.

Science.gov (United States)

Vecchio, Fabrizio; Miraglia, Francesca; Rossini, Paolo Maria

2018-05-01

The human brain is a complex container of interconnected networks. Network neuroscience is a recent venture aiming to explore the connection matrix built from the human brain or human "Connectome." Network-based algorithms provide parameters that define global organization of the brain; when they are applied to electroencephalographic (EEG) signals network, configuration and excitability can be monitored in millisecond time frames, providing remarkable information on their instantaneous efficacy also for a given task's performance via online evaluation of the underlying instantaneous networks before, during, and after the task. Here we provide an updated summary on the connectome analysis for the prediction of performance via the study of task-related dynamics of brain network organization from EEG signals.

Signal Attenuation Curve for Different Surface Detector Arrays

Science.gov (United States)

Vicha, J.; Travnicek, P.; Nosek, D.; Ebr, J.

2014-06-01

Modern cosmic ray experiments consisting of large array of particle detectors measure the signals of electromagnetic or muon components or their combination. The correction for an amount of atmosphere passed is applied to the surface detector signal before its conversion to the shower energy. Either Monte Carlo based approach assuming certain composition of primaries or indirect estimation using real data and assuming isotropy of arrival directions can be used. Toy surface arrays of different sensitivities to electromagnetic and muon components are assumed in MC simulations to study effects imposed on attenuation curves for varying composition or possible high energy anisotropy. The possible sensitivity of the attenuation curve to the mass composition is also tested for different array types focusing on a future apparatus that can separate muon and electromagnetic component signals.

Similarities and Differences Between Warped Linear Prediction and Laguerre Linear Prediction

NARCIS (Netherlands)

Brinker, Albertus C. den; Krishnamoorthi, Harish; Verbitskiy, Evgeny A.

2011-01-01

Linear prediction has been successfully applied in many speech and audio processing systems. This paper presents the similarities and differences between two classes of linear prediction schemes, namely, Warped Linear Prediction (WLP) and Laguerre Linear Prediction (LLP). It is shown that both

Prediction of infarction and reperfusion in stroke by flow- and volume-weighted collateral signal in MR angiography.

Science.gov (United States)

Ernst, M; Forkert, N D; Brehmer, L; Thomalla, G; Siemonsen, S; Fiehler, J; Kemmling, A

2015-02-01

In proximal anterior circulation occlusive strokes, collateral flow is essential for good outcome. Collateralized vessel intensity in TOF- and contrast-enhanced MRA is variable due to different acquisition methods. Our purpose was to quantify collateral supply by using flow-weighted signal in TOF-MRA and blood volume-weighted signal in contrast-enhanced MRA to determine each predictive contribution to tissue infarction and reperfusion. Consecutively (2009-2013), 44 stroke patients with acute proximal anterior circulation occlusion met the inclusion criteria with TOF- and contrast-enhanced MRA and penumbral imaging. Collateralized vessels in the ischemic hemisphere were assessed by TOF- and contrast-enhanced MRA using 2 methods: 1) visual 3-point collateral scoring, and 2) collateral signal quantification by an arterial atlas-based collateral index. Collateral measures were tested by receiver operating characteristic curve and logistic regression against 2 imaging end points of tissue-outcome: final infarct volume and percentage of penumbra saved. Visual collateral scores on contrast-enhanced MRA but not TOF were significantly higher in patients with good outcome. Visual collateral scoring on contrast-enhanced MRA was the best rater-based discriminator for final infarct volume 50% (area under the curve, 0.67; P = .04). Atlas-based collateral index of contrast-enhanced MRA was the overall best independent discriminator for final infarct volume of collateral index combining the signal of TOF- and contrast-enhanced MRA was the overall best discriminator for effective reperfusion (percentage of penumbra saved >50%; area under the curve, 0.89; P collateral assessment, TOF- and contrast-enhanced MRA both contain predictive signal information for penumbral reperfusion. This could improve risk stratification in further studies. © 2015 by American Journal of Neuroradiology.

Interpopulational Variations in Sexual Chemical Signals of Iberian Wall Lizards May Allow Maximizing Signal Efficiency under Different Climatic Conditions.

Science.gov (United States)

Martín, José; Ortega, Jesús; López, Pilar

2015-01-01

Sexual signals used in intraspecific communication are expected to evolve to maximize efficacy under a given climatic condition. Thus, chemical secretions of lizards might evolve in the evolutionary time to ensure that signals are perfectly tuned to local humidity and temperature conditions affecting their volatility and therefore their persistence and transmission through the environment. We tested experimentally whether interpopulational altitudinal differences in chemical composition of femoral gland secretions of male Iberian wall lizards (Podarcis hispanicus) have evolved to maximize efficacy of chemical signals in different environmental conditions. Chemical analyses first showed that the characteristics of chemical signals of male lizards differed between two populations inhabiting environments with different climatic conditions in spite of the fact that these two populations are closely related genetically. We also examined experimentally whether the temporal attenuation of the chemical stimuli depended on simulated climatic conditions. Thus, we used tongue-flick essays to test whether female lizards were able to detect male scent marks maintained under different conditions of temperature and humidity by chemosensory cues alone. Chemosensory tests showed that chemical signals of males had a lower efficacy (i.e. detectability and persistence) when temperature and dryness increase, but that these effects were more detrimental for signals of the highest elevation population, which occupies naturally colder and more humid environments. We suggest that the abiotic environment may cause a selective pressure on the form and expression of sexual chemical signals. Therefore, interpopulational differences in chemical profiles of femoral secretions of male P. hispanicus lizards may reflect adaptation to maximize the efficacy of the chemical signal in different climates.

Acoustic signal emission monitoring as a novel method to predict steam pops during radiofrequency ablation: preliminary observations.

Science.gov (United States)

Chik, William W B; Kosobrodov, Roman; Bhaskaran, Abhishek; Barry, Michael Anthony Tony; Nguyen, Doan Trang; Pouliopoulos, Jim; Byth, Karen; Sivagangabalan, Gopal; Thomas, Stuart P; Ross, David L; McEwan, Alistair; Kovoor, Pramesh; Thiagalingam, Aravinda

2015-04-01

Steam pop is an explosive rupture of cardiac tissue caused by tissue overheating above 100 °C, resulting in steam formation, predisposing to serious complications associated with radiofrequency (RF) ablations. However, there are currently no reliable techniques to predict the occurrence of steam pops. We propose the utility of acoustic signals emitted during RF ablation as a novel method to predict steam pop formation and potentially prevent serious complications. Radiofrequency generator parameters (power, impedance, and temperature) were temporally recorded during ablations performed in an in vitro bovine myocardial model. The acoustic system consisted of HTI-96-min hydrophone, microphone preamplifier, and sound card connected to a laptop computer. The hydrophone has the frequency range of 2 Hz to 30 kHz and nominal sensitivity in the range -240 to -165 dB. The sound was sampled at 96 kHz with 24-bit resolution. Output signal from the hydrophone was fed into the camera audio input to synchronize the video stream. An automated system was developed for the detection and analysis of acoustic events. Nine steam pops were observed. Three distinct sounds were identified as warning signals, each indicating rapid steam formation and its release from tissue. These sounds had a broad frequency range up to 6 kHz with several spectral peaks around 2-3 kHz. Subjectively, these warning signals were perceived as separate loud clicks, a quick succession of clicks, or continuous squeaking noise. Characteristic acoustic signals were identified preceding 80% of pops occurrence. Six cardiologists were able to identify 65% of acoustic signals accurately preceding the pop. An automated system identified the characteristic warning signals in 85% of cases. The mean time from the first acoustic signal to pop occurrence was 46 ± 20 seconds. The automated system had 72.7% sensitivity and 88.9% specificity for predicting pops. Easily identifiable characteristic acoustic emissions

MOTORCYCLE CRASH PREDICTION MODEL FOR NON-SIGNALIZED INTERSECTIONS

Directory of Open Access Journals (Sweden)

S. HARNEN

2003-01-01

Full Text Available This paper attempts to develop a prediction model for motorcycle crashes at non-signalized intersections on urban roads in Malaysia. The Generalized Linear Modeling approach was used to develop the model. The final model revealed that an increase in motorcycle and non-motorcycle flows entering an intersection is associated with an increase in motorcycle crashes. Non-motorcycle flow on major road had the greatest effect on the probability of motorcycle crashes. Approach speed, lane width, number of lanes, shoulder width and land use were also found to be significant in explaining motorcycle crashes. The model should assist traffic engineers to decide the need for appropriate intersection treatment that specifically designed for non-exclusive motorcycle lane facilities.

NESmapper: accurate prediction of leucine-rich nuclear export signals using activity-based profiles.

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Shunichi Kosugi

2014-09-01

Full Text Available The nuclear export of proteins is regulated largely through the exportin/CRM1 pathway, which involves the specific recognition of leucine-rich nuclear export signals (NESs in the cargo proteins, and modulates nuclear-cytoplasmic protein shuttling by antagonizing the nuclear import activity mediated by importins and the nuclear import signal (NLS. Although the prediction of NESs can help to define proteins that undergo regulated nuclear export, current methods of predicting NESs, including computational tools and consensus-sequence-based searches, have limited accuracy, especially in terms of their specificity. We found that each residue within an NES largely contributes independently and additively to the entire nuclear export activity. We created activity-based profiles of all classes of NESs with a comprehensive mutational analysis in mammalian cells. The profiles highlight a number of specific activity-affecting residues not only at the conserved hydrophobic positions but also in the linker and flanking regions. We then developed a computational tool, NESmapper, to predict NESs by using profiles that had been further optimized by training and combining the amino acid properties of the NES-flanking regions. This tool successfully reduced the considerable number of false positives, and the overall prediction accuracy was higher than that of other methods, including NESsential and Wregex. This profile-based prediction strategy is a reliable way to identify functional protein motifs. NESmapper is available at http://sourceforge.net/projects/nesmapper.

Potyviruses differ in their requirement for TOR signalling.

Science.gov (United States)

Ouibrahim, Laurence; Rubio, Ana Giner; Moretti, André; Montané, Marie-Hélène; Menand, Benoît; Meyer, Christian; Robaglia, Christophe; Caranta, Carole

2015-09-01

Potyviruses are important plant pathogens that rely on many plant cellular processes for successful infection. TOR (target of rapamycin) signalling is a key eukaryotic energy-signalling pathway controlling many cellular processes such as translation and autophagy. The dependence of potyviruses on active TOR signalling was examined. Arabidopsis lines downregulated for TOR by RNAi were challenged with the potyviruses watermelon mosaic virus (WMV) and turnip mosaic virus (TuMV). WMV accumulation was found to be severely altered while TuMV accumulation was only slightly delayed. In another approach, using AZD-8055, an active site inhibitor of the TOR kinase, WMV infection was found to be strongly affected. Moreover, AZD-8055 application can cure WMV infection. In contrast, TuMV infection was not affected by AZD-8055. This suggests that potyviruses have different cellular requirements for active plant TOR signalling.

Reduced Predictable Information in Brain Signals in Autism Spectrum Disorder

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Carlos eGomez

2014-02-01

Full Text Available Autism spectrum disorder (ASD is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information storage (AIS – a key quantity in the theory of distributed computation in biological networks. AIS is defined as the mutual information between the semi-infinite past of a process and its next state. It measures the amount of stored information that is used for computation of the next time step of a process. AIS is high for rich but predictable dynamics. We recorded magnetoencephalography (MEG signals in 13 ASD patients and 14 matched control subjects in a visual task. After a beamformer source analysis, twelve task-relevant sources were obtained. For these sources, stationary baseline activity was analyzed using AIS. Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both. Our study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD. The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

Observer performance in detecting multiple radiographic signals: prediction and analysis using a generalized ROC approach

International Nuclear Information System (INIS)

Metz, C.E.; Starr, S.J.; Lusted, L.B.

1975-01-01

The theories of decision processes and signal detection provide a framework for the evaluation of observer performance. Some radiologic procedures involve a search for multiple similar lesions, as in gallstone or pneumoconiosis examinations. A model is presented which attempts to predict, from the conventional receiver operating characteristic (ROC) curve describing the detectability of a single visual signal in a radiograph, observer performance in an experiment requiring detection of more than one such signal. An experiment is described which tests the validity of this model for the case of detecting the presence of zero, one, or two low-contrast radiographic images of a two-mm.-diameter lucite bead embedded in radiographic mottle. Results from six observers, including three radiologists, confirm the validity of the model and suggest that human observer performance for relatively complex detection tasks can be predicted from the results of simpler experiments

Dopamine reward prediction error coding.

Science.gov (United States)

Schultz, Wolfram

2016-03-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

Machine-Learning-Based Future Received Signal Strength Prediction Using Depth Images for mmWave Communications

OpenAIRE

Okamoto, Hironao; Nishio, Takayuki; Nakashima, Kota; Koda, Yusuke; Yamamoto, Koji; Morikura, Masahiro; Asai, Yusuke; Miyatake, Ryo

2018-01-01

This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks. The scheme provides the future RSS prediction of any mmWave links within the camera's view, including links where nodes are not transmitting frames. This enables network controllers to conduct network operations before line-of-sight path blockages degrade the RSS. Using the ML techniques, the prediction scheme automatically...

Brain Signal Analysis Using Different Types of Music

OpenAIRE

Siti Ayuni Mohd Nasir; Wan Mahani Hafizah Wan Mahmud

2015-01-01

Music is able to improve certain functions of human body physiologically and psychologically. Music also can improve attention, memory, and even mental math ability by listening to the music before performing any task. The purpose of this study is to study the relation between types of music and brainwaves signal that is differences in state of relaxation and attention states. The Electroencephalography (EEG) signal was recorded using PowerLab, Dual Bio Amp and computer to observes and record...

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

DEFF Research Database (Denmark)

Nielsen, Henrik; Engelbrecht, Jacob; Brunak, Søren

1997-01-01

We have developed a new method for the identification of signal peptides and their cleavage based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome...

Prediction of Cyclists Movement in Different Terrain Conditions

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A/L V.Nagarrettinam Mahesh

2018-01-01

Full Text Available In Malaysia, most of the accidents involving a bicycle and another vehicle are due to either the driver or rider ‘failing to look properly’. This is more significant with the government initiatives to support the use of bicycle making the carbon-free environment, a vision of TN50. This research addresses the safety aspect of the cyclists in terms of the driver’s point of view which improves cyclist visibility during driving. The proposed helmet system implements a rule-based algorithm which predicts the turning and braking movement of the cyclists. With this system, additional illumination and signaling are provided for the cyclists. The major challenge faced is the implementation of an algorithm for various situations of cycling. To ensure the system could be used on the road, the accuracy and speed of the automatic signaling system need to adhere. Situations that affects the output of the indicators include bicycle speed, the angle of turning, body tilt, duration of turn and random body movements. This paper implements a 3-axis accelerometer and a microcontroller in a data logger to acquire the required data which are analyzed in MATLAB. Using filtering technique, the acquired data are then be cleaned to remove noise due to vibration during cycling. The characteristics of braking and turning are then analyzed in the time domain as well as frequency domain to ensure the optimum algorithm used for gesture recognition and movement prediction. The algorithm is based on sliding window, FFT and threshold-based rule algorithm. The output based on the rule-based algorithm then illuminate the corresponding signals which provide the safety feature of the system.

Reducing Brain Signal Noise in the Prediction of Economic Choices: A Case Study in Neuroeconomics

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Raanju R. Sundararajan

2017-12-01

Full Text Available In order to reduce the noise of brain signals, neuroeconomic experiments typically aggregate data from hundreds of trials collected from a few individuals. This contrasts with the principle of simple and controlled designs in experimental and behavioral economics. We use a frequency domain variant of the stationary subspace analysis (SSA technique, denoted as DSSA, to filter out the noise (nonstationary sources in EEG brain signals. The nonstationary sources in the brain signal are associated with variations in the mental state that are unrelated to the experimental task. DSSA is a powerful tool for reducing the number of trials needed from each participant in neuroeconomic experiments and also for improving the prediction performance of an economic choice task. For a single trial, when DSSA is used as a noise reduction technique, the prediction model in a food snack choice experiment has an increase in overall accuracy by around 10% and in sensitivity and specificity by around 20% and in AUC by around 30%, respectively.

Differences in Signal Activation by LH and hCG are Mediated by the LH/CG Receptor's Extracellular Hinge Region.

Science.gov (United States)

Grzesik, Paul; Kreuchwig, Annika; Rutz, Claudia; Furkert, Jens; Wiesner, Burkhard; Schuelein, Ralf; Kleinau, Gunnar; Gromoll, Joerg; Krause, Gerd

2015-01-01

The human lutropin (hLH)/choriogonadotropin (hCG) receptor (LHCGR) can be activated by binding two slightly different gonadotropic glycoprotein hormones, choriogonadotropin (CG) - secreted by the placenta, and lutropin (LH) - produced by the pituitary. They induce different signaling profiles at the LHCGR. This cannot be explained by binding to the receptor's leucine-rich-repeat domain (LRRD), as this binding is similar for the two hormones. We therefore speculate that there are previously unknown differences in the hormone/receptor interaction at the extracellular hinge region, which might help to understand functional differences between the two hormones. We have therefore performed a detailed study of the binding and action of LH and CG at the LHCGR hinge region. We focused on a primate-specific additional exon in the hinge region, which is located between LRRD and the serpentine domain. The segment of the hinge region encoded by exon10 was previously reported to be only relevant to hLH signaling, as the exon10-deletion receptor exhibits decreased hLH signaling, but unchanged hCG signaling. We designed an advanced homology model of the hormone/LHCGR complex, followed by experimental characterization of relevant fragments in the hinge region. In addition, we examined predictions of a helical exon10-encoded conformation by block-wise polyalanine (helix supporting) mutations. These helix preserving modifications showed no effect on hormone-induced signaling. However, introduction of a structure-disturbing double-proline mutant LHCGR-Q303P/E305P within the exon10-helix has, in contrast to exon10-deletion, no impact on hLH, but only on hCG signaling. This opposite effect on signaling by hLH and hCG can be explained by distinct sites of hormone interaction in the hinge region. In conclusion, our analysis provides details of the differences between hLH- and hCG-induced signaling that are mainly determined in the L2-beta loop of the hormones and in the hinge

Signal predictions for a proposed fast neutron interrogation method

International Nuclear Information System (INIS)

Sale, K.E.

1992-12-01

We have applied the Monte Carlo radiation transport code COG) to assess the utility of a proposed explosives detection scheme based on neutron emission. In this scheme a pulsed neutron beam is generated by an approximately seven MeV deuteron beam incident on a thick Be target. A scintillation detector operating in the current mode measures the neutrons transmitted through the object as a function of time. The flight time of unscattered neutrons from the source to the detector is simply related to the neutron energy. This information along with neutron cross section excitation functions is used to infer the densities of H, C, N and O in the volume sampled. The code we have chosen to use enables us to create very detailed and realistic models of the geometrical configuration of the system, the neutron source and of the detector response. By calculating the signals that will be observed for several configurations and compositions of interrogated object we can investigate and begin to understand how a system that could actually be fielded will perform. Using this modeling capability many early on with substantial savings in time and cost and with improvements in performance. We will present our signal predictions for simple single element test cases and for explosive compositions. From these studies it is dear that the interpretation of the signals from such an explosives identification system will pose a substantial challenge

Influence of different envelope maskers on signal recognition and neuronal representation in the auditory system of a grasshopper.

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Daniela Neuhofer

Full Text Available BACKGROUND: Animals that communicate by sound face the problem that the signals arriving at the receiver often are degraded and masked by noise. Frequency filters in the receiver's auditory system may improve the signal-to-noise ratio (SNR by excluding parts of the spectrum which are not occupied by the species-specific signals. This solution, however, is hardly amenable to species that produce broad band signals or have ears with broad frequency tuning. In mammals auditory filters exist that work in the temporal domain of amplitude modulations (AM. Do insects also use this type of filtering? PRINCIPAL FINDINGS: Combining behavioural and neurophysiological experiments we investigated whether AM filters may improve the recognition of masked communication signals in grasshoppers. The AM pattern of the sound, its envelope, is crucial for signal recognition in these animals. We degraded the species-specific song by adding random fluctuations to its envelope. Six noise bands were used that differed in their overlap with the spectral content of the song envelope. If AM filters contribute to reduced masking, signal recognition should depend on the degree of overlap between the song envelope spectrum and the noise spectra. Contrary to this prediction, the resistance against signal degradation was the same for five of six masker bands. Most remarkably, the band with the strongest frequency overlap to the natural song envelope (0-100 Hz impaired acceptance of degraded signals the least. To assess the noise filter capacities of single auditory neurons, the changes of spike trains as a function of the masking level were assessed. Increasing levels of signal degradation in different frequency bands led to similar changes in the spike trains in most neurones. CONCLUSIONS: There is no indication that auditory neurones of grasshoppers are specialized to improve the SNR with respect to the pattern of amplitude modulations.

Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values.

Science.gov (United States)

Shackelford, Stacy; Yang, Shiming; Hu, Peter; Miller, Catriona; Anazodo, Amechi; Galvagno, Samuel; Wang, Yulei; Hartsky, Lauren; Fang, Raymond; Mackenzie, Colin

2015-10-01

Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p transfusion prediction (3-hour AUROC, 0.84; p transfusion AUROC, 0.91; p blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. Epidemiologic

Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

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Jennifer D. Atkins

2015-08-01

Full Text Available The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

Two different groups of signal sequence in M-superfamily conotoxins.

Science.gov (United States)

Wang, Qi; Jiang, Hui; Han, Yu-Hong; Yuan, Duo-Duo; Chi, Cheng-Wu

2008-04-01

M-superfamily conotoxins can be divided into four branches (M-1, M-2, M-3 and M-4) according to the number of amino acid residues in the third Cys loop. In general, it is widely accepted that the conotoxin signal peptides of each superfamily are strictly conserved. Recently, we cloned six cDNAs of novel M-superfamily conotoxins from Conus leopardus, Conus marmoreus and Conus quercinus, belonging to either M-1 or M-3 branch. These conotoxins, judging from the putative peptide sequences deducted from cDNAs, are rich in acidic residues and share highly conserved signal and pro-peptide region. However, they are quite different from the reported conotoxins of M-2 and M-4 branches even in their signal peptides, which in general are considered highly conserved for each superfamily of conotoxins. The signal sequences of M-1 and M-3 conotoxins composed of 24 residues start with MLKMGVVL-, while those of M-2 and M-4 conotoxins composed of 25 residues start with MMSKLGVL-. It is another example that different types of signal peptides can exist within a superfamily besides the I-conotoxin superfamily. In addition to the different disulfide connectivity of M-1 conotoxins from that of M-4 or M-2 conotoxins, the sequence alignment, preferential Cys codon usage and phylogenetic tree analysis suggest that M-1 and M-3 conotoxins have much closer relationship, being different from the conotoxins of other two branches (M-4 and M-2) of M-superfamily.

Altered neural reward and loss processing and prediction error signalling in depression

Science.gov (United States)

Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

2015-01-01

Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

Signaling Network Assessment of Mutations and Copy Number Variations Predict Breast Cancer Subtype-Specific Drug Targets

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Naif Zaman

2013-10-01

Full Text Available Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.

Causal Mathematical Logic as a guiding framework for the prediction of "Intelligence Signals" in brain simulations

Science.gov (United States)

Lanzalaco, Felix; Pissanetzky, Sergio

2013-12-01

A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the "Action functional" as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains "intelligence" processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of "non-explicitly programmed" complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent "intelligence Signals". To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general "Information Engine" model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype

Search for a QGP with a TPC spectrometer, and QGP signals predicted by new event generator

International Nuclear Information System (INIS)

Lindenbaum, S.J.

1988-01-01

The BNL/CCNY/Johns Hopkins/Rice Collaboration has developed and successfully tested a TPC Magnetic Spectrometer to search for OGP signals produced by ion beams at AGS. Test data with 14.5 GeV/c /times/ A Oxygen ions incident on a Pb target has been obtained. These include a 78-prong nuclear interaction in the MPS magnet which was pattern recognized with an efficiency ∼75%. A cascade and plasma event generator has also been developed, the predictions of which are used to illustrate how our technique can detect possible plasma signals at AGS and RHIC. A 4π tracking TPC magnetic spectrometer has been proposed for RHIC. The new event generator predicts striking central rapidity bump QGP signals at RHIC for p, /bar p/, π/sup +-/, K/sup +-/, etc., produced by 100 GeV/c /times/ A Au on Au collisions and these are presented. 2 refs., 13 figs., 1 tab

Differences in signal activation by LH and hCG are mediated by the LH/CG receptor`s extracellular hinge region

Directory of Open Access Journals (Sweden)

Paul eGrzesik

2015-09-01

Full Text Available The human lutropin/choriogonadotropin receptor (LHCGR can be activated by binding two slightly different gonadotropic glycoprotein hormones, choriogonadotropin (CG - secreted by the placenta, and lutropin (LH - produced by the pituitary. They induce different signaling profiles at the LHCGR. This cannot be explained by binding to the receptor's leucine-rich repeat domain (LRRD, as this binding is similar for the two hormones. We therefore speculate that there are previously unknown differences in the hormone/receptor interaction at the extracellular hinge region, which might help to understand functional differences between the two hormones. We have therefore performed a detailed study of the binding and action of LH and CG at the LHCGR hinge region. We focused on a primate-specific additional exon in the hinge region, which is located between LRRD and the serpentine domain. The segment of the hinge region encoded by exon10 was previously reported to be only relevant to hLH signaling, as the exon10-deletion receptor exhibits decreased hLH signaling, but unchanged hCG signaling. We designed an advanced homology model of the hormone/LHCGR complex, followed by experimental characterization of relevant fragments in the hinge region. In addition, we examined predictions of a helical exon10-encoded conformation by block-wise polyalanine (helix supporting mutations. These helix preserving modifications showed no effect on hormone induced signaling. However, introduction of a structure-disturbing double-proline mutant LHCGR-Q303P/E305P within the exon10-helix has, in contrast to exon10 deletion, no impact on hLH, but only on hCG signaling. This opposite effect on signaling by hLH and hCG can be explained by distinct sites of hormone interaction in the hinge region s. In conclusion, our analysis provides details of the differences between hLH- and hCG-induced signaling that are mainly determined in the L2-beta loop of the hormones and in the hinge region

Differences in Signal Activation by LH and hCG are Mediated by the LH/CG Receptor’s Extracellular Hinge Region

Science.gov (United States)

Grzesik, Paul; Kreuchwig, Annika; Rutz, Claudia; Furkert, Jens; Wiesner, Burkhard; Schuelein, Ralf; Kleinau, Gunnar; Gromoll, Joerg; Krause, Gerd

2015-01-01

The human lutropin (hLH)/choriogonadotropin (hCG) receptor (LHCGR) can be activated by binding two slightly different gonadotropic glycoprotein hormones, choriogonadotropin (CG) – secreted by the placenta, and lutropin (LH) – produced by the pituitary. They induce different signaling profiles at the LHCGR. This cannot be explained by binding to the receptor’s leucine-rich-repeat domain (LRRD), as this binding is similar for the two hormones. We therefore speculate that there are previously unknown differences in the hormone/receptor interaction at the extracellular hinge region, which might help to understand functional differences between the two hormones. We have therefore performed a detailed study of the binding and action of LH and CG at the LHCGR hinge region. We focused on a primate-specific additional exon in the hinge region, which is located between LRRD and the serpentine domain. The segment of the hinge region encoded by exon10 was previously reported to be only relevant to hLH signaling, as the exon10-deletion receptor exhibits decreased hLH signaling, but unchanged hCG signaling. We designed an advanced homology model of the hormone/LHCGR complex, followed by experimental characterization of relevant fragments in the hinge region. In addition, we examined predictions of a helical exon10-encoded conformation by block-wise polyalanine (helix supporting) mutations. These helix preserving modifications showed no effect on hormone-induced signaling. However, introduction of a structure-disturbing double-proline mutant LHCGR-Q303P/E305P within the exon10-helix has, in contrast to exon10-deletion, no impact on hLH, but only on hCG signaling. This opposite effect on signaling by hLH and hCG can be explained by distinct sites of hormone interaction in the hinge region. In conclusion, our analysis provides details of the differences between hLH- and hCG-induced signaling that are mainly determined in the L2-beta loop of the hormones and in the

Predicting future glacial lakes in Austria using different modelling approaches

Science.gov (United States)

Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

2017-04-01

Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

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Elize A Shirdel

2011-02-01

Full Text Available MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP.mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05, suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001, to be more studied (p<0.0002, and to have higher degree in the KEGG cancer pathway (p<0.0001, compared to intra-pathway microRNAs.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

When theory and biology differ: The relationship between reward prediction errors and expectancy.

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Williams, Chad C; Hassall, Cameron D; Trska, Robert; Holroyd, Clay B; Krigolson, Olave E

2017-10-01

Comparisons between expectations and outcomes are critical for learning. Termed prediction errors, the violations of expectancy that occur when outcomes differ from expectations are used to modify value and shape behaviour. In the present study, we examined how a wide range of expectancy violations impacted neural signals associated with feedback processing. Participants performed a time estimation task in which they had to guess the duration of one second while their electroencephalogram was recorded. In a key manipulation, we varied task difficulty across the experiment to create a range of different feedback expectancies - reward feedback was either very expected, expected, 50/50, unexpected, or very unexpected. As predicted, the amplitude of the reward positivity, a component of the human event-related brain potential associated with feedback processing, scaled inversely with expectancy (e.g., unexpected feedback yielded a larger reward positivity than expected feedback). Interestingly, the scaling of the reward positivity to outcome expectancy was not linear as would be predicted by some theoretical models. Specifically, we found that the amplitude of the reward positivity was about equivalent for very expected and expected feedback, and for very unexpected and unexpected feedback. As such, our results demonstrate a sigmoidal relationship between reward expectancy and the amplitude of the reward positivity, with interesting implications for theories of reinforcement learning. Copyright © 2017 Elsevier B.V. All rights reserved.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials

OpenAIRE

Cai, John; Bresell,; Steinberg,; Silberg,; Furlong,Stephen

2012-01-01

Zhaohui Cai,1,* Anders Bresell,2,* Mark H Steinberg,1 Debra G Silberg,1 Stephen T Furlong11AstraZeneca Pharmaceuticals, Wilmington, DE, USA; 2AstraZeneca Pharmaceuticals, Södertälje, Sweden*These authors contributed equally to this workPurpose: The goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information.Patients a...

γ-Aminobutyric acid (GABA) signalling in plants.

Science.gov (United States)

Ramesh, Sunita A; Tyerman, Stephen D; Gilliham, Matthew; Xu, Bo

2017-05-01

The role of γ-aminobutyric acid (GABA) as a signal in animals has been documented for over 60 years. In contrast, evidence that GABA is a signal in plants has only emerged in the last 15 years, and it was not until last year that a mechanism by which this could occur was identified-a plant 'GABA receptor' that inhibits anion passage through the aluminium-activated malate transporter family of proteins (ALMTs). ALMTs are multigenic, expressed in different organs and present on different membranes. We propose GABA regulation of ALMT activity could function as a signal that modulates plant growth, development, and stress response. In this review, we compare and contrast the plant 'GABA receptor' with mammalian GABA A receptors in terms of their molecular identity, predicted topology, mode of action, and signalling roles. We also explore the implications of the discovery that GABA modulates anion flux in plants, its role in signal transduction for the regulation of plant physiology, and predict the possibility that there are other GABA interaction sites in the N termini of ALMT proteins through in silico evolutionary coupling analysis; we also explore the potential interactions between GABA and other signalling molecules.

A Repeated Signal Difference for Recognising Patterns

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Kieran Greer

2016-08-01

Full Text Available This paper describes a new mechanism that might help with defining pattern sequences, by the fact that it can produce an upper bound on the ensemble value that can persistently oscillate with the actual values produced from each pattern. With every firing event, a node also receives an on/off feedback switch. If the node fires then it sends a feedback result depending on the input signal strength. If the input signal is positive or larger, it can store an ‘on’ switch feedback for the next iteration. If the signal is negative or smaller it can store an ‘off’ switch feedback for the next iteration. If the node does not fire, then it does not affect the current feedback situation and receives the switch command produced by the last active pattern event for the same neuron. The upper bound therefore also represents the largest or most enclosing pattern set and the lower value is for the actual set of firing patterns. If the pattern sequence repeats, it will oscillate between the two values, allowing them to be recognised and measured more easily, over time. Tests show that changing the sequence ordering produces different value sets, which can also be measured.

High-dimensional analysis of the aging immune system: verification of age-associated differences in immune signaling responses in healthy donors.

Science.gov (United States)

Longo, Diane M; Louie, Brent; Ptacek, Jason; Friedland, Greg; Evensen, Erik; Putta, Santosh; Atallah, Michelle; Spellmeyer, David; Wang, Ena; Pos, Zoltan; Marincola, Francesco M; Schaeffer, Andrea; Lukac, Suzanne; Railkar, Radha; Beals, Chan R; Cesano, Alessandra; Carayannopoulos, Leonidas N; Hawtin, Rachael E

2014-06-21

Single-cell network profiling (SCNP) is a multiparametric flow cytometry-based approach that simultaneously measures evoked signaling in multiple cell subsets. Previously, using the SCNP approach, age-associated immune signaling responses were identified in a cohort of 60 healthy donors. In the current study, a high-dimensional analysis of intracellular signaling was performed by measuring 24 signaling nodes in 7 distinct immune cell subsets within PBMCs in an independent cohort of 174 healthy donors [144 elderly (>65 yrs); 30 young (25-40 yrs)]. Associations between age and 9 immune signaling responses identified in the previously published 60 donor cohort were confirmed in the current study. Furthermore, within the current study cohort, 48 additional immune signaling responses differed significantly between young and elderly donors. These associations spanned all profiled modulators and immune cell subsets. These results demonstrate that SCNP, a systems-based approach, can capture the complexity of the cellular mechanisms underlying immunological aging. Further, the confirmation of age associations in an independent donor cohort supports the use of SCNP as a tool for identifying reproducible predictive biomarkers in areas such as vaccine response and response to cancer immunotherapies.

Influences of different sample preparation methods on tooth enamel ESR signals

International Nuclear Information System (INIS)

Zhang Wenyi; Jiao Ling; Zhang Liang'an; Pan Zhihong; Zeng Hongyu

2005-01-01

Objective: To study the influences of different sample preparation methods on tooth enamel ESR signals in order to reduce the effect of dentine on their sensitivities to radiation. Methods: The enamel was separated from dentine of non-irradiated adult teeth by mechanical, chemical, or both methods. The samples of different preparations were scanned by an ESR spectrometer before and after irradiation. Results: The response of ESR signals of samples prepared with different methods to radiation dose was significantly different. Conclusion: The selection of sample preparation method is very important for dose reconstruction by tooth enamel ESR dosimetry, especially in the low dose range. (authors)

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

Science.gov (United States)

Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

2018-05-17

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex.

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Yasuhiko Nakanishi

Full Text Available Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Phase-rectified signal averaging method to predict perinatal outcome in infants with very preterm fetal growth restriction- a secondary analysis of TRUFFLE-trial

NARCIS (Netherlands)

Lobmaier, Silvia M.; Mensing van Charante, Nico; Ferrazzi, Enrico; Giussani, Dino A.; Shaw, Caroline J.; Müller, Alexander; Ortiz, Javier U.; Ostermayer, Eva; Haller, Bernhard; Prefumo, Federico; Frusca, Tiziana; Hecher, Kurt; Arabin, Birgit; Thilaganathan, Baskaran; Papageorghiou, Aris T.; Bhide, Amarnath; Martinelli, Pasquale; Duvekot, Johannes J.; van Eyck, Jim; Visser, Gerard H A; Schmidt, Georg; Ganzevoort, Wessel; Lees, Christoph C.; Schneider, Karl T M; Bilardo, Caterina M.; Brezinka, Christoph; Diemert, Anke; Derks, Jan B.; Schlembach, Dietmar; Todros, Tullia; Valcamonico, Adriana; Marlow, Neil; van Wassenaer-Leemhuis, Aleid

2016-01-01

Background Phase-rectified signal averaging, an innovative signal processing technique, can be used to investigate quasi-periodic oscillations in noisy, nonstationary signals that are obtained from fetal heart rate. Phase-rectified signal averaging is currently the best method to predict survival

Phase-rectified signal averaging method to predict perinatal outcome in infants with very preterm fetal growth restriction- a secondary analysis of TRUFFLE-trial

NARCIS (Netherlands)

Lobmaier, Silvia M.; Mensing van Charante, Nico; Ferrazzi, Enrico; Giussani, Dino A.; Shaw, Caroline J.; Müller, Alexander; Ortiz, Javier U.; Ostermayer, Eva; Haller, Bernhard; Prefumo, Federico; Frusca, Tiziana; Hecher, Kurt; Arabin, Birgit; Thilaganathan, Baskaran; Papageorghiou, Aris T.; Bhide, Amarnath; Martinelli, Pasquale; Duvekot, Johannes J.; van Eyck, Jim; Visser, Gerard H. A.; Schmidt, Georg; Ganzevoort, Wessel; Lees, Christoph C.; Schneider, Karl T. M.; Bilardo, Caterina M.; Brezinka, Christoph; Diemert, Anke; Derks, Jan B.; Schlembach, Dietmar; Todros, Tullia; Valcamonico, Adriana; Marlow, Neil; van Wassenaer-Leemhuis, Aleid

2016-01-01

Phase-rectified signal averaging, an innovative signal processing technique, can be used to investigate quasi-periodic oscillations in noisy, nonstationary signals that are obtained from fetal heart rate. Phase-rectified signal averaging is currently the best method to predict survival after

Prediction of Above-elbow Motions in Amputees, based on Electromyographic(EMG Signals, Using Nonlinear Autoregressive Exogenous (NARX Model

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Ali Akbar Akbari

2014-08-01

Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are  and  , respectively. Conclusion  EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX  network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.

Dopamine reward prediction error coding

OpenAIRE

Schultz, Wolfram

2016-01-01

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards?an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less...

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides.

Science.gov (United States)

Tsirigos, Konstantinos D; Peters, Christoph; Shu, Nanjiang; Käll, Lukas; Elofsson, Arne

2015-07-01

TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Can the MRI signal of aggressive fibromatosis be used to predict its behavior?

International Nuclear Information System (INIS)

Castellazzi, G.; Vanel, D.; Le Cesne, A.; Le Pechoux, C.; Caillet, H.; Perona, F.; Bonvalot, S.

2009-01-01

Purpose: Aggressive fibromatosis is an invasive non-metastasizing soft-tissue tumor. Until recently, the standard treatment combined surgery and radiation therapy, but new studies reported that conservative strategies with or without medical treatment could be the best management. The aim of this study was to analyze and correlate the size and MR imaging signal features of aggressive fibromatosis with its behavior in order to choose the best treatment. Materials and methods: Between March 1985 and December 2005, 27 patients with at least 2 consecutive MRI examinations and no surgery or radiation therapy in between were recorded. There were 9 men and 18 women, and median age was 31 years. They underwent 107 MRI examinations of 47 lesions, 29 of which were medically treated, while the remaining 18 did not receive any drug administration. The size and signal changes of each lesion were studied over time on T2- and/or T1-weighted sequences after injection of contrast medium. RECIST criteria were used for size: only a 30% decrease or a 20% increase in the size of the main dimension was considered significant. We classified the appearance of the signal into six categories in order of increasing intensity and then we established the related variations over time. Results: The size of 79% of the lesions in the treated group and 82% in the untreated group remained stable. The initial signal of stable lesions or those exhibiting an increase in size was most frequently high. There was a high rate of signal stability over time, whatever the initial signal and size changes. Changes in size were not correlated with the initial MR signal. A decrease in size associated with a decreased signal was observed in three cases exclusively in the treated group. Conclusion: Fibromatoses are a group of soft-tissue tumors with variable characteristics on MRI, but it is not possible to predict their behavior based on the MRI signal

MRI investigation of normal fetal lung maturation using signal intensities on different imaging sequences

International Nuclear Information System (INIS)

Balassy, Csilla; Kasprian, Gregor; Weber, Michael; Hoermann, Marcus; Prayer, Daniela; Brugger, Peter C.; Csapo, Bence; Mittermayer, Christoph

2007-01-01

To purpose of this paper is to study the relation between normal lung maturation signal and changes in intensity ratios (SIR) and to determine which magnetic resonance imaging sequence provides the strongest correlation of normal lung SIs with gestational age. 126 normal singleton pregnancies (20-37 weeks) were examined with a 1.5 Tesla unit. Mean SIs for lungs, liver, and gastric fluid were assessed on six different sequences, and SIRs of lung/liver (LLSIR) and lung/gastric fluid (LGSIR) were correlated with gestational age for each sequence. To evaluate the feasibility of SIRs in the prediction of the state of the lung maturity, accuracy of the predicted SIRs (D*) was measured by calculating relative residuals (D*-D)/D for each sequence. LLSIRs showed significant changes in every sequence (p<0.05), while LGSIRs only on two sequences. Significant differences were shown for the mean of absolute residuals for both LLSIRs (p<0.001) and for LGSIRs (p=0.003). Relative residuals of LLSIRs were significantly smaller on T1-weighted sequence, whereas they were significantly higher for LGSIRs on FLAIR sequence. Fetal liver seems to be adequate reference for the investigation of lung maturation. T1-weighted sequence was the most accurate for the measurement of the lung SIs; thus, we propose to determine LLSIR on T1-weighted sequence when evaluating lung development. (orig.)

MRI investigation of normal fetal lung maturation using signal intensities on different imaging sequences

Energy Technology Data Exchange (ETDEWEB)

Balassy, Csilla; Kasprian, Gregor; Weber, Michael; Hoermann, Marcus; Prayer, Daniela [Medical University of Vienna, Department of Radiology, Vienna (Austria); Brugger, Peter C. [Medical University of Vienna, Center of Anatomy and Cell Biology, Vienna (Austria); Csapo, Bence [Medical University of Vienna, Department of Obstetrics and Gyneocology, Vienna (Austria); Mittermayer, Christoph [Medical University of Vienna, Department of Pediatrics, Vienna (Austria)

2007-03-15

To purpose of this paper is to study the relation between normal lung maturation signal and changes in intensity ratios (SIR) and to determine which magnetic resonance imaging sequence provides the strongest correlation of normal lung SIs with gestational age. 126 normal singleton pregnancies (20-37 weeks) were examined with a 1.5 Tesla unit. Mean SIs for lungs, liver, and gastric fluid were assessed on six different sequences, and SIRs of lung/liver (LLSIR) and lung/gastric fluid (LGSIR) were correlated with gestational age for each sequence. To evaluate the feasibility of SIRs in the prediction of the state of the lung maturity, accuracy of the predicted SIRs (D*) was measured by calculating relative residuals (D*-D)/D for each sequence. LLSIRs showed significant changes in every sequence (p<0.05), while LGSIRs only on two sequences. Significant differences were shown for the mean of absolute residuals for both LLSIRs (p<0.001) and for LGSIRs (p=0.003). Relative residuals of LLSIRs were significantly smaller on T1-weighted sequence, whereas they were significantly higher for LGSIRs on FLAIR sequence. Fetal liver seems to be adequate reference for the investigation of lung maturation. T1-weighted sequence was the most accurate for the measurement of the lung SIs; thus, we propose to determine LLSIR on T1-weighted sequence when evaluating lung development. (orig.)

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

International Nuclear Information System (INIS)

Ernst, Floris; Schweikard, Achim

2008-01-01

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

Energy Technology Data Exchange (ETDEWEB)

Ernst, Floris; Schweikard, Achim [University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck (Germany)

2008-06-15

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

Science.gov (United States)

Watanabe, Noriya; Sakagami, Masamichi; Haruno, Masahiko

2013-03-06

Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

Re-modulated technology of WDM-PON employing different DQPSK downstream signals

Science.gov (United States)

Gao, Chao; Xin, Xiang-jun; Yu, Chong-xiu

2012-11-01

This paper proposes a kind of modulation architecture for wavelength-division-multiplexing passive optical network (WDMPON) employing optical differential quadrature phase shift keying (DQPSK) downstream signals and two different modulation formats of re-modulated upstream signals. At the optical line terminal (OLT), 10 Gbit/s signal is modulated with DQPSK. At the optical network unit (ONU), part of the downstream signal is re-modulated with on-off keying (OOK) or inverse-return-to-zero (IRZ). Simulation results show the impact on the system employing NRZ, RZ and carrier-suppressed return-to-zero (CSRZ). The analyses also reflect that the architecture can restrain chromatic dispersion and channel crosstalk, which makes it the best architecture of access network in the future.

Numerical prediction and measurement of optoacoustic signals generated in PVA-H tissue phantoms

Science.gov (United States)

Melchert, Oliver; Blumenröther, Elias; Wollweber, Merve; Roth, Bernhard

2018-01-01

We present numerical simulations of optoacoustic (OA) signals, complementing laboratory experiments on melanin doped polyvinyl alcohol hydrogel (PVA-H) tissue phantoms. We review the computational approach to model the underlying mechanisms, i.e. optical absorption of laser energy and acoustic propagation of mechanical stress, geared toward experiments that involve absorbing media with homogeneous acoustic properties. We apply the numerical procedure to predict signals observed in the acoustic near- and farfield in both, forward and backward detection mode, in PVA-H tissue phantoms (i.e. an elastic solid). Further, we report on verification tests of our research code based on OA experiments on dye solution (i.e. a liquid) detailed in the literature and benchmark our 3D procedure via limiting cases described in terms of effectively 1D theoretical approaches.

NLStradamus: a simple Hidden Markov Model for nuclear localization signal prediction

Directory of Open Access Journals (Sweden)

Provart Nicholas

2009-06-01

Full Text Available Abstract Background Nuclear localization signals (NLSs are stretches of residues within a protein that are important for the regulated nuclear import of the protein. Of the many import pathways that exist in yeast, the best characterized is termed the 'classical' NLS pathway. The classical NLS contains specific patterns of basic residues and computational methods have been designed to predict the location of these motifs on proteins. The consensus sequences, or patterns, for the other import pathways are less well-understood. Results In this paper, we present an analysis of characterized NLSs in yeast, and find, despite the large number of nuclear import pathways, that NLSs seem to show similar patterns of amino acid residues. We test current prediction methods and observe a low true positive rate. We therefore suggest an approach using hidden Markov models (HMMs to predict novel NLSs in proteins. We show that our method is able to consistently find 37% of the NLSs with a low false positive rate and that our method retains its true positive rate outside of the yeast data set used for the training parameters. Conclusion Our implementation of this model, NLStradamus, is made available at: http://www.moseslab.csb.utoronto.ca/NLStradamus/

Gender differences in multiple sclerosis : induction of estrogen signaling in male and progesterone signaling in female lesions

NARCIS (Netherlands)

Luchetti, Sabina; van Eden, Corbert G; Schuurman, Karianne; van Strien, Miriam E; Swaab, Dick F; Huitinga, Inge

The basis of gender differences in the prevalence and clinical progression of multiple sclerosis (MS) is not understood. Here, we identify gender-specific responses in steroid synthesis and signaling in the brains of MS patients as possible contributors to these differences. We investigated gene

Genomic Signal Processing: Predicting Basic Molecular Biological Principles

Science.gov (United States)

Alter, Orly

2005-03-01

Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of

The human brain maintains contradictory and redundant auditory sensory predictions.

Directory of Open Access Journals (Sweden)

Marika Pieszek

Full Text Available Computational and experimental research has revealed that auditory sensory predictions are derived from regularities of the current environment by using internal generative models. However, so far, what has not been addressed is how the auditory system handles situations giving rise to redundant or even contradictory predictions derived from different sources of information. To this end, we measured error signals in the event-related brain potentials (ERPs in response to violations of auditory predictions. Sounds could be predicted on the basis of overall probability, i.e., one sound was presented frequently and another sound rarely. Furthermore, each sound was predicted by an informative visual cue. Participants' task was to use the cue and to discriminate the two sounds as fast as possible. Violations of the probability based prediction (i.e., a rare sound as well as violations of the visual-auditory prediction (i.e., an incongruent sound elicited error signals in the ERPs (Mismatch Negativity [MMN] and Incongruency Response [IR]. Particular error signals were observed even in case the overall probability and the visual symbol predicted different sounds. That is, the auditory system concurrently maintains and tests contradictory predictions. Moreover, if the same sound was predicted, we observed an additive error signal (scalp potential and primary current density equaling the sum of the specific error signals. Thus, the auditory system maintains and tolerates functionally independently represented redundant and contradictory predictions. We argue that the auditory system exploits all currently active regularities in order to optimally prepare for future events.

D2 receptor genotype and striatal dopamine signaling predict motor cortical activity and behavior in humans.

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Fazio, Leonardo; Blasi, Giuseppe; Taurisano, Paolo; Papazacharias, Apostolos; Romano, Raffaella; Gelao, Barbara; Ursini, Gianluca; Quarto, Tiziana; Lo Bianco, Luciana; Di Giorgio, Annabella; Mancini, Marina; Popolizio, Teresa; Rubini, Giuseppe; Bertolino, Alessandro

2011-02-14

Pre-synaptic D2 receptors regulate striatal dopamine release and DAT activity, key factors for modulation of motor pathways. A functional SNP of DRD2 (rs1076560 G>T) is associated with alternative splicing such that the relative expression of D2S (mainly pre-synaptic) vs. D2L (mainly post-synaptic) receptor isoforms is decreased in subjects with the T allele with a putative increase of striatal dopamine levels. To evaluate how DRD2 genotype and striatal dopamine signaling predict motor cortical activity and behavior in humans, we have investigated the association of rs1076560 with BOLD fMRI activity during a motor task. To further evaluate the relationship of this circuitry with dopamine signaling, we also explored the correlation between genotype based differences in motor brain activity and pre-synaptic striatal DAT binding measured with [(123)I] FP-CIT SPECT. Fifty healthy subjects, genotyped for DRD2 rs1076560 were studied with BOLD-fMRI at 3T while performing a visually paced motor task with their right hand; eleven of these subjects also underwent [(123)I]FP-CIT SPECT. SPM5 random-effects models were used for statistical analyses. Subjects carrying the T allele had greater BOLD responses in left basal ganglia, thalamus, supplementary motor area, and primary motor cortex, whose activity was also negatively correlated with reaction time at the task. Moreover, left striatal DAT binding and activity of left supplementary motor area were negatively correlated. The present results suggest that DRD2 genetic variation was associated with focusing of responses in the whole motor network, in which activity of predictable nodes was correlated with reaction time and with striatal pre-synaptic dopamine signaling. Our results in humans may help shed light on genetic risk for neurobiological mechanisms involved in the pathophysiology of disorders with dysregulation of striatal dopamine like Parkinson's disease. Copyright © 2010 Elsevier Inc. All rights reserved.

Rats do not respond differently in the presence of stimuli signaling wheel-running reinforcers of different durations.

Science.gov (United States)

Belke, Terry W

2007-05-01

Rats were exposed to a fixed interval 30 s schedule that produced opportunities to run of equal or unequal durations to assess the effect of differences in duration on responding. Each duration was signaled by a different stimulus. Wheel-running reinforcer duration pairs were 30 s 30 s, 50 s 10 s, and 55 s 5 s. An analysis of median postreinforcement pause duration and mean local lever-pressing rates broken down by previous reinforcer duration and duration of signaled upcoming reinforcer showed that postreinforcement pause duration was affected by the duration of the previous reinforcer but not by the stimulus signaling the duration of the upcoming reinforcer. Local lever-pressing rates were not affected by either previous or upcoming reinforcer duration. In general, the results are consistent with indifference between these durations obtained using a concurrent choice procedure.

Dynamics of response-conflict monitoring and individual differences in response control and behavioral control: an electrophysiological investigation using a stop-signal task.

Science.gov (United States)

Stahl, Jutta; Gibbons, Henning

2007-03-01

The aim of the present study was to investigate the functional significance of error (related) negativity Ne/ERN and individual differences in human action monitoring. A response-conflict model of Ne/ERN should be tested applying a stop-signal paradigm. After a few modifications of Ne/ERN response-conflict theory (Yeung N, Botvinick MM, Cohen JD. The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review 2004:111(4);931-959), strength and time course of response conflict could be modeled as a function of stop-signal delay. In Experiment 1, 35 participants performed a visual two-choice response-time task but tried to withhold the response if an auditory stop signal was presented. Probability of stopping errors was held at 50% using variable delays between visual and auditory stimuli. Experiment 2 (n=10) employed both auditory go and stop signals and confirmed that Ne/ERN effects are due to conflict induced by the auditory stop signal, and not the mere presence or absence of an additional stimulus. As predicted, amplitudes of both the stimulus-locked and response-locked Ne/ERN were largest for non-stopped responses, followed by successfully stopped and go responses. However, independently of response type Ne/ERN also increased with increasing stop-signal delay. Since longer delay invokes stronger response conflict, results specifically support the notion of Ne/ERN reflecting response-conflict monitoring. Furthermore, individual differences related to measures of response control and behavioral control were observed. Both low response control estimated from stop-task performance and high psychometric impulsivity were accompanied by smaller Ne/ERN amplitude on stop trials, suggesting reduced response-conflict monitoring. The present study supported the response-conflict view of Ne/ERN. Furthermore, the observed relationship between impulsivity and Ne/ERN amplitude suggested that individuals with low behavioral

Cluster synchronization transmission of different external signals in discrete uncertain network

Science.gov (United States)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.

Genetically determined measures of striatal D2 signaling predict prefrontal activity during working memory performance.

Science.gov (United States)

Bertolino, Alessandro; Taurisano, Paolo; Pisciotta, Nicola Marco; Blasi, Giuseppe; Fazio, Leonardo; Romano, Raffaella; Gelao, Barbara; Lo Bianco, Luciana; Lozupone, Madia; Di Giorgio, Annabella; Caforio, Grazia; Sambataro, Fabio; Niccoli-Asabella, Artor; Papp, Audrey; Ursini, Gianluca; Sinibaldi, Lorenzo; Popolizio, Teresa; Sadee, Wolfgang; Rubini, Giuseppe

2010-02-22

Variation of the gene coding for D2 receptors (DRD2) has been associated with risk for schizophrenia and with working memory deficits. A functional intronic SNP (rs1076560) predicts relative expression of the two D2 receptors isoforms, D2S (mainly pre-synaptic) and D2L (mainly post-synaptic). However, the effect of functional genetic variation of DRD2 on striatal dopamine D2 signaling and on its correlation with prefrontal activity during working memory in humans is not known. Thirty-seven healthy subjects were genotyped for rs1076560 (G>T) and underwent SPECT with [123I]IBZM (which binds primarily to post-synaptic D2 receptors) and with [123I]FP-CIT (which binds to pre-synaptic dopamine transporters, whose activity and density is also regulated by pre-synaptic D2 receptors), as well as BOLD fMRI during N-Back working memory. Subjects carrying the T allele (previously associated with reduced D2S expression) had striatal reductions of [123I]IBZM and of [123I]FP-CIT binding. DRD2 genotype also differentially predicted the correlation between striatal dopamine D2 signaling (as identified with factor analysis of the two radiotracers) and activity of the prefrontal cortex during working memory as measured with BOLD fMRI, which was positive in GG subjects and negative in GT. Our results demonstrate that this functional SNP within DRD2 predicts striatal binding of the two radiotracers to dopamine transporters and D2 receptors as well as the correlation between striatal D2 signaling with prefrontal cortex activity during performance of a working memory task. These data are consistent with the possibility that the balance of excitatory/inhibitory modulation of striatal neurons may also affect striatal outputs in relationship with prefrontal activity during working memory performance within the cortico-striatal-thalamic-cortical pathway.

Genetically determined measures of striatal D2 signaling predict prefrontal activity during working memory performance.

Directory of Open Access Journals (Sweden)

Alessandro Bertolino

2010-02-01

Full Text Available Variation of the gene coding for D2 receptors (DRD2 has been associated with risk for schizophrenia and with working memory deficits. A functional intronic SNP (rs1076560 predicts relative expression of the two D2 receptors isoforms, D2S (mainly pre-synaptic and D2L (mainly post-synaptic. However, the effect of functional genetic variation of DRD2 on striatal dopamine D2 signaling and on its correlation with prefrontal activity during working memory in humans is not known.Thirty-seven healthy subjects were genotyped for rs1076560 (G>T and underwent SPECT with [123I]IBZM (which binds primarily to post-synaptic D2 receptors and with [123I]FP-CIT (which binds to pre-synaptic dopamine transporters, whose activity and density is also regulated by pre-synaptic D2 receptors, as well as BOLD fMRI during N-Back working memory.Subjects carrying the T allele (previously associated with reduced D2S expression had striatal reductions of [123I]IBZM and of [123I]FP-CIT binding. DRD2 genotype also differentially predicted the correlation between striatal dopamine D2 signaling (as identified with factor analysis of the two radiotracers and activity of the prefrontal cortex during working memory as measured with BOLD fMRI, which was positive in GG subjects and negative in GT.Our results demonstrate that this functional SNP within DRD2 predicts striatal binding of the two radiotracers to dopamine transporters and D2 receptors as well as the correlation between striatal D2 signaling with prefrontal cortex activity during performance of a working memory task. These data are consistent with the possibility that the balance of excitatory/inhibitory modulation of striatal neurons may also affect striatal outputs in relationship with prefrontal activity during working memory performance within the cortico-striatal-thalamic-cortical pathway.

Differences in abundances of cell-signalling proteins in blood reveal novel biomarkers for early detection of clinical Alzheimer's disease.

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Mateus Rocha de Paula

Full Text Available BACKGROUND: In November 2007 a study published in Nature Medicine proposed a simple test based on the abundance of 18 proteins in blood to predict the onset of clinical symptoms of Alzheimer's Disease (AD two to six years before these symptoms manifest. Later, another study, published in PLoS ONE, showed that only five proteins (IL-1, IL-3, EGF, TNF- and G-CSF have overall better prediction accuracy. These classifiers are based on the abundance of 120 proteins. Such values were standardised by a Z-score transformation, which means that their values are relative to the average of all others. METHODOLOGY: The original datasets from the Nature Medicine paper are further studied using methods from combinatorial optimisation and Information Theory. We expand the original dataset by also including all pair-wise differences of z-score values of the original dataset ("metafeatures". Using an exact algorithm to solve the resulting Feature Set problem, used to tackle the feature selection problem, we found signatures that contain either only features, metafeatures or both, and evaluated their predictive performance on the independent test set. CONCLUSIONS: It was possible to show that a specific pattern of cell signalling imbalance in blood plasma has valuable information to distinguish between NDC and AD samples. The obtained signatures were able to predict AD in patients that already had a Mild Cognitive Impairment (MCI with up to 84% of sensitivity, while maintaining also a strong prediction accuracy of 90% on a independent dataset with Non Demented Controls (NDC and AD samples. The novel biomarkers uncovered with this method now confirms ANG-2, IL-11, PDGF-BB, CCL15/MIP-1; and supports the joint measurement of other signalling proteins not previously discussed: GM-CSF, NT-3, IGFBP-2 and VEGF-B.

Simulation and real-time replacement of missing plasma signals for disruption prediction: an implementation with APODIS

International Nuclear Information System (INIS)

Rattá, G A; Vega, J; Murari, A

2014-01-01

So far, the best results for real-time disruption prediction on the Joint European Torus (JET) have been achieved with the Advanced Predictor of Disruptions (APODIS). APODIS is a data-driven system whose latest version has been implemented in JET's real time-data network. It has been designed for the real-time analysis of features (mean and frequency values) corresponding to seven plasma signals in order to foresee upcoming disruptions. In this article, non-linear regression techniques are applied to create (off-line) signal models. The models are able to generate (in real-time) ‘synthetic’ signals. Therefore, these ‘synthetic’ signals can be used to replace the original ones in cases where they are in error or missing. APODIS has been tested under these conditions, emulating real-time operation. The simulation results demonstrate that once a signal in error is replaced by the generated ‘synthetic’ one, APODIS performance is considerably improved. The development of the regression models and the implications of the results are detailed and discussed in this paper. (paper)

A Geometrical-based Vertical Gain Correction for Signal Strength Prediction of Downtilted Base Station Antennas in Urban Areas

DEFF Research Database (Denmark)

Rodriguez, Ignacio; Nguyen, Huan Cong; Sørensen, Troels Bundgaard

2012-01-01

-based extension to standard empirical path loss prediction models can give quite reasonable accuracy in predicting the signal strength from tilted base station antennas in small urban macro-cells. Our evaluation is based on measurements on several sectors in a 2.6 GHz Long Term Evolution (LTE) cellular network......, with electrical antenna downtilt in the range from 0 to 10 degrees, as well as predictions based on ray-tracing and 3D building databases covering the measurement area. Although the calibrated ray-tracing predictions are highly accurate compared with the measured data, the combined LOS/NLOS COST-WI model...

Cortical feedback signals generalise across different spatial frequencies of feedforward inputs.

Science.gov (United States)

Revina, Yulia; Petro, Lucy S; Muckli, Lars

2017-09-22

Visual processing in cortex relies on feedback projections contextualising feedforward information flow. Primary visual cortex (V1) has small receptive fields and processes feedforward information at a fine-grained spatial scale, whereas higher visual areas have larger, spatially invariant receptive fields. Therefore, feedback could provide coarse information about the global scene structure or alternatively recover fine-grained structure by targeting small receptive fields in V1. We tested if feedback signals generalise across different spatial frequencies of feedforward inputs, or if they are tuned to the spatial scale of the visual scene. Using a partial occlusion paradigm, functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) we investigated whether feedback to V1 contains coarse or fine-grained information by manipulating the spatial frequency of the scene surround outside an occluded image portion. We show that feedback transmits both coarse and fine-grained information as it carries information about both low (LSF) and high spatial frequencies (HSF). Further, feedback signals containing LSF information are similar to feedback signals containing HSF information, even without a large overlap in spatial frequency bands of the HSF and LSF scenes. Lastly, we found that feedback carries similar information about the spatial frequency band across different scenes. We conclude that cortical feedback signals contain information which generalises across different spatial frequencies of feedforward inputs. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography.

Science.gov (United States)

Vahabi, Zahra; Amirfattahi, Rasoul; Shayegh, Farzaneh; Ghassemi, Fahimeh

2015-09-01

Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.

[Diagnostic efficiency of decline rate of signal intensity and apparent diffusion coefficient with different b values for differentiating benign and malignant breast lesions on diffusion-weighted 3.0T magnetic resonance imaging].

Science.gov (United States)

Jiang, Jing; Liu, Wanhua; Ye, Yuanyuan; Wang, Rui; Li, Fengfang; Peng, Chengyu

2014-06-17

To investigate the diagnostic efficiency of decline rate of signal intensity and apparent diffusion coefficient with different b values for differentiating benign and malignant breast lesions on diffusion-weighted 3.0 T magnetic resonance imaging. A total of 152 patients with 162 confirmed histopathologically breast lesions (85 malignant and 77 benign) underwent 3.0 T diffusion-weighted magnetic resonance imaging. Four b values (0, 400, 800 and 1 000 s/mm²) were used. The signal intensity and ADC values of breast lesions were measured respectively. The signal intensity decline rate (SIDR) and apparent diffusion coefficient decline rate (ADCDR) were calculated respectively. SIDR = (signal intensity of lesions with low b value-signal intensity of lesions with high b value)/signal intensity of lesions with low b value, ADCDR = (ADC value of lesions with low b value-ADC value of lesions with high b value) /ADC value of lesions with low b value. The independent sample t-test was employed for statistical analyses and the receiver operating characteristic (ROC) curve for evaluating the diagnosis efficiency of SIDR and ADCDR values. Significant differences were observed in SIDR between benign and malignant breast lesions with b values of 0-400, 400-800 and 800-1 000 s/mm². The sensitivities of SIDR for differentiating benign and malignant breast lesions were 61.2%, 68.2% and 67.1%, the specificities 74.0%, 85.7% and 67.5%, the diagnosis accordance rates 67.3%, 76.5% and 67.3%, the positive predictive values 72.2%, 84.1% and 69.5% and the negative predictive values 63.3%, 71.0% and 65.0% respectively. Significant differences were observed in ADCDR between benign and malignant breast lesions with b values of 400-800 s/mm² and 800-1 000 s/mm². The sensitivities of SDR for differentiating benign and malignant breast lesions were 80.0% and 65.9%, the specificities 72.7% and 65.0%, the diagnostic accordance rates 76.5% and 65.4%, the positive predictive values 76.4% and 67

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

Science.gov (United States)

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Protein Sorting Prediction

DEFF Research Database (Denmark)

Nielsen, Henrik

2017-01-01

and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.......Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths...

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution

Science.gov (United States)

Jia, Rui-Sheng; Gong, Yue; Peng, Yan-Jun; Sun, Hong-Mei; Zhang, Xing-Li; Lu, Xin-Ming

2018-05-01

Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follow α-stable distribution reveals that the microseismic signals have obvious pulse characteristics and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the microseismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.

Benchmarking the Algorithms to Detect Seasonal Signals Under Different Noise Conditions

Science.gov (United States)

Klos, A.; Bogusz, J.; Bos, M. S.

2017-12-01

Global Positioning System (GPS) position time series contain seasonal signals. Among the others, annual and semi-annual are the most powerful. Widely, these oscillations are modelled as curves with constant amplitudes, using the Weighted Least-Squares (WLS) algorithm. However, in reality, the seasonal signatures vary over time, as their geophysical causes are not constant. Different algorithms have been already used to cover this time-variability, as Wavelet Decomposition (WD), Singular Spectrum Analysis (SSA), Chebyshev Polynomial (CP) or Kalman Filter (KF). In this research, we employed 376 globally distributed GPS stations which time series contributed to the newest International Terrestrial Reference Frame (ITRF2014). We show that for c.a. 20% of stations the amplitudes of seasonal signal varies over time of more than 1.0 mm. Then, we compare the WD, SSA, CP and KF algorithms for a set of synthetic time series to quantify them under different noise conditions. We show that when variations of seasonal signals are ignored, the power-law character is biased towards flicker noise. The most reliable estimates of the variations were found to be given by SSA and KF. These methods also perform the best for other noise levels while WD, and to a lesser extend also CP, have trouble in separating the seasonal signal from the noise which leads to an underestimation in the spectral index of power-law noise of around 0.1. For real ITRF2014 GPS data we discovered, that SSA and KF are capable to model 49-84% and 77-90% of the variance of the true varying seasonal signals, respectively.

Testing the prediction error difference between two predictors

NARCIS (Netherlands)

van de Wiel, M.A.; Berkhof, J.; van Wieringen, W.N.

2009-01-01

We develop an inference framework for the difference in errors between 2 prediction procedures. The 2 procedures may differ in any aspect and possibly utilize different sets of covariates. We apply training and testing on the same data set, which is accommodated by sample splitting. For each split,

Causality and prediction: differences and points of contact

Directory of Open Access Journals (Sweden)

Luis Carlos Silva Ayçaguer, PhD

2014-09-01

Full Text Available This contribution presents the differences between those variables that might play a causal role in a certain process and those only valuable for predicting the outcome. Some considerations are made about the core intervention of the association and the temporal precedence and biases in both cases, the study of causality and predictive modeling. In that context, several relevant aspects related to the design of the corresponding studies are briefly reviewed and some of the mistakes that are often committed in handling both, causality and prediction, are illustrated.

Influence of dimension box differences and time differences during operations of red box for motorcycles at signalized intersection

Science.gov (United States)

Mulyadi, Agah Muhammad

2017-11-01

Performance of signalized intersection has declined due to a large number of motorcycles. The number of motorcycles reached 98.2 million units and the composition of motorcycles has reached around 81.7% of the total composition of vehicles in Indonesia (AISI, 2017). To solve that problem, the red box for motorcycles are provided at the signalized intersection. Red box for the motorcycle at signalized intersections was developed from the concept of Advance Stop Line (ASL) for bicycles. The Red Box was developed to split the queue between motorcycles and other vehicles when waiting at red light. This paper aims to evaluate the influence of the red box dimension and red time operation differences. The survey was conducted as many as 30 cycles of traffic signals per day. The data were analyzed using software IBM SPSS Statistics 20 by using Analysis of Variance (ANOVA) to obtain p-value (significant). The analysis shows that there are insignificant influences between the occupancy rates to the dimension of Red Box. Furthermore, that there is a significant difference that shows the dependency of only motorcycles in the Red Box Area towards red time operation.

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals.

Science.gov (United States)

Zaylaa, Amira; Oudjemia, Souad; Charara, Jamal; Girault, Jean-Marc

2015-09-01

This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Drosophila Perlecan gene trol regulates multiple signaling pathways in different developmental contexts

Directory of Open Access Journals (Sweden)

Perry Trinity L

2007-11-01

Full Text Available Abstract Background Heparan sulfate proteoglycans modulate signaling by a variety of growth factors. The mammalian proteoglycan Perlecan binds and regulates signaling by Sonic Hedgehog, Fibroblast Growth Factors (FGFs, Vascular Endothelial Growth Factor (VEGF and Platelet Derived Growth Factor (PDGF, among others, in contexts ranging from angiogenesis and cardiovascular development to cancer progression. The Drosophila Perlecan homolog trol has been shown to regulate the activity of Hedgehog and Branchless (an FGF homolog to control the onset of stem cell proliferation in the developing brain during first instar. Here we extend analysis of trol mutant phenotypes to show that trol is required for a variety of developmental events and modulates signaling by multiple growth factors in different situations. Results Different mutations in trol allow developmental progression to varying extents, suggesting that trol is involved in multiple cell-fate and patterning decisions. Analysis of the initiation of neuroblast proliferation at second instar demonstrated that trol regulates this event by modulating signaling by Hedgehog and Branchless, as it does during first instar. Trol protein is distributed over the surface of the larval brain, near the regulated neuroblasts that reside on the cortical surface. Mutations in trol also decrease the number of circulating plasmatocytes. This is likely to be due to decreased expression of pointed, the response gene for VEGF/PDGF signaling that is required for plasmatocyte proliferation. Trol is found on plasmatocytes, where it could regulate VEGF/PDGF signaling. Finally, we show that in second instar brains but not third instar brain lobes and eye discs, mutations in trol affect signaling by Decapentaplegic (a Transforming Growth Factor family member, Wingless (a Wnt growth factor and Hedgehog. Conclusion These studies extend the known functions of the Drosophila Perlecan homolog trol in both developmental and

Signaling and the Education Premium

OpenAIRE

Gregory Kurtzon

2004-01-01

A large portion of the rise in the education premium can be explained by a signaling theory of education which predicts that in the future, increases in the education level of the workforce will actually cause the education premium to rise, simply because different workers are being labeled as “highly educated†. This prediction is supported by past behavior of the high school education premium. It runs counter to the view that increases in the relative supply of high education workers wil...

Hierarchical learning induces two simultaneous, but separable, prediction errors in human basal ganglia.

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Diuk, Carlos; Tsai, Karin; Wallis, Jonathan; Botvinick, Matthew; Niv, Yael

2013-03-27

Studies suggest that dopaminergic neurons report a unitary, global reward prediction error signal. However, learning in complex real-life tasks, in particular tasks that show hierarchical structure, requires multiple prediction errors that may coincide in time. We used functional neuroimaging to measure prediction error signals in humans performing such a hierarchical task involving simultaneous, uncorrelated prediction errors. Analysis of signals in a priori anatomical regions of interest in the ventral striatum and the ventral tegmental area indeed evidenced two simultaneous, but separable, prediction error signals corresponding to the two levels of hierarchy in the task. This result suggests that suitably designed tasks may reveal a more intricate pattern of firing in dopaminergic neurons. Moreover, the need for downstream separation of these signals implies possible limitations on the number of different task levels that we can learn about simultaneously.

Extracting protein dynamics information from overlapped NMR signals using relaxation dispersion difference NMR spectroscopy.

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Konuma, Tsuyoshi; Harada, Erisa; Sugase, Kenji

2015-12-01

Protein dynamics plays important roles in many biological events, such as ligand binding and enzyme reactions. NMR is mostly used for investigating such protein dynamics in a site-specific manner. Recently, NMR has been actively applied to large proteins and intrinsically disordered proteins, which are attractive research targets. However, signal overlap, which is often observed for such proteins, hampers accurate analysis of NMR data. In this study, we have developed a new methodology called relaxation dispersion difference that can extract conformational exchange parameters from overlapped NMR signals measured using relaxation dispersion spectroscopy. In relaxation dispersion measurements, the signal intensities of fluctuating residues vary according to the Carr-Purcell-Meiboon-Gill pulsing interval, whereas those of non-fluctuating residues are constant. Therefore, subtraction of each relaxation dispersion spectrum from that with the highest signal intensities, measured at the shortest pulsing interval, leaves only the signals of the fluctuating residues. This is the principle of the relaxation dispersion difference method. This new method enabled us to extract exchange parameters from overlapped signals of heme oxygenase-1, which is a relatively large protein. The results indicate that the structural flexibility of a kink in the heme-binding site is important for efficient heme binding. Relaxation dispersion difference requires neither selectively labeled samples nor modification of pulse programs; thus it will have wide applications in protein dynamics analysis.

Extracting protein dynamics information from overlapped NMR signals using relaxation dispersion difference NMR spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Konuma, Tsuyoshi [Icahn School of Medicine at Mount Sinai, Department of Structural and Chemical Biology (United States); Harada, Erisa [Suntory Foundation for Life Sciences, Bioorganic Research Institute (Japan); Sugase, Kenji, E-mail: sugase@sunbor.or.jp, E-mail: sugase@moleng.kyoto-u.ac.jp [Kyoto University, Department of Molecular Engineering, Graduate School of Engineering (Japan)

2015-12-15

Protein dynamics plays important roles in many biological events, such as ligand binding and enzyme reactions. NMR is mostly used for investigating such protein dynamics in a site-specific manner. Recently, NMR has been actively applied to large proteins and intrinsically disordered proteins, which are attractive research targets. However, signal overlap, which is often observed for such proteins, hampers accurate analysis of NMR data. In this study, we have developed a new methodology called relaxation dispersion difference that can extract conformational exchange parameters from overlapped NMR signals measured using relaxation dispersion spectroscopy. In relaxation dispersion measurements, the signal intensities of fluctuating residues vary according to the Carr-Purcell-Meiboon-Gill pulsing interval, whereas those of non-fluctuating residues are constant. Therefore, subtraction of each relaxation dispersion spectrum from that with the highest signal intensities, measured at the shortest pulsing interval, leaves only the signals of the fluctuating residues. This is the principle of the relaxation dispersion difference method. This new method enabled us to extract exchange parameters from overlapped signals of heme oxygenase-1, which is a relatively large protein. The results indicate that the structural flexibility of a kink in the heme-binding site is important for efficient heme binding. Relaxation dispersion difference requires neither selectively labeled samples nor modification of pulse programs; thus it will have wide applications in protein dynamics analysis.

Monitoring and predicting cognitive state and performance via physiological correlates of neuronal signals.

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Russo, Michael B; Stetz, Melba C; Thomas, Maria L

2005-07-01

Judgment, decision making, and situational awareness are higher-order mental abilities critically important to operational cognitive performance. Higher-order mental abilities rely on intact functioning of multiple brain regions, including the prefrontal, thalamus, and parietal areas. Real-time monitoring of individuals for cognitive performance capacity via an approach based on sampling multiple neurophysiologic signals and integrating those signals with performance prediction models potentially provides a method of supporting warfighters' and commanders' decision making and other operationally relevant mental processes and is consistent with the goals of augmented cognition. Cognitive neurophysiological assessments that directly measure brain function and subsequent cognition include positron emission tomography, functional magnetic resonance imaging, mass spectroscopy, near-infrared spectroscopy, magnetoencephalography, and electroencephalography (EEG); however, most direct measures are not practical to use in operational environments. More practical, albeit indirect measures that are generated by, but removed from the actual neural sources, are movement activity, oculometrics, heart rate, and voice stress signals. The goal of the papers in this section is to describe advances in selected direct and indirect cognitive neurophysiologic monitoring techniques as applied for the ultimate purpose of preventing operational performance failures. These papers present data acquired in a wide variety of environments, including laboratory, simulator, and clinical arenas. The papers discuss cognitive neurophysiologic measures such as digital signal processing wrist-mounted actigraphy; oculometrics including blinks, saccadic eye movements, pupillary movements, the pupil light reflex; and high-frequency EEG. These neurophysiological indices are related to cognitive performance as measured through standard test batteries and simulators with conditions including sleep loss

Retroactive signaling in short signaling pathways.

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Jacques-Alexandre Sepulchre

Full Text Available In biochemical signaling pathways without explicit feedback connections, the core signal transduction is usually described as a one-way communication, going from upstream to downstream in a feedforward chain or network of covalent modification cycles. In this paper we explore the possibility of a new type of signaling called retroactive signaling, offered by the recently demonstrated property of retroactivity in signaling cascades. The possibility of retroactive signaling is analysed in the simplest case of the stationary states of a bicyclic cascade of signaling cycles. In this case, we work out the conditions for which variables of the upstream cycle are affected by a change of the total amount of protein in the downstream cycle, or by a variation of the phosphatase deactivating the same protein. Particularly, we predict the characteristic ranges of the downstream protein, or of the downstream phosphatase, for which a retroactive effect can be observed on the upstream cycle variables. Next, we extend the possibility of retroactive signaling in short but nonlinear signaling pathways involving a few covalent modification cycles.

Prediction of preterm deliveries from EHG signals using machine learning.

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Paul Fergus

Full Text Available There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography, could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term. The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial

Differential Dopamine Release Dynamics in the Nucleus Accumbens Core and Shell Reveal Complementary Signals for Error Prediction and Incentive Motivation.

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Saddoris, Michael P; Cacciapaglia, Fabio; Wightman, R Mark; Carelli, Regina M

2015-08-19

Mesolimbic dopamine (DA) is phasically released during appetitive behaviors, though there is substantive disagreement about the specific purpose of these DA signals. For example, prediction error (PE) models suggest a role of learning, while incentive salience (IS) models argue that the DA signal imbues stimuli with value and thereby stimulates motivated behavior. However, within the nucleus accumbens (NAc) patterns of DA release can strikingly differ between subregions, and as such, it is possible that these patterns differentially contribute to aspects of PE and IS. To assess this, we measured DA release in subregions of the NAc during a behavioral task that spatiotemporally separated sequential goal-directed stimuli. Electrochemical methods were used to measure subsecond NAc dopamine release in the core and shell during a well learned instrumental chain schedule in which rats were trained to press one lever (seeking; SL) to gain access to a second lever (taking; TL) linked with food delivery, and again during extinction. In the core, phasic DA release was greatest following initial SL presentation, but minimal for the subsequent TL and reward events. In contrast, phasic shell DA showed robust release at all task events. Signaling decreased between the beginning and end of sessions in the shell, but not core. During extinction, peak DA release in the core showed a graded decrease for the SL and pauses in release during omitted expected rewards, whereas shell DA release decreased predominantly during the TL. These release dynamics suggest parallel DA signals capable of supporting distinct theories of appetitive behavior. Dopamine signaling in the brain is important for a variety of cognitive functions, such as learning and motivation. Typically, it is assumed that a single dopamine signal is sufficient to support these cognitive functions, though competing theories disagree on how dopamine contributes to reward-based behaviors. Here, we have found that real

Laminar Differences in Associative Memory Signals in Monkey Perirhinal Cortex.

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Vogels, Rufin

2016-10-19

New research published in Neuron describes assignment of cortical layer to single neurons recorded in awake monkeys. Applying the procedure to perirhinal cortex, Koyano et al. (2016) found marked and unsuspected differences among layers in the coding of associative memory signals. Copyright © 2016. Published by Elsevier Inc.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

Science.gov (United States)

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Losing the Warning Signal: Drought Compromises the Cross-Talk of Signaling Molecules in Quercus ilex Exposed to Ozone

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Lorenzo Cotrozzi

2017-06-01

Full Text Available Understanding the interactions between drought and acute ozone (O3 stress in terms of signaling molecules and cell death would improve the predictions of plant responses to climate change. The aim was to investigate whether drought stress influences the responses of plants to acute episodes of O3 exposure. In this study, the behavior of 84 Mediterranean evergreen Quercus ilex plants was evaluated in terms of cross-talk responses among signaling molecules. Half of the sample was subjected to drought (20% of the effective daily evapotranspiration, for 15 days and was later exposed to an acute O3 exposure (200 nL L-1 for 5 h. First, our results indicate that in well-water conditions, O3 induced a signaling pathway specific to O3-sensitive behavior. Second, different trends and consequently different roles of phytohormones and signaling molecules (ethylene, ET; abscisic acid, ABA; salycilic acid, SA and jasmonic acid, JA were observed in relation to water stress and O3. A spatial and functional correlation between these signaling molecules was observed in modulating O3-induced responses in well-watered plants. In contrast, in drought-stressed plants, these compounds were not involved either in O3-induced signaling mechanisms or in leaf senescence (a response observed in water-stressed plants before the O3-exposure. Third, these differences were ascribable to the fact that in drought conditions, most defense processes induced by O3 were compromised and/or altered. Our results highlight how Q. ilex plants suffering from water deprivation respond differently to an acute O3 episode compared to well-watered plants, and suggest new effect to be considered in plant responses to environmental changes. This poses the serious question as to whether or not multiple high-magnitude O3 events (as predicted can change these cross-talk responses, thus opening it up possible further investigations.

A Signal Detection Approach in a Multiple Cohort Study: Different Admission Tools Uniquely Select Different Successful Students

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Linda van Ooijen-van der Linden

2018-05-01

Full Text Available Using multiple admission tools in university admission procedures is common practice. This is particularly useful if different admission tools uniquely select different subgroups of students who will be successful in university programs. A signal-detection approach was used to investigate the accuracy of Secondary School grade point average (SSGPA, an admission test score (ACS, and a non-cognitive score (NCS in uniquely selecting successful students. This was done for three consecutive first year cohorts of a broad psychology program. Each applicant's score on SSGPA, ACS, or NCS alone—and on seven combinations of these scores, all considered separate “admission tools”—was compared at two different (medium and high cut-off scores (criterion levels. Each of the tools selected successful students who were not selected by any of the other tools. Both sensitivity and specificity were enhanced by implementing multiple tools. The signal-detection approach distinctively provided useful information for decisions on admission instruments and cut-off scores.

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

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Daluwatte, C; Johannesen, L; Galeotti, L; Vicente, J; Strauss, D G; Scully, C G

2016-08-01

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.

A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

DEFF Research Database (Denmark)

Nielsen, Henrik; Engelbrecht, Jacob; Brunak, Søren

1997-01-01

We have developed a new method for the identication of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs signicantly better than previous prediction schemes, and can easily be applied to genome...

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Signalign: An Ontology of DNA as Signal for Comparative Gene Structure Prediction Using Information-Coding-and-Processing Techniques.

Science.gov (United States)

Yu, Ning; Guo, Xuan; Gu, Feng; Pan, Yi

2016-03-01

Conventional character-analysis-based techniques in genome analysis manifest three main shortcomings-inefficiency, inflexibility, and incompatibility. In our previous research, a general framework, called DNA As X was proposed for character-analysis-free techniques to overcome these shortcomings, where X is the intermediates, such as digit, code, signal, vector, tree, graph network, and so on. In this paper, we further implement an ontology of DNA As Signal, by designing a tool named Signalign for comparative gene structure analysis, in which DNA sequences are converted into signal series, processed by modified method of dynamic time warping and measured by signal-to-noise ratio (SNR). The ontology of DNA As Signal integrates the principles and concepts of other disciplines including information coding theory and signal processing into sequence analysis and processing. Comparing with conventional character-analysis-based methods, Signalign can not only have the equivalent or superior performance, but also enrich the tools and the knowledge library of computational biology by extending the domain from character/string to diverse areas. The evaluation results validate the success of the character-analysis-free technique for improved performances in comparative gene structure prediction.

Generation of earthquake signals

International Nuclear Information System (INIS)

Kjell, G.

1994-01-01

Seismic verification can be performed either as a full scale test on a shaker table or as numerical calculations. In both cases it is necessary to have an earthquake acceleration time history. This report describes generation of such time histories by filtering white noise. Analogue and digital filtering methods are compared. Different methods of predicting the response spectrum of a white noise signal filtered by a band-pass filter are discussed. Prediction of both the average response level and the statistical variation around this level are considered. Examples with both the IEEE 301 standard response spectrum and a ground spectrum suggested for Swedish nuclear power stations are included in the report

Simulation of Silicon Photomultiplier Signals

Science.gov (United States)

Seifert, Stefan; van Dam, Herman T.; Huizenga, Jan; Vinke, Ruud; Dendooven, Peter; Lohner, Herbert; Schaart, Dennis R.

2009-12-01

In a silicon photomultiplier (SiPM), also referred to as multi-pixel photon counter (MPPC), many Geiger-mode avalanche photodiodes (GM-APDs) are connected in parallel so as to combine the photon counting capabilities of each of these so-called microcells into a proportional light sensor. The discharge of a single microcell is relatively well understood and electronic models exist to simulate this process. In this paper we introduce an extended model that is able to simulate the simultaneous discharge of multiple cells. This model is used to predict the SiPM signal in response to fast light pulses as a function of the number of fired cells, taking into account the influence of the input impedance of the SiPM preamplifier. The model predicts that the electronic signal is not proportional to the number of fired cells if the preamplifier input impedance is not zero. This effect becomes more important for SiPMs with lower parasitic capacitance (which otherwise is a favorable property). The model is validated by comparing its predictions to experimental data obtained with two different SiPMs (Hamamatsu S10362-11-25u and Hamamatsu S10362-33-25c) illuminated with ps laser pulses. The experimental results are in good agreement with the model predictions.

Endocrinology of Species Differences in Sexually Dichromatic Signals

Science.gov (United States)

Diana K. Hews; Vanessa S. Quinn

2003-01-01

Many animals have conspicuous social signals. Often these signals are expressed in one sex and function in the context of mate choice, intrasexual competition, or both (Andersson 1994; Bradbury and Vehrencamp 1998). A more complete understanding of sex-specific signals will come from integrative studies within a phylogenetic context (Ryan, Autumn, and Wake 1998)....

Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

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Irina Popova

2013-08-01

Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.

Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards

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Cadena, Viviana; Porter, Warren P.; Kearney, Michael R.

2016-01-01

Many terrestrial ectotherms are capable of rapid colour change, yet it is unclear how these animals accommodate the multiple functions of colour, particularly camouflage, communication and thermoregulation, especially when functions require very different colours. Thermal benefits of colour change depend on an animal's absorptance of solar energy in both UV–visible (300–700 nm) and near-infrared (NIR; 700–2600 nm) wavelengths, yet colour research has focused almost exclusively on the former. Here, we show that wild-caught bearded dragon lizards (Pogona vitticeps) exhibit substantial UV–visible and NIR skin reflectance change in response to temperature for dorsal but not ventral (throat and upper chest) body regions. By contrast, lizards showed the greatest temperature-independent colour change on the beard and upper chest during social interactions and as a result of circadian colour change. Biophysical simulations of heat transfer predicted that the maximum temperature-dependent change in dorsal reflectivity could reduce the time taken to reach active body temperature by an average of 22 min per active day, saving 85 h of basking time throughout the activity season. Our results confirm that colour change may serve a thermoregulatory function, and competing thermoregulation and signalling requirements may be met by partitioning colour change to different body regions in different circumstances.

Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

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Paul eChorley

2011-05-01

Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

Optimization of a neural network model for signal-to-background prediction in gamma-ray spectrometry

International Nuclear Information System (INIS)

Dragovic, S.; Onjia, A. . E-mail address of corresponding author: sdragovic@inep.co.yu; Dragovic, S.)

2005-01-01

The artificial neural network (ANN) model was optimized for the prediction of signal-to-background (SBR) ratio as a function of the measurement time in gamma-ray spectrometry. The network parameters: learning rate (α), momentum (μ), number of epochs (E) and number of nodes in hidden layer (N) were optimized simultaneously employing variable-size simplex method. The most accurate model with the root mean square (RMS) error of 0.073 was obtained using ANN with online backpropagation randomized (OBPR) algorithm with α = 0.27, μ 0.36, E = 14800 and N = 9. Most of the predicted and experimental SBR values for the eight radionuclides ( 226 Ra, 214 Bi, 235 U, 40 K, 232 Th, 134 Cs, 137 Cs and 7 Be), studied in this work, reasonably agreed to within 15 %, which was satisfactory accuracy. (author)

Signal peptides and protein localization prediction

DEFF Research Database (Denmark)

Nielsen, Henrik

2005-01-01

In 1999, the Nobel prize in Physiology or Medicine was awarded to Gunther Blobel “for the discovery that proteins have intrinsic signals that govern their transport and localization in the cell”. Since the subcellular localization of a protein is an important clue to its function, the characteriz...

Learning the lipid language of plant signalling.

NARCIS (Netherlands)

van Leeuwen, W.; Okresz, L.; Bogre, L.; Munnik, T.

2004-01-01

Plant cells respond to different biotic and abiotic stresses by producing various uncommon phospholipids that are believed to play key roles in cell signalling. We can predict how they work because animal and yeast proteins have been shown to have specific lipid-binding domains, which act as docking

Characterisation of eddy current signals using different types of artificial neural networks

International Nuclear Information System (INIS)

Shyamsunder, M.T.; Rajagopalan, C.; Jayakumar, T.; Kalyanasundaram, P.; Baldev Raj; Ray, K.K.

1996-01-01

Eddy current testing is one of the important techniques in nondestructive testing. Automated characterisation of eddy current signals (ECS), either in the form of lissajous patterns (figure-of-eight) or individual voltage vs. time signals is an area of growing interest. This is particularly relevant in environments where the signal-to-noise ratio (SNR) of ECS are very poor. Intelligent, timely and precise interpretation of resulting data, is the key for improving the efficiency of NDT and E. A comprehensive study has been undertaken by the authors for the characterisation of ECS having poor SNR, using three types of artificial neural networks (ANNs). The types of ANNs used in this study are [a] the error-back propagation model, [b] the binary Hopfield model and [c] the Kohonen's self-organising maps model. Eddy current signals, acquired from different types of defects such as holes and notches on stainless steel type 316 sheets were used in this study. (author)

Search for QGP signals at AGS with a TPC spectrometer, and comparison of our event generator predictions for plasma model and cascade interactions

International Nuclear Information System (INIS)

Lindenbaum, S.J.; Foley, K.J.; Eiseman, S.E.

1988-01-01

We have developed and successfully tested a TPC Magnetic Spectrometer to search for QGP signals produced by ion beams at AGS. We also developed a cascade and plasma event generator the predictions of which are used to illustrate how our technique can detect possible plasma signals. 4 refs., 6 figs., 1 tab

Different cAMP sources are critically involved in G protein-coupled receptor CRHR1 signaling.

Science.gov (United States)

Inda, Carolina; Dos Santos Claro, Paula A; Bonfiglio, Juan J; Senin, Sergio A; Maccarrone, Giuseppina; Turck, Christoph W; Silberstein, Susana

2016-07-18

Corticotropin-releasing hormone receptor 1 (CRHR1) activates G protein-dependent and internalization-dependent signaling mechanisms. Here, we report that the cyclic AMP (cAMP) response of CRHR1 in physiologically relevant scenarios engages separate cAMP sources, involving the atypical soluble adenylyl cyclase (sAC) in addition to transmembrane adenylyl cyclases (tmACs). cAMP produced by tmACs and sAC is required for the acute phase of extracellular signal regulated kinase 1/2 activation triggered by CRH-stimulated CRHR1, but only sAC activity is essential for the sustained internalization-dependent phase. Thus, different cAMP sources are involved in different signaling mechanisms. Examination of the cAMP response revealed that CRH-activated CRHR1 generates cAMP after endocytosis. Characterizing CRHR1 signaling uncovered a specific link between CRH-activated CRHR1, sAC, and endosome-based signaling. We provide evidence of sAC being involved in an endocytosis-dependent cAMP response, strengthening the emerging model of GPCR signaling in which the cAMP response does not occur exclusively at the plasma membrane and introducing the notion of sAC as an alternative source of cAMP. © 2016 Inda et al.

Magnetic anomaly depth and structural index estimation using different height analytic signals data

Science.gov (United States)

Zhou, Shuai; Huang, Danian; Su, Chao

2016-09-01

This paper proposes a new semi-automatic inversion method for magnetic anomaly data interpretation that uses the combination of analytic signals of the anomaly at different heights to determine the depth and the structural index N of the sources. The new method utilizes analytic signals of the original anomaly at different height to effectively suppress the noise contained in the anomaly. Compared with the other high-order derivative calculation methods based on analytic signals, our method only computes first-order derivatives of the anomaly, which can be used to obtain more stable and accurate results. Tests on synthetic noise-free and noise-corrupted magnetic data indicate that the new method can estimate the depth and N efficiently. The technique is applied to a real measured magnetic anomaly in Southern Illinois caused by a known dike, and the result is in agreement with the drilling information and inversion results within acceptable calculation error.

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

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Abubakar M. Miyim

2014-11-01

Full Text Available The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS/Access Points (APs in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency.

Design and implementation of a multiband digital filter using FPGA to extract the ECG signal in the presence of different interference signals.

Science.gov (United States)

Aboutabikh, Kamal; Aboukerdah, Nader

2015-07-01

In this paper, we propose a practical way to synthesize and filter an ECG signal in the presence of four types of interference signals: (1) those arising from power networks with a fundamental frequency of 50Hz, (2) those arising from respiration, having a frequency range from 0.05 to 0.5Hz, (3) muscle signals with a frequency of 25Hz, and (4) white noise present within the ECG signal band. This was done by implementing a multiband digital filter (seven bands) of type FIR Multiband Least Squares using a digital programmable device (Cyclone II EP2C70F896C6 FPGA, Altera), which was placed on an education and development board (DE2-70, Terasic). This filter was designed using the VHDL language in the Quartus II 9.1 design environment. The proposed method depends on Direct Digital Frequency Synthesizers (DDFS) designed to synthesize the ECG signal and various interference signals. So that the synthetic ECG specifications would be closer to actual ECG signals after filtering, we designed in a single multiband digital filter instead of using three separate digital filters LPF, HPF, BSF. Thus all interference signals were removed with a single digital filter. The multiband digital filter results were studied using a digital oscilloscope to characterize input and output signals in the presence of differing sinusoidal interference signals and white noise. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the phase space structure of IP3 induced Ca2+ signalling and concepts for predictive modeling

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Falcke, Martin; Moein, Mahsa; TilÅ«naitÄ--, Agne; Thul, Rüdiger; Skupin, Alexander

2018-04-01

The correspondence between mathematical structures and experimental systems is the basis of the generalizability of results found with specific systems and is the basis of the predictive power of theoretical physics. While physicists have confidence in this correspondence, it is less recognized in cellular biophysics. On the one hand, the complex organization of cellular dynamics involving a plethora of interacting molecules and the basic observation of cell variability seem to question its possibility. The practical difficulties of deriving the equations describing cellular behaviour from first principles support these doubts. On the other hand, ignoring such a correspondence would severely limit the possibility of predictive quantitative theory in biophysics. Additionally, the existence of functional modules (like pathways) across cell types suggests also the existence of mathematical structures with comparable universality. Only a few cellular systems have been sufficiently investigated in a variety of cell types to follow up these basic questions. IP3 induced Ca2+signalling is one of them, and the mathematical structure corresponding to it is subject of ongoing discussion. We review the system's general properties observed in a variety of cell types. They are captured by a reaction diffusion system. We discuss the phase space structure of its local dynamics. The spiking regime corresponds to noisy excitability. Models focussing on different aspects can be derived starting from this phase space structure. We discuss how the initial assumptions on the set of stochastic variables and phase space structure shape the predictions of parameter dependencies of the mathematical models resulting from the derivation.

Death and Survival in Streptococcus mutans: Differing Outcomes of a Quorum-Sensing Signalling Peptide

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Vincent eLeung

2015-10-01

Full Text Available Bacteria are considered ‘social’ organisms able to communicate with one another using small hormone-like molecules (pheromones in a process called quorum-sensing. These signalling molecules increase in concentration as a function of bacterial cell density. For most human pathogens, quorum-sensing is critical for virulence and biofilm formation, and the opportunity to interfere with bacterial quorum-sensing could provide a sophisticated means for manipulating the composition of pathogenic biofilms, and possibly eradicating the infection. Streptococcus mutans is a well-characterized resident of the dental plaque biofilm, and is the major pathogen of dental caries (tooth decay. In S. mutans, its CSP quorum-sensing signalling peptide does not act as a classical quorum-sensing signal by accumulating passively in proportion to cell density. In fact, particular stresses such as those encountered in the oral cavity, induces the production of the CSP pheromone, suggesting that the pheromone most probably functions as a stress-inducible alarmone by triggering the signalling to the bacterial population to initiate an adaptive response that results in different phenotypic outcomes. This mini-review discusses two different CSP-induced phenotypes, bacterial ‘suicide’ and dormancy, and the underlying mechanisms by which S. mutans utilizes the same quorum-sensing signalling peptide to regulate two opposite phenotypes.

An MEG signature corresponding to an axiomatic model of reward prediction error.

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Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

Causal inference and temporal predictions in audiovisual perception of speech and music.

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Noppeney, Uta; Lee, Hwee Ling

2018-03-31

To form a coherent percept of the environment, the brain must integrate sensory signals emanating from a common source but segregate those from different sources. Temporal regularities are prominent cues for multisensory integration, particularly for speech and music perception. In line with models of predictive coding, we suggest that the brain adapts an internal model to the statistical regularities in its environment. This internal model enables cross-sensory and sensorimotor temporal predictions as a mechanism to arbitrate between integration and segregation of signals from different senses. © 2018 New York Academy of Sciences.

Auditory Warnings, Signal-Referent Relations, and Natural Indicators: Re-Thinking Theory and Application

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Petocz, Agnes; Keller, Peter E.; Stevens, Catherine J.

2008-01-01

In auditory warning design the idea of the strength of the association between sound and referent has been pivotal. Research has proceeded via constructing classification systems of signal-referent associations and then testing predictions about ease of learning of different levels of signal-referent relation strength across and within different…

TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

International Nuclear Information System (INIS)

Nasehi Tehrani, J; Wang, J; McEwan, A

2016-01-01

Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

TH-CD-207A-05: Lung Surface Deformation Vector Fields Prediction by Monitoring Respiratory Surrogate Signals

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Nasehi Tehrani, J; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States); McEwan, A [The University of Sydney, Sydney, New South Wales (Australia)

2016-06-15

Purpose: In this study, we developed and evaluated a method for predicting lung surface deformation vector fields (SDVFs) based on surrogate signals such as chest and abdomen motion at selected locations and spirometry measurements. Methods: A Patient-specific 3D triangular surface mesh of the lung region at end-expiration (EE) phase was obtained by threshold-based segmentation method. For each patient, a spirometer recorded the flow volume changes of the lungs; and 192 selected points at a regular spacing of 2cm X 2cm matrix points over a total area of 34cm X 24cm on the surface of chest and abdomen was used to detect chest wall motions. Preprocessing techniques such as QR factorization with column pivoting (QRCP) were employed to remove redundant observations of the chest and abdominal area. To create a statistical model between the lung surface and the corresponding surrogate signals, we developed a predictive model based on canonical ridge regression (CRR). Two unique weighting vectors were selected for each vertex on the surface of the lung, and they were optimized during the training process using the all other phases of 4D-CT except the end-inspiration (EI) phase. These parameters were employed to predict the vertices locations of a testing data set, which was the EI phase of 4D-CT. Results: For ten lung cancer patients, the deformation vector field of each vertex of lung surface mesh was estimated from the external motion at selected positions on the chest wall surface plus spirometry measurements. The average estimation of 98th percentile of error was less than 1 mm (AP= 0.85, RL= 0.61, and SI= 0.82). Conclusion: The developed predictive model provides a non-invasive approach to derive lung boundary condition. Together with personalized biomechanical respiration modelling, the proposed model can be used to derive the lung tumor motion during radiation therapy accurately from non-invasive measurements.

PKA catalytic subunit compartmentation regulates contractile and hypertrophic responses to β-adrenergic signaling

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Yang, Jason H.; Polanowska-Grabowska, Renata K.; Smith, Jeffrey S.; Shields, Charles W.; Saucerman, Jeffrey J.

2014-01-01

β-adrenergic signaling is spatiotemporally heterogeneous in the cardiac myocyte, conferring exquisite control to sympathetic stimulation. Such heterogeneity drives the formation of protein kinase A (PKA) signaling microdomains, which regulate Ca2+ handling and contractility. Here, we test the hypothesis that the nucleus independently comprises a PKA signaling microdomain regulating myocyte hypertrophy. Spatially-targeted FRET reporters for PKA activity identified slower PKA activation and lower isoproterenol sensitivity in the nucleus (t50 = 10.60±0.68 min; EC50 = 89.00 nmol/L) than in the cytosol (t50 = 3.71±0.25 min; EC50 = 1.22 nmol/L). These differences were not explained by cAMP or AKAP-based compartmentation. A computational model of cytosolic and nuclear PKA activity was developed and predicted that differences in nuclear PKA dynamics and magnitude are regulated by slow PKA catalytic subunit diffusion, while differences in isoproterenol sensitivity are regulated by nuclear expression of protein kinase inhibitor (PKI). These were validated by FRET and immunofluorescence. The model also predicted differential phosphorylation of PKA substrates regulating cell contractility and hypertrophy. Ca2+ and cell hypertrophy measurements validated these predictions and identified higher isoproterenol sensitivity for contractile enhancements (EC50 = 1.84 nmol/L) over cell hypertrophy (EC50 = 85.88 nmol/L). Over-expression of spatially targeted PKA catalytic subunit to the cytosol or nucleus enhanced contractile and hypertrophic responses, respectively. We conclude that restricted PKA catalytic subunit diffusion is an important PKA compartmentation mechanism and the nucleus comprises a novel PKA signaling microdomain, insulating hypertrophic from contractile β-adrenergic signaling responses. PMID:24225179

Impact of Different Active-Speech-Ratios on PESQ’s Predictions in Case of Independent and Dependent Losses (in Presence of Receiver-Side Comfort-Noise

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P. Pocta

2010-04-01

Full Text Available This paper deals with the investigation of PESQ’s behavior under independent and dependent loss conditions from an Active-Speech-Ratio perspective in presence of receiver-side comfort-noise. This reference signal characteristic is defined very broadly by ITU-T Recommendation P.862.3. That is the reason to investigate an impact of this characteristic on speech quality prediction more in-depth. We assess the variability of PESQ’s predictions with respect to Active-Speech-Ratios and loss conditions, as well as their accuracy, by comparing the predictions with subjective assessments. Our results show that an increase in amount of speech in the reference signal (expressed by the Active-Speech-Ratio characteristic may result in an increase of the reference signal sensitivity to packet loss change. Interestingly, we have found two additional effects in this investigated case. The use of higher Active-Speech-Ratios may lead to negative shifting effect in MOS domain and also PESQ’s predictions accuracy declining. Predictions accuracy could be improved by higher packet losses.

The analysis of transesophageal oxygen saturation photoplethysmography from different signal sources.

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Mou, Ling; Gong, Quan; Wei, Wei; Gao, Bo

2013-06-01

The photoplethysmography (PPG) signals detected by transesophageal oximetry sensor toward aorta arch (AA), descending aorta (DA), and left ventricle (LV) under the guidance of transesophageal echocardiography (TEE) were investigated, and the effects of filter application on PPG signals were evaluated. Eleven cardiac surgical patients were involved. After anesthesia was induced, the TEE probe with a modified pulse oximetry sensor was inserted. Under the guidance of TEE, the AA PPG, DA PPG and LV PPG were detected respectively when ventilator was on and off. The mean alternating current (AC) amplitudes and direct current (DC) values of original and filtered PPG signals were measured. The ratio of AC and DC value (AC/DC) and ventilation-induced AC variations were calculated. Satisfactory PPG waveforms were obtained in all patients under the guidance of TEE. The AC amplitude in LV PPG was significant larger than in AA and DA PPG, and both AC/DC and ventilation-induced AC variation in LV PPG were significantly higher than in AA PPG or DA PPG either. There were no significant differences of AC amplitude between filtered and ventilation off PPG signals. The AC amplitudes and AC/DC toward LV are significantly higher than transesophageal oximeter toward AA or DA, and the effect of mechanical ventilation on transesophageal PPG can be obviously reduced by filtering techniques.

Dopamine D1 signaling organizes network dynamics underlying working memory.

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Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

2016-06-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

Regional differences in prediction models of lung function in Germany

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Schäper Christoph

2010-04-01

Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.

A robust algorithm to solve the signal setting problem considering different traffic assignment approaches

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Adacher Ludovica

2017-12-01

Full Text Available In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM, particle swarm optimization (PSO and the genetic algorithm (GA are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA. Numerical experiments on a real test network are reported.

Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

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Pilge, Stefanie; Kreuzer, Matthias; Karatchiviev, Veliko; Kochs, Eberhard F; Malcharek, Michael; Schneider, Gerhard

2015-05-01

It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. Klinikum rechts der Isar, Technische Universität München, Munich. Forty adult patients undergoing elective surgery under general anaesthesia. Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high

Hydrogen Exchange Differences between Chemoreceptor Signaling Complexes Localize to Functionally Important Subdomains

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2015-01-01

The goal of understanding mechanisms of transmembrane signaling, one of many key life processes mediated by membrane proteins, has motivated numerous studies of bacterial chemotaxis receptors. Ligand binding to the receptor causes a piston motion of an α helix in the periplasmic and transmembrane domains, but it is unclear how the signal is then propagated through the cytoplasmic domain to control the activity of the associated kinase CheA. Recent proposals suggest that signaling in the cytoplasmic domain involves opposing changes in dynamics in different subdomains. However, it has been difficult to measure dynamics within the functional system, consisting of extended arrays of receptor complexes with two other proteins, CheA and CheW. We have combined hydrogen exchange mass spectrometry with vesicle template assembly of functional complexes of the receptor cytoplasmic domain to reveal that there are significant signaling-associated changes in exchange, and these changes localize to key regions of the receptor involved in the excitation and adaptation responses. The methylation subdomain exhibits complex changes that include slower hydrogen exchange in complexes in a kinase-activating state, which may be partially consistent with proposals that this subdomain is stabilized in this state. The signaling subdomain exhibits significant protection from hydrogen exchange in complexes in a kinase-activating state, suggesting a tighter and/or larger interaction interface with CheA and CheW in this state. These first measurements of the stability of protein subdomains within functional signaling complexes demonstrate the promise of this approach for measuring functionally important protein dynamics within the various physiologically relevant states of multiprotein complexes. PMID:25420045

Predicting climate-induced range shifts: model differences and model reliability.

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Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Corticospinal signals recorded with MEAs can predict the volitional forearm forces in rats.

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Guo, Yi; Mesut, Sahin; Foulds, Richard A; Adamovich, Sergei V

2013-01-01

We set out to investigate if volitional components in the descending tracts of the spinal cord white matter can be accessed with multi-electrode array (MEA) recording technique. Rats were trained to press a lever connected to a haptic device with force feedback to receive sugar pellets. A flexible-substrate multi-electrode array was chronically implanted into the dorsal column of the cervical spinal cord. Field potentials and multi-unit activities were recorded from the descending axons of the corticospinal tract while the rat performed a lever pressing task. Forelimb forces, recorded with the sensor attached to the lever, were reconstructed using the hand position data and the neural signals through multiple trials over three weeks. The regression coefficients found from the trial set were cross-validated on the other trials recorded on same day. Approximately 30 trials of at least 2 seconds were required for accurate model estimation. The maximum correlation coefficient between the actual and predicted force was 0.7 in the test set. Positional information and its interaction with neural signals improved the correlation coefficient by 0.1 to 0.15. These results suggest that the volitional information contained in the corticospinal tract can be extracted with multi-channel neural recordings made with parenchymal electrodes.

Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test.

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Ring, Patrick; Neyse, Levent; David-Barett, Tamas; Schmidt, Ulrich

2016-01-01

This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT) and whether they differ by gender. After participants completed the CRT, they predicted their own (i), the other participants' (ii), men's (iii), and women's (iv) number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women's predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one's ability relative to same and opposite sex peers.

Intra-arterial high signals on arterial spin labeling perfusion images predict the occluded internal carotid artery segment

International Nuclear Information System (INIS)

Sogabe, Shu; Satomi, Junichiro; Tada, Yoshiteru; Kanematsu, Yasuhisa; Kuwayama, Kazuyuki; Yagi, Kenji; Yoshioka, Shotaro; Mizobuchi, Yoshifumi; Mure, Hideo; Yamaguchi, Izumi; Kitazato, Keiko T.; Nagahiro, Shinji; Abe, Takashi; Harada, Masafumi; Yamamoto, Nobuaki; Kaji, Ryuji

2017-01-01

Arterial spin labeling (ASL) involves perfusion imaging using the inverted magnetization of arterial water. If the arterial arrival times are longer than the post-labeling delay, labeled spins are visible on ASL images as bright, high intra-arterial signals (IASs); such signals were found within occluded vessels of patients with acute ischemic stroke. The identification of the occluded segment in the internal carotid artery (ICA) is crucial for endovascular treatment. We tested our hypothesis that high IASs on ASL images can predict the occluded segment. Our study included 13 patients with acute ICA occlusion who had undergone angiographic and ASL studies within 48 h of onset. We retrospectively identified the high IAS on ASL images and angiograms and recorded the occluded segment and the number of high IAS-positive slices on ASL images. The ICA segments were classified as cervical (C1), petrous (C2), cavernous (C3), and supraclinoid (C4). Of seven patients with intracranial ICA occlusion, five demonstrated high IASs at C1-C2, suggesting that high IASs could identify stagnant flow proximal to the occluded segment. Among six patients with extracranial ICA occlusion, five presented with high IASs at C3-C4, suggesting that signals could identify the collateral flow via the ophthalmic artery. None had high IASs at C1-C2. The mean number of high IAS-positive slices was significantly higher in patients with intra- than extracranial ICA occlusion. High IASs on ASL images can identify slow stagnant and collateral flow through the ophthalmic artery in patients with acute ICA occlusion and help to predict the occlusion site. (orig.)

Intra-arterial high signals on arterial spin labeling perfusion images predict the occluded internal carotid artery segment

Energy Technology Data Exchange (ETDEWEB)

Sogabe, Shu; Satomi, Junichiro; Tada, Yoshiteru; Kanematsu, Yasuhisa; Kuwayama, Kazuyuki; Yagi, Kenji; Yoshioka, Shotaro; Mizobuchi, Yoshifumi; Mure, Hideo; Yamaguchi, Izumi; Kitazato, Keiko T.; Nagahiro, Shinji [Tokushima University Graduate School, Department of Neurosurgery, Tokushima (Japan); Abe, Takashi; Harada, Masafumi [Tokushima University Graduate School, Department of Radiology, Tokushima (Japan); Yamamoto, Nobuaki; Kaji, Ryuji [Tokushima University Graduate School, Department of Clinical Neurosciences, Institute of Biomedical Biosciences, Tokushima (Japan)

2017-06-15

Arterial spin labeling (ASL) involves perfusion imaging using the inverted magnetization of arterial water. If the arterial arrival times are longer than the post-labeling delay, labeled spins are visible on ASL images as bright, high intra-arterial signals (IASs); such signals were found within occluded vessels of patients with acute ischemic stroke. The identification of the occluded segment in the internal carotid artery (ICA) is crucial for endovascular treatment. We tested our hypothesis that high IASs on ASL images can predict the occluded segment. Our study included 13 patients with acute ICA occlusion who had undergone angiographic and ASL studies within 48 h of onset. We retrospectively identified the high IAS on ASL images and angiograms and recorded the occluded segment and the number of high IAS-positive slices on ASL images. The ICA segments were classified as cervical (C1), petrous (C2), cavernous (C3), and supraclinoid (C4). Of seven patients with intracranial ICA occlusion, five demonstrated high IASs at C1-C2, suggesting that high IASs could identify stagnant flow proximal to the occluded segment. Among six patients with extracranial ICA occlusion, five presented with high IASs at C3-C4, suggesting that signals could identify the collateral flow via the ophthalmic artery. None had high IASs at C1-C2. The mean number of high IAS-positive slices was significantly higher in patients with intra- than extracranial ICA occlusion. High IASs on ASL images can identify slow stagnant and collateral flow through the ophthalmic artery in patients with acute ICA occlusion and help to predict the occlusion site. (orig.)

Effect of injection current and temperature on signal strength in a laser diode optical feedback interferometer.

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Al Roumy, Jalal; Perchoux, Julien; Lim, Yah Leng; Taimre, Thomas; Rakić, Aleksandar D; Bosch, Thierry

2015-01-10

We present a simple analytical model that describes the injection current and temperature dependence of optical feedback interferometry signal strength for a single-mode laser diode. The model is derived from the Lang and Kobayashi rate equations, and is developed both for signals acquired from the monitoring photodiode (proportional to the variations in optical power) and for those obtained by amplification of the corresponding variations in laser voltage. The model shows that both the photodiode and the voltage signal strengths are dependent on the laser slope efficiency, which itself is a function of the injection current and the temperature. Moreover, the model predicts that the photodiode and voltage signal strengths depend differently on injection current and temperature. This important model prediction was proven experimentally for a near-infrared distributed feedback laser by measuring both types of signals over a wide range of injection currents and temperatures. Therefore, this simple model provides important insight into the radically different biasing strategies required to achieve optimal sensor sensitivity for both interferometric signal acquisition schemes.

Early warning signals of abrupt temperature change in different regions of China over the past 50 years

International Nuclear Information System (INIS)

Tong Ji-Long; Wu Hao; Hou Wei; He Wen-Ping; Zhou Jie

2014-01-01

In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann–Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5–10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method. (geophysics, astronomy, and astrophysics)

Age differences in recall and predicting recall of action events and words.

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McDonald-Miszczak, L; Hubley, A M; Hultsch, D F

1996-03-01

Age differences in recall and prediction of recall were examined with different memory tasks. We asked 36 younger (19-28 yrs) and 36 older (60-81 yrs) women to provide both global and item-by-item predictions of their recall, and then to recall either (a) Subject Performance Tasks (SPTs), (b) verb-noun word-pairs memorized in list-like fashion (Word-Pairs), or (c) nonsense verb-noun word-pairs (Nonsense-Pairs) over three experimental trials. Based on previous research, we hypothesized that these tasks would vary in relative difficulty and flexibility of encoding. The results indicated that (a) age differences in global predictions (task specific self-efficacy) and recall performance across trials were minimized with SPT as compared with verbal materials, (b) global predictions were higher and more accurate for SPT as compared to verbal materials, and (c) item-by-item predictions were most accurate for materials encoded with the most flexibility (Nonsense Pairs). The results suggest that SPTs may provide some level of environmental support to reduce age differences in performance and task-specific self-efficacy, but that memory monitoring may depend on specific characteristics of the stimuli (i.e., flexibility of encoding) rather than their verbal or nonverbal nature.

Gender Differences in Performance Predictions: Evidence from the Cognitive Reflection Test

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Patrick Ring

2016-11-01

Full Text Available This paper studies performance predictions in the 7-item Cognitive Reflection Test (CRT and whether they differ by gender. After participants completed the CRT, they predicted their own (i, the other participants’ (ii, men’s (iii, and women’s (iv number of correct answers. In keeping with existing literature, men scored higher on the CRT than women and both men and women were too optimistic about their own performance. When we compare gender-specific predictions, we observe that men think they perform significantly better than other men and do so significantly more than women. The equality between women’s predictions about their own performance and their female peers cannot be rejected. Our findings contribute to the growing literature on the underpinnings of behavior in economics and in psychology by uncovering gender differences in confidence about one’s ability relative to same and opposite sex peers.

Discrete dynamic modeling of cellular signaling networks.

Science.gov (United States)

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

An algorithm for modularization of MAPK and calcium signaling pathways: comparative analysis among different species.

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Nayak, Losiana; De, Rajat K

2007-12-01

Signaling pathways are large complex biochemical networks. It is difficult to analyze the underlying mechanism of such networks as a whole. In the present article, we have proposed an algorithm for modularization of signal transduction pathways. Unlike studying a signaling pathway as a whole, this enables one to study the individual modules (less complex smaller units) easily and hence to study the entire pathway better. A comparative study of modules belonging to different species (for the same signaling pathway) has been made, which gives an overall idea about development of the signaling pathways over the taken set of species of calcium and MAPK signaling pathways. The superior performance, in terms of biological significance, of the proposed algorithm over an existing community finding algorithm of Newman [Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103(23):8577-82] has been demonstrated using the aforesaid pathways of H. sapiens.

Vicarious reinforcement learning signals when instructing others.

Science.gov (United States)

Apps, Matthew A J; Lesage, Elise; Ramnani, Narender

2015-02-18

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providing positive or negative feedback. We examined activity time-locked to the students' responses, when teachers infer student predictions and know actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher's ACC signals when a student's predictions are wrong. In line with our hypothesis, activity in the teacher's ACC covaried with the PE values in the model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors. Copyright © 2015 Apps et al.

A PREDICTIVE STUDY: CARBON MONOXIDE EMISSION MODELING AT A SIGNALIZED INTERSECTION

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FREDDY WEE LIANG KHO

2014-02-01

Full Text Available CAL3QHC dispersion model was used to predict the present and future carbonmonoxide (CO levels at a busy signalized intersection. This study attempted to identify CO “hot-spots” at nearby areas of the intersection during typical A.M. and P.M. peak hours. The CO concentration “hot-spots” had been identified at 101 Commercial Park and the simulated maximum 1-hour Time-Weighted Average (1-h TWA ground level CO concentrations of 18.3 ppm and 18.6 ppm had been observed during A.M. and P.M. peaks, respectively in year 2006. This study shows that there would be no significant increment in CO level for year 2014 although a substantial increase in the number of vehicles is assumed to affect CO levels. It was also found that CO levels would be well below the Malaysian Ambient Air Quality Guideline of 30 ppm (1-h TWA. Comparisons between the measured and simulated CO levels using quantitative data analysis technique and statistical methods indicated that CAL3QHC dispersion model correlated well with measured data.

Real time implementation of a linear predictive coding algorithm on digital signal processor DSP32C

International Nuclear Information System (INIS)

Sheikh, N.M.; Usman, S.R.; Fatima, S.

2002-01-01

Pulse Code Modulation (PCM) has been widely used in speech coding. However, due to its high bit rate. PCM has severe limitations in application where high spectral efficiency is desired, for example, in mobile communication, CD quality broadcasting system etc. These limitation have motivated research in bit rate reduction techniques. Linear predictive coding (LPC) is one of the most powerful complex techniques for bit rate reduction. With the introduction of powerful digital signal processors (DSP) it is possible to implement the complex LPC algorithm in real time. In this paper we present a real time implementation of the LPC algorithm on AT and T's DSP32C at a sampling frequency of 8192 HZ. Application of the LPC algorithm on two speech signals is discussed. Using this implementation , a bit rate reduction of 1:3 is achieved for better than tool quality speech, while a reduction of 1.16 is possible for speech quality required in military applications. (author)

Consistency of test behaviour and individual difference in prescision of prediction

NARCIS (Netherlands)

Meijer, R.R.

1998-01-01

Ghiselli ((1956, 1960) argued that the precision of prediction on the basis of a test may vary for different individuals. To quantify the individual precision of prediction he compared the observed criterion scores with the expected criterion scores estimated on the basis of the total scores on a

Risk factors predict post-traumatic stress disorder differently in men and women

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Elklit Ask

2008-11-01

Full Text Available Abstract Background About twice as many women as men develop post-traumatic stress disorder (PTSD, even though men as a group are exposed to more traumatic events. Exposure to different trauma types does not sufficiently explain why women are more vulnerable. Methods The present work examines the effect of age, previous trauma, negative affectivity (NA, anxiety, depression, persistent dissociation, and social support on PTSD separately in men and women. Subjects were exposed to either a series of explosions in a firework factory near a residential area or to a high school stabbing incident. Results Some gender differences were found in the predictive power of well known risk factors for PTSD. Anxiety predicted PTSD in men, but not in women, whereas the opposite was found for depression. Dissociation was a better predictor for PTSD in women than in men in the explosion sample but not in the stabbing sample. Initially, NA predicted PTSD better in women than men in the explosion sample, but when compared only to other significant risk factors, it significantly predicted PTSD for both men and women in both studies. Previous traumatic events and age did not significantly predict PTSD in either gender. Conclusion Gender differences in the predictive value of social support on PTSD appear to be very complex, and no clear conclusions can be made based on the two studies included in this article.

Data-driven quantification of the robustness and sensitivity of cell signaling networks

International Nuclear Information System (INIS)

Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J; Das, Jayajit

2013-01-01

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems. (paper)

Dividend policy as a signaling mechanism under different market conditions: Evidence from the Casablanca Stock Exchange

DEFF Research Database (Denmark)

Farooq, Omar; Saoud, Siham; Agnaou, Samir

2012-01-01

Does the signaling value of dividend policy depend on market conditions? Do investors respond to dividend policy differently in different periods? This study answers these questions by using a sample of firms from the Casablanca Stock Exchange during the period between 2003 and 2007. We find a si...... growth period. One of the reasons for our results may be that investors pay lesser attention to the signaling value of dividends during the periods when they are earning higher returns on their investments.......Does the signaling value of dividend policy depend on market conditions? Do investors respond to dividend policy differently in different periods? This study answers these questions by using a sample of firms from the Casablanca Stock Exchange during the period between 2003 and 2007. We find...... a significantly negative relationship between dividend payout ratio and stock price volatility during the stable growth period. We also show a significantly positive relationship between dividend payout ratio and stock returns during the same period. However, this relationship turns insignificant during the high...

Comparative proteomic analysis to dissect differences in signal transduction in activating TSH receptor mutations in the thyroid.

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Krause, Kerstin; Boisnard, Alexandra; Ihling, Christian; Ludgate, Marian; Eszlinger, Markus; Krohn, Knut; Sinz, Andrea; Fuhrer, Dagmar

2012-02-01

In the thyroid, cAMP controls both thyroid growth and function. Gain-of-function mutations in the thyroid-stimulating hormone receptor (TSHR) lead to constitutive cAMP formation and are a major cause of autonomous thyroid adenomas. The impact of activating TSHR mutations on the signal transduction network of the thyrocyte is not fully understood. To gain more insights into constitutive TSHR signaling, rat thyrocytes (FRTL-5 cells) with stable expression of three activating TSHR mutants (mutTSHR: A623I, L629F and Del613-621), which differ in their functional characteristics in vitro, were analyzed by a quantitative proteomic approach and compared to the wild-type TSHR (WT-TSHR). This study revealed (1) differences in the expression of Rab proteins suggesting an increased TSHR internalization in mutTSHR but not in the WT-TSHR; (2) differential stimulation of PI3K/Akt signaling in mutTSHR vs. WT-TSHR cells, (3) activation of Epac, impairing short-time Akt phosphorylation in both, mutTSHR and WT-TSHR cells. Based on the analysis of global changes in protein expression patterns, our findings underline the complexity of gain-of-function TSHR signaling in thyrocytes, which extends beyond pure cAMP and/or IP formation. Moreover, evidence for augmented endocytosis in the mutTSHR, adds to a new concept of TSHR signaling in thyroid autonomy. Further studies are required to clarify whether the observed differences in Rab, PI3K and Epac signaling may contribute to differences in the phenotypic presentation, i.e. stimulation of function and growth of thyroid autonomy in vivo. Copyright © 2011 Elsevier Ltd. All rights reserved.

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

Science.gov (United States)

Terfve, Camille; Cokelaer, Thomas; Henriques, David; MacNamara, Aidan; Goncalves, Emanuel; Morris, Melody K; van Iersel, Martijn; Lauffenburger, Douglas A; Saez-Rodriguez, Julio

2012-10-18

Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

Ultrasonic Detection of Small Crack in Studs[Bolts] by Time Difference of Thread Signals(TDTS)

International Nuclear Information System (INIS)

Suh, D. M.; Park, D. Y.; Kim, C. K.

1990-01-01

It is difficult to detect such flaws as stress - corrosion cracking or corrosion wastage(loss of bolt diameter) in the threads. In many cases the critical size of a flaw is very small(1-2 mm order). This paper describes how it is possible to discriminate small flaw indications in threads using the time difference or thread signals(TDTS) by a signal-conditioning technique

Color and behavior differently predict competitive outcomes for divergent stickleback color morphs

Science.gov (United States)

Lehto, Whitley R; Lierheimer, V Faith

2018-01-01

Abstract Our knowledge of how male competition contributes to speciation is dominated by investigations of competition between within-species morphs or closely related species that differ in conspicuous traits expressed during the breeding season (e.g. color, song). In such studies, it is important to consider the manner in which putatively sexually selected traits influence the outcome of competitive interactions within and between types because these traits can communicate information about competitor quality and may not be utilized by homotypic and heterotypic receivers in the same way. We studied the roles of breeding color and aggressive behaviors in competition within and between two divergent threespine stickleback Gasterosteus aculeatus color types. Our previous work in this system showed that the switch from red to black breeding coloration is associated with changes in male competition biases. Here, we find that red and black males also use different currencies in competition. Winners of both color types performed more aggressive behaviors than losers, regardless of whether the competitor was of the same or opposite color type. But breeding color differently predicted competitive outcomes for red and black males. Males who were redder at the start of competition were more likely to win when paired with homotypic competitors and less likely to win when paired with heterotypic competitors. In contrast, black color, though expressed in the breeding season and condition dependent, was unrelated to competitive outcomes. Placing questions about the role of male competition in speciation in a sexual signal evolution framework may provide insight into the “why and how” of aggression biases and asymmetries in competitive ability between closely related morphs and species. PMID:29492044

Predictive value of cognition for different domains of outcome in recent-onset schizophrenia.

Science.gov (United States)

Holthausen, Esther A E; Wiersma, Durk; Cahn, Wiepke; Kahn, René S; Dingemans, Peter M; Schene, Aart H; van den Bosch, Robert J

2007-01-15

The aim of this study was to see whether and how cognition predicts outcome in recent-onset schizophrenia in a large range of domains such as course of illness, self-care, interpersonal functioning, vocational functioning and need for care. At inclusion, 115 recent-onset patients were tested on a cognitive battery and 103 patients participated in the follow-up 2 years after inclusion. Differences in outcome between cognitively normal and cognitively impaired patients were also analysed. Cognitive measures at inclusion did not predict number of relapses, activities of daily living and interpersonal functioning. Time in psychosis or in full remission, as well as need for care, were partly predicted by specific cognitive measures. Although statistically significant, the predictive value of cognition with regard to clinical outcome was limited. There was a significant difference between patients with and without cognitive deficits in competitive employment status and vocational functioning. The predictive value of cognition for different social outcome domains varies. It seems that cognition most strongly predicts work performance, where having a cognitive deficit, regardless of the nature of the deficit, acts as a rate-limiting factor.

Agent-specific learning signals for self-other distinction during mentalising.

Directory of Open Access Journals (Sweden)

Sam Ereira

2018-04-01

Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

Predicting the performance of a power amplifier using large-signal circuit simulations of an AlGaN/GaN HFET model

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Bilbro, Griff L.; Hou, Danqiong; Yin, Hong; Trew, Robert J.

2009-02-01

We have quantitatively modeled the conduction current and charge storage of an HFET in terms its physical dimensions and material properties. For DC or small-signal RF operation, no adjustable parameters are necessary to predict the terminal characteristics of the device. Linear performance measures such as small-signal gain and input admittance can be predicted directly from the geometric structure and material properties assumed for the device design. We have validated our model at low-frequency against experimental I-V measurements and against two-dimensional device simulations. We discuss our recent extension of our model to include a larger class of electron velocity-field curves. We also discuss the recent reformulation of our model to facilitate its implementation in commercial large-signal high-frequency circuit simulators. Large signal RF operation is more complex. First, the highest CW microwave power is fundamentally bounded by a brief, reversible channel breakdown in each RF cycle. Second, the highest experimental measurements of efficiency, power, or linearity always require harmonic load pull and possibly also harmonic source pull. Presently, our model accounts for these facts with an adjustable breakdown voltage and with adjustable load impedances and source impedances for the fundamental frequency and its harmonics. This has allowed us to validate our model for large signal RF conditions by simultaneously fitting experimental measurements of output power, gain, and power added efficiency of real devices. We show that the resulting model can be used to compare alternative device designs in terms of their large signal performance, such as their output power at 1dB gain compression or their third order intercept points. In addition, the model provides insight into new device physics features enabled by the unprecedented current and voltage levels of AlGaN/GaN HFETs, including non-ohmic resistance in the source access regions and partial depletion of

Acquisition and deconvolution of seismic signals by different methods to perform direct ground-force measurements

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Poletto, Flavio; Schleifer, Andrea; Zgauc, Franco; Meneghini, Fabio; Petronio, Lorenzo

2016-12-01

We present the results of a novel borehole-seismic experiment in which we used different types of onshore-transient-impulsive and non-impulsive-surface sources together with direct ground-force recordings. The ground-force signals were obtained by baseplate load cells located beneath the sources, and by buried soil-stress sensors installed in the very shallow-subsurface together with accelerometers. The aim was to characterize the source's emission by its complex impedance, function of the near-field vibrations and soil stress components, and above all to obtain appropriate deconvolution operators to remove the signature of the sources in the far-field seismic signals. The data analysis shows the differences in the reference measurements utilized to deconvolve the source signature. As downgoing waves, we process the signals of vertical seismic profiles (VSP) recorded in the far-field approximation by an array of permanent geophones cemented at shallow-medium depth outside the casing of an instrumented well. We obtain a significant improvement in the waveform of the radiated seismic-vibrator signals deconvolved by ground force, similar to that of the seismograms generated by the impulsive sources, and demonstrates that the results obtained by different sources present low values in their repeatability norm. The comparison evidences the potentiality of the direct ground-force measurement approach to effectively remove the far-field source signature in VSP onshore data, and to increase the performance of permanent acquisition installations for time-lapse application purposes.

A signal detection theory analysis of an unconscious perception effect.

Science.gov (United States)

Haase, S J; Theios, J; Jenison, R

1999-07-01

The independent observation model (Macmillan & Creelman, 1991) is fitted to detection-identification data collected under conditions of heavy masking. The model accurately predicts a quantitative relationship between stimulus detection and stimulus identification over a wide range of detection performance. This model can also be used to offer a signal detection interpretation of the common finding of above-chance identification following a missed signal. While our finding is not a new one, the stimuli used in this experiment (redundant three-letter strings) differ slightly from those used in traditional signal detection work. Also, the stimuli were presented very briefly and heavily masked, conditions typical in the study of unconscious perception effects.

Use of a MS-electronic nose for prediction of early fungal spoilage of bakery products.

Science.gov (United States)

Marín, S; Vinaixa, M; Brezmes, J; Llobet, E; Vilanova, X; Correig, X; Ramos, A J; Sanchis, V

2007-02-28

A MS-based electronic nose was used to detect fungal spoilage (measured as ergosterol concentration) in samples of bakery products. Bakery products were inoculated with different Eurotium, Aspergillus and Penicillium species, incubated in sealed vials and their headspace sampled after 2, 4 and 7 days. Once the headspace was sampled, ergosterol content was determined in each sample. Different electronic nose signals were recorded depending on incubation time. Both the e-nose signals and ergosterol levels were used to build models for prediction of ergosterol content using e-nose measurements. Accuracy on prediction of those models was between 87 and 96%, except for samples inoculated with Penicillium corylophilum where the best predictions only reached 46%.

Signaling equilibria in sensorimotor interactions.

Science.gov (United States)

Leibfried, Felix; Grau-Moya, Jordi; Braun, Daniel A

2015-08-01

Although complex forms of communication like human language are often assumed to have evolved out of more simple forms of sensorimotor signaling, less attention has been devoted to investigate the latter. Here, we study communicative sensorimotor behavior of humans in a two-person joint motor task where each player controls one dimension of a planar motion. We designed this joint task as a game where one player (the sender) possesses private information about a hidden target the other player (the receiver) wants to know about, and where the sender's actions are costly signals that influence the receiver's control strategy. We developed a game-theoretic model within the framework of signaling games to investigate whether subjects' behavior could be adequately described by the corresponding equilibrium solutions. The model predicts both separating and pooling equilibria, in which signaling does and does not occur respectively. We observed both kinds of equilibria in subjects and found that, in line with model predictions, the propensity of signaling decreased with increasing signaling costs and decreasing uncertainty on the part of the receiver. Our study demonstrates that signaling games, which have previously been applied to economic decision-making and animal communication, provide a framework for human signaling behavior arising during sensorimotor interactions in continuous and dynamic environments. Copyright © 2015 Elsevier B.V. All rights reserved.

An integrated approach for signal validation in nuclear power plants

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Kerlin, T.W.; Gloeckler, O.; Frei, Z.; Qualls, L.; Morgenstern, V.

1987-08-01

A signal validation system, based on several parallel signal processing modules, is being developed at the University of Tennessee. The major modules perform (1) general consistency checking (GCC) of a set of redundant measurements, (2) multivariate data-driven modeling of dynamic signal components for maloperation detection, (3) process empirical modeling for prediction and redundancy generation, (4) jump, pulse, noise detection, and (5) an expert system for qualitative signal validation. A central database stores information related to sensors, diagnostics rules, past system performance, subsystem models, etc. We are primarily concerned with signal validation during steady-state operation and slow degradations. In general, the different modules will perform signal validation during all operating conditions. The techniques have been successfully tested using PWR steam generator simulation, and efforts are currently underway in applying the techniques to Millstone-III operational data. These methods could be implemented in advanced reactors, including advanced liquid metal reactors

Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

Science.gov (United States)

Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

2007-11-21

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

Science.gov (United States)

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Attractiveness Differences Between Twins Predicts Evaluations of Self and Co-Twin

Science.gov (United States)

Principe, Connor P.; Rosen, Lisa H.; Taylor-Partridge, Teresa; Langlois, Judith H.

2012-01-01

One of the most consistent findings in psychology shows that people prefer and make positive attributions about attractive compared with unattractive people. The goal of the current study was to determine the power of attractiveness effects by testing whether these social judgments are made where attractiveness differences are smallest: between twins. Differences in facial attractiveness predicted twins’ evaluations of self and their co-twin (n = 158; 54 male). In twin pairs, the more attractive twin judged their less attractive sibling as less physically attractive, athletic, socially competent, and emotionally stable. The less attractive twin did the reverse. Given that even negligible differences in facial attractiveness predicted self and co-twin attitudes, these results provide the strongest test yet of appearance-based stereotypes. PMID:23467329

Social benefits of luxury brands as costly signals of wealth and status

NARCIS (Netherlands)

Nelissen, R.M.A.; Meijers, M.H.C.

2011-01-01

Drawing from costly signaling theory, we predicted that luxury consumption enhances status and produces benefits in social interactions. Across seven experiments, displays of luxury — manipulated through brand labels on clothes — elicited different kinds of preferential treatment, which even

Silent communication: toward using brain signals.

Science.gov (United States)

Pei, Xiaomei; Hill, Jeremy; Schalk, Gerwin

2012-01-01

From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?

An Intelligent Model for Stock Market Prediction

Directory of Open Access Journals (Sweden)

IbrahimM. Hamed

2012-08-01

Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

Enhanced signal dispersion in saturation transfer difference experiments by conversion to a 1D-STD-homodecoupled spectrum

Energy Technology Data Exchange (ETDEWEB)

Martin-Pastor, Manuel; Vega-Vazquez, Marino [Universidade de Santiago de Compostela, Laboratorio Integral de Dinamica e Estructura de Biomoleculas Jose R. Carracido, Unidade de Resonancia Magnetica, Edificio CACTUS, RIAIDT (Spain); Capua, Antonia De [Seconda Universita degli Studi di Napoli, Dipartimento di Scienze Ambientali (Italy); Canales, Angeles [Centro de Investigaciones Biologicas, CSIC, Departamento de Estructura y funcion de proteinas (Spain); Andre, Sabine; Gabius, Hans-Joachim [Ludwig-Maximilians-Universitaet, Institut fuer Physiologische Chemie, Tieraerztliche Fakultaet (Germany); Jimenez-Barbero, Jesus [Centro de Investigaciones Biologicas, CSIC, Departamento de Estructura y funcion de proteinas (Spain)], E-mail: JJbarbero@cib.csic.es

2006-10-15

The saturation transfer difference (STD) experiment is a rich source of information on topological aspects of ligand binding to a receptor. The epitope mapping is based on a magnetization transfer after signal saturation from the receptor to the ligand, where interproton distances permit this process. Signal overlap in the STD spectrum can cause difficulties to correctly assign and/or quantitate the measured enhancements. To address this issue we report here a modified version of the routine experiment and a processing scheme that provides a 1D-STD homodecoupled spectrum (i.e. an experiment in which all STD signals appear as singlets) with line widths similar to those in original STD spectrum. These refinements contribute to alleviate problems of signal overlap. The experiment is based on 2D-J-resolved spectroscopy, one of the fastest 2D experiments under conventional data sampling in the indirect dimension, and provides excellent sensitivity, a key factor for the difference experiments.

Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ?

Science.gov (United States)

Kramer, Andrew A; Higgins, Thomas L; Zimmerman, Jack E

2015-02-01

To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences. Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort. Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013. Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions. None. We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Directory of Open Access Journals (Sweden)

Snowdon Stuart

2009-07-01

Full Text Available Abstract Background Metabolomics experiments using Mass Spectrometry (MS technology measure the mass to charge ratio (m/z and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50% of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data.

Predictability of Conversation Partners

Science.gov (United States)

Takaguchi, Taro; Nakamura, Mitsuhiro; Sato, Nobuo; Yano, Kazuo; Masuda, Naoki

2011-08-01

Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cell-phone locations, [C. Song , ScienceSCIEAS0036-8075 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one’s conversation partners is defined as the degree to which one’s next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual’s predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community—in the sense of an abundance of surrounding triangles—tend to have low predictability, and those bridging different communities tend to have high predictability.

Predictability of Conversation Partners

Directory of Open Access Journals (Sweden)

Taro Takaguchi

2011-09-01

Full Text Available Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cell-phone locations, [C. Song et al., Science 327, 1018 (2010SCIEAS0036-8075] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one’s conversation partners is defined as the degree to which one’s next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by long-tailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual’s predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community—in the sense of an abundance of surrounding triangles—tend to have low predictability, and those bridging different communities tend to have high predictability.

Dynamic Trading with Predictable Returns and Transaction Costs

DEFF Research Database (Denmark)

Gârleanu, Nicolae; Heje Pedersen, Lasse

2013-01-01

We derive a closed-form optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean-reversion speeds. The optimal strategy is characterized by two principles: (1) aim in front of the target, and (2) trade partially toward the current...

Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness.

Science.gov (United States)

Hodges-Simeon, Carolyn R; Gaulin, Steven J C; Puts, David A

2010-12-01

Low mean fundamental frequency (F(0)) in men's voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fertile and non-fertile menstrual cycle phases for desirability in short-term and long-term relationships. Five vocal parameters were analyzed: mean F(0) (an acoustic correlate of vocal fold size), F(0) variation, intensity (loudness), utterance duration, and formant dispersion (D(f), an acoustic correlate of vocal tract length). Parallel but separate ratings of speech transcripts served as controls for content. Multiple regression analyses were used to examine the independent contributions of each of the predictors. Physical dominance was predicted by low F(0) variation and physically dominant word content. Social dominance was predicted only by socially dominant word content. Ratings of attractiveness by women were predicted by low mean F(0), low D(f), high intensity, and attractive word content across cycle phase and mating context. Low D(f) was perceived as attractive by fertile-phase women only. We hypothesize that competitors and potential mates may attend more strongly to different components of men's voices because of the different types of information these vocal parameters provide.

Dopamine reward prediction errors reflect hidden state inference across time

Science.gov (United States)

Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Physical signals for protein–DNA recognition

International Nuclear Information System (INIS)

Cao, Xiao-Qin; Zeng, Jia; Yan, Hong

2009-01-01

This paper discovers consensus physical signals around eukaryotic splice sites, transcription start sites, and replication origin start and end sites on a genome-wide scale based on their DNA flexibility profiles calculated by three different flexibility models. These salient physical signals are localized highly rigid and flexible DNAs, which may play important roles in protein–DNA recognition by the sliding search mechanism. The found physical signals lead us to a detailed hypothetical view of the search process in which a DNA-binding protein first finds a genomic region close to the target site from an arbitrary starting location by three-dimensional (3D) hopping and intersegment transfer mechanisms for long distances, and subsequently uses the one-dimensional (1D) sliding mechanism facilitated by the localized highly rigid DNAs to accurately locate the target flexible binding site within 30 bp (base pair) short distances. Guided by these physical signals, DNA-binding proteins rapidly search the entire genome to recognize a specific target site from the 3D to 1D pathway. Our findings also show that current promoter prediction programs (PPPs) based on DNA physical properties may suffer from lots of false positives because other functional sites such as splice sites and replication origins have similar physical signals as promoters do

Predicting Electrocardiogram and Arterial Blood Pressure Waveforms with Different Echo State Network Architectures

Science.gov (United States)

2014-11-01

Predicting Electrocardiogram and Arterial Blood Pressure Waveforms with Different Echo State Network Architectures Allan Fong, MS1,3, Ranjeev...the medical staff in Intensive Care Units. The ability to predict electrocardiogram and arterial blood pressure waveforms can potentially help the...type of neural network for mining, understanding, and predicting electrocardiogram and arterial blood pressure waveforms. Several network

Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

Science.gov (United States)

Griffin, James M.; Diaz, Fernanda; Geerling, Edgar; Clasing, Matias; Ponce, Vicente; Taylor, Chris; Turner, Sam; Michael, Ernest A.; Patricio Mena, F.; Bronfman, Leonardo

2017-02-01

By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 μm. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level).

Predictability and environmental drivers of chlorophyll fluctuations vary across different time scales and regions of the North Sea

Science.gov (United States)

Blauw, Anouk N.; Benincà, Elisa; Laane, Remi W. P. M.; Greenwood, Naomi; Huisman, Jef

2018-02-01

Phytoplankton concentrations display strong temporal variability at different time scales. Recent advances in automated moorings enable detailed investigation of this variability. In this study, we analyzed phytoplankton fluctuations at four automated mooring stations in the North Sea, which measured phytoplankton abundance (chlorophyll) and several environmental variables at a temporal resolution of 12-30 min for two to nine years. The stations differed in tidal range, water depth and freshwater influence. This allowed comparison of the predictability and environmental drivers of phytoplankton variability across different time scales and geographical regions. We analyzed the time series using wavelet analysis, cross correlations and generalized additive models to quantify the response of chlorophyll fluorescence to various environmental variables (tidal and meteorological variables, salinity, suspended particulate matter, nitrate and sea surface temperature). Hour-to-hour and day-to-day fluctuations in chlorophyll fluorescence were substantial, and mainly driven by sinking and vertical mixing of phytoplankton cells, horizontal transport of different water masses, and non-photochemical quenching of the fluorescence signal. At the macro-tidal stations, these short-term phytoplankton fluctuations were strongly driven by the tides. Along the Dutch coast, variation in salinity associated with the freshwater influence of the river Rhine played an important role, while in the central North Sea variation in weather conditions was a major determinant of phytoplankton variability. At time scales of weeks to months, solar irradiance, nutrient conditions and thermal stratification were the dominant drivers of changes in chlorophyll concentrations. These results show that the dominant drivers of phytoplankton fluctuations differ across marine environments and time scales. Moreover, our findings show that phytoplankton variability on hourly to daily time scales should not be

Visual-haptic integration with pliers and tongs: signal ‘weights’ take account of changes in haptic sensitivity caused by different tools

Directory of Open Access Journals (Sweden)

Chie eTakahashi

2014-02-01

Full Text Available When we hold an object while looking at it, estimates from visual and haptic cues to size are combined in a statistically optimal fashion, whereby the ‘weight’ given to each signal reflects their relative reliabilities. This allows object properties to be estimated more precisely than would otherwise be possible. Tools such as pliers and tongs systematically perturb the mapping between object size and the hand opening. This could complicate visual-haptic integration because it may alter the reliability of the haptic signal, thereby disrupting the determination of appropriate signal weights. To investigate this we first measured the reliability of haptic size estimates made with virtual pliers-like tools (created using a stereoscopic display and force-feedback robots with different ‘gains’ between hand opening and object size. Haptic reliability in tool use was straightforwardly determined by a combination of sensitivity to changes in hand opening and the effects of tool geometry. The precise pattern of sensitivity to hand opening, which violated Weber’s law, meant that haptic reliability changed with tool gain. We then examined whether the visuo-motor system accounts for these reliability changes. We measured the weight given to visual and haptic stimuli when both were available, again with different tool gains, by measuring the perceived size of stimuli in which visual and haptic sizes were varied independently. The weight given to each sensory cue changed with tool gain in a manner that closely resembled the predictions of optimal sensory integration. The results are consistent with the idea that different tool geometries are modelled by the brain, allowing it to calculate not only the distal properties of objects felt with tools, but also the certainty with which those properties are known. These findings highlight the flexibility of human sensory integration and tool-use, and potentially provide an approach for optimising the

An investigation into the signals leakage from a smartcard based on different runtime code

CSIR Research Space (South Africa)

Frieslaar, I

2015-09-01

Full Text Available September 2015 at the Arabella Hotel & Spa in the heart of the Kogelberg Biosphere Reserve near Hermanus, Western Cape, South Africa An Investigation into the Signals Leakage From a Smartcard based on Different Runtime Code Ibraheem Frieslaar...

Macroevolution of perfume signalling in orchid bees.

Science.gov (United States)

Weber, Marjorie G; Mitko, Lukasz; Eltz, Thomas; Ramírez, Santiago R

2016-11-01

Theory predicts that both stabilising selection and diversifying selection jointly contribute to the evolution of sexual signalling traits by (1) maintaining the integrity of communication signals within species and (2) promoting the diversification of traits among lineages. However, for many important signalling traits, little is known about whether these dynamics translate into predictable macroevolutionary signatures. Here, we test for macroevolutionary patterns consistent with sexual signalling theory in the perfume signals of neotropical orchid bees, a group well studied for their chemical sexual communication. Our results revealed both high species-specificity and elevated rates of evolution in perfume signals compared to nonsignalling traits. Perfume complexity was correlated with the number of congeners in a species' range, suggesting that perfume evolution may be tied to the remarkably high number of orchid bee species coexisting together in some neotropical communities. Finally, sister-pair comparisons were consistent with both rapid divergence at speciation and character displacement upon secondary contact. Together, our results provide new insight into the macroevolution of sexual signalling in insects. © 2016 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

Directory of Open Access Journals (Sweden)

Martens-Uzunova Elena S

2010-10-01

Full Text Available Abstract Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

Science.gov (United States)

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

Adiposity signals predict vocal effort in Alston's singing mice.

Science.gov (United States)

Burkhard, Tracy T; Westwick, Rebecca R; Phelps, Steven M

2018-04-25

Advertisement displays often seem extravagant and expensive, and are thought to depend on the body condition of a signaller. Nevertheless, we know little about how signallers adjust effort based on condition, and few studies find a strong relationship between natural variation in condition and display. To examine the relationship between body condition and signal elaboration more fully, we characterized physiological condition and acoustic displays in a wild rodent with elaborate vocalizations, Alston's singing mouse, Scotinomys teguina We found two major axes of variation in condition-one defined by short-term fluctuations in caloric nutrients, and a second by longer-term variation in adiposity. Among acoustic parameters, song effort was characterized by high rates of display and longer songs. Song effort was highly correlated with measures of adiposity. We found that leptin was a particularly strong predictor of display effort. Leptin is known to influence investment in other costly traits, such as immune function and reproduction. Plasma hormone levels convey somatic state to a variety of tissues, and may govern trait investment across vertebrates. Such measures offer new insights into how animals translate body condition into behavioural and life-history decisions. © 2018 The Author(s).

Human Splicing Finder: an online bioinformatics tool to predict splicing signals

OpenAIRE

Desmet, Francois-Olivier; Hamroun, Dalil; Lalande, Marine; Collod-Beroud, Gwenaelle; Claustres, Mireille; Beroud, Christophe

2009-01-01

International audience; Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effec...

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly

Energy Technology Data Exchange (ETDEWEB)

Shen, Ming; Zhang, Qilin [Fudan University, Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Shanghai (China); Shanghai Pituitary Tumor Center, Shanghai (China); Liu, Wenjuan; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yang, Yeping [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical College, Shanghai (China); Wang, Meng [Fudan University, Department of Endocrinology and Metabolism, Huashan Hospital, Shanghai Medical College, Shanghai (China); Soochow University, Division of Endocrinology, the Second Affiliated Hospital, Suzhou (China); Zhu, Jingjing [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Neuropathology, Huashan Hospital, Shanghai Medical College, Shanghai (China); Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Zhao, Yao; Wang, Yongfei [Fudan University, Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Shanghai (China); Shanghai Pituitary Tumor Center, Shanghai (China); Yao, Zhenwei [Shanghai Pituitary Tumor Center, Shanghai (China); Fudan University, Department of Radiology, Huashan Hospital, Shanghai Medical College, Shanghai (China); Lu, Yun [Fudan University, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Shanghai (China)

2016-11-15

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly. (orig.)

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly

International Nuclear Information System (INIS)

Shen, Ming; Zhang, Qilin; Liu, Wenjuan; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yang, Yeping; Wang, Meng; Zhu, Jingjing; Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Zhao, Yao; Wang, Yongfei; Yao, Zhenwei; Lu, Yun

2016-01-01

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly. (orig.)

Predictive value of T2 relative signal intensity for response to somatostatin analogs in newly diagnosed acromegaly.

Science.gov (United States)

Shen, Ming; Zhang, Qilin; Liu, Wenjuan; Wang, Meng; Zhu, Jingjing; Ma, Zengyi; He, Wenqiang; Li, Shiqi; Shou, Xuefei; Li, Yiming; Zhang, Zhaoyun; Ye, Hongying; He, Min; Lu, Bin; Yao, Zhenwei; Lu, Yun; Qiao, Nidan; Ye, Zhao; Zhang, Yichao; Yang, Yeping; Zhao, Yao; Wang, Yongfei

2016-11-01

The difficulty of predicting the efficacy of somatostatin analogs (SSA) is not fully resolved. Here, we quantitatively evaluated the predictive value of relative signal intensity (rSI) on T1- and T2-weighted magnetic resonance imaging (MRI) for the short-term efficacy (3 months) of SSA therapy in patients with active acromegaly and assessed the correlation between MRI rSI and expression of somatostatin receptors (SSTR). This was a retrospective review of prospectively recorded data. Ninety-two newly diagnosed patients (37 males and 55 females) with active acromegaly were recruited. All patients were treated with pre-surgical SSA, followed by reassessment and transspenoidal surgery. rSI values were generated by calculating the ratio of SI in the tumor to the SI of normal frontal white matter. The Youden indices were calculated to determine the optimal cutoff of rSI to determine the efficacy of SSA. The correlation between rSI and expression of SSTR2/5 was analyzed by the Spearman rank correlation coefficient. T2 rSI was strongly correlated with biochemical sensitivity to SSA. The cutoff value of T2 rSI to distinguish biochemical sensitivity was 1.205, with a positive predictive value (PPV) of 81.5 % and a negative predictive value (NPV) of 77.3 %. No correlation was found between MRI and tumor size sensitivity. Moreover, T2 rSI was negatively correlated with the expression of SSTR5. T2 rSI correlates with the expression of SSTR5 and quantitatively predicts the biochemical efficacy of SSA in acromegaly.

Prediction of non-canonical polyadenylation signals in human genomic sequences based on a novel algorithm using a fuzzy membership function.

Science.gov (United States)

Kamasawa, Masami; Horiuchi, Jun-Ichi

2009-05-01

Computational prediction of polyadenylation signals (PASes) is essential for analysis of alternative polyadenylation that plays crucial roles in gene regulations by generating heterogeneity of 3'-UTR of mRNAs. To date, several algorithms that are mostly based on machine learning methods have been developed to predict PASes. Accuracies of predictions by those algorithms have improved significantly for the last decade. However, they are designed primarily for prediction of the most canonical AAUAAA and its common variant AUUAAA whereas other variants have been ignored in their predictions despite recent studies indicating that non-canonical variants of AAUAAA are more important in the polyadenylation process than commonly recognized. Here we present a new algorithm "PolyF" employing fuzzy logic to confer an advance in computational PAS prediction--enable prediction of the non-canonical variants, and improve the accuracies for the canonical A(A/U)UAAA prediction. PolyF is a simple computational algorithm that is composed of membership functions defining sequence features of downstream sequence element (DSE) and upstream sequence element (USE), together with an inference engine. As a result, PolyF successfully identified the 10 single-nucleotide variants with approximately the same or higher accuracies compared to those for A(A/U)UAAA. PolyF also achieved higher accuracies for A(A/U)UAAA prediction than those by commonly known PAS finder programs, Polyadq and Erpin. Incorporating the USE into the PolyF algorithm was found to enhance prediction accuracies for all the 12 PAS hexamers compared to those using only the DSE, suggesting an important contribution of the USE in the polyadenylation process.

Relation between stability and resilience determines the performance of early warning signals under different environmental drivers.

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Dai, Lei; Korolev, Kirill S; Gore, Jeff

2015-08-11

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as "indicators for loss of resilience." We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability-resilience relation needs to be better understood for the application of early warning signals in different scenarios.

Functional Divergence in the Role of N-Linked Glycosylation in Smoothened Signaling.

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Suresh Marada

2015-08-01

Full Text Available The G protein-coupled receptor (GPCR Smoothened (Smo is the requisite signal transducer of the evolutionarily conserved Hedgehog (Hh pathway. Although aspects of Smo signaling are conserved from Drosophila to vertebrates, significant differences have evolved. These include changes in its active sub-cellular localization, and the ability of vertebrate Smo to induce distinct G protein-dependent and independent signals in response to ligand. Whereas the canonical Smo signal to Gli transcriptional effectors occurs in a G protein-independent manner, its non-canonical signal employs Gαi. Whether vertebrate Smo can selectively bias its signal between these routes is not yet known. N-linked glycosylation is a post-translational modification that can influence GPCR trafficking, ligand responsiveness and signal output. Smo proteins in Drosophila and vertebrate systems harbor N-linked glycans, but their role in Smo signaling has not been established. Herein, we present a comprehensive analysis of Drosophila and murine Smo glycosylation that supports a functional divergence in the contribution of N-linked glycans to signaling. Of the seven predicted glycan acceptor sites in Drosophila Smo, one is essential. Loss of N-glycosylation at this site disrupted Smo trafficking and attenuated its signaling capability. In stark contrast, we found that all four predicted N-glycosylation sites on murine Smo were dispensable for proper trafficking, agonist binding and canonical signal induction. However, the under-glycosylated protein was compromised in its ability to induce a non-canonical signal through Gαi, providing for the first time evidence that Smo can bias its signal and that a post-translational modification can impact this process. As such, we postulate a profound shift in N-glycan function from affecting Smo ER exit in flies to influencing its signal output in mice.

T2-weighted signal intensity-selected volumetry for prediction of pathological complete response after preoperative chemoradiotherapy in locally advanced rectal cancer.

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Kim, Sungwon; Han, Kyunghwa; Seo, Nieun; Kim, Hye Jin; Kim, Myeong-Jin; Koom, Woong Sub; Ahn, Joong Bae; Lim, Joon Seok

2018-06-01

To evaluate the diagnostic value of signal intensity (SI)-selected volumetry findings in T2-weighted magnetic resonance imaging (MRI) as a potential biomarker for predicting pathological complete response (pCR) to preoperative chemoradiotherapy (CRT) in patients with rectal cancer. Forty consecutive patients with pCR after preoperative CRT were compared with 80 age- and sex-matched non-pCR patients in a case-control study. SI-selected tumor volume was measured on post-CRT T2-weighted MRI, which included voxels of the treated tumor exceeding the SI (obturator internus muscle SI + [ischiorectal fossa fat SI - obturator internus muscle SI] × 0.2). Three blinded readers independently rated five-point pCR confidence scores and compared the diagnostic outcome with SI-selected volumetry findings. The SI-selected volumetry protocol was validated in 30 additional rectal cancer patients. The area under the receiver-operating characteristic curve (AUC) of SI-selected volumetry for pCR prediction was 0.831, with an optimal cutoff value of 649.6 mm 3 (sensitivity 0.850, specificity 0.725). The AUC of the SI-selected tumor volume was significantly greater than the pooled AUC of readers (0.707, p volumetry in post-CRT T2-weighted MRI can help predict pCR after preoperative CRT in patients with rectal cancer. • Fibrosis and viable tumor MRI signal intensities (SIs) are difficult to distinguish. • T2 SI-selected volumetry yields high diagnostic performance for assessing pathological complete response. • T2 SI-selected volumetry is significantly more accurate than readers and non-SI-selected volumetry. • Post-chemoradiation therapy T2-weighted MRI SI-selected volumetry facilitates prediction of pathological complete response.

The predictive ability of different customer feedback metrics for retention

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de Haan, Evert; Verhoef, Peter C.; Wiesel, Thorsten

This study systematically compares different customer feedback metrics (CFMs) - namely customer satisfaction, the Net Promoter Score, and the Customer Effort Score - to test their ability to predict retention across a wide range of industries. We classify the CFMs according to a time focus (past,

Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

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Niknazar, Hamid; Nasrabadi, Ali Motie

Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

On the prediction of the Free Core Nutation

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Belda Palazón, Santiago; Ferrándiz, José M.; Heinkelmann, Robert; Nilsson, Tobias; Schuh, Harald; Modiri, Sadegh

2017-04-01

Consideration of the Free Core Nutation (FCN) model is obliged for improved modelling of the Celestial Pole Offsets (CPO), since it is the major source of inaccuracy or unexplained time variability with respect to the current IAU2000 nutation theory. FCN is excited from various geophysical sources and thus it cannot be known until it is inferred from observations. However, given that the variations of the FCN signal are slow and seldom abrupt, we examine whether the availability of new FCN empirical models (i.e., Malkin 2007; Krásná et al. 2013; Belda et al. 2016) can be exploited to make reasonably accurate predictions of the FCN signal before observing it. In this work we study CPO predictions for the FCN model provided by Belda et al. 2016, in which the amplitude coefficients were estimated by using a sliding window with a width of 400 days and with a minimal displacement between the subsequent fits (one-day step). Our results exhibit two significant features: (1) the prediction of the FCN signal can be done on the basis of its prior amplitudes with a mean error of about 30 microarcseconds per year, with an apparent linear trend; and (2) the Weighted Root Mean Square (wrms) of the differences between the CPO produced by the IERS (International Earth Rotation and Reference Systems Service) and our predicted FCN exhibit an exponential slow-growing pattern, with a wmrs close to 120 microarcseconds along several months. Therefore a substantial improvement with respect to the CPO operational predictions of the IERS Rapid Service/Prediction Centre can be achieved.

Photoplethysmography Signal Analysis for Optimal Region-of-Interest Determination in Video Imaging on a Built-In Smartphone under Different Conditions

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Yunyoung Nam

2017-10-01

Full Text Available Smartphones and tablets are widely used in medical fields, which can improve healthcare and reduce healthcare costs. Many medical applications for smartphones and tablets have already been developed and widely used by both health professionals and patients. Specifically, video recordings of fingertips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal, it is possible to estimate a continuous heart rate and a respiratory rate. To estimate the heart rate and respiratory rate accurately, which pixel regions of the color bands give the most optimal signal quality should be investigated. In this paper, we investigate signal quality to determine the best signal quality by the largest amplitude values for three different smartphones under different conditions. We conducted several experiments to obtain reliable PPG signals and compared the PPG signal strength in the three color bands when the flashlight was both on and off. We also evaluated the intensity changes of PPG signals obtained from the smartphones with motion artifacts and fingertip pressure force. Furthermore, we have compared the PSNR of PPG signals of the full-size images with that of the region of interests (ROIs.

Early detection of structual changes in random signal

International Nuclear Information System (INIS)

Kuroda, Yoshiteru; Yokota, Katsuhiro

1981-01-01

Early detection of structual changes in observed random signal is very important from the point of system diagnosis. In this paper, the following procedures are applied to this problem and the results are compared. (1) auto-regressive model to random signal to calculate the prediction error, i.e., the defference between observed and predicted values. (2) auto-regressive method to caluculate the sum of the prediction error. (3) a method is based on AIC (Akaike Information Criterion). Simulation is made of these procedures, indicating their merits and demerits as a diagostic tools. (author)

Application of neural computing paradigms for signal validation

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

1989-01-01

Signal validation and process monitoring problems often require the prediction of one or more process variables in a system. The feasibility of applying neural network paradigms to relate one variable with a set of other related variables is studied. The backpropagation network (BPN) is applied to develop models of signals from both a commercial power plant and the EBR-II. Modification of the BPN algorithm is studied with emphasis on the speed of network training and the accuracy of prediction. The prediction of process variables in a Westinghouse PWR is presented in this paper

CAMS prototype extension: Integration of data acquisition, signal validation, tracking simulator, predictive simulator, state identification, and probabilistic safety assessment

International Nuclear Information System (INIS)

Fantoni, Paolo; Iguchi, Yukihiro; Meyer, Geir; Soerensen, Aimar; Van Dyck, Claude

1996-04-01

CAMS (Computerized Accident Management Support) is a system that will provide assistance to the staff in the control room, in the technical support centre, and in a national safety centre. These three groups of users do not need the same type of support. Support is offered in identification of the plant state, in assessment of the future development of the accident, and in planning of accident mitigation strategies. Last year the predictive part of the system was tested at a safety exercise arranged by the Swedish Nuclear Inspectorate, and found to be a useful tool, with potential for further development. Now, new methods are added in signal validation, state identification, tracking simulation, predictive simulation, risk monitoring, and man-machine interface design. A prototype will be demonstrated at Loen in May 1996. This prototype is still under development. The purpose of this prototype is to test those methods in a simulated environment to verify that the developed functions, using different techniques, can work together producing the desired result in an efficient way. The plan is to test these techniques at power plants. During the CAMS design, a considerable effort has been given to maintain the generality of the CAMS concept; although the referenced process has been so far a BWR nuclear plant, the use of this structure and design can be applied to other processes, including non-nuclear processes. The research programme is carried out in close cooperation with member organizations (author)

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

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Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

2016-01-01

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

Signal Control for Reducing Vehicle NOx and CO2 Emissions Based on Prediction of Arrival Traffic Flows at Intersections

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Oda, Toshihiko

Nitrogen oxide (NOx) and carbon dioxide (CO2) emissions from vehicles have been increasing every year because of the growing number of vehicles, and they cause serious environmental problems such as air pollution and global warming. To alleviate these problems, this paper proposes a new traffic signal control method for reducing vehicle NOx and CO2 emissions on arterial roads. To this end, we first model the amount of vehicle emissions as a function of the traffic delay and the number of stops at intersections. This step is necessary because it is difficult to obtain the amount of emissions directly using traffic control systems. Second, we introduce a signal control model in which the control parameters are continuously updated on the basis of predictions of arrival traffic flows at intersections. The signal timings are calculated in such a manner so as to minimize the weighted sum of the two emissions, which depend on the traffic flow. To evaluate the validity of this method, simulation experiments are carried out on an arterial road. The experiments show that the proposed method significantly outperforms existing methods in reducing both the emissions and travel time.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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Kumari Sonal Choudhary

2016-06-01

Full Text Available Epithelial to mesenchymal transition (EMT is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR, are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E and mesenchymal (EGFR_M networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

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Choudhary, Kumari Sonal; Rohatgi, Neha; Halldorsson, Skarphedinn; Briem, Eirikur; Gudjonsson, Thorarinn; Gudmundsson, Steinn; Rolfsson, Ottar

2016-06-01

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

Determination of the X, Y coordinates of a pulsed ultrasonic source of signals

International Nuclear Information System (INIS)

Sokolov, B.V.; Shemyakin, V.V.

1975-01-01

A range of problems in predicting the emergency state of large-scale vessel housings are determined for subsequent solution involving acoustic emission phenomena. The authors specify the position of a given problem and present substantial grounds for selecting the minimum number of group signal receivers for unambiguous calculation of the location of the source. Relationships are obtained between X, Y - the coordinates of the pulse signal source - and experimentally measured time differences in recording of signals by group receivers. A criterion is given for selecting the true signal group combination when the receivers simultaneously record waves from several sources. Specific suggestions are made regarding the experimental information to be stored in a central computer for subsequent processing [ru

Spontaneous Alpha Power Lateralization Predicts Detection Performance in an Un-Cued Signal Detection Task.

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Gonzalo Boncompte

Full Text Available Focusing one's attention by external guiding stimuli towards a specific area of the visual field produces systematical neural signatures. One of the most robust is the change in topological distribution of oscillatory alpha band activity across parieto-occipital cortices. In particular, decreases in alpha activity over contralateral and/or increases over ipsilateral scalp sites, respect to the side of the visual field where attention was focused. This evidence comes mainly from experiments where an explicit cue informs subjects where to focus their attention, thus facilitating detection of an upcoming target stimulus. However, recent theoretical models of attention have highlighted a stochastic or non-deterministic component related to visuospatial attentional allocation. In an attempt to evidence this component, here we analyzed alpha activity in a signal detection paradigm in the lack of informative cues; in the absence of preceding information about the location (and time of appearance of target stimuli. We believe that the unpredictability of this situation could be beneficial for unveiling this component. Interestingly, although total alpha power did not differ between Seen and Unseen conditions, we found a significant lateralization of alpha activity over parieto-occipital electrodes, which predicted behavioral performance. This effect had a smaller magnitude compared to paradigms in which attention is externally guided (cued. However we believe that further characterization of this spontaneous component of attention is of great importance in the study of visuospatial attentional dynamics. These results support the presence of a spontaneous component of visuospatial attentional allocation and they advance pre-stimulus alpha-band lateralization as one of its neural signatures.

Transionospheric propagation predictions

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Klobucher, J. A.; Basu, S.; Basu, S.; Bernhardt, P. A.; Davies, K.; Donatelli, D. E.; Fremouw, E. J.; Goodman, J. M.; Hartmann, G. K.; Leitinger, R.

1979-01-01

The current status and future prospects of the capability to make transionospheric propagation predictions are addressed, highlighting the effects of the ionized media, which dominate for frequencies below 1 to 3 GHz, depending upon the state of the ionosphere and the elevation angle through the Earth-space path. The primary concerns are the predictions of time delay of signal modulation (group path delay) and of radio wave scintillation. Progress in these areas is strongly tied to knowledge of variable structures in the ionosphere ranging from the large scale (thousands of kilometers in horizontal extent) to the fine scale (kilometer size). Ionospheric variability and the relative importance of various mechanisms responsible for the time histories observed in total electron content (TEC), proportional to signal group delay, and in irregularity formation are discussed in terms of capability to make both short and long term predictions. The data base upon which predictions are made is examined for its adequacy, and the prospects for prediction improvements by more theoretical studies as well as by increasing the available statistical data base are examined.

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability.

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Armbruster-Genç, Diana J N; Ueltzhöffer, Kai; Fiebach, Christian J

2016-04-06

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories. Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential

Different minimally important clinical difference (MCID) scores lead to different clinical prediction rules for the Oswestry disability index for the same sample of patients.

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Schwind, Julie; Learman, Kenneth; O'Halloran, Bryan; Showalter, Christopher; Cook, Chad

2013-05-01

Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. To explore the relationship between predictive models and the MCID cutpoint on the ODI. Data were collected from 16 outpatient physical therapy clinics in 10 states. Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 001). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. The primary limitations include sub-optimal sample size and study design. There is extreme variability among predictive models created using different MCIDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

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Pant, Jeevan K; Krishnan, Sridhar

2014-04-01

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks

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Rouzbeh Forouzandeh Jonaghani

2018-01-01

Full Text Available In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc., the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF and cumulative distribution function (CDF of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions.

Signal modulation as a mechanism for handicap disposal

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Gavassa, Sat; Silva, Ana C.; Gonzalez, Emmanuel; Stoddard, Philip K.

2012-01-01

Signal honesty may be compromised when heightened competition provides incentive for signal exaggeration. Some degree of honesty might be maintained by intrinsic handicap costs on signalling or through imposition of extrinsic costs, such as social punishment of low quality cheaters. Thus, theory predicts a delicate balance between signal enhancement and signal reliability that varies with degree of social competition, handicap cost, and social cost. We investigated whether male sexual signals of the electric fish Brachyhypopomus gauderio would become less reliable predictors of body length when competition provides incentives for males to boost electric signal amplitude. As expected, social competition under natural field conditions and in controlled lab experiments drove males to enhance their signals. However, signal enhancement improved the reliability of the information conveyed by the signal, as revealed in the tightening of the relationship between signal amplitude and body length. Signal augmentation in male B. gauderio was independent of body length, and thus appeared not to be curtailed through punishment of low quality (small) individuals. Rather, all individuals boosted their signals under high competition, but those whose signals were farthest from the predicted value under low competition boosted signal amplitude the most. By elimination, intrinsic handicap cost of signal production, rather than extrinsic social cost, appears to be the basis for the unexpected reinforcement of electric signal honesty under social competition. Signal modulation may provide its greatest advantage to the signaller as a mechanism for handicap disposal under low competition rather than as a mechanism for exaggeration of quality under high competition. PMID:22665940

Detection of different-time-scale signals in the length of day variation based on EEMD analysis technique

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Wenbin Shen

2016-05-01

Full Text Available Scientists pay great attention to different-time-scale signals in the length of day (LOD variations ΔLOD, which provide signatures of the Earth's interior structure, couplings among different layers, and potential excitations of ocean and atmosphere. In this study, based on the ensemble empirical mode decomposition (EEMD, we analyzed the latest time series of ΔLOD data spanning from January 1962 to March 2015. We observed the signals with periods and amplitudes of about 0.5 month and 0.19 ms, 1.0 month and 0.19 ms, 0.5 yr and 0.22 ms, 1.0 yr and 0.18 ms, 2.28 yr and 0.03 ms, 5.48 yr and 0.05 ms, respectively, in coincidence with the results of predecessors. In addition, some signals that were previously not definitely observed by predecessors were detected in this study, with periods and amplitudes of 9.13 d and 0.12 ms, 13.69 yr and 0.10 ms, respectively. The mechanisms of the LOD fluctuations of these two signals are still open.

Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker

International Nuclear Information System (INIS)

Vedam, S.S.; Kini, V.R.; Keall, P.J.; Ramakrishnan, V.; Mostafavi, H.; Mohan, R.

2003-01-01

The aim of this work was to quantify the ability to predict intrafraction diaphragm motion from an external respiration signal during a course of radiotherapy. The data obtained included diaphragm motion traces from 63 fluoroscopic lung procedures for 5 patients, acquired simultaneously with respiratory motion signals (an infrared camera-based system was used to track abdominal wall motion). During these sessions, the patients were asked to breathe either (i) without instruction, (ii) with audio prompting, or (iii) using visual feedback. A statistical general linear model was formulated to describe the relationship between the respiration signal and diaphragm motion over all sessions and for all breathing training types. The model parameters derived from the first session for each patient were then used to predict the diaphragm motion for subsequent sessions based on the respiration signal. Quantification of the difference between the predicted and actual motion during each session determined our ability to predict diaphragm motion during a course of radiotherapy. This measure of diaphragm motion was also used to estimate clinical target volume (CTV) to planning target volume (PTV) margins for conventional, gated, and proposed four-dimensional (4D) radiotherapy. Results from statistical analysis indicated a strong linear relationship between the respiration signal and diaphragm motion (p<0.001) over all sessions, irrespective of session number (p=0.98) and breathing training type (p=0.19). Using model parameters obtained from the first session, diaphragm motion was predicted in subsequent sessions to within 0.1 cm (1 σ) for gated and 4D radiotherapy. Assuming a 0.4 cm setup error, superior-inferior CTV-PTV margins of 1.1 cm for conventional radiotherapy could be reduced to 0.8 cm for gated and 4D radiotherapy. The diaphragm motion is strongly correlated with the respiration signal obtained from the abdominal wall. This correlation can be used to predict diaphragm

Measuring soil sydric content by the attenuation of a microwave signal

International Nuclear Information System (INIS)

Orden, S.; Goldberg, M.; Landini, A.; Sainato, C.; Bottini, L.; Arrigo, N.

1995-01-01

Measuring soil water content by means of microwave signal attenuation. The attenuation of microwave signal was used to measure the moisture of various soils. Samples of three soils with different textures and organic matter contents were used. The attenuation of the transmitted electromagnetic signal was measured for each sample with different values of soil moisture. Linear regression models were used to fit the experimental values obtained, and the 95% prediction interval was estimated for the attenuation. From the comparison between the moisture values obtained with this method and those of the gravimetric method, the advantages of the first one are seen, both in speed and in the possibility to estimate the in situ moisture, even if this method has a greater relative error. This method would be useful to operate an automatic control irrigation system, preventing hydric stress when the values of soil moisture reach near field capacity. (author) [es

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

Science.gov (United States)

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Discrimination of amygdala response predicts future separation anxiety in youth with early deprivation.

Science.gov (United States)

Green, Shulamite A; Goff, Bonnie; Gee, Dylan G; Gabard-Durnam, Laurel; Flannery, Jessica; Telzer, Eva H; Humphreys, Kathryn L; Louie, Jennifer; Tottenham, Nim

2016-10-01

Significant disruption in caregiving is associated with increased internalizing symptoms, most notably heightened separation anxiety symptoms during childhood. It is also associated with altered functional development of the amygdala, a neurobiological correlate of anxious behavior. However, much less is known about how functional alterations of amygdala predict individual differences in anxiety. Here, we probed amygdala function following institutional caregiving using very subtle social-affective stimuli (trustworthy and untrustworthy faces), which typically result in large differences in amygdala signal, and change in separation anxiety behaviors over a 2-year period. We hypothesized that the degree of differentiation of amygdala signal to trustworthy versus untrustworthy face stimuli would predict separation anxiety symptoms. Seventy-four youths mean (SD) age = 9.7 years (2.64) with and without previous institutional care, who were all living in families at the time of testing, participated in an fMRI task designed to examine differential amygdala response to trustworthy versus untrustworthy faces. Parents reported on their children's separation anxiety symptoms at the time of scan and again 2 years later. Previous institutional care was associated with diminished amygdala signal differences and behavioral differences to the contrast of untrustworthy and trustworthy faces. Diminished differentiation of these stimuli types predicted more severe separation anxiety symptoms 2 years later. Older age at adoption was associated with diminished differentiation of amygdala responses. A history of institutional care is associated with reduced differential amygdala responses to social-affective cues of trustworthiness that are typically exhibited by comparison samples. Individual differences in the degree of amygdala differential responding to these cues predict the severity of separation anxiety symptoms over a 2-year period. These findings provide a biological

Analysis of simulated ECT signals obtained at tubesheet and tube expansion area

International Nuclear Information System (INIS)

Song, Sung Chul; Lee, Yun Tai; Jung, Hee Sung; Shin, Young Kil

2006-01-01

Steam generator(SG) tubes are expanded inside tubesheet holes by using explosive or hydraulic methods to be fixed in the tubesheet. In the tube expansion process, it is important to minimize the crevice gap between tubesheet and expanded tube. In this paper, absolute and differential signals are predicted by a numerical method for several different locations of tube expansion inside and outside the tubesheet and signal variations due to tubesheet, tube expansion and operating frequency are observed. Results show that low frequency is good for detecting tubesheet location in both types of signals and high frequency is suitable for sizing of tube diameter as well as the detection of transition region. Also learned is that the absolute signal is good for measuring tube diameter, while the differential signal is good for locating the top of tubesheet and both ends of the transition region.

Flanking signal and mature peptide residues influence signal peptide cleavage

Directory of Open Access Journals (Sweden)

Ranganathan Shoba

2008-12-01

Full Text Available Abstract Background Signal peptides (SPs mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I, and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i eukaryotes (Euk (ii Gram-positive (Gram+ bacteria, and (iii Gram-negative (Gram- bacteria. Results In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups. Conclusion We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs.

Differences between signal currents for both polarities of applied voltages on cavity ionization chambers

International Nuclear Information System (INIS)

Takata, N.

2000-01-01

It is necessary to obtain precise values of signal currents for the measurement of exposure rates for gamma rays with cavity ionization chambers. Signal currents are usually expected to have the same absolute values for both polarities of applied voltages. In the case of cylindrical cavity ionization chambers, volume recombination loss of ion pairs depends on the polarity of the applied voltage. This is because the values of mobility are different for positive and negative ions. It was found, however, that values of signal currents from a cylindrical ionization chamber change slightly more with a negative than with a positive applied voltage, even after being corrected for volume recombination loss. Moreover, absolute values of saturation currents, which are obtained by extrapolation of correction of initial recombination and diffusion loss, were larger for the negative than for the positive applied voltage. It is known from an experiment with parallel plate ionization chambers that when negative voltage is applied to the repeller electrode, the saturated signal current decreases with an increase in the applied voltage. This is because secondary electrons are accelerated and the stopping power of air for these electrons decreases. When positive voltage is applied, the reverse is true. The effects of acceleration and deceleration of secondary electrons by the electric field thus seem to cause a tendency opposite to the experimental results on the signal currents from cylindrical ionization chambers. The experimental results for the cylindrical ionization chamber can be explained as follows. When negative voltage is applied, secondary electrons are attracted to the central (collecting) electrode. Consequently, the path length of the trajectories of these secondary electrons in the ionization volume increases and signal current increases. The energy gain from the electric field by secondary electrons which stop in the ionization chamber also contributes to the

The Theory of Linear Prediction

CERN Document Server

Vaidyanathan, PP

2007-01-01

Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vecto

Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.

Science.gov (United States)

Lin, Shih-Yun; Lai, Ying-Chih; Hsia, Chi-Chun; Su, Pei-Fang; Chang, Chih-Han

2017-09-01

This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HR net ). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HR net , respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.

Basketball predictions in the NCAAB and NBA: Similarities and differences

OpenAIRE

Zimmermann , Albrecht

2016-01-01

International audience; Most work on predicting the outcome of basketball matches so far has focused on NCAAB games. Since NCAAB and professional (NBA) basketball have a number of differences, it is not clear to what degree these results can be transferred. We explore a number of different representations, training settings, and classifiers, and contrast their results on NCAAB and NBA data. We find that adjusted efficiencies work well for the NBA, that the NCAAB regular season is not ideal fo...

Orbiter CCTV video signal noise analysis

Science.gov (United States)

Lawton, R. M.; Blanke, L. R.; Pannett, R. F.

1977-01-01

The amount of steady state and transient noise which will couple to orbiter CCTV video signal wiring is predicted. The primary emphasis is on the interim system, however, some predictions are made concerning the operational system wiring in the cabin area. Noise sources considered are RF fields from on board transmitters, precipitation static, induced lightning currents, and induced noise from adjacent wiring. The most significant source is noise coupled to video circuits from associated circuits in common connectors. Video signal crosstalk is the primary cause of steady state interference, and mechanically switched control functions cause the largest induced transients.

Prediction of reflood behavior for tests with differing axial power shapes using WCOBRA/TRAC

International Nuclear Information System (INIS)

Bajorek, S.M.; Hochreiter, L.E.

1991-01-01

The rector core power shape can vary over the fuel cycle due to load follow, control rod movement, burnup effects and Xenon transients. a best estimate thermal-hydraulic code must be able to accurately predict the reflooding behavior for different axial power shapes in order to find the power shapes effects on the loss-of-coolant peak cladding temperature. Several different reflood heat transfer experiments have been performed at the same or similar PWR reflood conditions with different axial power shapes. These experiments have different rod diameters, were full length, 3.65 m (12 feet) in height, and had simple egg crate grids. The WCOBRA/TRAC code has been used to model several different tests from these three experiments to examine the code's capability to predict the reflood transient for different power shapes, with a consistent model and noding scheme. This paper describes these different experiments, their power shapes, and the test conditions. The WCOBRA/TRAC code is described as well as the noding scheme, and the calculated results will be compared in detail with the test data rod temperatures. An overall assessment of the code's predictions of these experiments is presented

An analysis of reactivity prediction during the reactor start-up process

International Nuclear Information System (INIS)

Bajgl, Josef; Krysl, Vaclav; Svarny, Jiri

2015-01-01

The different VVER-440 core fuel loadings subcriticality evaluations are performed during the start-up process by boron dilution or control assembly withdrawn by macrocode MOBY-DICK calculations. The dynamic reactivity and quasicritical reactivity are compared and sensitivity of reactivity prediction at the low boundary of start-up interval (ρ = -0,01) has been provided on the basis of different modelling of ionization chamber (IC) response calculation. Special attention is paid to the impact of power distribution and spontaneous fission distribution form factor on IC response correction during control assembly movement. Precision and robustness of different corrections of IC signal processing in real core start-up processed IC signals was evaluated.

Myxovirus resistance, osteopontin and suppressor of cytokine signaling 3 polymorphisms predict hepatitis C virus therapy response in an admixed patient population: comparison with IL28B.

Science.gov (United States)

Angelo, Ana Luiza Dias; Cavalcante, Lourianne Nascimento; Abe-Sandes, Kiyoko; Machado, Taísa Bonfim; Lemaire, Denise Carneiro; Malta, Fernanda; Pinho, João Renato; Lyra, Luiz Guilherme Costa; Lyra, Andre Castro

2013-10-01

Suppressor of cytokine signaling 3, myxovirus resistance protein and osteopontin gene polymorphisms may influence the therapeutic response in patients with chronic hepatitis C, and an association with IL28 might increase the power to predict sustained virologic response. Our aims were to evaluate the association between myxovirus resistance protein, osteopontin and suppressor of cytokine signaling 3 gene polymorphisms in combination with IL28B and to assess the therapy response in hepatitis C patients treated with pegylated-interferon plus ribavirin. Myxovirus resistance protein, osteopontin, suppressor of cytokine signaling 3 and IL28B polymorphisms were analyzed by PCR-restriction fragment length polymorphism, direct sequencing and real-time PCR. Ancestry was determined using genetic markers. We analyzed 181 individuals, including 52 who were sustained virologic responders. The protective genotype frequencies among the sustained virologic response group were as follows: the G/G suppressor of cytokine signaling 3 (rs4969170) (62.2%); T/T osteopontin (rs2853744) (60%); T/T osteopontin (rs11730582) (64.3%); and the G/T myxovirus resistance protein (rs2071430) genotype (54%). The patients who had ≥3 of the protective genotypes from the myxovirus resistance protein, the suppressor of cytokine signaling 3 and osteopontin had a greater than 90% probability of achieving a sustained response (pC/C IL28B genotype was present in 58.8% of the subjects in this group. The sustained virological response rates increased to 85.7% and 91.7% by analyzing C/C IL28B with the T/T osteopontin genotype at rs11730582 and the G/G suppressor of cytokine signaling 3 genotype, respectively. Genetic ancestry analysis revealed an admixed population. Hepatitis C genotype 1 patients who were responders to interferon-based therapy had a high frequency of multiple protective polymorphisms in the myxovirus resistance protein, osteopontin and suppressor of cytokine signaling 3 genes. The combined

Speech Intelligibility Prediction Based on Mutual Information

DEFF Research Database (Denmark)

Jensen, Jesper; Taal, Cees H.

2014-01-01

This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related...... to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using...... a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting...

Mechanical signaling coordinates the embryonic heartbeat

Science.gov (United States)

Chiou, Kevin K.; Rocks, Jason W.; Chen, Christina Yingxian; Cho, Sangkyun; Merkus, Koen E.; Rajaratnam, Anjali; Robison, Patrick; Tewari, Manorama; Vogel, Kenneth; Majkut, Stephanie F.; Prosser, Benjamin L.; Discher, Dennis E.; Liu, Andrea J.

2016-01-01

In the beating heart, cardiac myocytes (CMs) contract in a coordinated fashion, generating contractile wave fronts that propagate through the heart with each beat. Coordinating this wave front requires fast and robust signaling mechanisms between CMs. The primary signaling mechanism has long been identified as electrical: gap junctions conduct ions between CMs, triggering membrane depolarization, intracellular calcium release, and actomyosin contraction. In contrast, we propose here that, in the early embryonic heart tube, the signaling mechanism coordinating beats is mechanical rather than electrical. We present a simple biophysical model in which CMs are mechanically excitable inclusions embedded within the extracellular matrix (ECM), modeled as an elastic-fluid biphasic material. Our model predicts strong stiffness dependence in both the heartbeat velocity and strain in isolated hearts, as well as the strain for a hydrogel-cultured CM, in quantitative agreement with recent experiments. We challenge our model with experiments disrupting electrical conduction by perfusing intact adult and embryonic hearts with a gap junction blocker, β-glycyrrhetinic acid (BGA). We find this treatment causes rapid failure in adult hearts but not embryonic hearts—consistent with our hypothesis. Last, our model predicts a minimum matrix stiffness necessary to propagate a mechanically coordinated wave front. The predicted value is in accord with our stiffness measurements at the onset of beating, suggesting that mechanical signaling may initiate the very first heartbeats. PMID:27457951

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Directory of Open Access Journals (Sweden)

Fei Gao

2015-01-01

Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue

Directory of Open Access Journals (Sweden)

Li Wang

2015-07-01

Full Text Available In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

Directory of Open Access Journals (Sweden)

Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

Study on characteristics of EMR signals induced from fracture of rock samples and their application in rockburst prediction in copper mine

Science.gov (United States)

Liu, Xiaofei; Wang, Enyuan

2018-06-01

A rockburst is a dynamic disaster that occurs during underground excavation or mining which has been a serious threat to safety. Rockburst prediction and control are as important as any other underground engineering in deep mines. For this paper, we tested electromagnetic radiation (EMR) signals generated during the deformation and fracture of rock samples from a copper mine under uniaxial compression, tension, and cycle-loading experiments, analyzed the changes in the EMR intensity, pulse number, and frequency corresponding to the loading, and a high correlation between these EMR parameters and the applied loading was observed. EMR apparently reflects the deformation and fracture status to the loaded rock. Based on this experimental work, we invented the KBD5-type EMR monitor and used it to test EMR signals generated in the rock surrounding the Hongtoushan copper mine. From the test results, it is determined the responding characteristics of EMR signals generated by changes in mine-generated stresses and stress concentrations and it is proposed that this EMR monitoring method can be used to provide early warning for rockbursts.

Differences in TGF-β1 signaling and clinicopathologic characteristics of histologic subtypes of gastric cancer.

Science.gov (United States)

Pak, Kyung Ho; Kim, Dong Hoon; Kim, Hyunki; Lee, Do Hyung; Cheong, Jae-Ho

2016-02-04

Aberrant TGF-β1 signaling is suggested to be involved in gastric carcinogenesis. However, the role of TGF-β1 in intestinal-type [i-GC] and diffuse-type [d-GC] gastric cancer remains largely unknown. In this study, we evaluated the expression of TGF-β1 signaling molecules and compared the clinicopathological features of i-GC and d-GC. Patients (n=365, consecutive) who underwent curative gastrectomy for gastric adenocarcinoma in 2005 were enrolled. We performed immunohistochemical staining of TGF-β1, TGF-β1 receptor-2 (TβR2), Smad4, p-ERK1/2, TGF-activated kinase (TAK)1, and p-Akt in 68 paraffin-embedded tumor blocks (33 i-GC and 35 d-GC), scored the expression according to the extent of staining, and evaluated differences between the histologic subtypes. Patients with d-GC differed from those with i-GC as follows: younger and more likely to be female; more aggressive stage; higher recurrence rate. The expression of TGF-β1 and TβR2 was higher in i-GC (P = 0.05 and P Smad4, a representative molecule of the Smad-dependent pathway, was decreased in both subtypes. TAK1 and p-Akt, two major molecules involved in the Smad-independent pathway, were over-expressed (69 ~87% of cases stained), without a statistically significant difference between i-GC and d-GC. Of note, the expression of p-ERK1/2, a Smad-independent pathway, was significantly increased in i-GC (P = 0.008). The clinicopathological characteristics vary in different histologic gastric cancer subtypes. Although TGF-β1 signaling in gastric cancer cells appears hyper-activated in i-GC compared to d-GC, the Smad-dependent pathway seems down-regulated while the Smad-independent pathway seems up-regulated in both histologic subtypes.

Discrimination of Cylinders with Different Wall Thicknesses using Neural Networks and Simulated Dolphin Sonar Signals

DEFF Research Database (Denmark)

Andersen, Lars Nonboe; Au, Whitlow; Larsen, Jan

1999-01-01

This paper describes a method integrating neural networks into a system for recognizing underwater objects. The system is based on a combination of simulated dolphin sonar signals, simulated auditory filters and artificial neural networks. The system is tested on a cylinder wall thickness...... difference experiment and demonstrates high accuracy for small wall thickness differences. Results from the experiment are compared with results obtained by a false killer whale (pseudorca crassidens)....

An Online Full-Body Motion Recognition Method Using Sparse and Deficient Signal Sequences

Directory of Open Access Journals (Sweden)

Chengyu Guo

2014-01-01

Full Text Available This paper presents a method to recognize continuous full-body human motion online by using sparse, low-cost sensors. The only input signals needed are linear accelerations without any rotation information, which are provided by four Wiimote sensors attached to the four human limbs. Based on the fused hidden Markov model (FHMM and autoregressive process, a predictive fusion model (PFM is put forward, which considers the different influences of the upper and lower limbs, establishes HMM for each part, and fuses them using a probabilistic fusion model. Then an autoregressive process is introduced in HMM to predict the gesture, which enables the model to deal with incomplete signal data. In order to reduce the number of alternatives in the online recognition process, a graph model is built that rejects parts of motion types based on the graph structure and previous recognition results. Finally, an online signal segmentation method based on semantics information and PFM is presented to finish the efficient recognition task. The results indicate that the method is robust with a high recognition rate of sparse and deficient signals and can be used in various interactive applications.

Macrocell path loss prediction using artificial intelligence techniques

Science.gov (United States)

Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

2014-04-01

The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

Computational identification of signalling pathways in Plasmodium falciparum.

Science.gov (United States)

Oyelade, Jelili; Ewejobi, Itunu; Brors, Benedikt; Eils, Roland; Adebiyi, Ezekiel

2011-06-01

Malaria is one of the world's most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a network under modified biologically motivated constraints. We predicted several important signalling transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by the merozoites. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design

Knowledge Prediction of Different Students’ Categories Trough an Intelligent Testing

Directory of Open Access Journals (Sweden)

Irina Zheliazkova

2015-02-01

Full Text Available Student’s modelling, prediction, and grouping have remained open research issues in the multi-disciplinary area of educational data mining. The purpose of this study is to predict the correct knowledge of different categories of tested students: good, very good, and all. The experimental data set was gathered from an intelligent post-test performance containing student’s correct, missing, and wrong knowledge, time undertaken, and final mark. The proposed procedure applies consequently correlation analysis, simple and multiple liner regression using a power specialized tool for programming by the teacher. The finding is that the accuracy of the procedure is satisfactory for the three students’ categories. The experiment also confirms some findings of other researchers and previous authors’ team studies.

HiggsSignals. Confronting arbitrary Higgs sectors with measurements at the Tevatron and the LHC

Energy Technology Data Exchange (ETDEWEB)

Bechtle, Philip [Bonn Univ. (Germany). Physikalisches Inst.; Heinemeyer, Sven [Instituto de Fisica de Cantabria (CSIC-UC), Santander (Spain); Staal, Oscar [Stockholm Univ. (Sweden). The Oskar Klein Centre; Stefaniak, Tim [Bonn Univ. (Germany). Physikalisches Inst.; Bonn Univ. (Germany). Bethe Center for Theoretical Physics; Weiglein, Georg [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)

2013-05-15

HiggsSignals is a Fortran90 computer code that allows to test the compatibility of Higgs sector predictions against Higgs rates and masses measured at the LHC or the Tevatron. Arbitrary models with any number of Higgs bosons can be investigated using a model-independent input scheme based on HiggsBounds. The test is based on the calculation of a {chi}{sup 2} measure from the predictions and the measured Higgs rates and masses, with the ability of fully taking into account systematics and correlations for the signal rate predictions, luminosity and Higgs mass predictions. It features two complementary methods for the test. First, the peak-centered method, in which each observable is defined by a Higgs signal rate measured at a specific hypothetical Higgs mass, corresponding to a tentative Higgs signal. Second, the mass-centered method, where the test is evaluated by comparing the signal rate measurement to the theory prediction at the Higgs mass predicted by the model. The program allows for the simultaneous use of both methods, which is useful in testing models with multiple Higgs bosons. The code automatically combines the signal rates of multiple Higgs bosons if their signals cannot be resolved by the experimental analysis. We compare results obtained with HiggsSignals to official ATLAS and CMS results for various examples of Higgs property determinations and find very good agreement. A few examples of HiggsSignals applications are provided, going beyond the scenarios investigated by the LHC collaborations. For models with more than one Higgs boson we recommend to use HiggsSignals and HiggsBounds in parallel to exploit the full constraining power of Higgs search exclusion limits and the measurements of the signal seen at m{sub H} {approx} 125.5 GeV.

Different Signal Enhancement Pathways of Attention and Consciousness Underlie Perception in Humans.

Science.gov (United States)

van Boxtel, Jeroen J A

2017-06-14

It is not yet known whether attention and consciousness operate through similar or largely different mechanisms. Visual processing mechanisms are routinely characterized by measuring contrast response functions (CRFs). In this report, behavioral CRFs were obtained in humans (both males and females) by measuring afterimage durations over the entire range of inducer stimulus contrasts to reveal visual mechanisms behind attention and consciousness. Deviations relative to the standard CRF, i.e., gain functions, describe the strength of signal enhancement, which were assessed for both changes due to attentional task and conscious perception. It was found that attention displayed a response-gain function, whereas consciousness displayed a contrast-gain function. Through model comparisons, which only included contrast-gain modulations, both contrast-gain and response-gain effects can be explained with a two-level normalization model, in which consciousness affects only the first level and attention affects only the second level. These results demonstrate that attention and consciousness can effectively show different gain functions because they operate through different signal enhancement mechanisms. SIGNIFICANCE STATEMENT The relationship between attention and consciousness is still debated. Mapping contrast response functions (CRFs) has allowed (neuro)scientists to gain important insights into the mechanistic underpinnings of visual processing. Here, the influence of both attention and consciousness on these functions were measured and they displayed a strong dissociation. First, attention lowered CRFs, whereas consciousness raised them. Second, attention manifests itself as a response-gain function, whereas consciousness manifests itself as a contrast-gain function. Extensive model comparisons show that these results are best explained in a two-level normalization model in which consciousness affects only the first level, whereas attention affects only the second level

Predictive analytics technology review: Similarity-based modeling and beyond

Energy Technology Data Exchange (ETDEWEB)

Herzog, James; Doan, Don; Gandhi, Devang; Nieman, Bill

2010-09-15

Over 11 years ago, SmartSignal introduced Predictive Analytics for eliminating equipment failures, using its patented SBM technology. SmartSignal continues to lead and dominate the market and, in 2010, went one step further and introduced Predictive Diagnostics. Now, SmartSignal is combining Predictive Diagnostics with RCM methodology and industry expertise. FMEA logic reengineers maintenance work management, eliminates unneeded inspections, and focuses efforts on the real issues. This integrated solution significantly lowers maintenance costs, protects against critical asset failures, and improves commercial availability, and reduces work orders 20-40%. Learn how.

Audiovisual biofeedback improves motion prediction accuracy.

Science.gov (United States)

Pollock, Sean; Lee, Danny; Keall, Paul; Kim, Taeho

2013-04-01

The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.

Design principles of paradoxical signaling in the immune system

Science.gov (United States)

Hart, Yuval

A widespread feature of cell-cell signaling systems is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T-cells. The role of such paradoxical signaling remains unclear. We suggest that this mechanism provides homeostatic concentration of cells, independent of initial conditions. The crux of the paradoxical mechanism is the combination of a positive and a negative feedback loops creating two stable states - an OFF state and an ON state. Experimentally, we found that CD4 + cells grown in culture with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels (ON-state). Below an initial threshold, cell density decayed to extinction (OFF-state). Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.

Individual differences in episodic memory abilities predict successful prospective memory output monitoring.

Science.gov (United States)

Hunter Ball, B; Pitães, Margarida; Brewer, Gene A

2018-02-07

Output monitoring refers to memory for one's previously completed actions. In the context of prospective memory (PM) (e.g., remembering to take medication), failures of output monitoring can result in repetitions and omissions of planned actions (e.g., over- or under-medication). To be successful in output monitoring paradigms, participants must flexibly control attention to detect PM cues as well as engage controlled retrieval of previous actions whenever a particular cue is encountered. The current study examined individual differences in output monitoring abilities in a group of younger adults differing in attention control (AC) and episodic memory (EM) abilities. The results showed that AC ability uniquely predicted successful cue detection on the first presentation, whereas EM ability uniquely predicted successful output monitoring on the second presentation. The current study highlights the importance of examining external correlates of PM abilities and contributes to the growing body of research on individual differences in PM.

Fast digitizing and digital signal processing of detector signals

International Nuclear Information System (INIS)

Hannaske, Roland

2008-01-01

A fast-digitizer data acquisition system recently installed at the neutron time-of-flight experiment nELBE, which is located at the superconducting electron accelerator ELBE of Forschungszentrum Dresden-Rossendorf, is tested with two different detector types. Preamplifier signals from a high-purity germanium detector are digitized, stored and finally processed. For a precise determination of the energy of the detected radiation, the moving-window deconvolution algorithm is used to compensate the ballistic deficit and different shaping algorithms are applied. The energy resolution is determined in an experiment with γ-rays from a 22 Na source and is compared to the energy resolution achieved with analogously processed signals. On the other hand, signals from the photomultipliers of barium fluoride and plastic scintillation detectors are digitized. These signals have risetimes of a few nanoseconds only. The moment of interaction of the radiation with the detector is determined by methods of digital signal processing. Therefore, different timing algorithms are implemented and tested with data from an experiment at nELBE. The time resolutions achieved with these algorithms are compared to each other as well as to reference values coming from analog signal processing. In addition to these experiments, some properties of the digitizing hardware are measured and a program for the analysis of stored, digitized data is developed. The analysis of the signals shows that the energy resolution achieved with the 10-bit digitizer system used here is not competitive to a 14-bit peak-sensing ADC, although the ballistic deficit can be fully corrected. However, digital methods give better result in sub-ns timing than analog signal processing. (orig.)

Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling.

Directory of Open Access Journals (Sweden)

Masanao Sato

Full Text Available Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2. This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i the components of the network are highly interconnected; and (ii negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector

The Relationship Between Acoustic Signal Typing and Perceptual Evaluation of Tracheoesophageal Voice Quality for Sustained Vowels.

Science.gov (United States)

Clapham, Renee P; van As-Brooks, Corina J; van Son, Rob J J H; Hilgers, Frans J M; van den Brekel, Michiel W M

2015-07-01

To investigate the relationship between acoustic signal typing and perceptual evaluation of sustained vowels produced by tracheoesophageal (TE) speakers and the use of signal typing in the clinical setting. Two evaluators independently categorized 1.75-second segments of narrow-band spectrograms according to acoustic signal typing and independently evaluated the recording of the same segments on a visual analog scale according to overall perceptual acoustic voice quality. The relationship between acoustic signal typing and overall voice quality (as a continuous scale and as a four-point ordinal scale) was investigated and the proportion of inter-rater agreement as well as the reliability between the two measures is reported. The agreement between signal type (I-IV) and ordinal voice quality (four-point scale) was low but significant, and there was a significant linear relationship between the variables. Signal type correctly predicted less than half of the voice quality data. There was a significant main effect of signal type on continuous voice quality scores with significant differences in median quality scores between signal types I-IV, I-III, and I-II. Signal typing can be used as an adjunct to perceptual and acoustic evaluation of the same stimuli for TE speech as part of a multidimensional evaluation protocol. Signal typing in its current form provides limited predictive information on voice quality, and there is significant overlap between signal types II and III and perceptual categories. Future work should consider whether the current four signal types could be refined. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Understanding the Mind or Predicting Signal-Dependent Action? Performance of Children With and Without Autism on Analogues of the False-Belief Task

OpenAIRE

Bowler, D. M.; Briskman, J.; Gurvidi, N.; Fornells-Ambrojo, M.

2005-01-01

To evaluate the claim that correct performance on unexpected transfer false-belief tasks specifically involves mental-state understanding, two experiments were carried out with children with autism, intellectual disabilities, and typical development. In both experiments, children were given a standard unexpected transfer false-belief task and a mental-state-free, mechanical analogue task in which participants had to predict the destination of a train based on true or false signal information....

High-Density Signal Interface Electromagnetic Radiation Prediction for Electromagnetic Compatibility Evaluation.

Energy Technology Data Exchange (ETDEWEB)

Halligan, Matthew

2017-11-01

Radiated power calculation approaches for practical scenarios of incomplete high- density interface characterization information and incomplete incident power information are presented. The suggested approaches build upon a method that characterizes power losses through the definition of power loss constant matrices. Potential radiated power estimates include using total power loss information, partial radiated power loss information, worst case analysis, and statistical bounding analysis. A method is also proposed to calculate radiated power when incident power information is not fully known for non-periodic signals at the interface. Incident data signals are modeled from a two-state Markov chain where bit state probabilities are derived. The total spectrum for windowed signals is postulated as the superposition of spectra from individual pulses in a data sequence. Statistical bounding methods are proposed as a basis for the radiated power calculation due to the statistical calculation complexity to find a radiated power probability density function.

A Feasibility Study on Detection of Insider Threats based on Human Bio-signals

Energy Technology Data Exchange (ETDEWEB)

Suh, Young A; Yim, Man-Sung [KAIST, Daejeon (Korea, Republic of)

2016-10-15

The insider threat means that trusted workers in an organization might carry out harmful acts from the negligent use of classified data to potentially sabotage the workplace. Surveys and studies conducted over the last decade have consistently shown the critical nature of the insider threats problem, in both government and private sectors. The shortcomings of existing systems, such as mental self-assessment and peer review, are very subjective, biased-assessments and employed infrequently. To overcome these limitations, this study investigates the feasibility of detecting and predicting an insider threat by using human biodata, from smart wearable devices. This paper showed the feasibility of predicting and detecting insider threats using EEG, GSR and ECG signals. In the section 2.1, two research hypotheses were established to identify the significant difference on EEG, GSR and ECG signals when the subject decided bad action and is the placed in deceit situation. These hypotheses were tested using two kinds of pilot experiments in the form of input (stimulus) and output (checking response of physiological signals and reaction time)

Cellular and circuit properties supporting different sensory coding strategies in electric fish and other systems.

Science.gov (United States)

Marsat, Gary; Longtin, André; Maler, Leonard

2012-08-01

Neural codes often seem tailored to the type of information they must carry. Here we contrast the encoding strategies for two different communication signals in electric fish and describe the underlying cellular and network properties that implement them. We compare an aggressive signal that needs to be quickly detected, to a courtship signal whose quality needs to be evaluated. The aggressive signal is encoded by synchronized bursts and a predictive feedback input is crucial in separating background noise from the communication signal. The courtship signal is accurately encoded through a heterogenous population response allowing the discrimination of signal differences. Most importantly we show that the same strategies are used in other systems arguing that they evolved similar solutions because they faced similar tasks. Copyright © 2012 Elsevier Ltd. All rights reserved.

Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

Directory of Open Access Journals (Sweden)

Ravindra Kumar

2017-09-01

Full Text Available Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83

Active diagnosis of hybrid systems - A model predictive approach

DEFF Research Database (Denmark)

Tabatabaeipour, Seyed Mojtaba; Ravn, Anders P.; Izadi-Zamanabadi, Roozbeh

2009-01-01

A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and fault...... can be used as a test signal for sanity check at the commissioning or for detection of faults hidden by regulatory actions of the controller. The method is tested on the two tank benchmark example. ©2009 IEEE....

Working Group 3: Greenhouse signal detection

International Nuclear Information System (INIS)

Barnett, T.; Ellsaesser, H.; Groisman, P.Ya.; Grotch, S.; Jenkins, G.; Karoly, D.; Riches, M.; Santer, B.; Schoenwiese, C.; Vinnikov, K.; Zwiers, F.

1990-01-01

Quantitative efforts to detect the greenhouse-gas signal (GHG) in nature are in their infancy. The reasons for this state of affairs are numerous. It is only in the last few years that GCMs have advanced to the point where their simulations of GHG signals might be marginally believable. Without reasonably good a priori predictions of expected GHG signals from the models, the detection problem is moot. The observational data sets describing changes in the global climate system over the last 50-100 years needed for adequate detection studies have also only come into existence in the last five years. Finally, no coherent, generally-agreed-on detection strategy has been developed by the scientific community interested in the GHG problem. The lack of adequate model predictions and observational sets are largely responsible for this latter condition. The rudimentary detection efforts that have been conducted have generally been based on recognizing the fingerprint of GHG signals in the oceans and atmosphere. GCM results for 1 x 2 x CO 2 equilibrium runs have been used to search for GHG effects induced in tropospheric air and ocean surface temperature fields since the early 1900s. No significant effect has been found

Individual differences in working memory capacity predict visual attention allocation.

Science.gov (United States)

Bleckley, M Kathryn; Durso, Francis T; Crutchfield, Jerry M; Engle, Randall W; Khanna, Maya M

2003-12-01

To the extent that individual differences in working memory capacity (WMC) reflect differences in attention (Baddeley, 1993; Engle, Kane, & Tuholski, 1999), differences in WMC should predict performance on visual attention tasks. Individuals who scored in the upper and lower quartiles on the OSPAN working memory test performed a modification of Egly and Homa's (1984) selective attention task. In this task, the participants identified a central letter and localized a displaced letter flashed somewhere on one of three concentric rings. When the displaced letter occurred closer to fixation than the cue implied, high-WMC, but not low-WMC, individuals showed a cost in the letter localization task. This suggests that low-WMC participants allocated attention as a spotlight, whereas those with high WMC showed flexible allocation.

Acoustic Signals and Systems

DEFF Research Database (Denmark)

2008-01-01

The Handbook of Signal Processing in Acoustics will compile the techniques and applications of signal processing as they are used in the many varied areas of Acoustics. The Handbook will emphasize the interdisciplinary nature of signal processing in acoustics. Each Section of the Handbook...... will present topics on signal processing which are important in a specific area of acoustics. These will be of interest to specialists in these areas because they will be presented from their technical perspective, rather than a generic engineering approach to signal processing. Non-specialists, or specialists...... from different areas, will find the self-contained chapters accessible and will be interested in the similarities and differences between the approaches and techniques used in different areas of acoustics....

Predicting persuasion-induced behavior change from the brain.

Science.gov (United States)

Falk, Emily B; Berkman, Elliot T; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D

2010-06-23

Although persuasive messages often alter people's self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance.

Individual differences in the propensity to approach signals vs goals promote different adaptations in the dopamine system of rats.

Science.gov (United States)

Flagel, Shelly B; Watson, Stanley J; Robinson, Terry E; Akil, Huda

2007-04-01

The way an individual responds to cues associated with rewards may be a key determinant of vulnerability to compulsive behavioral disorders. We studied individual differences in Pavlovian conditioned approach behavior and examined the expression of neurobiological markers associated with the dopaminergic system, the same neural system implicated in incentive motivational processes. Pavlovian autoshaping procedures consisted of the brief presentation of an illuminated retractable lever (conditioned stimulus) followed by the response-independent delivery of a food pellet (unconditioned stimulus), which lead to a Pavlovian conditioned response. In situ hybridization was performed on brains obtained either following the first or last (fifth) day of training. Two phenotypes emerged. Sign-trackers (ST) exhibited behavior that seemed to be largely controlled by the cue that signaled impending reward delivery; whereas goal-trackers (GT) preferentially approached the location where the reward was delivered. Following a single training session, ST showed greater expression of dopamine D1 receptor mRNA relative to GT. After 5 days of training, GT exhibited greater expression levels of tyrosine hydroxylase, dopamine transporter, and dopamine D2 receptor mRNA relative to ST. These findings suggest that the development of approach behavior towards signals vs goal leads to distinct adaptations in the dopamine system. The sign-tracker vs goal-tracker phenotype may prove to be a valuable animal model to investigate individual differences in the way incentive salience is attributed to environmental stimuli, which may contribute to the development of addiction and other compulsive behavioral disorders.

Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

Science.gov (United States)

Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan

2004-09-01

Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics

Prediction error, ketamine and psychosis: An updated model.

Science.gov (United States)

Corlett, Philip R; Honey, Garry D; Fletcher, Paul C

2016-11-01

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms - which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis. © The Author(s) 2016.

Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

Science.gov (United States)

Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

2014-09-01

The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

Prediction of the optimum hybridization conditions of dot-blot-SNP analysis using estimated melting temperature of oligonucleotide probes.

Science.gov (United States)

Shiokai, Sachiko; Kitashiba, Hiroyasu; Nishio, Takeshi

2010-08-01

Although the dot-blot-SNP technique is a simple cost-saving technique suitable for genotyping of many plant individuals, optimization of hybridization and washing conditions for each SNP marker requires much time and labor. For prediction of the optimum hybridization conditions for each probe, we compared T (m) values estimated from nucleotide sequences using the DINAMelt web server, measured T (m) values, and hybridization conditions yielding allele-specific signals. The estimated T (m) values were comparable to the measured T (m) values with small differences of less than 3 degrees C for most of the probes. There were differences of approximately 14 degrees C between the specific signal detection conditions and estimated T (m) values. Change of one level of SSC concentrations of 0.1, 0.2, 0.5, and 1.0x SSC corresponded to a difference of approximately 5 degrees C in optimum signal detection temperature. Increasing the sensitivity of signal detection by shortening the exposure time to X-ray film changed the optimum hybridization condition for specific signal detection. Addition of competitive oligonucleotides to the hybridization mixture increased the suitable hybridization conditions by 1.8. Based on these results, optimum hybridization conditions for newly produced dot-blot-SNP markers will become predictable.

A causal link between prediction errors, dopamine neurons and learning.

Science.gov (United States)

Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

2013-07-01

Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

Optimizing signal output: effects of viscoelasticity and difference frequency on vibroacoustic radiation of tissue-mimicking phantoms

Science.gov (United States)

Namiri, Nikan K.; Maccabi, Ashkan; Bajwa, Neha; Badran, Karam W.; Taylor, Zachary D.; St. John, Maie A.; Grundfest, Warren S.; Saddik, George N.

2018-02-01

Vibroacoustography (VA) is an imaging technology that utilizes the acoustic response of tissues to a localized, low frequency radiation force to generate a spatially resolved, high contrast image. Previous studies have demonstrated the utility of VA for tissue identification and margin delineation in cancer tissues. However, the relationship between specimen viscoelasticity and vibroacoustic emission remains to be fully quantified. This work utilizes the effects of variable acoustic wave profiles on unique tissue-mimicking phantoms (TMPs) to maximize VA signal power according to tissue mechanical properties, particularly elasticity. A micro-indentation method was utilized to provide measurements of the elastic modulus for each biological replica. An inverse relationship was found between elastic modulus (E) and VA signal amplitude among homogeneous TMPs. Additionally, the difference frequency (Δf ) required to reach maximum VA signal correlated with specimen elastic modulus. Peak signal diminished with increasing Δf among the polyvinyl alcohol specimen, suggesting an inefficient vibroacoustic response by the specimen beyond a threshold of resonant Δf. Comparison of these measurements may provide additional information to improve tissue modeling, system characterization, as well as insights into the unique tissue composition of tumors in head and neck cancer patients.

Reduced endogenous Ca2+ buffering speeds active zone Ca2+ signaling.

Science.gov (United States)

Delvendahl, Igor; Jablonski, Lukasz; Baade, Carolin; Matveev, Victor; Neher, Erwin; Hallermann, Stefan

2015-06-09

Fast synchronous neurotransmitter release at the presynaptic active zone is triggered by local Ca(2+) signals, which are confined in their spatiotemporal extent by endogenous Ca(2+) buffers. However, it remains elusive how rapid and reliable Ca(2+) signaling can be sustained during repetitive release. Here, we established quantitative two-photon Ca(2+) imaging in cerebellar mossy fiber boutons, which fire at exceptionally high rates. We show that endogenous fixed buffers have a surprisingly low Ca(2+)-binding ratio (∼ 15) and low affinity, whereas mobile buffers have high affinity. Experimentally constrained modeling revealed that the low endogenous buffering promotes fast clearance of Ca(2+) from the active zone during repetitive firing. Measuring Ca(2+) signals at different distances from active zones with ultra-high-resolution confirmed our model predictions. Our results lead to the concept that reduced Ca(2+) buffering enables fast active zone Ca(2+) signaling, suggesting that the strength of endogenous Ca(2+) buffering limits the rate of synchronous synaptic transmission.

Modelling of polysomnographic respiratory measurements for artefact detection and signal restoration

International Nuclear Information System (INIS)

Rathnayake, S I; Abeyratne, U R; Hukins, C; Duce, B

2008-01-01

Polysomnography (PSG), which incorporates measures of sleep with measures of EEG arousal, air flow, respiratory movement and oxygenation, is universally regarded as the reference standard in diagnosing sleep-related respiratory diseases such as obstructive sleep apnoea syndrome. Over 15 channels of physiological signals are measured from a subject undergoing a typical overnight PSG session. The signals often suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artefact-corrupted signal segments are visually detected and removed from further consideration. This is a highly time-consuming process, and subjective judgement is required for the job. During typical sleep scoring sessions, the target is the detection of segments of diagnostic interest, and signal restoration is not utilized for distorted segments. In this paper, we propose a novel framework for artefact detection and signal restoration based on the redundancy among respiratory flow signals. We focus on the air flow (thermistor sensors) and nasal pressure signals which are clinically significant in detecting respiratory disturbances. The method treats the respiratory system and other organs that provide respiratory-related inputs/outputs to it (e.g., cardiovascular, brain) as a possibly nonlinear coupled-dynamical system, and uses the celebrated Takens embedding theorem as the theoretical basis for signal prediction. Nonlinear prediction across time (self-prediction) and signals (cross-prediction) provides us with a mechanism to detect artefacts as unexplained deviations. In addition to detection, the proposed method carries the potential to correct certain classes of artefacts and restore the signal. In this study, we categorize commonly occurring artefacts and distortions in air flow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. The results we obtained from a database of clinical

Predicting mass loading as a function of pressure difference across prefilter/HEPA filter systems

International Nuclear Information System (INIS)

Novick, V.J.; Klassen, J.F.; Monson, P.R.

1992-01-01

The purpose of this work is to develop a methodology for predicting the mass loading and pressure drop effects on a prefilter/ HEPA filter system. The methodology relies on the use of empirical equations for the specific resistance of the aerosol loaded filter as a function of the particle diameter. These correlations relate the pressure difference across a filter to the mass loading on the filter and accounts for aerosol particle density effects. These predictions are necessary for the efficient design of new filtration systems and for risk assessment studies of existing filter systems. This work specifically addresses the prefilter/HEPA filter Airborne Activity Confinement Systems (AACS) at the Savannah River Plant. In order to determine the mass loading on the system, it is necessary to establish the efficiency characteristics for the prefilter, the mass loading characteristics of the prefilter measured as a function of pressure difference across the prefilter, and the mass loading characteristics of the HEPA filter as a function of pressure difference across the filter. Furthermore, the efficiency and mass loading characteristics need to be determined as a function of the aerosol particle diameter. A review of the literature revealed that no previous work had been performed to characterize the prefilter material of interest. In order to complete the foundation of information necessary to predict total mass loadings on prefilter/HEPA filter systems, it was necessary to determine the prefilter efficiency and mass loading characteristics. The measured prefilter characteristics combined with the previously determined HEPA filter characteristics allowed the resulting pressure difference across both filters to be predicted as a function of total particle mass for a given particle distribution. These predictions compare favorably to experimental measurements (±25%)

Safety analysis of urban signalized intersections under mixed traffic.

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S, Anjana; M V L R, Anjaneyulu

2015-02-01

This study examined the crash causative factors of signalized intersections under mixed traffic using advanced statistical models. Hierarchical Poisson regression and logistic regression models were developed to predict the crash frequency and severity of signalized intersection approaches. The prediction models helped to develop general safety countermeasures for signalized intersections. The study shows that exclusive left turn lanes and countdown timers are beneficial for improving the safety of signalized intersections. Safety is also influenced by the presence of a surveillance camera, green time, median width, traffic volume, and proportion of two wheelers in the traffic stream. The factors that influence the severity of crashes were also identified in this study. As a practical application, the safe values of deviation of green time provided from design green time, with varying traffic volume, is presented in this study. This is a useful tool for setting the appropriate green time for a signalized intersection approach with variations in the traffic volume. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: extending the predictions to different years and different sites

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Reggente, Matteo; Dillner, Ann M.; Takahama, Satoshi

2016-02-01

Organic carbon (OC) and elemental carbon (EC) are major components of atmospheric particulate matter (PM), which has been associated with increased morbidity and mortality, climate change, and reduced visibility. Typically OC and EC concentrations are measured using thermal-optical methods such as thermal-optical reflectance (TOR) from samples collected on quartz filters. In this work, we estimate TOR OC and EC using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE Teflon) filters using partial least square regression (PLSR) calibrated to TOR OC and EC measurements for a wide range of samples. The proposed method can be integrated with analysis of routinely collected PTFE filter samples that, in addition to OC and EC concentrations, can concurrently provide information regarding the functional group composition of the organic aerosol. We have used the FT-IR absorbance spectra and TOR OC and EC concentrations collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA). We used 526 samples collected in 2011 at seven sites to calibrate the models, and more than 2000 samples collected in 2013 at 17 sites to test the models. Samples from six sites are present both in the calibration and test sets. The calibrations produce accurate predictions both for samples collected at the same six sites present in the calibration set (R2 = 0.97 and R2 = 0.95 for OC and EC respectively), and for samples from 9 of the 11 sites not included in the calibration set (R2 = 0.96 and R2 = 0.91 for OC and EC respectively). Samples collected at the other two sites require a different calibration model to achieve accurate predictions. We also propose a method to anticipate the prediction error; we calculate the squared Mahalanobis distance in the feature space (scores determined by PLSR) between new spectra and spectra in the calibration set. The squared Mahalanobis distance provides a crude method for assessing the

Hippo pathway phylogenetics predicts monoubiquitylation of Salvador and Merlin/Nf2.

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Robert G Wisotzkey

Full Text Available Recently we employed phylogenetics to predict that the cellular interpretation of TGF-β signals is modulated by monoubiquitylation cycles affecting the Smad4 signal transducer/tumor suppressor. This prediction was subsequently validated by experiments in flies, frogs and mammalian cells. Here we apply a phylogenetic approach to the Hippo pathway and predict that two of its signal transducers, Salvador and Merlin/Nf2 (also a tumor suppressor are regulated by monoubiquitylation. This regulatory mechanism does not lead to protein degradation but instead serves as a highly efficient "off/on" switch when the protein is subsequently deubiquitylated. Overall, our study shows that the creative application of phylogenetics can predict new roles for pathway components and new mechanisms for regulating intercellular signaling pathways.

Reward positivity: Reward prediction error or salience prediction error?

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Heydari, Sepideh; Holroyd, Clay B

2016-08-01

The reward positivity is a component of the human ERP elicited by feedback stimuli in trial-and-error learning and guessing tasks. A prominent theory holds that the reward positivity reflects a reward prediction error signal that is sensitive to outcome valence, being larger for unexpected positive events relative to unexpected negative events (Holroyd & Coles, 2002). Although the theory has found substantial empirical support, most of these studies have utilized either monetary or performance feedback to test the hypothesis. However, in apparent contradiction to the theory, a recent study found that unexpected physical punishments also elicit the reward positivity (Talmi, Atkinson, & El-Deredy, 2013). The authors of this report argued that the reward positivity reflects a salience prediction error rather than a reward prediction error. To investigate this finding further, in the present study participants navigated a virtual T maze and received feedback on each trial under two conditions. In a reward condition, the feedback indicated that they would either receive a monetary reward or not and in a punishment condition the feedback indicated that they would receive a small shock or not. We found that the feedback stimuli elicited a typical reward positivity in the reward condition and an apparently delayed reward positivity in the punishment condition. Importantly, this signal was more positive to the stimuli that predicted the omission of a possible punishment relative to stimuli that predicted a forthcoming punishment, which is inconsistent with the salience hypothesis. © 2016 Society for Psychophysiological Research.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

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Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Different populations of subthalamic neurons encode cocaine vs. sucrose reward and predict future error.

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Lardeux, Sylvie; Paleressompoulle, Dany; Pernaud, Remy; Cador, Martine; Baunez, Christelle

2013-10-01

The search for treatment of cocaine addiction raises the challenge to find a way to diminish motivation for the drug without decreasing it for natural rewards. Subthalamic nucleus (STN) inactivation decreases motivation for cocaine while increasing motivation for food, suggesting that STN can dissociate different rewards. Here, we investigated how rat STN neurons respond to cues predicting cocaine or sucrose and to reward delivery while rats are performing a discriminative stimuli task. We show that different neuronal populations of STN neurons encode cocaine and sucrose. In addition, we show that STN activity at the cue onset predicts future error. When changing the reward predicted unexpectedly, STN neurons show capacities of adaptation, suggesting a role in reward-prediction error. Furthermore, some STN neurons show a response to executive error (i.e., "oops neurons") that is specific to the missed reward. These results position the STN as a nexus where natural rewards and drugs of abuse are coded differentially and can influence the performance. Therefore, STN can be viewed as a structure where action could be taken for the treatment of cocaine addiction.

A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

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Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

Gender differences in the factors predicting initial engagement at cardiac rehabilitation.

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Galdas, Paul Michael; Harrison, Alexander Stephen; Doherty, Patrick

2018-01-01

To determine whether there are gender differences in the factors that predict attendance at the initial cardiac rehabilitation baseline assessment (CR engagement) after referral. Using data from the National Audit of Cardiac Rehabilitation, we analysed data on 95 638 patients referred to CR following a cardiovascular diagnosis/treatment between 2013 and 2016. Eighteen factors that have been shown in previous research to be important predictors of CR participation were investigated and grouped into four categories: sociodemographic factors, cardiac risk factors, patient medical status and service-level factors. Logistic binary regression models were built for male patients and female patients, assessing the likelihood for CR engagement. Each included predictors such as age, number of comorbidities and social deprivation score. There were no important differences in the factors that predict the likelihood of CR engagement in men and women. Seven factors associated with a reduced probability of CR engagement, and eight factors associated with increased probability, were identified. Fourteen of the 15 factors identified as predicting the likelihood for engagement/non-engagement were the same for both men and women. Increasing age, being South Asian or non-white ethnicity (other than Black) and being single were all associated with a reduced likelihood of attending an initial CR baseline assessment in both men and women. Male patients with diabetes were 11% less likely to engage with CR; however, there was no significant association in women. Results showed that the overwhelmingly important determinant of CR engagement observed in both men and women was receiving an invitation to attend an assessment session (OR 4.223 men/4.033women; pgender differences in predictors of CR uptake should probably be more nuanced and informed by the stage of the patient care pathway.

Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.

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Raia, Valentina; Schilling, Marcel; Böhm, Martin; Hahn, Bettina; Kowarsch, Andreas; Raue, Andreas; Sticht, Carsten; Bohl, Sebastian; Saile, Maria; Möller, Peter; Gretz, Norbert; Timmer, Jens; Theis, Fabian; Lehmann, Wolf-Dieter; Lichter, Peter; Klingmüller, Ursula

2011-02-01

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

Assessing the Influence of Spatio-Temporal Context for Next Place Prediction using Different Machine Learning Approaches

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Jorim Urner

2018-04-01

Full Text Available For next place prediction, machine learning methods which incorporate contextual data are frequently used. However, previous studies often do not allow deriving generalizable methodological recommendations, since they use different datasets, methods for discretizing space, scales of prediction, prediction algorithms, and context data, and therefore lack comparability. Additionally, the cold start problem for new users is an issue. In this study, we predict next places based on one trajectory dataset but with systematically varying prediction algorithms, methods for space discretization, scales of prediction (based on a novel hierarchical approach, and incorporated context data. This allows to evaluate the relative influence of these factors on the overall prediction accuracy. Moreover, in order to tackle the cold start problem prevalent in recommender and prediction systems, we test the effect of training the predictor on all users instead of each individual one. We find that the prediction accuracy shows a varying dependency on the method of space discretization and the incorporated contextual factors at different spatial scales. Moreover, our user-independent approach reaches a prediction accuracy of around 75%, and is therefore an alternative to existing user-specific models. This research provides valuable insights into the individual and combinatory effects of model parameters and algorithms on the next place prediction accuracy. The results presented in this paper can be used to determine the influence of various contextual factors and to help researchers building more accurate prediction models. It is also a starting point for future work creating a comprehensive framework to guide the building of prediction models.

Chameleons communicate with complex colour changes during contests: different body regions convey different information.

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Ligon, Russell A; McGraw, Kevin J

2013-01-01

Many animals display static coloration (e.g. of feathers or fur) that can serve as a reliable sexual or social signal, but the communication function of rapidly changing colours (as in chameleons and cephalopods) is poorly understood. We used recently developed photographic and mathematical modelling tools to examine how rapid colour changes of veiled chameleons Chamaeleo calyptratus predict aggressive behaviour during male-male competitions. Males that achieved brighter stripe coloration were more likely to approach their opponent, and those that attained brighter head coloration were more likely to win fights; speed of head colour change was also an important predictor of contest outcome. This correlative study represents the first quantification of rapid colour change using organism-specific visual models and provides evidence that the rate of colour change, in addition to maximum display coloration, can be an important component of communication. Interestingly, the body and head locations of the relevant colour signals map onto the behavioural displays given during specific contest stages, with lateral displays from a distance followed by directed, head-on approaches prior to combat, suggesting that different colour change signals may evolve to communicate different information (motivation and fighting ability, respectively).

Polarized cellular patterns of endocannabinoid production and detection shape cannabinoid signaling in neurons

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Delphine eLadarre

2015-01-01

Full Text Available Neurons display important differences in plasma membrane composition between somatodendritic and axonal compartments, potentially leading to currently unexplored consequences in G-protein-coupled-receptor signaling. Here, by using highly-resolved biosensor imaging to measure local changes in basal levels of key signaling components, we explored features of type-1 cannabinoid receptor (CB1R signaling in individual axons and dendrites of cultured rat hippocampal neurons. Activation of endogenous CB1Rs led to rapid, Gi/o-protein- and cAMP-mediated decrease of cyclic-AMP-dependent protein kinase (PKA activity in the somatodendritic compartment. In axons, PKA inhibition was significantly stronger, in line with axonally-polarized distribution of CB1Rs. Conversely, inverse agonist AM281 produced marked rapid increase of basal PKA activation in somata and dendrites, but not in axons, removing constitutive activation of CB1Rs generated by local production of the endocannabinoid 2-arachidonoylglycerol (2-AG. Interestingly, somatodendritic 2-AG levels differently modified signaling responses to CB1R activation by Δ9-THC, the psychoactive compound of marijuana, and by the synthetic cannabinoids WIN55,212-2 and CP55,940. These highly contrasted differences in sub-neuronal signaling responses warrant caution in extrapolating pharmacological profiles, which are typically obtained in non-polarized cells, to predict in vivo responses of axonal (i.e. presynaptic GPCRs. Therefore, our results suggest that enhanced comprehension of GPCR signaling constraints imposed by neuronal cell biology may improve the understanding of neuropharmacological action.

Estrogen signaling modulates allergic inflammation and contributes to sex differences in asthma.

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Aleksander eKeselman

2015-11-01

Full Text Available Asthma is a chronic airway inflammatory disease that afflicts approximately 300 million people worldwide. It is characterized by airway constriction that leads to wheezing, coughing, and shortness of breath. The most common treatments are corticosteroids and β2-adrenergic receptor antagonists, which target inflammation and airway smooth muscle constriction, respectively. The incidence and severity of asthma is greater in women than in men, and women are more prone to develop corticosteroid-resistant or hard-to-treat asthma. Puberty, menstruation, pregnancy, menopause, and oral contraceptives are known to contribute to disease outcome in women, potentially suggesting a role for estrogen and other hormones impacting allergic inflammation. Currently, the mechanisms underlying these sex differences are poorly understood, although the effect of sex hormones, such as estrogen, on allergic inflammation is gaining interest. Asthma presents as a heterogeneous disease. In typical Th2-type allergic asthma, interleukin-4 and interleukin-13 predominate, driving IgE production and recruitment of eosinophils into the lungs. Chronic Th2-inflammation in the lung results in structural changes and activation of multiple immune cell types, leading to a deterioration of lung function over time. Most immune cells express estrogen receptors (ERα, ERβ, or the membrane-bound G-protein-coupled estrogen receptor to varying degrees and can respond to the hormone. Together these receptors have demonstrated the capacity to regulate a spectrum of immune functions, including adhesion, migration, survival, wound healing, and antibody and cytokine production. This review will cover the current understanding of estrogen signaling in allergic inflammation and discuss how this signaling may contribute to sex differences in asthma and allergy.

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

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Md. Rabiul Islam

2012-01-01

Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

Neural net prediction of tokamak plasma disruptions

International Nuclear Information System (INIS)

Hernandez, J.V.; Lin, Z.; Horton, W.; McCool, S.C.

1994-10-01

The computation based on neural net algorithms in predicting minor and major disruptions in TEXT tokamak discharges has been performed. Future values of the fluctuating magnetic signal are predicted based on L past values of the magnetic fluctuation signal, measured by a single Mirnov coil. The time step used (= 0.04ms) corresponds to the experimental data sampling rate. Two kinds of approaches are adopted for the task, the contiguous future prediction and the multi-timescale prediction. Results are shown for comparison. Both networks are trained through the back-propagation algorithm with inertial terms. The degree of this success indicates that the magnetic fluctuations associated with tokamak disruptions may be characterized by a relatively low-dimensional dynamical system

Altered carotid plaque signal among different repetition times on T1-weighted magnetic resonance plaque imaging with self-navigated radial-scan technique

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Narumi, Shinsuke; Ohba, Hideki; Mori, Kiyofumi; Ohura, Kazumasa; Ono, Ayumi; Terayama, Yasuo [Iwate Medical University, Department of Neurology and Gerontology, Morioka (Japan); Sasaki, Makoto [Iwate Medical University, Advanced Medical Research Center, Morioka (Japan); Ogasawara, Kuniaki [Iwate Medical University, Department of Neurosurgery, Morioka (Japan); Hitomi, Jiro [Iwate Medical University, Department of Anatomy, Morioka (Japan)

2010-04-15

Magnetic resonance (MR) plaque imaging for carotid arteries is usually performed by using an electrocardiograph (ECG)-gating technique to eliminate pulsation-related artifacts, which can affect the plaque signals because of varied repetition time (TR) among patients. Hence, we investigated whether differences in TR causes signal alterations of the carotid plaque by using a non-gated plaque imaging technique. We prospectively examined 19 patients with carotid stenosis by using a T1-weighted self-navigated radial-scan technique with TRs of 500, 700, and 900 ms. The signal intensity of the carotid plaque was measured, and the contrast ratio (CR) relative to the adjacent muscle was calculated. CRs of the carotid plaques were 1.39 {+-} 0.39, 1.29 {+-} 0.29, and 1.23 {+-} 0.24 with TRs of 500, 700, and 900 ms, respectively, and were significantly different. Among the plaques, those with a hyperintensity signal (CR > 1.5) and moderate-intensity signal (CR 1.2-1.5) at 500 ms showed a TR-dependent signal decrease (hyperintensity plaques, 1.82 {+-} 0.26; 1.61 {+-} 0.19; and 1.48 {+-} 0.17; moderate-intensity plaques, 1.33 {+-} 0.08; 1.26 {+-} 0.08; and 1.19 {+-} 0.07), while those with an isointensity signal (CR < 1.2) remained unchanged regardless of TR (0.96 {+-} 0.12, 0.96 {+-} 0.11, and 0.97 {+-} 0.13). The signal intensity of the carotid plaque on T1-weighted imaging significantly varies among different TRs and tends to decrease with longer TR. MR plaque imaging with short and constant TR settings that the ECG-gating method cannot realize would be preferable for evaluating plaque characteristics. (orig.)

Cognitive functioning differentially predicts different dimensions of older drivers' on-road safety.

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Aksan, Nazan; Anderson, Steve W; Dawson, Jeffrey; Uc, Ergun; Rizzo, Matthew

2015-02-01

The extent to which deficits in specific cognitive domains contribute to older drivers' safety risk in complex real-world driving tasks is not well understood. We selected 148 drivers older than 70 years of age both with and without neurodegenerative diseases (Alzheimer disease-AD and Parkinson disease-PD) from an existing driving database of older adults. Participant assessments included on-road driving safety and cognitive functioning in visuospatial construction, speed of processing, memory, and executive functioning. The standardized on-road drive test was designed to examine multiple facets of older driver safety including navigation performance (e.g., following a route, identifying landmarks), safety errors while concurrently performing secondary navigation tasks ("on-task" safety errors), and safety errors in the absence of any secondary navigation tasks ("baseline" safety errors). The inter-correlations of these outcome measures were fair to moderate supporting their distinctiveness. Participants with diseases performed worse than the healthy aging group on all driving measures and differences between those with AD and PD were minimal. In multivariate analyses, different domains of cognitive functioning predicted distinct facets of driver safety on road. Memory and set-shifting predicted performance in navigation-related secondary tasks, speed of processing predicted on-task safety errors, and visuospatial construction predicted baseline safety errors. These findings support broad assessments of cognitive functioning to inform decisions regarding older driver safety on the road and suggest navigation performance may be useful in evaluating older driver fitness and restrictions in licensing. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison on batch anaerobic digestion of five different livestock manures and prediction of biochemical methane potential (BMP) using different statistical models.

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Kafle, Gopi Krishna; Chen, Lide

2016-02-01

There is a lack of literature reporting the methane potential of several livestock manures under the same anaerobic digestion conditions (same inoculum, temperature, time, and size of the digester). To the best of our knowledge, no previous study has reported biochemical methane potential (BMP) predicting models developed and evaluated by solely using at least five different livestock manure tests results. The goal of this study was to evaluate the BMP of five different livestock manures (dairy manure (DM), horse manure (HM), goat manure (GM), chicken manure (CM) and swine manure (SM)) and to predict the BMP using different statistical models. Nutrients of the digested different manures were also monitored. The BMP tests were conducted under mesophilic temperatures with a manure loading factor of 3.5g volatile solids (VS)/L and a feed to inoculum ratio (F/I) of 0.5. Single variable and multiple variable regression models were developed using manure total carbohydrate (TC), crude protein (CP), total fat (TF), lignin (LIG) and acid detergent fiber (ADF), and measured BMP data. Three different kinetic models (first order kinetic model, modified Gompertz model and Chen and Hashimoto model) were evaluated for BMP predictions. The BMPs of DM, HM, GM, CM and SM were measured to be 204, 155, 159, 259, and 323mL/g VS, respectively and the VS removals were calculated to be 58.6%, 52.9%, 46.4%, 81.4%, 81.4%, respectively. The technical digestion time (T80-90, time required to produce 80-90% of total biogas production) for DM, HM, GM, CM and SM was calculated to be in the ranges of 19-28, 27-37, 31-44, 13-18, 12-17days, respectively. The effluents from the HM showed the lowest nitrogen, phosphorus and potassium concentrations. The effluents from the CM digesters showed highest nitrogen and phosphorus concentrations and digested SM showed highest potassium concentration. Based on the results of the regression analysis, the model using the variable of LIG showed the best (R(2

Predicting diet and consumption rate differences between and within species using gut ecomorphology.

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Griffen, Blaine D; Mosblack, Hallie

2011-07-01

1. Rapid environmental changes and pressing human needs to forecast the consequences of environmental change are increasingly driving ecology to become a predictive science. The need for effective prediction requires both the development of new tools and the refocusing of existing tools that may have previously been used primarily for purposes other than prediction. One such tool that historically has been more descriptive in nature is ecomorphology (the study of relationships between ecological roles and morphological adaptations of species and individuals). 2. Here, we examine relationships between diet and gut morphology for 15 species of brachyuran crabs, a group of pervasive and highly successful consumers for which trophic predictions would be highly valuable. 3. We show that patterns in crab stomach volume closely match some predictions of metabolic theory and demonstrate that individual diet differences and associated morphological variation reflect, at least in some instances, individual choice or diet specialization. 4. We then present examples of how stomach volume can be used to predict both the per cent herbivory of brachyuran crabs and the relative consumption rates of individual crabs. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

A common signal detection model accounts for both perception and discrimination of the watercolor effect.

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Devinck, Frédéric; Knoblauch, Kenneth

2012-03-21

Establishing the relation between perception and discrimination is a fundamental objective in psychophysics, with the goal of characterizing the neural mechanisms mediating perception. Here, we show that a procedure for estimating a perceptual scale based on a signal detection model also predicts discrimination performance. We use a recently developed procedure, Maximum Likelihood Difference Scaling (MLDS), to measure the perceptual strength of a long-range, color, filling-in phenomenon, the Watercolor Effect (WCE), as a function of the luminance ratio between the two components of its generating contour. MLDS is based on an equal-variance, gaussian, signal detection model and yields a perceptual scale with interval properties. The strength of the fill-in percept increased 10-15 times the estimate of the internal noise level for a 3-fold increase in the luminance ratio. Each observer's estimated scale predicted discrimination performance in a subsequent paired-comparison task. A common signal detection model accounts for both the appearance and discrimination data. Since signal detection theory provides a common metric for relating discrimination performance and neural response, the results have implications for comparing perceptual and neural response functions.

Predicting memory performance in normal ageing using different measures of hippocampal size

International Nuclear Information System (INIS)

Lye, T.C.; Creasey, H.; Kril, J.J.; Grayson, D.A.; Piguet, O.; Bennett, H.P.; Ridley, L.J.; Broe, G.A.

2006-01-01

A number of different methods have been employed to correct hippocampal volumes for individual variation in head size. Researchers have previously used qualitative visual inspection to gauge hippocampal atrophy. The purpose of this study was to determine the best measure(s) of hippocampal size for predicting memory functioning in 102 community-dwelling individuals over 80 years of age. Hippocampal size was estimated using magnetic resonance imaging (MRI) volumetry and qualitative visual assessment. Right and left hippocampal volumes were adjusted by three different estimates of head size: total intracranial volume (TICV), whole-brain volume including ventricles (WB+V) and a more refined measure of whole-brain volume with ventricles extracted (WB). We compared the relative efficacy of these three volumetric adjustment methods and visual ratings of hippocampal size in predicting memory performance using linear regression. All four measures of hippocampal size were significant predictors of memory performance. TICV-adjusted volumes performed most poorly in accounting for variance in memory scores. Hippocampal volumes adjusted by either measure of whole-brain volume performed equally well, although qualitative visual ratings of the hippocampus were at least as effective as the volumetric measures in predicting memory performance in community-dwelling individuals in the ninth or tenth decade of life. (orig.)

Sensitivity analysis of intracellular signaling pathway kinetics predicts targets for stem cell fate control.

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Alborz Mahdavi

2007-07-01

Full Text Available Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli. We developed and validated a computational model of signal transducer and activator of transcription-3 (Stat3 pathway kinetics, a signaling network involved in embryonic stem cell (ESC self-renewal. Our analysis identified novel pathway responses; for example, overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation. We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation. Our analysis demonstrates that signaling activation and desensitization (the inability to respond to ligand restimulation is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3, nuclear phosphatase activity, inhibition by suppressor of cytokine signaling, and receptor trafficking. This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal.

Different requirements for GFRα2-signaling in three populations of cutaneous sensory neurons.

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Kupari, Jussi; Airaksinen, Matti S

2014-01-01

Many primary sensory neurons in mouse dorsal root ganglia (DRG) express one or several GFRα's, the ligand-binding receptors of the GDNF family, and their common signaling receptor Ret. GFRα2, the principal receptor for neurturin, is expressed in most of the small nonpeptidergic DRG neurons, but also in some large DRG neurons that start to express Ret earlier. Previously, GFRα2 has been shown to be crucial for the soma size of small nonpeptidergic nociceptors and for their target innervation of glabrous epidermis. However, little is known about this receptor in other Ret-expressing DRG neuron populations. Here we have investigated two populations of Ret-positive low-threshold mechanoreceptors that innervate different types of hair follicles on mouse back skin: the small C-LTMRs and the large Aβ-LTMRs. Using GFRα2-KO mice and immunohistochemistry we found that, similar to the nonpeptidergic nociceptors, GFRα2 controls the cell size but not the survival of both C-LTMRs and Aβ-LTMRs. In contrast to the nonpeptidergic neurons, GFRα2 is not required for the target innervation of C-LTMRs and Aβ-LTMRs in the back skin. These results suggest that different factors drive target innervation in these three populations of neurons. In addition, the observation that the large Ret-positive DRG neurons lack GFRα2 immunoreactivity in mature animals suggests that these neurons switch their GFRα signaling pathways during postnatal development.

Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation

International Nuclear Information System (INIS)

Karlen, W; Kobayashi, K; Dumont, G A; Ansermino, J M

2012-01-01

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO 2 ). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms. (paper)

Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.

Science.gov (United States)

Karlen, W; Kobayashi, K; Ansermino, J M; Dumont, G A

2012-10-01

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

Embedding supplemental data in a digital video signal

NARCIS (Netherlands)

2005-01-01

An MPEG-encoded video signal includes groups of pictures (GOPs), each GOP having an intraframe coded (I) picture and a series of predictively encoded (P) pictures and bidirectionally predictively encoded (B) pictures. Usually, the GOP structure IBBPBBP . . . is used. However, in order to embed a

Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

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Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

2015-01-01

Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

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Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

2008-10-01

A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

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Donglei Du

Full Text Available Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A What is the general difference between signal emitting and receiving in a protein interactome? B Which proteins are among the top ranked in directional ranking? C Are high ranked proteins more evolutionarily conserved than low ranked ones? D Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

Systematic differences in signal emitting and receiving revealed by PageRank analysis of a human protein interactome.

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Du, Donglei; Lee, Connie F; Li, Xiu-Qing

2012-01-01

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell.

Gene expression profiling reveals different molecular patterns in G-protein coupled receptor signaling pathways between early- and late-onset preeclampsia.

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Liang, Mengmeng; Niu, Jianmin; Zhang, Liang; Deng, Hua; Ma, Jian; Zhou, Weiping; Duan, Dongmei; Zhou, Yuheng; Xu, Huikun; Chen, Longding

2016-04-01

Early-onset preeclampsia and late-onset preeclampsia have been regarded as two different phenotypes with heterogeneous manifestations; To gain insights into the pathogenesis of the two traits, we analyzed the gene expression profiles in preeclamptic placentas. A whole genome-wide microarray was used to determine the gene expression profiles in placental tissues from patients with early-onset (n = 7; 36 weeks) preeclampsia and their controls who delivered preterm (n = 5; 36 weeks). Genes were termed differentially expressed if they showed a fold-change ≥ 2 and q-value preeclampsia (177 genes were up-regulated and 450 were down-regulated). Gene ontology analysis identified significant alterations in several biological processes; the top two were immune response and cell surface receptor linked signal transduction. Among the cell surface receptor linked signal transduction-related, differentially expressed genes, those involved in the G-protein coupled receptor protein signaling pathway were significantly enriched. G-protein coupled receptor signaling pathway related genes, such as GPR124 and MRGPRF, were both found to be down-regulated in early-onset preeclampsia. The results were consistent with those of western blotting that the abundance of GPR124 was lower in early-onset compared with late-onset preeclampsia. The different gene expression profiles reflect the different levels of transcription regulation between the two conditions and supported the hypothesis that they are separate disease entities. Moreover, the G-protein coupled receptor signaling pathway related genes may contribute to the mechanism underlying early- and late-onset preeclampsia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Age-Related Differences in Goals: Testing Predictions from Selection, Optimization, and Compensation Theory and Socioemotional Selectivity Theory

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Penningroth, Suzanna L.; Scott, Walter D.

2012-01-01

Two prominent theories of lifespan development, socioemotional selectivity theory and selection, optimization, and compensation theory, make similar predictions for differences in the goal representations of younger and older adults. Our purpose was to test whether the goals of younger and older adults differed in ways predicted by these two…

CCTOP: a Consensus Constrained TOPology prediction web server.

Science.gov (United States)

Dobson, László; Reményi, István; Tusnády, Gábor E

2015-07-01

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Design principles of nuclear receptor signaling: how complex networking improves signal transduction

Science.gov (United States)

Kolodkin, Alexey N; Bruggeman, Frank J; Plant, Nick; Moné, Martijn J; Bakker, Barbara M; Campbell, Moray J; van Leeuwen, Johannes P T M; Carlberg, Carsten; Snoep, Jacky L; Westerhoff, Hans V

2010-01-01

The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018

Variations in Environmental Signals in Tree-Ring Indices in Trees with Different Growth Potential.

Directory of Open Access Journals (Sweden)

Polona Hafner

Full Text Available We analysed two groups of Quercus robur trees, growing at nearby plots with different micro-location condition (W-wet and D-dry in the floodplain Krakovo forest, Slovenia. In the study we compared the growth response of two different tree groups to environmental variables, the potential signal stored in earlywood (EW structure and the potential difference of the information stored in carbon isotope discrimination of EW and latewood (LW. For that purpose EW and LW widths and carbon isotope discrimination for the period 1970-2008 AD were measured. EW and LW widths were measured on stained microscopic slides and chronologies were standardised using the ARSTAN program. α-cellulose was extracted from pooled EW and LW samples and homogenized samples were further analysed using an elemental analyser and IRMS. We discovered that W oaks grew significantly better over the whole analysed period. The difference between D and W oaks was significant in all analysed variables with the exception of stable carbon isotope discrimination in latewood. In W oaks, latewood widths correlated with summer (June to August climatic variables, while carbon isotope discrimination was more connected to River Krka flow during the summer. EW discrimination correlated with summer and autumn River Krka flow of the previous year, while latewood discrimination correlated with flow during the current year. In the case of D oaks, the environmental signal appears to be vague, probably due to less favourable growth conditions resulting in markedly reduced increments. Our study revealed important differences in responses to environmental factors between the two oak groups of different physiological conditions that are preconditioned by environmental stress. Environmental information stored in tree-ring features may vary, even within the same forest stand, and largely depends on the micro-environment. Our analysis confirmed our assumptions that separate EW and LW analysis of widths and

Modeling signal-to-noise ratio of otoacoustic emissions in workers exposed to different industrial noise levels

Directory of Open Access Journals (Sweden)

Parvin Nassiri

2016-01-01

Full Text Available Introduction: Noise is considered as the most common cause of harmful physical effects in the workplace. A sound that is generated from within the inner ear is known as an otoacoustic emission (OAE. Distortion-product otoacoustic emissions (DPOAEs assess evoked emission and hearing capacity. The aim of this study was to assess the signal-to-noise ratio in different frequencies and at different times of the shift work in workers exposed to various levels of noise. It was also aimed to provide a statistical model for signal-to-noise ratio (SNR of OAEs in different frequencies based on the two variables of sound pressure level (SPL and exposure time. Materials and Methods: This case–control study was conducted on 45 workers during autumn 2014. The workers were divided into three groups based on the level of noise exposure. The SNR was measured in frequencies of 1000, 2000, 3000, 4000, and 6000 Hz in both ears, and in three different time intervals during the shift work. According to the inclusion criterion, SNR of 6 dB or greater was included in the study. The analysis was performed using repeated measurements of analysis of variance, spearman correlation coefficient, and paired samples t-test. Results: The results showed that there was no statistically significant difference between the three exposed groups in terms of the mean values of SNR (P > 0.05. Only in signal pressure levels of 88 dBA with an interval time of 10:30–11:00 AM, there was a statistically significant difference between the right and left ears with the mean SNR values of 3000 frequency (P = 0.038. The SPL had a significant effect on the SNR in both the right and left ears (P = 0.023, P = 0.041. The effect of the duration of measurement on the SNR was statistically significant in both the right and left ears (P = 0.027, P < 0.001. Conclusion: The findings of this study demonstrated that after noise exposure during the shift, SNR of OAEs reduced from the

Auditory working memory predicts individual differences in absolute pitch learning.

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Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

2015-07-01

Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

Embedding supplemental data in a digital video signal

NARCIS (Netherlands)

2005-01-01

An MPEG-encoded video signal includes groups of pictures (GOPs), each GOP having an intraframe coded (I) picture and a series of predictively encoded (P) pictures and bi-directionally predictively (B) pictures. Usually, the GOP structure IBBPBBP . . . is used. However, in order to embed a watermark

Sign epistasis caused by hierarchy within signalling cascades

NARCIS (Netherlands)

Nghe, Philippe; Kogenaru, Manjunatha; Tans, S.J.

2018-01-01

Sign epistasis is a central evolutionary constraint, but its causal factors remain difficult to predict. Here we use the notion of parameterised optima to explain epistasis within a signalling cascade, and test these predictions in Escherichia coli. We show that sign epistasis arises from the

Early warning signal for interior crises in excitable systems.

Science.gov (United States)

Karnatak, Rajat; Kantz, Holger; Bialonski, Stephan

2017-10-01

The ability to reliably predict critical transitions in dynamical systems is a long-standing goal of diverse scientific communities. Previous work focused on early warning signals related to local bifurcations (critical slowing down) and nonbifurcation-type transitions. We extend this toolbox and report on a characteristic scaling behavior (critical attractor growth) which is indicative of an impending global bifurcation, an interior crisis in excitable systems. We demonstrate our early warning signal in a conceptual climate model as well as in a model of coupled neurons known to exhibit extreme events. We observed critical attractor growth prior to interior crises of chaotic as well as strange-nonchaotic attractors. These observations promise to extend the classes of transitions that can be predicted via early warning signals.

Selection of personalized patient therapy through the use of knowledge-based computational models that identify tumor-driving signal transduction pathways.

Science.gov (United States)

Verhaegh, Wim; van Ooijen, Henk; Inda, Márcia A; Hatzis, Pantelis; Versteeg, Rogier; Smid, Marcel; Martens, John; Foekens, John; van de Wiel, Paul; Clevers, Hans; van de Stolpe, Anja

2014-06-01

Increasing knowledge about signal transduction pathways as drivers of cancer growth has elicited the development of "targeted drugs," which inhibit aberrant signaling pathways. They require a companion diagnostic test that identifies the tumor-driving pathway; however, currently available tests like estrogen receptor (ER) protein expression for hormonal treatment of breast cancer do not reliably predict therapy response, at least in part because they do not adequately assess functional pathway activity. We describe a novel approach to predict signaling pathway activity based on knowledge-based Bayesian computational models, which interpret quantitative transcriptome data as the functional output of an active signaling pathway, by using expression levels of transcriptional target genes. Following calibration on only a small number of cell lines or cohorts of patient data, they provide a reliable assessment of signaling pathway activity in tumors of different tissue origin. As proof of principle, models for the canonical Wnt and ER pathways are presented, including initial clinical validation on independent datasets from various cancer types. ©2014 American Association for Cancer Research.

Comparison of Different Approaches to Predict the Performance of Pumps As Turbines (PATs

Directory of Open Access Journals (Sweden)

Mauro Venturini

2018-04-01

Full Text Available This paper deals with the comparison of different methods which can be used for the prediction of the performance curves of pumps as turbines (PATs. The considered approaches are four, i.e., one physics-based simulation model (“white box” model, two “gray box” models, which integrate theory on turbomachines with specific data correlations, and one “black box” model. More in detail, the modeling approaches are: (1 a physics-based simulation model developed by the same authors, which includes the equations for estimating head, power, and efficiency and uses loss coefficients and specific parameters; (2 a model developed by Derakhshan and Nourbakhsh, which first predicts the best efficiency point of a PAT and then reconstructs their complete characteristic curves by means of two ad hoc equations; (3 the prediction model developed by Singh and Nestmann, which predicts the complete turbine characteristics based on pump shape and size; (4 an Evolutionary Polynomial Regression model, which represents a data-driven hybrid scheme which can be used for identifying the explicit mathematical relationship between PAT and pump curves. All approaches are applied to literature data, relying on both pump and PAT performance curves of head, power, and efficiency over the entire range of operation. The experimental data were provided by Derakhshan and Nourbakhsh for four different turbomachines, working in both pump and PAT mode with specific speed values in the range 1.53–5.82. This paper provides a quantitative assessment of the predictions made by means of the considered approaches and also analyzes consistency from a physical point of view. Advantages and drawbacks of each method are also analyzed and discussed.

Differential genomic effects on signaling pathways by two different CeO2 nanoparticles in HepG2 cells

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U.S. Environmental Protection Agency — Differential genomic effects on signaling pathways by two different CeO2 nanoparticles in HepG2 cells. This dataset is associated with the following publication:...

Signal analysis approach to ultrasonic evaluation of diffusion bond quality

International Nuclear Information System (INIS)

Thomas, Graham; Chinn, Diane

1999-01-01

Solid state bonds like the diffusion bond are attractive techniques for joining dissimilar materials since they are not prone to the defects that occur with fusion welding. Ultrasonic methods can detect the presence of totally unbonded regions but have difficulty sensing poor bonded areas where the substrates are in intimate contact. Standard ultrasonic imaging is based on amplitude changes in the signal reflected from the bond interface. Unfortunately, amplitude alone is not sensitive to bond quality. We demonstrated that there is additional information in the ultrasonic signal that correlates with bond quality. In our approach, we interrogated a set of dissimilar diffusion bonded samples with broad band ultrasonic signals. The signals were digitally processed and the characteristics of the signals that corresponded to bond quality were determined. These characteristics or features were processed with pattern recognition algorithms to produce predictions of bond quality. The predicted bond quality was then compared with the destructive measurement to assess the classification capability of the ultrasonic technique

Predicting chemical environments of bacteria from receptor signaling.

Directory of Open Access Journals (Sweden)

Diana Clausznitzer

2014-10-01

Full Text Available Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer (FRET experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine.

Cell-Cell Contact Area Affects Notch Signaling and Notch-Dependent Patterning.

Science.gov (United States)

Shaya, Oren; Binshtok, Udi; Hersch, Micha; Rivkin, Dmitri; Weinreb, Sheila; Amir-Zilberstein, Liat; Khamaisi, Bassma; Oppenheim, Olya; Desai, Ravi A; Goodyear, Richard J; Richardson, Guy P; Chen, Christopher S; Sprinzak, David

2017-03-13

During development, cells undergo dramatic changes in their morphology. By affecting contact geometry, these morphological changes could influence cellular communication. However, it has remained unclear whether and how signaling depends on contact geometry. This question is particularly relevant for Notch signaling, which coordinates neighboring cell fates through direct cell-cell signaling. Using micropatterning with a receptor trans-endocytosis assay, we show that signaling between pairs of cells correlates with their contact area. This relationship extends across contact diameters ranging from micrometers to tens of micrometers. Mathematical modeling predicts that dependence of signaling on contact area can bias cellular differentiation in Notch-mediated lateral inhibition processes, such that smaller cells are more likely to differentiate into signal-producing cells. Consistent with this prediction, analysis of developing chick inner ear revealed that ligand-producing hair cell precursors have smaller apical footprints than non-hair cells. Together, these results highlight the influence of cell morphology on fate determination processes. Copyright © 2017 Elsevier Inc. All rights reserved.

Conflict adaptation is predicted by the cognitive, but not the affective alexithymia dimension

Directory of Open Access Journals (Sweden)

Michiel ede Galan

2014-07-01

Full Text Available Stimulus-induced response conflict (e.g., in Simon or Stroop tasks is often reduced after conflict trials—the Gratton effect. It is generally assumed that this effect is due to a strengthening of the representation of the current intention or goal, which in turn increases the degree of stimulus and/or response control. Recent evidence suggests that the motivational signal driving the Gratton effect might be affective in nature. If so, individual differences in either the strength of affective signals and/or the ability to interpret such signals might explain individual differences in cognitive-control adjustments as reflected in the Gratton effect. We tested this hypothesis by relating individual sizes of the Gratton effect in a Simon task to scores on the affective and the cognitive dimension of the Bermond/Vorst Alexithymia Questionnaire (BVAQ—which we assumed to assess individual differences in affective-signal strength and ability to interpret affective signals, respectively. Results show that the cognitive, but not the affective dimension predicted control adjustment, while the accuracy of heartbeat detection was only (and only weakly related to online control. This suggests that the motivation to fine-tune one’s cognitive-control operations is mediated by, and may depend on one’s ability to interpret one’s own affective signals.

Differences in gene expression profiles and signaling pathways in rhabdomyolysis-induced acute kidney injury.

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Geng, Xiaodong; Wang, Yuanda; Hong, Quan; Yang, Jurong; Zheng, Wei; Zhang, Gang; Cai, Guangyan; Chen, Xiangmei; Wu, Di

2015-01-01

Rhabdomyolysis is a threatening syndrome because it causes the breakdown of skeletal muscle. Muscle destruction leads to the release of myoglobin, intracellular proteins, and electrolytes into the circulation. The aim of this study was to investigate the differences in gene expression profiles and signaling pathways upon rhabdomyolysis-induced acute kidney injury (AKI). In this study, we used glycerol-induced renal injury as a model of rhabdomyolysis-induced AKI. We analyzed data and relevant information from the Gene Expression Omnibus database (No: GSE44925). The gene expression data for three untreated mice were compared to data for five mice with rhabdomyolysis-induced AKI. The expression profiling of the three untreated mice and the five rhabdomyolysis-induced AKI mice was performed using microarray analysis. We examined the levels of Cyp3a13, Rela, Aldh7a1, Jun, CD14. And Cdkn1a using RT-PCR to determine the accuracy of the microarray results. The microarray analysis showed that there were 1050 downregulated and 659 upregulated genes in the rhabdomyolysis-induced AKI mice compared to the control group. The interactions of all differentially expressed genes in the Signal-Net were analyzed. Cyp3a13 and Rela had the most interactions with other genes. The data showed that Rela and Aldh7a1 were the key nodes and had important positions in the Signal-Net. The genes Jun, CD14, and Cdkn1a were also significantly upregulated. The pathway analysis classified the differentially expressed genes into 71 downregulated and 48 upregulated pathways including the PI3K/Akt, MAPK, and NF-κB signaling pathways. The results of this study indicate that the NF-κB, MAPK, PI3K/Akt, and apoptotic pathways are regulated in rhabdomyolysis-induced AKI.

An attempt to detect the greenhouse-gas signal in a transient GCM simulation

International Nuclear Information System (INIS)

Barnett, T.P.

1990-01-01

Results from the GISS model forced by transient greenhouse-gas (GHG) increases are used to demonstrate methods of detecting the theoretically predicted GHG signal. The signal predicted to occur in the surface temperature of the world's ocean since 1958 is not found in the observations but this is not surprising since the signal was small in the first place. The main result of the study is to demonstrate many of the key issues/difficulties that attend the detection problem

A study of MRI gradient echo signals from discrete magnetic particles with considerations of several parameters in simulations.

Science.gov (United States)

Kokeny, Paul; Cheng, Yu-Chung N; Xie, He

2018-05-01

Modeling MRI signal behaviors in the presence of discrete magnetic particles is important, as magnetic particles appear in nanoparticle labeled cells, contrast agents, and other biological forms of iron. Currently, many models that take into account the discrete particle nature in a system have been used to predict magnitude signal decays in the form of R2* or R2' from one single voxel. Little work has been done for predicting phase signals. In addition, most calculations of phase signals rely on the assumption that a system containing discrete particles behaves as a continuous medium. In this work, numerical simulations are used to investigate MRI magnitude and phase signals from discrete particles, without diffusion effects. Factors such as particle size, number density, susceptibility, volume fraction, particle arrangements for their randomness, and field of view have been considered in simulations. The results are compared to either a ground truth model, theoretical work based on continuous mediums, or previous literature. Suitable parameters used to model particles in several voxels that lead to acceptable magnetic field distributions around particle surfaces and accurate MR signals are identified. The phase values as a function of echo time from a central voxel filled by particles can be significantly different from those of a continuous cubic medium. However, a completely random distribution of particles can lead to an R2' value which agrees with the prediction from the static dephasing theory. A sphere with a radius of at least 4 grid points used in simulations is found to be acceptable to generate MR signals equivalent from a larger sphere. Increasing number of particles with a fixed volume fraction in simulations reduces the resulting variance in the phase behavior, and converges to almost the same phase value for different particle numbers at each echo time. The variance of phase values is also reduced when increasing the number of particles in a fixed

Predicting Smartphone Operating System from Personality and Individual Differences.

Science.gov (United States)

Shaw, Heather; Ellis, David A; Kendrick, Libby-Rae; Ziegler, Fenja; Wiseman, Richard

2016-12-01

Android and iPhone devices account for over 90 percent of all smartphones sold worldwide. Despite being very similar in functionality, current discourse and marketing campaigns suggest that key individual differences exist between users of these two devices; however, this has never been investigated empirically. This is surprising, as smartphones continue to gain momentum across a variety of research disciplines. In this article, we consider if individual differences exist between these two distinct groups. In comparison to Android users, we found that iPhone owners are more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. Key differences in personality were also observed with iPhone users displaying lower levels of Honesty-Humility and higher levels of emotionality. Following this analysis, we were also able to build and test a model that predicted smartphone ownership at above chance level based on these individual differences. In line with extended self-theory, the type of smartphone owned provides some valuable information about its owner. These findings have implications for the increasing use of smartphones within research particularly for those working within Computational Social Science and PsychoInformatics, where data are typically collected from devices and applications running a single smartphone operating system.

Intra-Tumour Signalling Entropy Determines Clinical Outcome in Breast and Lung Cancer

Science.gov (United States)

Banerji, Christopher R. S.; Severini, Simone; Caldas, Carlos; Teschendorff, Andrew E.

2015-01-01

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample’s genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers. PMID:25793737

Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

Directory of Open Access Journals (Sweden)

Christopher R S Banerji

2015-03-01

Full Text Available The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers.

Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations.

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Ou, Jian; Chen, Yongguang; Zhao, Feng; Liu, Jin; Xiao, Shunping

2017-03-19

The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Predicting Persuasion-Induced Behavior Change from the Brain

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Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

2011-01-01

Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

Cloud prediction of protein structure and function with PredictProtein for Debian.

Science.gov (United States)

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells.

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Ruth Merkle

2016-08-01

Full Text Available Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC, is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO. However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR. The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in

Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose

Science.gov (United States)

Lever, Melissa; Lim, Hong-Sheng; Kruger, Philipp; Nguyen, John; Trendel, Nicola; Abu-Shah, Enas; Maini, Philip Kumar; van der Merwe, Philip Anton

2016-01-01

T cells must respond differently to antigens of varying affinity presented at different doses. Previous attempts to map peptide MHC (pMHC) affinity onto T-cell responses have produced inconsistent patterns of responses, preventing formulations of canonical models of T-cell signaling. Here, a systematic analysis of T-cell responses to 1 million-fold variations in both pMHC affinity and dose produced bell-shaped dose–response curves and different optimal pMHC affinities at different pMHC doses. Using sequential model rejection/identification algorithms, we identified a unique, minimal model of cellular signaling incorporating kinetic proofreading with limited signaling coupled to an incoherent feed-forward loop (KPL-IFF) that reproduces these observations. We show that the KPL-IFF model correctly predicts the T-cell response to antigen copresentation. Our work offers a general approach for studying cellular signaling that does not require full details of biochemical pathways. PMID:27702900

A study of pedestrian compliance with traffic signals for exclusive and concurrent phasing.

Science.gov (United States)

Ivan, John N; McKernan, Kevin; Zhang, Yaohua; Ravishanker, Nalini; Mamun, Sha A

2017-01-01

This paper describes a comparison of pedestrian compliance at traffic signals with two types of pedestrian phasing: concurrent, where both pedestrians and vehicular traffic are directed to move in the same directions at the same time, and exclusive, where pedestrians are directed to move during their own dedicated phase while all vehicular traffic is stopped. Exclusive phasing is usually perceived to be safer, especially by senior and disabled advocacy groups, although these safety benefits depend upon pedestrians waiting for the walk signal. This paper investigates whether or not there are differences between pedestrian compliance at signals with exclusive pedestrian phasing and those with concurrent phasing and whether these differences continue to exist when compliance at exclusive phasing signals is evaluated as if they had concurrent phasing. Pedestrian behavior was observed at 42 signalized intersections in central Connecticut with both concurrent and exclusive pedestrian phasing. Binary regression models were estimated to predict pedestrian compliance as a function of the pedestrian phasing type and other intersection characteristics, such as vehicular and pedestrian volume, crossing distance and speed limit. We found that pedestrian compliance is significantly higher at intersections with concurrent pedestrian phasing than at those with exclusive pedestrian phasing, but this difference is not significant when compliance at exclusive phase intersections is evaluated as if it had concurrent phasing. This suggests that pedestrians treat exclusive phase intersections as though they have concurrent phasing, rendering the safety benefits of exclusive pedestrian phasing elusive. No differences were observed for senior or non-senior pedestrians. Published by Elsevier Ltd.

cGMP signalling : different ways to create a pathway

NARCIS (Netherlands)

Roelofs, Jeroen; Smith, Janet L.; Haastert, Peter J.M. van

Recently, a novel cGMP signalling cascade was uncovered in Dictyostelium, a eukaryote that diverged from the lineage leading to metazoa after plants and before yeast. In both Dictyostelium and metazoa, the ancient cAMP-binding (cNB) motif of bacterial CAP has been modified and assembled with other

Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill using the North American Multi-model Ensemble

Science.gov (United States)

Pegion, K.; DelSole, T. M.; Becker, E.; Cicerone, T.

2016-12-01

Predictability represents the upper limit of prediction skill if we had an infinite member ensemble and a perfect model. It is an intrinsic limit of the climate system associated with the chaotic nature of the atmosphere. Producing a forecast system that can make predictions very near to this limit is the ultimate goal of forecast system development. Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. Quantification of the predictability is also important for providing a scientific basis for relaying to stakeholders what kind of climate information can be provided to inform decision-making and what kind of information is not possible given the intrinsic predictability of the climate system. One challenge with predictability estimates is that different prediction systems can give different estimates of the upper limit of skill. How do we know which estimate of predictability is most representative of the true predictability of the climate system? Previous studies have used the spread-error relationship and the autocorrelation to evaluate the fidelity of the signal and noise estimates. Using a multi-model ensemble prediction system, we can quantify whether these metrics accurately indicate an individual model's ability to properly estimate the signal, noise, and predictability. We use this information to identify the best estimates of predictability for 2-meter temperature, precipitation, and sea surface temperature from the North American Multi-model Ensemble and compare with current skill to indicate the regions with potential for improving skill.

Dual roles for spike signaling in cortical neural populations

Directory of Open Access Journals (Sweden)

Dana eBallard

2011-06-01

Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

Exposure to lateral collision in signalized intersections with protected left turn under different traffic control strategies.

Science.gov (United States)

Midenet, Sophie; Saunier, Nicolas; Boillot, Florence

2011-11-01

This paper proposes an original definition of the exposure to lateral collision in signalized intersections and discusses the results of a real world experiment. This exposure is defined as the duration of situations where the stream that is given the right-of-way goes through the conflict zone while road users are waiting in the cross-traffic approach. This measure, obtained from video sensors, makes it possible to compare different operating conditions such as different traffic signal strategies. The data from a real world experiment is used, where the adaptive real-time strategy CRONOS (ContRol Of Networks by Optimization of Switchovers) and a time-plan strategy with vehicle-actuated ranges alternately controlled an isolated intersection near Paris. Hourly samples with similar traffic volumes are compared and the exposure to lateral collision is different in various areas of the intersection and various traffic conditions for the two strategies. The total exposure under peak hour traffic conditions drops by roughly 5 min/h with the CRONOS strategy compared to the time-plan strategy, which occurs mostly on entry streams. The results are analyzed through the decomposition of cycles in phase sequences and recommendations are made for traffic control strategies. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting the effect of spectral subtraction on the speech recognition threshold based on the signal-to-noise ratio in the envelope domain

DEFF Research Database (Denmark)

Jørgensen, Søren; Dau, Torsten

2011-01-01

rarely been evaluated perceptually in terms of speech intelligibility. This study analyzed the effects of the spectral subtraction strategy proposed by Berouti at al. [ICASSP 4 (1979), 208-211] on the speech recognition threshold (SRT) obtained with sentences presented in stationary speech-shaped noise....... The SRT was measured in five normal-hearing listeners in six conditions of spectral subtraction. The results showed an increase of the SRT after processing, i.e. a decreased speech intelligibility, in contrast to what is predicted by the Speech Transmission Index (STI). Here, another approach is proposed......, denoted the speech-based envelope power spectrum model (sEPSM) which predicts the intelligibility based on the signal-to-noise ratio in the envelope domain. In contrast to the STI, the sEPSM is sensitive to the increased amount of the noise envelope power as a consequence of the spectral subtraction...

Selection of References in Wind Turbine Model Predictive Control Design

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Hovgaard, Tobias

2015-01-01

a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...

Profit through predictability: The MRF difference at optimax

Science.gov (United States)

Light, Brandon

2007-05-01

In the manufacturing business, there is one product that matters, money. Whether making shoelaces or aircraft carriers a business that doesn't also make a profit doesn't stay around long. Being able to predict operational expenses is critical to determining a product's sale price. Priced too high a product won't sell, too low profit goes away. In the business of precision optics manufacturing, predictability has been often impossible or had large error bars. Manufacturing unpredictability made setting price a challenge. What if predictability could improve by changing the polishing process? Would a predictable, deterministic process lead to profit? Optimax Systems has experienced exactly that. Incorporating Magnetorheological Finishing (MRF) into its finishing process, Optimax saw parts categorized financially as "high risk" become a routine product of higher quality, delivered on time and within budget. Using actual production figures, this presentation will show how much incorporating MRF reduced costs, improved output and increased quality all at the same time.

A Bayesian test for periodic signals in red noise

Science.gov (United States)

Vaughan, S.

2010-02-01

Many astrophysical sources, especially compact accreting sources, show strong, random brightness fluctuations with broad power spectra in addition to periodic or quasi-periodic oscillations (QPOs) that have narrower spectra. The random nature of the dominant source of variance greatly complicates the process of searching for possible weak periodic signals. We have addressed this problem using the tools of Bayesian statistics; in particular, using Markov Chain Monte Carlo techniques to approximate the posterior distribution of model parameters, and posterior predictive model checking to assess model fits and search for periodogram outliers that may represent periodic signals. The methods developed are applied to two example data sets, both long XMM-Newton observations of highly variable Seyfert 1 galaxies: RE J1034 + 396 and Mrk 766. In both cases, a bend (or break) in the power spectrum is evident. In the case of RE J1034 + 396, the previously reported QPO is found but with somewhat weaker statistical significance than reported in previous analyses. The difference is due partly to the improved continuum modelling, better treatment of nuisance parameters and partly to different data selection methods.

A comparison of different methods for predicting coal devolatilisation kinetics

Energy Technology Data Exchange (ETDEWEB)

Arenillas, A.; Rubiera, F.; Pevida, C.; Pis, J.J. [Instituto Nacional del Carbon, CSIC, Apartado 73, 33080 Oviedo (Spain)

2001-04-01

Knowledge of the coal devolatilisation rate is of great importance because it exerts a marked effect on the overall combustion behaviour. Different approaches can be used to obtain the kinetics of the complex devolatilisation process. The simplest are empirical and employ global kinetics, where the Arrhenius expression is used to correlate rates of mass loss with temperature. In this study a high volatile bituminous coal was devolatilised at four different heating rates in a thermogravimetric analyser (TG) linked to a mass spectrometer (MS). As a first approach, the Arrhenius kinetic parameters (k and A) were calculated from the experimental results, assuming a single step process. Another approach is the distributed-activation energy model, which is more complex due to the assumption that devolatilisation occurs through several first-order reactions, which occur simultaneously. Recent advances in the understanding of coal structure have led to more fundamental approaches for modelling devolatilisation behaviour, such as network models. These are based on a physico-chemical description of coal structure. In the present study the FG-DVC (Functional Group-Depolymerisation, Vaporisation and Crosslinking) computer code was used as the network model and the FG-DVC predicted evolution of volatile compounds was compared with the experimental results. In addition, the predicted rate of mass loss from the FG-DVC model was used to obtain a third devolatilisation kinetic approach. The three methods were compared and discussed, with the experimental results as a reference.

Is Einsteinian no-signalling violated in Bell tests?

Science.gov (United States)

Kupczynski, Marian

2017-11-01

Relativistic invariance is a physical law verified in several domains of physics. The impossibility of faster than light influences is not questioned by quantum theory. In quantum electrodynamics, in quantum field theory and in the standard model relativistic invariance is incorporated by construction. Quantum mechanics predicts strong long range correlations between outcomes of spin projection measurements performed in distant laboratories. In spite of these strong correlations marginal probability distributions should not depend on what was measured in the other laboratory what is called shortly: non-signalling. In several experiments, performed to test various Bell-type inequalities, some unexplained dependence of empirical marginal probability distributions on distant settings was observed. In this paper we demonstrate how a particular identification and selection procedure of paired distant outcomes is the most probable cause for this apparent violation of no-signalling principle. Thus this unexpected setting dependence does not prove the existence of superluminal influences and Einsteinian no-signalling principle has to be tested differently in dedicated experiments. We propose a detailed protocol telling how such experiments should be designed in order to be conclusive. We also explain how magical quantum correlations may be explained in a locally causal way.

Is Einsteinian no-signalling violated in Bell tests?

Directory of Open Access Journals (Sweden)

Kupczynski Marian

2017-11-01

Full Text Available Relativistic invariance is a physical law verified in several domains of physics. The impossibility of faster than light influences is not questioned by quantum theory. In quantum electrodynamics, in quantum field theory and in the standard model relativistic invariance is incorporated by construction. Quantum mechanics predicts strong long range correlations between outcomes of spin projection measurements performed in distant laboratories. In spite of these strong correlations marginal probability distributions should not depend on what was measured in the other laboratory what is called shortly: non-signalling. In several experiments, performed to test various Bell-type inequalities, some unexplained dependence of empirical marginal probability distributions on distant settings was observed. In this paper we demonstrate how a particular identification and selection procedure of paired distant outcomes is the most probable cause for this apparent violation of no-signalling principle. Thus this unexpected setting dependence does not prove the existence of superluminal influences and Einsteinian no-signalling principle has to be tested differently in dedicated experiments. We propose a detailed protocol telling how such experiments should be designed in order to be conclusive. We also explain how magical quantum correlations may be explained in a locally causal way.

Analysis of infant cry through weighted linear prediction cepstral coefficients and Probabilistic Neural Network.

Science.gov (United States)

Hariharan, M; Chee, Lim Sin; Yaacob, Sazali

2012-06-01

Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy.

Science.gov (United States)

Kim, Eunjung; Kim, Jae-Young; Smith, Matthew A; Haura, Eric B; Anderson, Alexander R A

2018-03-01

During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor

Delay-based Passenger Car Equivalent at Signalized Intersections in Iran

Directory of Open Access Journals (Sweden)